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Carbon felt electrode coated with WS2 enables a high-performance polysulfide/ferricyanide flow battery

Rare Metals 2024年第10期43卷 5039-5047页

作者： Liang-Yu Li Su Yan Ying-Jia Huang Fang-Fang Zhong Jin-Chao Cao Mei Ding Chuan-Kun Jia College of Materials Science and Engineering Changsha University of Science and Technology Institute of Energy Storage technology College of Energy and Power EngineeringChangsha University of Science and Technology Binzhou Institute of technology Weiqiao-UCAS Science and Technology Park

Polysulfide/ferricyanide flow batteries （S/Fe RFBs）,with the advantages of abundant earth reservation low cost,high safety,and environmental friendliness,have attracted significant interest and demonstrated noteworthy potential for practical ***,the battery performance,including the energy efficiency （EE）,voltage efficiency （VE）,and power density of the S/Fe RFBs remains low owing to the slow redox kinetics of polysulfide *** address these concerns,WS2was selected as the booster and deposited on a commercial carbon felt electrode （WS2–CF） to stimulate the redox reactions of polysulfide *** better hydrophilicity and smaller charge-transfer resistance,WS2–CF exhibits enhanced electrochemical activity toward polysulfide redox ***,the battery performance of S/Fe RFB with WS2–CF as the anode has been improved,with EE of 84%,VE of 84%,and a peak power density of 175.7 mW·cm-2,which are all higher than the cell only with the bare carbon felt （CF） as electrodes (76%,77% and 155.8 mW·cm-2,respectively).Furthermore,the cycling life of the S/Fe RFB with WS2–CF has been prolonged to 2200 cycles with a capacity retention of 96% a 40 mA·cm-2because of the good stability of WS2–CF as the ***,under the same conditions,the S/Fe RFB without WS2–CF terminated after 1500 cycles with a fast capacity *** successful utilization of WS2as a booster on an electrode provides an efficient strategy for obtaining advanced S/Fe RFBs for practical applications.

关键词： Carbon felt Energy storage Polysulfide/ferricyanide flow battery Tungsten disulfide

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A model reduction approach for discrete-time linear time-variant systems with delayed inputs

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Science China(Information Sciences) 2024年第4期67卷 197-217页

作者： Ai-Guo WU Guang-Ren DUAN Yu WANG Jie ZHANG School of Mechanical Engineering and Automation Harbin Institute of Technology(Shenzhen) HIT Wuhu Robot technology Research Institute Center for Control Science and technology Southern University of Science and Technology

A model reduction approach is presented for discrete-time linear time-variant input-delayed systems. According to this proposed approach, a dynamical variable is constructed by taking advantage of the current state and historical information of input. It is revealed that the behavior of this dynamical variable is governed by a discrete-time linear delay-free system. It is worth noting that the presented variable transformation does not require the system matrix to be invertible. Based on the reduced delay-free models,stabilizing control laws can be easily obtained for the original delayed system. For the case with a single input delay, the constructed variable is an exact prediction for the future state, and thus the stabilizing control law could be designed by replacing the future state with its prediction. Finally, three discrete-time periodic systems with delayed input are employed to illustrate how to utilize the presented model reduction approaches.

关键词： reduction approach predictor-based feedback time-varying systems delayed input periodic system

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Image attention transformer network for indoor 3D object detection

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Science China(Technological Sciences) 2024年第7期67卷 2176-2190页

作者： REN KeYan YAN Tong HU ZhaoXin HAN HongGui ZHANG YunLu Faculty of Information technology Beijing University of TechnologyBeijing 100124China

Point clouds and RGB images are both critical data for 3D object detection. While recent multi-modal methods combine them directly and show remarkable performances, they ignore the distinct forms of these two types of data. For mitigating the influence of this intrinsic difference on performance, we propose a novel but effective fusion model named LI-Attention model, which takes both RGB features and point cloud features into consideration and assigns a weight to each RGB feature by attention ***, based on the LI-Attention model, we propose a 3D object detection method called image attention transformer network(IAT-Net) specialized for indoor RGB-D scene. Compared with previous work on multi-modal detection, IAT-Net fuses elaborate RGB features from 2D detection results with point cloud features in attention mechanism, meanwhile generates and refines 3D detection results with transformer model. Extensive experiments demonstrate that our approach outperforms stateof-the-art performance on two widely used benchmarks of indoor 3D object detection, SUN RGB-D and NYU Depth V2, while ablation studies have been provided to analyze the effect of each module. And the source code for the proposed IAT-Net is publicly available at https://***/wisper181/IAT-Net.

关键词： 3D object detection transformer attention mechanism

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Satellite anomaly detection based on reconstruction discrepancy theory utilizing a new dual-branch reconstruction model

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Science China Technological Sciences 2024年第10期67卷 3294-3307页

作者： ZHAO HaoTian QIU Shi LIU Ming CAO XiBin Research Center of Satellite technology School of AstronauticsHarbin Institute of Technology

To enhance the accuracy of deep learning methods based on reconstruction discrepancy in satellite anomaly detection tasks, this study proposes a dual-branch reconstruction model(DBRM) and designs a comprehensive satellite anomaly detection framework around this model. Firstly, we introduce the temporal-channel mixer(TC-Mixer) module, which mainly comprises a self-attention layer for capturing long-range temporal dependencies in telemetry data, and two types of feed-forward networks(FFN) for extracting complex patterns in the temporal and channel dimension of telemetry data. This design endows the TC-Mixer module with robust capabilities for extracting complicated dependencies in telemetry data. Secondly, with the TC-Mixer module as the main component, we designed the DBRM. This model utilizes a shared latent representation layer, allowing the regeneration branch and forecasting branch of the DBRM to share most of the feature extraction network architecture. This approach significantly enhances the model's regression accuracy while reducing computational complexity. Thirdly, using the DBRM as the core network model, we devised a comprehensive satellite anomaly detection framework. This includes an anomaly criterion that considers the reconstruction discrepancy of both the regeneration and forecasting branches, the peak-over-threshold(POT) method for anomaly thresholding, and the MIC-based feature engineering method, etc. Finally, we conducted comparative experiments with several SOTA anomaly detection algorithms on two public and one private satellite anomaly detection datasets. The experimental results validate the effectiveness and superiority of our proposed method.

关键词： anomaly detection reconstruction discrepancy dual-branch reconstruction model (DBRM) satellite telemetry data time series

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Role of knowledge economy in managing demand-based environmental Kuznets Curve

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Geoscience Frontiers 2024年第4期15卷 397-410页

作者： Rukhsana Kalim Shajara Ul-Durar Mubasher Iqbal Noman Arshed Muhammad Shahbaz Department of Economics and Statistics Dr.Hassan Murad School of ManagementUniversity of Management and TechnologyLahorePakistan University of Sunderland The School of Business ManagementEdinburgh BuildingChester RoadSunderland SR13SDUnited Kingdom Department of Economics Division of Management and Administrative SciencesUniversity of EducationLahorePakistan Department of International Trade and Finance School of Management and EconomicsBeijing Institute of TechnologyBeijingChina Center for Sustainable Energy and Economic Development Gulf University for Science and TechnologyHawallyKuwait

Aggregate demand or supply at equilibrium is commonly used as a representative of the macroeconomic activity of an economy whereby aggregate demand denotes the behaviour of individuals and ***,aggregate demand can also directly affect environmental deterioration via changes in aggregate *** study tried to explore this relationship,known as the demand-based Environmental Kuznets Curve(Demand EKC)and the role of different knowledge economy *** economy indicators are proposed to influence consumption patterns,altering the demand EKC that empirical studies have *** this purpose,secondary data for 147 countries were collected from 2008 to 2018,also classified as *** study found that aggregate demand significantly affects carbon *** long-run results are estimated using the Fully Modified Ordinary Least Square *** factors like renewable energy consumption,population density,and financial development significantly affect carbon emissions in sample *** study has incorporated four pillars of a knowledge-based economy and the results showed that these indicators helped reduce consumption-related CO_(2) emissions.

关键词： Kuznets Curve Knowledge economy Higher education Institutional quality Innovation technology

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A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss

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Journal of Wuhan University of technology(Materials Science) 2024年第1期39卷 160-166页

作者： A Visagan P Ganesh Department of Production technology Madras Institute of TechnologyChennai-600044India

We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.

关键词： single point incremental forming AISI 316L taguchi grey relation analysis principal component analysis surface roughness scanning electron microscopy

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Minimal Context-Switching Data Race Detection with Dataflow Tracking

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Journal of Computer Science & technology 2024年第1期39卷 211-226页

作者：郑龙李洋辛杰刘海峰郑然廖小飞金海 National Engineering Research Center for Big Data technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing technology and System Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race ***,this constraint-based approach has serious limitations on helping programmers analyze and understand data ***,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the ***,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving *** ad hoc recommendation imposes too many context switches,which complicates the data race *** address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context ***,we reduce the false positives by ana-lyzing and tracking the dataflow in the *** this means,DFTracker thus reduces the unnecessary analysis of false race *** further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data *** experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race ***,DFTracker can be used as an effective tool to understand the data race for programmers.

关键词： data race satisfiability modulo theory multi-threaded program dynamic detection

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Implementation of blockchain technology in integrated IoT networks for constructing scalable ITS systems in India

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Blockchain(Research and Applications) 2024年第2期5卷 99-125页

作者： Arya Kharche Sanskar Badholia Ram Krishna Upadhyay School of technology Gati Shakti VishwavidyalayaVadodaraGujarat390004India

The implementation of blockchain technology in integrated IoT networks for constructing scalable Intelligent Transportation Systems(ITSs)in India has the potential to revolutionize the way we approach *** leveraging the power of IoT and blockchain,we can create a highly secure,transparent,and efficient system that can transform the way we move people and ***,one of the world’s most populous countries,has a highly congested and inefficient transportation system that often leads to delays,accidents,and waste of time and *** integration of IoT and blockchain can help address these issues by enabling real-time monitoring,tracking,and optimization of traffic flows,thereby reducing congestion,improving safety,and increasing the overall efficiency of the transportation *** paper explores the potential of blockchain technology in the context of integrated IoT networks for constructing scalable ITS systems in *** methodology followed is to develop a proof-of-concept blockchain-based application for ITS,implement the blockchain solution into the existing ITS infrastructure,and ensure proper integration and compatibility with other *** thorough research and maintenance to ensure the reliability and sustainability of such blockchain-based *** research discusses the various benefits and challenges of this approach and the various applications of this technology in the transportation sector,including the green sustainability *** results find various ways in which such implementations of blockchain and IoT-Machine Learning(IoT-ML)can revolutionize transportation systems.

关键词： Transportation systems IoT Blockchain Machine learning Safety

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A mixed Nash equilibrium solution for visibility-based pursuit-evasion game with multiple obstacles

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Science China(Information Sciences) 2024年第9期67卷 330-332页

作者： Shaoming BU Li LIANG Youqing WANG College of Information Science and technology Beijing University of Chemical Technology

This study primarily investigates a visibility-based pursuitevasion game with multiple obstacles. The pursuer aims to maintain surveillance over the evader as long as possible, while the evader's objective is to escape the pursuer's surveillance as quickly as possible. We use the explicit strategy method to analyze the winning conditions and optimal strategy of the player in the case of corner obstacles and propose a 'Present-Future-Past'(PFP) loop structure to extend the strategy to the case of multiple obstacles.

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HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

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Science China(Information Sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN Cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU School of Computer Science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and technology Wuhan University of Science and Technology School of Future technology South China University of Technology Shenzhen Research Institute of Hunan University School of Computer Science Hunan University of Technology and Business Institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

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