Pub Date : 2022-07-01DOI: 10.1109/msmc.2022.3150144
Moloud Abdar, A. Khosravi, Sheikh Mohammed Shariful Islam, Usha R. Acharya, A. Vasilakos
Different terms such as trust, certainty, and uncertainty are of great importance in the real world and play a critical role in artificial intelligence (AI) applications. The implied assumption is that the level of trust in AI can be measured in different ways. This principle can be achieved by distinguishing uncertainties in predicting AI methods used in medical studies. Hence, it is necessary to propose effective uncertainty quantification (UQ) and measurement methods to have trustworthy AI (TAI) clinical decision support systems (CDSSs). In this study, we present practical guidelines for developing and using UQ methods while applying various AI techniques for medical data analysis.
{"title":"The need for quantification of uncertainty in artificial intelligence for clinical data analysis: increasing the level of trust in the decision-making process","authors":"Moloud Abdar, A. Khosravi, Sheikh Mohammed Shariful Islam, Usha R. Acharya, A. Vasilakos","doi":"10.1109/msmc.2022.3150144","DOIUrl":"https://doi.org/10.1109/msmc.2022.3150144","url":null,"abstract":"Different terms such as trust, certainty, and uncertainty are of great importance in the real world and play a critical role in artificial intelligence (AI) applications. The implied assumption is that the level of trust in AI can be measured in different ways. This principle can be achieved by distinguishing uncertainties in predicting AI methods used in medical studies. Hence, it is necessary to propose effective uncertainty quantification (UQ) and measurement methods to have trustworthy AI (TAI) clinical decision support systems (CDSSs). In this study, we present practical guidelines for developing and using UQ methods while applying various AI techniques for medical data analysis.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"3 1","pages":"28-40"},"PeriodicalIF":3.2,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79813769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/msmc.2022.3185685
{"title":"IEEE Feedback","authors":"","doi":"10.1109/msmc.2022.3185685","DOIUrl":"https://doi.org/10.1109/msmc.2022.3185685","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"11 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78750883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/msmc.2022.3153747
Toru Usami, M. Deng
In this article, a method used for tip-position coordinate control of a three-degree-of-freedom (DOF) soft actuator is proposed.In general, the behavior of pneumatic soft actuators is simple. However, the actuator, which consists of three artificial muscles, is capable of more complex motions compared to conventional soft actuators. By designing a model and control system that can handle multiple input patterns, various motions are possible. In addition, a machine learning technique called multioutput support vector regression (M-SVR) is used as a method to compensate for the complexity of multiple-input, multiple-output systems. First, a model that can be used to design a control system is offered. Then, a control system is designed, using the recommended model and machine learning approaches. Furthermore, the effectiveness of the proposed system is verified by experiments.
{"title":"Applying an MSVR Method to Forecast a Three-Degree-of-Freedom Soft Actuator for a Nonlinear Position Control System: Simulation and Experiments","authors":"Toru Usami, M. Deng","doi":"10.1109/msmc.2022.3153747","DOIUrl":"https://doi.org/10.1109/msmc.2022.3153747","url":null,"abstract":"In this article, a method used for tip-position coordinate control of a three-degree-of-freedom (DOF) soft actuator is proposed.In general, the behavior of pneumatic soft actuators is simple. However, the actuator, which consists of three artificial muscles, is capable of more complex motions compared to conventional soft actuators. By designing a model and control system that can handle multiple input patterns, various motions are possible. In addition, a machine learning technique called multioutput support vector regression (M-SVR) is used as a method to compensate for the complexity of multiple-input, multiple-output systems. First, a model that can be used to design a control system is offered. Then, a control system is designed, using the recommended model and machine learning approaches. Furthermore, the effectiveness of the proposed system is verified by experiments.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"19 1","pages":"61-69"},"PeriodicalIF":3.2,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74065158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/msmc.2022.3178557
Haibin Zhu
{"title":"More Diverse Investigations and More Collaborations Make More Contributions to the Community [Editorial]","authors":"Haibin Zhu","doi":"10.1109/msmc.2022.3178557","DOIUrl":"https://doi.org/10.1109/msmc.2022.3178557","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"1 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88955507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/msmc.2022.3177350
{"title":"Special Issue on Sensing, Control, and Learning for Human-Assisted Robots [Call for Papers]","authors":"","doi":"10.1109/msmc.2022.3177350","DOIUrl":"https://doi.org/10.1109/msmc.2022.3177350","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"26 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82257007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/msmc.2022.3168994
G. Narayanan, M. Ali, Jianan Wang, S. A. Kauser, A. Diab, H. I. A. Ghaffar
The Takagi–Sugeno (T–S) fuzzy-based impulsive consensus problem of a fractional-order multiagent system (FOMAS) with an average dwell time (ADT) is investigated. FOMASs are nonlinear systems, and they are modeled as linear subsystems using a T–S fuzzy model. We study a T–S fuzzy FOMAS subject to a class of impulse time sequences with the ADT approach. In this article, an impulsive control scheme is proposed to make the tracking error converge in a finite-time consensus into a small neighborhood of origin. Based on impulsive fractional differential equations theory, the Lyapunov functional approach, and the ADT technique, an impulsive controller is designed to achieve finite-time consensus of a T–S fuzzy FOMAS. Finally, through numerical as well as practical examples, the effectiveness and superiority of the proposed approach are validated.
{"title":"Impulsive Consensus of Fractional-Order Takagi–Sugeno Fuzzy Multiagent Systems With Average Dwell Time Approach and Its Applications: Achieving Finite-Time Consensus","authors":"G. Narayanan, M. Ali, Jianan Wang, S. A. Kauser, A. Diab, H. I. A. Ghaffar","doi":"10.1109/msmc.2022.3168994","DOIUrl":"https://doi.org/10.1109/msmc.2022.3168994","url":null,"abstract":"The Takagi–Sugeno (T–S) fuzzy-based impulsive consensus problem of a fractional-order multiagent system (FOMAS) with an average dwell time (ADT) is investigated. FOMASs are nonlinear systems, and they are modeled as linear subsystems using a T–S fuzzy model. We study a T–S fuzzy FOMAS subject to a class of impulse time sequences with the ADT approach. In this article, an impulsive control scheme is proposed to make the tracking error converge in a finite-time consensus into a small neighborhood of origin. Based on impulsive fractional differential equations theory, the Lyapunov functional approach, and the ADT technique, an impulsive controller is designed to achieve finite-time consensus of a T–S fuzzy FOMAS. Finally, through numerical as well as practical examples, the effectiveness and superiority of the proposed approach are validated.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"73 1","pages":"41-50"},"PeriodicalIF":3.2,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86244507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cyberphysical-social systems (CPSS) integrate human, machine, and information into large-scale automated systems and generate complex heterogeneous big data from multiple sources. Knowledge graphs play a pivotal role in energizing the data with huge volume and uneven quality to drive CPSS intelligent applications and services, thus attracting intense research interests from scholars. The Resource Description Framework (RDF) describes knowledge in the form of subject-predicate-object triples and interpreted as directed labeled graphs. However, the graph structure doesn’t have flexible operability and direct computability in the theoretical framework, although it can be understood intuitively. Therefore, we proposed a tensor-based knowledge analysis framework in this article, which supports the representation, fusion, and reasoning of knowledge graphs. First, we employ Boolean tensors to represent heterogeneous knowledge graphs completely. Then, we present a series of graph tensor operations for the modification, extraction, and aggregation of high-order knowledge graphs. Furthermore, we perform tensor 1-mode product operation between the knowledge graph representation tensor and the entity representation tensor to obtain the relation path tensor, so as to infer the relationship between any two entities. Finally, we demonstrate the practicality and effectiveness of the proposed model by implementing a case study.
{"title":"Tensor-Based Knowledge Fusion and Reasoning for Cyberphysical-Social Systems: Theory and Framework","authors":"Jing Yang, L. Yang, Yuan Gao, Huazhong Liu, Hao Wang, Xia Xie","doi":"10.1109/MSMC.2021.3114538","DOIUrl":"https://doi.org/10.1109/MSMC.2021.3114538","url":null,"abstract":"Cyberphysical-social systems (CPSS) integrate human, machine, and information into large-scale automated systems and generate complex heterogeneous big data from multiple sources. Knowledge graphs play a pivotal role in energizing the data with huge volume and uneven quality to drive CPSS intelligent applications and services, thus attracting intense research interests from scholars. The Resource Description Framework (RDF) describes knowledge in the form of subject-predicate-object triples and interpreted as directed labeled graphs. However, the graph structure doesn’t have flexible operability and direct computability in the theoretical framework, although it can be understood intuitively. Therefore, we proposed a tensor-based knowledge analysis framework in this article, which supports the representation, fusion, and reasoning of knowledge graphs. First, we employ Boolean tensors to represent heterogeneous knowledge graphs completely. Then, we present a series of graph tensor operations for the modification, extraction, and aggregation of high-order knowledge graphs. Furthermore, we perform tensor 1-mode product operation between the knowledge graph representation tensor and the entity representation tensor to obtain the relation path tensor, so as to infer the relationship between any two entities. Finally, we demonstrate the practicality and effectiveness of the proposed model by implementing a case study.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"39 1","pages":"31-38"},"PeriodicalIF":3.2,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76123576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-01DOI: 10.1109/MSMC.2021.3136983
Ming Hou, Guoyin Wang, L. Trajković, K. Plataniotis, S. Kwong, Mengchu Zhou, E. Tunstel, I. Rudas, J. Kacprzyk, Henry Leung
A brain-inspired intelligent adaptive system (IAS) framework is developed toward fundamental breakthroughs in the cognitive bottleneck of humans and the incompetence of artificial intelligence (AI) under indeterministic conditions or with insufficient data. IASs have led to defense science and technology innovations for interaction-centered design (ICD) methodologies, human–autonomy symbiosis initiatives, and a trust framework synergizing key strategies on intention, measurability, performance, adaptivity, communication, transparency, and security (IMPACTS) for trustworthy mission-critical autonomous systems. These paradigms of emerging technologies empower highly automated systems to think and behave like humans for generating collective intelligence. IAS-based technologies have not only fostered the development of a series of novel theories and methodologies, such as brain-inspired systems and the ICD approach, but also have paved unprecedented paths to innovative applications in the defense and general industries.
{"title":"Frontiers of Brain-Inspired Autonomous Systems: How Does Defense R&D Drive the Innovations?","authors":"Ming Hou, Guoyin Wang, L. Trajković, K. Plataniotis, S. Kwong, Mengchu Zhou, E. Tunstel, I. Rudas, J. Kacprzyk, Henry Leung","doi":"10.1109/MSMC.2021.3136983","DOIUrl":"https://doi.org/10.1109/MSMC.2021.3136983","url":null,"abstract":"A brain-inspired intelligent adaptive system (IAS) framework is developed toward fundamental breakthroughs in the cognitive bottleneck of humans and the incompetence of artificial intelligence (AI) under indeterministic conditions or with insufficient data. IASs have led to defense science and technology innovations for interaction-centered design (ICD) methodologies, human–autonomy symbiosis initiatives, and a trust framework synergizing key strategies on intention, measurability, performance, adaptivity, communication, transparency, and security (IMPACTS) for trustworthy mission-critical autonomous systems. These paradigms of emerging technologies empower highly automated systems to think and behave like humans for generating collective intelligence. IAS-based technologies have not only fostered the development of a series of novel theories and methodologies, such as brain-inspired systems and the ICD approach, but also have paved unprecedented paths to innovative applications in the defense and general industries.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"85 1","pages":"8-20"},"PeriodicalIF":3.2,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79936081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-01DOI: 10.1109/MSMC.2021.3118893
Mohd Hammad Khan, Devdutt Sharma, N. Prasanth, S. Raja
Bitcoin is the world’s most traded cryptocurrency and highly popular among cryptocurrency investors and miners. However, its volatility makes it a risky investment, which leads to the need for accurate and fast price-prediction models. This article proposes a Bitcoin price-prediction model using a long short-term memory (LSTM) network in a distributed environment. A tensor processing unit (TPU) has been used to provide the distributed environment for the model. The results show that the TPU-based model performed significantly better than a conventional CPU-based model.
{"title":"Bitcoin Price Prediction in a Distributed Environment Using a Tensor Processing Unit: A Comparison With a CPU-Based Model","authors":"Mohd Hammad Khan, Devdutt Sharma, N. Prasanth, S. Raja","doi":"10.1109/MSMC.2021.3118893","DOIUrl":"https://doi.org/10.1109/MSMC.2021.3118893","url":null,"abstract":"Bitcoin is the world’s most traded cryptocurrency and highly popular among cryptocurrency investors and miners. However, its volatility makes it a risky investment, which leads to the need for accurate and fast price-prediction models. This article proposes a Bitcoin price-prediction model using a long short-term memory (LSTM) network in a distributed environment. A tensor processing unit (TPU) has been used to provide the distributed environment for the model. The results show that the TPU-based model performed significantly better than a conventional CPU-based model.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"22 1","pages":"39-43"},"PeriodicalIF":3.2,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81813971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-01DOI: 10.1109/msmc.2022.3149118
Yuying Dong, Jiliang Luo
{"title":"The 18th IEEE International Conference On Networking, Sensing and Control [Conference Reports]","authors":"Yuying Dong, Jiliang Luo","doi":"10.1109/msmc.2022.3149118","DOIUrl":"https://doi.org/10.1109/msmc.2022.3149118","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"31 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80776822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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