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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Smeriglio, Riccardo Rosell-Mirmi, Joana Radeva, Petia Abante, Jordi Universitat de Barcelona Dpt. of Physics Spain Politecnico di Torino Control and Computer Engineering Dpt. Turin Italy Headache Research Group Vall d'Hebron Institut de Recerca Catalonia Barcelona Spain Universitat de Barcelona Institute of Neurosciences Dpt. of Mathematics & Computer Science Catalonia Barcelona Spain Laboratory of Stem Cells & Regenerative Medicine Creatio Production & Validation Center of Advanced Therapies Spain Universitat de Barcelona Institute of Neurosciences Dpt. of Biomedical Sciences Spain Dpt. of Mathematics & Computer Science Catalonia Barcelona Spain

ISBN: (纸本)9798350356632

Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the performance of the models. Moreover, interactions at the protein level can have profound implications in understanding the genetic etiology of diseases and, in turn, for drug development. In this article, we propose a novel approach for phenotype prediction based on graph neural networks (GNNs) that naturally incorporates existing protein interaction networks into the model. As a result, our approach can naturally discover relevant epistatic interactions. We assess the potential of this approach using simulations and comparing it to linear and other non-linear approaches. We also study the performance of the proposed GNN-based methods in predicting Alzheimer's disease, one of the most complex neurodegenerative diseases, where our GNN approach outperform state of the art methods. In addition, we show that our proposal is able to discover critical interactions in the Alzheimer's disease. Our findings highlight the potential of GNNs in predicting phenotypes and discovering the underlying mechanisms of complex diseases. © 2024 IEEE.

关键词： Prediction models

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A Comprehensive Methodology of Field-Oriented control Design With Parameter Variation Analysis for Interior Permanent Magnet Synchronous Machine Drives

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IEEE Access 2025年 13卷 89524-89541页

作者： Tiago Davi Curi Busarello Abdullah Bubshait Oruganti V. S. R. Varaprasad Abdulhakeem Alsaleem Marcelo Godoy Simões Department of Control Automation and Computation Engineering Federal University of Santa Catarina Florianopolis Brazil Department of Electrical Engineering College of Engineering King Faisal University Al Ahsa Saudi Arabia Smart Transportation Electrification and Energy Research (STEER) Group Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada Department of Electrical Engineering College of Engineering Qassim University Buraydah Saudi Arabia School of Technology and Innovations University of Vaasa Vaasa Finland

This paper proposes a comprehensive methodology for Field-Oriented control (FOC) with parameter variation analysis for Interior Permanent Magnet Synchronous Machines (IPMSM). The modeling approach for an IPMSM is first presented, followed by a step-by-step procedure for designing a vector-controlled strategy. The formulation is based on a dq-rotating reference frame aligned with the rotor shaft position. A key distinction of this methodology from traditional approaches is that the current controller is designed in the time domain based on desired time constants, while the speed control is formulated within a frequency response domain framework. The proposed hybrid approach enables accurate tuning of the Proportional-Integrator (PI) controllers for both current and speed control loops. Additionally, a parameter variation analysis is conducted to enhance the proposed methodology. The validity region for the design procedure is presented, ensuring that for any wide speed variation of the machine, loop gains are properly tuned. One of the main advantages of the proposed methodology is that it provides a fast, reliable, and accurate technique for implementing IPMSM drive systems. Results from a controller Hardware-in-the-Loop (C-HIL) setup with an external microcontroller are presented. The comprehensive design approach is validated under two different IPMSM parameter sets, demonstrating its effectiveness.

关键词： PI control Stators Tuning Mathematical models Velocity control Rotors Vectors Stator windings Robustness Microcontrollers

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research on Geomembrane Leakage Detection Method Based on Quadruped Robot

Research on Geomembrane Leakage Detection Method Based on Qu...

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Chinese automation Congress (CAC)

作者： Pengcheng Zhang Ningning Hu Xiao Wang Qingbiao Xi Haohao Zhao Xingchen Wu Linfeng Qin Dongli Yuan Investigation & Testing Research Institute Shanghai Investigation Design & Research Institute Co. Ltd. Shanghai China System Department 2 Xi'an ASN Technology Group Co. Ltd. Xi'an China School of Electronic and Control Engineering Chang'an University Xi'an China School of Automation Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Aiming at the problem of leakage detection of seepage control geomembranes in landfills, a fully autonomous dipole method detection system based on a quadruped robot equipped with a dipole sensor is designed. The feasibility and superiority of using a quadruped robot are analyzed on the basis of the detection principle of the dipole method. Due to the problems existing in the process of upgrading the artificial static detection to autonomous dynamic detection, a dynamic detection method based on a quadruped robot is designed while the structure construction of the dipole method on the quadruped robot is completed. The experimental results show that the efficiency of geomembrane leakage detection can be improved with detection system method employed in this paper.

关键词： Legged locomotion Costs automation Landfills Dogs Robot sensing systems Software Quadrupedal robots Intelligent control

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TPCH: Tensor-interacted Projection and Cooperative Hashing for Multi-view Clustering

arXiv

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arXiv 2024年

作者： Wang, Zhongwen Li, Xingfeng Sun, Yinghui Sun, Quansen Sun, Yuan Ling, Han Dai, Jian Ren, Zhenwen School of Computer Science and Engineering Nanjing University of Science and Technology China School of Computer Science and Engineering Southeast University Bangladesh College of Computer Science Sichuan University China Southwest Automation Research Institute China South Industries Group Corporation China School of National Defence Science and Technology Southwest University of Science and Technology China

In recent years, anchor and hash-based multi-view clustering methods have gained attention for their efficiency and simplicity in handling large-scale data. However, existing methods often overlook the interactions among multi-view data and higher-order cooperative relationships during projection, negatively impacting the quality of hash representation in low-dimensional spaces, clustering performance, and sensitivity to noise. To address this issue, we propose a novel approach named Tensor-Interacted Projection and Cooperative Hashing for Multi-View Clustering(TPCH). TPCH stacks multiple projection matrices into a tensor, taking into account the synergies and communications during the projection process. By capturing higher-order multi-view information through dual projection and Hamming space, TPCH employs an enhanced tensor nuclear norm to learn more compact and distinguishable hash representations, promoting communication within and between views. Experimental results demonstrate that this refined method significantly outperforms state-of-the-art methods in clustering on five large-scale multi-view datasets. Moreover, in terms of CPU time, TPCH achieves substantial acceleration compared to the most advanced current methods. The code is available at https://***/jankin-wang/TPCH. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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DL-RLSTM: An Anomaly Detection Framework for High Dimensional Time Series Data

DL-RLSTM: An Anomaly Detection Framework for High Dimensiona...

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2021 China automation Congress, CAC 2021

作者： Wang, Shuai Liu, Zhihong Xiao, Qianke Lin, Jie Long, Dan An, Dou School of Computer Science and Technology Xi'an Jiaotong University China Automation Research Institute Co. Ltd. of China South Industries Group Corporation China School of Automation Science and Engineering Xi'an Jiaotong University China

ISBN: (纸本)9781665426473

With the development of communication technology and the popularization of Internet of Things (IoT) applications in daily life, anomaly detection for time series data has been paid more attentions. To mitigate the imbalance and incorrectness of data labels in anomaly detection, unsupervised anomaly detection has been widely used to detect abnormal data. Although a number of efforts on unsupervised anomaly detection has been developed, most of these schemes focus on detecting abnormal data with low-dimensional and achieve undulatory efficiency on detecting abnormal data with high-dimension. In addition, most of the existing schemes determine the data as abnormalities with the thresholds defined by expertise experience, resulting in undulatory efficiency on abnormal data detection as well. To address these issues, in our paper, an advanced Double Layer-RLSTM framework, namely DL-RLSTM, is proposed to detect the abnormal time series data with high-dimension effectively. Particularly, two neural network layers are considered in our DL-RLSTM framework, in which the RLSTM network is introduced in the first neural network layer to extract the temporal feature of time series data. The second neural network layer in our DL-RLSTM framework are served as the autoencoder to reconstruct temporal feature of time series data extracted by the first neural network layer. Different from the traditional projection-based dimensionality reduction, our DL-RLSTM framework can maximize the retention of temporal feature of time series data. Additionally, K-Means is introduced in our DL-RLSTM framework to determine the abnormal time series data according to the anomaly scores obtained from the autoencoder. By doing this, the participation of expertise experience on abnormality thresholds determination can be minimized. Via evaluations, the results show that our DL-RLSTM framework can achieve better detection efficiency on high dimensional abnormal time series data in comparison with existing

关键词： Feature extraction

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An Asymmetric Direct-Connected Network for Rolling Bearing Fault Diagnosis

An Asymmetric Direct-Connected Network for Rolling Bearing F...

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2021 China automation Congress, CAC 2021

作者： Long, Dan Qiu, Feng Xu, Manfei Lin, Jie Wang, Shuai An, Dou School of Computer Science and Technology Xi'an Jiaotong University China Automation Research Institute Co. Ltd. of China South Industries Group Corporation China School of Automation Science and Engineering Xi'an Jiaotong University China

ISBN: (纸本)9781665426473

Rolling bearings have be extensively applied in applications of intelligent manufacturing, such as rotating machinery, and the fault diagnosis of rolling bearings is critical to mitigate the production and economics losses. Deep learning, taking the advantage in signal processing, have been widely used in fault diagnosis. However, existing deep-learning-based fault diagnosis cannot be directly and easily applied to process the signal of rolling bearing. In addition, some existing scheme suffer from severe performance degradation in variable noisy environments or variable working loads. To address these issues, in this paper, an asymmetric direct-connected network, namely ADNet, is proposed to effectively diagnose faults of rolling bearing with deep learning technologies. To be specific, in our ADNet, the one-dimensional bearing signal firstly is converted into the recurrence plot (i.e., two-dimensional image), and multiple direct connection blocks (DCBs) that are nested with asymmetric convolution transformation (ACT) modules are proposed to extracted the detailed convolved feature map of faults. Finally, through the softmax classifier, the faults can be effectively identified by our ADNet based on the detailed convolved feature map of faults. Via evaluations, the data demonstrate that our ADNet can achieve great scalability and accuracy on faults diagnosis of rolling bearing in both variable noisy environments and variable working loads. © 2021 IEEE

关键词： Failure analysis

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Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features 1

Audio Deepfake Detection Based on a Combination of F0 Inform...

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1st International Workshop on Deepfake Detection for Audio Multimedia, DDAM 2022

作者： Xue, Jun Fan, Cunhang Lv, Zhao Tao, Jianhua Yi, Jiangyan Zheng, Chengshi Wen, Zhengqi Yuan, Minmin Shao, Shegang School of Computer Science and Technology Anhui University Hefei China NLPR Institute of Automation Chinese Academy of Sciences Beijing China Institute of Acoustics Chinese Academy of Sciences Beijing China Qiyuan Laboratory Beijing China National Environmental Protection Engineering and Technology Center for Road Traffic Noise Control Research Institute of Highway Ministry of Transport Beijing China

ISBN: (纸本)9781450394963

Recently, pioneer research works have proposed a large number of acoustic features (log power spectrogram, linear frequency cepstral coefficients, constant Q cepstral coefficients, etc.) for audio deepfake detection, obtaining good performance, and showing that different subbands have different contributions to audio deepfake detection. However, this lacks an explanation of the specific information in the subband, and these features also lose information such as phase. Inspired by the mechanism of synthetic speech, the fundamental frequency (F0) information is used to improve the quality of synthetic speech, while the F0 of synthetic speech is still too average, which differs significantly from that of real speech. It is expected that F0 can be used as important information to discriminate between bonafide and fake speech, while this information cannot be used directly due to the irregular distribution of F0. Insteadly, the frequency band containing most of F0 is selected as the input feature. Meanwhile, to make full use of the phase and full-band information, we also propose to use real and imaginary spectrogram features as complementary input features and model the disjoint subbands separately. Finally, the results of F0, real and imaginary spectrogram features are fused. Experimental results on the ASVspoof 2019 LA dataset show that our proposed system is very effective for the audio deepfake detection task, achieving an equivalent error rate (EER) of 0.43%, which surpasses almost all systems. © 2022 Association for Computing Machinery.

关键词： Feature extraction

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A review on the application of grid-forming control in power system

A review on the application of grid-forming control in power...

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2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Yao, Bo Hu, Qinran Quan, Xiangjun Li, Wei Zhu, Ling Nanjing Normal University School of Electrical and Automation Engineering Nanjing China Nari Group Corporation State Grid Electric Power Research Institute School of Electrical Engineering Southeast University State Key Laboratory of Smart Grid Protection and Control Nanjing China Nari Group Corporation State Grid Electric Power Research Institute State Key Laboratory of Smart Grid Protection and Control China

ISBN: (纸本)9781665414395

Due to the continuous improvement of new energy penetration, grid-forming control has become a hot issue in grid-connected technology. Grid-forming control simulates the mechanical and electrical transient characteristics of the synchronous unit. The power or load using the converter has the inertia, damping, frequency and voltage adjustment, and other operating characteristics of the synchronous unit machine technology. This paper introduces the development process and basic concept of grid-forming control. It introduces the application of grid-forming control from three aspects of grid connection, frequency modulation, and virtual inertia. This paper focuses on the current problems of grid-forming control, such as control parameter tuning, power oscillation, and parallel grid-forming control circulation. At the same time, the prospect of research direction is put forward given the above problems. © 2021 IEEE.

关键词： Damping

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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

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Chinese automation Congress (CAC)

作者： Yanxing Bai Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene.

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