Team-Centered Perspective for Adaptive Automation Design
Lawrence J.Prinzel
Langley Research Center, Hampton, Virginia
Abstract
Automation represents a very active area of human factors research. The journal, Human Factors, published a special issue on automation in 1985. Since then, hundreds of scientific studies have been published examining the nature of automation and its interaction with human performance. However, despite a dramatic increase in research investigating human factors issues in aviation automation, there remain areas that need further exploration. This NASA Technical Memorandum describes a new area of automation design and research, called “adaptive automation.” It discusses the concepts and outlines the human factors issues associated with the new method of adaptive function allocation. The primary focus is on human-centered design, and specifically on ensuring that adaptive automation is from a team-centered perspective. The document shows that adaptive automation has many human factors issues common to traditional automation design. Much like the introduction of other new technologies and paradigm shifts, adaptive automation presents an opportunity to remediate current problems but poses new ones for human-automation interaction in aerospace operations. The review here is intended to communicate the philosophical perspective and direction of adaptive automation research conducted under the Aerospace Operations Systems (AOS), Physiological and Psychological Stressors and Factors (PPSF) project.
Key words:Adaptive Automation; Human-Centered Design; Automation; Human Factors
Introduction
"During the 1970s and early 1980s...the concept of automating as much as possible was considered appropriate. The expected benefit was a reduction in
pilot workload and increased safety...Although many of these benefits have been
realized, serious questions have arisen and incidents/accidents that have occurred
which question the underlying assumptions that a maximum available
automation is ALWAYS appropriate or that we understand how to design
automated systems so that they are fully compatible with the capabilities and
limitations of the humans in the system."
---- ATA, 1989
The Air Transport Association of America (ATA) Flight Systems Integration Committee(1989) made the above statement in response to the proliferation of automation in aviation. They noted that technology improvements, such as the ground proximity warning system, have had dramatic benefits; others, such as the electronic library system, offer marginal benefits at best. Such observations have led many in the human factors community, most notably Charles Billings (1991; 1997) of NASA, to assert that automation should be approached from a "human-centered design" perspective.
The period from 1970 to the present was marked by an increase in the use of electronic display units (EDUs); a period that Billings (1997) calls "information" and “management automation." The increased use of altitude, heading, power, and navigation displays; alerting and warning systems, such as the traffic alert and collision avoidance system (TCAS) and ground proximity warning system (GPWS; E-GPWS; TAWS); flight management systems (FMS) and flight guidance (e.g., autopilots; autothrottles) have "been accompanied by certain costs, including an increased cognitive burden on pilots, new information requirements that have required additional training, and more complex, tightly coupled, less observable systems" (Billings, 1997). As a result, human factors research in aviation has focused on the effects of information and management automation. The issues of interest include over-reliance on automation, "clumsy" automation (e.g., Wiener, 1989), digital versus analog control, skill degradation, crew coordination, and data overload (e.g., Billings, 1997). Furthermore, research has also been directed toward situational awareness (mode & state awareness; Endsley, 1994; Woods & Sarter, 1991) associated with complexity, coupling, autonomy, and inadequate feedback. Finally, human factors research has introduced new automation concepts that will need to be integrated into the existing suite of aviation
automation.
Clearly, the human factors issues of automation have significant implications for safety in aviation. However, what exactly do we mean by automation? The way we choose to define automation has considerable meaning for how we see the human role in modern aerospace systems. The next section considers the concept of automation, followed by an examination of human factors issues of human-automation interaction in aviation. Next, a potential remedy to the problems raised is described, called adaptive automation. Finally, the human-centered design philosophy is discussed and proposals are made for how the philosophy can be applied to this advanced form of automation. The perspective is considered in terms of the Physiological /Psychological Stressors & Factors project and directions for research on adaptive automation.
Automation in Modern Aviation
Definition.Automation refers to "...systems or methods in which many of the processes of production are automatically performed or controlled by autonomous machines or electronic devices" (Parsons, 1985). Automation is a tool, or resource, that the human operator can use to perform some task that would be difficult or impossible without machine aiding (Billings, 1997). Therefore, automation can be thought of as a process of substituting the activity of some device or machine for some human activity; or it can be thought of as a state of technological development (Parsons, 1985). However, some people (e.g., Woods, 1996) have questioned whether automation should be viewed as a substitution of one agent for another (see "apparent simplicity, real complexity" below). Nevertheless, the presence of automation has pervaded almost every aspect of modern lives. From the wheel to the modern jet aircraft, humans have sought to improve the quality of life. We have built machines and systems that not only make work easier, more efficient, and safe, but also give us more leisure time. The advent of automation has further enabled us to achieve this end. With automation, machines can now perform many of the activities that we once had to do. Our automobile transmission will shift gears for us. Our airplanes will fly themselves for us. All we have to do is turn the machine on and off. It has even been suggested that one day there may not be a need for us to do even that. However, the increase in “cognitive” accidents resulting from faulty human-automation interaction have led many in the human factors community to conclude that such a statement may be premature.
Automation Accidents. A number of aviation accidents and incidents have been directly attributed to automation. Examples of such in aviation mishaps include (from Billings, 1997):
DC-10 landing in control wheel steering A330 accident at Toulouse
B-747 upset over Pacific DC-10 overrun at JFK, New York
B-747 uncommandedroll,Nakina,Ont. A320 accident at Mulhouse-Habsheim
A320 accident at Strasbourg A300 accident at Nagoya
B-757 accident at Cali, Columbia A320 accident at Bangalore
A320 landing at Hong Kong B-737 wet runway overruns
A320 overrun at Warsaw B-757 climbout at Manchester
A310 approach at Orly DC-9 wind shear at Charlotte
Billings (1997) notes that each of these accidents has a different etiology, and that human factors investigation of causes show the matter to be complex. However, what is clear is that the percentage of accident causes has fundamentally shifted from machine-caused to human-caused (estimations of 60-80% due to human error) etiologies, and the shift is attributable to the change in types of automation that have evolved in aviation.
Types of Automation
There are a number of different types of automation and the descriptions of them vary considerably. Billings (1997) offers the following types of automation:
?Open-Loop Mechanical or Electronic Control.Automation is controlled by gravity or spring motors driving gears and cams that allow continous and repetitive motion. Positioning, forcing, and timing were dictated by the mechanism and environmental factors (e.g., wind). The automation of factories during the Industrial Revolution would represent this type of automation.
?Classic Linear Feedback Control. Automation is controlled as a function of differences between a reference setting of desired output and the actual output. Changes are made to system parameters to re-set the automation to conformance. An example of this type of automation would be flyball governor on the steam engine. What engineers call conventional proportional-integral-derivative (PID) control would also fit in this category of automation.
?Optimal Control. A computer-based model of controlled processes is driven by the same control inputs as that used to control the automated process. The model output is used to project future states and is thus used to determine the next control input. A "Kalman filtering" approach is used to estimate the system state to determine what the best control input should be.
?Adaptive Control. This type of automation actually represents a number of approaches to controlling automation, but usually stands for automation that changes dynamically in response to a change in state. Examples include the use of "crisp" and "fuzzy" controllers, neural networks, dynamic control, and many other nonlinear methods.
Levels of Automation
In addition to “types” of automation, we can also conceptualize different “levels” of automation control that the operator can have. A number of taxonomies have been put forth, but perhaps the best known is the one proposed by Tom Sheridan of Massachusetts Institute of Technology (MIT). Sheridan (1987) listed 10 levels of automation control:
1. The computer offers no assistance, the human must do it all
2. The computer offers a complete set of action alternatives
3. The computer narrows the selection down to a few
4. The computer suggests a selection, and
5. Executes that suggestion if the human approves, or
6. Allows the human a restricted time to veto before automatic execution, or
7. Executes automatically, then necessarily informs the human, or
8. Informs the human after execution only if he asks, or
9. Informs the human after execution if it, the computer, decides to
10. The computer decides everything and acts autonomously, ignoring the human
The list covers the automation gamut from fully manual to fully automatic. Although different researchers define adaptive automation differently across these levels, the consensus is that adaptive automation can represent anything from Level 3 to Level 9. However, what makes adaptive automation different is the philosophy of the approach taken to initiate adaptive function allocation and how such an approach may address the impact of current automation technology.
Impact of Automation Technology
Advantages of Automation. Wiener (1980; 1989) noted a number of advantages to automating human-machine systems. These include increased capacity and productivity, reduction of small errors, reduction of manual workload and mental fatigue, relief from routine operations, more precise handling of routine operations, economical use of machines, and decrease of performance variation due to individual differences. Wiener and Curry (1980) listed eight reasons for the increase in flight-deck automation: (a) Increase in available technology, such as FMS, Ground Proximity Warning System (GPWS), Traffic Alert and
Collision Avoidance System (TCAS), etc.; (b) concern for safety; (c) economy, maintenance, and reliability; (d) workload reduction and two-pilot transport aircraft certification; (e) flight maneuvers and navigation precision; (f) display flexibility; (g) economy of cockpit space; and (h) special requirements for military missions.
Disadvantages of Automation. Automation also has a number of disadvantages that have been noted. Automation increases the burdens and complexities for those responsible for operating, troubleshooting, and managing systems. Woods (1996) stated that automation is "...a wrapped package -- a package that consists of many different dimensions bundled together as a hardware/software system. When new automated systems are introduced into a field of practice, change is precipitated along multiple dimensions." As Woods (1996) noted, some of these changes include: ( a) adds to or changes the task, such as device setup and initialization, configuration control, and operating sequences; (b) changes cognitive demands, such as requirements for increased situational awareness; (c) changes the roles of people in the system, often relegating people to supervisory controllers; (d) automation increases coupling and integration among parts of a system often resulting in data overload and "transparency"; and (e) the adverse impacts of automation is often not appreciated by those who advocate the technology. These changes can result in lower job satisfaction (automation seen as dehumanizing human roles), lowered vigilance, fault-intolerant systems, silent failures, an increase in cognitive workload, automation-induced failures, over-reliance, complacency, decreased trust, manual skill erosion, false alarms, and a decrease in mode awareness (Wiener, 1989).
Adaptive Automation
Disadvantages of automation have resulted in increased interest in advanced automation concepts. One of these concepts is automation that is dynamic or adaptive in nature (Hancock & Chignell, 1987; Morrison, Gluckman, & Deaton, 1991; Rouse, 1977; 1988). In an aviation context, adaptive automation control of tasks can be passed back and forth between the pilot and automated systems in response to the changing task demands of modern aircraft. Consequently, this allows for the restructuring of the task environment based upon (a) what is automated, (b) when it should be automated, and (c) how it is automated (Rouse, 1988; Scerbo, 1996). Rouse
(1988) described criteria for adaptive aiding systems:
The level of aiding, as well as the ways in which human and aid
interact, should change as task demands vary. More specifically,
the level of aiding should increase as task demands become such
that human performance will unacceptably degrade without
aiding. Further, the ways in which human and aid interact should
become increasingly streamlined as task demands increase.
Finally, it is quite likely that variations in level of aiding and
modes of interaction will have to be initiated by the aid rather than
by the human whose excess task demands have created a situation
requiring aiding. The term adaptive aiding is used to denote aiding
concepts that meet [these] requirements.
Adaptive aiding attempts to optimize the allocation of tasks by creating a mechanism for determining when tasks need to be automated (Morrison, Cohen, & Gluckman, 1993). In adaptive automation, the level or mode of automation can be modified in real time. Further, unlike traditional forms of automation, both the system and the pilot share control over changes in the state of automation (Scerbo, 1994; 1996). Parasuraman, Bahri, Deaton, Morrison, and Barnes (1992) have argued that adaptive automation represents the optimal coupling of the level of pilot workload to the level of automation in the tasks. Thus, adaptive automation invokes automation only when task demands exceed the pilot's capabilities. Otherwise, the pilot retains manual control of the system functions. Although concerns have been raised about the dangers of adaptive automation (Billings & Woods, 1994; Wiener, 1989), it promises to regulate workload, bolster situational awareness, enhance vigilance, maintain manual skill levels, increase task involvement, and generally improve pilot performance.
Strategies for Invoking Automation
Perhaps the most critical challenge facing system designers seeking to implement automation concerns how changes among modes or levels of automation will be accomplished (Parasuraman et al., 1992; Scerbo, 1996). Traditional forms of automation usually start with some task or functional analysis and attempt to fit the operational tasks necessary to the abilities of the human or the system. The approach often takes the form of a functional allocation analysis (e.g., Fitt's List) in which an attempt is made to determine whether the human or the system is better suited to do each task. However, many in the field have pointed out the problem with trying to equate the two in automated systems, as each have special characteristics that impede simple classification taxonomies. Such ideas as these have led some to suggest other ways of determining human-automation mixes. Although certainly not exhaustive, some of these ideas are presented below.
Dynamic Workload Assessment.One approach involves the dynamic assessment of
measures that index the operators' state of mental engagement. (Parasuraman et al., 1992; Rouse,1988). The question, however, is what the "trigger" should be for the allocation of functions between the pilot and the automation system. Numerous researchers have suggested that adaptive systems respond to variations in operator workload (Hancock & Chignell, 1987; 1988; Hancock, Chignell & Lowenthal, 1985; Humphrey & Kramer, 1994; Reising, 1985; Riley, 1985; Rouse, 1977), and that measures of workload be used to initiate changes in automation modes. Such measures include primary and secondary-task measures, subjective workload measures, and physiological measures. The question, however, is what adaptive mechanism should be used to determine operator mental workload (Scerbo, 1996).
Performance Measures. One criterion would be to monitor the performance of the operator (Hancock & Chignel, 1987). Some criteria for performance would be specified in the system parameters, and the degree to which the operator deviates from the criteria (i.e., errors), the system would invoke levels of adaptive automation. For example, Kaber, Prinzel, Clammann, & Wright (2002) used secondary task measures to invoke adaptive automation to help with information processing of air traffic controllers. As Scerbo (1996) noted, however,"...such an approach would be of limited utility because the system would be entirely reactive."
Psychophysiological Measures.Another criterion would be the cognitive and attentional state of the operator as measured by psychophysiological measures (Byrne & Parasuraman, 1996). An example of such an approach is that by Pope, Bogart, and Bartolome (1996) and Prinzel, Freeman, Scerbo, Mikulka, and Pope (2000) who used a closed-loop system to dynamically regulate the level of "engagement" that the subject had with a tracking task. The system indexes engagement on the basis of EEG brainwave patterns.
Human Performance Modeling.Another approach would be to model the performance of the operator. The approach would allow the system to develop a number of standards for operator performance that are derived from models of the operator. An example is Card, Moran, and Newell (1987) discussion of a "model human processor." They discussed aspects of the human processor that could be used to model various levels of human performance. Another example is Geddes (1985) and his colleagues (Rouse, Geddes, & Curry, 1987-1988) who provided a model to invoke automation based upon system information, the environment, and expected operator behaviors (Scerbo, 1996).
Mission Analysis. A final strategy would be to monitor the activities of the mission or task (Morrison & Gluckman, 1994). Although this method of adaptive automation may be the
most accessible at the current state of technology, Bahri et al. (1992) stated that such monitoring systems lack sophistication and are not well integrated and coupled to monitor operator workload or performance (Scerbo, 1996). An example of a mission analysis approach to adaptive automation is Barnes and Grossman (1985) who developed a system that uses critical events to allocate among automation modes. In this system, the detection of critical events, such as emergency situations or high workload periods, invoked automation.
Adaptive Automation Human Factors Issues
A number of issues, however, have been raised by the use of adaptive automation, and many of these issues are the same as those raised almost 20 years ago by Curry and Wiener (1980). Therefore, these issues are applicable not only to advanced automation concepts, such as adaptive automation, but to traditional forms of automation already in place in complex systems (e.g., airplanes, trains, process control).
Although certainly one can make the case that adaptive automation is "dressed up" automation and therefore has many of the same problems, it is also important to note that the trend towards such forms of automation does have unique issues that accompany it. As Billings & Woods (1994) stated, "[i]n high-risk, dynamic environments...technology-centered automation has tended to decrease human involvement in system tasks, and has thus impaired human situation awareness; both are unwanted consequences of today's system designs, but both are dangerous in high-risk systems. [At its present state of development,] adaptive ("self-adapting") automation represents a potentially serious threat ... to the authority that the human pilot must have to fulfill his or her responsibility for flight safety."
The Need for Human Factors Research.Nevertheless, such concerns should not preclude us from researching the impact that such forms of advanced automation are sure to have on h uman performance. Consider Hancock’s (1996; 1997) examination of the "teleology for technology." He suggests that automation shall continue to impact our lives requiring humans to co-evolve with the technology; Hancock called this "techneology."
What Peter Hancock attempts to communicate to the human factors community is that automation will continue to evolve whether or not human factors chooses to be part of it. As Wiener and Curry (1980) conclude: "The rapid pace of automation is outstripping one's ability to comprehend all the implications for crew performance. It is unrealistic to call for a halt to cockpit automation until the manifestations are completely understood. We do, however, call for those designing, analyzing, and installing automatic systems in the cockpit to do so carefully; to recognize the behavioral effects of automation; to avail themselves of present and
future guidelines; and to be watchful for symptoms that might appear in training and operational settings." The concerns they raised are as valid today as they were 23 years ago. However, this should not be taken to mean that we should capitulate. Instead, because Wiener and Curry’s observation suggests that it may be impossible to fully research any new technology before implementation, we need to form a taxonomy and research plan to maximize human factors input for concurrent engineering of adaptive automation.
Classification of Human Factors Issues. Kantowitz and Campbell (1996) identified some of the key human factors issues to be considered in the design of advanced automated systems. These include allocation of function, stimulus-response compatibility, and mental models. Scerbo (1996) further suggested the need for research on teams, communication, and training and practice in adaptive automated systems design. The impact of adaptive automation systems on monitoring behavior, situational awareness, skill degradation, and social dynamics also needs to be investigated. Generally however, Billings (1997) stated that the problems of automation share one or more of the following characteristics: Brittleness, opacity, literalism, clumsiness, monitoring requirement, and data overload. These characteristics should inform design guidelines for the development, analysis, and implementation of adaptive automation technologies. The characteristics are defined as: ?Brittleness refers to "...an attribute of a system that works well under normal or usual conditions but that does not have desired behavior at or close to some margin of its operating envelope."
?Opacity reflects the degree of understanding of how and why automation functions as it does. The term is closely associated with "mode awareness" (Sarter & Woods, 1994), "transparency"; or "virtuality" (Schneiderman, 1992).
?Literalism concern the "narrow-mindedness" of the automated system; that is, the flexibility of the system to respond to novel events.
?Clumsiness was coined by Wiener (1989) to refer to automation that reduced workload demands when the demands are already low (e.g., transit flight phase), but increases them when attention and resources are needed elsewhere (e.g., descent phase of flight). An example is when the co-pilot needs to re-program the FMS, to change the plane's descent path, at a time when the co-pilot should be scanning for other planes.
?Monitoring requirement refers to the behavioral and cognitive costs associated with increased "supervisory control" (Sheridan, 1987; 1991).
?Data overload points to the increase in information in modern automated contexts (Billings, 1997).
These characteristics of automation have relevance for defining the scope of human
factors issues likely to plague adaptive automation design if significant attention is not directed toward ensuring human-centered design. The human factors research community has noted that these characteristics can lead to human factors issues of allocation of function (i.e., when and how should functions be allocated adaptively); stimulus-response compatibility and new error modes; how adaptive automation will affect mental models, situation models, and representational models; concerns about mode unawareness and situation awareness decay; manual skill decay and the “out-of-the-loop” performance problem; clumsy automation and task/workload management; and issues related to the design of automation. This last issue points to the significant concern in the human factors community of how to design adaptive automation so that it reflects what has been called “team-centered”; that is, successful adaptive automation will likely embody the concept of the “electronic team member”. However, past research (e.g., Pilots Associate Program) has shown that designing automation to reflect such a role has significantly different requirements than those arising in traditional automation design. The field is currently focused on answering the questions, “what is it that defines one as a team member?” and “how does that definition translate into designing automation to reflect that role?” Unfortunately, the literature also shows that the answer is not transparent and, therefore, adaptive automation must first tackle its own unique and difficult problems before it may be considered a viable prescription to current human-automation interaction problems. The next section describes the concept of the electronic team member and then discusses the literature with regard to team dynamics, coordination, communication, shared mental models, and the implications of these for adaptive automation design.
Adaptive Automation as Electronic Team Member
Layton, Smith, and McCoy (1994) stated that the design of automated systems should be from a team-centered approach; the design should allow for the coordination between machine agents and human practitioners. However, many researchers have noted that automated systems tend to fail as team players (Billings, 1991; Malin & Schreckenghost, 1992; Malin et al., 1991;Sarter & Woods, 1994; Scerbo, 1994; 1996; Woods, 1996). The reason is what Woods (1996) calls “apparent simplicity, real complexity.”
Apparent Simplicity, Real Complexity.Woods (1996) stated that conventional wisdom about automation makes technology change seem simple. Automation can be seen as simply changing the human agent for a machine agent. Automation further provides for more options and methods, frees up operator time to do other things, provides new computer graphics and interfaces, and reduces human error. However, the reality is that technology change has often
resulted in the design of automated systems that are strong, silent, clumsy, and difficult to direct. Woods (1996) stated that these types of systems are “are not team players." The literature has described these as:
?Strong automation refers to automation that act autonomously and possess authority.
A number of researchers (Billings, 1997; Norman, 1990; Sarter & Woods, 1994; Wiener,1989) have noted that increased autonomy and authority creates new monitoring and coordination demands for operators of a system (Woods, 1996; Scerbo, 1996).
?Automation can also be silent; that is, the automation does not provide feedback about its activities. Sarter and Woods (1994) have noted that many operators often ask "what is it [the automation] doing?" This has been termed "mode awareness." Automation that is strong and silent lacks "transparency" (Billings, 1997) or "virtuality" (Mayher, 1992). The operator is not often aware what the system is doing and why; therefore, strong systems tend not "to communicate effectively" (Matlin & Schrenkenghost, 1992).
?Clumsy automation (Wiener, 1989) refers to automation that lightens crew workload when it is already low, but increases workload when situational demands become greater. As stated earlier, automation often requires human intervention to manage the automation at times when human attention should be focused elsewhere (e.g., scanning for traffic).
?Automation can also be difficult to direct when systems are designed to be intricate and laborious to manage.
A Team-Centered Approach. Billings (1997) argued for a "human-centered approach" that drives system design from the users' perspective. "Human-centered automation means automation designed to work cooperatively with human operators in pursuit of stated objectives." Although some (Sheridan, 1995) questioned the utility of the concept suggesting that it was an "oxymoron," Billings noted that humans are responsible for the outcomes of automation; therefore, humans should be the primary focus of any "team-centered" design. According to Billings, a human-centered design requires that the operator have authority and responsibility, that the operator must be always be informed, that operators must be able to monitor the automation, that the automation must be predictable, that the automation must also monitor the human, and that the two must communicate intent with each other. Billings suggested that these should serve as guidelines in the design of adaptive automation technology. Although human-centered automation is currently fashionable, its precise definition is difficult to pin down. It can mean:
?Allocating to the human the tasks best suited to the human and allocating to the automation the tasks best suited to it
?Maintaining the human operator as the final authority over the automation, or keeping the human in command
?Keeping the human operator in the decision and control loop
?Keeping the human operator involved in the system
?Keeping the human operator informed
?Making the human operator's job easier, more enjoyable, or more satisfying through automation
?Empowering or enhancing the human operation to the greatest extent possible through automation
?Generating trust in the automation by the human operator
?Giving the operator computer-based advice about everything he or she might want to know
?Engineering the automation to reduce human error and keep response variability to the minimum
?Casting the operator in the role of supervisor of subordinate automation control system(s)
?Achieving the best combination of human and automatic control, best being defined by explicit system objectives
?Making it easy to train operators to use automation effectively, minimizing training time and costs
?Creating similarity and commonality in various models and derivatives that may be operated by same person
?Allowing the human operator to monitor the automation
?Making the automated systems predictable
?Allowing the automated systems to monitor the human operator
?Designing each element of the system to have knowledge of the other's intent
?Ensuring automation has the characteristics of team players
Characteristics of Team Players. Malin and Schreckenghost (1992) discussed some of the characteristics of team players important for developing coupled, automated human-computer interaction. First, a team player is reliable; that is, a team member should reliably perform assigned tasks, which may require alternate ways of completing the assignment. Thus, intelligent systems should be designed to be both robust and flexible. A robust and flexible system would be able to perform when "things don't go as expected." Second, a team player communicates effectively with others. Intelligent systems must provide enough information so that the human understands what the computer is trying to do and why.
However, the system must not provide so much information that the human becomes overloaded with it. Next, a team player must coordinate activities with others; that is team members must make sure that their performance does not interfere with others' performance. Furthermore, team members should be able to monitor each other and "back each other up." Therefore, intelligent systems must be designed so that both the computer and human are able to monitor each others' activities and exchange information about the tasks being performed. Finally, a team player is guided by a coach. The human, therefore, is both a team member who performs tasks, but also is a "coach"who manages and supervises the activities of the system. However, in some situations, the human may not be the "expert" and, therefore, the computer should be designed to provide advice (i.e., an expert system).
Factors Affecting Team Performance. In addition to characteristics of what makes a team player, it is also important for design of adaptive automation to consider those factors involved in optimal team performance. Nieva, Fleishman, and Rieck (1978) discussed four factors that have been shown to affect team performance. First, the knowledge, skills, and abilities (KSAs) are important contributions to successful team performance. The greater the KSAs, the greater the potential for team success. Next, task characteristics determine how individual and interdependent activities will impact the team cohesiveness. Third, team characteristics are important determinants of successful performance. Finally, the environment affects the ability of the team to accomplish directed objectives, such as winning games or completing stated tasks.
Fleishman and Zaccaro (1992) incorporated these four factors into a model of team behavior that emphasizes micro- and macro-level contributions to team behavior. They posited seven categories of team functions: motivation, systems monitoring, orientation, resource distribution, timing, response coordination, and procedure maintenance. These are defined as:
?Motivation concerns the stated team goals and other causal factors that engender team participation and cooperation.
?The systems monitoring functions refer to measurement issues regarding how team progress should be assessed.
?Orientation was defined as those factors that influence the acquisition and distribution of information, such as details about team goals, potential problems, delimiting factors, and so on.
?Resource distribution refers to the identification and allocation of tasks to individual members based upon their KSAs.
?Timing specifies the pace of work at both the individual and team levels.
?Response coordination identifies the sequence and timing of team member responses.
?Finally, procedure maintenance refers to the supervision of team behavior for compliance with stated and implicit organizational and societal norms and policies.
Scerbo (1994) compared these seven team functions to aviation automation. He suggested that there are a number of analogs for these functions to advanced automation concepts, such as adaptive automation (he specifically examined the taxonomy in the Pilot Associate (PA) and Adaptive Function Allocation for Intelligent Cockpits (AFAIC) program). Scerbo suggested that these characteristic programs illustrate the growing team-centered approach for automation design. However, he also noted that there are still many team-centered issues that remain to be explored in adaptive automation design (Scerbo, 1996). These team-centered issues include adaptive automation interface design and communication, supervisory control, and thus involved in situation awareness maintenance and mental model development.
Adaptive Automation Interface.Scerbo (1996), in discussing the team-centered issues for adaptive automation, suggested that the design of adaptive automation should depend largely on the design of the interface. Effective communication among team members is critical for successful performance. However, Wiener (1993) stated that advanced automation has decreased crew coordination and resource management. Wiener (1989) cited some cockpit observations that identify several crew coordination issues:
?"Compared to traditional models, it is physically difficult for one pilot to see what the other is doing.... Though some carriers have a procedure that requires the captain (or pilot flying) to approve any changes entered into the CDU before they are executed, this is seldom done; often he or she is working on the CDU on another page [on the FMS] at the same time."
?"Automation tends to induce a breakdown of the traditional (and stated) roles and duties of the pilot-flying versus pilot-not-flying and a less clear demarcation of 'who does what' that in traditional cockpits. In aircraft in the past, the standardization of allocation of duties and functions has been one of the foundations of cockpit safety."
Therefore, in general, automated systems tend to decrease the communication among operators.
Furthermore, automation may increase pilot error because the usual checks are impractical to implement (e.g., checking the accuracy of programming the FMS). In fact, Sears (1986) noted that 26% of aviation accidents were caused by inadequate cross check by the second crewmember. With adaptive automation, this figure has the potential to increase dramatically if human engineering doesn’t ensure sufficient transparent interface between flightcrew, ATC, and other aviation operators and adaptive automation.
红外数据通信技术外文翻译文献(文档含中英文对照即英文原文和中文翻译) Infrared Remote Control System Abstract Red outside data correspondence the technique be currently within the scope of world drive extensive usage of a kind of wireless conjunction technique, drive numerous hardware and software platform support. Red outside the transceiver product have cost low, small scaled turn, the baud rate be quick, point to point SSL, be free from electromagnetism thousand Raos
etc. characteristics, can realization information at dissimilarity of the product fast, convenience, safely exchange and transmission, at short distance wireless deliver aspect to own very obvious of advantage. Along with red outside the data deliver a technique more and more mature, the cost descend, red outside the transceiver necessarily will get at the short distance communication realm more extensive of application. The purpose that design this system is transmit customer’s operation information with infrared rays for transmit media, then demodulate original signal with receive circuit. It use coding chip to modulate signal and use decoding chip to demodulate signal. The coding chip is PT2262 and decoding chip is PT2272. Both chips are made in Taiwan. Main work principle is that we provide to input the information for the PT2262 with coding keyboard. The input information was coded by PT2262 and loading to high frequent load wave whose frequent is 38 kHz, then modulate infrared transmit dioxide and radiate space outside when it attian enough power. The receive circuit receive the signal and demodulate original information. The original signal was decoded by PT2272, so as to drive some circuit to accomplish customer’s operation demand. Keywords: Infrared dray；Code；Decoding；LM386；Red outside transceiver 1 Introduction 1.1 research the background and significance Infrared Data Communication Technology is the world wide use of a wireless connection technology, by the many hardware and software platforms supported. Is a data through electrical pulses and infrared optical pulse switch between the wireless data transceiver technology.
中等分辨率制备分离的 快速色谱技术 W. Clark Still,* Michael K a h n , and Abhijit Mitra Departm(7nt o/ Chemistry, Columbia Uniuersity,1Veu York, Neu; York 10027 ReceiLied January 26, 1978 我们希望找到一种简单的吸附色谱技术用于有机化合物的常规净化。这种技术是适于传统的有机物大规模制备分离，该技术需使用长柱色谱法。尽管这种技术得到的效果非常好，但是其需要消耗大量的时间，并且由于频带拖尾经常出现低复原率。当分离的样本剂量大于1或者2g时，这些问题显得更加突出。近年来，几种制备系统已经进行了改进，能将分离时间减少到1-3h，并允许各成分的分辨率ΔR f≥（使用薄层色谱分析进行分析）。在这些方法中，在我们的实验室中，媒介压力色谱法1和短柱色谱法2是最成功的。最近，我们发现一种可以将分离速度大幅度提升的技术，可用于反应产物的常规提纯，我们将这种技术称为急骤色谱法。虽然这种技术的分辨率只是中等（ΔR f≥），而且构建这个系统花费非常低，并且能在10-15min内分离重量在的样本。4 急骤色谱法是以空气压力驱动的混合介质压力以及短柱色谱法为基础，专门针对快速分离，介质压力以及短柱色谱已经进行了优化。优化实验是在一组标准条件5下进行的，优化实验使用苯甲醇作为样本，放在一个20mm*5in.的硅胶柱60内，使用Tracor 970紫外检测器监测圆柱的输出。分辨率通过持续时间（r）和峰宽（w,w/2）的比率进行测定的（Figure 1），结果如图2-4所示，图2-4分别放映分辨率随着硅胶颗粒大小、洗脱液流速和样本大小的变化。
毕业设计说明书 英文文献及中文翻译 学院：专 2011年6月 电子与计算机科学技术软件工程
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转型衰退时期的土木工程研究 Sergios Lambropoulosa[1], John-Paris Pantouvakisb, Marina Marinellic 摘要 最近的全球经济和金融危机导致许多国家的经济陷入衰退，特别是在欧盟的周边。这些国家目前面临的民用建筑基础设施的公共投资和私人投资显著收缩，导致在民事特别是在民用建筑方向的失业。因此，在所有国家在经济衰退的专业发展对于土木工程应届毕业生来说是努力和资历的不相称的研究，因为他们很少有机会在实践中积累经验和知识，这些逐渐成为过时的经验和知识。在这种情况下，对于技术性大学在国家经济衰退的计划和实施的土木工程研究大纲的一个实质性的改革势在必行。目的是使毕业生拓宽他们的专业活动的范围，提高他们的就业能力。 在本文中，提出了土木工程研究课程的不断扩大，特别是在发展的光毕业生的潜在的项目，计划和投资组合管理。在这个方向上，一个全面的文献回顾，包括ASCE体为第二十一世纪，IPMA的能力的基础知识，建议在其他：显著增加所提供的模块和项目管理在战略管理中添加新的模块，领导行为，配送管理，组织和环境等；提供足够的专业训练五年的大学的研究；并由专业机构促进应届大学生认证。建议通过改革教学大纲为土木工程研究目前由国家技术提供了例证雅典大学。 1引言 土木工程研究（CES）蓬勃发展，是在第二次世界大战后。土木工程师的出现最初是由重建被摧毁的巨大需求所致，目的是更多和更好的社会追求。但是很快，这种演变一个长期的趋势，因为政府为了努力实现经济发展，采取了全世界的凯恩斯主义的理论，即公共基础设施投资作为动力。首先积极的结果导致公民为了更好的生活条件（住房，旅游等）和增加私人投资基础设施而创造机会。这些现象再国家的发展中尤为为明显。虽然前景并不明朗（例如，世界石油危机在70年代），在80年代领先的国家采用新自由主义经济的方法（如里根经济政策），这是最近的金融危机及金融危机造成的后果（即收缩的基础设施投资，在技术部门的高失业率），消除发展前途无限的误区。 技术教育的大学所认可的大量研究土木工程部。旧学校拓展专业并且新的学校建成，并招收许多学生。由于高的职业声望，薪酬，吸引高质量的学校的学生。在工程量的增加和科学技术的发展，导致到极强的专业性，无论是在研究还是工作当中。结构工程师，液压工程师，交通工程师等，都属于土木工程。试图在不同的国家采用专业性的权利，不同的解决方案，，从一个统一的大学学历和广泛的专业化的一般职业许可证。这个问题在许多其他行业成为关键。国际专业协会的专家和机构所确定的国家性检查机构，经过考试后，他们证明不仅是行业的新来者，而且专家通过时间来确定进展情况。尽管在很多情况下，这些证书虽然没有国家接受，他们赞赏和公认的世界。 在试图改革大学研究（不仅在土木工程）更接近市场需求的过程中，欧盟确定了1999博洛尼亚宣言，它引入了一个二能级系统。第一级度（例如，一个三年的学士）是进入
软件工程专业BIOS资料外文翻译文献 What is the Basic Input Output System (BIOS)? BIOS is an acronym for Basic Input Output System. It is the program that stores configuration details about your computer hardware and enables your computer to boot up. Every time your computer is switched on the BIOS loads configuration data into main memory, performs a routine diagnostic test on your hardware, then loads the operating system. The BIOS resides in a ROM (Read-Only memory) chip, which is mounted on the motherboard, usually in a socket so it is removable. To the right is an example of what a BIOS chip may look like in your motherboard. This is a PLCC 32 pin type BIOS chip. It is a very common type. Every computer has BIOS. There are many types but the most common type of BIOS 's come from: AMI, Award and Phoenix. Motherboard manufacturers buy or lease the BIOS source code from these companies. The BIOS tells the operating system in your computer how to boot up, where to load everything, what to load, what memory and CPU are present and much more. A good comparison to further understand the
用于多跳认知无线电网络的分布式网络编码控制信道 Alfred Asterjadhi等著 1 前言 大多数电磁频谱由政府机构长期指定给公司或机构专门用于区域或国家地区。由于这种资源的静态分配，许可频谱的许多部分在许多时间和/或位置未使用或未被充分利用。另一方面，几种最近的无线技术在诸如IEEE802.11，蓝牙，Zigbee之类的非许可频段中运行，并且在一定程度上对WiMAX进行操作;这些技术已经看到这样的成功和扩散，他们正在访问的频谱- 主要是2.4 GHz ISM频段- 已经过度拥挤。为了为这些现有技术提供更多的频谱资源，并且允许替代和创新技术的潜在开发，最近已经提出允许被许可的设备（称为次要用户）访问那些许可的频谱资源，主要用户未被使用或零星地使用。这种方法通常被称为动态频谱接入（DSA），无线电设备发现和机会性利用未使用或未充分利用的频谱带的能力通常称为认知无线电（CR）技术。 DSA和CR最近都引起了无线通信和网络界的极大关注。通常设想两种主要应用。第一个是认知无线接入（CW A），根据该认知接入点，认知接入点负责识别未使用的许可频谱，并使用它来提供对次用户的接入。第二个应用是我们在这个技术中研究的应用，它是认知自组织网络（CAN），也就是使用 用于二级用户本身之间通信的无许可频谱，用于诸如点对点内容分发，环境监控，安全性等目的，灾难恢复情景通信，军事通信等等。 设计CAN系统比CW A有更多困难，主要有两个原因。第一是识别未使用的频谱。在CW A中，接入点的作用是连接到互联网，因此可以使用简单的策略来推断频谱可用性，例如查询频谱调节器在其地理位置的频谱可用性或直接与主用户协商频谱可用性或一些中间频谱经纪人另一方面，在CAN中，与频谱调节器或主要用户的缺乏直接通信需要二级用户能够使用检测技术自己识别未使用的频谱。第二个困难是辅助用户协调媒体访问目的。在CW A中存在接入点和通常所有二级用户直接与之通信（即，网络是单跳）的事实使得直接使用集中式媒体接入控制（MAC）解决方案，如时分多址（TDMA）或正交频分多址（OFDMA）。相反，预计CAN将跨越多跳，缺少集中控制器;而对于传统的单通道多跳自组织网络而言，这个问题的几个解决方案是已知的，因为假设我们处理允许设备访问的具有成
New technique of the computer network Abstract The 21 century is an ages of the information economy, being the computer network technique of representative techniques this ages, will be at very fast speed develop soon in continuously creatively, and will go deep into the people's work, life and study. Therefore, control this technique and then seem to be more to deliver the importance. Now I mainly introduce the new technique of a few networks in actuality live of application. keywords Internet Network System Digital Certificates Grid Storage 1. Foreword Internet turns 36, still a work in progress Thirty-six years after computer scientists at UCLA linked two bulky computers using a 15-foot gray cable, testing a new way for exchanging data over networks, what would ultimately become the Internet remains a work in progress. University researchers are experimenting with ways to increase its capacity and speed. Programmers are trying to imbue Web pages with intelligence. And work is underway to re-engineer the network to reduce Spam (junk mail) and security troubles. All the while threats loom: Critics warn that commercial, legal and political pressures could hinder the types of innovations that made the Internet what it is today. Stephen Crocker and Vinton Cerf were among the graduate students who joined UCLA professor Len Klein rock in an engineering lab on Sept. 2, 1969, as bits of meaningless test data flowed silently between the two computers. By January, three other "nodes" joined the fledgling network.
项目成本控制 一、引言 项目是企业形象的窗口和效益的源泉。随着市场竞争日趋激烈，工程质量、文明施工要求不断提高，材料价格波动起伏，以及其他种种不确定因素的影响，使得项目运作处于较为严峻的环境之中。由此可见项目的成本控制是贯穿在工程建设自招投标阶段直到竣工验收的全过程，它是企业全面成本管理的重要环节，必须在组织和控制措施上给于高度的重视，以期达到提高企业经济效益的目的。 二、概述 工程施工项目成本控制，指在项目成本在成本发生和形成过程中，对生产经营所消耗的人力资源、物资资源和费用开支，进行指导、监督、调节和限制，及时预防、发现和纠正偏差从而把各项费用控制在计划成本的预定目标之内，以达到保证企业生产经营效益的目的。 三、施工企业成本控制原则 施工企业的成本控制是以施工项目成本控制为中心，施工项目成本控制原则是企业成本管理的基础和核心，施工企业项目经理部在对项目施工过程进行成本控制时，必须遵循以下基本原则。 3.1 成本最低化原则。施工项目成本控制的根本目的，在于通过成本管理的各种手段，促进不断降低施工项目成本，以达到可能实现最低的目标成本的要求。在实行成本最低化原则时，应注意降低成本的可能性和合理的成本最低化。一方面挖掘各种降低成本的能力，使可能性变为现实；另一方面要从实际出发，制定通过主观努力可能达到合理的最低成本水平。 3.2 全面成本控制原则。全面成本管理是全企业、全员和全过程的管理，亦称“三全”管理。项目成本的全员控制有一个系统的实质性内容，包括各部门、各单位的责任网络和班组经济核算等等，应防止成本控制人人有责，人人不管。项目成本的全过程控制要求成本控制工作要随着项目施工进展的各个阶段连续 进行，既不能疏漏，又不能时紧时松，应使施工项目成本自始至终置于有效的控制之下。 3.3 动态控制原则。施工项目是一次性的，成本控制应强调项目的中间控制，即动态控制。因为施工准备阶段的成本控制只是根据施工组织设计的具体内容确
中英文对照外文翻译 (文档含英文原文和中文翻译) Application Fundamentals Android applications are written in the Java programming language. The compiled Java code — along with any data and resource files required by the application — is bundled by the aapt tool into an Android package, an archive file marked by an .apk suffix. This file is the vehicle for distributing the application and installing it on mobile devices; it's the file users download to their devices. All the code in a single .apk file is considered to be one application. In many ways, each Android application lives in its own world: 1. By default, every application runs in its own Linux process. Android starts the process when any of the application's code needs to be executed, and shuts down the process when it's no longer needed and system resources are required by other applications. 2. Each process has its own virtual machine (VM), so application code runs in isolation from the code of all other applications. 3. By default, each application is assigned a unique Linux user ID. Permissions are set so that the application's files are visible only to that user and only to the application itself — although there are ways to export them to other applications as well. It's possible to arrange for two applications to share the same user ID, in which case they will be able to see each other's files. To conserve system resources, applications with the same ID can also arrange to run in the same Linux process, sharing the same
5G无线通信网络中英文对照外文翻译文献(文档含英文原文和中文翻译)
翻译： 5G无线通信网络的蜂窝结构和关键技术 摘要 第四代无线通信系统已经或者即将在许多国家部署。然而，随着无线移动设备和服务的激增，仍然有一些挑战尤其是4G所不能容纳的，例如像频谱危机和高能量消耗。无线系统设计师们面临着满足新型无线应用对高数据速率和机动性要求的持续性增长的需求，因此他们已经开始研究被期望于2020年后就能部署的第五代无线系统。在这篇文章里面，我们提出一个有内门和外门情景之分的潜在的蜂窝结构，并且讨论了多种可行性关于5G无线通信系统的技术，比如大量的MIMO技术，节能通信，认知的广播网络和可见光通信。面临潜在技术的未知挑战也被讨论了。 介绍 信息通信技术（ICT）创新合理的使用对世界经济的提高变得越来越重要。无线通信网络在全球ICT战略中也许是最挑剔的元素，并且支撑着很多其他的行业，它是世界上成长最快最有活力的行业之一。欧洲移动天文台（EMO）报道2010年移动通信业总计税收1740亿欧元,从而超过了航空航天业和制药业。无线技术的发展大大提高了人们在商业运作和社交功能方面通信和生活的能力无线移动通信的显著成就表现在技术创新的快速步伐。从1991年二代移动通信系统(2G)的初次登场到2001年三代系统（3G）的首次起飞，无线移动网络已经实现了从一个纯粹的技术系统到一个能承载大量多媒体内容网络的转变。4G无线系统被设计出来用来满足IMT-A技术使用IP面向所有服务的需求。在4G系统中，先进的无线接口被用于正交频分复用技术（OFDM），多输入多输出系统（MIMO）和链路自适应技术。4G无线网络可支持数据速率可达1Gb/s的低流度，比如流动局域无线访问，还有速率高达100M/s的高流速，例如像移动访问。LTE系统和它的延伸系统LTE-A，作为实用的4G系统已经在全球于最近期或不久的将来部署。 然而，每年仍然有戏剧性增长数量的用户支持移动宽频带系统。越来越多的
英文翻译 A comprehensive overview of substations Along with the economic development and the modern industry developments of quick rising, the design of the power supply system become more and more completely and system. Because the quickly increase electricity of factories, it also increases seriously to the dependable index of the economic condition, power supply in quantity. Therefore they need the higher and more perfect request to the power supply. Whether Design reasonable, not only affect directly the base investment and circulate the expenses with have the metal depletion in colour metal, but also will reflect the dependable in power supply and the safe in many facts. In a word, it is close with the economic performance and the safety of the people. The substation is an importance part of the electric power system, it is consisted of the electric appliances equipments and the Transmission and the Distribution. It obtains the electric power from the electric power system, through its function of transformation and assign, transport and safety. Then transport the power to every place with safe, dependable, and economical. As an important part of power’s transport and control, the transformer substation must change the mode of the traditional design and control, then can adapt to the modern electric power system, the development of modern industry and the of trend of the society life. Electric power industry is one of the foundations of national industry and national economic development to industry, it is a coal, oil, natural gas, hydropower, nuclear power, wind power and other energy conversion into electrical energy of the secondary energy industry, it for the other departments of the national economy fast and stable development of the provision of adequate power, and its level of development is a reflection of the country's economic development an important indicator of the level. As the power in the industry and the importance of the national economy, electricity transmission and distribution of electric energy used in these areas is an indispensable component.。Therefore, power transmission and distribution is critical. Substation is to enable superior power plant power plants or power after adjustments to the lower load of books is an important part of power transmission. Operation of its functions, the capacity of a direct impact on the size of the lower load power, thereby affecting the industrial production and power consumption.Substation system if a link failure, the system will protect the part of action. May result in power outages and so on, to the production and living a great disadvantage. Therefore, the substation in the electric power system for the protection of electricity reliability,
专业资料 学院： 专业：土木工程 姓名： 学号： 外文出处：Structural Systems to resist (用外文写) Lateral loads 附件：1.外文资料翻译译文；2.外文原文。
附件1：外文资料翻译译文 抗侧向荷载的结构体系 常用的结构体系 若已测出荷载量达数千万磅重，那么在高层建筑设计中就没有多少可以进行极其复杂的构思余地了。确实，较好的高层建筑普遍具有构思简单、表现明晰的特点。 这并不是说没有进行宏观构思的余地。实际上，正是因为有了这种宏观的构思，新奇的高层建筑体系才得以发展，可能更重要的是：几年以前才出现的一些新概念在今天的技术中已经变得平常了。 如果忽略一些与建筑材料密切相关的概念不谈，高层建筑里最为常用的结构体系便可分为如下几类： 1.抗弯矩框架。 2.支撑框架，包括偏心支撑框架。 3.剪力墙，包括钢板剪力墙。 4.筒中框架。 5.筒中筒结构。 6.核心交互结构。 7. 框格体系或束筒体系。 特别是由于最近趋向于更复杂的建筑形式，同时也需要增加刚度以抵抗几力和地震力，大多数高层建筑都具有由框架、支撑构架、剪力墙和相关体系相结合而构成的体系。而且，就较高的建筑物而言，大多数都是由交互式构件组成三维陈列。 将这些构件结合起来的方法正是高层建筑设计方法的本质。其结合方式需要在考虑环境、功能和费用后再发展，以便提供促使建筑发展达到新高度的有效结构。这并
不是说富于想象力的结构设计就能够创造出伟大建筑。正相反，有许多例优美的建筑仅得到结构工程师适当的支持就被创造出来了，然而，如果没有天赋甚厚的建筑师的创造力的指导，那么，得以发展的就只能是好的结构，并非是伟大的建筑。无论如何，要想创造出高层建筑真正非凡的设计，两者都需要最好的。 虽然在文献中通常可以见到有关这七种体系的全面性讨论，但是在这里还值得进一步讨论。设计方法的本质贯穿于整个讨论。设计方法的本质贯穿于整个讨论中。 抗弯矩框架 抗弯矩框架也许是低，中高度的建筑中常用的体系，它具有线性水平构件和垂直构件在接头处基本刚接之特点。这种框架用作独立的体系，或者和其他体系结合起来使用，以便提供所需要水平荷载抵抗力。对于较高的高层建筑，可能会发现该本系不宜作为独立体系，这是因为在侧向力的作用下难以调动足够的刚度。 我们可以利用STRESS，STRUDL 或者其他大量合适的计算机程序进行结构分析。所谓的门架法分析或悬臂法分析在当今的技术中无一席之地，由于柱梁节点固有柔性，并且由于初步设计应该力求突出体系的弱点，所以在初析中使用框架的中心距尺寸设计是司空惯的。当然，在设计的后期阶段，实际地评价结点的变形很有必要。 支撑框架 支撑框架实际上刚度比抗弯矩框架强，在高层建筑中也得到更广泛的应用。这种体系以其结点处铰接或则接的线性水平构件、垂直构件和斜撑构件而具特色，它通常与其他体系共同用于较高的建筑，并且作为一种独立的体系用在低、中高度的建筑中。
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中英文翻译 附件1：外文资料翻译译文 通用移动通信系统的回顾 1.1 UMTS网络架构 欧洲/日本的3G标准，被称为UMTS。 UMTS是一个在IMT-2000保护伞下的ITU-T 批准的许多标准之一。随着美国的CDMA2000标准的发展，它是目前占主导地位的标准，特别是运营商将cdmaOne部署为他们的2G技术。在写这本书时，日本是在3G 网络部署方面最先进的。三名现任运营商已经实施了三个不同的技术：J - PHONE 使用UMTS，KDDI拥有CDMA2000网络，最大的运营商NTT DoCoMo正在使用品牌的FOMA（自由多媒体接入）系统。 FOMA是基于原来的UMTS协议，而且更加的协调和标准化。 UMTS标准被定义为一个通过通用分组无线系统（GPRS）和全球演进的增强数据
技术（EDGE）从第二代GSM标准到UNTS的迁移，如图。这是一个广泛应用的基本原理，因为自2003年4月起，全球有超过847万GSM用户，占全球的移动用户数字的68％。重点是在保持尽可能多的GSM网络与新系统的操作。 我们现在在第三代（3G）的发展道路上，其中网络将支持所有类型的流量：语音，视频和数据，我们应该看到一个最终的爆炸在移动设备上的可用服务。此驱动技术是IP协议。现在，许多移动运营商在简称为2.5G的位置，伴随GPRS的部署，即将IP骨干网引入到移动核心网。在下图中，图2显示了一个在GPRS网络中的关键部件的概述，以及它是如何适应现有的GSM基础设施。 SGSN和GGSN之间的接口被称为Gn接口和使用GPRS隧道协议（GTP的，稍后讨论）。引进这种基础设施的首要原因是提供连接到外部分组网络如，Internet或企业Intranet。这使IP协议作为SGSN和GGSN之间的运输工具应用到网络。这使得数据服务，如移动设备上的电子邮件或浏览网页，用户被起诉基于数据流量，而不是时间连接基础上的数据量。3G网络和服务交付的主要标准是通用移动通信系统，或UMTS。首次部署的UMTS是发行'99架构，在下面的图3所示。 在这个网络中，主要的变化是在无线接入网络（RAN的）CDMA空中接口技术的引进，和在传输部分异步传输模式作为一种传输方式。这些变化已经引入，主要是为了支持在同一网络上的语音，视频和数据服务的运输。核心网络保持相对不变，主要是软件升级。然而，随着目前无线网络控制器使用IP与3G的GPRS业务支持节点进行通信，IP协议进一步应用到网络。 未来的进化步骤是第4版架构，如图4。在这里，GSM的核心被以语音IP技术为基础的IP网络基础设施取代。 海安的发展分为两个独立部分：媒体网关（MGW）和MSC服务器（MSS）的。这基本上是打破外连接的作用和连接控制。一个MSS可以处理多个MGW，使网络更具有扩展性。 因为现在有一些在3G网络的IP云，合并这些到一个IP或IP/ ATM骨干网是很有意义的（它很可能会提供两种选择运营商）。这使IP权利拓展到整个网络，一直到BTS(基站收发信台)。这被称为全IP网络，或推出五架构，如图五所示。在HLR/ VLR/VLR/EIR被推广和称为HLR的子系统（HSS）。 现在传统的电信交换的最后残余被删除，留下完全基于IP协议的网络运营，并