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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Surendra Singh Choudhary Rashmi Saini Rahul Raj Choudhary Laxman Singh Khangarot Department: Computer Science & Engineering Govt. Engineering College Bikaner Computer Science & Engineering G. B. Pant Institute of Engineering and Technology Uttarakhand Department: Electronics and Instrument & Control Engineering Govt. Engineering College Bikaner Computer Science & Engineering Indian Institute of Technology Roorkee Uttarakhand

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

The Artificial Neural Networks (ANNs) models have provided mixed results to solve linear problems, so it is not widely used for any data types. The autoregressive integrated moving average (ARIMA) model is a massive, very popular, and connivance linear model for long time series forecasting. Mainly used to construct different hybrid models for forecasting other time series data in the last decade. The study analyzes ARIMA and Holt-Winters (HW) forecasting procedure analysis. The machine learning and deep learning model have good frameworks that can apply to multiple forecasting processes with good precision and accuracy. Autoregressive Integrated moving average (ARIMA) and Holt-Winters (HW) approaches have been used to forecasting time series climatic variables. The two different forecasting methods are generated, and the output of both methods is compared by the climate variables rainfall and temperature for monthly data of 2001-2021.

关键词： Temperature sensors Temperature distribution Rain Time series analysis Stochastic processes Predictive models Data models Autocorrelation Forecasting Meteorology

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A Learning Framework Combining Distillation-Generated Replay and Development Network in Continual Visual Scene Cognition for Autonomous Robot

A Learning Framework Combining Distillation-Generated Replay...

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International Conference on control and Automation (ICCA)

作者： Yuyang Zhang Zhi Zheng College of Computer and Cyber Security Fujian Normal University Fuzhou China College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

Continual learning for autonomous robots in complex environments is a challenging problem. Human beings have the lifelong ability to continuously acquire, adjust, and transfer knowledge. Although we tend to gradually forget previously learned knowledge throughout our lives, in very few cases does learning new knowledge catastrophically affect what we have already learned. Incremental learning aims to address a common flaw in model training: catastrophic forgetting. The primary drawback of most existing replay-based incremental learning is that they require a lot of additional computational resources and storage space to recall old knowledge. When the number of tasks keeps increasing, either the training cost becomes higher, or the representativeness of samples diminishes. In order to mitigate catastrophic forgetting and save storage space, we propose a new autonomous developmental neural network that combines distillation-generated replay(DGR-DN). Experimental results show that our approach not only has the better ability to integrate and refine new knowledge in new data, prevent significant interference from new input on existing knowledge, but also requires less storage space compared to existing generative replay networks. We verify the effectiveness of the method in the real environment scene, and also verify the generality using the MNISIT dataset. Through these experiments, it can be seen in the scene dataset that the recognition rate has increased by 300% compared to not using the generated network, and the storage space is reduced by more than 30% compared to the network that used generation. In the MNIST dataset, our approach significantly reduces storage space by over 50%, while preserving recognition rates at a level similar to the baseline.

关键词： Knowledge engineering Training Visualization Incremental learning Training data Interference Generators

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Efficient Content Reconstruction for High Dynamic Range Imaging

Efficient Content Reconstruction for High Dynamic Range Imag...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiang Zhang Tao Hu Jiashuang He Qingsen Yan College of Information and Control Engineering Xi’an University of Architecture and Technology School of Computer Science Northwestern Polytechnical University

High Dynamic Range (HDR) images can be reconstructed from multiple Low Dynamic Range (LDR) images using existing deep neural network (DNN) techniques. Despite notable advancements, DNN-based methods still exhibit ghosting artifacts when handling LDR images with saturation and significant motion. Recent Diffusion models (DMs) have been introduced in HDR imaging, showcasing promising performance, especially in achieving visually perceptible results. However, DMs typically require numerous inference iterations to recover the clean image from Gaussian noise, demanding substantial computational resources. Additionally, DM only learns a probability distribution of the added noise in each step but neglects image space constraints on HDR images, limiting distortion-based metrics. To tackle these challenges, we propose an efficient network that integrates DM modules into existing regression-based models, providing reliable content reconstruction for HDR while avoiding limitations in distortion-based metrics.

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Impact of Parametric Uncertainties on the Steady-State Performances of DFIG-Based WECS 10th

Impact of Parametric Uncertainties on the Steady-State Perfo...

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10th EAI International Conference on Advancements of Science and Technology, EAI ICAST 2022

作者： Ayenew, Endalew Biru, Getachew Mulatu, Asrat Berhanu, Milkias Electrical Power and Control Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia School of Electrical and Computer Engineering Addis Ababa Institute of Technology Addis Ababa University Addis Ababa Ethiopia Center of Excellence for Sustainable Energy College of Electrical and Mechanical Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia

ISBN: (纸本)9783031411724

This chapter presents the study carried out to discover uncertain parameters in a model of a doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS) and to investigate their effects on WECS plant while harvesting energy from wind at varying speeds. This is carried out by analytically formulating the steady-state models of a DFIG-based WECS and computing the parameters of these models at different wind speeds that drive wind turbines at rotor speed from sub-synchronous to super-synchronous. The main uncertain parameters in this system are the mutual reactances between stator and rotor windings, and the reactance of rotor winding. These variations happen due to the varying wind speed. The effect of parametric uncertainties on the steady-state performances of DFIG-based WECS is examined. The results depicted that the steady-state performances of DFIG-based WECS are influenced by uncertain phenomena. Typically a 0.5% change in mutual reactances caused up to 10 kW change in the generator output power. Mainly, it causes instability in the performances of the DFIG-based WECS and slightly reduces overall efficiency at a higher value of the wind speed. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： DFIG-based WECS HAWT Performances of WECS Uncertain parameter

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Sea ice type identification method based on MEMS sensor network combined with PCA-BPMIV-GRU

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Yang, Le Quan, Xiyu Zhang, Jiankui Liu, Yunjing Zhao, Shiqi Fu, Changzeng Zhan, Zhikun Li, Lianliang Lv, Xiaoyong Zhao, Yuliang Northeastern University at Qinhuangdao Hebei Key Laboratory of MicroNano Precision Optical Sensing and Measurement Technology The School of Control Engineering Qinhuangdao066004 China Northeastern University at Qinhuangdao College of Control Science and Engineering Qinhuangdao066099 China Northeastern University at Qinhuangdao College of Computer Science and Engineering Qinhuangdao066004 China

Sea ice poses a significant hazard to vessels navigating in high-latitude maritime regions. To effectively prevent the collision between ships and large sea ice (such as icebergs) and other similar accidents, it is crucial to identify sea ice and its types in a timely and accurate manner. However, traditional sea ice identification techniques, such as optical and radar remote sensing, are often limited by visibility conditions, suffering from poor accuracy, high measurement costs, and low identification efficiency. This paper proposes a sea ice identification method based on a vibration sensor network, combined with the PCA-BPMIV-GRU strategy, which achieves low cost, high efficiency, and high accuracy in sea ice type detection. Firstly, a cost-effective MEMS vibration sensor network is deployed to capture vibration information of sea ice. The data collected from six sensors at different locations at the same time are treated as independent samples to construct a dataset, which contains time-frequency domain characteristic data of three sea-ice types: broken ice, flat ice, and open water. Secondly, the dimension reduction and feature optimization of vibration data were carried out by combining Principal Component Analysis (PCA) and the improved Mean Influence Value Optimization algorithm (BPMIV). This integration of unsupervised and supervised algorithms effectively eliminated unnecessary feature information, significantly improving model identification efficiency. Finally, seven commonly used classification algorithms are compared for the identification of three types of sea ice, evaluating their identification accuracy and efficiency under different dimensionality reduction methods. The results confirm that the Gated Recurrent Unit (GRU) model achieves the best comprehensive performance. Experimental data shows that the proposed method reduces the number of features to 9, resulting in a 24.2 % decrease in parameters compared to the GRU model alone, while achieving

关键词： Frequency domain analysis

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HL-HDR: Multi-Exposure High Dynamic Range Reconstruction with High-Low Frequency Decomposition

HL-HDR: Multi-Exposure High Dynamic Range Reconstruction wit...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiang Zhang Genggeng Chen Tao Hu Kangzhen Yang Fan Zhang Qingsen Yan College of Information and Control Engineering Xi’an University of Architecture and Technology School of Computer Science Northwestern Polytechnical University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Generating high-quality High Dynamic Range (HDR) images in dynamic scenes is particularly challenging. Recent Transformer have been introduced in HDR imaging, demonstrating promising performance, particularly in scenarios involving large-scale motion compared to previous CNN-based methods. However, Transformer-based methods face hurdles capturing local details and come with high computational complexity, hindering further progress. In this paper, inspired by the distinct characteristics of high and low-frequency in image patterns, we propose a Frequency Decomposition Processing Block (FDPB) for ghost-free HDR imaging. In the image reconstruction process, FDPB decouples features into resolution-invariant high-frequency features and resolution-reduced low-frequency features to separately address local and global information. Specifically, considering the characteristics of different frequencies, for the high-frequency components, we design a Local Feature Extractor (LFE) based on CNN to extract local feature maps. Meanwhile, for the low-frequency components, we propose a Global Feature Extractor (GFE) that learns long-range dependencies through carefully designed Transformer modules. Importantly, the downscaled low-frequency features exploit Transformer’s remote learning capabilities while substantially reducing self-attention computational costs. By incorporating the FDPB as basic components, we further build a Low/High-Frequency Aware Network (HL-HDR), a hierarchical network to reconstruct high-quality ghost-free HDR images. Extensive experiments on four public datasets confirm the superior performance of the proposed method, both in terms of quantitative and qualitative evaluations.

关键词： Image resolution Neural networks Imaging Feature extraction Transformers High dynamic range Image restoration

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Domain Adversarial Interaction Network for Cross-Domain Fault Diagnosis 4th

Domain Adversarial Interaction Network for Cross-Domain Fau...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Lu, Weikai Chen, Jian Zheng, Hao Fan, Haoyi Wei, Eng Yee Cao, Xinrong Zhang, Deyang School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Henan Provincial Institute of Scientific and Technical Information Zhengzhou450003 China

ISBN: (纸本)9783031200984

Intelligent fault diagnosis has been widely used in the industry and plays a crucial role in the health management of machinery. In recent years, unsupervised domain adaptation (UDA) has been applied to fault diagnosis, showing excellent performance under variable working conditions. However, most existing UDA-based methods do not consider the temporal relations in the fault signal, resulting in sub-optimal performance. In this paper, we proposed a domain adversarial interaction network (DAIN) to solve this problem. By downsampling sub-sequences of fault signals and interacting with their features, DAIN can obtain feature representations containing the temporal relations. In addition, domain adversarial learning and maximum mean discrepancy (MMD) are applied to DAIN to align the domain discrepancy and distribution discrepancy of source and target domains. We conducted extensive experiments on the public Paderborn University (PU) dataset, and the results demonstrate that the proposed method can achieve higher cross-domain fault diagnosis accuracy than the existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Failure analysis

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EiffHDR: An Efficient Network for Multi-Exposure High Dynamic Range Imaging

EiffHDR: An Efficient Network for Multi-Exposure High Dynami...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiang Zhang Qiang Zhu Tao Hu Qingsen Yan College of Information and Control Engineering Xi’an University of Architecture and Technology School of Computer Science Northwestern Polytechnical University

While recent progress in Multi-exposure HDR imaging is promising, the growing complexity of state-of-the-art (SOTA) methods poses challenges for their analysis and comparison. In this paper, we analyze the motivations and approaches behind previous SOTA works and introduce EiffHDR, an efficient Multi-exposure HDR imaging technique. In contrast to prior methods employing multiple branches spatial attention mechanisms, EiffHDR adopts a streamlined gating mechanism for information flow control at both spatial and channel levels, enabling implicit alignment. Subsequently, we process these features through proposed Efficient Merging Network, facilitating long-range correlations and multi-scale information perception, ultimately producing high-quality HDR images. Our experiments demonstrate that EiffHDR not only achieves outstanding performance but also significantly reduces computational complexity, making it a valuable contribution to the field.

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Sensing Metric-Based Hybrid Underlay/Overlay Transmission Framework for CR-IoT Networks for Smart City Applications 7

Sensing Metric-Based Hybrid Underlay/Overlay Transmission Fr...

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7th IET Smart Cities Symposium, SCS 2023

作者： Bala, Indu Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed School of Electrical and Electronics Engineering Lovely Professional University Punjab India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results. © The Institution of engineering & Technology 2023.

关键词： Smart city

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Drosophila Larvae-Inspired Soft Crawling Robot with Multimodal Locomotion and Versatile Applications

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Research 2025年第1期7卷 35-46页

作者： Qin Fang Jingyu Zhang Yinhui He Nenggan Zheng Yue Wang Rong Xiong Zhefeng Gong Haojian Lu State Key Laboratory of Industrial Control and Technology Zhejiang UniversityHangzhou 310027China Institute of Cyber-Systems and Control Zhejiang UniversityHangzhou 310027China Medical School of Zhejiang University Hangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Stomatology Hospital School of StomatologyZhejiang University School of MedicineZhejiang Provincial Clinical Research Center for Oral DiseasesKey Laboratory of Oral Biomedical Research of Zhejiang ProvinceCancer Center of Zhejiang UniversityEngineering Research Center of Oral Biomaterials and Devices of Zhejiang ProvinceHangzhou 310000China

Soft crawling robots have been widely studied and applied because of their excellent environmental adaptability and flexible ***,most existing soft crawling robots typically exhibit a single-motion mode and lack diverse *** by Drosophila larvae,this paper proposes a compact soft crawling robot(weight,13 g;length,165 mm;diameter,35 mm)with multimodal locomotion(forward,turning,rolling,and twisting).Each robot module uses 4 sets of high-power-density shape memory alloy actuators,endowing it with 4 degrees of motion *** analyze the mechanical characteristics of the robot modules through experiments and simulation *** plug-and-play modules can be quickly assembled to meet different motion and task *** soft crawling robot can be remotely operated with an external controller,showcasing multimodal motion on various material *** a narrow maze,the robot demonstrates agile movement and effective maneuvering around *** addition,leveraging the inherent bistable characteristics of the robot modules,we used the robot modules as anchoring units and installed a microcamera on the robot's head for pipeline *** robot completed the inspection in horizontal,vertical,curved,and branched pipelines,adjusted the camera view,and twisted a valve in the pipeline for the first *** research highlights the robot's superior locomotion and application capabilities,providing an innovative strategy for the development of lightweight,compact,and multifunctional soft crawling robots.

关键词： multimodal locomotion versatile applications Drosophila larvae soft crawling robot weight soft crawling robots shape memory alloy actuators

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