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EK-Net++: Real-time scene text detection with expand kernel distance and Epoch Adaptive Weight

Expert Systems with Applications 2025年 267卷

作者： Zhu, Boyuan Chen, Xi Tang, Quan Chen, C.L. Philip Liu, Fagui Computer Science and Engineering South China University of Technology Guangzhou510006 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518066 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Recently, scene text detection has received significant attention due to its wide applications. Accurate detection in complex scenes of multiple scales, orientations, and curvature remains a challenge. Component-based methods have emerged as the dominant approach for offering a more flexible way to achieve superior performance. However, Component-based methods employ the Vatti clipping algorithm for multi-instance training to handle the difficulty of arbitrary-shaped text. Yet we identify several bias results from these approaches called the "shrinked kernel" leading to decreased accuracy. Additionally, almost all text detection methods fail to consider the characteristics of different datasets and directly assign fixed loss weights to multi-instances. To address these limitations, we propose a new approach named Expand Kernel Network (EK-Net++), which presents the expand kernel distance map to compensate for the previous deficiency and incorporates three-stage regression to complete integral instance detection. Furthermore, we design the Epoch Adaptive Weight algorithm which dynamically distinguishes the characteristics of different datasets. EK-Net++ not only achieves precise positioning of arbitrary-shaped text but also strikes a balance between performance and speed. Evaluation results show that EK-Net++ attains state-of-the-art or competitive performance compared to other advanced methods, e.g., F-measure of 86.72% at 35.42 FPS on ICDAR 2015, F-measure of 85.83% at 40.13 FPS on CTW1500. © 2024 Elsevier Ltd

关键词： Epoch Adaptive Weight Expand kernel distance Real-time Scene text detection

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Two Enhanced-Rate Power Allocation Strategies for Active IRS-Assisted Wireless Network

Two Enhanced-Rate Power Allocation Strategies for Active IRS...

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computer Communication and Artificial Intelligence (CCAI), International Conference on

作者： Qiankun Cheng Rongen Dong Wenlong Cai Ruiqi Liu Feng Shu Jiangzhou Wang Hainan University School of Information and Communication Engineering Haikou China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China Wireless and Computing Research Institute ZTE Corporation Beijing China University of Kent School of Engineering Canterbury U.K.

ISBN: (数字)9798350362763

ISBN: (纸本)9798350362770

Due to its ability of overcoming the impact of double-fading effect, active intelligent reflecting surface (IRS) has attracted a lot of attention. Unlike passive IRS, active IRS should be supplied by power, thus adjusting power between base station (BS) and IRS having a direct impact on the system rate performance. In this paper, the active IRS-aided network under a total power constraint is modeled with an ability of adjusting power between BS and IRS. Given the transmit beamforming at BS and reflecting beamforming at IRS, the SNR expression is derived to be a function of power allocation (PA) factor, and the optimization of maximizing the SNR is given. Subsequently, two high-performance PA strategies, enhanced multiple random initialization Newton's (EMRIN) and Taylor polynomial approximation (TPA), are proposed. The former is to improve the rate performance of classic Newton's method to avoid involving a local optimal point by using multiple random initializations. To reduce its high computational complexity, the latter provides a closed-form solution by making use of the first-order Taylor polynomial approximation to the original SNR function. Actually, using TPA, the original optimization problem is transformed into a problem of finding a root for a third-order polynomial. Simulation results are as follows: the first-order TPA of SNR fit its exact expression well, the proposed two PA methods performs much better than fixed PA in accordance with rate, and approaches exhaustive search as the number of IRS reflecting elements goes to large-scale.

关键词： Array signal processing Wireless networks Simulation Polynomials Vectors Resource management Newton method

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A Weighted Collaborative Prediction Method of RUL for Integrated Circuits in Multi-Failure Modes Based on Transformer

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IFAC-PapersOnLine 2023年第2期56卷 7632-7637页

作者： Zibo Zhang Long Jia Xinyu Zou Chang Chen Wei Chen Chunyun Lan Jian Ma Institution of Reliability Engineering Beihang University Beijing 100191 China Science & Technology on Reliability & Environmental Engineering Laboratory Beijing 100191 China School of Reliability and Systems Engineering Beihang University Beijing 10091 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Beijing Aerospace Institute of Automatic Control 100854 Beijing China

Accurately predicting the Remaining Useful Life (RUL) of integrated circuits in multi-failure modes is critical for ensuring the safe and efficient operation of electronic devices. In this paper, we propose a novel RUL prediction method that combines a locally weighted regression method, a Transformer deep neural network, and a multi-failure modes weighted collaborative prediction approach. We first use a locally weighted regression method to eliminate noise from the original feature time series. Then, the feature time series is input into the proposed classification model to classify the failure modes and establish the failure mode weighted function. Finally, the feature time series is fed into the proposed regression model for RUL prediction with weighted processing to obtain accurate RUL predictions. Our experimental results demonstrate that our method effectively captures the degradation information of integrated circuits and generates precise RUL predictions. We demonstrate the efficacy of our method on a circuit stimulation model, showing that it outperforms several state-of-the-art RUL prediction methods. Our proposed method has the potential to enhance the reliability and safety of electronic devices in various applications.

关键词： Integrated circuits RUL prediction multi-failure modes locally weighted regression method Transformer deep neural network weighted collaborative prediction

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AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion

arXiv

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arXiv 2024年

作者： Xie, Yuting Guo, Xianda Wang, Cong Liu, Kunhua Chen, Long School of Computer Science and Engineering Sun Yat-Sen University China School of Computer Science Wuhan University China State Key Laboratory for Management and Control of Complex Systems The Institute of Automation Chinese Academy of Sciences China School of Mechanical and Automotive Engineering Qingdao University of Technology China

Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance. © 2024, CC BY.

关键词： automatic guided vehicles

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Aspect-Based Capsule Network With Mutual Attention for Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第3期2卷 228-237页

作者： Yang, Zhenyu Wang, Xuesong Cheng, Yuhu Liu, GuoJing The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The Qilu University of Technology Shandong Academy of Sciences Software Integration Institute Jinan250353 China The Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The School of Computer Science and Technology Qilu University of Technology Jinan250353 China

Review text is a valuable source of information for recommendation systems and often contains rich semantics with user preferences and item attributes. Recently, mainstream recommendation approaches have been using deep learning techniques to utilize review text. These efforts employ an affinity matrix to model the correlations between users and items and in turn aggregate the contextual features of users and items to form latent representations with some interpretability. However, constructing this simple interaction does not capture the complex correlations between users and items. Furthermore, a single representation of latent features is not sufficient to express user preferences and item attributes in reviews with multiple granularities. To this end, we propose an aspect-based capsule network and mutual attention for recommendation systems, named Acapnet. We design a novel contextual mutual attention mechanism for modeling the fine-grained correlations in the contextual features of users and items. We employ reverse dynamic routing to aggregate user and item context features into aspect features (capsules) for rating predictions;the vector length of a capsule can be used as a basis for estimating aspect importance. Extensive experiments are conducted on five real-world datasets with different features. The results show that the proposed Acapnet is superior to recent state-of-the-art models in terms of performance. Further analysis reveals that our model effectively mitigates the homogeneity among various aspect capsules with strong interpretability. © 2021 IEEE.

关键词： Recommender systems

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Distributed Active Disturbance Rejection control of Multiple Mobile Robots for Online Workload Partition

Distributed Active Disturbance Rejection Control of Multiple...

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2023 China Automation Congress, CAC 2023

作者： Li, Xin Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350303759

Considering the actual multi-agent coverage process, the motion trajectories are seriously affected by disturbances and noise. In this paper, a cooperative control method based on active disturbance rejection controller (ADRC) for multiple mecanum-wheeled robots (MWMR) is proposed to improve its robustness. In particular, to suppress the total disturbances during the running of each robot and reduce large computation and observation cost of high order ADRC, a reduced-order cascaded ADRC (RC-ADRC) is proposed. Moreover, distributed controller is proposed to track the expected trajectories of multiple mecanum-wheeled robots under disturbances. Compared with the proposed RC-ADRC based cooperative control scheme and PID based cooperative control one, the favorable motion trajectory tracking performance is obtained. Simulation results verify the superiority of the method. © 2023 IEEE.

关键词： controllers

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Application of an Efficient Blockchain PBFT Optimization Algorithm for Environmental Monitoring 2

Application of an Efficient Blockchain PBFT Optimization Alg...

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2nd International Conference on computer science and Blockchain, CCSB 2022

作者： Zhao, Manyu Liu, Wei He, Kai Shandong Technology and Business University School of Information and Electronic Engineering Yantai Shandong264005 China Key Laboratory of Sensing Technology and Control in Universities of Shandong Yantai Shandong264005 China Shandong Technology and Business University School of Computer Science and Technology School of Artificial Intelligence Yantai Shandong264005 China

ISBN: (纸本)9798350331882

Blockchain technology has changed the centralized management model of the existing environmental monitoring system by virtue of its decentralized and non-tampering characteristics. However, with the increase in the amount of monitoring data, the environmental monitoring blockchain system is faced with a large number of computing and communication overhead caused by data consensus, which affects the normal collection, calculation, and upload of environmental monitoring data. Aiming at the above problems, this paper proposes a credit grouping Byzantine fault-tolerant(CGBFT) mechanism which is suitable for environmental monitoring blockchain scenarios. First, the reliability analysis is performed based on the authenticity and integrity of the data transmitted by the monitoring nodes to obtain the credit value of each node as the basis for selecting the master node. Second, the consistency protocol of PBFT is optimized for the characteristics of the environmental monitoring blockchain system to meet the data consensus strategy of high throughput and reliable fault tolerance. The scheme analysis shows that the improved PBFT consensus mechanism can effectively solve the problems of high communication overhead and low efficiency in the system performance. © 2022 IEEE.

关键词： Blockchain

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A Reinforcement Learning Based Dynamic Multi-Objective Constrained Evolutionary Algorithm for Open-Pit Mine Truck Scheduling

A Reinforcement Learning Based Dynamic Multi-Objective Const...

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2023 China Automation Congress, CAC 2023

作者： Qiu, Junxiang Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo- Exploration Ministry of Education Wuhan430074 China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350303759

Aiming at the truck scheduling problem in the open-pit mine scenario, a truck scheduling model based on real-time ore blending is established, and an adaptive evolution algorithm for truck scheduling based on DCNSGA-III is proposed. In the established scheduling model, the real-time grade variance of the crushing plant is minimized as one of the optimization objectives, and the Q-learning algorithm is introduced to adaptively select one of the most effective operators during the search process. Experiments show that the proposed method can effectively control the grade fluctuation of the ore flow and better scheduling schemes are obtained in comparison with algorithms equipped with the traditional search operator selection methods. © 2023 IEEE.

关键词： Evolutionary algorithms

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Kernel-Guided Texture-Aware Blind Super-Resolution via Uncertainty Learning

Kernel-Guided Texture-Aware Blind Super-Resolution via Uncer...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Tingting Zhao Zuoyong Li Li Quan Kun Zeng Shenghua Teng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China College of Computer Science and Big Data Fuzhou University Fuzhou China

The accuracy of kernel estimation is critical to the performance of blind super-resolution (SR). Traditional kernel estimation methods usually use L1 or L2 loss function to minimize the difference between the estimated kernel and the ground-truth kernel. This tends to make the estimated kernel converge towards the average of all possible kernels, deviating from the ground-truth kernel. To improve the performance of kernel estimation, this paper proposes an uncertainty loss for training a kernel estimation network, focusing on regions with high uncertainty (variance) in the kernel. In addition, a texture-aware SR network is proposed that utilizes the Gumbel Softmax trick to pay more attention to the complex regions of the image texture, thus improving the SR performance. Extensive experiments on synthetic datasets show that our approach achieves promising performance.

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CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS BY DEEP SUBSPACE ENCODING

arXiv

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arXiv 2022年

作者： Beintema, Gerben I. Schoukens, Maarten Tóth, Roland Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. This paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. The proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. © 2022, CC BY.

关键词： Cost functions

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