International Journal of Network Dynamics and Intelligence最新文献

{"title":"Information Fusion over Network Dynamics with Unknown Correlations: An Overview","authors":"Wangyan Li, Fuwen Yang","doi":"10.53941/ijndi0201003","DOIUrl":"https://doi.org/10.53941/ijndi0201003","url":null,"abstract":"Survey/review study\u0000Information Fusion over Network Dynamics with Unknown Correlations: An Overview\u0000\u0000Wangyan Li 1, and Fuwen Yang 2,*\u0000\u0000\u00001 College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China\u00002 Griffth School of Engineering, Griffth University, Gold Coast Campus, QLD 4222, Australia\u0000* Correspondence: fuwen.yang@griffth.edu.au\u0000 \u0000 \u0000Received: 24 October 2022\u0000Accepted: 22 November 2022\u0000Published: \u0000 \u0000\u0000Abstract: Unknown correlations (UCs) generally exist in a wide spectrum of practical multi-source information fusion problems, and thereby, their corresponding fusion problems have become one of the most important topics in information fusion domain. During the past three decades, the research on this topic has been growing rapidly and extensively, and, as a result, various important advances have been reported. In this overview, we intend to summarize the culmination of years of development in the field of information fusion under UCs as a roadmap. First, the potential reasons leading to UCs are investigated. According to the unknown nature of correlations, we further divide UCs into two categories, i.e., fully UCs, and partially UCs. For each category, the corresponding fusion methods are reviewed. Next, this roadmap witnesses the recent development of information fusion under UCs in a distributed way thanks to the popularity of distributed sensing technology. In particular, the distributed fusion techniques based on consensus, diffusion, and multi-object tracking strategies for UCs are examined. Finally, some future perspectives on information fusion under UCs are pointed out.","PeriodicalId":183984,"journal":{"name":"International Journal of Network Dynamics and Intelligence","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133850160","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}

{"title":"A Review of Techniques on Gait-Based Person Re-Identification","authors":"Babak H. Rahi, Liao Maozhen, Man Qi","doi":"10.53941/ijndi0201005","DOIUrl":"https://doi.org/10.53941/ijndi0201005","url":null,"abstract":"Survey/review study\u0000A Review of Techniques on Gait-Based Person Re-Identification\u0000\u0000Babak Rahi 1,*, Maozhen Li 1, and Man Qi 2\u0000\u0000\u00001 Department of Electronics and Computer Engineering, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom\u00002 The School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom\u0000* Correspondence: babak.h.rahi@hotmail.com\u0000 \u0000 \u0000Received: 16 October 2022\u0000Accepted: 14 December 2022\u0000Published: \u0000 \u0000\u0000Abstract: Person re-identification at a distance across multiple non-overlapping cameras has been an active research area for years. In the past ten years, short-term Person re-identification techniques have made great strides in accuracy using only appearance features in limited environments. However, massive intra-class variations and inter-class confusion limit their ability to be used in practical applications. Moreover, appearance consistency can only be assumed in a short time span from one camera to the other. Since the holistic appearance will change drastically over days and weeks, the technique, as mentioned above, will be ineffective. Practical applications usually require a long-term solution in which the subject's appearance and clothing might have changed after the elapse of a significant period. Facing these problems, soft biometric features such as Gait has stirred much interest in the past years. Nevertheless, even Gait can vary with illness, ageing and emotional states, walking surfaces, shoe types, clothes types, carried objects (by the subject) and even environment clutters. Therefore, Gait is considered as a temporal cue that could provide biometric motion information. On the other hand, the shape of the human body could be viewed as a spatial signal which can produce valuable information. So extracting discriminative features from both spatial and temporal domains would benefit this research. This article examines the main approaches used in gait analysis for re-identification over the past decade. We identify several relevant dimensions of the problem and provide a taxonomic analysis of current research. We conclude by reviewing the performance levels achievable with current technology and providing a perspective on the most challenging and promising research directions.","PeriodicalId":183984,"journal":{"name":"International Journal of Network Dynamics and Intelligence","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128990051","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}

{"title":"Network Learning for Biomarker Discovery","authors":"Yulian Ding, Minghan Fu, Ping Luo, Fang-Xiang Wu","doi":"10.53941/ijndi0201004","DOIUrl":"https://doi.org/10.53941/ijndi0201004","url":null,"abstract":"Survey/review study\u0000Network Learning for Biomarker Discovery\u0000\u0000Yulian Ding 1, Minghan Fu 1, Ping Luo 2, and Fang-Xiang Wu 1,3,4,*\u0000\u0000\u00001 Division of Biomedical Engineering, University of Saskatchewan, S7N 5A9, Saskatoon, Canada\u00002 Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada\u00003 Department of Computer Sciences, University of Saskatchewan, S7N 5A9, Saskatoon, Canada\u00004 Department of Mechanical Engineering, University of Saskatchewan, S7N 5A9, Saskatoon, Canada\u0000* Correspondence: faw341@mail.usask.ca\u0000 \u0000 \u0000Received: 14 October 2022\u0000Accepted: 5 December 2022\u0000Published: \u0000 \u0000\u0000Abstract: Everything is connected and thus networks are instrumental in not only modeling complex systems with many components, but also accommodating knowledge about their components. Broadly speaking, network learning is an emerging area of machine learning to discover knowledge within networks. Although networks have permeated all subjects of sciences, in this study we mainly focus on network learning for biomarker discovery. We first overview methods for traditional network learning which learn knowledge from networks with centrality analysis. Then, we summarize the network deep learning, which are powerful machine learning models that integrate networks (graphs) with deep neural networks. Biomarkers can be placed in proper biological networks as vertices or edges and network learning applications for biomarker discovery are discussed. We finally point out some promising directions for future work about network learning.","PeriodicalId":183984,"journal":{"name":"International Journal of Network Dynamics and Intelligence","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125555126","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}

{"title":"Inaugural Issue for International Journal of Network Dynamics and Intelligence","authors":"Zidong Wang","doi":"10.53941/ijndi0101001","DOIUrl":"https://doi.org/10.53941/ijndi0101001","url":null,"abstract":"Editorial\u0000Inaugural Issue for International Journal of Network Dynamics and Intelligence\u0000\u0000Zidong Wang *\u0000\u0000\u0000Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom\u0000* Correspondence: Email: zidong.wang@brunel.ac.uk. Tel.: +44-1895-266021. Fax: +44-1895-251686\u0000 \u0000 \u0000Received: 26 December 2022\u0000Accepted: 26 December 2022\u0000Published: 27 December 2022\u0000","PeriodicalId":183984,"journal":{"name":"International Journal of Network Dynamics and Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130547394","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}

{"title":"Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications","authors":"Qichun Zhang, Yuyang Zhou","doi":"10.53941/ijndi0101010","DOIUrl":"https://doi.org/10.53941/ijndi0101010","url":null,"abstract":"Survey/review study\u0000Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications\u0000\u0000Qichun Zhang 1, and Yuyang Zhou 2,*\u0000\u0000\u00001 Department of Computer Science, University of Bradford, Bradford BD7 1DP, United Kingdom\u00002 School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, United Kingdom\u0000* Correspondence: y.zhou@napier.ac.uk\u0000 \u0000 \u0000Received: 24 October 2022\u0000Accepted: 18 November 2022\u0000Published: 22 December 2022\u0000 \u0000\u0000Abstract: Non-Gaussian randomness widely exists in complex dynamical systems, in which the traditional mean-variance index cannot fully reflect the systematic characteristics. To improve the performance of control design subjected to non-Gaussian noises, stochastic distribution control (SDC) theory was proposed in the 1990s, where the output probability density function (PDF) has been investigated as an additional system variable. Following this framework, SDC has been extended to other research subjects in control systems such as filter design, fault diagnosis, and so on. It shows that SDC supplies an important solution to enhance the accuracy of system design, which is further beneficial to almost all the topics subject to non-Gaussian randomness. Meanwhile, the theoretical results of the SDC have been applied to several practical industrial applications. As data science raises based on the development of industrial artificial intelligence, SDC has been further developed recently focusing on data-driven design and multi-agent systems. To explore the new challenges with the evolution of SDC, e.g. unknown system models, unknown noise distributions, strong non-stationary transient dynamics, stability analysis and industrial applications, this survey summarises the most recent published results in the last 5 years of SDC work in terms of modelling, control, filtering, fault diagnosis, and industrial applications. Based on the technical analysis, potential future work is discussed in the end.","PeriodicalId":183984,"journal":{"name":"International Journal of Network Dynamics and Intelligence","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129933933","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}

{"title":"Individual-Based Modelling of Animal Brucellosis Spread with the Use of Complex Networks","authors":"E. Pinto, E. G. Nepomuceno, A. Campanharo","doi":"10.53941/ijndi0101011","DOIUrl":"https://doi.org/10.53941/ijndi0101011","url":null,"abstract":"Article\u0000Individual-Based Modelling of Animal Brucellosis Spread with the Use of Complex Networks\u0000\u0000E. R. Pinto 1, E. G. Nepomuceno 2,*, and A. S. L. O. Campanharo 1\u0000\u0000\u00001 Department of Biostatistics, Institute of Biosciences São Paulo State University, Botucatu, São Paulo 18618-970, Brazil\u00002 Department of Electronic Engineering and Centre for Ocean Energy Research, Maynooth University, Maynooth, Ireland\u0000* Correspondence: erivelton.nepomuceno@mu.ie\u0000 \u0000 \u0000Received: 12 October 2022\u0000Accepted: 23 November 2022\u0000Published: 22 December 2022\u0000 \u0000\u0000Abstract: The principal purpose of this work was to study the spread of brucellosis in the state of São Paulo with the help of the complex network theory and to propose control measures for its eradication. For this, the scale-free model of complex networks, widely known in the literature, was used. The effect of vaccination was verified in each of the municipalities in the state of São Paulo and it was observed that when heterogeneity is not taken into account, vaccination becomes ineffective for the eradication of the disease.","PeriodicalId":183984,"journal":{"name":"International Journal of Network Dynamics and Intelligence","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130170656","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}

{"title":"Deep Common Spatial Pattern based Motor Imagery Classification with Improved Objective Function","authors":"Nanxi Yu, Rui Yang, Mengjie Huang","doi":"10.53941/ijndi0101007","DOIUrl":"https://doi.org/10.53941/ijndi0101007","url":null,"abstract":"Survey/review study\u0000Deep Common Spatial Pattern Based Motor Imagery Classification with Improved Objective Function\u0000\u0000Nanxi Yu 1,2, Rui Yang 1, and Mengjie Huang 1,*\u0000\u0000\u00001 School of Electrical Engineering, Electronics & Computer Science, University of Liverpool, Liverpool, L69 3BX, United Kingdom\u00002 Department of Biostatistics, Graduate School of Arts and Sciences, Yale University, New Haven, CT 06511, United States\u0000* Correspondence: Mengjie.Huang@liverpool.ac.uk\u0000 \u0000 \u0000Received: 12 October 2022\u0000Accepted: 28 November 2022\u0000Published: 22 December 2022\u0000 \u0000\u0000Abstract: Common spatial pattern (CSP) technique has been very popular in terms of electroencephalogram (EEG) features extraction in motor imagery (MI)-based brain-computer interface (BCI). Through the simultaneous diagonalization of the covariance matrices, CSP intends to transform data into another mapping with data of different categories having maximal differences in their measures of dispersion. This paper shows the objective function realized by original CSP method could be inaccurate by regularizing the estimated spatial covariance matrix from EEG data by trace, leading to some flaws in the features to be extracted. In order to deal with this problem, a novel deep CSP (DCSP) model with optimal objective function is proposed in this paper. The benefits of the proposed DCSP method over original CSP method are verified with experiments on two EEG based MI datasets where the classification accuracy is effectively improved.","PeriodicalId":183984,"journal":{"name":"International Journal of Network Dynamics and Intelligence","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133218812","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}

{"title":"Adaptive Dynamic Programming for Networked Control Systems Under Communication Constraints: A Survey of Trends and Techniques","authors":"Xueli Wang, Ying Sun, Derui Ding","doi":"10.53941/ijndi0101008","DOIUrl":"https://doi.org/10.53941/ijndi0101008","url":null,"abstract":"Survey/review study\u0000Adaptive Dynamic Programming for Networked Control Systems under Communication Constraints: A Survey of Trends and Techniques\u0000\u0000Xueli Wang 1, Ying Sun 1,*, and Derui Ding 2\u0000\u0000\u00001 Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China\u00002 School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3122, Australia\u0000* Correspondence: yingsun1991@163.com\u0000 \u0000 \u0000Received: 22 September 2022\u0000Accepted: 28 November 2022\u0000Published: 22 December 2022\u0000 \u0000\u0000Abstract: The adaptive dynamic programming (ADP) technology has been widely used benefiting from its recursive structure in forward and the prospective conception of reinforcement learning. Furthermore, ADP-based control issues with communication constraints arouse ever-increasing research consideration in theoretical analysis and engineering applications due mainly to the wide participation of digital communications in industrial systems. The latest development of ADP-based optimal control with communication constraints is systematically surveyed in this paper. To this end, the development of ADP-based dominant methods is first investigated from their structures and implementation. Then, technical challenges and corresponding approaches are comprehensively and thoroughly discussed and the existing results are reviewed according to the constraint types. Furthermore, some applications of the ADP method in practical systems are summarized. Finally, future topics are lighted on ADP-based control issues.","PeriodicalId":183984,"journal":{"name":"International Journal of Network Dynamics and Intelligence","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131872460","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}

{"title":"Real-Time Sensing and Fault Diagnosis for Transmission Lines","authors":"F. Shakiba, Milad Shojaee, S. Azizi, Megnchu Zhou","doi":"10.53941/ijndi0101004","DOIUrl":"https://doi.org/10.53941/ijndi0101004","url":null,"abstract":"Article\u0000Real-Time Sensing and Fault Diagnosis for Transmission Lines\u0000\u0000Fatemeh Mohammadi Shakiba 1, Milad Shojaee 1, S. Mohsen Azizi 1,2, and Mengchu Zhou 1,*\u0000\u0000\u00001 Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark 07102, NJ, USA.\u00002 The school of Applied Engineering and Technology, New Jersey Institute of Technology, Newark 07102, NJ, USA.\u0000* Correspondence: mengchu.zhou@njit.edu\u0000 \u0000 \u0000Received: 12 October 2022\u0000Accepted: 8 November 2022\u0000Published: 22 December 2022\u0000 \u0000\u0000Abstract: Protection of high voltage transmission lines is one of the crucial problems in the power system engineering. Accurate and timely detection and identification of transmission line short circuit faults can considerably improve and simplify their recovery process and hence save the costs associated with the downtime of a power system. Hence, it is essential that a robust and reliable fault diagnosis system completes its operation within an acceptable time window after fault occurrence in the presence of uncertainties and disturbances in the system. The significant costs of mistakenly detected or undetected faults based on the conventional techniques motivate us to present a robust detection and identification system by using the convolutional neural networks. The robustness of this technique is analyzed for the variations of the phase difference between two connected buses, fault resistance, source inductance fluctuations, fault inception angle, local bus voltage fluctuations, and measurement noises. The time delay analysis is also conducted to indicate that the presented technique is able to detect, identify, and estimate the location of faults before tripping relays and circuit breakers disconnect a faulty region.","PeriodicalId":183984,"journal":{"name":"International Journal of Network Dynamics and Intelligence","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132492354","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}

{"title":"A Mini Review of Node Centrality Metrics in Biological Networks","authors":"Mengyuan Wang, Haiying Wang, Huiru Zheng","doi":"10.53941/ijndi0101009","DOIUrl":"https://doi.org/10.53941/ijndi0101009","url":null,"abstract":"Survey/review study\u0000A Mini Review of Node Centrality Metrics in Biological Networks\u0000\u0000Mengyuan Wang 1,2, Haiying Wang 1, and Huiru Zheng 1,*\u0000\u0000\u00001 School of Computing, Ulster University, Belfast, BT15 1ED, United Kingdom\u00002 Scotland’s Rural College, Edinburgh, EH25 9RG, United Kingdom\u0000* Correspondence: h.zheng@ulster.ac.uk\u0000 \u0000 \u0000Received: 31 October 2022\u0000Accepted: 21 November 2022\u0000Published: 22 December 2022\u0000 \u0000\u0000Abstract: The diversity of nodes in a complex network causes each node to have varying significance, and the important nodes often have a significant impact on the structure and function of the network. Although the interpretation of the results of biological networks must always depend on the topological study of nodes, there is presently no consensus on how to use these metrics, and most network analyses always result in a basic interpretation of a limited number of metrics. To thoroughly comprehend biological networks, it is necessary to consistently understand the notion of node centrality. Therefore, for 10 typical nodal metrics in biological networks, the study first assesses their current applications, advantages, disadvantages as well as potential applications. Then, a review of previous studies is provided, and suggestions are made correspondingly for the purpose of improving biological topology algorithms. Finally, the following recommendations are made in this study: (1) a comprehensive and accurate assessment of node centrality necessitates the use of multiple metrics, including both the target node and its surroundings, and density of maximum neighbourhood component(DMNC) can be used as a complement to other node centrality metrics; (2) different centrality metrics can be applied to identify nodes with different functions, which in this study are mapped as modular surroundings, bridging roles, and susceptibility; and (3) the following groups of node centrality can often be verified against each other, including degree and maximum neighbourhood component (MNC), eccentricity, closeness and radiality; stress and betweenness.","PeriodicalId":183984,"journal":{"name":"International Journal of Network Dynamics and Intelligence","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125556683","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}