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Optimal weight support vector regression ensemble with cluster-based subsampling for electricity price forecasting

Journal of Intelligent and Fuzzy Systems 2024年第1期1卷 1-16页

作者： Li, Yuerong Zhang, Yuhua Che, Jinxing School of Science Nanchang Institute of Technology Jiangxi Nanchang China Key Laboratory of Engineering Mathematics and Advanced Computing Nanchang Institute of Technology Jiangxi Nanchang China

Accurate prediction of short-term electricity price is the key to obtain economic benefit and also an important index of power system planning and management. Support vector regression (SVR) based ensemble works have gained remarkable achievements in terms of high accuracy and steady performance, but they are highly dependent on data representativeness and have a high computational complexity O (k ∗ N 3) of data samples and parameter selection. To further improve the data representativeness and reduce its computational complexity, this paper develops a new approach to forecast electricity price via optimal weighted ensemble. In the model, the cluster-based subsampling algorithm is proposed to categorize the inputs being seasonally decomposed into several groups, and representative data are drawn from each group in a certain proportion to ensure that each subset trained with SVR has the same representativeness and features. Moreover, the optimal weighted combination method is presented to assign weights to the sub-SVRs to obtain the optimal support vector regression ensemble model (OWSSVRE). The experimental results show that the improved support vector regression ensemble model with the same features and representativeness of the subset has better performance in electricity price forecasting. As a result, it is suitable to support decision making in the energy and other sectors. © 2024 - IOS Press. All rights reserved.

关键词： K-means clustering

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Fibrillar adhesives with unprecedented adhesion strength,switchability and scalability

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National science Review 2024年第10期11卷 20-33页

作者： Changhong Linghu Yangchengyi Liu Xudong Yang Dong Li Yee Yuan Tan Mohamed Haziq Bin Mohamed Hafiz Mohammad Fadhli Bin Rohani Zihao Du Jiangtao Su Yan Li Yucheng Huo Hanyan Xu Xiufeng Wang Yifan Wang Jing Yu Huajian Gao K.Jimmy Hsia School of Mechanical and Aerospace Engineering Nanyang Technological University School of Materials Science and Engineering Xiangtan University Department of Engineering Mechanics Zhejiang University School of Materials Science and Engineering Nanyang Technological University Institute of High-Performance Computing A*STAR Mechano-X Institute Applied Mechanics Laboratory Department of Engineering Mechanics Tsinghua University School of Chemistry Chemical Engineering and BiotechnologyNanyang Technological University

Bio-inspired fibrillar adhesives have received worldwide attention but their potentials have been limited by a trade-off between adhesion strength and adhesion switchability, and a size scale effect that restricts the fibrils to micro/nanoscales. Here, we report a class of adhesive fibrils that achieve unprecedented adhesion strength（～2 MPa）, switchability（～2000）, and scalability（up to millimeter-scale at the single fibril level）,by leveraging the rubber-to-glass（R2G） transition in shape memory polymers（SMPs）. Moreover, R2G SMP fibrillar adhesive arrays exhibit a switchability of >1000（with the aid of controlled buckling） and an adhesion efficiency of 57.8%, with apparent contact area scalable to 1000 mm2, outperforming existing fibrillar adhesives. We further demonstrate that the SMP fibrillar adhesives can be used as soft grippers and reusable superglue devices that are capable of holding and releasing heavy objects >2000 times of their own weight. These findings represent significant advances in smart fibrillar adhesives for numerous applications,especially those involving high-payload scenarios.

关键词： JKR-DMT transition rubber-to-glass (R2G) transition R2G fibrillar adhesives shape memory polymers adhesion switchability and scalability

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Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of Computer science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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Evaluating RISC-V Vector Instruction Set Architecture Extension with Computer Vision Workloads

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Journal of Computer science & Technology 2023年第4期38卷 807-820页

作者：李若时彭平邵志远金海郑然 National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan 430074China Services Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074 China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Computer vision(CV)algorithms have been extensively used for a myriad of applications *** the multimedia data are generally well-formatted and regular,it is beneficial to leverage the massive parallel processing power of the underlying platform to improve the performances of CV *** Instruction Multiple Data(SIMD)instructions,capable of conducting the same operation on multiple data items in a single instruction,are extensively employed to improve the efficiency of CV *** this paper,we evaluate the power and effectiveness of RISC-V vector extension(RV-V)on typical CV algorithms,such as Gray Scale,Mean Filter,and Edge *** our examinations,we show that compared with the baseline OpenCV implementation using scalar instructions,the equivalent implementations using the RV-V(version 0.8)can reduce the instruction count of the same CV algorithm up to 24x,when processing the same input ***,the actual performances improvement measured by the cycle counts is highly related with the specific implementation of the underlying RV-V *** our evaluation,by using the vector co-processor(with eight execution lanes)of Xuantie C906,vector-version CV algorithms averagely exhibit up to 2.98x performances speedups compared with their scalar counterparts.

关键词： RISC-V vector extension single instruction multiple data(SIMD) computer vision OpenCV

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Mean-square exponential stability of stochastic Volterra systems in infinite dimensions

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science China(Information sciences) 2024年第10期67卷 298-314页

作者： Lin FU Shiguo PENG Feiqi DENG Quanxin ZHU School of Automation Guangdong University of Technology School of Automation Science and Engineering South China University of Technology Key Laboratory of Computing and Stochastic Mathematics(Ministry of Education) School of Mathematics and StatisticsHunan Normal University

Over the past decade, the study of stability theory in integro-differential systems has grown significantly owing to their relevance in solving physical and engineering problems, such as viscoelasticity and thermo-viscoelasticity in materials with memory properties. This paper concentrates on a class of infinite-dimensional stochastic integro-differential systems. We establish the well-posedness of the system and identify mild solutions to the system and an abstract stochastic Cauchy problem. This identification is identified by employing a semigroup approach combined with Yosida approximation. We derive sufficient conditions that ensure the mean-square exponential stability of mild solutions to the system boils down to the boundedness of a certain function and a norm estimate for the stochastic part. These conditions are implemented through the semigroup approach and the composition operator method. Illustrative examples are provided and the obtained theoretical results are validated by numerical simulations.

关键词： stochastic integro-differential equations mean-square exponential stability stochastic distributed parameter systems Hardy space composition operators

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Omni-Detection of Adversarial Examples with Diverse Magnitudes

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China Communications 2024年第12期21卷 139-151页

作者： Ke Jianpeng Wang Wenqi Yang Kang Wang Lina Ye Aoshuang Wang Run Key Laboratory of Aerospace Information Security and Trusted Computing(Wuhan University) Ministry of EducationWuhan 430072China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China

Deep neural networks(DNNs)are poten-tially susceptible to adversarial examples that are ma-liciously manipulated by adding imperceptible pertur-bations to legitimate inputs,leading to abnormal be-havior of *** of methods have been pro-posed to defend against adversarial ***-ever,the majority of them are suffering the follow-ing weaknesses:1)lack of generalization and prac-ticality.2)fail to deal with unknown *** ad-dress the above issues,we design the adversarial na-ture eraser(ANE)and feature map detector(FMD)to detect fragile and high-intensity adversarial examples,***,we apply the ensemble learning method to compose our detector,dealing with adver-sarial examples with diverse magnitudes in a divide-and-conquer *** results show that our approach achieves 99.30%and 99.62%Area un-der Curve(AUC)scores on average when tested with various Lp norm-based attacks on CIFAR-10 and Im-ageNet,***,our approach also shows its potential in detecting unknown attacks.

关键词： adversarial example detection ensemble learning feature maps fragile and high-intensity ad-versarial examples

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge computing

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Ming Northeastern University School of Computer Science and Engineering Engineering Research Center of Security Technology of Complex Network System Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system information. We first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multi-per-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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A DECOUPLED,LINEARLY IMPLICIT AND UNCONDITIONALLY ENERGY STABLE SCHEME FOR THE COUPLED CAHN-HILLIARD SYSTEMS

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Journal of Computational Mathematics 2025年第3期43卷 708-730页

作者： Dan Zhao Dongfang Li Yanbin Tang Jinming Wen School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China College of Information Science and Technology Jinan UniversityGuangzhou 510632China

We present a decoupled,linearly implicit numerical scheme with energy stability and mass conservation for solving the coupled Cahn-Hilliard *** time-discretization is done by leap-frog method with the scalar auxiliary variable(SAV)*** only needs to solve three linear equations at each time step,where each unknown variable can be solved *** is shown that the semi-discrete scheme has second-order accuracy in the temporal *** convergence results are proved by a rigorous analysis of the boundedness of the numerical solution and the error estimates at different *** examples are presented to further confirm the validity of the methods.

关键词： Coupled Cahn-Hilliard system Leap-frog method Scalar auxiliary variable Error estimate

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A FRAMEWORK FOR INFERENCE INSPIRED BY HUMAN MEMORY MECHANISMS 12

A FRAMEWORK FOR INFERENCE INSPIRED BY HUMAN MEMORY MECHANISM...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zeng, Xiangyu Lin, Jie Hu, Piao Huang, Ruizheng Zhang, Zhicheng Laboratory of Intelligent Collaborative Computing School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

How humans and machines make sense of current inputs for relation reasoning and question-answering while putting the perceived information into context of our past memories, has been a challenging conundrum in cognitive science and artificial intelligence. Inspired by human brain's memory system and cognitive architectures, we propose a PMI framework that consists of perception, memory and inference components. Notably, the memory module comprises working and long-term memory, with the latter endowed with a higher-order structure to retain extensive and complex relational knowledge and experience. Through a differentiable competitive write access, current perceptions update working memory, which is later merged with long-term memory via outer product associations, reducing information conflicts and averting memory overflow. In the inference module, relevant information is retrieved from two separate memory origins and associatively integrated to attain a more comprehensive and precise interpretation of current perceptions. We exploratively apply our PMI to improve prevailing Transformers and CNN models on question-answering tasks like bAbI-20k and Sort-of-CLEVR datasets, as well as detecting equilateral triangles, language modeling and image classification tasks, and in each case, our PMI enhancements consistently outshine their original counterparts significantly. Visualization analyses reveal that relational memory consolidation, along with the interaction and integration of information from diverse memory sources, substantially contributes to the model effectiveness on inference tasks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Modeling languages

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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