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arXiv 2024年

作者： Yuan, Xiaoying Xu, Tingfa Liu, Xincong Wang, Ying Qin, Haolin Fang, Yuqiang Li, Jianan Beijing Institute of Technology Beijing100081 China The Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing101416 China The Key Laboratory of Photoelectronic Imaging Technology and System Ministry of Education of China Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401135 China

In the realm of unmanned aerial vehicle (UAV) tracking, Siamese-based approaches have gained traction due to their optimal balance between efficiency and precision. However, UAV scenarios often present challenges such as insufficient sampling resolution, fast motion and small objects with limited feature information. As a result, temporal context in UAV tracking tasks plays a pivotal role in target location, overshadowing the target’s precise features. In this paper, we introduce MT-Track, a streamlined and efficient multi-step temporal modeling framework designed to harness the temporal context from historical frames for enhanced UAV tracking. This temporal integration occurs in two steps: correlation map generation and correlation map refinement. Specifically, we unveil a unique temporal correlation module that dynamically assesses the interplay between the template and search region features. This module leverages temporal information to refresh the template feature, yielding a more precise correlation map. Subsequently, we propose a mutual transformer module to refine the correlation maps of historical and current frames by modeling the temporal knowledge in the tracking sequence. This method significantly trims computational demands compared to the raw transformer. The compact yet potent nature of our tracking framework ensures commendable tracking outcomes, particularly in extended tracking scenarios. Comprehensive tests across four renowned UAV benchmarks substantiate the superior efficacy of our approach, delivering real-time performance at 84.7 FPS on a single GPU. Real-world test on the NVIDIA AGX hardware platform achieves a speed exceeding 30 FPS, validating the practicality of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Machine learning for membrane bioreactor research:principles,methods,applications,and a tutorial

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Frontiers of Environmental science & Engineering 2025年第3期19卷 119-144页

作者： Yizhe Lai Kang Xiao Yifan He Xian Liu Jihua Tan Wenchao Xue Aiqian Zhang Xia Huang Beijing Yanshan Earth Critical Zone National Research Station College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101408China Key Laboratory of Earth System Numerical Modeling and Application University of Chinese Academy of SciencesBeijing 101408China Environmental Chemistry Faculty College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101408China State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Department of Energy Environmentand Climate ChangeSchool of EnvironmentResourcesand DevelopmentAsian Institute of TechnologyP.O.Box 4Klong LuangPathumthani 12120Thailand State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China

Membrane fouling poses a significant challenge to the sustainable development of membrane bioreactor(MBR)technologies for wastewater *** accurate prediction of the membrane filtration process is of great importance for identifying and controlling *** learning methods address the limitations of traditional statistical approaches,such as low accuracy,poor generalization ability,and slow convergence,particularly in predicting complex filtration and fouling processes within the realm of big *** article provides an in-depth exposition of machine learning *** study then reviews advances in MBRs that utilize machine learning methods,including artificial neural networks(ANN),support vector machines(SVM),decision trees,and ensemble *** on current literature,this study summarizes and compares the model input and output characteristics(including foulant characteristics,solution environments,filtration conditions,operating conditions,and time factors),as well as the selection of models and optimization *** modeling procedures of SVM,random forest(RF),back propagation neural network(BPNN),long short-term memory(LSTM),and genetic algorithm-back propagation(GA-BP)methods are elucidated through a tutorial *** simulation results demonstrated that all five methods yielded accurate predictions with R2>***,the existing challenges in the implementation of machine learning models in MBRs were *** is notable that integration of deep learning,automated machine learning(AutoML)and explainable artificial intelligence(XAI)may facilitate the deployment of models in practical engineering *** insights presented here are expected to facilitate the establishment of an intelligent control framework for MBR processes in future endeavors.

关键词： Membrane bioreactor Machine learning Pollutant removal Membrane fouling Model prediction

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Enhancing Autonomy Capability in Regional Power Grids:A Strategic Planning Approach with Multiple Autonomous Evaluation Indexes

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Energy Engineering 2024年第9期121卷 2449-2477页

作者： Jie Ma Tong Zhao Yuanzhao Hao Wenwen Qin Haozheng Yu Mingxuan Du Yuanhong Liu Liang Zhang Shixia Mu Cuiping Li Junhui Li Planning and Assessment Center Department State Grid Henan Economic Research InstituteZhengzhou450000China Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China Development Planning Department State Grid Henan Electric Power CompanyZhengzhou450000China Innovation Development Research Center State Grid Shanghai Energy Internet Research Institute Co.Ltd.Shanghai201210China State Grid Simulation Center China Electric Power Research Institute Co.Ltd.Beijing100192China

After the integration of large-scale DistributedGeneration(DG)into the distribution network,the randomness and volatility of its output result in a reduction of spatiotemporal alignment between power generation and demand in the distribution network,exacerbating the phenomenon of wind and solar power *** a novel power system model,the fundamental concept of Regional Autonomous Power Grids(RAPGs)is to achieve localized management and energy autonomy,thereby facilitating the effective consumption of ***,this paper proposes a distributed resource planning strategy that enhances the autonomy capabilities of regional power grids by considering multiple evaluation indexes for ***,a regional Energy Storage(ES)configuration strategy is *** strategy can select a suitable reference value for the upper limit of ES configuration based on the regional load andDGoutput to maximize the elimination of source load deviations in the region as the upper limit constraint of ES ***,a control strategy for regional ES is proposed,the charging and discharging reference line of ES is set,and multiple autonomy and economic indexes are used as objective functions to select different proportions of ES to control the distributed resources of the regional power grid and establish evaluation indexes of the internal regional generation and load power ratio,the proportion of power supply matching hours,new energy consumption rate and tie line power imbalance outside the region to evaluate changes in the regional autonomy *** final simulation results showthat in the real regional grid example,the planning method in the planning year in the region of the overall power supply matching hour ratio and new energy consumption rate increased by 3.9%and 4.8%on average,and the power imbalance of the tie line decreased by 7.8%on *** proposed planning approach enables the maximization of regional autonomy while effectively smoothing the fluct

关键词： Regional autonomous power grid distributed generation distributed energy storage regional planning strategy evaluation index

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An Interval Fractional Electric Power system Planning Model for CO2 Emission Intensity Reduction: A Case Study of Fujian, China 14th

An Interval Fractional Electric Power System Planning Model ...

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14th International Conference on Environmental science and technology, ICEST 2023

作者： Zhao, S.H. Li, J.W. Liu, J. Li, Y.P. Fujian Engineering and Research Center of Rural Sewage Treatment and Water Safety School of Environmental Science and Engineering Xiamen University of Technology Xiamen361024 China State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Beijing Normal University Beijing100875 China

ISBN: (纸本)9783031546839

Using low-carbon/zero-carbon energy sources and negative emission technologies, and economic policies have proven to be effective measures for electric power plants’ CO2 emission mitigation. This study constructs an interval fractional electric power system planning (IFES) model for minimizing CO2 emission intensity considering the interactions between the measures and other constraints under uncertainty. The model is applied to Fujian Province as a real-case study, which contains electricity mix improvement, three types of CO2 emission reduction technologies, one policy. It was found that: (i) system carbon emission intensity is [170, 213] g/RMB;(ii) The proportion of clean energy power generation in Fujian Province will reach [92.32, 93.13]% in the 2055–2060, with nuclear energy, wind power and photovoltaic power generation ranking the top three;(iii) The electricity mix improvement has made the greatest contribution to the carbon neutrality of the power system (74.62%), and the contributions of CCS, DAC, and ESC are (9.11, 5.25, 11.02%) respectively. The research results provide scientific planning solutions for carbon reduction in the power industry in Fujian Province. The developed IF-ESP model can also be used in other regions around the world. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electric power system planning

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Operational Effectiveness Evaluation of Reconnaissance Satellite system based on system Dynamics Model 2

Operational Effectiveness Evaluation of Reconnaissance Satel...

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2nd IEEE International Conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Han, Chi Xiong, Wei Liu, Wenwen Yu, Xiaolan Jian, Ping Aerospace Information Institute Space Engineering University Beijing China Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing China

ISBN: (纸本)9781665428767

In order to study changes in the effectiveness of reconnaissance satellite system (RSS) in supporting maritime operations, based on the key factors of dynamic confrontation system of the offensive and defensive parties, the feedback relationships between variables are analyzed. According to the feedback relationship, a system dynamics (SD) model based on Lanchester equation was established and simulated on the VENSIM platform to compare and analyze operational effectiveness of different RSS allocation schemes. simulation analysis shows that RSS can increase the rate of detection and acquisition of information and accelerate the loss of the enemy. At the same time, when the sensor parameters are similar, increasing the number of weapons can significantly reduce the loss, which provides a certain theoretical support for decision-making analysis in combat. © 2021 IEEE.

关键词： Feedback

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MTL-JER: Meta-Transfer Learning for Low-Resource Joint Entity and Relation Extraction

MTL-JER: Meta-Transfer Learning for Low-Resource Joint Entit...

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Neural Networks, Information and Communication Engineering (NNICE), International Conference on

作者： Da Peng Zhongmin Pei Delin Mo Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing China Institute of War Studies Academy of Military Sciences

Joint entity and relation extraction has achieved impressive advances in NLP, such as document understanding and knowledge graph construction. The typical methods for entity and relation extraction typically break down the joint task into several smaller components or stages for ease of implementation, but this leads to a loss of the interconnected knowledge in the triple. Hence, we propose to model the triple in one module jointly. Furthermore, the labeling of a joint entity and relation extraction tasks is costly and domain-specific; therefore, it is important to improve its performance on low-resource data and domain adaption. To address this issue, we suggest using two sources that are rich in information, namely pre-trained models on large data and multi-domain text corpora. Pretraining allows us to provide the model with the fundamental ability to perform joint entity and relationship extraction. Second, through meta-learning on multi-domain text, we can improve the model's generalization capabilities, enabling it to perform well even with limited data. We present MTL-JER, a Meta-Transfer Learning method for Joint Entity and Relation Extraction in low-resource settings in this paper. Using exhaustive experiments on five datasets, we prove that our model obtains optimal results.

关键词： Adaptation models Training data Knowledge graphs Artificial neural networks Data models Data mining Labeling

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Effectiveness Evaluation of Reconnaissance Satellite system based on Analytic Hierarchy Process and Cloud Model 5

Effectiveness Evaluation of Reconnaissance Satellite System ...

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5th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Han, Chi Xiong, Wei Xiong, Minghui Liu, Zhen Liu, Wenwen Yu, Xiaolan Jian, Ping Space Engineering University Aerospace Information Institute Beijing China Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing China

ISBN: (纸本)9781665415989

Considering the complexity of effectiveness evaluation index of reconnaissance satellite system (RSS), the cloud model theory and AHP method are combined. A fuzzy evaluation method of combat effectiveness based on cloud model is proposed. A multi-layer evaluation index system is constructed through the AHP method, and the weight of each level index is calculated. Combining the multi-dimensional cloud model with the AHP method, a quantitative fuzzy evaluation method for qualitative indicators and quantitative data is designed, and the qualitative evaluation results are obtained. The experimental results show that the proposed evaluation method can effectively realize the fuzzy quantitative evaluation under the complex evaluation index system with good performance in terms of calculation accuracy and fuzziness. © 2021 IEEE.

关键词： Analytic hierarchy process

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Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 8943-8956页

作者： Shan, Pufang Chen, Jialei Fu, Chong Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science The University of Auckland Auckland New Zealand

Automated skin lesion classification in dermoscopy images remains challenging due to the existence of artefacts and intrinsic cutaneous features, diversity of lesion morphology, insufficiency of training data, and class imbalance problem. To address these challenges, we propose a new densely connected convolutional network termed AttDenseNet-121, which is obtained by integrating the convolutional block attention module (CBAM) into DenseNet-121. CBAM is a simple yet efficient attention module, which is further improved by adding a novel pooling layer (multiscale-pooling) to effectively enhance its attention capability. The optimized CBAM strengthens the representation power of DenseNet-121 by emphasizing meaningful features and suppressing unnecessary ones, thus significantly enhancing the classification performance. Besides, to handle the imbalanced dataset, we employ an improved focal loss function rather than the traditional cross-entropy loss function to train AttDenseNet-121. The improved focal loss is calculated from a different perspective compared with the original focal loss. It makes the distribution of positive samples and negative samples in each batch the same as that in the original data, successfully mitigating the negative influence of class imbalance on this multi-class classification task. We conduct extensive experiments on the public benchmark dataset (HAM10000) and the results indicate that our proposed method achieves superior performance compared with that of the baselines and state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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On the Outage of Far-Reach Communications via RIS-Empowered Satellites

On the Outage of Far-Reach Communications via RIS-Empowered ...

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2024 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, PACRIM 2024

作者： Wang, Fan Jiang, Ruihong Liu, Yuan Bao, Tingnan Peng, Mugen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Research Center for Space Computing System Zhejiang Laboratory Hangzhou311100 China School of Electrical Engineering and Computer Science University of Ottawa OttawaON Canada

ISBN: (纸本)9798350362312

The rising demand for reliable, high-bandwidth satellite communications in far-reach areas drives the integration of low earth orbit (LEO) satellites into future 6G networks. In this paper, we investigate the deployment of reconfigurable intelligent surface (RIS) technology and rate splitting multiple access (RSMA) within LEO satellite systems to improve communication quality and system capacity. We derive closed-form expressions for outage probability (OP) and reliable throughput that validate the theoretical advantages of these technologies under various signal-to-noise ratio conditions. Numerical results demonstrate that, compared with traditional space division multiple access (SDMA) and non-orthogonal multiple access (NOMA) approaches, our RSMA and RIS-integrated framework significantly can reduce OP and enhance reliable throughput with an appropriate power-splitting factor, showcasing its superiority in RIS-enabled satellite communication scenarios. © 2024 IEEE.

关键词： Satellite communication systems

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Constructing a Raman and surface-enhanced Raman scattering spectral reference library for fine-particle analysis

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Journal of Environmental sciences 2022年第8期34卷 1-13页

作者： Hui Chen Fengkui Duan Kebin He Jingjing Du Zhenli Sun Suhua Wang Key Laboratory of Resources and Environmental System Optimization of Ministry of Education College of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing 102206China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Guangdong Provincial Key Laboratory of Petrochemcial Pollution Processes and Control School of Environmental Science and EngineeringGuangdong University of Petrochemical TechnologyMaoming 525000 China

Fine particles associated with haze pollution threaten the health of over 400 million people in China. Owing to excellent non-destructive fingerprint recognition characteristics, Raman and surface-enhanced Raman scattering(SERS) are often used to analyze the composition of fine particles to determine their physical and chemical properties as well as reaction mechanisms. However, there is no comprehensive Raman spectral library of fine particles. Furthermore, various studies that used SERS for fine-particle composition analysis showed that the uniqueness of the SERS substrates and different excitation wavelengths can produce a different spectrum for the same fine-particle component. To overcome this limitation, we conducted SERS experiments with a portable Raman spectrometer using two common SERS substrates(silver(Ag) foil and gold nanoparticles(Au NPs)) and a 785 nm laser. Herein, we introduced three main particle component types(sulfate-nitrate-ammonium(SNA), organic material, and soot) with a total of 39 chemical substances. We scanned the solid Raman, liquid Raman, and SERS spectra of these substances and constructed a fine-particle reference library containing 105 spectra. Spectral results indicated that for soot and SNA, the differences in characteristic peaks mainly originated from the solid-liquid phase transition;Ag foil had little effect on this difference, while the Au NPs caused a significant red shift in the peak positions of polycyclic aromatic hydrocarbons. Moreover, with various characteristic peak positions in the three types of spectra, we could quickly and correctly distinguish substances. We hope that this spectral library will aid in the future identification of fine particles.

关键词： Raman Surface-enhanced Raman scattering(SERS) Spectral library Fine-particle

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