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Chinese control and Decision Conference, CCDC

作者： Dong Liang Yi Dong Chaoli Wang Engang Tian Department of Control Science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

The cooperative output regulation problem has been extensively studied on the basis of the distributed observer approach. However, the majority of the existing research assumes that the dynamics is known previously. To remove this condition, the cooperative output regulation problem is further solved via the data-driven framework where the dynamics of the plant is unknown. First, a data-driven distributed observer is established to estimate the state of the leader with unknown dynamics subject to external inputs. Second, the unknown regulator equations are solved using the iterative recurrent neural network approach. Third, the state-based data-driven distributed control law is synthesized to solve the problem. The optimal gains are derived by solving convex optimization problems using input and state data. Finally, a numerical example is presented to verify the feasibility of the proposed framework.

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Investigation of Effective Electrode Structure for Metal Removal from Composite Materials Using Pulsed Discharge

Investigation of Effective Electrode Structure for Metal Rem...

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Pulsed Power Conference, International Conference on High-Power Particle Beams and International Symposium on Electromagnetic Launch Technology (EAPPC/BEAMS/EML), Euro-Asian

作者： Takuto Doi Shota Oishi Takashi Sakugawa Kaisei Nishimura Shinichiro Shobako Shinichi Shimasaki Tomohiko Yamashita Department of Computer Science & Electrical Engineering Graduate School of Science & Technology Kumamoto University Kumamoto Japan Department of Electro-Mechanical Systems Engineering National Institute of Technology Kagawa College Takamatsu Japan

ISBN: (数字)9789038661353

ISBN: (纸本)9798350352733

The authors investigate the potential of pulsed power technology in recycling of E-waste. Applying the pulsed discharge can separate composite materials into plastic and metal. In this study, pulsed discharge was applied to indium tin oxide (ITO) coated plastic films. We used two electrode types: rod-to-rod electrode and a pair of flat plate electrodes to investigate the effect of electrode structure on the metal removal area in detail. A series of single pulse discharges were applied at various electrode distances. As a result, it was revealed that metal can be removed over a wide range by the pulsed discharge using a pair of flat plate electrodes. When the gap between the electrodes was 30 mm, the removal area by the flat plate electrodes was approximately 3.4 times that by the rod electrodes. Analysis of the current density also revealed that the metal removal area was greatly affected by the current density.

关键词： Electrodes Particle beams Composite materials Shape Indium tin oxide Metals Plastic films Discharges (electric) Recycling Current density

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Intrusion Detection Mechanism Using Deep Learning

Intrusion Detection Mechanism Using Deep Learning

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IoT Based control Networks and Intelligent systems (ICICNIS), International Conference on

作者： P. Ananthi K. Nirmaladevi Naveen Kumar S Department of Computer Technology-PG Kongu Engineering College Erode Tamilnadu Department of Computer Applications Madanapalle Institute of Technology & Science M.Sc Software Systems Kongu Engineering College Erode India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

In today's connected and data-driven world, networks and digital systems need to be protected from malicious attacks. The effectiveness of conventional Intrusion Detection systems (IDS) in recognizing and impeding novel threats and intricate methodologies has its limits. This study introduces a new method for improving network security by using deep learning techniques in the design and development of an intrusion detection system (IDS). The suggested Intrusion Detection System uses deep learning techniques, specifically Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs), to identify and react to known and new security threats. Data can be taught to be encoded into a lower-dimensional representation and then decoded back into its original form using autoencoders (AEs). Autoencoders are a cutting-edge intrusion detection system that can lower false positives and raise overall detection accuracy because of their capacity to automatically learn from and adjust to the ever-changing threat landscape. They enhance computer networks capacity to spot irregularities and possible security threats. With the deep learning model obtaining a high accuracy of 99% in real-world threat detection, the results show that deep learning is effective in developing intrusion detection systems.

关键词： Deep learning Accuracy Autoencoders Intrusion detection Network security Threat assessment Real-time systems computer security Optimization Long short term memory

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Distributed Nash Equilibrium Seeking with Stochastic Event-Triggered Mechanism

arXiv

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arXiv 2023年

作者： Huo, Wei Tsang, Kam Fai Elvis Yan, Yamin Johansson, Karl Henrik Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

In this paper, we study the problem of consensus-based distributed Nash equilibrium (NE) seeking where a network of players, abstracted as a directed graph, aim to minimize their own local cost functions non-cooperatively. Considering the limited energy of players and constrained bandwidths, we propose a stochastic event-triggered algorithm by triggering each player with a probability depending on certain events, which improves communication efficiency by avoiding continuous communication. We show that the distributed algorithm with the developed event-triggered communication scheme converges to the exact NE exponentially if the underlying communication graph is strongly connected. Moreover, we prove that our proposed event-triggered algorithm is free of Zeno behavior. Finally, numerical simulations for a spectrum access game are provided to illustrate the effectiveness of the proposed mechanism by comparing it with some existing event-triggered methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Directed graphs

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EnsembleCAM: Unified Visualization for Explainable Cervical Cancer Identification

EnsembleCAM: Unified Visualization for Explainable Cervical ...

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International Research Conference on Smart Computing and systems engineering (SCSE)

作者： Niruthikka Sritharan Nishaanthini Gnanavel Prathushan Inparaj Dulani Meedeniya Pratheepan Yogarajah Department of Computer Science and Engineering University of Moratuwa Moratuwa Sri Lanka School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

Cervical cancer, which is ranked fourth among cancers affecting women, is highly treatable when detected early through the pap smear test. Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), analyze pap smear images, yet their "Black-Box" nature raises transparency concerns in medical diagnostics. This paper introduces a solution named EnsembleCAM to enhance interpretability by unifying visual explanations through the combination of diverse Class Activation Maps (CAMs). Using the Herlev Dataset, we employ data pre-processing, data augmentation techniques, develop an XceptionNet based binary classification model with an accuracy of 89% and apply GradCAM, GradCAM++, Score-CAM, Eigen-CAM and LayerCAM on this classifier. Then, the novel EnsembleCAM is constructed taking the median of activation maps from the five individual CAM methods. The analysis of activation maps of each CAM method and EnsembleCAM confirmed that in activation maps of EnsembleCAM, higher activation values were more concentrated around the nucleus which is the most important region in indicating cervical malignancy. The evaluation using pixel flipping performance metric also proved that the EnsembleCAM effectively recognises regions vital to the model's decision-making through its steepest drop in the mean prediction score when the pixels in the region contributing most to the model's decision were flipped.

关键词： Visualization Decision making Closed box Predictive models Cams Reliability Medical diagnosis

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Recognition of Handwritten Tamazight Characters Using ResNet, MobileNet and VGG Transfer Learning 23

Recognition of Handwritten Tamazight Characters Using ResNet...

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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Fattahi, Jaouhar Sghaier, Feriel Mejri, Mohamed Manai, Elyes Ghayoula, Ridha Department of Computer Science and Software Engineering Laval University 2325 rue de l'Université QuébecQCG1V 0A6 Canada Ecole Nationale d'Ingénieurs de Carthage Université de Carthage Tunis Tunisia Faculty of Engineering Information Systems Department University of Moncton New Brunswick Canada

ISBN: (纸本)9781643685380

The Tamazight civilization stands as a significant cultural entity, marked by its linguistic diversity, historical legacy, and scriptural traditions, which collectively enrich the cultural tapestry of North Africa. Among these traditions, the Tamazight handwritten script assumes particular importance, embodying centuries of cultural identity and artistic expression. Recognizing the imperative of safeguarding this cultural heritage, our study focuses on Tamazight handwritten character recognition. Leveraging the strategic application of Transfer Learning, we explore its efficacy in this domain. Transfer Learning presents a robust framework wherein pre-existing models are adapted for specific tasks despite limited data availability. Our research employs three prominent Transfer Learning architectures: VGG, ResNet, and MobileNet. Through a rigorous comparative analysis, we discern the efficacy of these methodologies in the context of Tamazight handwritten character recognition. Our findings underscore the potential of Transfer Learning to significantly augment the accuracy and efficiency of script recognition systems, thereby advancing the overarching objective of preserving and propagating the Tamazight cultural heritage. © 2024 IOS Press. All rights reserved.

关键词： Transfer learning

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Real-Time Notifier for Blockchain Events

Real-Time Notifier for Blockchain Events

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Roedunet International Conference (RoEduNet)

作者： Cristian Tănase-Paris Costin Carabaș Nicolae Țăpuș Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

Blockchain technology gained much traction in the last few years. These decentralized databases offer security, immutability, and scalability across various applications. Decentralized applications generate vast amounts of data, known as events, that are recorded on the blockchain and are public to anyone. Some people may see opportunities for financial gains in these events and would like to know when they occur. This paper proposes a solution to process and deliver those events as real-time alerts to the users. It uses existing technologies such as message queues, multithreading, and asynchronous processing and integrates them into a scalable architecture. The results we achieved in this paper show that for an evenly distributed network traffic, which does not entirely consists of transaction bursts, the proposed solution offers reliability, efficiency, and a suitable delivery time to those wishing to integrate it into their projects. With time, this solution, or improved architectures, may form the basis of the following big-data architectures for processing blockchain events.

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An Apriori Algorithm-Based Association Rule Analysis to detect Human Suicidal Behaviour

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Procedia computer science 2023年 219卷 1279-1288页

作者： Md. Mehedi Hassan Asif Karim Swarnali Mollick Sami Azam Eva Ignatious A S M Farhan Al Haque Computer Science and Engineering North Western University Khulna Bangladesh College of Engineering IT and Environment Charles Darwin University NT Australia Computer Science and Engineering Northern University of Business and Technology Khulna Bangladesh Department of Electronic Systems Aalborg University Copenhagen Denmark

Suicide is a major cause of death. It is also a complex public health issue and often preventable with timely intervention. Overall, the rate of suicide is increasing for various reasons. In our study, we use an association rule analysis to find the most important rules to predict suicidal behavior from an available data set. One of the most powerful machine learning algorithms available for identifying associations within databases is the Apriori algorithm. We used this algorithm to analyze association rules of suicidal behavior using a dataset of 1250 instances and 27 impactful features. These include daily activities, family background, and answers to mental questionnaires and have been analyzed to find combinations that are associated with suicidal behavior. The study has resulted in some key rules for human suicidal behavior. The Apriori method has been used to identify the eight most significant rules with the support of 0.25 and the confidence of 0.90.

关键词： Suicide Suicidal Behaviour Behaviour Analysis Association Rule Mining Apriori

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SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint Learning

arXiv

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arXiv 2024年

作者： Feng, Yi Guo, Zizhan Chen, Qijun Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Shanghai Institute of Intelligent Science and Technology The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

Unsupervised monocular depth estimation frameworks have shown promising performance in autonomous driving. However, existing solutions primarily rely on a simple convolutional neural network for ego-motion recovery, which struggles to estimate precise camera poses in dynamic, complicated real-world scenarios. These inaccurately estimated camera poses can inevitably deteriorate the photometric reconstruction and mislead the depth estimation networks with wrong supervisory signals. In this article, we introduce SCIPaD, a novel approach that incorporates spatial clues for unsupervised depth-pose joint learning. Specifically, a confidence-aware feature flow estimator is proposed to acquire 2D feature positional translations and their associated confidence levels. Meanwhile, we introduce a positional clue aggregator, which integrates pseudo 3D point clouds from DepthNet and 2D feature flows into homogeneous positional representations. Finally, a hierarchical positional embedding injector is proposed to selectively inject spatial clues into semantic features for robust camera pose decoding. Extensive experiments and analyses demonstrate the superior performance of our model compared to other state-of-the-art methods. Remarkably, SCIPaD achieves a reduction of 22.2% in average translation error and 34.8% in average angular error for camera pose estimation task on the KITTI Odometry dataset. Our source code is available at https://***/SCIPaD. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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Prototyping Multi-Protocol Communication to enable semantic interoperability for Demand response Services

Prototyping Multi-Protocol Communication to enable semantic ...

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2021 IEEE International Conference on Communications, control, and Computing Technologies for Smart Grids, SmartGridComm 2021

作者： Galkin, Nikolai Yang, Chen-Wei Nordstrom, Lars Vyatkin, Valeriy Electrical and Space Engineering Luleå University of Technology Department of Computer Science Luleå Sweden Department of Electric Power and Energy Systems Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9781665415026

In future demand response scenarios, a multitude of different types of resources are potentially to be used, e.g., electric vehicles, flexible residential loads, and battery storage systems. To solve the problem of real-time communication of data among the various resources, it is likely that several different communication protocols and most importantly differing semantic models, must be used. An aggregator utilizing several types of resources is therefore potentially faced with a problem of semantic interoperability. This paper addresses this challenge by presenting a testbed consisting of a microgrid model integrated with several controllers communicating with industrial grade protocols for demand response, including IEC 61850, OpenChargePoint protocol (OCPP), OpenADR and UDP. The testbed forms a basis for further development of a semantic canvas to enable forecasting, activation and clearing of heterogenous demand response resources. © 2021 IEEE.

关键词： Multi agent systems

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