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arXiv 2019年

作者： Franzoni, Valentina Lepri, Michele Milani, Alfredo Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Mathematics and Computer Science University of Perugia Italy Perugia Italy

In this work, we introduce a novel method for entity resolution author disambiguation in bibliographic networks. Such a method is based on a 2-steps network traversal using topological similarity measures for rating candidate nodes. Topological similarity is widely used in the Link Prediction application domain to assess the likelihood of an unknown link. A similarity function can be a good approximation for equality, therefore can be used to disambiguate, basing on the hypothesis that authors with many common co-authors are similar. Our method has experimented on a graph-based representation of the public DBLP computer science database. The results obtained are extremely encouraging regarding Precision, Accuracy, and Specificity. Further good aspects are the locality of the method for disambiguation assessment which avoids the need to know the global network, and the exploitation of only a few data, e.g. author name and paper title (i.e., co-authorship data). Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

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Erratum: An improved point spread function for complex susceptibility-based magnetic particle imaging (2022 Meas. Sci. Technol. 33 095402)

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Measurement science and Technology 2023年第7期34卷 079601-079601页

作者： Tao Jiang Wentong Yi Zhongzhou Du Wenzhong Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 People’s Republic of China China-Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan 430074 People’s Republic of China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou 450001 People’s Republic of China

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Network Parameter Generation for One-Shot Object Detection

Network Parameter Generation for One-Shot Object Detection

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2019 IEEE International Conference on Unmanned Systems, ICUS 2019

作者： Wang, Kang Zhao, Jiajia Wang, Yang Zhou, Shuigeng Fudan University Shanghai Key Lab of Intelligent Information Processing School of Computer Science Shanghai200433 China Beijing Electro-Mechanical Engineer Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Nanchang University School of Information Engineering 330031 China

ISBN: (纸本)9781728137926

Recent object detection models have achieved satisfactory performance by deep learning with large-scale annotated datasets. However, these models often perform poorly when the training examples are not sufficient enough, especially for one-shot object detection where there is only one training example of each target class. In this paper, we propose a network parameter generation framework for effective one-shot object detection. By leveraging the semantic relationship between the source-domain and the target-domain (new classes), our framework can generate network parameters for the new classes directly. Furthermore, we develop two implementation schemes for this framework: weighting-based parameter generation and regression-based parameter generation. These two schemes use different information to construct the relationship and transfer knowledge from the source-domain to the target-domain. Experiments on the PASCAL-OSCD benchmark show that our methods can significantly boost the performance of one-shot object detection. © 2019 IEEE.

关键词： Object detection

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Boundary-aware dynamic re-weighting for semi-supervised medial image segmentation

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Expert Systems with Applications 2025年 287卷

作者： Jiang, Weili Li, Jiawen Li, Yihao Wei, Xifei Huang, Jianping Quellec, Gwenolé Si, Weixin Ou, Chubin School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu China Norla Institute of Technical Physics Chengdu China United Imaging Healthcare Shanghai China Weizhi Meditech Co. Ltd. Guangdong Foshan China LaTIM UMR 1101 Inserm Brest France Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology China Guangdong General Hospital Guangdong Guangzhou China

Different from traditional semi-supervised learning (SSL), semi-supervised medical image segmentation faces two significant challenges: (1) the imbalanced distribution of labeled data causes models to bias towards majority classes;(2) the distribution discrepancy between labeled and unlabeled samples induces confirmation bias in pseudo-labels. Inspired by clinical practice, where experienced doctors utilize intrinsic features from the interior of target organs to clarify ambiguous boundaries and focus on minority classes, we propose a novel boundary-aware dynamic re-weighting network (BDRN). First, we utilize edge filters to generate visually different but semantically aligned views, compelling two sub-networks to learn informative features from organ interiors and boundaries, respectively. Second, we extract the boundary and interior regions using morphological operators and introduce a shape constraint to enhance feature learning. Additionally, a conflict-adversarial module promotes segmentation consistency between different views. Finally, we propose a dynamic re-weighting strategy based on the effective number to improve attention to imbalanced classes. Experiments demonstrate that our method significantly improves segmentation performance, achieving state-of-the-art results on CT and MR images. Ablation studies further confirm the efficacy of boundary consistency constraints and dynamic re-weighting. The segmentation Dice score for minority organs (e.g., esophagus) on the Synapse dataset is improved by 17.1 %, 46.2 %, and 49.4 % using 10 %, 20 %, and 40 % labeled data, respectively. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

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An Efficient and Privacy-preserving Energy Trading Scheme based on Blockchain

An Efficient and Privacy-preserving Energy Trading Scheme ba...

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IEEE Global Communications Conference

作者： Xin Lu Zhitao Guan Xiao Zhou Longfei Wu Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power University China Department of Mathematics and Computer Science Fayetteville State University Fayetteville USA Department of Computer and Information Science Temple University Philadelphia PA USA Department of Computer Science and Engineering Qatar University Doha Qatar

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Distributed transaction model has gradually replaced the traditional centralized transaction model and has become the leading direction of development in energy trading. As the underlying support, blockchain technology is attracting more and more attention due to its advantages, i.e., integrity and non-repudiation. However, most blockchain-based trading models face the problem of privacy protection. In this paper, to solve this problem, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is introduced as the core algorithm to reconstruct the transaction model. Specifically, we build a general model for distributed transaction called PP-BCTS (Privacy-Preserving Blockchain Trading Scheme). It can achieve fine-grained access control through transaction arbitration in ciphertext form. This design can maximize the protection of private information and can greatly improve the security and reliability of the transaction model. Additionally, a credibility-based equity proof consensus mechanism is proposed in PP-BCTS, which can greatly improve the operational efficiency. Security analysis and experimental evaluations are conducted to prove the validity and practicability of our proposed scheme.

关键词： Privacy Access control Encryption Data privacy

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Fast ORB-SLAM without Keypoint Descriptors

arXiv

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arXiv 2020年

作者： Fu, Qiang Yu, Hongshan Wang, Xiaolong Yang, Zhengeng He, Yong Zhang, Hong Mian, Ajmal The National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University China The Department of Computing Science University of Alberta Canada Quanzhou Institute of Industrial Design and Machine Intelligence Innovation Hunan University China The Southern University of Science and Technology Shenzhen China The School of Mathematics and Information Science Shaanxi Normal University China The Department of Computer Science The University of Western Australia WA6009 Australia

Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 [1] is a benchmark method in this domain, however, it consumes significant time for computing descriptors that never get reused unless a frame is selected as a keyframe. To overcome these problems, we present FastORB-SLAM which is light-weight and efficient as it tracks keypoints between adjacent frames without computing descriptors. To achieve this, a two stage descriptor-independent keypoint matching method is proposed based on sparse optical flow. In the first stage, we predict initial keypoint correspondences via a simple but effective motion model and then robustly establish the correspondences via pyramid-based sparse optical flow tracking. In the second stage, we leverage the constraints of the motion smoothness and epipolar geometry to refine the correspondences. In particular, our method computes descriptors only for keyframes. We test FastORB-SLAM on TUM and ICL-NUIM RGB-D datasets and compare its accuracy and efficiency to nine existing RGB-D SLAM methods. Qualitative and quantitative results show that our method achieves state-of-the-art accuracy and is about twice as fast as the ORB-SLAM2. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical flows

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Kernelized support tensor train machines

arXiv

引用

arXiv 2020年

作者： Chen, Cong Batselier, Kim Yu, Wenjian Wong, Ngai Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong Delft Center for Systems and Control Delft University of Technology Delft Netherlands BNRist Department of Computer Science and Technology Tsinghua University Beijing100084 China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

Tensor, a multi-dimensional data structure, has been exploited recently in the machine learning community. Traditional machine learning approaches are vector- or matrix-based, and cannot handle tensorial data directly. In this paper, we propose a tensor train (TT)-based kernel technique for the first time, and apply it to the conventional support vector machine (SVM) for image classification. Specifically, we propose a kernelized support tensor train machine that accepts tensorial input and preserves the intrinsic kernel property. The main contributions are threefold. First, we propose a TT-based feature mapping procedure that maintains the TT structure in the feature space. Second, we demonstrate two ways to construct the TT-based kernel function while considering consistency with the TT inner product and preservation of information. Third, we show that it is possible to apply different kernel functions on different data modes. In principle, our method tensorizes the standard SVM on its input structure and kernel mapping scheme. Extensive experiments are performed on real-world tensor data, which demonstrates the superiority of the proposed scheme under few-sample high-dimensional inputs. Copyright © 2020, The Authors. All rights reserved.

关键词： Tensors

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Three-Phase D-STATCOM with Digital Current control Strategy in abc-Reference Frame

Three-Phase D-STATCOM with Digital Current Control Strategy ...

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International Conference on Computing, Mathematics and engineering Technologies (iCoMET)

作者： Tiago Davi Curi Busarello Kamran Zeb Muhammad Asim Sarwar Hee Je Kim Depart. Control Automation and Computation Engineering Federal University of Santa Catarina - UFSC Blumenau Brazil School of Electrical Engineering School of Electrical Engineering and Computer Science. Pusan National University National University of Sciences and Technology Busan South Korea Department Of Electrical Engineering Sukkur IBA University Pakistan School of Electrical Engineering Pusan National University Busan South Korea

ISBN: (数字)9781728149707

ISBN: (纸本)9781728149714

This paper proposes a Three-phase D-STATCOM with digital current control strategy in abc- reference frame. The proposal D-STATCOM does not use a PLL and the reactive power is transferred to the grid through controlling the D-STATCOM output current. The current controller has one zero and two poles. This type of controller is also known as Type-2 controller. The procedure to tune the controller is explained. The Conservative Power Theory (CPT) is used to compute the reactive power of the loads. A case study shows the behavior of the D-STACOM during initialization, during load transitions and during steady-state conditions. The results show the efficacy of the proposed D-STATCOM.

关键词： Automatic voltage control Reactive power Transfer functions Phase locked loops Mathematical model Filtering theory

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Improved ECO Algorithm Based on Residual Neural Network

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Journal of Physics: Conference Series 2021年第1期1732卷

作者： Huifang Qi Changmeng Wang Bo Li School of Information Science and Engineering Yanshan University Qinhuangdao China School of Electrical Engineering Yanshan University Qinhuangdao China Key Laboratory of industrial computer control engineering in Hebei Province Yanshan University Qinhuangdao China

Video target tracking is one of hot fields of computer vision, and its application is also very extensive. However, due to the complexity and variability of tracking environment, which brings some challenges to the research of target tracking. ECO(Efficient Convolution Operators) Algorithm is proposed based on convolutional neural network in three aspects. Firstly, residual neural network ResNet50 is adopted instead of convolutional neural network to extract the appearance features of target, and deeper residual neural network is applied to obtain more abundant target semantic information, so as to improve the tracking effect of tracking ***, sample space classification strategy is improved. Different weights are assigned to shallow feature and deep feature, which make the deep feature play a more important role and improve the effect of target tracking. Finally, the method of scale estimation is improved so that better bounding boxes can be estimated with the scale changing. Experimental results show that the distance accuracy and success rate of the algorithm.

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control processes in mechatronic systems with friction: Research and modeling 10

Control processes in mechatronic systems with friction: Rese...

引用

10th International Scientific Conference BALTTRIB 2019

作者： Malafeev, S.I. Malafeev, S.S. Kopeykin, A.I. Department of Science Research Joint Power Co. Ltd. Moscow Russia Department of Computer Engineering and Control Systems Vladimir State University named after Alexander and Nikolay Stoletovs Vladimir Russia Vladimir Polytechnic College Vladimir Russia

Effect of friction on the motion control processes in mechatronic systems is considered. The analysis of modern friction modeling methods is carried out. Features of processes at low speeds: accuracy, oscillations and stability have been studied on base of macroscopic models of friction. Friction in the mechatronic system causes the formation of complex nonlinear speed feedback, which depends on many factors and has different signs at different speeds. Oscillations and a violation of the stability of the system are possible with positive feedback at low speed. The conditions of stable operation under frictional load for a DC motor are derived. Correction methods for mechatronic systems with friction are considered. Effective control in mechatronic systems achieve using the principle of subordinate coordinate control. The results of modeling systems with the main feedback on the angular speed and angle of rotation are presented. Copyright © 2019 The Authors.

关键词： Friction

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