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Multi-timescale feature extraction method of wastewater treatment process based on adaptive entropy

Chinese Journal of Chemical engineering 2024年第12期76卷 264-271页

作者： Honggui Han Yaqian Zhao Xiaolong Wu Hongyan Yang School of Information Science and Technology Beijing University of TechnologyBeijing 100124China College of Environmental Sciences and Engineering Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China

In wastewater treatment systems,extracting meaningful features from process data is essential for effective monitoring and ***,the multi-time scale data generated by different sampling frequencies pose a challenge to accurately extract *** solve this issue,a multi-timescale feature extraction method based on adaptive entropy is ***,the expert knowledge graph is constructed by analyzing the characteristics of wastewater components and water quality data,which can illustrate various water quality parameters and the network of relationships among ***,multiscale entropy analysis is used to investigate the inherent multi-timescale patterns of water quality data in depth,which enables us to minimize information loss while uniformly optimizing the ***,we harness partial least squares for feature extraction,resulting in an enhanced representation of sample data and the iterative enhancement of our expert knowledge *** experimental results show that the multi-timescale feature extraction algorithm can enhance the representation of water quality data and improve monitoring capabilities.

关键词： Feature extraction Knowledge graph Wastewater treatment process Adaptive entropy

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DDT-Net:Deep Detail Tracking Network for Image Tampering Detection

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Computers, Materials & Continua 2025年第5期83卷 3451-3469页

作者： Jim Wong Zhaoxiang Zang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China College of Computer and Information Technology ChinaThree Gorges UniversityYichang443002China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China

In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

关键词： Image forensics image tampering detection image manipulation detection noise flow Bayar

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JNMR: Joint Non-Linear Motion Regression for Video Frame Interpolation

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IEEE Transactions on Image Processing 2023年 32卷 5283-5295页

作者： Liu, Meiqin Xu, Chenming Yao, Chao Lin, Chunyu Zhao, Yao Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Video frame interpolation (VFI) aims to generate predictive frames by motion-warping from bidirectional references. Most examples of VFI utilize spatiotemporal semantic information to realize motion estimation and interpolation. However, due to variable acceleration, irregular movement trajectories, and camera movement in real-world cases, they can not be sufficient to deal with non-linear middle frame estimation. In this paper, we present a reformulation of the VFI as a joint non-linear motion regression (JNMR) strategy to model the complicated inter-frame motions. Specifically, the motion trajectory between the target frame and multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. Then, a comprehensive joint distribution is constructed to connect all temporal motions. Moreover, to reserve more contextual details for joint regression, the feature learning network is devised to explore clarified feature expressions with dense skip-connection. Later, a coarse-to-fine synthesis enhancement module is utilized to learn visual dynamics at different resolutions with multi-scale textures. The experimental VFI results show the effectiveness and significant improvement of joint motion regression over the state-of-the-art methods. The code is available at https://***/ruhig6/JNMR. © 1992-2012 IEEE.

关键词： Interpolation

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μ-stability of multiple equilibria in Cohen-Grossberg neural networks and its application to associative memory

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science China(Technological sciences) 2023年第9期66卷 2611-2624页

作者： LIU Yang WANG Zhen XIAO Min LI YuXia SHEN Hao College of Mathematics and Systems Science Shandong University of Science and TechnologyQingdao 266590China College of Automation&College of Artificial Intelligence Nanjing University of Posts and TelecommunicationsNanjing 210023China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Electrical and Information Engineering Anhui University of TechnologyMa’anshan 243002China

In this paper, the μ-stability of multiple equilibrium points(EPs) in the Cohen-Grossberg neural networks(CGNNs) is addressed by designing a kind of discontinuous activation function(AF). Under some criteria, CGNNs with this AF are shown to possess at least 5^(n)EPs, of which 3^(n)EPs are locally μ-stable. Compared with the saturated AF or the sigmoidal AF, CGNNs with the designed AF can produce many more total/stable EPs. Therefore, when CGNNs with the designed discontinuous AF are applied to associative memory, they can store more prototype patterns. Moreover, the AF is expanded to a more general version to further increase the number of total/stable equilibria. The CGNNs with the expanded AF are found to produce(2k+3)^(n)EPs, of which (k+2)^(n)EPs are locally μ-stable. By adjusting two parameters in the AF, the number of sufficient conditions ensuring the μ-stability of multiple equilibria can be decreased. This finding implies that the computational complexity can be greatly *** numerical examples and an application to associative memory are illustrated to verify the correctness of the obtained results.

关键词： associative memory Cohen-Grossberg neural networks discontinuous activation functions multiple equilibria μ-stability

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Improved YOLOv5s Tomato Leaf Small Disease Spot Detection Method for Embedded Devices

Improved YOLOv5s Tomato Leaf Small Disease Spot Detection Me...

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2024 International Conference on Remote Sensing, Mapping, and Image Processing, RSMIP 2024

作者： Li, Xiaojing Fang, Jiandong Zhao, Yvdong College of Information Engineering Inner Mongolia University of Technology China Inner Mongolia Key Laboratory of Perceptual Technology and Intelligent Systems China

ISBN: (纸本)9781510680012

In the field of tomato disease spot detection, despite significant advancements in convolutional neural network technology, challenges still persist in detecting small-sized tomato disease spots. This study aims to enhance the tomato disease spot detection model to improve the accuracy of detecting small disease spots on tomatoes. Firstly, the concept of DenseNet is introduced, and a Dense_C3 module is constructed, which incorporates two 3×3 convolutional layers within the Bottleneck of C3 to achieve dense feature propagation and reuse. Secondly, the Convolutional Block Attention Module (CBAM) is employed to weight the feature maps, enhancing the focus on local and spatial information to enhance model accuracy. To validate the effectiveness of the proposed improvements, experiments were conducted on tomato disease spots of different scales, including Septoria Leaf Spot, Early Blight, Late Blight, and Tomato Yellow Leaf Curl Virus. The experimental results demonstrate that the introduction of the Dense_C3 structure and CBAM improves the detection accuracy for small-scale targets. In a comprehensive evaluation, the improved YOLOv5s model shows increases of 2.4% in F1 score and 2.1% in mAP. © 2024 SPIE.

关键词： Viruses

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DP-GenFL: a local differentially private federated learning system through generative data

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science China(information sciences) 2023年第8期66卷 275-276页

作者： Jun LI Kang WEI Chuan MA Feng SHU School of Electronic and Optical Engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education School of Information and Communication Engineering Hainan University

With the rapid development of the Internet of Things(Io T),the amount of data from intelligent devices is propagating at unprecedented scales. Meanwhile, machine learning(ML),which relies heavily on such data, is revolutionizing many aspects of our lives [1]. However, conventional centralized ML offers little scalability for efficiently processing this huge amount of data.

关键词： Learning systems

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RobFL: Robust Federated Learning via Feature Center Separation and Malicious Center Detection 40

RobFL: Robust Federated Learning via Feature Center Separati...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhou, Ting Liu, Ning Song, Bo Lv, Hongtao Guo, Deke Liu, Lei National University of Defense Technology Science and Technology Laboratory on Information Systems Engineering Changsha China School of Software Shandong University Jinan China Shandong Research Institute of Industrial Technology Jinan China

ISBN: (纸本)9798350317152

In recent years, the integration of federated learning and deep learning technologies has become increasingly prevalent in privacy-preserved scenarios, such as smart health applications and automatic financial support. However, the inherent robustness issue in deep learning poses potential risks to federated learning systems when subjected to various attack methods. These attacks can inflict damage during the training and testing phases, perturbing models and inputs. To enhance the robustness of existing federated learning systems, we propose a novel framework called RobFL. This framework incorporates a unique feature learning module -feature center separation learning -that is specifically designed to increase the margins between different classes in the feature space, thereby augmenting the difficulty of attacks employing imperceptible perturbations on inputs. Furthermore, we design a malicious center detection method to detect malicious clients and mitigate their adverse impact. Extensive experiments substantiate the robustness of our proposed framework, RobFL, demonstrating its resilience against both evasion attacks and poisoning attacks. © 2024 IEEE.

关键词： Anomaly detection

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High-timing-precision detection of single X-ray photons by superconducting nanowires

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National science Review 2024年第1期11卷 194-203页

作者： Shuya Guo Jingrou Tan Hengbin Zhang Jinguang Wang Tianhao Ji Labao Zhang Xiaolong Hu Jian Chen Jun Xie Kai Zou Yun Meng Xiaomin Bei Ling-An Wu Qi Chen Hao Wang Xuecou Tu Xiaoqing Jia Qing-Yuan Zhao Lin Kang Peiheng Wu Research Institute of Superconductor Electronics Nanjing University Qian Xuesen Laboratory of Space Technology Beijing National Laboratory of Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Hefei National Laboratory School of Precision Instrument and Optoelectronic Engineering Tianjin University Key Laboratory of Optoelectronic Information Science and Technology Ministry of Education

Precisely acquiring the timing information of individual X-ray photons is important in both fundamental research and practical applications. The timing precision of commonly used X-ray single-photon detectors remains in the range of one hundred picoseconds to microseconds. In this work, we report on high-timing-precision detection of single X-ray photons through the fast transition to the normal state from the superconductive state of superconducting nanowires. We successfully demonstrate a free-running X-ray single-photon detector with a timing resolution of 20.1 ps made of 100-nm-thick niobium nitride film with an active area of 50 μm by 50 μm. By using a repeated differential timing measurement on two adjacent X-ray single-photon detectors, we demonstrate a precision of 0.87 ps in the arrival-time difference of X-ray photon measurements. Therefore, our work significantly enhances the timing precision in X-ray photon counting, opening a new niche for ultrafast X-ray photonics and many associated applications.

关键词： single-photon detection timing precision superconductor ultrafast phenomenon pulsar X-ray navigation

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ER‐Mapping: An extrinsic robust coloured mapping system using residual evaluation and selection

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IET Cyber-systems and Robotics 2024年第2期6卷 97-108页

作者： Changjian Jiang Zeyu Wan Ruilan Gao Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhouChina Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province HangzhouChina

The colour-enhanced point cloud map is increasingly being employed in fields such as robotics,3D reconstruction and virtual *** authors propose ER-Mapping(Extrinsic Robust coloured Mapping system using residual evaluation and selection).ER-Mapping consists of two components:the simultaneous localisation and mapping(SLAM)subsystem and the colouring *** SLAM subsystem reconstructs the geometric structure,where it employs a dynamic threshold-based residual selection in LiDAR-inertial odometry to improve mapping *** the other hand,the col-ouring subsystem focuses on recovering texture information from input images and innovatively utilises 3D–2D feature selection and optimisation methods,eliminating the need for strict hardware time synchronisation and highly accurate extrinsic *** were conducted in both indoor and outdoor *** results demonstrate that our system can enhance accuracy,reduce computational costs and achieve extrinsic robustness.

关键词： robot vision sensor fusion simultaneous localisation and mapping(robots)

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Multi-Mode Inflow Prediction for Urban Transportation systems 14th

Multi-Mode Inflow Prediction for Urban Transportation System...

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14th International Conference on Frontier Computing, FC 2024

作者： Hung, Chih-Chieh Hung, Min-Hsien Chang, Jia-Wei Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789819623945

Traffic congestion poses significant challenges to modern cities, leading to increased energy use, pollution, and long commute times. Optimizing public transit systems and encouraging their use is an effective solution. However, public transit integrates various modes like subways, buses, and shared bikes, requiring transfers between modes. Therefore, understanding the relationships between modes and accurately predicting passenger flows for each mode is crucial. Accurate predictions enable better capacity allocation, scheduling, and seamless transfers between modes, enhancing accessibility and convenience to attract more riders. Existing multi-modal prediction methods typically focus on station-to-region or region-to-region flows, overlooking station-to-station predictions that can better capture inter-modal relationships and provide fine-grained insights for traffic management. This study proposes a multi-modal framework for MRT and YouBike data that employs a spatial-temporal module to effectively model the spatiotemporal dependencies in the data. Evaluations on real-world datasets demonstrate the framework outperforms benchmark methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Traffic congestion

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