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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： T. Mangayarkarasi D. Najumnissa Jamal S. Kaja Mohideen Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai India B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Electronics and Instrumentation Engineering B.S.Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

In present days, the recent encroachment in the medicinal field and abnormality detection methodologies mutually by instrumentation also clinical methods has reached to a height in such a way that the clinical physicians are able to give an appropriate warning for the early detection of any disease and cure. This is widely applied in detecting abnormality of vital organs like kidney, heart, liver, gallbladder, etc. In this proposed work, the detection of prominent abnormalities in kidneys using US scanned images is analyzed using segmentation technique for feature extraction. The extracted features are saved as .csv file in which the significant Max intensity features exceeds threshold value detected as abnormal (cyst, calculi, tumor) image otherwise normal.

关键词： Image segmentation Ultrasonic imaging Instruments Noise Watersheds Feature extraction Medical diagnosis

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A Payment System for Electric Vehicles Charging and Peer-to-Peer Energy Trading 7

A Payment System for Electric Vehicles Charging and Peer-to-...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Kaushal, Rajanish Kumar Jain, Shubhi Shankar Rao, R Gowri Thirumalaimuthu, Babu Rawat, Ruchira Natrayan, L. Chandigarh University Department of Electrical Engineering Punjab Mohali Gharuan India Swami Keshvanand Institute of Technology Management and Gramothan Department of Electronics and Communication Rajasthan Jaipur302018 India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Physics Tamil Nadu Chennai600062 India St. Joseph's College of Engineering Department of Instrumentation and Control Engineering Tamil Nandu Chennai600119 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350341485

The popularity of vehicles based on electricity is increasing as they are considered a sustainable mode of transportation. The charging infrastructure for electric vehicles are suffering various issues. There are limited availability of public charging stations, the high costs associated with installing private charging stations, and the high variability of electricity prices. The peer-to-peer (P2P) energy stability will be seen as a potential solution to facilitate electric vehicle (EV) charging in a manner that is both cost-effective and environmentally sustainable. The paper presents a novel system energy trading and charging payment for electric vehicles (EVs). The desired system enables EV owners to exchange surplus electricity with other EV owners in their location. The system being proposed utilizes blockchain technology to guarantee secure and transparent transactions. Additionally, it incorporates a smart contract to automate the trading and charging process. The system utilizes a dynamic pricing mechanism that adjusts in real-time to reflect the current electricity prices. This feature is designed to encourage electric vehicle owners to engage in energy stabilization during maximum usage hours. The Optimistic results shows that the described system has the efficacy on minimising the cost of vehicle charging and to decrease peak demand on the grid. Additionally, the system encourages the utilization of renewable energy sources. © 2023 IEEE.

关键词： Electric vehicles

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Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator System

arXiv

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arXiv 2023年

作者： Zhang, Ruining Han, Haoran Lv, Maolong Yang, Qisong Cheng, Jian School of Information and Communication Engineering University of Electronic Science and Technology of China China Glasgow School University of Electronic Science and Technology of China China Air Traffic Control and Navigation College Air Force Engineering University China Xi’an Institute of High-Tech China

Extensive utilization of deep reinforcement learning (DRL) policy networks in diverse continuous control tasks has raised questions regarding performance degradation in expansive state spaces where the input state norm is larger than that in the training environment. This paper aims to uncover the underlying factors contributing to such performance deterioration when dealing with expanded state spaces, using a novel analysis technique known as state division. In contrast to prior approaches that employ state division merely as a post-hoc explanatory tool, our methodology delves into the intrinsic characteristics of DRL policy networks. Specifically, we demonstrate that the expansion of state space induces the activation function tanh to exhibit saturability, resulting in the transformation of the state division boundary from nonlinear to linear. Our analysis centers on the paradigm of the double-integrator system, revealing that this gradual shift towards linearity imparts a control behavior reminiscent of bang-bang control. However, the inherent linearity of the division boundary prevents the attainment of an ideal bang-bang control, thereby introducing unavoidable overshooting. Our experimental investigations, employing diverse RL algorithms, establish that this performance phenomenon stems from inherent attributes of the DRL policy network, remaining consistent across various optimization algorithms. © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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A Practical Quantum Network Coding Protocol Based on Non-Maximally Entangled State

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Computers, Materials & Continua 2021年第8期68卷 2651-2663页

作者： Zhen-Zhen Li Zi-Chen Li Xiu-Bo Chen Zhiguo Qu Xiaojun Wang Haizhu Pan School of Information Engineering Beijing Institute of Graphic CommunicationBeijing102600China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing210044China School of Electronic Engineering Dublin City UniversityDublin9Ireland College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China

In many earlier works,perfect quantum state transmission over the butterfly network can be achieved via quantum network coding protocols with the assist of maximally entangled ***,in actual quantum networks,a maximally entangled state as auxiliary resource is hard to be obtained or easily turned into a non-maximally entangled state subject to all kinds of environmental ***,we propose a more practical quantum network coding scheme with the assist of non-maximally entangled *** this paper,a practical quantum network coding protocol over grail network is proposed,in which the non-maximally entangled resource is assisted and even the desired quantum state can be perfectly *** achievable rate region,security and practicability of the proposed protocol are discussed and *** practical quantum network coding protocol proposed over the grail network can be regarded as a useful attempt to help move the theory of quantum network coding towards practicability.

关键词： Quantum network coding non-maximally entangled state quantum grail network practical protocol

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Industrial Semantic communications for Tool Wear Monitoring in Industrial IoT Networks

Industrial Semantic Communications for Tool Wear Monitoring ...

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IEEE International Conference on communications in China Workshops (ICCC)

作者： Mingqiang Zhang Yajuan Yang Baokun Song Zexin Chen Xiaocong Ma Dongyang Li Na Li Feng Cao Yanping Zhang Haixia Zhang School of Cyber Science and Engineering Qufu Normal University Qufu China College of Oceanography and Space Informatics China University of Petroleum (East China) Huangdao China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Control Science and Engineering Shandong University Jinan China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Shandong University Jinan China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

In this paper, we propose an Industrial Semantic communications (ISC) framework to ensure efficient and in-telligent communication in Industrial Internet-of-Things (IIoT) networks, and further apply the proposed framework to the task of tool wear monitoring. A semantic-aware tool wear monitoring network (TWM-Net) is designed to accurately identify the idling state from tool wear data, thus assisting the semantic data transmission process of the ISC framework. Then, a demo software system is implemented to verify the delay performance of the proposed ISC framework. Experimental results demonstrated that the proposed ISC framework can facilitate the semantic information exchange between edge industrial system and cloud platform together with the deep compressed sensing method, and significantly reduce the amount of data transmissions in IIoT networks. The source code and dataset is available through https://***/pnixsoft/TWM-Net.

关键词： Vibrations Source coding Software systems Multitasking Delays Data communication Monitoring Information exchange Industrial Internet of Things Testing

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The Research Process, Application, and the Future Development of Pulse-Position Modulation 4

The Research Process, Application, and the Future Developmen...

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2022 4th International Conference on Wireless communications and Smart Grid, ICWCSG 2022

作者： Tang, Wenzhe Wang, Sirui Xu, Yimeng Yu, Zhouyue Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom School of Electronic Engineering Xidian University Shaanxi Province Xi'an710071 China College of Electronics and Information Engineering Tongji University Shanghai201804 China School of Information and Communication Guilin University of Electronic Technology Guangxi Province Guilin541214 China

With the development of digital communication, pulse-position modulation technology has become an important technology in wireless communication due to its efficient bandwidth utilization and sensitive detection efficiency. This study aims to provide some basic information on PPM. In the trend of rapid development of PPM, researchers have found that PPM plays an irreplaceable role, no matter in daily life or scientific research and development. To arouse people's interest in PPM research, more people are attracted to participate in the study. It began with an introduction to the development process of PPM, and briefly lists the advantages and disadvantages of PPM and the development trend to help readers quickly understand its background knowledge of it. The second part started to work on different modulations based on the previous research on PPM. Various modulation methods are related to each other, and the proposal of each modulation method is based on the shortcomings of existing modulation methods. This study discusses how PPM is used in experiments. Moreover, each experiment's measurement values and corresponding advantages are analyzed through charts, like Bit Error Rate (BER) and Signal to Noise Ratio (SNR) conducted in each experiment. This study draws a conclusion by discussing the application and the broad horizon for future developments mainly used in communication engineering led by light communication. However, in recent years, PPM has been introduced in many other fields, such as detection and mechanic. © Published under licence by IOP Publishing Ltd.

关键词： Pulse position modulation

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Research on Autonomous Maneuvering Strategies of UAV Swarm for Saturation Attack

Research on Autonomous Maneuvering Strategies of UAV Swarm f...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Zhang, Tingting Zuo, Yi Zhang, Dongning Ding, Fei Huang, Jun Lin, Yun College of Command and Control Engineering Army Engineering University of the Pla Nanjing China Key Laboratory of Information System Requirements Nanjing China Fifty-fourth Research Institute of China Electronics Technology Group Corporation Shijiazhuang China Internet of Things Academy Nanjing University of Posts and Telecommunications Nanjing China Jsti Group Nanjing China Harbin Engineering University Department of Information and Communication Engineering Harbin China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

In order to solve the problem of unmanned aerial Vehicle (UAV) cluster saturation attack, the autonomous maneuver strategy of UAV is studied. Based on the distributed partial observable Markov decision process, the autonomous maneuver decision model of UAV is constructed, and the Recurrent Multi-Agent Deep Deterministic Policy Gradient (RMADDPG) is used to learn the maneuver strategy of UAV cluster saturation attack. Combined with the typical characteristics of saturation attack scenario, two reward functions of global reward and local reward for UAV maneuver are designed. The trained UAVs can effectively perform suicide saturation attack task. According to the mission target design evaluation index of saturation attack, the Monte Carlo analysis method is used to compare the proposed method with particle swarm optimization (PSO) algorithm in three aspects: inter aircraft collision avoidance, NFZ avoidance and simultaneous attack. The experimental results show that the trained UAVs can effectively perform the suicide saturation attack task under the condition of a small amount of communication, which provides a method for the application of multi-agent deep reinforcement learning to UAV cluster operations. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Adversarial Examples Detection of Electromagnetic Signal Based on GAN

Adversarial Examples Detection of Electromagnetic Signal Bas...

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International Conference on Big Data Computing and communications (BIGCOM)

作者： Jiawei Zhu Jiangpeng Li Dongwei Xu Chuntao Gu Qi Xuan Shunling Wang Security Control Laboratory Science and Technology on Communication Information Jiaxing China Institute of Cyberspace Security Zhejiang University of Technology College of Information Engineering Zhejiang University of Technology Hangzhou China

In the field of signal modulation classification, deep neural networks (DNNs) perform very well and have good generalization ability. However, the DNN model is extremely vulnerable. The model gives a false output with high confidence from an input example, which formed only by deliberately adding tiny disturbances. Such elaborate examples that make the model misclassify are called adversarial examples. In this paper, for the detection of adversarial examples in electromagnetic signals, we propose a method based on generative adversarial networks (GANs), a novel strategy to defend deep neural networks against such attacks. First of all, by experiment we came to the conclusion that the confidence of the DNN model for normal examples is stable, and the confidence for adversarial examples is unstable. We trained a classifier that performs well in extracting electromagnetic signal features. Then, We feed the normal examples to the generative adversarial network, the generator generates examples with similar data distribution to the clean examples. We send such pairs of examples to the classifier to calculate a threshold T, and the same operations will be performed on the test examples. Finally, the absolute error E of each pair of test examples will be calculated and compared with the threshold T to achieve electromagnetic signal adversarial examples detection. We define the method in the paper as Detect-GAN. The experimental results show that Detect-GAN can effectively defend against common and powerful attack algorithm. Comparing with other detection methods, Detect-GAN achieves more competitive performance.

关键词： Deep learning Neural networks Modulation Big Data Generative adversarial networks Feature extraction Generators

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Act Now: A Novel Online Forecasting Framework for Large-Scale Streaming Data

arXiv

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arXiv 2024年

作者： Liang, Daojun Zhang, Haixia Wang, Jing Yuan, Dongfeng Zhang, Minggao School of Information Science and Engineering Shandong University China School of Control Science and Engineering Shandong University China Ocean College Jiangsu University of Science and Technology China School of Qilu Transportation Shandong University China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration China

In this paper, we find that existing online forecasting methods have the following issues: 1) They do not consider the update frequency of streaming data and directly use labels (future signals) to update the model, leading to information leakage. 2) Eliminating information leakage can exacerbate concept drift and online parameter updates can damage prediction accuracy. 3) Leaving out a validation set cuts off the model’s continued learning. 4) Existing GPU devices cannot support online learning of large-scale streaming data. To address the above issues, we propose a novel online learning framework, Act-Now, to improve the online prediction on large-scale streaming data. Firstly, we introduce a Random Subgraph Sampling (RSS) algorithm designed to enable efficient model training. Then, we design a Fast Stream Buffer (FSB) and a Slow Stream Buffer (SSB) to update the model online. FSB updates the model immediately with the consistent pseudo- and partial labels to avoid information leakage. SSB updates the model in parallel using complete labels from earlier times. Further, to address concept drift, we propose a Label Decomposition model (Lade) with statistical and normalization flows. Lade forecasts both the statistical variations and the normalized future values of the data, integrating them through a combiner to produce the final predictions. Finally, we propose to perform online updates on the validation set to ensure the consistency of model learning on streaming data. Extensive experiments demonstrate that the proposed Act-Now framework performs well on large-scale streaming data, with an average 28.4% and 19.5% performance improvement, respectively. Experiments can be reproduced via https://***/Anoise/Act-Now. © 2024, CC BY.

关键词： Spatio-temporal data

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Acceleration of Formation control Based on Hessian Matrix of Artificial Potential Field

Acceleration of Formation Control Based on Hessian Matrix of...

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Chinese control Conference (CCC)

作者： Haoran Han Zhilong Xi Maolong Lv Jian Cheng School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu P. R. China Air Traffic Control and Navigation College Air Force Engineering University Xian P. R. China

The formation control of multi-agent system has become a hotspot in recent years. One of the most commonly used methods is to use artificial potential field (APF) to generate the attractive and repulsive force to organize the agents into a low-energy and stable formation. In this paper, we regard the APF as the difference in the desired position, and introduce the derivative of the gradient of the APF with respect to time, which is simplified as the Hessian matrix of the APF, into the formation control from the perspective of trajectory tracking. The energy-based Lyapunov function is selected to prove the stability and collision avoidance ability of the proposed control law. Moreover, we show that the maneuverability of the multi-agent system is enormously improved with the additional Hessian matrix term.

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