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Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on

Issue 6 • Date Nov. 2011

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  • Table of contents

    Page(s): C1 - 1037
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  • IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans publication information

    Page(s): C2
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  • Evaluation of the Impact of User-Cognitive Styles on the Assessment of Text Summarization

    Page(s): 1038 - 1051
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1350 KB) |  | HTML iconHTML  

    Text summarization techniques have been found to be effective with regard to helping users find relevant information faster. The effectiveness and efficiency of a user's performance in an information-seeking task can greatly be improved if he/she needs to only look at a summary that includes the relevant information presented in his/her preferred manner. On the other hand, if the main idea is misrepresented and/or omitted altogether from a summary, it may take users more time to solve a target problem or, even worse, lead users to make incorrect decisions. There is an important need to design a personalized text summarization system that takes into account both what a user is currently interested in and how a user perceives information. The latter factor is referred to as a user's cognitive styles. Although there are some existing approaches that have employed a user's interests to help in the design of a personalized text summarization system, there has been inadequate focus on exploring cognitive styles. This paper aims at studying the impact of a user's cognitive styles when assessing multidocument summaries. In particular, we choose two dimensions of a user's cognitive style - the analytic/wholist and verbal/imagery dimensions - and study their impacts on how a user assesses a summary that was generated from a set of documents. In particular, the type of a document set refers to whether the set's content is loosely or closely related. We use a document set type to explore if there are any differences in the users' assessments of summaries that were generated from sets of different types. The results of this paper show that different users have different assessments with regard to information coverage and the way that information is presented in both loosely and closely related document sets. In addition, we found that the coherency ratings that were given to summaries from the two types of document sets were significantly different between the analytic a- d wholist groups. This result leads us to investigate the impact of a user's cognitive styles and the following two factors that directly relate to the coherence of a summary: 1) graph entropy and 2) the percentage of stand-alone concepts. We found that these two factors and a user's cognitive styles affect a user's ratings on the coherency of a summary. View full abstract»

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  • Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty

    Page(s): 1052 - 1063
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (781 KB) |  | HTML iconHTML  

    This paper proposes to maintain player's engagement by adapting game difficulty according to player's emotions assessed from physiological signals. The validity of this approach was first tested by analyzing the questionnaire responses, electroencephalogram (EEG) signals, and peripheral signals of the players playing a Tetris game at three difficulty levels. This analysis confirms that the different difficulty levels correspond to distinguishable emotions, and that, playing several times at the same difficulty level gives rise to boredom. The next step was to train several classifiers to automatically detect the three emotional classes from EEG and peripheral signals in a player-independent framework. By using either type of signals, the emotional classes were successfully recovered, with EEG having a better accuracy than peripheral signals on short periods of time. After the fusion of the two signal categories, the accuracy raised up to 63%. View full abstract»

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  • A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors

    Page(s): 1064 - 1076
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    This paper presents a framework for hand gesture recognition based on the information fusion of a three-axis accelerometer (ACC) and multichannel electromyography (EMG) sensors. In our framework, the start and end points of meaningful gesture segments are detected automatically by the intensity of the EMG signals. A decision tree and multistream hidden Markov models are utilized as decision-level fusion to get the final results. For sign language recognition (SLR), experimental results on the classification of 72 Chinese Sign Language (CSL) words demonstrate the complementary functionality of the ACC and EMG sensors and the effectiveness of our framework. Additionally, the recognition of 40 CSL sentences is implemented to evaluate our framework for continuous SLR. For gesture-based control, a real-time interactive system is built as a virtual Rubik's cube game using 18 kinds of hand gestures as control commands. While ten subjects play the game, the performance is also examined in user-specific and user-independent classification. Our proposed framework facilitates intelligent and natural control in gesture-based interaction. View full abstract»

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  • Designing Buildings for Real Occupants: An Agent-Based Approach

    Page(s): 1077 - 1091
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (963 KB) |  | HTML iconHTML  

    Building information modeling is only beginning to incorporate human factors, although buildings are sites where humans and technologies interact with globally significant consequences. Some buildings fail to perform as their designers intended, in part because users do not or cannot properly operate the building, and some occupants behave differently than designers expect. Innovative buildings, e.g., green buildings, are particularly susceptible to usability problems. This paper presents a framework for prospectively measuring the usability of designs before buildings are constructed, while there is still time to improve the design. The framework, which was implemented as an agent-based computer simulation model, tests how well buildings are likely to perform, given realistic occupants. An illustrative model for lighting design shows that this modeling approach has practical efficacy, demonstrating that, to the extent that users exhibit heterogeneous behaviors and preferences, designs that allow greater local control and ease of operation perform better. View full abstract»

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  • Hidden Markov Model and Auction-Based Formulations of Sensor Coordination Mechanisms in Dynamic Task Environments

    Page(s): 1092 - 1106
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1651 KB) |  | HTML iconHTML  

    In this paper, multistage auction-based intelligence, surveillance, and reconnaissance (ISR) sensor coordination mechanisms are investigated in the context of dynamic and uncertain mission environments such as those faced by expeditionary strike groups. Each attribute of the mission task is modeled using a hidden Markov model (HMM) with controllable emission matrices, corresponding to each ISR asset package (subset of sensors). For each HMM-asset package pair, we evaluate a matrix of information gains (uncertainty reduction measures). The elements of this matrix depend on the asset coordination structure and the concomitant delays accrued. We consider three coordination structures (distributed ISR coordination, ISR officer serving as a coordinator, and ISR officer serving as a commander) here. We evaluate these structures on a hypothetical mission scenario that requires the monitoring of ISR activities in multiple geographic regions. The three structures are evaluated by comparing the task state estimation error cost, as well as travel, waiting, and assignment delays. The results of the analysis were used as a guide in the design of a mission scenario and asset composition for a team-in-the-loop experimentation. Our solution has the potential to be a mixed initiative decision support tool to an ISR coordinator/commander, where the human provides possible ISR asset package-task pairings and the tool evaluates the efficacy of the assignment in terms of task accuracy and delays. We also apply our approach to a hypothetical disaster management scenario involving chemical contamination and discuss the computational complexity of our approach. View full abstract»

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  • Decision Making of Networked Multiagent Systems for Interaction Structures

    Page(s): 1107 - 1121
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    Networked multiagent systems are very popular in large-scale application environments. In networked multiagent systems, the interaction structures can be shaped into the form of networks where each agent occupies a position that is determined by such agent's relations with others. To avoid collisions between agents, the decision of each agent's strategies should match its own interaction position, so that the strategies available to all agents are in line with their interaction structures. Therefore, this paper presents a novel decision-making model for networked multiagent strategies based on their interaction structures, where the set of strategies for an agent is conditionally decided by other agents within its dependence interaction substructure. With the presented model, the resulting strategies available to all agents can minimize the collisions of multiagents regarding their interaction structures, and the model can produce the same resulting strategies for the isomorphic interaction structures. Furthermore, this paper uses a multiagent citation network as a case study to demonstrate the effectiveness of the presented decision-making model. View full abstract»

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  • Socioscope: Human Relationship and Behavior Analysis in Social Networks

    Page(s): 1122 - 1143
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1947 KB) |  | HTML iconHTML  

    In this paper, we propose a socioscope model for social-network and human-behavior analysis based on mobile-phone call-detail records. Because of the diversity and complexity of human social behavior, no one technique will detect every attribute that arises when humans engage in social behaviors. We use multiple probability and statistical methods for quantifying social groups, relationships, and communication patterns and for detecting human-behavior changes. We propose a new index to measure the level of reciprocity between users and their communication partners. This reciprocity index has application in homeland security, detection of unwanted calls (e.g., spam), telecommunication presence, and product marketing. For the validation of our results, we used real-life call logs of 81 users which contain approximately 500 000 h of data on users' location, communication, and device-usage behavior collected over eight months at the Massachusetts Institute of Technology (MIT) by the Reality Mining Project group. Also, call logs of 20 users collected over six months by the University of North Texas (UNT) Network Security team are used. The MIT and UNT data sets contain approximately 5000 callers. The experimental results show that our model is effective. View full abstract»

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  • Analyzing and Visualizing Web Opinion Development and Social Interactions With Density-Based Clustering

    Page(s): 1144 - 1155
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    Due to the advancement of Web 2.0 technologies, a large volume of Web opinions is available on social media sites such as Web forums and Weblogs. These technologies provide a platform for Internet users around the world to communicate with each other and express their opinions. Analysis of developing Web opinions is potentially valuable for discovering ongoing topics of interests of the public like terrorist and crime detection, understanding how topics evolve together with the underlying social interaction between participants, and identifying important participants who have great influence in various topics of discussions. Nonetheless, the work of analyzing and clustering Web opinions is extremely challenging. Unlike regular documents, Web opinions are short and sparse text messages with noisy content. Typical document clustering techniques with the goal of clustering all documents applied to Web opinions produce unsatisfactory performance. In this paper, we investigated the density-based clustering algorithm and proposed the scalable distance-based clustering technique for Web opinion clustering. We conducted experiments and benchmarked with the density-based algorithm to show that the new algorithm obtains higher microaccuracy and macroaccuracy. This Web opinion clustering technique enables the identification of themes within discussions in Web social networks and their development, as well as the interactions of active participants. We also developed interactive visualization tools, which make use of the identified topic clusters to display social network development, the network topology similarity between topics, and the similarity values between participants. View full abstract»

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  • Combinatorial Algorithm for Reliability Analysis of Multistate Systems With Propagated Failures and Failure Isolation Effect

    Page(s): 1156 - 1165
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (293 KB) |  | HTML iconHTML  

    This paper considers the reliability analysis of multistate systems (MSSs) subject to propagated failure with global effect (PFGE) and failure isolation effect. The PFGE can be caused by an imperfect fault coverage despite the presence of fault-tolerant mechanism or by a destructive effect of failures that originate from some system components on other components. The failure isolation effect is caused by functional dependence among system components, where the failure of some component can prevent the propagation of failures that originate from other components within the same system. Existing approaches for simultaneously addressing PFGE and failure isolation are limited to binary-state systems in which the system and its components exhibit two and only two states: operation or failure. In practice, however, many systems are MSS in which the system and/or its components may exhibit multiple performance levels corresponding to different states ranging from perfect operation to complete failure. In this paper, a separable and combinatorial methodology is proposed for evaluating the reliability of MSS subject to both PFGE and the failure isolation effect. The proposed method has no limitation on the type of time-to-failure distributions for the system components and is applicable to MSS with any arbitrary system structure. Application and advantages of the proposed method are illustrated through a detailed analysis of an example of a multistate memory system. View full abstract»

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  • Dynamic Checking and Solution to Temporal Violations in Concurrent Workflow Processes

    Page(s): 1166 - 1181
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (793 KB) |  | HTML iconHTML  

    Current methods that deal with concurrent workflow temporal violations only focus on checking whether there are any temporal violations. They are not able to point out the path where the temporal violation happens and thus cannot provide specific solutions. This paper presents an approach based on a sprouting graph to find out the temporal violation paths in concurrent workflow processes as well as possible solutions to resolve the temporal violations. First, we model concurrent workflow processes with time workflow net and a sprouting graph. Second, we update the sprouting graph at the checking point. Finally, we find out the temporal violation paths and provide solutions. We apply the approach in a real business scenario to illustrate its advantages: 1) It can dynamically check temporal constraints of multiple concurrent workflow processes with resource constraints; 2) it can give the path information in the workflow processes where the temporal violation happens; and 3) it can provide solution to the temporal violation based on the analysis. View full abstract»

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  • Due-Date Management Through Iterative Bidding

    Page(s): 1182 - 1198
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (540 KB) |  | HTML iconHTML  

    This paper proposes an iterative bidding framework for integrated due-date management (DDM) decision making. We focus on a type of make-to-order environment in which a firm needs to quote due dates and prices and to schedule the production of a variety of job orders required by a large group of customers. In most cases, customers prefer shorter due dates. However, given limited production capacity and various cost constraints, the firm has to balance the attractiveness of its due-date quotations and the reliability of delivering accepted job orders. The key issue is how to integrate DDM decisions such that high-quality solutions, which benefit both the firm and the customers, can be obtained. We study the integrated DDM in an economic setting where customers are modeled as self-interested agents and the objective of the firm is to maximize social welfare. We present an iterative bidding framework as a decentralized decision support tool which enables the integration of key DDM decisions. Effective solutions are achieved through the automated negotiation between the firm and its customers. We provide analytical results on the application of the proposed framework to two special cases of the integrated DDM. We also evaluate the performance of the framework on general DDM problems through a computational study. View full abstract»

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  • Early Detection of Numerical Typing Errors Using Data Mining Techniques

    Page(s): 1199 - 1212
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    This paper studies the applications of data mining techniques in early detection of numerical typing errors by human operators through a quantitative analysis of multichannel electroencephalogram (EEG) recordings. Three feature extraction techniques were developed to capture temporal, morphological, and time-frequency (wavelet) characteristics of EEG data. Two most commonly used data mining techniques, namely, linear discriminant analysis (LDA) and support vector machine (SVM), were employed to classify EEG samples associated with correct and erroneous keystrokes. The leave-one-error-pattern-out and leave-one-subject-out cross-validation methods were designed to evaluate the in- and cross-subject classification performances, respectively. For the in-subject classification, the best testing performance had a sensitivity of 62.20% and a specificity of 51.68%, which were achieved by SVM using morphological features. For the cross-subject classification, the best testing performance was achieved by LDA using temporal features, based on which it had a sensitivity of 68.72% and a specificity of 49.45%. In addition, the receiver operating characteristic (ROC) analysis revealed that the averaged values of the area under ROC curves of LDA and SVM for the in- and cross-subject classifications were both greater than 0.60 using the EEG 300 ms prior to the keystrokes. The classification results of this study indicated that the EEG patterns of erroneous keystrokes might be different from those of the correct ones. As a result, it may be possible to predict erroneous keystrokes prior to error occurrence. The classification problem addressed in this study is extremely challenging due to the very limited number of erroneous keystrokes made by each subject and the complex spatiotemporal characteristics of the EEG data. However, the outcome of this study is quite encouraging, and it is promising to develop a prospective early detection system for erroneous keystrokes based on brain-wa- - ve signals. View full abstract»

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  • On Data Collection Using Mobile Robot in Wireless Sensor Networks

    Page(s): 1213 - 1224
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    A novel data-collecting algorithm using a mobile robot to acquire sensed data from a wireless sensor network (WSN) that possesses partitioned/islanded WSNs is proposed in this paper. This algorithm permits the improvement of data-collecting performance by the base station by identifying the locations of partitioned/islanded WSNs and navigating a mobile robot to the desired location. To identify the locations of the partitioned/islanded WSNs, two control approaches, a global- and local-based approach, are proposed. Accordingly, the navigation strategy of the robot can be scheduled based on time and location using three scheduling strategies: time based, location based, and dynamic moving based. With these strategies, the mobile robot can collect the sensed data from the partitioned/islanded WSNs. Therefore, the efficiency of sensed data collected by the base station in partitioned/islanded WSNs is improved. Through simulation under the environment of an ns-2 simulator, the results, from various aspects, show that the collecting strategies proposed can dramatically improve sensed data-collecting performance in partitioned or islanded WSNs. View full abstract»

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  • Online Updating Belief-Rule-Base Using the RIMER Approach

    Page(s): 1225 - 1243
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    In order to determine the parameters of belief-rule-base (BRB) accurately, several optimization methods have been proposed for training BRB, on the basis of a generic rule-base inference methodology using the evidential reasoning (RIMER) approach. These optimization methods are implemented offline, and such are not suitable for training BRB in a dynamic fashion. In this paper, two recursive algorithms are proposed to update BRB online that can simulate dynamic systems. The main feature of the proposed algorithms is that only partial input and output information is required, which can be incomplete or vague, numerical or judgmental, or mixed. If the internal structure of a BRB is initially decided using expert judgments, domain-specific knowledge and/or commonsense rules, the proposed algorithms can be used to fine-tune the initial BRB online, once input and output datasets become available. Using the proposed algorithms, there is no need to collect a complete set of data before a BRB can be trained, which is necessary if the BRB is used to simulate a dynamic system. A numerical example and a case study are reported to demonstrate the potential of the algorithms for online fault diagnosis. View full abstract»

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  • Spectral Measure of Structural Robustness in Complex Networks

    Page(s): 1244 - 1252
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    We introduce the concept of natural connectivity as a measure of structural robustness in complex networks. The natural connectivity characterizes the redundancy of alternative routes in a network by quantifying the weighted number of closed walks of all lengths. This definition leads to a simple mathematical formulation that links the natural connectivity to the spectrum of a network. The natural connectivity can be regarded as an average eigenvalue that changes strictly monotonically with the addition or deletion of edges. We calculate both analytically and numerically the natural connectivity of three typical networks: regular ring lattices, random graphs, and random scale-free networks. We also compare the proposed natural connectivity to other structural robustness measures within a scenario of edge elimination and demonstrate that the natural connectivity provides sensitive discrimination of structural robustness that agrees with our intuition. View full abstract»

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  • Minimum-Cost Consensus Models Under Aggregation Operators

    Page(s): 1253 - 1261
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    In group decision making, consensus models are decision aid tools and help experts modify their individual opinions to reach a closer agreement. Based on the concept of minimum-cost consensus, this paper proposes a novel framework to achieve minimum-cost consensus under aggregation operators. Analytical results indicate that the proposed framework reduces to the consensus model of Ben-Arieh when the selected aggregation operator is the ordered weighted averaging (OWA) operator with weight vector (1/2, ..., 0, ..., 1/2)T. Furthermore, this paper closely examines the minimum-cost consensus models with a linear cost function under the common aggregation operators (e.g., the weighted averaging operator and the OWA operator). Linear-programming-based approaches are also developed to solve these models. The results of this paper significantly contribute to efforts to develop the consensus model of Ben-Arieh et al. View full abstract»

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  • Distributed Diagnosis Using a Condensed Representation of Diagnoses With Application to an Automotive Vehicle

    Page(s): 1262 - 1267
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    In fault detection and isolation, diagnostic test results are commonly used to compute a set of diagnoses, where each diagnosis points at a set of components which might behave abnormally. In distributed systems consisting of multiple control units, the test results in each unit can be used to compute local diagnoses while all test results in the complete system give the global diagnoses. It is an advantage for both repair and fault-tolerant control to have access to the global diagnoses in each unit since these diagnoses represent all test results in all units. However, when the diagnoses, for example, are to be used to repair a unit, only the components that are used by the unit are of interest. The reason for this is that it is only these components that could have caused the abnormal behavior. However, the global diagnoses might include components from the complete system and therefore often include components that are superfluous for the unit. Motivated by this observation, a new type of diagnosis is proposed, namely, the condensed diagnosis. Each unit has a unique set of condensed diagnoses which represents the global diagnoses. The benefit of the condensed diagnoses is that they only include components used by the unit while still representing the global diagnoses. The proposed method is applied to an automotive vehicle, and the results from the application study show the benefit of using condensed diagnoses compared to global diagnoses. View full abstract»

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  • Online Updating With a Probability-Based Prediction Model Using Expectation Maximization Algorithm for Reliability Forecasting

    Page(s): 1268 - 1277
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    Recently, a novel prediction model based on the evidential reasoning (ER) approach is developed to forecast reliability in engineering systems. In order to determine the parameters of the ER-based prediction model, some optimization models have been proposed to train the ER-based prediction model. However, these models are implemented in an offline fashion and thus it is very expensive to train and retrain them when new information is available. This correspondence paper is concerned with developing the recursive algorithms for updating the ER-based prediction model from the probability-based point of view. Using the recursive expectation maximization algorithm, two recursive algorithms are proposed for updating the parameters of the ER-based prediction model under judgmental and numerical outputs, respectively. As such, the proposed algorithms can be used to fine tune the ER-based prediction model online once new information becomes available. We verify the proposed method via a realistic example with missile reliability data. View full abstract»

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  • Special issue on decision making in human and machine vision

    Page(s): 1278 - 1279
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  • IEEE Foundation [advertisement]

    Page(s): 1280
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  • 2011 Index IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans Vol. 41

    Page(s): 1281 - 1295
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  • IEEE Systems, Man, and Cybernetics Society Information

    Page(s): C3
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  • IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans Information for authors

    Page(s): C4
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Aims & Scope

The fields of systems engineering and human machine systems: systems engineering includes efforts that involve issue formulation, issue analysis and modeling, and decision making and issue interpretation at any of the lifecycle phases associated with the definition, development, and implementation of large systems.

 

This Transactions ceased production in 2012. The current retitled publication is IEEE Transactions on Systems, Man, and Cybernetics: Systems.

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Meet Our Editors

Editor-in-Chief
Dr. Witold Pedrycz
University of Alberta