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OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs)

Vehicular Communications 2022年 36卷

作者： Rahmani, Amir Masoud Ali, Saqib Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Javaheri, Danial Lalbakhsh, Pooia Hassan Ahmed, Omed Hosseinzadeh, Mehdi Lee, Sang-Woong Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Chosun University Gwangju 61452 South Korea Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Clayton 3800 VIC Australia Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Flying ad-hoc networks (FANETs) have many applications in military, industrial and agricultural areas. Due to specific features of FANETs, such as high-speed nodes, low density of nodes in the network, and rapid changes in the topology, most routing protocols designed for mobile ad hoc networks (MANETs) or vehicular ad hoc networks (VANETs) are not compatible with FANETs. In this paper, we propose a fuzzy logic-based routing approach called OLSR+ for FANETs. In this scheme, we seek to improve the optimized link state routing protocol (OLSR) so that it can efficiently be used in FANETs. OLSR+ includes four main phases: 1) Discovering neighboring nodes. In this phase, we propose a new and efficient technique for estimating the lifetime of the link between two unmanned ariel vehicles (UAVs) based on the link quality, distance, relative velocity, and movement direction. 2) Selecting multipoint relays (MPRs). In this phase, we present a fuzzy mechanism for selecting a set of MPR nodes. According to this mechanism, when a node has higher residual energy, higher link lifetime, and more neighborhood degree compared to others, it achieves more fitness to be selected as MPR. 3) Discovering the network topology. In this phase, we modify the format of the topology control (TC) message and add two fields, including route energy and route lifetime to this message. 4) Calculating the routing table. In OLSR+, we consider two parameters, including route energy and route lifetime, for establishing stable paths. Finally, we simulate OLSR+ using NS3 and compare its performance with two methods, namely greedy optimized link state routing (G-OLSR) and optimized link state routing (OLSR). The simulation results show that OLSR+ successfully reduces delay compared to G-OLSR and OLSR. In addition, it has higher packet delivery rate and throughput than others. Also, it improves energy consumption in the network. However, OLSR+ has more routing overhead than G-OLSR. © 2022 Elsevier Inc.

关键词： Artificial intelligence (AI) Flying ad hoc networks (FANETs) Fuzzy logic Routing Unmanned ariel vehicles (UAVs)

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Learn to predict sets using feed-forward neural networks

arXiv

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arXiv 2020年

作者： Rezatofighi, Seyed Hamid Zhu, Alan Tianyu Kaskman, Roman Motlagh, Farbod T. Shi, Javen Qinfeng Milan, Anton Cremers, Daniel Leal-Taixé, Laura Reid, Ian The Department of Data Science and AI Faculty of Information Technology Monash University Melbourne Australia Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia The School of Computer Science The university of Adelaide Australia Amazon Technical University of Munich Germany

This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a set is not fixed in advance. Many real-world problems, such as image tagging and object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors, matrices or tensors. We present a novel approach for learning to predict sets with unknown permutation and cardinality using deep neural networks. In our formulation we define a likelihood for a set distribution represented by a) two discrete distributions defining the set cardinally and permutation variables, and b) a joint distribution over set elements with a fixed cardinality. Depending on the problem under consideration, we define different training models for set prediction using deep neural networks. We demonstrate the validity of our set formulations on relevant vision problems such as: 1) multi-label image classification where we outperform the other competing methods on the PASCAL VOC and MS COCO datasets, 2) object detection, for which our formulation outperforms popular state-of-the-art detectors, and 3) a complex CAPTCHA test, where we observe that, surprisingly, our set-based network acquired the ability of mimicking arithmetics without any rules being coded. Copyright © 2020, The Authors. All rights reserved.

关键词： Object detection

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Correction: PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification

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Journal of imaging informatics in medicine 2025年 2025 Feb 4页

作者： Mevan Ekanayake Kamlesh Pawar Zhifeng Chen Gary Egan Zhaolin Chen Monash Biomedical Imaging Monash University Clayton VIC 3800 Australia. Department of Electrical and Computer Systems Engineering Monash University Clayton VIC 3800 Australia. Department of Data Science and AI Faculty of IT Monash University Clayton VIC 3800 Australia. School of Psychological Sciences Monash University Clayton VIC 3800 Australia. Monash Biomedical Imaging Monash University Clayton VIC 3800 Australia. zhaolin.chen@monash.edu. Department of Data Science and AI Faculty of IT Monash University Clayton VIC 3800 Australia. zhaolin.chen@monash.edu.

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Efficient Recursive Implementation of Spatial-Temporal Gaussian Process Regression

Efficient Recursive Implementation of Spatial-Temporal Gauss...

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Chinese Control Conference (CCC)

作者： Junpeng Zhang Ye Kuang Tianshi Chen Xiaochen Lu Feng Yin Renxin Zhong School of Science and Engineering and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Shenzhen P. R. China Guangzhou Yunchuang Data Science and Technology Co. Ltd. Guangzhou P. R. China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

The current implementation of the spatial-temporal Gaussian process regression has computational complexity O(NM 3 ), where N and M are the number of temporal and spatial data, respectively, and thus can only be applied to data with large N but relatively small M. In this work, we show that by exploring the Kronecker structure in the state-space model realization of the spatial-temporal Gaussian process, we can extend the current implementation with a coordinate transformation and an output transformation (corresponding to data preprocessing), such that the computational complexity is reduced to O(M 3 +NM 2 +NM) and therefore the proposed implementation can be applied to data with large N and moderately large M. Moreover, the proposed implementation can be parallelized and the computational complexity can be further lowered if parallel computing is adopted.

关键词： Gaussian processes Kernel Estimation Computational complexity State-space methods Kalman filters Contracts

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The route to chaos in routing games: When is price of anarchy too optimistic? 20

The route to chaos in routing games: When is price of anarch...

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Proceedings of the 34th International Conference on Neural Information Processing systems

作者： Thiparat Chotibut Fryderyk Falniowski Michał Misiurewicz Georgios Piliouras Chula Intelligent and Complex Systems Department of Physics Faculty of Science Chulalongkorn University Department of Mathematics Cracow University of Economics Department of Mathematical Sciences Indiana Univ.-Purdue Univ. Indianapolis Engineering Systems and Design Singapore Univ. of Technology and Design

ISBN: (纸本)9781713829546

Routing games are amongst the most studied classes of games in game theory. Their most well-known property is that learning dynamics typically converge to equilibria implying approximately optimal performance (low Price of Anarchy). We perform a stress test for these classic results by studying the ubiquitous learning dynamics, Multiplicative Weights Update (MWU), in different classes of congestion games, uncovering intricate non-equilibrium phenomena. We study MWU using the actual game costs without applying cost normalization to [0, 1]. Although this non-standard assumption leads to large regret, it captures realistic agents' behaviors. Namely, as the total demand increases, agents respond more aggressively to unbearably large *** start with the illustrative case of non-atomic routing games with two paths of linear cost, and show that every system has a carrying capacity, above which it becomes unstable. If the equilibrium flow is a symmetric 50 - 50% split, the system exhibits one period-doubling bifurcation. Although the Price of Anarchy is equal to one, in the large population limit the time-average social cost for all but a zero measure set of initial conditions converges to its worst possible value. For asymmetric equilibrium flows, increasing the demand eventually forces the system into Li-Yorke chaos with positive topological entropy and periodic orbits of all possible periods. Remarkably, in all non-equilibrating regimes, the time-average flows on the paths converge exactly to the equilibrium flows, a property akin to no-regret learning in zero-sum games. We extend our results to games with arbitrarily many strategies, polynomial cost functions, non-atomic as well as atomic routing games, and heterogenous users.

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SpinalXNet: Transfer Learning with Modified Fully Connected Layer for X-Ray Image Classification

SpinalXNet: Transfer Learning with Modified Fully Connected ...

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Recent Advances in systems science and engineering (RASSE), IEEE International Conference on

作者： Keshav Kumar Sadia Khanam Md Mahbub Islam Bhuiyan Mohammad Reza Chalak Qazani Subrota Kumar Mondal Houshyar Asadi H M Dipu Kabir Abbas Khorsavi Saeid Nahavandi Centre of Energy Excellence Gyancity Research Lab Motihari India Dhaka Dental College Dhaka Bangladesh Smartify Pty Ltd Sydney NSW Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Australia Faculty of Information Technology Macau University of Science and Technology Macao Harvard Paulson School of Engineering and Applied Sciences Harvard University Allston USA

ISBN: (纸本)9781665434423

Over the past year, COVID-19 has become a global pandemic and people across the globe have suffered a lot from this pandemic. The rate of transmitting the coronavirus in people is very quick. A rapid diagnosis can potentially help governments in identifying the pattern of transmission. There are some tests available but those tests take a long time to give the report. So, in this work, we have proposed a model that will distinguish between normal people, COVID affected people, and pneumonia affected people with the help of an X-ray. X-ray images are considered because taking an X-ray image is very little time-consuming. In this work, we have trained the X-ray images with a novel Deep Learning approach with SpinalNet architecture, and that distinguishes normal people, COVID affected people, and pneumonia affected people. After training the model we have achieved a very good accuracy for the SpinalNet architecture that is 96.12% while the traditional model provides 95.50% accuracy. We present precision, recall, and F1 scores of COVID and Pneumonia classes. We also present our results and codes with execution details. This paper contains the link to Kaggle notebooks with execution details. The applied Spinalnet transfer learning code is available in our GitHub repository: https://***/dipuk0506/SpinalNet

关键词： COVID-19 Training Codes Pandemics Pulmonary diseases Transfer learning Government

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mdx: A Cloud Platform for Supporting data science and Cross-Disciplinary Research Collaborations

arXiv

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arXiv 2022年

作者： Suzumura, Toyotaro Sugiki, Akiyoshi Takizawa, Hiroyuki Imakura, Akira Nakamura, Hiroshi Taura, Kenjiro Kudoh, Tomohiro Hanawa, Toshihiro Sekiya, Yuji Kobayashi, Hiroki Matsushima, Shin Kuga, Yohei Nakamura, Ryo Jiang, Renhe Kawase, Junya Hanai, Masatoshi Miyazaki, Hiroshi Ishizaki, Tsutomu Shimotoku, Daisuke Miyamoto, Daisuke Aida, Kento Takefusa, Atsuko Kurimoto, Takashi Sasayama, Koji Kitagawa, Naoya Fujiwara, Ikki Tanimura, Yusuke Aoki, Takayuki Endo, Toshio Ohshima, Satoshi Fukazawa, Keiichiro Date, Susumu Uchibayashi, Toshihiro Information Technology Center The University of Tokyo Japan Information Initiative Center Hokkaido University Japan Cyberscience Center Tohoku University Japan Faculty of Engineering Information and Systems University of Tsukuba Japan Division for Information and Communication Systems The University of Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Japan Center for Cloud Research and Development National Institute of Informatics Japan Research and Development Center for Academic Networks National Institute of Informatics Japan Research Center for Open Science and Data Platform National Institute of Informatics Japan Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Japan Global Scientific Information and Computing Center Tokyo Institute of Technology Japan Information Technology Center Nagoya University Japan Academic Center for Computing and Media Studies Kyoto University Japan Cybermedia Center Osaka University Japan Research Institute for Information Technology Kyushu University Japan

The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks. We have built such a nation-wide cloud platform, called "mdx" to meet this need. The mdx platform’s virtualization service, jointly operated by 9 national universities and 2 national research institutes in Japan, launched in 2021, and more features are in development. Currently mdx is used by researchers in a wide variety of domains, including materials informatics, geo-spatial information science, life science, astronomical science, economics, social science, and computer science. This paper provides an the overview of the mdx platform, details the motivation for its development, reports its current status, and outlines its future plans. © 2022, CC BY.

关键词： Internet of things

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Two-DOF Speed Control of Permanent Magnet Synchronous Machine with a novel Parameter Identification Method

Two-DOF Speed Control of Permanent Magnet Synchronous Machin...

引用

Chinese Control Conference (CCC)

作者： Mingjin Hu Wei Hua Xudong Wu Chengdong Wu Dazhi Wang Zhentao Liu Xiaofang Yuan Wennong Zhang School of Electrical Engineering Southeast University Nanjing Faculty of Robot Science and Engineering Northeastern University Shenyang College of Information Science and Engineering Northeastern University Shenyang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 College of Electrical and Information Engineering Hunan University Changsha Inovance Technology Co Ltd Shenzhen

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In this paper, a two-degree-of-freedom (2-DOF) speed control scheme for permanent magnet synchronous machines is developed. Both the reference-tracking and disturbance rejection performance are taken into account and only a single parameter related to expected bandwidth determines the dynamics. To improve the performance of speed control, a novel identification method for mechanical parameters is also proposed. The effectiveness of the proposed method is verified by simulations.

关键词： Bandwidth Velocity control Torque Simulation Transfer functions Rotors Parameter estimation

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Evolution of diabetes prediction using the fusion of ANOVA, ADASYN technique and XGBoost based on body composition data

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Journal of diabetes and metabolic disorders 2025年第2期24卷 151页

作者： Mohammad Ali Nematollahi Javad Hassannataj Joloudari Omid Zare Mohammad Maftoun Nima Shadkam Danial Sharifrazi Roohallah Alizadehsani Arefeh Asadollahi Department of Computer Sciences Fasa University Fasa Iran. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran. Department of Computer Engineering Babol Branch Islamic Azad University Babol Iran. Department of Computer Engineering Technical and Vocational University (TVU) Tehran *** Iran. Department of Computer Science University of Verona Verona Italy. Department of Artificial Intelligence Technical and Engineering Faculty South Tehran Branch Islamic Azad University Tehran Iran. Department of Computer Engineering Technical and Engineering Faculty West Tehran Branch Islamic Azad University Tehran Iran. Institute for Intelligent Systems Research and Innovation Deakin University Geelong VIC 3216 Australia. Noncommunicable Diseases Research Center Fasa University of Medical Sciences Fasa Iran.

Diabetes is known as a chronic illness with severe consequences. The rising morbidity rates predict a stunning growth in the global diabetes population, approaching 642 million by 2040, implying that one out of every ten people will be affected. This worrying number highlights the critical need for collaborative efforts from industry and academics to accelerate innovation and foster growth in diabetes risk prediction, eventually saving lives. As the frequency of life-threatening diseases, such as diabetes, rises, Medical Decision Support systems (MDSS) continue to prove their usefulness in supporting healthcare professionals, particularly physicians, in clinical decision-making procedures. Due to the advancement of technology, machine-learning techniques have made headlines in the early prediction of diabetes. In this paper, we employed machine learning techniques and the Analysis of Variance (ANOVA) method to explore associations between regional body fat distribution and diabetes mellitus in a community adult population, aiming to assess predictive capabilities. We used individual standard classifiers and ensemble learning methods to conduct a retrospective analysis of a portion of data based on body composition. To address the class imbalance problem in the target variable, we also applied three oversampling methods to provide more accurate predictions via learning algorithms. The results demonstrate that XGBoost, based on the Adaptive Synthetic Sampling (ADASYN) method, outperforms the state-of-the-art by achieving an accuracy value of 92.04%. This model exhibits more effectiveness for diabetes prediction compared to other models.

关键词： ADASYN ANOVA Diabetes prediction Machine learning

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Passivity properties for regulation of DC networks with stochastic load demand

arXiv

引用

arXiv 2020年

作者： Silani, Amirreza Cucuzzella, Michele Scherpen, Jacquelien M.A. Yazdanpanah, Mohammad Javad Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this paper we present new (stochastic) passivity properties for Direct Current (DC) power networks, where the unknown and unpredictable load demand is modelled by a stochastic process. More precisely, the considered power network consists of distributed generation units supplying ZIP loads, i.e., nonlinear loads comprised of impedance (Z), current (I) and power (P) components. Differently from the majority of the results in the literature, where each of these components is assumed to be constant, we consider time-varying loads whose dynamics are described by a class of stochastic differential equations. Finally, we prove that an existing distributed control scheme achieving current sharing and (average) voltage regulation ensures the asymptotic stochastic stability of the controlled network. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric power transmission networks

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