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2020 IEEE Sustainable Power and Energy Conference, iSPEC 2020

作者： Chen, Peijie Pan, Tianhong School of Electrical and Information Engineering Jiangsu University Zhenjiang China Key Laboratory of Intelligent Computing and Signal Processing of Minister of Education School of Electrical Engineering and Automation Anhui University Hefei China

ISBN: (纸本)9781728191645

Permanent magnet synchronous motor (PMSM) has gradually become the main driving motor for electric vehicles (EVs). Reducing the electrical loss of PMSM can effectively improve the cruising distance of EVs after a single charge. In order to improve the system operation efficiency, the economic factors of the system are considered, and a drive system for PMSM using the economic model predictive control (EMPC) has been proposed in this paper. Firstly, the mathematical model of PMSM is described, and the electrical loss of the system is investigated. Then, economic performance indicators are embedded into the cost function, and the optimal control law is obtained by solving the optimization problem with constraints. Case studies demonstrate that the proposed EMPC not only improves the dynamic response of the motor, but also reduces the loss of the system. In this way, the system operation efficiency is improved and the purpose of energy saving is achieved. © 2020 IEEE.

关键词： Model predictive control

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A family of diameter perfect constant-weight codes from Steiner systems

arXiv

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arXiv 2022年

作者： Shi, Minjia Xia, Yuhong Krotov, Denis S. Key Laboratory of Intel Ligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China State Key Laboratory of Integrated Service Networks Xidian University Xian710071 China School of Mathematical Sciences Anhui University Hefei230601 China Sobolev Institute of Mathematics Novosibirsk630090 Russia

If S is a transitive metric space, then |C|·|A| ≤ |S| for any distanced code C and a set A, "anticode", of diameter less than d. For every Steiner S(t, k, n) system S, we show the existence of a q-ary constant-weight code C of length n, weight k (or n − k), and distance d = 2k − t + 1 (respectively, d = n − t + 1) and an anticode A of diameter d−1 such that the pair (C, A) attains the code–anticode bound and the supports of the codewords of C are the blocks of S (respectively, the complements of the blocks of S). We study the problem of estimating the minimum value of q for which such a code exists, and find that minimum for small values of *** Codes 94B25, 05B05 Copyright © 2022, The Authors. All rights reserved.

关键词： C (programming language)

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Study of Novel Sparse Array Design Based on the Maximum Inter-Element Spacing Criterion

arXiv

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arXiv 2022年

作者： Shi, Wanlu Li, Yingsong de Lamare, Rodrigo C. The College of Information and Communication Engineering Harbin Engineering University Harbin150001 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China G¨¢vea Rio de Janeiro Brazil The University of York United Kingdom

A novel sparse array (SA) structure is proposed based on the maximum inter-element spacing (IES) constraint (MISC) criterion. Compared with the traditional MISC array, the proposed SA configurations, termed as improved MISC (IMISC) has significantly increased uniform degrees of freedom (uDOF) and reduced mutual coupling. In particular, the IMISC arrays are composed of six uniform linear arrays (ULAs), which can be determined by an IES set. The IES set is constrained by two parameters, namely the maximum IES and the number of sensors. The uDOF of the IMISC arrays is derived and the weight function of the IMISC arrays is analyzed as well. The proposed IMISC arrays have a great advantage in terms of uDOF against the existing SAs, while their mutual coupling remains at a low level. Simulations are carried out to demonstrate the advantages of the IMISC arrays. Copyright © 2022, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

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Semi-Supervised Clustering Framework for Fine-grained Scene Graph Generation 39

Semi-Supervised Clustering Framework for Fine-grained Scene ...

引用

39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Yang, Jiarui Wang, Chuan Zhang, Jun Wu, Shuyi Jinjing, Zhao Liu, Zeming Yang, Liang Shanghai Key Lab of Intell. Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Institute of Information Engineering CAS China School of Computer Science and Technology Beijing JiaoTong University China Guangdong Provincial Key Lab of Intell. Info. Processing & Shenzhen Key Lab of Media Security Shenzhen University China Information Research Center of Military Science PLA Academy of Military Science China National Key Laboratory of Science and Technology on Information System Security China School of Computer Science and Engineering Beihang University China School of Artificial Intelligence Hebei University of Technology China

ISBN: (纸本)157735897X

Scene Graph Generation (SGG) aims to detect all objects and identify their pairwise relationships existing in the scene. Considering the substantial human labor costs, existing scene graph annotations are often sparse and biased, which result in confusion training with low-frequency predicates. In this work, we design a Semi-Supervised Clustering framework for Scene Graph Generation (SSC-SGG) that uses the sparse labeled data to guide the generation of effective pseudo-labels from unlabeled object pairs, thus enriching the labeled sample space, especially for low-frequency interaction samples. We approach from the perspective of clustering, reducing the problem of confirmation bias in a self-training manner. Specifically, we first enhance the model’s robustness to feature extraction via prototype-based clustering, aggregating different relationship augmented features onto the same prototype. Secondly, we design a dynamic pseudo-label assignment algorithm based on a mini-batch, which adjusts the detection sensitivity to different frequency samples from the historical assignment. Finally, we conduct joint training on the pseudo-labels and the labeled data. We conduct experiments on various SGG models and achieve substantial overall performance improvements, demonstrating the effectiveness of SSC-SGG. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

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Distilling Knowledge from Heterogeneous Architectures for Semantic Segmentation

arXiv

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arXiv 2025年

作者： Huang, Yanglin Hu, Kai Zhang, Yuan Chen, Zhineng Gao, Xieping Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University China School of Computer Science Fudan University China Key Laboratory for Artificial Intelligence and International Communication Hunan Normal University China

Current knowledge distillation (KD) methods for semantic segmentation focus on guiding the student to imitate the teacher’s knowledge within homogeneous architectures. However, these methods overlook the diverse knowledge contained in architectures with different inductive biases, which is crucial for enabling the student to acquire a more precise and comprehensive understanding of the data during distillation. To this end, we propose for the first time a generic knowledge distillation method for semantic segmentation from a heterogeneous perspective, named HeteroAKD. Due to the substantial disparities between heterogeneous architectures, such as CNN and Transformer, directly transferring cross-architecture knowledge presents significant challenges. To eliminate the influence of architecture-specific information, the intermediate features of both the teacher and student are skillfully projected into an aligned logits space. Furthermore, to utilize diverse knowledge from heterogeneous architectures and deliver customized knowledge required by the student, a teacher-student knowledge mixing mechanism (KMM) and a teacher-student knowledge evaluation mechanism (KEM) are introduced. These mechanisms are performed by assessing the reliability and its discrepancy between heterogeneous teacher-student knowledge. Extensive experiments conducted on three main-stream benchmarks using various teacher-student pairs demonstrate that our HeteroAKD outperforms state-of-the-art KD methods in facilitating distillation between heterogeneous architectures. Copyright © 2025, The Authors. All rights reserved.

关键词： Students

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Multi-modal Remote Sensing Image Registration via Modality Perception and Self-Supervised Position Estimation

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Xiao, Yun Zhang, Chunlei Jiang, Bo Chen, Yuan Tang, Jin Anhui University Key Laboratory of Intelligent Computing & Signal Processing (Anhui University) Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Hefei 230601 China Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei 230601 China Anhui University School of Internet Hefei 230601 China

Multi-modal remote sensing images registration ensures that images from different sensors or modalities are spatial and informational consistent for effective comparison and analysis. However, due to the non-linear modality gaps that exist between images, making it difficult to focus only on the spatial position differences of the images and ignore the modality gaps. In this paper, to address this issue, we propose a new framework for Multi-Modal remote sensing image Registration, named MMRNet. The proposed framework comprises the following main aspects. First, a novel self-supervised Positional Misalignment Estimator (PME) is designed for multi-modal image registration. PME is able to efficiently overcome the modality gaps and learn the positional differences between multi-modal images more reliably, optimizing the registration loss by minimizing the positional differences directly. Then, a new paradigm of modality translation, termed Modality Perception Module (MPM), is introduced to effectively learn modality gaps and perform modality translation in the case of positional misalignment. Finally, we further design the modality perception guidance loss to supervise the modality translation task, which can encourage the fidelity of the generated pseudo-modality images. Our registration network integrates both rigid registration model and non-rigid registration model. Experimental results demonstrate that the proposed registration framework can obtain obviously superior performance in both rigid and non-rigid image registration tasks on optical-SAR data, optical-map data and optical-infrared data. © 1980-2012 IEEE.

关键词： image registration modality translation Multi-modal learning remote sensing processing rigid and non-rigid transformation

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Adversarial Testing: A Novel On-Line Testing Method for Deep Learning Processors

Adversarial Testing: A Novel On-Line Testing Method for Deep...

引用

Asian Test Symposium (ATS)

作者： Wen Li Ying Wang Kaiwei Zou Huawei Li Xiaowei Li School of Computer and Information Technology Shanxi University Taiyuan Shanxi China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan Shanxi China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Electronic Engineering Tsinghua University Beijing China

Deep neural networks have shown outstanding performance on complex tasks. Recently, various researches have been developed to pursue fast and energy-efficient deep learning accelerators. However, devices may suffer from hard defects and hardware variability during its lifetime, which poses severe challenges to deep learning accelerators. To protect edge deep learning accelerators from fault-induced failures, we leverage the adversarial deep learning technique to tailor a lightweight online fault detection method for neural network accelerator chips. The proposed Adversarial Testing scheme (AT) is a function-level testing method outcompeting conventional test in several ways: negligible run-time overhead, super sensitivity to subtle hardware variations, which reduces chip over-kills and also the unnecessary diagnosis operations. The evaluation results show that AT can accurately detect fault occurrence and ensure the normal use of deep learning accelerator during its lifetime.

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Novel Sparse Array Design Based on the Maximum Inter-Element Spacing Criterion

TechRxiv

引用

TechRxiv 2022年

作者： Shi, Wanlu Li, Yingsong de Lamare, Rodrigo C. College of Information and Communication Engineering Harbin Engineering University Harbin150001 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China G¨¢vea Rio de Janeiro Brazil The University of York United Kingdom

关键词： Degrees of freedom (mechanics)

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Denoiser-Guided Image Deconvolution with Arbitrary Boundaries and Incomplete Observations

SSRN

引用

SSRN 2023年

作者： He, Liangtian Gao, Shaobing Deng, Liang-Jian Wang, Yilun Wang, Chao Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China College of Computer Science Sichuan University Chengdu610065 China Chengdu611731 China American Family Insurance ChicagoIL60654 United States College of Mathematics Physics and Information Zhejiang Ocean University Zhoushan316022 China

Non-blind image deconvolution (NBID) has been a significant and long-standing research topic in the field of imaging sciences. While enduring efforts have been made over the past decades, they are extensively focusing on the scenario where the blur process is artificially assumed, e.g., typically under periodic boundary condition, as the involved matrix inversion can be efficiently tackled using the fast Fourier transforms (FFTs). However, despite its popularity, these boundary conditions are not always realistic assumptions and can lead to severe artifacts in the recovered image. Therefore, image deconvolution with arbitrary boundaries remains as a fundamental problem to be investigated. In this paper, we propose a principled framework with the aid of Plug-and-Play (PnP) scheme to address NBID in a more realistic scenario of unknown boundary. Specifically, the local and nonlocal denoiser priors with implicit optimization objectives are incorporated into a unified model, and the degradation operator is formulated as a composition of a convolution and a spatial mask. Two kinds of experiments: deconvolution, deconvolution with inpainting, all with unknown boundaries are tested. Both quantitative and qualitative evaluations demonstrate that the proposed denoiser-guided PnP framework is highly flexible and effective to cope with the general image deconvolution tasks. © 2023, The Authors. All rights reserved.

关键词： Fast Fourier transforms

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Co-training based prediction of multi-label protein–protein interactions

引用

Computers in Biology and Medicine 2024年 177卷 108623-108623页

作者： Tang, Tao Zhang, Xiaocai Li, Weizhuo Wang, Qing Liu, Yuansheng Cao, Xiaofeng School of Modern Posts Nanjing University of Posts and Telecommunications 9 Wenyuan Rd Jiangsu Nanjing210023 China 1 Fusionopolis Way Singapore138632 Singapore School of Management Nanjing University of Posts and Telecommunications 9 Wenyuan Rd Jiangsu Nanjing210023 China College of Computer Science and Electronic Engineering Hunan University 2 Lushan Rd Hunan Changsha410086 China Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education Anhui University 111 Jiulong Road Anhui Hefei230601 China School of Artificial Intelligence Jilin University 2699 Qianjin St Changchun Jilin130012 China

Prediction of protein–protein interaction (PPI) types enhances the comprehension of the underlying structural characteristics and functions of proteins, which gives rise to a multi-label classification problem. The nominal features describe the physicochemical characteristics of proteins directly, establishing a more robust correlation with the interaction types between proteins than ordered features. Motivated by this, we propose a multi-label PPI prediction model referred to as CoMPPI (Co-training based Multi-Label prediction of Protein–Protein Interaction). This approach aims to maximize the utility of both ordered and nominal features extracted from protein sequences. Specifically, CoMPPI incorporates graph convolutional network (GCN) and 1D convolution operation to process the complementary subsets of features individually, leveraging both local and contextualized information in a more efficient way. In addition, two multi-type PPI datasets were constructed to eliminate the duplication in previous datasets. We compare the performance of CoMPPI with three state-of-the-art methods on three datasets partitioned using distinct schemes (Breadth-first search, Depth-first search, and Random), CoMPPI consistently outperforms the other methods across all cases, demonstrating improvements ranging from 3.81% to 32.40% in Micro-F1. The subsequent ablation experiment confirms the efficacy of employing the co-training framework for multi-label PPI prediction, indicating promising avenues for future advancements in this domain. © 2024 Elsevier Ltd

关键词： Proteins

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