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12th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2021 / 11th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2021

作者： Deng, Yujuan Fu, Yunfang Zhang, Huitao Liu, Xin Liu, Zhiguo School of Computer Science and Engineering Shijiazhuang University Shijiazhuang050035 China Hebei Prov. Internet of Things Intelligent Perception and Application Technology Innovation Center Shijiazhuang050035 China Shijiazhuang Public Security Bureau Shijiazhuang050035 China

The study and identification of protein post-translational modification(PTM) sites in rice seeds plays an important role in breeding and yield increasing in intelligent agriculture. Deep learning, especially deep convolutional neural networks(CNN) become more and more popular in intelligent agriculture because of its excellent image processing and data analysis capabilities. In this paper, the Denoised-Oversampling is adopted to denoise the samples and balance the data. The protein sequences are transformed into corresponding numerical feature vectors. Then, the processed feature subset is input to the deep convolution network. In fully connected layers, we used the L2 regularization method to prevent the overfitting problem. The phosphorylation sites are predicted by the sigmoid classifier. Finally, in the experimental part, in order to truly reflect the predictive power of the model, we divided the dataset into training set, validation set and independent test set. The prediction accuracy of the independent test set is 83.20%. The test result shows that the proposed algorithm has better prediction performance than the existing phosphorylation prediction algorithms. © 2021 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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Global Exponential Rendezvous Control of Nonholonomic Unicycle Vehicles with Directed Communication Topology

Global Exponential Rendezvous Control of Nonholonomic Unicyc...

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Chinese Intelligent Systems Conference, CISC 2020

作者： Zhao, Yixin Xing, Bin Huang, Wei Luo, Shiyu Li, Peng Xie, Wenjing The School of Computer and Information Science Southwest University Chongqing400715 China Chongqing Innovation Center of Industrial Big-Data Co. Ltd. National Engineering Laboratory for Industrial Big-Data Application Technology Chongqing400065 China

ISBN: (纸本)9789811584572

This paper studies the full-state rendezvous control problem of multiple nonholonomic unicycle systems. A distributed state feedback time-varying control law is derived by exploiting model transformation, input-output feedback linearization technology, linear cooperation control theory and graph theory. Based on the carefully constructed output-like variable, the input-output feedback linearization approach overcomes the underactuated control challenge, makes the choice of controller parameters independent of initial states of vehicles, and allows the linear control theory applicable, rather than using the Lyapunov method and hence requiring the information flow undirected. Stability analysis proves that all the unicycle vehicles globally exponentially converge to a common fixed location and orientation, provided that the communication topology is directed and having a spanning tree. A numerical simulation is implemented for five nonholonomic unicycle vehicles, demonstrating the effectiveness of the proposed control scheme. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicles

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EVALUATION OF PERFORMANCE OF A CI ENGINE FUELED WITH BIODIESEL PRODUCED FROM UNUSED ALGAE

Journal of Solid Waste Technology and Management

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Journal of Solid Waste Technology and Management 2023年第4期49卷 359-364页

作者： Karmakar, Rachan Kumar, Nitin Rajor, Anita Kundu, Krishnendu Kumar, Rajesh Gahlot, Dinesh Chaudhury, Bhaskerpratap Ragit, S.S. Kumar, Amit Gorai, Sanjoy Gangwar, Pavan SInghal, Rishabh Bhattacharya, Sourish Sharma, Rachna Savita Singh, Narpinder Kumar, Neeraj Graphic Era Deemed to be University Uttarakhand Dehradun India Dept. of Mechanical Engineering Roorkee Institute of Technology Uttarakhand Roorkee India School of Energy and Environment Thapar University Punjab Patiala India Dept. of Biofuel CSIR CMERI CoEFM Punjab Ludhiana India Dept. of Chemistry S.S.J. University Campus Almora Uttarakhand India Dept. of Physics Chandigarh Group of Colleges Punjab Jhanjheri India Dept. of Mechanical Engineering Guru Nanak Institute of Technology Maharashtra Nagpur India Dept. of Computer Application Quantum University Uttarakhand Roorkee India Dept. of Electronics and Communication Engineering IMS Engineering College U.P. Gaziabad India Dept. of Electrical Engineering COER University Uttarakhand Roorkee India Process Design and Engineering Cell Discipline CSIR Central Salt and Marine Chemicals Research Institute Gujrat Bhavnagar India Dept. of Applied Science Roorkee Institute of Technology Uttarakhand Roorkee India School of Agriculture Graphic Era Hill University Uttarakhand Dehradun India Dept. of Food Science and Technology Graphic Era Deemed to be University Uttarakhand Dehradun India

Abundant availability and easy culture process of algae make it a better resource than other vegetable crops for biodiesel production. The oil (with 21% FFA content) extracted from the unused, mixed culture of algae in this experiment was used to produce biodiesel by an ‘acid esterification followed by alkaline esterification’ procedure. After confirming the properties of the biodiesel to be within the limit of ASTM standard, three biodiesel blends (B10, B20 and B30) were used in an internal combustion engine (Four stroke single cylinder VCR engine) and the performance of the engine was observed at different engine loads (0%, 20%, 40%, 80%, 100%, 120%). The little higher brake specific fuel consumption (0.22kg/KWh, 0.25 kg/KWh, 0.26kg/KWh and 0.21kg/KWh respectively for B10, B20, B30 and petro-diesel at overload condition), lower brake power (3.41 kW, 3.37 kW, 3.25 kW, 3.39 kW for diesel, B10, B20 and B30 respectively for B10, B20, B30 and petro-diesel) and mechanical efficiency (63.34, 51.43%, 52.06% and 51.43% for petro-diesel, B10, B20 and B30 respectively) for biodiesel blends took place which might be the results of lower calorific value (40800 kJ/kg), higher density (875.27kg/m3) and viscosity (3.14 mm2/s) of the algal biodiesel than diesel. © 2023 Widener University school of Civil engineering. All rights reserved.

关键词： Biodiesel

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DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation

arXiv

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arXiv 2025年

作者： Guo, Hanghui Zhu, Jia Di, Shimin Shi, Weijie Chen, Zhangze Xu, Jiajie School of Computer Science and Technology Zhejiang Normal University China The Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China School of Computer Science and Engineering Southeast University China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong School of Computer Science and Technology Soochow University China

Dynamic Retrieval-augmented Generation (RAG) has shown great success in mitigating hallucinations in large language models (LLMs) during generation. However, existing dynamic RAG methods face significant limitations in two key aspects: 1) Lack of an effective mechanism to control retrieval triggers, and 2) Lack of effective scrutiny of retrieval content. To address these limitations, we propose an innovative dynamic RAG method, DioR (Adaptive Cognitive Detection and Contextual Retrieval Optimization), which consists of two main components: adaptive cognitive detection and contextual retrieval optimization, specifically designed to determine when retrieval is needed and what to retrieve for LLMs is useful. Experimental results demonstrate that DioR achieves superior performance on all tasks, demonstrating the effectiveness of our work. Copyright © 2025, The Authors. All rights reserved.

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Efficient and Privacy-Enhancing Non-Interactive Periocular Authentication for Access Control Services

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IEEE Transactions on Network and Service Management 2025年

作者： Li, Yating Wang, Le Xue, Liang Liu, Jingwei Lin, Xiaodong Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China University of Guelph School of Computer Science GuelphN1G 2W1 Canada York University School of Information Technology North YorkM3J 1P3 Canada

Periocular authentication has emerged as an increasingly prominent approach in access control services, especially in situations of face occlusion. However, its limited feature area and low information complexity make it susceptible to tampering and identity forgery. To address these vulnerabilities, we propose a practical privacy-enhancing non-interactive periocular authentication scheme for access control services. It enables encrypted cosine similarity matching on an untrusted remote authentication server by leveraging random masking (RM) and dual-trapdoor homomorphic encryption (DTHE). Additionally, a weight control matrix is introduced to enhance authentication accuracy by assigning importance to different feature dimensions. To accommodate devices with varying trust levels, we employ adaptive authentication strategies. For trusted mobile devices, we implement secure single-factor authentication based on periocular features, while for external devices with unknown security status, we enforce a two-factor authentication mechanism integrating periocular features with tokens to mitigate unauthorized access risks. Additionally, our scheme conceals users’ true identities from external devices during authentication. Security analysis demonstrates that our solution effectively mitigates tampering and replay attacks in the network while preventing privacy leakage. As validated by experimental results, the proposed scheme enables efficient authentication of obscured faces. © 2004-2012 IEEE.

关键词： Differential privacy

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Intelligent Fault Diagnosis of Rotating Machine Via Expansive Dual-Attention Fusion Transformer Enhanced by Semi-Supervisedlearning

SSRN

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SSRN 2023年

作者： Liu, Sijie Li, Jin Zhou, Nan Chen, Geng Lu, Kuan Wu, Yafeng School of Power and Energy Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi’an710129 China School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Two Shaanxi Xi’an710129 China

The precise identification of faults is an essential component in the effective operation and maintenance of rotating machinery(RM). The growing adoption of deep learning can possibly be attributed to the abundance of automatically generated monitoring data and its strong capacity to effectively learn and detect errors. The efficacy of deep learning techniques is consistently high when a limited amount of fault data is available or when a two-stage diagnostic approach is utilized, including manual feature extraction followed by fault classification. To address these challenges, we offer a novel architecture called Expansive Dual-Attention Fusion Transformer (EDAF-Transformer). This structure combines Convolutional Attention Neural Network with Transfomer, and it is specifically designed to handle limited labeled data. Additionally, we boost the performance of EDAF-Transformer by the implementation of a semi-supervised approach. First, we introduce a primary theory transformer. Then Expansive Dual-branch Attention Module (EDAEM) consists of the Expansive multi-scale channel attention (EMSCA) with multi-scale learning, which is designed to expand the receptive field and fused multi-scale information. Therefore, the EDAFM and TwinSparse Self-Attneiton Fusion Transformer encoder Module are integrated in a cascaded manner to construct a deep network for intelligent fault diagnosis. Moreover, an uncertainty self-training semisupervised strategy based on a balanced unlabeled sample selection is implemented, which could further enhance the generalization of EDAF-Transformer and achieve a better performance. The proposed semi-EDAF-Transformer fault diagnosis method has been extensively tested on publicly available motor bearing data as well as a specially arranged transmission shaft dataset. These experiments demonstrate that this method outperforms other intelligent fault diagnosis methods. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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MACCoM: A multiple attention and convolutional cross-mixer framework for detailed 2D biomedical image segmentation

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computers in Biology and Medicine 2024年 179卷 108847-108847页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Ejiyi, Makuachukwu Bennedith Ukwuoma, Chiagoziem Ejiyi, Thomas Ugochukwu Muoka, Gladys Wavinya Gyarteng, Emmanuel S.A. Bamisile, Olusola O. Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria Department of Pure and Industrial Chemistry University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Chengdu China

The UNet architecture, which is widely used for biomedical image segmentation, has limitations like blurred feature maps and over- or under-segmented regions. To overcome these limitations, we propose a novel network architecture called MACCoM (Multiple Attention and Convolutional Cross-Mixer) – an end-to-end depthwise encoder-decoder fully convolutional network designed for binary and multi-class biomedical image segmentation built upon deeperUNet. We proposed a multi-scope attention module (MSAM) that allows the model to attend to diverse scale features, preserving fine details and high-level semantic information thus useful at the encoder-decoder connection. As the depth increases, our proposed spatial multi-head attention (SMA) is added to facilitate inter-layer communication and information exchange, enabling the network to effectively capture long-range dependencies and global context. MACCoM is also equipped with a convolutional cross-mixer we proposed to enhance the feature extraction capability of the model. By incorporating these modules, we effectively combine semantically similar features and reduce artifacts during the early stages of training. Experimental results on 4 biomedical datasets crafted from 3 datasets of varying modalities consistently demonstrate that MACCoM outperforms or matches state-of-the-art baselines in the segmentation tasks. With Breast Ultrasound Image (BUSI), MACCoM recorded 99.06 % Jaccard, 77.58 % Dice, and 93.92 % Accuracy, while recording 99.50 %, 98.44 %, and 99.29 % respectively for Jaccard, Dice, and Accuracy on the Chest X-ray (CXR) images used. The Jaccard, Dice, and Accuracy for the High-Resolution Fundus (HRF) images are 95.77 %, 74.35 %, and 95.95 % respectively. The findings here highlight MACCoM's effectiveness in improving segmentation performance and its valuable potential in image analysis. © 2024 Elsevier Ltd

关键词： Mixers (machinery)

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BurstBalancer: Do Less, Better Balance for Large-scale Data Center Traffic

BurstBalancer: Do Less, Better Balance for Large-scale Data ...

引用

International Conference on Network Protocols

作者： Zirui Liu Yikai Zhao Zhuochen Fan Tong Yang Xiaodong Li Ruwen Zhang Kaicheng Yang Zheng Zhong Yi Huang Cong Liu Jing Hu Gaogang Xie Bin Cui National Engineering Laboratory for Big Data Analysis Technology and Application School of Computer Science Peking University Beijing China Peng Cheng Laboratory Shenzhen China Huawei Technologies CNIC CAS

ISBN: (数字)9781665482349

ISBN: (纸本)9781665482356

Layer-3 load balancing is a key topic in the networking field. It is well acknowledged that flowlet is the most promising solution because of its good trade-off between load balance and packet reordering. However, we find its one significant limitation: it makes the forwarding paths of flows unpredictable. To address this limitation, this paper presents BurstBalancer, a simple yet efficient load balancing system with a sketch, named BalanceSketch. Our design philosophy is doing less changes to keep the forwarding path of most flows fixed, which guides the design of BalanceSketch and balance operations. We have fully implemented BurstBalancer in a small-scale testbed built with Tofino switches, and conducted large-scale NS-2 simulations. Our results show that BurstBalancer achieves 5%∼35% smaller FCT than LetFlow in symmetric topology and up to 30× smaller FCT in asymmetric topology, while 58× fewer flows suffer from path changing. All related codes are open-sourced at Github 2 2 https://***/BurstBalancer/Burst-Balancer.

关键词： Data centers Philosophical considerations Protocols Codes Network topology Load management Topology

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A High-Performance Standalone Planar FAIMS System for Effective Detection of Chemical Warfare Agents

SSRN

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SSRN 2023年

作者： Hao, Jie Feng, Rong Li, Junhui Gao, Wenqing Yu, Jiancheng Tang, Keqi Institute of Mass Spectrometry Zhejiang Engineering Research Center of Advanced Mass spectrometry and Clinical Application Ningbo University Ningbo315211 China Zhenhai Institute of Mass Spectrometry Ningbo315211 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China School of Material Science and Chemical Engineering Ningbo University Ningbo315211 China

A high-performance standalone planar field asymmetric waveform ion mobility spectrometry (p-FAIMS) system with a deconvolution algorithm (two-step particle swarm optimization algorithm, TSPSO) for overlapping peaks was developed to effectively detect chemical warfare agents (CWAs). Four CWA simulants were applied in this study to systemically evaluate the performance of the standalone p-FAIMS system. The experimental results showed that each CWA simulant in the mixture can be positively identified by carefully comparing the compensation voltage (CV) value of each peak in the FAIMS spectra for the mixture to the ones in the spectra acquired by using the same FAIMS system for the pure CWA simulant standards. The FAIMS spectrum of the CWA simulant mixture might consist of multiple overlapping peaks, which would be difficult to accurately determine the CV value for each CWA simulant peak. This problem has been effectively resolved in this study by deconvoluting the overlapping peaks via the TSPSO algorithm. As the effective peak deconvolution via TSPSO requires the degree of overlap between each FAIMS peak to be lower than a specific value, the flow rate of FAIMS carrier gas was degreased to further improve the resolution of the p-FAIMS system. After the accurate deconvolution, the resolution of original FAIMS spectrum can also be enhanced to achieve baseline separation by using TSPSO algorithm to narrow the peak width of each peak. The experimental results in this study proved that this system can achieve effective detection of the CWAs and also offered an effective new method for the high-resolution detection of other complex samples. © 2023, The Authors. All rights reserved.

关键词： Deconvolution

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A Surrogate-Assist Quasi-Affine Transformation Evolutionary for Multi-Objective Optimization of Empty Train Deployment on Heavy-Haul Railways

SSRN

引用

SSRN 2023年

作者： Du, Zhi-Gang Pan, Jeng-Shyang Xu, He-Ying Chu, Shu-Chuan Ni, Shao-Quan School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu610031 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031 China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031 China

In recent years, surrogate-assisted evolutionary algorithms have become increasingly popular for solving expensive and time-consuming single-objective and multi-objective problems. However, in multi-objective optimization, the Pareto non-dominated solution set space can quickly become computationally intractable, making it challenging to select which samples to evaluate using the expensive fitness function. In this paper, we propose a novel surrogate-assist QUasi-Affine TRansformation Evolutionary algorithm (SA-QUATRE/MO) for solving multi-objective optimization problems. The SA-QUATRE/MO algorithm uses a radial basis neural network as a surrogate model to improve the speed of the algorithm operation by replacing the expensive fitness evaluation with the surrogate model. To ensure the excellence and diversity of the selected samples while keeping the archived sample space fixed, we propose a technique called Vector Space Sampling, which samples objective points in the current set of non-dominated solutions by dividing several sub-vector spaces. Additionally, we propose an uncertain sample infilling strategy to select samples for real fitness evaluation using a designed uncertainty function. We compare the SA-QUATRE/MO algorithm with three state-of-the-art algorithms for multi-objective problems in the DTLZ, MaF, and ZDT test function suites. Finally, we apply the SA-QUATRE/MO algorithm to solve the optimization problem of the empty train deployment at the loading end of a heavy-haul railway. We build a model based on the S12 section of a certain railway and optimize it using the SA-QUATRE/MO algorithm, demonstrating the effectiveness and practicality of our proposed approach. © 2023, The Authors. All rights reserved.

关键词： Vector spaces

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