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Modeling and Simulation of Two Axes Gimbal Using Fuzzy control

computers, Materials & Continua 2022年第7期72卷 93-107页

作者： Ayman A.Aly Mohamed O.Elhabib Bassem F.Felemban B.Saleh Dac-Nhuong Le Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Control and Mechatronics Division School of Electrical EngineeringUTMJohor Bahru81300Malaysia Institute of Research and Development Duy Tan UniversityDanang550000Vietnam School of Computer Science Duy Tan UniversityDanang550000Vietnam

The application of the guided missile seeker is to provide stability to the sensor’s line of sight toward a target by isolating it from the missile motion and *** main objective of this paper is not only to present the physical modeling of two axes gimbal system but also to improve its performance through using fuzzy logic controlling *** paper is started by deriving the mathematical model for gimbals motion using Newton’s second law,followed by designing the mechanical parts of model using SOLIDWORKS and converted to xml file to connect dc motors and sensors using MATLAB/***,a Mamdani-type fuzzy and a Proportional-Integral-Derivative(PID)controllers were designed using MATLAB *** performance of both controllers was evaluated and tested for different types of input *** simulation results showed that self-tuning fuzzy controller provides better performance,since no overshoot,small steady-state error and small settling time compared to PID controller.

关键词： Gimbal system self-tuning fuzzy proportional-integral-derivative(PID)control cross coupling

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A novel high-through cell micronucleus detection algorithm based on improved YOLOv5 4

A novel high-through cell micronucleus detection algorithm b...

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4th International Conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

作者： Wei, Weiyi Li, Jingyu Wu, Xiaoqin Zhang, Hangjian College of Computer Science and Engineering Northwest Normal University Lanzhou China Radiology Department Gansu Provincial Center for Disease Control and Prevention Lanzhou China Business School Northwest Normal University Lanzhou China

ISBN: (纸本)9798350341690

In response to the challenges associated with cell micronucleus image recognition, namely small target sizes, high class similarity, and limited sample data, A novel cell micronucleus detection algorithm named YOLOv5-ECA is proposed based on YOLOv5. The proposed algorithm gets hyper-accurate micronucleus identification through a series of improvements. To begin with, the anchor frame size is recalibrated based on a meticulously constructed dataset. This process serves to expedite convergence speed while bolstering accuracy. Subsequently, in C3 module, a coordinate attention (CA) is introduced, which enlarging the receptive field and enabling the model to allocate greater focus to crucial elements within processed images. Ultimately, a superficial detection layer is incorporated, thereby enhancing the precision of cell micronucleus identification through multi-scale detection. Comparative analysis with the original YOLOv5 which demonstrates a 2% increase in average precision, accuracy, and a 1.3% enhancement in recall for the YOLOv5-ECA algorithm based on experimental results. © 2023 IEEE.

关键词： Cells

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Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach

arXiv

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arXiv 2024年

作者： Dong, Wei Zhang, Xing Chen, Bihui Yan, Dawei Lin, Zhijun Yan, Qingsen Wang, Peng Yang, Yang School of Computer Science and Engineering University of Electronic Science and Technology of China China College of information and control engineering Xi'an University of Architecture and Technology China School of Computer Science Northwestern Polytechnical University China

Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features poses a key challenge. Currently, there is a lack of focus on guiding this delicate trade-off. In this study, we approach the problem from the perspective of Singular Value Decomposition (SVD) of pretrained parameter matrices, providing insights into the tuning dynamics of existing methods. Building upon this understanding, we propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy. This strategy not only enhances flexibility in parameter tuning but also ensures that new parameters do not deviate excessively from the pretrained model through a residual design. Extensive experiments demonstrate that our method achieves competitive performance across various downstream image classification tasks, all while maintaining comparable new parameters. We believe this work takes a step forward in offering a unified perspective for interpreting existing methods and serves as motivation for the development of new approaches that move closer to effectively considering the crucial trade-off mentioned above. Our code is available at https://***/zstarN70/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Singular value decomposition

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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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No-regret Exploration in Shuffle Private Reinforcement Learning

arXiv

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arXiv 2024年

作者： Bai, Shaojie Talebi, Mohammad Sadegh Zhao, Chengcheng Cheng, Peng Chen, Jiming College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Computer Science University of Copenhagen Copenhagen2100 Denmark

Differential privacy (DP) has recently been introduced into episodic reinforcement learning (RL) to formally address user privacy concerns in personalized services. Previous work mainly focuses on two trust models of DP: the central model, where a central agent is responsible for protecting users’ sensitive data, and the (stronger) local model, where the protection occurs directly on the user side. However, they either require a trusted central agent or incur a significantly higher privacy cost, making it unsuitable for many scenarios. This work introduces a trust model stronger than the central model but with a lower privacy cost than the local model, leveraging the emerging shuffle model of privacy. We present the first generic algorithm for episodic RL under the shuffle model, where a trusted shuffler randomly permutes a batch of users’ data before sending it to the central agent. We then instantiate the algorithm using our proposed shuffle Privatizer, relying on a shuffle private binary summation mechanism. Our analysis shows that the algorithm achieves a near-optimal regret bound comparable to that of the centralized model and significantly outperforms the local model in terms of privacy cost. © 2024, CC BY-NC-ND.

关键词： Differential privacy

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Reversible Data Hiding Scheme Based on Prediction Error Bit-plane Compression

Reversible Data Hiding Scheme Based on Prediction Error Bit-...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Guo Yuan Chao Heng Zhai Ping Liu Ziqi Li Haibo Li Wenpeng College of computer science and technology Heilongjiang University Harbin China College of Computer and Control Engineering Qiqihar University Qiqihar China

How to maximize embedding capacity is one of the current challenges in the field of reversible data hiding. A reversible data hiding scheme is proposed based on the rearrangement and compression of prediction error bit-planes in this paper. The image holder first predicts the pixels to obtain the error map, then decomposes them into bit-planes, rearranges and compresses them to maximize the embedding space, and embeds the auxiliary information and secret information into the freed space according to certain rules to generate a cryptographic image. After acquiring the cryptographic image, the image receiver extracts the embedded secret information and recovers the original image using the auxiliary information. In this paper, a rearrangement strategy based on full pixel correlation was built to maximize compression efficiency and further compress the auxiliary information to free up more embeddable space and improve embedding capacity. A large number of test has revealed that the embedding rate of this method is 30% higher on average than the current state-of-the-art algorithm.

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LoBee: Bidirectional Communication between LoRa and ZigBee based on Physical-Layer CTC

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IEEE Transactions on Wireless Communications 2025年

作者： Gao, Demin Wang, Haoyu Chen, YongRui Ye, Qiaolin Wang, Weizheng Guo, Xiuzhen Wang, Shuai Liu, Yunhuai He, Tian Nanjing Forestry University College of Information Science and Technology Artificial Intelligence Nanjing210037 China University of Minnesota The Department of Computer Science and Engineering 55414 United States University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China Southeast University The School of Computer Science and Engineering Nanjing211189 China City University of Hong Kong Department of Computer Science 999077 Hong Kong College of Control Science and Engineering Zhejiang 310027 China Peking University The Department of Computer Science and Engineering Beijing100871 China University of Minnesota The Department of Computer Science and Engineering Minneapolis55414 United States

LoRa networks operating in a star topology, this configuration creates a single point of failure and may limit scalability and reliability in areas that are large and geographically dispersed. In order to improve the overall transmission capabilities of the network, recent studies show that adding LoRa to the ZigBee devices effectively disseminates network management. By doing so, the strengths of both technologies can be leveraged, with LoRa serving as the long-range transmitter and ZigBee functioning as the mesh network. In this study, we present LoBee, a novel bidirectional communication method between LoRa and ZigBee that relies on Physical-Layer Cross-Technology Communication. Despite the fact that LoRa and ZigBee utilize different modulation techniques, ZigBee devices can detect and recognize LoRa chirps through the process of sampling the received signal strength. For the transmissions from ZigBee to LoRa devices, we carefully select the input chips to generate specific waveforms, where LoBee detects the preamble of a ZigBee frame based on the locations of the repeated peaks. Our evaluation, which was conducted using USRP and commodity devices, demonstrates that LoBee is capable of achieving concurrent bidirectional wireless communications, with a data rate of approximately 639.38 bits per second from LoRa to ZigBee and from ZigBee to LoRa with more than 90% frame reception rate in the 2.4 GHz frequency band. © 2002-2012 IEEE.

关键词： Zigbee

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ScCADE: A Superior Tool for Predicting Perturbation Responses in Single-Cell Gene Expression Using Contrastive Learning and Attention Mechanisms

ScCADE: A Superior Tool for Predicting Perturbation Response...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ou, Jingfeng Li, Jiawei Xia, Zhiliang Dai, Shurui Ding, Yulian Guo, Yan Jiang, Limin Tang, Jijun Southern University of Science and Technology College of Engineering Shenzhen518055 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Miami Department of Public Health Sciences MiamiFL33136 United States

ISBN: (纸本)9798350386226

The advent of single-cell transcriptomics has revolutionized our ability to analyze cellular heterogeneity and dynamics at a fine resolution, yet covering the vast array of potential perturbations remains challenging due to biological variability. To address this, we propose scCADE, a novel computational approach utilizing contrastive learning and an attention mechanism to decouple gene expression signatures and predict cellular responses to perturbations. scCADE excels in predicting responses in cells to perturbations observed in other cells but not yet seen in the target cells. Through rigorous ablation studies and validation across three datasets involving drug and gene editing perturbations, scCADE consistently outperformed existing methods, underscoring its efficacy and potential to advance genomics and personalized medicine by accurately forecasting responses to novel perturbations. © 2024 IEEE.

关键词： Contrastive Learning

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FlexPDD: Enabling Proportional Delay Differentiation Service on Programmable Switches

FlexPDD: Enabling Proportional Delay Differentiation Service...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xinyue Jiang Dezhang Kong Zhengyan Zhou Di Wang Shuangxi Chen Chunming Wu College of Computer Science and Technology Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China Jiaxing Vocational and Technical College Jiangxing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Quality-of-Service (QoS) guarantees are crucial for meeting the diverse performance requirements of applications in packet networks. The Proportional Delay Differentiation (PDD) model offers relative service differentiation based on the delay requirements of different traffic classes. However, implementing PDD on current hardware switches faces challenges due to the lack of inherent queuing behavior description in switch ASICs. This paper introduces FlexPDD, a dynamic and adaptive packet prioritization mechanism designed to implement the PDD model on programmable switches. FlexPDD leverages the flexibility of programmable switch to adjust the mapping between packet classes and output queues dynamically, ensuring precise control over delay differentiation. Our implementation of FlexPDD on a Barefoot Tofino switch and an NS3 simulator demonstrates its feasibility and effectiveness. The results indicate that FlexPDD successfully maintains approximate delay differentiation among service classes proportional to their delay weights, highlighting its potential as a practical solution for achieving advanced service differentiation in modern network infrastructures.

关键词： Adaptation models Quality of service Switches control systems Hardware Scheduling Delays Global communication Faces

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CoTV: Cooperative control for Traffic Light Signals and Connected Autonomous Vehicles Using Deep Reinforcement Learning

CoTV: Cooperative Control for Traffic Light Signals and Conn...

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IEEE Symposium on Intelligent Vehicle

作者： Jiaying Guo Long Cheng Shen Wang School of Computer Science University College Dublin Ireland School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

The target of reducing travel time only is insufficient to support the development of future smart transportation systems. To align with the United Nations Sustainable Development Goals (UN-SDG), a further reduction in fuel consumption and emissions, improvements in traffic safety, and the ease of infrastructure deployment and maintenance should also be considered. Most existing research in sustainable urban traffic control adjusts either traffic light signals or vehicle speed. Adaptive traffic light signal control can increase the intersection throughput and reduce travel time as well as energy consumption and emissions. Connected Autonomous Vehicles (CAVs) can proactively control vehicle acceleration to achieve more stable traffic nearby with relatively higher driving velocity (i.e., lower fuel consumption and CO 2 emissions) and maintain a safe distance from the surrounding traffic (i.e., longer time-to-collision).

关键词： Energy consumption Traffic control Throughput Deep reinforcement learning Smart transportation Safety Maintenance

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