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Integral Sliding Mode Control for Automated Vehicles under Coding-Decoding Mechanisms with Constrained Bit Rate

IEEE Internet of Things Journal 2025年第11期12卷 16214-16226页

作者： Zhang, Mingming Ding, Derui Shen, Bo Ge, Xiaohua University of Shanghai for Science and Technology Department of Control Science and Engineering Shanghai200093 China Donghua University College of Information Science and Technology Shanghai201620 China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia

In this paper, an integral sliding mode (ISM) control scheme is developed to tackle platooning control issues of vehicle systems under both an improved constant-Time headway (CTH) spacing strategy and a coding-decoding communication protocol (CDCP) with constrained bit rate. The utilization of CDCPs can effectively reduce communication burden and promote data security, while the improved CTH spacing policy based on relative velocities can ensure high traffic efficiency as well as platoon safety. First, an ISM surface is constructed to handle matched unmodeled dynamics by resorting to the improved CTH spacing strategy and the decoded information governed by CDCPs. Both an equivalent ISM law and a practical ISM controller are constructed in terms of such an ISM surface, reflecting the effect of both spacing strategies and decoded information. Then, a generally dynamical model of platoon tracking errors involving an unusual coupling with the adopted spacing strategy is derived by leveraging both the equivalent ISM law and a matrix transformation technique. Moreover, the reachability of the proposed ISM surface and the required platooning performance are profoundly discussed and sufficient conditions to determine the required gain parameters are deduced with essential inequality techniques. In terms of vehicle tracking errors, the upper boundary is determined by both the bit rate assigned and the spacing strategy employed. Finally, the effectiveness of the devised ISM control scheme is professionally evaluated through Carsim's simulation instances. © 2014 IEEE.

关键词： Decoding

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Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm

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Journal of Artificial Intelligence and technology 2024年第2期4卷 179-187页

作者： Tianke Fang Zhenxing Hui William P.Rey Aihua Yang Bin Liu Zhiying Xie School of Information Technology Mapua UniversityManilaPhilippines School of Computing and Information Engineering Xiamen University of TechnologyXiamenChina School of Information Media Zhangzhou Vocational College of Science and TechnologyZhangzhouChina College of Software Engineering Xiamen University of TechnologyXiamenChina

Historical architecture is an important carrier of cultural and historical heritage in a country and region,and its protection and restoration work plays a crucial role in the inheritance of cultural ***,the damage and destruction of buildings urgently need to be repaired due to the ancient age of historical buildings and the influence of natural environment and human ***,an artificial intelligence repair technology based on three-dimensional(3D)point cloud(PC)reconstruction and generative adversarial networks(GANs)was proposed to improve the precision and efficiency of repair ***,in-depth research on the principles and algorithms of 3D PC data processing and GANs should be ***,a digital restoration frameworkwas constructed by combining these two artificial intelligence technologies to achieve precise and efficient restoration of historical buildings through continuous adversarial learning *** experimental results showed that the errors in the restoration of palace buildings,defense walls,pagodas,altars,temples,and mausoleums were 0.17,0.12,0.13,0.11,and 0.09,*** technique can significantly reduce the error while maintaining the high-precision repair *** technology with artificial intelligence as the core has excellent accuracy and stability in the digital *** provides a new technical means for the digital restoration of historical buildings and has important practical significance for the protection of cultural heritage.

关键词： 3D PC reconstruction artificial intelligence repair GAN algorithm stereoscopic vision historical buildings

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Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud computing Environment

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Computer Systems Science & engineering 2023年第3期44卷 1881-1895页

作者： S.Stewart Kirubakaran V.P.Arunachalam S.Karthik S.K annan Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia School of Computing Science and Engineering Vellore Institute of TechnologyBhopalMadhya PradheshIndia

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud ***,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data *** addressing and handling the security-related issues on Cloud,several models were *** that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud *** preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data ***,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the ***,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

关键词： Third-party auditor(TPA) efficient auditing technique(EAT) cloud service provider(CSP) data user(DU) data security privacy-preserving cloud computing cloud security

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Enhanced CNN Model for Lettuce Disease Identification in Indoor Aeroponic Vertical Farming Systems 4

Enhanced CNN Model for Lettuce Disease Identification in Ind...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Rajendiran, Gowtham Rethnaraj, Jebakumar Malaisamy, Janani Dept. of Computing Technologies School of Computing College of Engineering & Technology Srm Institute of Science & Technology Kattankulathur Tamil Nadu Chengalpattu603203 India

ISBN: (纸本)9798331540364

India, like many other nations, heavily relies on agricultural exports to fund its national budget. Economic losses may occur when crop and plant diseases reduce production quality and quantity. Hence, it is crucial to detect infections early on in crop farming. Commercial crops grown using aeroponic vertical farming techniques include lettuce in particular. To increase the crop yield, the crop should be free from disease-causing agents like viral, bacterial, or fungal. As a response, scientists have utilized deep learning algorithms for the automatic identification of abnormalities in both indoor and outdoor farming crops by capturing leaf images. In this work, fine-tuned convolutional neural networks (CNN) were utilized to identify diseases in the aeroponic lettuce crops in an effective manner. Further, the Convolutional Neural Networks (CNN) were fine-tuned by training them on lettuce images. While testing, a higher accuracy of 95.6258% was achieved. Hence, this research work signifies that the proposed model would be appropriate for lettuce disease classification in controlled environment agriculture. © 2024 IEEE.

关键词： Convolutional neural networks

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Novel multi‐domain attention for abstractive summarisation

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CAAI Transactions on Intelligence technology 2023年第3期8卷 796-806页

作者： Chunxia Qu Ling Lu Aijuan Wang Wu Yang Yinong Chen College of Computer Science and Engineering Chongqing University of TechnologyChongqingChina School of Computing and Augmented Intelligence Arizona State UniversityTempeArizonaUSA

The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary,and the summary generated by models lacks the cover of the subject of source document due to models'small *** order to make up these disadvantages,a multi‐domain attention pointer(MDA‐Pointer)abstractive summarisation model is proposed in this ***,the model uses bidirectional long short‐term memory to encode,respectively,the word and sentence sequence of source document for obtaining the semantic representations at word and sentence ***,the multi‐domain attention mechanism between the semantic representations and the summary word is established,and the proposed model can generate summary words under the proposed attention mechanism based on the words and ***,the words are extracted from the vocabulary or the original word sequences through the pointer network to form the summary,and the coverage mechanism is introduced,respectively,into word and sentence level to reduce the redundancy of sum-mary ***,experiment validation is conducted on CNN/Daily Mail *** evaluation indexes of the model without and with the coverage mechanism are improved respectively,and the results verify the validation of model proposed by this paper.

关键词： abstractive summarisation attention mechanism Bi‐LSTM coverage mechanism pointer network

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Deep Learning to Effectively Detect and Eliminate Real-Time Network Intrusions 3

Deep Learning to Effectively Detect and Eliminate Real-Time ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Harikesh, S. Sujatha, G. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai India

ISBN: (纸本)9798331527549

Network traffic has been growing exponentially for the past several years. As a result, it has become more and more difficult to detect cyber intrusions - especially in unbalanced environments, where the attacks are usually covered up by normal traffic. This investigation is set to deal with the problem by coming up with an intrusion detection system that uses a deep learning approach to effectively find and eliminate the unbalanced network intrusions. The process includes data preprocessing for balancing the classes, feature engineering for choosing optimal features, and comparison of CNN and RNN models for improving detection accuracy. The tests have shown precision of 93% and recall of 88% which in total brought 99.8% accuracy. Also, the real-time deployment has demonstrated the model’s ability to dynamically traffic patterns, which induced false positives to be reduced remarkably. The results help in the improvement of intrusion detection systems by providing a scalable, adaptive, and efficient way to handle constantly altering cyber threats. © 2025 IEEE.

关键词： Network intrusion

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A Deep Learning-Based Method for Crowd Counting Using Shunting Inhibition Mechanism

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5733-5745页

作者： Tivive, Fok Hing Chi Bouzerdoum, Abdesselam Phung, Son Lam Le, Hoang Thanh Baali, Hamza University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Hamad Bin Khalifa University Information and Computing Technology Division College of Science and Engineering Doha Qatar

Image-based crowd counting has gained significant attention due to its widespread applications in security and surveillance. Recent advancements in deep learning have led to the development of numerous methods that have achieved remarkable success in accurately counting crowds. However, many of the existing deep learning methods, which have large model sizes, are unsuitable for deployment on edge devices. This article introduces a novel network architecture and processing element designed to create an efficient and compact deep learning model for crowd counting. The processing element, referred to as the shunting inhibitory neuron, generates complex decision boundaries, making it more powerful than the traditional perceptron. It is employed in both the encoder and decoder modules of the proposed model for feature extraction. Furthermore, the decoder includes alternating convolutional and transformer layers, which provide local receptive fields and global self-attention, respectively. This design captures rich contextual information that is used for generating accurate segmentation and density maps. The self-attention mechanism is implemented using convolution modulation instead of matrix multiplication to reduce computational costs. Experiments conducted on three challenging crowd counting datasets demonstrate that the proposed deep learning network, which comprises a small model size, achieves crowd counting performance comparable to that of state-of-the-art techniques. Codes are available at https://***/ftivive/SINet. © 2024 IEEE.

关键词： Deep learning

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Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1458-1475页

作者： Jing Liang Hongyu Lin Caitong Yue Ponnuthurai Nagaratnam Suganthan Yaonan Wang School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001 School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China KINDI Center for Computing Research Qatar UniversityDohaQatarand also with the Nanyang Technology UniversitySingapore College of Electrical and Information Engineering and also with the National Engineering Research Center for Robot Visual Perception and Control TechnologyHunan UniversityChangsha 410082China IEEE

In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective *** paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision *** to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal *** proposed algorithm adopts a dual-population framework and an improved environmental selection *** utilizes a convergence archive to help the first population improve the quality of *** improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first *** combination of these two strategies helps to effectively balance and enhance conver-gence and diversity *** addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is *** proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

关键词： Benchmark functions diversity measure evolution-ary algorithms multimodal multiobjective optimization.

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Optimizing Building Energy Consumption using Smart Grid Data 5

Optimizing Building Energy Consumption using Smart Grid Data

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Neelakandan, Viduran Voleti, Sai Suraj Srinivasan, Dhanush Gouthaman, P. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331530013

This research discusses how data analytics techniques can be applied in the enhancement of building energy management. It is therefore with the purpose of stimulating the right utilization of the practically inexhaustible quantity of data obtained through smart grids that this research seeks to design original approaches to energy management, building performance, and counteracting of negative impacts on the environment. Since Apache spark process is very efficient we do an excellent job in pre-processing and analyzing the data collected. Combining high-level artificial intelligence decision making tools like linear regression as well as the random forest, the relationship was established between energy utilization and determinants, forecast patterns and create accurate prognosis models. In order to present the results in a format that can be easily understood with the help of Tableau to build a data visualization where depending on the values chosen by the stakeholders further steps can be taken in achieving a more efficient use of energy and less expenditure. In our work, we strive to generate knowledge that will support improved energy efficiency of the built environment, and therefore a more sustainable future. The implications of our work have the ability to affect the processes within and energy usage of buildings, saving energies and making cities greener. Furthermore, our work can help with the creation of further reference points for future research in the field of building energy optimization. © 2024 IEEE.

关键词： Big Data Building Efficiency Energy Optimization Machine Learning Predictive Analytics Smart Grids Sustainability

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Feature Contrast Difference and Enhanced Network for RGB-D Indoor Scene Classification in Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 17610-17621页

作者： Zhou, Wujie Jian, Bitao Liu, Yuanyuan Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou310023 China China University of Geosciences School of Computer Science and Technology Wuhan430074 China Nanyang Technological University College of Computing and Data Science 308232 Singapore

The era of smart connectivity spawned by the Internet of Things (IoT) has made the need to achieve environmental perception and understanding of different scenarios increasingly urgent. Among the many scenarios, indoor scene classification has attracted much attention because of its its relevance to the daily lives of people, ranging from comfort regulation in living spaces to the optimal allocation of resources in offices, and a variety of approaches for this task have emerged. However, increasing accuracy remains a crucial objective due to the complexity and disorder of indoor scenes. Therefore, we propose a feature contrast difference and enhanced network for RGB-D indoor scene classification, FCDENet. First, the RGB and Depth images express different information. Therefore, we built a feature contrast difference module for the first two low-level features to extend the receptive fields of the different features, utilizing differential contrast to complement each other. Second, the high-level feature semantic information is abstract. Therefore, we introduced information cluster blocks, which are used to aggregate feature points with similar attributes into compact clusters after being parsed by an initial frequency transform, enabling instantiated representations of the semantic information. Finally, to further enhance the integrated features, we introduced a wavelet transform block in the cross-layer decoding process. In contrast to conventional decoding methods, we employed a wavelet transform for initial denoising cross-layer features and used multiple pooling structures to supplement local information, gradually weighting to achieve higher prediction accuracy. Extensive experiments on two typical indoor datasets, NYUDv2 and SUN RGB-D, show that our results exhibit excellent performance. In addition, to better demonstrate the reliability of the method, we conducted generalizability experiments on other datasets, and the proposed method provides a robust soluti

关键词： Wavelet decomposition

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