Automation: What It Means to Us Around the World

Abstract

The meaning of the term automation is reviewed through its definition and related definitions, historical evolution, technological progress, benefits and risks, and domains and levels of applications. A survey of 331 people around the world adds insights to the current meaning of automation to people, with regard to: What is your definition of automation? Where did you encounter automation first in your life? and What is the most important contribution of automation to society? The survey respondents include 12 main aspects of the definition in their responses; 62 main types of first automation encounter; and 37 types of impacts, mostly benefits but also two benefit–risks combinations: replacing humans, and humansʼ inability to complete tasks by themselves. The most exciting contribution of automation found in the survey was to encourage/inspire creative work; inspire newer solutions. Minor variations were found in different regions of the world. Responses about the first automation encounter are somewhat related to the age of the respondent, e.g., pneumatic versus digital control, and to urban versus farming childhood environment. The chapter concludes with several emerging trends in bioinspired automation, collaborative control and automation, and risks to anticipate and eliminate.

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The Meaning of Automation

What is the meaning of automation? When discussing this term and concept with many colleagues, leading experts in various aspects of automation, control theory, robotics engineering, and computer science during the development of this Handbook of Automation, many of them had different definitions; they even argued vehemently that in their language, or their region of the world, or their professional domain, automation has a unique meaning and we are not sure it is the same meaning for other experts. But there has been no doubt, no confusion, and no hesitation that automation is powerful; it has tremendous and amazing impact on civilization, on humanity, and it may carry risks.

So what is automation? This chapter introduces the meaning and definition of automation, at an introductory, overview level. Specific details and more theoretical definitions are further explained and illustrated throughout the following parts and chapters of this handbook. A survey of 331 participants from around the world was conducted and is presented in Sect. 3.5.

Definitions and Formalism

Automation, in general, implies operating or acting, or self-regulating, independently, without human intervention. The term evolves from automatos, in Greek, meaning acting by itself, or by its own will, or spontaneously. Automation involves machines, tools, devices, installations, and systems that are all platforms developed by humans to perform a given set of activities without human involvement during those activities. But there are many variations of this definition. For instance, before modern automation (specifically defined in the modern context since about 1950s), mechanization was a common version of automation. When automatic control was added to mechanization as an intelligence feature, the distinction and advantages of automation became clear. In this chapter, we review these related definitions and their evolvement, and survey how people around the world perceive automation. Examples of automation are described, including ancient to early examples in Table 3.1, examples from the Industrial Revolution in Table 3.3, and modern and emerging examples in Table 3.4. From the general definition of automation, the automation formalism is presented in Fig. 3.1 with four main elements: platform, autonomy, process, and power source. Automation platforms are illustrated in Table 3.2.

Table 3.1 Automation examples: ancient to early history

Table 3.3 Automation examples: Industrial Revolution to 1920

Table 3.4 Automation examples: modern and emerging

Fig. 3.1

Automation formalism. Automation comprises four basic elements. See representative illustrations of platforms, autonomy, process, and power source in Tables 3.1–3.2, 3.6, and the automation cases below, in Sect. 3.3

Table 3.2 Automation platforms

This automation formalism can help us review some early examples that may also fall under the definition of automation (before the term automation was even coined), and differentiate from related terms, such as mechanization, cybernetics, artificial intelligence, and robotics.

Automaton

An automaton (plural: automata, or automatons) is an autonomous machine that contains its own power source and can perform without human intervention a complicated series of decisions and actions, in response to programs and external stimuli. Since the term automaton is used for a specific autonomous machine, tool or device, it usually does not include automation platforms such as automation infrastructure, automatic installations, or automation systems such as automation software (even though some use the term software automaton to imply computing procedures). The scholar Al-Jazari from al-Jazira, Mesopotamia designed pioneering programmable automatons in 1206, as a set of dolls, or humanoid automata. Today, the most typical automatons are what we define as robots.

Robot

A robot is a mechanical device that can be programmed to perform a variety of tasks of manipulation and locomotion under automatic control. Thus, a robot could also be an automaton. But unlike an automaton, a robot is usually designed for highly variable and flexible, purposeful motions and activities, and for specific operation domains, e.g., surgical robot, service robot, welding robot, toy robot, etc. General Motors implemented the first industrial robot, called UNIMATE, in 1961 for die-casting at an automobile factory in New Jersey. By now, millions of robots are routinely employed and integrated throughout the world.

Robotics

The science and technology of designing, building, and applying robots, computer-controlled mechanical devices, such as automated tools and machines. Science fiction author and scientist Isaac Asimov coined the term robotics in 1941 to describe the technology of robots and predicted the rise of a significant robot industry, e.g., in his foreword to [3.1]:

Since physics and most of its subdivisions routinely have the ``-icsʼʼ suffix, I assumed that robotics was the proper scientific term for the systematic study of robots, of their construction, maintenance, and behavior, and that it was used as such.

Robotics and Automation

Robotics is an important subset of automation (Fig. 3.2). For instance, of the 25 automation examples in Tables 3.1, 3.3 and 3.4, examples 4 in Table 3.1 and 7 in Table 3.4 are about robots. Beyond robotics, automation includes:

• Infrastructure, e.g., water supply, irrigation, power supply, telecommunication

• Nonrobot devices, e.g., timers, locks, valves, and sensors

• Automatic and automated machines, e.g., flour mills, looms, lathes, drills, presses, vehicles, and printers

• Automatic inspection machines, measurement workstations, and testers

• Installations, e.g., elevators, conveyors, railways, satellites, and space stations

• Systems, e.g., computers, office automation, Internet, cellular phones, and software packages.

Common to both robotics and automation are use of automatic control, and evolution with computing and communication progress. As in automation, robotics also relies on four major components, including a platform, autonomy, process, and power source, but in robotics, a robot is often considered a machine, thus the platform is mostly a machine, a tool or device, or a system of tools and devices. While robotics is, in a major way, about automation of motion and mobility, automation beyond robotics includes major areas based on software, decision-making, planning and optimization, collaboration, process automation, office automation, enterprise resource planning automation, and e-Services. Nevertheless, there is clearly an overlap between automation and robotics; while to most people a robot means a machine with certain automation intelligence, to many an intelligent elevator, or highly automated machine tool, or even a computer may also imply a robot.

Fig. 3.2

The relation between robotics and automation: The scope of automation includes applications: (1a) with just computers, (1b) with various automation platforms and applications, but without robots; (2) automation including also some robotics; (3) automation with robotics

Cybernetics

Cybernetics is the scientific study of control and communication in organisms, organic processes, and mechanical and electronic systems. It evolves from kibernetes, in Greek, meaning pilot, or captain, or governor, and focuses on applying technology to replicate or imitate biological control systems, often called today bioinspired, or system biology. Cybernetics, a book by Norbert Wiener, who is attributed with coining this word, appeared in 1948 and influenced artificial intelligence research. Cybernetics overlaps with control theory and systems theory.

Cyber

Cyber- is a prefix, as in cybernetic, cybernation, or cyborg. Recently, cyber has assumed a meaning as a noun, meaning computers and information systems, virtual reality, and the Internet. This meaning has emerged because of the increasing importance of these automation systems to society and daily life.

Artificial Intelligence (AI)

Artificial intelligence (AI) is the ability of a machine system to perceive anticipated or unanticipated new conditions, decide what actions must be performed under these conditions, and plan the actions accordingly. The main areas of AI study and application are knowledge-based systems, computer sensory systems, language processing systems, and machine learning. AI is an important part of automation, especially to characterize what is sometimes called intelligent automation (Sect. 3.4.2).

It is important to note that AI is actually human intelligence that has been implemented on machines, mainly through computers and communication. Its significant advantages are that it can function automatically, i.e., without human intervention during its operation/function; it can combine intelligence from many humans, and improve its abilities by automatic learning and adaptation; it can be automatically distributed, duplicated, shared, inherited, and if necessary, restrained and even deleted. With the advent of these abilities, remarkable progress has been achieved. There is also, however, an increasing risk of running out of control (Sect. 3.6), which must be considered carefully as with harnessing any other technology.

Early Automation

The creative human desire to develop automation, from ancient times, has been to recreate natural activities, either for enjoyment or for productivity with less human effort and hazard. It should be clear, however, that the following six imperatives have been proven about automation:

• 1. Automation has always been developed by people

• 2. Automation has been developed for the sake of people

• 3. The benefits of automation are tremendous

• 4. Often automation performs tasks that are impossible or impractical for humans

• 5. As with other technologies, care should be taken to prevent abuse of automation, and to eliminate the possibilities of unsafe automation

• 6. Automation is usually inspiring further creativity of the human mind.

The main evolvement of automation has followed the development of mechanics and fluidics, civil infrastructure and machine design, and since the 20th century, of computers and communication. Examples of ancient automation that follow the formal definition (Table 3.1) include flying and chirping birds, sundial clocks, irrigation systems, and windmills. They all include the four basic automation elements, and have a clear autonomous process without human intervention, although they are mostly predetermined or predefined in terms of their control program and organization. But not all these ancient examples replace previously used human effort: Some of them would be impractical or even impossible for humans, e.g., displaying time, or moving large quantities of water by aqueducts over large distances. This observation is important, since, as evident from the definition surveys (Sect. 3.5):

• 1. In defining automation, over one-quarter of those surveyed associate automation with replacing humans, hinting somber connotation that humans are losing certain advantages. Many resources erroneously define automation as replacement of human workers by technology. But the definition is not about replacing humans, as many automation examples involve activities people cannot practically perform, e.g., complex and fast computing, wireless telecommunication, microelectronics manufacturing, and satellite-based positioning. The definition is about the autonomy of a system or process from human involvement and intervention during the process (independent of whether humans could or could not perform it themselves). Furthermore, automation is rarely disengaged from people, who must maintain and improve it (or at least replace its batteries).

• 2. Humans are always involved with automation, to a certain degree, from its development, to, at certain points, supervising, maintaining, repairing, and issuing necessary commands, e.g., at which floor should this elevator stop for me?

Describing automation, Buckingham [3.3] quotes Aristotle (384–322 BC): “When looms weave by themselves humanʼs slavery will end.” Indeed, the reliance on a process that can proceed successfully to completion autonomously, without human participation and intervention, is an essential characteristic of automation. But it took over 2000 years since Aristotleʼs prediction till the automatic loom was developed during the Industrial Revolution.

Industrial Revolution

Some scientists (e.g., Truxal [3.4]) define automation as applying machines or systems to execute tasks that involve more elaborate decision-making. Certain decisions were already involved in ancient automation, e.g., where to direct the irrigation water. More control sophistication was indeed developed later, beginning during the Industrial Revolution (see examples in Table 3.3).

During the Industrial Revolution, as shown in the examples, steam and later electricity became the main power sources of automation systems and machines, and autonomy of process and decision-making increasingly involved feedback control models.

Modern Automation

The term automation in its modern meaning was actually attributed in the early 1950s to D.S. Harder, a vice-president of the Ford Motor Company, who described it as a philosophy of manufacturing. Towards the 1950s, it became clear that automation could be viewed as substitution by mechanical, hydraulic, pneumatic, electric, and electronic devices for a combination of human efforts and decisions. Critics with humor referred to automation as substitution of human error by mechanical error. Automation can also be viewed as the combination of four fundamental principles: mechanization; process continuity; automatic control; and economic, social, and technological rationalization.

Mechanization

Mechanization is defined as the application of machines to perform work. Machines can perform various tasks, at different levels of complexity. When mechanization is designed with cognitive and decision-making functions, such as process control and automatic control, the modern term automation becomes appropriate. Some machines can be rationalized by benefits of safety and convenience. Some machines, based on their power, compactness, and speed, can accomplish tasks that could never be performed by human labor, no matter how much labor or how effectively the operation could be organized and managed. With increased availability and sophistication of power sources and of automatic control, the level of autonomy of machines and mechanical system created a distinction between mechanization and the more autonomous form of mechanization, which is automation (Sect. 3.4).

Process Continuity

Process continuity is already evident in some of the ancient automation examples, and more so in the Industrial Revolution examples (Tables 3.1 and 3.3). For instance, windmills could provide relatively uninterrupted cycles of grain milling. The idea of continuity is to increase productivity, the useful output per labor-hour. Early in the 20th century, with the advent of mass production, it became possible to better organize workflow. Organization of production flow and assembly lines, and automatic or semiautomatic transfer lines increased productivity beyond mere mechanization. The emerging automobile industry in Europe and the USA in the early 1900s utilized the concept of moving work continuously, automatically or semiautomatically, to specialized machines and workstations. Interesting problems that emerged with flow automation included balancing the work allocation and regulating the flow.

Automatic Control

A key mechanism of automatic control is feedback, which is the regulation of a process according to its own output, so that the output meets the conditions of a predetermined, set objective. An example is the windmill that can adjust the orientation of it blades by feedback informing it of the changing direction of the current wind. Another example is the heating system that can stop and restart its heating or cooling process according to feedback from its thermostat. Wattʼs flyball governor applied feedback from the position of the rotating balls as a function of their rotating speed to automatically regulate the speed of the steam engine. Charles Babbage analytical engine for calculations applied the feedback principle in 1840 (see more on the development of automatic control in Chap. 4).

Automation Rationalization

Rationalization means a logical and systematic analysis, understanding, and evaluation of the objectives and constraints of the automation solution. Automation is rationalized by considering the technological and engineering aspects in the context of economic, social, and managerial considerations, including also: human factors and usability, organizational issues, environmental constraints, conservation of resources and energy, and elimination of waste (Chaps. 40 and 41).

Soon after automation enabled mass production in factories of the early 20th century and workers feared for the future of their jobs, the US Congress held hearings in which experts explained what automation means to them (Table 3.5). From our vantage point several generations later, it is interesting to read these definitions, while we already know about automation discoveries yet unknown at that time, e.g., laptop computers, robots, cellular telephones and personal digital assistants, the Internet, and more.

Table 3.5 Definitions of automation (after [3.2])

A relevant question is: Why automate? Several prominent motivations are the following, as has been indicated by the survey participants (Sect. 3.5):

• 1. Feasibility: Humans cannot handle certain operations and processes, either because of their scale, e.g., micro- and nanoparticles are too small, the amount of data is too vast, or the process happens too fast, for instance, missile guidance; microelectronics design, manufacturing and repair; and database search.

• 2. Productivity: Beyond feasibility, computers, automatic transfer machines, and other equipment can operate at such high speed and capacity that it would be practically impossible without automation, for instance, controlling consecutive, rapid chemical processes in food production; performing medicine tests by manipulating atoms or molecules; optimizing a digital image; and placing millions of colored dots on a color television (TV) screen.

• 3. Safety: Automation sensors and devices can operate well in environments that are unsafe for humans, for example, under extreme temperatures, nuclear radiation, or in poisonous gas.

• 4. Quality and economy: Automation can save significant costs on jobs performed without it, including consistency, accuracy, and quality of manufactured products and of services, and saving labor, safety, and maintenance costs.

• 5. Importance to individuals, to organizations, and to society: Beyond the above motivations, service- and knowledge-based automation reduces the need for middle managers and middle agents, thus reducing or eliminating the agency costs and removing layers of bureaucracy, for instance, Internet-based travel services and financial services, and direct communication between manufacturing managers and line operators, or cell robots. Remote supervision and telecollaboration change the nature, sophistication, skills and training requirements, and responsibility of workers and their managers. As automation gains intelligence and competencies, it takes over some employment skills and opens up new types of work, skills, and service requirements.

• 6. Accessibility: Automation enables better accessibility for all people, including disadvantaged and disabled people. Furthermore, automation opens up new types of employment for people with limitations, e.g., by integration of speech and vision recognition interfaces.

• 7. Additional motivations: Additional motivations are the competitive ability to integrate complex mechanization, advantages of modernization, convenience, and improvement in quality of life.

To be automated, a system must follow the motivations listed above. The modern and emerging automation examples in Table 3.4 and the automation cases in Sect. 3.3 illustrate these motivations, and the mechanization, process continuity, and automatic control features.

Certain limits and risks of automation need also be considered. Modern, computer-controlled automation must be programmable and conform to definable procedures, protocols, routines, and boundaries. The limits also follow the boundaries imposed by the four principles of automation. Can it be mechanized? Is there continuity in the process? Can automatic control be designed for it? Can it be rationalized? Theoretically, all continuous processes can be automatically controlled, but practically such automation must be rationalized first; for instance, jet engines may be continuously advanced on conveyors to assembly cells, but if the demand for these engines is low, there is no justification to automate their flow. Furthermore, all automation must be designed to operate within safe boundaries, so it does not pose hazards to humans and to the environment.

Domains of Automation

Some unique meanings of automation are associated with the domain of automation. Several examples of well-known domains are listed here:

• Detroit automation – Automation of transfer lines and assembly lines adopted by the automotive industry [3.5].

• Flexible automation – Manufacturing and service automation consisting of a group of processing stations and robots operating as an integrated system under computer control, able to process a variety of different tasks simultaneously, under automatic, adaptive control or learning control [3.5]. Also known as flexible manufacturing system (FMS), flexible assembly system, or robot cell, which are suitable for medium demand volume and medium variety of flexible tasks. Its purpose is to advance from mass production of products to more customer-oriented and customized supply. For higher flexibility with low demand volume, stand-alone numerically controlled (NC) machines and robots are preferred. For high demand volume with low task variability, automatic transfer lines are designed. The opposite of flexible automation is fixed automation, such as process-specific machine tools and transfer lines, lacking task flexibility. For mass customization (mass production with some flexibility to respond to variable customer demands), transfer lines with flexibility can be designed (see more on automation flexibility in Sect. 3.4).

• Office automation – Computer and communication machinery and software used to improve office procedures by digitally creating, collecting, storing, manipulating, displaying, and transmitting office information needed for accomplishing office tasks and functions [3.6,7]. Office automation became popular in the 1970s and 1980s when the desktop computer and the personal computer emerged.

Other examples of well-known domains of automation have been factory automation (e.g., [3.8]), healthcare automation (e.g., [3.9]), workflow automation (e.g., [3.10]), and service automation (e.g., [3.11]). More domain examples are illustrated in Table 3.6.

Table 3.6 Automation domains

Throughout these different domains, automation has been applied for various organization functions. Five hierarchical layers of automation are shown in the automation pyramid (Fig. 3.3), which is a common depiction of how to organize automation implementation.

Fig. 3.3

The automation pyramid: organizational layers

Brief History of Automation

Automation has evolved, as described in Table 3.7, along three automation generations.

Table 3.7 Brief history of automation events

First Generation: Before Automatic Control (BAC)

Early automation is characterized by elements of process autonomy and basic decision-making autonomy, but without feedback, or with minimal feedback. The period is generally from prehistory till the 15th century. Some examples of basic automatic control can be found earlier than the 15th century, at least in conceptual design or mathematical definition. Automation examples of the first generation can also be found later, whenever automation solutions without automatic control could be rationalized.

Second Generation: Before Computer Control (BCC)

Automation with advantages of automatic control, but before the introduction and implementation of the computer, especially the digital computer, belongs to this generation. Automatic control emerging during this generation offered better stability and reliability, more complex decision-making, and in general better control and automation quality. The period is between the 15th century and the 1940s. It would be generally difficult to rationalize in the future any automation with automatic control and without computers; therefore, future examples of this generation will be rare.

Third Generation: Automatic Computer Control (ACC)

The progress of computers and communication has significantly impacted the sophistication of automatic control and its effectiveness. This generation began in the 1940s and continues today. Further refinement of this generation can be found in Sect. 3.4, discussing the levels of automation.

See also Table 3.8 for examples discovered or implemented during the three automation generations.

Table 3.8 Automation generations

Automation Cases

Ten automation cases are illustrated in this section to demonstrate the meaning and scope of automation in different domains.

Case A: Steam Turbine Governor (Fig. 3.4)

Fig. 3.4

(a) Steam turbine generator. (b) Governor block diagram (PLC: programmable logic controller; T&T: trip and throttle). A turbine generator designed for on-site power and distributed energy ranging from 0.5 to 100 MW. Turbine generator sets produce power for pulp and paper mills, sugar, hydrocarbon, petrochemical and process industries; palm oil, ethanol, waste-to-energy, other biomass burning facilities, and other installations (with permission from Dresser-Rand)

Source

Courtesy of Dresser-Rand Co., Houston (http://www.dresser-rand.com/).

Process

Operates a steam turbine used to drive a compressor or generator.

Platform

Device, integrated as a system with programmable logic controller (PLC).

Autonomy

Semiautomatic and automatic activation/deactivation and control of the turbine speed; critical speed-range avoidance; remote, auxiliary, and cascade speed control; loss of generator and loss of utility detection; hot standby ability; single and dual actuator control; programmable governor parameters via operatorʼs screen and interface, and mobile computer. A manual mode is also available: The operator places the system in run mode which opens the governor valve to the full position, then manually opens the T&T valve to idle speed, to warm up the unit. After warm-up, the operator manually opens the T&T valve to full position, and as the turbineʼs speed approaches the rated (desirable) speed, the governor takes control with the governor valve. In semiautomatic and automatic modes, once the operator places the system in run mode, the governor takes over control.

Case B: Bioreactor (Fig. 3.5)

Fig. 3.5

Bioreactor system configured for microbial or cell culture applications. Optimization studies and screening and testing of strains and cell lines are of high importance in industry and research and development (R&D) institutes. Large numbers of tests are required and they must be performed in as short a time as possible. Tests should be performed so that results can be validated and used for further process development and production (with permission from Applikon Biotechnology)

Source

Courtesy of Applikon Biotechnology Co., Schiedam ( http://www.pharmaceutical-technology.com/contractors/automation/applikon-technology/).

Process

Microbial or cell culture applications that can be validated, conforming with standards for equipment used in life science and food industries, such as good automated manufacturing practice (GAMP).

Platform

System or installation, including microreactors, single-use reactors, autoclavable glass bioreactors, and stainless-steel bioreactors.

Autonomy

Bioreactor functions with complete measurement and control strategies and supervisory control and data acquisition (SCADA), including sensors and a cell retention device.

Case C: Digital Photo Processing (Fig. 3.6)

Fig. 3.6

Adobe Photoshop functions for digital image editing and processing: (a) automating functional actions, such as shadow, frame, reflection, and other visual effects; (b) selecting brush and palette for graphic effects; (c) setting color and saturation values (with permission from Adobe Systems Inc., 2008)

Source

Adobe Systems Incorporated San Jose, California (http://adobe.com).

Process

Editing, enhancing, adding graphic features, removing stains, improving resolution, cropping and sizing, and other functions to process photo images.

Platform

Software system.

Autonomy

The software functions are fully automatic once activated by a user. The software can execute them semiautomatically under user control, or action series can be automated too.

Case D: Robotic Painting (Fig. 3.7)

Fig. 3.7

A robotic painting line: (a) the facility; (b) programmer using interface to plan offline or online, experiment, optimize, and verify control programs for the line; (c) robotic painting facility design simulator (with permission from ABB)

Source

Courtesy of ABB Co., Zürich (http://www.ABB.com).

Process

Automatic painting under automatic control of car body movement, door opening and closing, paint-pump functions, fast robot motions to optimize finish quality, and minimize paint waste.

Platform

Automatic tools, machines, and robots, including sensors, conveyors, spray-painting equipment, and integration with planning and programming software systems.

Autonomy

Flexibility of motions; collision avoidance; coordination of conveyor moves, robotsʼ motions, and paint pump operations; programmability of process and line operations.

Case E: Assembly Automation (Fig. 3.8)

Fig. 3.8

Pharmaceutical pad-stick automatic assembly cell

Source

Courtesy of Adapt Automation Inc., Santa Ana, California (www.adaptautomation.com/Medical.html).

Process

Hopper and two bowl feeders feed specimen sticks through a track to where they are picked and placed, two up, into a double nest on a 12-position indexed dial plate. Specimen pads are fed and placed on the sticks. Pads are fully seated and inspected. Rejected and good parts are separated into their respective chutes.

Platform

System of automatic tools, machines, and robots.

Autonomy

Automatic control through a solid-state programmable controller which operates the sequence of device operations with a control panel. Control programs include main power, emergency stop, manual, automatic, and individual operations controls.

Case F: Computer-Integrated Elevator Production (Fig. 3.9)

Fig. 3.9

Elevator swing return computer-integrated production system: Three levels of automation systems are integrated, including (1) link to computer-aided design (CAD) for individual customer elevator specifications and customized finish, (2) link to direct numerical control (DNC) of workcell machine and manufacturing activities, (3) link to management information system (MIS) and computer integrated manufacturing system (CIM) for accounting and shipping management (source: [3.12])

Process

Fabrication and production execution and management.

Platform

Automatic tools, machines, and robots, integrated with system-of-systems, comprising a production/manufacturing installation, with automated material handling equipment, fabrication and finishing machines and processes, and software and communication systems for production planning and control, robotic manipulators, and cranes. Human operators and supervisors are also included.

Autonomy

Automatic control, including knowledge-based control of laser, press, buffing, and sanding machines/cells; automated control of material handling and material flow, and of management information flow.

Case G: Water Treatment (Fig. 3.10)

Fig. 3.10

(a) Municipal water treatment system in compliance with the Safe Drinking Water Federal Act (courtesy of City of Kewanee, IL; Engineered Fluid, Inc.; and Rockwell Automation Co.). (b) Wastewater treatment and disposal (courtesy of Rockwell Automation Co.)

Source

Rockwell Automation Co., Cleveland (www.rockwellautomation.com).

Process

Water treatment by reverse osmosis filtering system (Fig. 3.10a) and water treatment and disposal ((Fig. 3.10b). When preparing to filter impurities from the city water, the controllers activate the pumps, which in turn flush wells to clean water sufficiently before it flows through the filtering equipment (Fig. 3.10a) or activating complete system for removal of grit, sediments, and disposal of sludge to clean water supply (Fig. 3.10b).

Platform

Installation including water treatment plant with a network of pumping stations, integrated with programmable and supervisory control and data acquisition (SCADA) control, remote communication software system, and human supervisory interfaces.

Autonomy

Monitoring and tracking the entire water treatment and purification system.

Case H: Digital Document Workflow (Fig. 3.11)

Fig. 3.11

Document imaging and color printing workflow: (a) streamlined workflow by FreeFlow. (b) Detail shows document scanner workstation with automatic document feeder and image processing (courtesy of Xerox Co., Norwalk)

Source

Xerox Co., Norwalk (http://www.xerox.com).

Process

On-demand customized processing, production, and delivery of print, email, and customized web sites.

Platform

Network of devices, machines, robots, and software systems integrated within a system-of-systems with media technologies.

Autonomy

Integration of automatic workflow of document image capture, processing, enhancing, preparing, producing, and distributing.

Case I: Ship Building Automation (Fig. 3.12)

Fig. 3.12

Automation and control systems in shipbuilding: (a) production management through enterprise resource planning (ERP) systems. Manufacturing automation in shipbuilding examples: (b) overview; (c) automatic panel welding robots system; (d) sensor application in membrane tank fabrication; (e) propeller grinding process by robotic automation. Automatic ship operation systems examples: (f) overview; (g) alarm and monitoring system; (h) integrated bridge system; (i) power management system; (j) engine monitoring system (source: [3.13]) (with permission from Hyundai Heavy Industries)

Source

[3.13]; Korea Shipbuilderʼs Association, Seoul (http://www.koshipa.or.kr); Hyundai Heavy Industries, Co., Ltd., Ulsan (http://www.hhi.co.kr).

Process

Shipbuilding manufacturing process control and automation; shipbuilding production, logistics, and service management; ship operations management.

Platform

Devices, tools, machines and multiple robots; system-of-software systems; system of systems.

Autonomy

Automatic control of manufacturing processes and quality assurance; automatic monitoring, planning and decision support software systems; integrated control and collaborative control for ship operations and bridge control of critical automatic functions of engine, power supply systems, and alarm systems.

Case J: Energy Power Substation Automation (Fig. 3.13)

Fig. 3.13

Integrated power substation control system: (a) overview; (b) substation automation platform chassis (courtesy of GE Energy Co., Atlanta) (LAN – local area network, DA – data acquisition, UR – universal relay, MUX – multiplexor, HMI – human-machine interface)

Source

GE Energy Co., Atlanta (http://www.gepower.com/prod serv/products/substation automation/en/downloads/po.pdf).

Process

Automatically monitoring and activating backup power supply in case of breakdown in the power generation and distribution. Each substation automation platform has processing capacity to monitor and control thousands of input–output points and intelligent electronic devices over the network.

Platform

Devices integrated with a network of system-of-systems, including substation automation platforms, each communicating with and controlling thousands of power network devices.

Autonomy

Power generation, transmission, and distribution automation, including automatic steady voltage control, based on user-defined targets and settings; local/remote control of distributed devices; adjustment of control set-points based on control requests or control input values; automatic reclosure of tripped circuit breakers following momentary faults; automatic transfer of load and restoration of power to nonfaulty sections if possible; automatically locating and isolating faults to reduce customersʼ outage times; monitoring a network of substations and moving load off overloaded transformers to other stations as required.

These ten case studies cover a variety of automation domains. They also demonstrate different level of intelligence programmed into the automation application, different degrees of automation, and various types of automation flexibility. The meaning of these automation characteristics is explained in the next section.

Flexibility, Degrees, and Levels of Automation

Increasingly, solutions of societyʼs problems cannot be satisfied by, and therefore cannot mean the automation of just a single, repeated process. By automating the networking and integration of devices and systems, they are able to perform different and variable tasks. Increasingly, this ability also requires cooperation (sharing of information and resources) and collaboration (sharing in the execution and responses) with other devices and systems. Thus, devices and systems have to be designed with inherent flexibility, which is motivated by the clientsʼ requirements. With growing demand for service and product variety, there is also an increase in the expectations by users and customers for greater reliability, responsiveness, and smooth interoperability. Thus, the meaning of automation also involves the aspects of its flexibility, degree, and levels.

To enable design for flexibility, certain standards and measures have been and will continue to be established. Automation flexibility, often overlapping with the level of automation intelligence, depends on two main considerations:

• 1. The number of different states that can be assumed automatically

• 2. The length of time and amount of effort (setup process) necessary to respond and execute a change of state.

The number of different possible states and the cost of changes required are linked with two interrelated measures of flexibility: application flexibility and adaptation flexibility (Fig. 3.14). Both measures are concerned with the possible situations of the system and its environment. Automation solutions may address only switching between undisturbed, standard operations and nominal, variable situations, or can also aspire to respond when operations encounter disruptions and transitions, such as errors and conflicts, or significant design changes.

Fig. 3.14

Application flexibility and adaptation flexibility in machining automation (after [3.14])

Application flexibility measures the number of different work states, scenarios, and conditions a system can handle. It can be defined as the probability that an arbitrary task, out of a given class of such tasks, can be carried out automatically. A relative comparison between the application flexibility of alternative designs is relevant mostly for the same domain of automation solutions. For instance, in Fig. 3.14 it is the domain of machining.

Adaptation flexibility is a measure of the time duration and the cost incurred for an automation device or system to transition from one given work state to another. Adaptation flexibility can also be measured only relatively, by comparing one automation device or system with another, and only for one defined change of state at a time. The change of state involves being in one possible state prior to the transition, and one possible state after it.

A relative estimate of the two flexibility measures (dimensions) for several implementations of machine tools automation is illustrated in Fig. 3.14. For generality, both measures are calibrated between 0 and 1.

Degree of Automation

Another dimension of automation, besides measures of its inherent flexibility, is the degree of automation. Automation can mean fully automatic or semi-automatic devices and systems, as exemplified in case A (Sect. 3.3.1), with the steam turbine speed governor, and in case E (Sect. 3.3.5), with a mix of robots and operators in elevator production. When a device or system is not fully automatic, meaning that some, or more frequent human intervention is required, they are considered automated, or semiautomatic, equivalent terms implying partial automation.

A measure of the degree of automation, between fully manual to fully automatic, has been used to guide the design rationalization and compare between alternative solutions. Progression in the degree of automation in machining is illustrated in Fig. 3.14. The increase of automated partial functions is evident when comparing the drilling machine with the more flexible machines that can also drill and, in addition, are able to perform other processes such as milling.

The degree of automation can be defined as the fraction of automated functions out of the overall functions of an installation or system. It is calculated as the ratio between the number of automated operations, and the total number of operations that need to be performed, resulting in a value between 0 and 1. Thus, for a device or system with partial automation, where not all operations or functions are automatic, the degree of automation is less than 1. Practically, there are several methods to determine the degree of automation. The derived value requires a description of the method assumptions and steps. Typically, the degree of automation is associated with characteristics of:

• 1. Platform (device, system, etc.)

• 2. Group of platforms

• 3. Location, site

• 4. Plant, facility

• 5. Process and its scope

• 6. Process measures, e.g., operation cycle

• 7. Automatic control

• 8. Power source

• 9. Economic aspects

• 10. Environmental effects.

In determining the degree of automation of a given application, whether the following functions are also supposed to be considered must also be specified:

• 1. Setup

• 2. Organization, reorganization

• 3. Control and communication

• 4. Handling (of parts, components, etc.)

• 5. Maintenance and repair

• 6. Operation and process planning

• 7. Construction

• 8. Administration.

For example, suppose we consider the automation of a document processing system (case H, Sect. 3.3.8) which is limited to only the scanning process, thus omitting other workflow functions such as document feeding, joining, virtual inspection, and failure recovery. Then if the scanning is automatic, the degree of automation would be 1. However, if the other functions are also considered and they are not automatic, then the value would be less than 1.

Methods to determine the degree of automation divide into two categories:

• Relative determination applying a graded scale, containing all the functions of a defined domain process, relative to a defined system and the corresponding degrees of automation. For any given device or system in this domain, the degree of automation is found through comparison with the graded scale. This procedure is similar to other graded scales, e.g., Mohsʼ hardness scale, and Beaufort wind-speed scale. This method is illustrated in Fig. 3.15, which shows an example of the graded scale of mechanization and automation, following the scale developed by Bright [3.15].

• Relative determination by a ratio between the autonomous and nonautonomous measures of reference. The most common measure of reference is the number of decisions made during the process under consideration (Table 3.9). Other useful measures of reference for this determination are the comparative ratios of:

  • Rate of service quality

  • Human labor

  • Time measures of effort

  • Cycle time

  • Number of mobility and motion functions

  • Program steps.

Fig. 3.15

Automation scale: scale for comparing grades of automation (after [3.15])

Table 3.9 Degree of automation: calculation by the ratio of decision types

To illustrate the method in reference to decisions made during the process, consider an example for case B (Sect. 3.3.2). Suppose in the bioreactor system process there is a total of seven decisions made automatically by the devices and five made by a human laboratory supervisor. Because these decisions are not similar, the degree of automation cannot be calculated simply as the ratio 7/(7 + 5) ≈ 0.58. The decisions must be weighted by their complexity, which is usually assessed by the number of control program commands or steps (Table 3.9). Hence, the degree of automation can be calculated as:

Now designers can compare this automation design against relatively more and less elaborate options. Rationalization will have to assess the costs, benefits, risks, and acceptability of the degree of automation for each alternative design.

Whenever the degree of automation is determined by a method, the following conditions must be followed:

• 1. The method is able to reproduce the same procedure consistently.

• 2. Comparison is only done between objective measures.

• 3. Degree values should be calibrated between 0 and 1 to simplify calculations and relative comparisons.

Levels of Automation, Intelligence, and Human Variability

There is obviously an inherent relation between the level of automation flexibility, the degree of automation, and the level of intelligence of a given automation application. While there are no absolute measures of any of them, their meaning is useful and intriguing to inventors, designers, users, and clients of automation. Levels of automation are shown in Table 3.10 based on the intelligent human ability they represent.

Table 3.10 Levels of automation (updated and expanded after [3.16])

It is interesting to note that the progression in our ability to develop and implement higher levels of automation follows the progress in our understanding of relatively more complex platforms; more elaborate control, communication, and solutions of computational complexity; process and operation programmability; and our ability to generate renewable, sustainable, and mobile power sources.

Worldwide Surveys: What Does Automation Mean to People?

With known human variability, we are all concerned about automation, enjoy its benefits, and wonder about its risks. But all individuals do not share our attitude towards automation equally, and it does not mean the same to everyone. We often hear people say:

• Iʼll have to ask my grandson to program the video recorder.

• I hate my cell-phone.

• I canʼt imagine my life without a cell-phone.

• Those dumb computers.

• Sorry, I do not use elevators, Iʼll climb the six floors and meet you there!

etc.

In an effort to explore the meaning of automation to people around the world, a random, nonscientific survey was conducted during 2007–2008 by the author, with the help of the following colleagues: Carlos Pereira (Brazil), Jose Ceroni (Chile), Alexandre Dolgui (France), Sigal Berman, Yael Edan, and Amit Gil (Israel), Kazayuhi Ishii, Masayuki Matsui, Jing Son, and Tetsuo Yamada (Japan), Jeong Wootae (Korea), Luis Basañez and Raúl Suárez Feijóo (Spain), Chin-Yin Huang (Taiwan), and Xin W. Chen (USA). Since the majority of the survey participants are students, undergraduate and graduate, and since they migrate globally, the respondents actually originate from all continents. In other words, while it is not a scientific survey, it carries a worldwide meaning.

The survey population includes 331 respondents, from three general categories:

• 1. Undergraduate students of engineering, science, management, and medical sciences (251)

• 2. Graduate students from the same disciplines (70)

• 3. Nonstudents, experts, and novices in automation (10).

Three questions were posed in this survey:

• How do you define automation (do not use a dictionary)?

• When and where did you encounter and recognize automation first in your life (probably as a child)?

• What do you think is the major impact/contribution of automation to humankind (only one)?

The answers are summarized in Tables 3.11–3.13.

Table 3.11 How do you define automation (do not use a dictionary)?

Table 3.12 When and where did you encounter and recognize automation first in your life (probably as a child)?

Table 3.13 What do you think is the major impact/contribution of automation to humankind (only one)?

How Do We Define Automation?

The key answer to this was question was (Table 3.11, no. 3): operate without or with little human participation (24%). This answer reflects a meaning that corresponds well with the definition in the beginning of this chapter. It was the most popular response in Asia–Pacific and South America.

Overall, the 12 types of definition meanings follow three main themes: How automation works (answer nos. 2, 6, 9, 11, total 30%); automation replaces humans, works without them, or augments their functions (answer nos. 1, 3, 7, 10, total 54%); automation improves (answer nos. 4, 5, 8, 12, total 16%).

Interestingly, the overall most popular answer (answer no. 1; 27%) is a partially wrong answer. (It was actually the significantly most popular response only in Europe and Israel.) Replacing human work may represent legacy fear of automation. This answer lacks the recognition that most automation applications are performing tasks humans cannot accomplish. The latter is also a partial, yet positive, meaning of automation and is addressed by answer no. 7 (2%).

Answer nos. 2, 6, 9, and 11 (total 29%) represent a factual meaning of how automation is implemented. Answer 10, found only in North America responses, addresses a meaning of automation that narrows it down to information technology. Answer nos. 4, 5, and 12 (total 14%) imply that automation means improvements and assistance. Finally, answer no. 8, promote human value (2%, but found only in Asia–Pacific, and Europe and Israel) may reflect cultural meaning more than a definition.

In addition to the regional response variations mentioned above, it turns out that the first four answers in Table 3.11 comprise the majority of meaning in each region: 73% for Asia–Pacific; 89% for Europe and Israel and for South America; and 92% for North America (86% worldwide). In Asia–Pacific, four other answer types each comprised 4–6% of the 12 answer types.

When and Where Did We Encounter Automation First in Our Life?

The key answer to this question was: automated manufacturing machine or factory (16%), which was the top answer in North and South America, but only the third response in Asia–Pacific and in Europe and Israel. The top answer in Asia–Pacific is shared by vending machine and automatic door (14% each), and in Europe and Israel the top answer was car, truck, motorcycle, and components (14%). Overall, the 62 types of responses to this question represent a wide range of what automation means to us.

There are variations in responses between regions, with only two answers – no. 1, automated manufacturing machine, factory, and no. 2, vending machine: snacks, candy, drink, tickets – shared by at least three of the four regions surveyed. Automatic door is in the top three only in Asia–Pacific; car, truck, motorcycle, and components is in the top three only in Europe and Israel; computer is in the top three only in North America; and robot is in the top three only in South America.

Of the 62 response types, almost 40 appear in only one region (or are negligible in other regions), and it is interesting to relate the regional context of the first encounter with automation. For instance:

• Answers no. 34, train (unmanned) and no. 36, automated grinding machine for sharpening knives, appear as responses mostly in Asia–Pacific (3%)

• Answers no. 12, dishwasher (4%); no. 35, x-ray machine (2%); and no. 58, traffic light (1%) appear as responses mostly in Europe and Israel

• Answers no. 19, barcode scanner (4%); no. 38, automatic bottle filling (2%); no. 39, automatic toll collection (2%); and no. 59, treadmill, (2%) appear as responses mostly in North America.

What Do We Think Is the Major Impact/Contribution of Automation to Humankind?

Thirty-seven types of impacts or benefits were found in the survey responses overall. The most inspiring impact and benefit of automation found is answer no. 4, encourage/inspire creative work; inspire newer solutions (6%).

The most popular response was: save time, increase productivity/efficiency; 24/7 operations (22%). The key risk identified, answer no. 16, was: replace people; people lose jobs (2%, but interestingly, this was not found in South America). Another risky impact identified is answer no. 35, people lose abilities to complete tasks, (1%, only in Europe and Israel). Nevertheless, the majority (98%) identified 35 types of positive impacts and benefits.

Emerging Trends

Many of us perceive the meaning of the automatic and automated factories and gadgets of the 20th and 21st century as outstanding examples of the human spirit and human ingenuity, no less than art; their disciplined organization and synchronized complex of carefully programmed functions and services mean to us harmonious expression, similar to good music (when they work).

Clearly, there is a mixture of emotions towards automation: Some of us are dismayed that humans cannot usually be as accurate, or as fast, or as responsive, attentive, and indefatigable as automation systems and installations. On the other hand, we sometimes hear the word automaton or robot describing a person or an organization that lacks consideration and compassion, let alone passion. Let us recall that automation is made by people and for the people. But can it run away by its own autonomy and become undesirable? Future automation will advance in micro- and nanosystems and systems-of-systems. Bioinspired automation and bioinspired collaborative control theory will significantly improve artificial intelligence, and the quality of robotics and automation, as well as the engineering of their safety and security. In this context, it is interesting to examine the role of automation in the 20th and 21st centuries.

Automation Trends of the 20th and 21st Centuries

The US National Academy of Engineering, which includes US and worldwide experts, compiled the list shown in Table 3.14 as the top 20 achievements that have shaped a century and changed the world [3.17]. The table adds columns indicating the role that automation has played in each achievement, and clearly, automation has been relevant in all of them and essential to most of them.

Table 3.14 Top engineering achievements in the 20th century [3.17] and the role of automation

The US National Academy of Engineers has also compiled a list of the grand challenges for the 21st century. These challenges are listed in Table 3.15 with the anticipated and emerging role of automation in each. Again, automation is relevant to all of them and essential to most of them.

Table 3.15 Grand engineering challenges for the 21st century [3.17] and the role of automation

Some of the main trends in automation are described next.

Bioautomation

Bioinspired automation, also known as bioautomation or evolutionary automation, is emerging based on the trend of bioinspired computing, control, and AI. They influence traditional automation and artificial intelligence in the methods they offer for evolutionary machine learning, as opposed to what can be described as generative methods (sometimes called creationist methods) used in traditional programming and learning. In traditional methods, intelligence is typically programmed from top down: Automation engineers and programmers create and implement the automation logic, and define the scope, functions, and limits of its intelligence. Bioinspired automation, on the other hand, is also created and implemented by automation engineers and programmers, but follows a bottom-up decentralized and distributed approach. Bioinspired techniques often involve a method of specifying a set of simple rules, followed and iteratively applied by a set of simple and autonomous manmade organisms. After several generations of rule repetition, initially manmade mechanisms of self-learning, self-repair, and self-organization enable self-evolution towards more complex behaviors. Complexity can result in unexpected behaviors, which may be robust and more reliable; can be counterintuitive compared with the original design; but can potentially become undesirable, out-of-control, and unsafe behaviors. This subject has been under intense research and examination in recent years.

Natural evolution and system biology (biology-inspired automation mechanisms for systems engineering) are the driving analogies of this trend: concurrent steps and rules of responsive selection, interdependent recombination, reproduction, mutation, reformation, adaptation, and death-and-birth can be defined, similar to how complex organisms function and evolve in nature. Similar automation techniques are used in genetic algorithms, artificial neural networks, swarm algorithms, and other emerging evolutionary automation systems. Mechanisms of self-organization, parallelism, fault tolerance, recovery, backup, and redundancy are being developed and researched for future automation, in areas such as neuro-fuzzy techniques, biorobotics, digital organisms, artificial cognitive models and architectures, artificial life, bionics, and bioinformatics. See related topics in many following handbook chapters, particularly, 29, 75 and 76.

Collaborative Control Theory and e-Collaboration

Collaboration of humans, and its advantages and challenges are well known from prehistory and throughout history, but have received increased attention with the advent of communication technology. Significantly better enabled and potentially streamlined and even optimized through e-Collaboration (based on communication via electronic means), it is emerging as one of the most powerful trends in automation, with telecommunication, computer communication, and wireless communication influencing education and research, engineering and business, healthcare and service industries, and global society in general. Those developments, in turn, motivate and propel further applications and theoretical investigations into this highly intelligent level of automation (Table 3.10, level A₉ and higher).

Interesting examples of the e-Collaboration trend include wikis, which since the early 2000s have been increasingly adopted by enterprises as collaborative software, enriching static intranets and the Internet. Examples of e-Collaborative applications that emerged in the 1990s include project communication for coplanning, sharing the creation and editing of design documents as codesign and codocumentation, and mutual inspiration for collaborative innovation and invention through cobrainstorming.

Beyond human–human automation-supported collaboration through better and more powerful communication technology, there is a well-known but not yet fully understood trend for collaborative e-Work. Associated with this field is collaborative control theory (CCT), which is under development. Collaborative e-Work is motivated by significantly improved performance of humans leveraging their collaborative automatic agents. The latter, from software automata (e.g., constructive bots as opposed to spam and other destructive bots) to automation devices, multisensors, multiagents, and multirobots can operate in a parallel, autonomous cyberspace, thus multiplying our productivity and increasing our ability to design sustainable systems and operations. A related important trend is the emergence of active middleware for collaboration support of device networks and of human team networks and enterprises.

More about this subject area can be found in several chapters of this handbook, particularly in Chaps. 12, 14, 26, and 88.

Risks of Automation

As civilization increasingly depends on automation and looks for automation to support solutions of its serious problems, the risks associated with automation must be understood and eliminated. Failures of automation on a very large scale are most risky. Just a few examples of disasters caused by automation failures are nuclear accidents; power supply disruptions and blackout; Federal Aviation Administration control systems failures causing air transportation delays and shutdowns; cellular communication network failures; and water supply failures. The impacts of severe natural and manmade disasters on automated infrastructure are therefore the target of intense research and development. In addition, automation experts are challenged to apply automation to enable sustainability and better mitigate and eliminate natural and manmade disasters, such as security, safety, and health calamities.

Need for Dependability, Survivability, Security, and Continuity of Operation

Emerging efforts are addressing better automation dependability and security by structured backup and recovery of information and communication systems. For instance, with service orientation that is able to survive, automation can enable gradual and degraded services by sustaining critical continuity of operations until the repair, recovery, and resumption of full services. Automation continues to be designed with the goal of preventing and eliminating any conceivable errors, failures, and conflicts, within economic constraints. In addition, the trend of collaborative flexibility being designed into automation frameworks encourages reconfiguration tools that redirect available, safe resources to support the most critical functions, rather that designing absolutely failure-proof system.

With the trend towards collaborative, networked automation systems, dependability, survivability, security, and continuity of operations are increasingly being enabled by autonomous self-activities, such as:

• Self-awareness and situation awareness

• Self-configuration

• Self-explaining and self-rationalizing

• Self-healing and self-repair

• Self-optimization

• Self-organization

• Self-protection for security.

Other dimensions of emerging automation risks involve privacy invasion, electronic surveillance, accuracy and integrity concerns, intellectual and physical property protection and security, accessibility issues, confidentiality, etc. Increasingly, people ask about the meaning of automation, how can we benefit from it, yet find a way to contain its risks and powers. At the extreme of this concern is the automation singularity [3.18].

Automation singularity follows the evident acceleration of technological developments and discoveries. At some point, people ask, is it possible that superhuman machines can take over the human race? If we build them too autonomous, with collaborative ability to self-improve and self-sustain, would they not eventually be able to exceed human intelligence? In other words, superintelligent machines may autonomously, automatically, produce discoveries that are too complex for humans to comprehend; they may even act in ways that we consider out of control, chaotic, and even aimed at damaging and overpowering people. This emerging trend of thought will no doubt energize future research on how to prevent automation from running on its own without limits. In a way, this is the 21st century human challenge of never play with fire.

Conclusion

This chapter explores the meaning of automation to people around the world. After review of the evolution of automation and its influence on civilization, its main contributions and attributes, a survey is used to summarize highlights of the meaning of automation according to people around the world. Finally, emerging trends in automation and concerns about automation are also described. They can be summarized as addressing three general questions:

• 1. How can automation be improved and become more useful and dependable?

• 2. How can we limit automation from being too risky when it fails?

• 3. How can we develop better automation that is more autonomous and performs better, yet does not take over our humanity?

These topics are discussed in detail in the chapters of this handbook.

Further Reading

• U. Alon: An Introduction to Systems Biology: Design Principles of Biological Circuits (Chapman Hall, New York 2006)

• R.C. Asfahl: Robots and Manufacturing Automation, 2nd edn. (Wiley, New York 1992)

• M.V. Butz, O. Sigaud, G. Pezzulo, G. Baldassarre (Eds.): Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior (Springer, Berlin Heidelberg 2007)

• Center for Chemical Process Safety (CCPS): Guidelines for Safe and Reliable Instrumented Protective Systems (Wiley, New York 2007)

• K. Collins: PLC Programming For Industrial Automation (Exposure, Goodyear 2007)

• C.H. Dagli, O. Ersoy, A.L. Buczak, D.L. Enke, M. Embrechts: Intelligent Engineering Systems Through Artificial Neural Networks: Smart Systems Engineering, Comput. Intell. Architect. Complex Eng. Syst., Vol. 17 (ASME, New York 2007)

• R.C. Dorf, R.H. Bishop: Modern Control Systems (Pearson, Upper Saddle River 2007)

• R.C. Dorf, S.Y. Nof (Eds.): International Encyclopedia of Robotics and Automation (Wiley, New York 1988)

• K. Elleithy (ed.): Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering (Springer, Berlin Heidelberg 2008)

• K. Evans: Programming of CNC Machines, 3rd edn. (Industrial Press, New York 2007)

• M. Fewster, D. Graham: Software Test Automation: Effective Use of Test Execution Tools (Addison-Wesley, Reading 2000)

• P.G. Friedmann: Automation and Control Systems Economics, 2nd edn. (ISA, Research Triangle Park 2006)

• C.Y. Huang, S.Y. Nof: Automation technology. In: Handbook of Industrial Engineering, ed. by G. Salvendy (Wiley, New York 2001), 3rd edn., Chap. 5

• D.C. Jacobs, J.S. Yudken: The Internet, Organizational Change and Labor: The Challenge of Virtualization (Routledge, London 2003)

• S. Jaderstrom, J. Miller (Eds.): Complete Office Handbook: The Definitive Reference for Todayʼs Electronic Office, 3rd edn. (Random House, New York 2002)

• T.R. Kochtanek, J.R. Matthews: Library Information Systems: From Library Automation to Distributed Information Access Solutions (Libraries Unlimited, Santa Barbara 2002)

• E.M. Marszal, E.W. Scharpf: Safety Integrity Level Selection: Systematic Methods Including Layer of Protection Analysis (ISA, Research Triangle Park 2002)

• A.P. Mathur: Foundations of Software Testing (Addison-Wesley, Reading 2008)

• D.F. Noble: Forces of Production: A Social History of Industrial Automation (Oxford Univ. Press, Cambridge 1986)

• R. Parasuraman, T.B. Sheridan, C.D. Wickens: A model for types and levels of human interaction with automation, IEEE Trans. Syst. Man Cyber. 30(3), 286–197 (2000)

• G.D. Putnik, M.M. Cunha (Eds.): Encyclopedia of Networked and Virtual Organizations (Information Science Reference, 2008)

• R.K. Rainer, E. Turban: Introduction to Information Systems, 2nd edn. (Wiley, New York 2009)

• R.L. Shell, E.L. Hall (Eds.): Handbook of Industrial Automation (CRC, Boca Raton 2000)

• T.B. Sheridan: Humans and Automation: System Design and Research Issues (Wiley, New York 2002)

• V. Trevathan: A Guide to the Automation Body of Knowledge, 2nd edn. (ISA, Research Triangle Park 2006)

• L. Wang, K.C. Tan: Modern Industrial Automation Software Design (Wiley-IEEE Press, New York 2006)

• N. Wiener: Cybernetics, or the Control and Communication in the Animal and the Machine, 2nd edn. (MIT Press, Cambridge 1965)

• T.J. Williams, S.Y. Nof: Control models. In: Handbook of Indus trial Engineering, ed. by G. Salvendy (Wiley, New York 1992), 2nd edn., Chap. 9