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11th International Conference on Signal and Information Processing, Network and Computers, ICSINC 2023

作者： Lu, Mingquan Zhao, Xingyuan Yu, Wentong Department of Electronic Engineering Tsinghua University Beijing100084 China Shenzhen Aerospace Smart City System Technology Co. Ltd. Shenzhen518057 China

ISBN: (纸本)9789819721153

Wastewater treatment and its quality prediction have always been pivotal for environmental protection and public health. Accurate forecasting of wastewater quality parameters is essential for optimizing treatment processes and ensuring water safety. In light of this, our study proposes a hybrid model integrating Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Convolutional Neural Network-Long Short-Term Memory (Conv1D-LSTM) for wastewater quality prediction. DBSCAN enables anomaly detection in the influent data, reducing noise impact. The Conv1D-LSTM model automatically extracts features and captures time series dependencies. Experiments demonstrate superior performance of the proposed model over LSTM alone in both single-step and multi-step forecasting of ammonia nitrogen levels, with lower RMSE, MAE and RMSLE. The integration of DBSCAN and Conv1D-LSTM can effectively leverage their respective strengths for enhanced wastewater quality prediction. Further research will focus on multi-variable prediction and incorporating data smoothing techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection

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Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

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Frontiers of Earth Science 2024年第1期18卷 30-43页

作者： Xianyu GUO Junjun YIN Kun LI Jian YANG Huimin ZOU Fukun YANG School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China College of Global Change and Earth System Science Beijing Normal UniversityBeijing 100875China

Rice is an important food crop for human *** distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern *** polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and ***,the use of machine learning methods to build classification models is a controversial *** this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice ***,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data ***,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data *** addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice *** results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than *** them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,*** the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.

关键词： compact polarimetric(CP)SAR rice paddy machine learning fine classification multitemporal

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Axial Attention MLP-Mixer: A New Architecture for Image Segmentation 9

Axial Attention MLP-Mixer: A New Architecture for Image Segm...

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9th IEEE International Conference on Communications and electronics, ICCE 2022

作者： Lai, Hong-Phuc Tran, Thi-Thao Pham, Van-Truong Hanoi University of Science and Technology School of Electrical and Electronic Engineering Department of Automation Engineering Viet Nam

ISBN: (数字)9781665497459

ISBN: (纸本)9781665497442

Recently, the MLP-Mixer model has received much attention in vision problems. The advantage of this model is that by using only multi-layer perceptron (MLP) blocks, the model could build well the long-range dependencies of the input patches when pre-trained on huge data sets. Recognizing the importance of information positions in patch processing and the advantage of using MLPs, in this study, we proposed an Axial Attention MLP-Mixer model, shorted as AxialAtt-MLP-Mixer for image segmentation problem. In particular, inspired by advanced attention mechanisms along with position embedding, we proposed a new token layer that replaces the token mixing in the MLP-Mixer model to make the model more aware of global information. In addition, we propose a new model using MLP-Mixer architecture and an axial attention token layer. Through evaluation on two datasets: GlaS and Data Science Bowl 2018, we indicate the superiority of the proposed method along with the ability to get good results right on small datasets without pre-training. © 2022 IEEE.

关键词： Image segmentation

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A Load Forecasting Model Based on Sequence-to-Image Preprocessing Framework

A Load Forecasting Model Based on Sequence-to-Image Preproce...

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2024 International Automatic Control Conference, CACS 2024

作者： Yao, Leehter Wu, Bo-Yi Ku, Cheng-Yu Teo, J.C. National Taipei University of Technology Department of Electrical Engineering Taipei Taiwan National Taipei University of Technology Institute of Automation Technology Taipei Taiwan National Taipei University of Technology Taipei Taiwan UCSI University Electrical and Electronic Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350354904

The smart grid is currently composed of many distributed energy resources. To manage these sources, Short-Term Load Forecasting (STLF) is regarded as a crucial role in the power dispatching and energy management domain. To improve the performance and accuracy of the STLF, this paper proposes a Sequence-to-Image preprocessing approach and Rolling Forecasting framework (S2I-RF) to help the model better catch the multi-level seasonality of the load series and overcome the cumulative error associated with the multi-step forecasting. To better extract the spatial pattern and the temporal relationship of the load images, a structure of the hybrid neural network model CNN-ConvBiLSTM-ECANet (CCBE-Net) is proposed. The technique can significantly improve the performance of the STLF in real-time or day-ahead forecasting. © 2024 IEEE.

关键词： Electric load forecasting

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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Computer Modeling in engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Automated object recognition in high-resolution optical remote sensing imagery

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National Science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

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GCA-Net: Geometrical Constraints-based Advanced Network for Polyp Segmentation 9

GCA-Net: Geometrical Constraints-based Advanced Network for ...

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9th NAFOSTED Conference on Information and Computer Science, NICS 2022

作者： Nguyen, Quang-Vinh Tran, Thi-Thao Pham, Van-Truong School of Electrical and Electronic Engineering Hanoi University of Science and Technology Department of Automation Engineering Viet Nam

ISBN: (纸本)9781665454223

Colonoscopy is a common procedure for detecting and screening colorectal polyps, however, it poses a number of challenges because of the properties of images and the features of polyps inside, which can vary in shape, color, condition, and clear non-distinction with the surrounding context. Deep learning-based approaches have recently emerged, which considerably boost polyp segmentation procedures and gradually convert and replace out-of-date methods. Nevertheless, determining an effective measure remains a significant difficulty, which motivates us to propose GCA-Net for polyp segmentation. We first employ EfficientNetV2 as the backbone to extract improved salient features, then pass them via two modules, SE-PASPP and SE-RFB, to capture more contexture before feeding them into a dual-asymmetrical partial decoder to build a final resolution map. Finally, we develop a novel loss function based on the region-based strength of Dice loss and the geometrical constraints-based advantages of Active Contour with Elastica (ACE) loss. The suggested method's preeminence is demonstrated by comparing results to over state-of-the-art approaches in both learning and generalization assessment. © 2022 IEEE.

关键词： Image segmentation

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Finite-time robust performance improvement for non-linear systems in the presence of input non-linearity and external disturbance

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IET Control Theory and Applications 2023年第6期17卷 672-682页

作者： Wang, Haoping Ghaffari, Valiollah Mobayen, Saleh Bartoszewicz, Andrzej Fekih, Afef Assawinchaichote, Wudhichai School of Automation Nanjing University of Science and Technology Nanjing China Department of Electrical Engineering Persian Gulf University Bushehr Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou Taiwan Institute of Automatic Control Lodz University of Technology Lodz Poland Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA United States Department of Electronic and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

The main focus of this research is on improving the performance of dynamic systems with actuator non-linearities and time-varying disturbances. To this end, using the concept of finite-time stability, a novel observer is presented to estimate the disturbances in systems with input non-linearities. Then, introducing an innovative sliding manifold, a robust observer-based technique is derived to guarantee the finite-time stability of the sliding surface. Hence, the closed-loop system would be stable in the presence of hard non-linearities and time-varying disturbances. The performance of the proposed approach was assessed in using a simulation study of two numerical examples. The obtained results and comparison with a conventional method confirmed the benefits of the suggested approach. © 2022 The Authors. IET Control Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and technology.

关键词： Closed loop systems

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Research on Cogging Torque of Interior Permanent-Magnet Synchronous Motor Caused by Manufacturing Tolerances 4

Research on Cogging Torque of Interior Permanent-Magnet Sync...

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4th International Symposium on Electrical, electronics and Information engineering, ISEEIE 2024

作者： Zhong, Xin Liu, Yingzhen Ou, Jing Liang, Dawei Chen, Liang Xu, Dianguo School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Department of Electronic and Electrical Engineering University of Sheffield Sheffield United Kingdom Corporate Research Center Midea Group Shanghai China

ISBN: (纸本)9798350355772

The Interior permanent-magnet synchronous motor (IPMSM) is widely used in servo motors due to its high-power density and excellent dynamic performance. However, the high cogging torque and torque ripple reduce the control accuracy of the servo system. Manufacturing tolerances inevitably exist during the manufacture of the stator and rotor, which makes the stator and rotor not an idealized symmetrical structure and results in higher cogging torque and torque ripple. Therefore, this paper investigates the effect of manufacturing tolerance on the cogging torque of IPMSM through theoretical analysis and finite element analysis (FEA). Firstly, the cogging torque is analyzed theoretically based on the energy method. In order to clearly show the relationship between the magnitude of different cogging torque components and the number of poles and slots of the motor, mathematical formulas for cogging torque taking into account manufacturing tolerances are derived. Secondly, through finite element analysis, it is verified that the rotor and stator manufacturing tolerance generate the low-frequency harmonic components of the cogging torque. The sensitivity of different types of motors and pole and slot combination of manufacturing tolerances to the cogging torque is analyzed, which provides motor designers with methods and directions for reducing cogging torque. © 2024 IEEE.

关键词： Permanent magnets

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Aspect Ratio Optimization for YASA Machines Under Thermal Constraints

Aspect Ratio Optimization for YASA Machines Under Thermal Co...

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2024 International Conference on Electrical Machines, ICEM 2024

作者： Ding, Hanju Wang, Yu Geng, Weiwei Zhang, Jinrong Sun, Yaojie Zhu, Zi-Qiang School of Information Science and Technology Fudan University Shanghai China School of Automation Nanjing University of Science and Technology Nanjing China University of Shefield Department of Electronic and Electrical Engineering Sheffield United Kingdom

ISBN: (纸本)9798350370607

Aspect ratio of axial field permanent magnet machines (AFPM), i.e., the ratio of axial length to diameter of machines, is a key dimensional parameter. It significantly influences the torque capability and loss performances under specific thermal constraint. This paper analytically establishes the relationship between output torque and aspect ratio, as well as other design parameters of yokeless and segmented armature (YASA) AFPM machines. The prescriptive loss conditions are established, which correspond to different thermal limitations. It is found analytically, as verified by finite element analysis, that for YASA AFPM machines, the optimal aspect ratio exists and is significantly affected by the machine size and relatively less affected by the operating speed. © 2024 IEEE.

关键词： Aspect ratio

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