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IEEE Transactions on Intelligent Vehicles 2023年 1-10页

作者： Xue, Jun Liu, Ziniu Liu, Guanjun Zhou, Ziyuan Zhang, Kaiwen Tang, Ying Wang, Jiacun Department of Control Science and Engineering Tongji University Shanghai China Department of Computer Science Tongji University Shanghai China Department of Electrical and Computer Engineering Rowan University Glassboro NJ USA Department of Computer Science and Software Engineering Monmouth University West Long Branch NJ USA

Unmanned Aerial Vehicles (UAVs) have extensive applications such as logistics transportation and aerial photography. However, UAVs are sensitive to winds. Traditional control methods, such as proportional- integral-derivative controllers, generally fail to work well when the strength and direction of winds are changing frequently. In this work deep reinforcement learning algorithms are combined with a domain randomization method to learn robust wind-resistant hovering policies. A novel reward function is designed to guide learning. This reward function uses a constant reward to maintain a continuous flight of a UAV as well as a weight of the horizontal distance error to ensure the stability of the UAV at altitude. A five-dimensional representation of actions instead of the traditional four dimensions is designed to strengthen the coordination of wings of a UAV. We theoretically explain the rationality of our reward function based on the theories of Q-learning and reward shaping. Experiments in the simulation and real-world application both illustrate the effectiveness of our method. To the best of our knowledge, it is the first paper to use reinforcement learning and domain randomization to explore the problem of robust wind-resistant hovering control of quadrotor UAVs, providing a new way for the study of wind-resistant hovering and flying of UAVs. IEEE

关键词： Heuristic algorithms

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A novel member enhancement-based clustering ensemble algorithm

A novel member enhancement-based clustering ensemble algorit...

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作者： He, Yulin Yang, Jin Cheng, Yingchao Du, Xueqin Huang, Joshua Zhexue Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Clustering ensemble is a popular approach for identifying data clusters that combines the clustering results from multiple base clustering algorithms to produce more accurate and robust data clusters. However, the performance of clustering ensemble algorithms is highly dependent on the quality of clustering members. To address this problem, this paper proposes a member enhancement-based clustering ensemble (MECE) algorithm that selects the ensemble members by considering their distribution consistency. MECE has two main components, called heterocluster splitting and homocluster merging. The first component estimates two probability density functions (p.d.f.s) estimated on the sample points of an heterocluster and represents them using a Gaussian distribution and a Gaussian mixture model. If the random numbers generated by these two p.d.f.s have different probability distributions, the heterocluster is then split into smaller clusters. The second component merges the clusters that have high neighborhood densities into a homocluster, where the neighborhood density is measured using a novel evaluation criterion. In addition, a co-association matrix is presented, which serves as a summary for the ensemble of diverse clusters. A series of experiments were conducted to evaluate the feasibility and effectiveness of the proposed ensemble member generation algorithm. Results show that the proposed MECE algorithm can select high quality ensemble members and as a result yield the better clusterings than six state-of-the-art ensemble clustering algorithms, that is, cluster-based similarity partitioning algorithm (CSPA), meta-clustering algorithm (MCLA), hybrid bipartite graph formulation (HBGF), evidence accumulation clustering (EAC), locally weighted evidence accumulation (LWEA), and locally weighted graph partition (LWGP). Specifically, MECE algorithm has the nearly 23% higher average NMI, 27% higher average ARI, 15% higher average FMI, and 10% higher average purity than CSPA

关键词： Clustering algorithms

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Classification and Segmentation of Gastroenterology Images Using Saliency Mapping in Convolutional Neural Network 21

Classification and Segmentation of Gastroenterology Images U...

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21st International Bhurban Conference on Applied sciences and Technology, IBCAST 2024

作者： Khan, Yusra Shaukat, Arslan Riaz, Farhan Department of Computer and Software Engineering Islamabad Pakistan School of Computer Science University of Lincoln Lincoln United Kingdom

ISBN: (纸本)9798331516680

This research work has been done towards the treatment of Gastrointestinal (GI) Cancer by experimenting as to how a computer aided design can help oncologists classify and segment GI Cancer using Gastroenterology (GE) Images. The concept of Attention Module by the name of Convolution Block Attention Module (CBAM) using Convolutional Neural Network (CNN) layers has been utilized to achieve saliency mapping in these images. The different layers of the CNN are tweaked and the hyper parameters are consistently checked and changed to achieve maximum accuracy. The CBAM Algorithm is divided into Channel Attention (CA) and Spatial Attention (SA) module. The Channel Attention has been implemented in the HSV Color Space for finding the best color space for segmentation purposes of the Chromo endoscopy medical images. Classification, followed by segmentation, is again implemented using CNN based model for normal and abnormal cancer images. For classification, 92% test accuracy has been achieved, while 70% Dice Similarity Coefficient has been obtained for segmentation. The results achieved are competitive as compared to the previous reported results on the GE images dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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A PPO-based Channel Hopping Sequence Framework for Time Slot Channel Hopping 24

A PPO-based Channel Hopping Sequence Framework for Time Slot...

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12th International Conference on Communications and Broadband Networking, ICCBN 2024

作者： He, Zhihang Niu, Xianhua Qi, Chao Wang, Yaoxuan Zhou, Enzhi Zeng, Bosen School of Computer and Software Engineering Xihua University China School of Computer Science Chengdu Normal University China

ISBN: (纸本)9798400717109

As a key communication technology in IEEE 802.15.4, Time Slot Channel Hopping (TSCH) enhances transmission reliability and interference immunity by scheduling of time slots and channel assignments. This paper presents a TSCH framework designed to improve packet delivery ratio and anti-interference performance by generating optimal channel hopping sequences that achieve the Lempel-Greenberger bound. The framework employs the Proximal Policy Optimization (PPO) algorithm to predict channel quality, thereby facilitating the selection of higher quality channels in response the dynamic interference changes. Simulation results demonstrate a 7.9% improvement in packet delivery ratio performance compared to previous studies. © 2024 ACM.

关键词： Frequency hopping

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3-Multi ranked encryption with enhanced security in cloud computing

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Digital Communications and Networks 2023年第2期9卷 313-326页

作者： YeEun Kim Junggab Son Reza M.Parizi Gautam Srivastava Heekuck Oh Major in Bio Artificial Intelligence Dept.of Computer Science and EngineeringHanyang UniversityAnsanRepublic of Korea Department of Computer Science Kennesaw State UniversityMariettaUSA Department of Software Engineering Kennesaw State UniversityMariettaUSA Department of Mathematics and Computer Science Brandon UniversityBrandonCanada Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Dept.of Computer Science and Engineering Hanyang UniversityAnsanRepublic of Korea

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when *** refer to a model that satisfies all of the conditions a 3-multi ranked search ***,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation *** is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real *** this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security *** proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same ***,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different *** evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

关键词： Searchable encryption Multi-keyword search Multiple data owners/users Ranked search Enhanced security

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Quick Hosting Capacity Evaluation Based on Distributed Dispatching for Smart Distribution Network Planning with Distributed Generation

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 128-140页

作者： Bing Sun Ruipeng Jing Leijiao Ge Yuan Zeng Shimeng Dong Luyang Hou the School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China State Grid Corporation of China BeijingChina the School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijingChina

The smart distribution network(SDN)is integrat ing increasing distributed generation(DG)and energy storage(ES).Hosting capacity evaluation is important for SDN plan ning with *** and ES are usually invested by users or a third party,and they may form friendly microgrids(MGs)and operate *** centralized dispatching meth od no longer suits for hosting capacity evaluation of SDN.A quick hosting capacity evaluation method based on distributed optimal dispatching is ***,a multi-objective DG hosting capacity evaluation model is established,and the host ing capacity for DG is determined by the optimal DG planning *** steady-state security region method is applied to speed up the solving process of the DG hosting capacity evalua tion ***,the optimal dispatching models are estab lished for MG and SDN respectively to realize the operating *** the distributed dispatching strategy,the dual-side optimal operation of SDN-MGs can be realized by several iterations of power exchange ***,an SDN with four MGs is conducted considering multiple flexible *** shows that the DG hosting capacity of SDN oversteps the sum of the maximum active power demand and the rated branch ***,the annual DG electricity oversteps the maximum active power demand value.

关键词： Smart distribution network(SDN) microgrid hosting capacity multi-objective optimization distributed opti mal dispatching flexible resource

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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

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Stroke Disease Prediction Based on Multi-Model Ensemble Learning 5

Stroke Disease Prediction Based on Multi-Model Ensemble Lear...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Cui, Xiaojian School of Computer Science and Engineering School of Software Guangxi Normal University Guilin China

ISBN: (纸本)9798350385557

Stroke is a kind of acute cerebrovascular disease, which is caused by the sudden rupture of blood vessels in the brain or the blockage of blood vessels that can not flow into the brain and cause brain tissue damage. Stroke has the characteristics of acute onset and rapid progress. Once stroke, it can lead to extremely serious consequences, and many people suffer from it. But because of the lack of effective treatment for stroke, prevention is the best response. So we use multi-model ensemble learning method to train a model. It can analyze biological signals and lifestyle habits to predict the probability of a person having a stroke. People can adjust their living habits in time according to the probability of stroke. In order to reduce the probability of stroke, protect their own life and health. According to our experiments, the accuracy of the trained model is high, which can help people to prevent stroke disease. © 2024 IEEE.

关键词： Learning systems

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Communication-Efficient Federated Learning Over-the-Air With Sparse One-Bit Quantization

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IEEE Transactions on Wireless Communications 2024年第10期23卷 15673-15689页

作者： Oh, Junsuk Lee, Donghyun Won, Dongwook Noh, Wonjong Cho, Sungrae Chung-Ang University School of Computer Science and Engineering Seoul06974 Korea Republic of Hallym University School of Software Chuncheon24252 Korea Republic of

Federated learning (FL) is a framework for realizing distributed machine learning in an environment where training samples are distributed to each device. Recently, FL has employed over-the-air computation enabling all devices to transmit learning model updates simultaneously. This work proposes a communication-efficient sparse one-bit analog aggregation (SOBAA) method, incorporating new power control, layer-wise scaled one-bit quantization, layer-wise sparsification, and an error-feedback mechanism. We derive a tight upper bound of the expected convergence rate of the proposed SOBAA as a closed-form expression. From this expression, we explicitly identify the relationship between the convergence rate and compression and aggregation errors. Based on the theoretical convergence analysis, we formulate a joint optimization problem of the compression ratio and power control to minimize compression and aggregation errors, leading to the fastest convergence. In each communication round, the optimization problem is decomposed, and solved in a computationally efficient and feasible way. From this solution, we characterize the trade-off between learning performance and communication cost. Through extensive experiments on well-known MNIST and CIFAR-10 datasets, we confirm that the proposed method provides an enhanced trade-off performance between test accuracy and communication costs and a faster convergence rate than the other state-of-the-art methods. In addition, it is proven that the proposed method is more effective for more complex datasets and learning models. © 2024 IEEE.

关键词： Fading channels

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Enhancing Waste Management Sustainability Through Smart and Incentivized Sorting 15

Enhancing Waste Management Sustainability Through Smart and ...

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15th Annual Undergraduate Research Conference on Applied Computing, URC 2024

作者： Woga, Muluberhan Negash, Rediet Bekera, Ahmedin Alemu, Nathanael Zaki, Nazar Department of Computer Science and Software Engineering Al Ain15551 United Arab Emirates

ISBN: (纸本)9798331527341

This paper presents an intelligent waste sorting system that utilizes computer vision and deep learning to accurately categorize waste items. Moreover, the system incentivizes proper waste disposal through a rewards scheme. Testing indicated over 90% accuracy in classifying waste into multiple categories. This sustainable solution has strong potential to address critical waste management challenges. © 2024 IEEE.

关键词： Deep learning

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