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Capacity Allocation of Renewable Energy Sources Considering Complementarity

CSEE Journal of Power and Energy systems 2024年第4期10卷 1415-1426页

作者： Yinzhe Hu Kaigui Xie Bo Hu Hengming Tai Changzheng Shao Chunyan Li Xueying Yu State Key Laboratory of Power Transmission Equipment and System Security at the Chongqing University Chongqing 400030China Department of Electrical and Computer Engineering University of TulsaTulsaOK 74104USA

The outputs of renewable energy sources(RESs)are inherently variable and uncertain,such as wind power(WP)and photovoltaic(PV).However,the outputs of various types of RESs in different regions are *** the capacity of RESs could be properly allocated during system planning,variability of the total output could be ***,system reliability and renewable energy(RE)consumption could be *** paper proposes an analytical model for optimal complementary capacity allocation of RESs to decrease variability of the total *** model considers the capacity ratio of RESs as decision variables and the coefficient of variation(CV)of the total output as the objective *** proposed approach transforms the single-level optimization model into a bilevel optimization model and derives an analytical equation that can directly calculate the optimal complementary capacity ratio(OCCR)of system *** studies on wind and solar farms in Xinjiang and Qinghai,China,are performed to verify the effectiveness of the proposed analytical allocation method.

关键词： Analytical equation capacity allocation complementary optimization renewable energy variability

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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

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Frontiers of computer Science 2024年第4期18卷 1-23页

作者： Hongru GAO Xiaofei LIAO Zhiyuan SHAO Kexin LI Jiajie CHEN Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in *** most real-world scenarios,entities and their relationships are subject to constant *** that record such changes are called dynamic *** recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics *** the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing *** the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing ***-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph *** comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph *** addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.

关键词： dynamic graphs graph processing graph algorithms GPUs

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BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation

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Frontiers of computer Science 2023年第5期17卷 103-118页

作者： Jinwei LUO Mingkai HE Weike PAN Zhong MING College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China

Session-based recommendation(SBR)and multibehavior recommendation(MBR)are both important problems and have attracted the attention of many researchers and *** from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics,heterogeneous SBR(HSBR)that exploits different types of behavioral information(e.g.,examinations like clicks or browses,purchases,adds-to-carts and adds-to-favorites)in sequences is more consistent with real-world recommendation scenarios,but it is rarely *** efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of ***,all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of ***,all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of *** limitation hinders the development of HSBR and results in unsatisfactory *** a response,we propose a novel behavior-aware graph neural network(BGNN)for *** BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a ***,our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified *** then conduct extensive empirical studies on three real-world datasets,and find that our BGNN outperforms the best baseline by 21.87%,18.49%,and 37.16%on average correspondingly.A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our *** exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can e

关键词： session-based recommendation graph neural network heterogeneous behaviors

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A Hybrid Approach for Maximum Power Point Tracker on PV systems 16

A Hybrid Approach for Maximum Power Point Tracker on PV Syst...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Ekogha, Ely Ondo Owolawi, Pius Tshwane University of Technology Computer System Engineering Gauteng Pretoria South Africa

ISBN: (纸本)9798350371154

As the usage of photovoltaic is emerging to palliate the degradation of the atmosphere and the natural environment. Photovoltaic (PV) systems appear to be the key for green energy production for households and industrial sectors. However, due to the dynamic change of load for a PV system or the variations of weather conditions, most PV systems are equipped with a maximum power point tracker (MPPT) to operate at their optimum capabilities. However, the efficiency of the MPPT controller varies from the techniques and algorithms used thus affecting the operations of the PV system. This paper proposes a hybrid technique based on Artificial neural network (ANN) combines with perturbation and observation (P&O) or incremental conductance (IC) algorithms using MATLAB Simulink for tracking the ideal maximum power point under uniform and sudden change of weather conditions. A comparison is depicted with conventional techniques, the findings suggest that the hybrid ANN-IC technique has an improved accuracy of 98 % and response time 0,154 seconds to MPP under uniform weather. The model presents a fast-tracking response under rapidly changing conditions for a convergence time of 1.013 μ s. Keywords-Maximum power point (MPP) tracker, artificial neural network (ANN), perturbation and observation (P&O), incremental conductance (IC). © 2024 IEEE.

关键词： Maximum power point trackers

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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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Predictive Modeling of Child Identity Risk using Machine and Deep Learning: A systematic Literature Review 2

Predictive Modeling of Child Identity Risk using Machine and...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Dighe, Akshada Garg, Bindu Department of Computer Engineering BVDUCOE Pune India Department of Computer Science and Business System BVDUCOE Pune India

ISBN: (纸本)9798331541217

Intellectual disability (ID), a developmental condition often stemming from prenatal or postnatal factors, exerts a profound influence on individuals' lives and necessitates timely intervention. Conventional screening methods are resource-intensive and demand specialized medical expertise. This study introduces a novel, non-invasive approach to ID screening by meticulously analyzing facial features and vocal characteristics extracted from interview *** the extraction of facial measurements and speech patterns, we devised a Craniofacial Variability Index (CVI) to assess the likelihood of ID. Furthermore, advanced machine learning techniques were strategically employed to augment the predictive accuracy of this index. Our empirical findings unequivocally demonstrate promising results, with an accuracy rate reaching up to 80% when synergizing CVI scores and phonetic features. This research underscores the potential of a non-invasive, AI-powered screening instrument for ID, offering a more accessible and efficient alternative to prevailing practices. Future investigations will concentrate on refining the model and validating its efficacy across diverse populations. © 2024 IEEE.

关键词： Adversarial machine learning

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ReCSA:a dedicated sort accelerator using ReRAM-based content addressable memory

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Frontiers of computer Science 2023年第2期17卷 1-13页

作者： Huize LI Hai JIN Long ZHENG Yu HUANG Xiaofei LIAO National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

With the increasing amount of data,there is an urgent need for efficient sorting algorithms to process large data *** sorting algorithms have attracted much attention because they can take advantage of different hardware's *** the traditional hardware sort accelerators suffer“memory wall”problems since their multiple rounds of data transmission between the memory and the *** this paper,we utilize the in-situ processing ability of the ReRAM crossbar to design a new ReCAM array that can process the matrix-vector multiplication operation and the vector-scalar comparison in the same array *** this designed ReCAM array,we present ReCSA,which is the first dedicated ReCAM-based sort *** hardware designs,we also develop algorithms to maximize memory utilization and minimize memory exchanges to improve sorting *** sorting algorithm in ReCSA can process various data types,such as integer,float,double,and *** also present experiments to evaluate the performance and energy efficiency against the state-of-the-art sort *** experimental results show that ReCSA has 90.92×,46.13×,27.38×,84.57×,and 3.36×speedups against CPU-,GPU-,FPGA-,NDP-,and PIM-based platforms when processing numeric data *** also has 24.82×,32.94×,and 18.22×performance improvement when processing string data sets compared with CPU-,GPU-,and FPGA-based platforms.

关键词： ReCAM parallel sorting architecture design processing-in-memory

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AMTLUS: Attention-guided multi-task learning with uncertainty estimation in skin lesion segmentation and classification

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Multimedia Tools and Applications 2024年第37期83卷 84885-84909页

作者： Kasukurthi, Aravinda Davuluri, Rajya Lakshmi Department of Computer Science & amp Engineering JNTU-GV Andhra Pradesh Vizianagaram India Department of Computer Science and Business System R.V.R. & ampJ.C. College of Engineering Andhra Pradesh Guntur India

Skin lesion segmentation and classification from dermoscopic images have emerged as pivotal research topics, playing vibrant role in early detection and diagnosis of skin diseases, including melanoma. Previous studies have employed various deep learning models for skin lesion segmentation and classification, enabling the automatic learning of complex and discriminative features from dermoscopic images. However, inherent challenges arise due to the variance in skin lesion shape, size, and contrast, leading to intrinsic limitations of former models, such as Isolated Representation Learning, Uniform Attention, Limited Model Generalization, Reduced Model Interpretability, and Uncertainty. To address these limitations and propel the field forward, this paper introduces a novel frameworkcalled AMTLUS that leverages Multi-Task Learning (MTL) in conjunction with deep Attention Mechanisms and Uncertainty Estimation. The integration of MTL facilitates joint training of segmentation and classification tasks, enabling shared representation learning and efficient utilization of data. Incorporating attention mechanisms dynamically focuses on informative regions within dermoscopic images, improving segmentation accuracy and feature extraction for classification. Uncertainty estimation techniques quantify model confidence, offering probabilistic interpretations for improved reliability and interpretability. Our widespread experiments conducted on the ISIC-2016 dataset demonstrate superior accuracy and reliability, showcasing the proposed model’s capability to identify challenging cases. This deep learning framework represents a significant advancement in automated skin lesion analysis, enhancing early detection and diagnosis of skin diseases, including melanoma. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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SSVEP Based BCI Control of an Exoskeleton 18th

SSVEP Based BCI Control of an Exoskeleton

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18th International Conference on Interdisciplinarity in engineering, INTER-ENG 2024

作者： Sibișanu, Remus Constantin Olar, Marius Leonard Roșca, Sebastian Daniel Leba, Monica Computer and System Control Engineering Department University of Petrosani 20 Universitatii Petrosani332006 Romania

ISBN: (纸本)9783031816840

This paper explores recent innovation in the field of robotic teleoperation, presenting a state-of-the-art system for a robotic arm, configurable as an exoskeleton or prosthetic limb. Based on noninvasive neural headset technology, the system captures movement intentions directly from the user’s neural activity. By precisely interpreting neural signals and translating them into robotic arm movements, the system bridges the gap between human intention and robotic action. Its uniqueness lies in its ability to interpret the subtleties of human intent through non-invasive means, providing intuitive and fluid control over the robotic arm. The system is distinguished by the efficient use of rich neural activity, thus improving usability and opening up new possibilities for people with mobility impairments. The paper analyzes technical complexities, detailing the functionality of neural headsets, signal decoding algorithms, and the command formation process for the robotic arm. It also presents empirical evidence of the effectiveness of the system and discusses possible applications and future improvements. This research not only contributes to robotic teleoperation, but also paves the way for more human and empathetic interactions with machines in the future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Exoskeleton (Robotics)

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Reconstruction Attacks in Template-Based ECG Biometric Recognition systems

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IEEE Internet of Things Journal 2024年第8期11卷 14971-14984页

作者： Lee, Song-Hong Nien, Cing-Ping Wu, Shun-Chi Swindlehurst, A. Lee National Tsing Hua University Department of Engineering and System Science Hsinchu30013 Taiwan University of California Department of Electrical Engineering and Computer Science IrvineCA92697 United States

The success of Internet of Things (IoT) services will be determined by how the security of the IoT devices and the networks to which they are connected can be guaranteed. Integrating biometric recognition technology into IoT systems has gained popularity as a means of accomplishing this objective. Electrocardiograms (ECGs) have become a promising biometric tool for security because their intrinsic and dynamic nature makes them difficult to steal and forge for replay attacks. However, as with other biometric-based security approaches, attacks on ECG biometric systems have been developed. This study examines the vulnerability of template-based ECG biometric systems to reconstruction attacks. These attacks involve exposing the biometric templates of registered subjects in a hacked database and attempting to reconstruct their ECGs to spoof the system. Both deep learning models and 'heuristic' approaches are used to perform this task, depending on the intruder's level of knowledge of the template construction. Several reconstruction attack strategies are proposed and evaluated for fiducial- and PCA-based systems using ECGs from the Physikalisch-Technische Bundesanstalt database of 285 subjects. The experimental results demonstrate a reconstruction similarity of at least 0.93 and an increase in the false-positive identification-error rate of more than 73%. © 2014 IEEE.

关键词： Electrocardiograms

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