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Towards More Efficient EfficientDets and Real-Time Marine Debris Detection

IEEE Robotics and Automation Letters 2023年第4期8卷 2134-2141页

作者： Zocco, Federico Lin, Tzu-Chieh Huang, Ching-I Wang, Hsueh-Cheng Khyam, Mohammad Omar Van, Mien Queen's University Belfast Centre for Intelligent Autonomous Manufacturing Systems School of Electronics Electrical Engineering and Computer Science Northern IrelandBT7 1NN United Kingdom Hsinchu City30010 Taiwan Institute of Electrical and Control Engineering Hsinchu City30010 Taiwan Labs Hsinchu Taiwan Central Queensland University School of Engineering and Technology Norman Gardens QLD4701 Australia

Marine debris is a problem both for the health of marine environments and for the human health since tiny pieces of plastic called 'microplastics' resulting from the debris decomposition over the time are entering the food chain at any levels. For marine debris detection and removal, autonomous underwater vehicles (AUVs) are a potential solution. In this letter, we focus on the efficiency of AUV vision for real-time marine debris detection. First, we improved the efficiency of a class of state-of-the-art object detectors, namely EfficientDets [1], by 1.5% AP on D0, 2.6% AP on D1, 1.2% AP on D2 and 1.3% AP on D3 without increasing the GPU latency (see Fig. 1). Subsequently, we created and made publicly available a dataset for the detection of in-water plastic bags and bottles and trained our improved EfficientDets on this and on two public datasets for marine debris detection. Finally, we began the testing of real-time detection performance on a simulator of marine environments. © 2016 IEEE.

关键词： Real time systems

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Long Short-Term Memory Neural Equalizer

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2023年第1期2卷 13-22页

作者： Wang, Zihao Xu, Zhifei He, Jiayi Delingette, Hervé Fan, Jun The French National Institute for Research in Computer Science and Control Le Chesnay-Rocquencourt78150 France Université Côte d’Azur Nice06000 France The Detoolic Technology Co. Ltd Ningbo China The Missouri University of Science and Technology RollaMO65401 United States The Electromagnetic Compatibility Laboratory Missouri University of Science and Technology RollaMO65401 United States The Université Côte d’Azur FranceFrench Interdisciplinary Institutes of Artificial Intelligence Université Côte d’Azur 06000 Nice Nice06000 France

A trainable neural equalizer based on the long short-term memory (LSTM) neural network architecture is proposed in this article to recover the channel output signal. The current widely used solution for the transmission line signal recovery is generally realized through a decision feedback equalizer (DFE) or: Feed forward equalizer (FFE) combination. The novel learning-based equalizer is suitable for highly nonlinear signal restoration, thanks to its recurrent design. The effectiveness of the LSTM equalizer (LSTME) is shown through an advance design system simulation channel signal equalization task, including a quantitative and qualitative comparison with an FFE–DFE combination. The LSTM neural network shows good equalization results compared with that of the FFE–DFE combination. The advantage of a trainable LSTME lies in its ability to learn its parameters in a flexible manner and to tackle complex scenarios without any hardware modification. This can reduce the equalizer implantation cost for variant transmission channels and bring additional portability in practical applications. © 2023 IEEE.

关键词： Long short-term memory

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Achieving adaptively secure data access control with privacy protection for lightweight IoT devices

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Science China(Information Sciences) 2021年第6期64卷 144-157页

作者： Zhitao GUAN Wenti YANG Liehuang ZHU Longfei WU Ruimiao WANG School of Control and Computer Engineering North China Electric Power University School of Computer Beijing Institute of Technology Department of Mathematics and Computer Science Fayetteville State University

The Internet of things(IoT) technology has been used in a wide range of fields, ranging from industrial manufacturing to daily lives. The IoT system contains numerous resource-constrained lightweight devices such as wireless sensors and radio frequency identification(RFID) tags. A massive amount of sensitive data is generated and transmitted by these devices to a variety of users. The complexity of the IoT system places a high demand on security. Therefore, it is necessary to develop an encryption scheme with access control to provide flexible and secure access to the sensitive data. The ciphertext policy attribute-based encryption(CP-ABE) scheme is a potential solution. However, the long ciphertext as well as the slow encryption and decryption operations in traditional ABE schemes make it inappropriate for most IoT systems,which require low latency and contain many devices with limited memory size and computing capability. In this paper, we propose a modified CP-ABE scheme with constant length of ciphertext and low computation overhead in the encryption and decryption phases. Additionally, our scheme is proven to be adaptively secure under the standard model. Moreover, two enhanced schemes are developed to prevent authorized users from leaking data and protect the privacy of data owners by combining chameleon hash, bloom filters and CP-ABE, respectively. Finally, the experimental evaluation and analysis prove the feasibility of our scheme.

关键词： IoT CP-ABE constant-size ciphertexts adaptively secure privacy protection

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite Systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Saliency-Aware Regularized Graph Neural Network

arXiv

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arXiv 2024年

作者： Pei, Wenjie Xu, WeiNa Wu, Zongze Li, Weichao Wang, Jinfan Lu, Guangming Wang, Xiangrong Department of Computer Science Harbin Institute of Technology at Shenzhen Shenzhen518172 China Institute of Future Networks Southern University of Science and Technology Shenzhen518055 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518066 China

The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when aggregating features of neighboring nodes, and obtain the representation for the entire graph by aggregating node features. Such methods have two potential limitations: 1) the global node saliency w.r.t. graph classification is not explicitly modeled, which is crucial since different nodes may have different semantic relevance to graph classification;2) the graph representation directly aggregated from node features may have limited effectiveness to reflect graph-level information. In this work, we propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification, which consists of two core modules: 1) a traditional graph neural network serving as the backbone for learning node features and 2) the Graph Neural Memory designed to distill a compact graph representation from node features of the backbone. We first estimate the global node saliency by measuring the semantic similarity between the compact graph representation and node features. Then the learned saliency distribution is leveraged to regularize the neighborhood aggregation of the backbone, which facilitates the message passing of features for salient nodes and suppresses the less relevant nodes. Thus, our model can learn more effective graph representation. We demonstrate the merits of SAR-GNN by extensive experiments on seven datasets across various types of graph data. Code will be released. © 2024, CC BY.

关键词： Graph neural networks

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Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy

arXiv

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arXiv 2024年

作者： Huo, Wei Liu, Changxin Ding, Kemi Johansson, Karl Henrik Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

This paper investigates the use of the cubic-regularized Newton method within a federated learning framework while addressing two major concerns that commonly arise in federated learning: privacy leakage and communication bottleneck. We introduce a federated learning algorithm called Differentially Private Federated Cubic Regularized Newton (DP-FCRN). By leveraging second-order techniques, our algorithm achieves lower iteration complexity compared to first-order methods. We also incorporate noise perturbation during local computations to ensure privacy. Furthermore, we employ sparsification in uplink transmission, which not only reduces the communication costs but also amplifies the privacy guarantee. Specifically, this approach reduces the necessary noise intensity without compromising privacy protection. We analyze the convergence properties of our algorithm and establish the privacy guarantee. Finally, we validate the effectiveness of the proposed algorithm through experiments on a benchmark dataset. © 2024, CC BY.

关键词： Federated learning

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Risk Identification Method for Smuggled Frozen Meat in Urban Public Safety Awareness and Early Warning

Risk Identification Method for Smuggled Frozen Meat in Urban...

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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Li, Xueru Zhang, Yan Liu, Lingjie Tian, Ye Zhang, Yue Harbin Engineering University College of Computer Science and Technology Harbin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Xidian University Hangzhou Institute of Technology Hangzhou China Key Laboratory of Peace-Building Big Data of Zhejiang Province Zhejiang China

ISBN: (纸本)9798350357677

With the continuous growth in demand for meat and frozen products in the cold chain market, illegally smuggled frozen goods have quickly entered the market due to their low prices, leading to a series of food safety issues. This article proposes a method for identifying the risks of smuggled frozen meat for urban public safety awareness using the YOLOv7 model. The YOLOv7 model is used to detect data captured at various traffic checkpoints, obtaining spatiotemporal information of frozen goods vehicles. This information is then combined with map data to generate vehicle trajectory information. The trajectory similarity between the frozen vehicle trajectories and the specialized database of frozen transportation routes is analyzed using the EDwP distance algorithm. The EDwP algorithm employs a threshold-free approach, dynamically interpolating using the concept of projection, and automatically adapting to non-uniform sampling rates to address the issue of inconsistent trajectory data sampling rates. The experimental results of the YOLOv7-EDwP algorithm were verified by verifying the alerts generated in the recent period through on-site verification by a third-party inspection agency, with a recognition accuracy of 90.77%. This holds significant significance in the detection and warning of smuggled frozen meat. © 2023 IEEE.

关键词： Transportation routes

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State controller with Improved Response Speed for Linear Discrete SISO Systems

State Controller with Improved Response Speed for Linear Dis...

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2022 International Conference Automatics and Informatics, ICAI 2022

作者： Nikolov, Nikola Alexandrova, Mariela Gaziyeva, Rano Ubaydullayeva, Shakhnoza Technical University of Varna Faculty of Computer Sciences and Automation Department of Automation Varna9010 Bulgaria National Research University Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Department Automation and Control of Technology Process in Production Tashkent100000 Uzbekistan

ISBN: (数字)9781665476256

ISBN: (纸本)9781665476256

This paper presents an opportunity to create a linear discrete system (LDS) with a modal state controller for Single Input Single Output objects control. The created controller has an integrating element and a relationship between the set point (SP) and the control object (CO), implemented with an inverse model on the object. The proposed system guarantees handling of the perturbances and providing an improvement in the system performance as in PID-controller systems. In structure proposed, an LQR state controller could be used instead of the MSC. © 2022 IEEE.

关键词： Three term control systems

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HTPSeg: A Semantic Segmentation Database for House-Tree-Person Psychological Test

HTPSeg: A Semantic Segmentation Database for House-Tree-Pers...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Hao Wang Jin Wang Ting Pan Bingfeng Zhang Weifeng Liu Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao China College of Control Science and Engineering China University of Petroleum (East China) Qingdao China

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

The House Tree Person (HTP) test is widely recommended for clinical application of mental illness. Traditionally, therapists assess a patient's mental state by analyzing the content of their HTP drawings manually, which is time-consuming and susceptible to the therapist's subjective influences. Recently, using intelligent diagnostic models to tackle HTP test has attracted much attention. However, most existing models attempt to make a binary classification for HTP drawings, i.e., positive or negative psychological states, making details that can reflect the psychological state of the patient lost. To address the above challenge, in this paper, we introduce the semantic segmentation task into the HTP test for the first time to generate pixel-level semantic information. We first construct a semantic segmentation dataset about HTP psychological diagnosis named HTPSeg. Subsequently, to overcome the domain gap between HTP drawings and natural images, we propose to use the Low-Rank Adaptation (LoRA) fine-tuning strategy to adapt the Segment Anything Model (SAM) to the task of HTP drawing analysis. Specifically, we integrate the rank decomposition matrices into the projection layer of the transformer block in SAM's image encoder for fine-tuning. Additionally, we freeze the Mask Decoder and fine-tune the Prompt Encoder using default embeddings. Extensive experiments indicate the effectiveness and efficiency of the proposed method. The dataset and code will be available at https://***/clown06/HTPSeg.

关键词： Analytical models Adaptation models Heavily-tailed distribution Semantic segmentation Computational modeling Semantics Psychology Feature extraction Transformers Matrix decomposition

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Spectral Clustering and Multi-Modal Stable Matching for Enhanced Management of Photovoltaic Stations and Virtual Power Plants

Spectral Clustering and Multi-Modal Stable Matching for Enha...

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International Conference on Green Energy and Applications (ICGEA)

作者： Yang Yang Boon Han Lim Yicheng Xu Yong Zhang Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenzhen China Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Malaysia Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen University of Advanced Technology Shenzhen China

ISBN: (数字)9798331530778

ISBN: (纸本)9798331530785

As renewable energy sources like photovoltaic (PV) stations expand, efficient management and operation are becoming increasingly important for maintaining grid stability and optimizing resource allocation. In this paper, we introduce a novel framework that integrates spectral clustering and multi-modal stable matching to enhance the coordination of PV stations and Virtual Power Plants (VPPs). We begin by applying spectral clustering to group PV stations based on geographic locations and key weather conditions. Our custom-built similarity matrix for PV stations, combined with our theoretical insights, suggests that using fewer eigenvectors in spectral clustering not only reduces computation time but also improves clustering accuracy. This method ensures that each cluster of PV stations shares similar environmental conditions, allowing for more effective collective management. In the second stage, we utilize multi-modal stable matching to assign these PV clusters to VPPs. Unlike traditional stable matching, the multi-modal approach considers multiple factors, such as energy demand, geographic proximity, and VPP capacity, resulting in a more balanced and stable allocation. We validate our approach through experiments on both synthetic and real-world PV station data. The results demonstrate that our framework leads to more accurate grouping of PV stations, facilitating more effective decision-making across multiple criteria. This also enhances work offloading when tasks become intensive on certain nodes. Our work presents a promising solution for managing large-scale renewable energy resources in future power systems, improving both efficiency and stability.

关键词： Photovoltaic systems Energy consumption Stability criteria Clustering algorithms Virtual power plants Power system stability Approximation algorithms Resource management Thermal stability Meteorology

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