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Enhancing photovoltaic energy forecasting:a progressive approach using wavelet packet decomposition

Clean Energy 2024年第3期8卷 95-108页

作者： Khaled Ferkous Mawloud Guermoui Abderahmane Bellaour Tayeb boulmaiz Nadjem Bailek LMTESE Laboratory of Materials Technology of Energy Systems and EnvironmentUniversitéde Ghardaia47000Algeria Renewable Energy Applied Research Unit(URAER) Ghardaia47000Algeria Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer ScienceFaculty of Sciences and TechnologyAhmed Draia University of AdrarAdrar 01000Algeria

Accurate photovoltaic(PV)energy forecasting plays a crucial role in the efficient operation of PV power *** study presents a novel hybrid machine-learning(ML)model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour *** proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting *** evaluations demonstrate the superiority of the proposed approach over conventional ML methods,including Gaussian process regression,extreme learning machines,artificial neural networks and support vector machines,across all *** proposed model exhibits lower normalized root mean square error(nRMSE)(2.116%)and root mean square error(RMSE)(208.233 kW)values,along with a higher coefficient of determination(R^(2))of 99.881%.Furthermore,the exceptional performance of the model is maintained even when tested with various prediction ***,as the forecast horizon extends from 1.5 to 5.5 hours,the prediction accuracy decreases,evident by the increase in the RMSE(710.839 kW)and nRMSE(7.276%),and a decrease in R2(98.462%).Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior *** study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model,suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy,thereby contributing to more efficient grid management.

关键词： short photovoltaic power forecasting wavelet packet decomposition sub-series reconstruction machine learning in energy forecasting sustainable power stations renewable energy

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Multivariate Time Series Forecasting Using Recurrent Neural Network for a Complex System

Multivariate Time Series Forecasting Using Recurrent Neural ...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： El Abassi, Rida Idrais, Jaafar El Moudene, Yassine Sabour, Abderrahim IMI Laboratory Department of Mathematics Ibn Zohr University Agadir Morocco Department of Computer Science Ibn Zohr University Agadir Morocco

ISBN: (纸本)9783031262531

A complex system is a combination of interacting parameters, having the capacity to generate a new type of collective behavior through self-organization. This work focuses on the automatic processing of non-preprocessed multivariate time series obtained from a complex system, in particular the critical cases process of the SARS-Cov2 pandemic in Morocco. We have established a multivariate neural network which the target variable is the critical cases process, the system uses three input parameters, the active cases, the dead cases and the critical cases. We adopt the average error percentage (MAPE) to measure the performance of the model for which we found MAPE = 14.61% as the best performance depending on the tests performed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Time series

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Rescue Under-Motivated Learners Who Studied Through MOOCs by Prediction and Intervention 1

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3rd International Conference on Novel and Intelligent Digital Systems, NiDS 2023

作者： Mosbah, Hadjer Boussaha, Karima Drissi, Samia Department of Mathematics and Computer Science Faculty of Science and Technology Mohamed-Cherif Messaadia University Souk Ahras Algeria University of Constantine 3 - Salah Boubnider Constantine Algeria Department of Mathematics and Computer Science University of Oum El Bouaghi Oum El Bouaghi Algeria Department of Mathematics and Computer Science LIM Laboratory Mohamed-Cherif Messaadia University Souk Ahras Algeria

ISBN: (数字)9783031440977

ISBN: (纸本)9783031440960

Under certain circumstances, E-learning can prevent students from interrupting their educational process about what happened during the lockdown, which caused education facilities from different levels to close. As a result, traditional learning has been replaced by online learning and the popularity of MOOCs is increasing and rapidly spreading all over the globe. Even after the crisis, the conventional form of learning is being complimented by online education nowadays. However, MOOCs struggle with low completion rates and high dropouts even though a large number of students have joined the courses. A variety of factors may be contributing to the low completion rates, especially the lack of interaction. As a result, predicting MOOC dropouts is an interesting research topic. The problem we intend to address in our research is to develop a system that enables the delivery of customized interventions based on the classification of students who could have motivational barriers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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On heterogeneity or sub-classes aspect in construction of stylometric input datasets 26

On heterogeneity or sub-classes aspect in construction of st...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Stanczyk, Urszula Baron, Grzegorz Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Stylometric analysis of texts relies on learning characteristic traits of writing styles for authors. Once these patterns are discovered, they can be compared to the ones present in other text samples, to recognise their authorship. This recognition can be compromised if input datasets are prepared without taking into consideration possible stratification of the input space, leading to specific grouping of datapoints, or sub-classes within distinguished classes. The paper shows research dedicated to construction of various structures of input datasets, and combinations of such structures between train and test sets. In the research the influence of different stratification forms on the performance of selected popular classification systems was observed. To minimise the number of influencing factors, a task of authorship attribution was performed as binary classification with balanced classes. Stylometric descriptors exploited belonged to lexical and syntactic group, giving frequencies of occurrence for chosen style-markers. It resulted in real-valued attributes and these values were explored without applying discretisation, in order to avoid the possible bias of this procedure on observations. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022)

关键词： Knowledge based systems

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Simple Designs of Power-Law Basic and Inverse Filters 5

Simple Designs of Power-Law Basic and Inverse Filters

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5th International IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2023

作者： Nako, Julia Tsirimokou, Georgia Psychalinos, Costas University of Patras Electronics Laboratory Physics Department Rio Patras Greece University of Ioannina Department of Computer Science & Engineering Ioannina Greece

ISBN: (纸本)9798350381030

The standard power-law filter functions of order less than one, as well as their inverse counterparts, are implemented using only one active element in this work. The corresponding transfer functions are realized as impedances ratio, and the associated non-integer-order impedances are approximated using a suitable approximation technique, for fitting their magnitude and phase frequency responses. The employment of the Current Feedback Operational Amplifier as active element also offers the capability of interconnecting the filter stages without the requirement of extra buffer stages. The behavior of the presented schemes is validated through simulation results, obtained using the OrCAD PSpice simulator. © 2023 IEEE.

关键词： Frequency response

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A Multi-Modal Approach For Context-Aware Network Traffic Classification 48

A Multi-Modal Approach For Context-Aware Network Traffic Cla...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Pang, Bo Fu, Yongquan Ren, Siyuan Shen, Siqi Wang, Ye Liao, Qing Jia, Yan Xiamen University Xiamen China Department of Computer Science and Technology Shenzhen China National University of Defense Technology College of Computer Changsha China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781728163277

Network traffic classification is important for network security and management. State-of-the-art classifiers use deep learning techniques to automatically extract feature vectors from the traffic, which however lose important context of the communication sessions and encapsulated text semantics. In this paper, we present a Multi-Modal Classification method named MTCM to systematically exploit the context for the classification task. We build an adaptive context-aware feature extraction framework over varying-length and dynamic packet sequences, based on the attention-aware graph neural networks and BERT. We next automatically fusion multimodal features with the Multi-Layer Perception (MLP) that unifies the graph and semantic features for the packet stream. Extensive evaluation with real-world application and abnormal network datasets show that MTCM outperforms state- of-the-art deep learning methods, and is robust for different classes of traffic data sets. © 2023 IEEE.

关键词： context-aware graph neural network multi-modal learning traffic classification

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Multi-objective evolutionary optimization for hardware-aware neural network pruning

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Fundamental Research 2024年第4期4卷 941-950页

作者： Wenjing Hong Guiying Li Shengcai Liu Peng Yang Ke Tang Guangdong Provincial Key Laboratory of Brain-Inspired Inteligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Research Institute of Trustworthy Autonomous Systems Southern University of Science and TechnologyShenzhen 518055China Department of Statistics and Data Science Southern University of Science and TechnologyShenzhen 518055China

Neural network pruning is a popular approach to reducing the computational complexity of deep neural *** recent years,as growing evidence shows that conventional network pruning methods employ inappropriate proxy metrics,and as new types of hardware become increasingly available,hardware-aware network pruning that incorporates hardware characteristics in the loop of network pruning has gained growing attention,Both network accuracy and hardware efficiency(latency,memory consumption,etc.)are critical objectives to the success of network pruning,but the conflict between the multiple objectives makes it impossible to find a single optimal *** studies mostly convert the hardware-aware network pruning to optimization problems with a single *** this paper,we propose to solve the hardware-aware network pruning problem with Multi-Objective Evolutionary Algorithms(MOEAs).Specifically,we formulate the problem as a multi-objective optimization problem,and propose a novel memetic MOEA,namely HAMP,that combines an efficient portfoliobased selection and a surrogate-assisted local search,to solve *** studies demonstrate the potential of MOEAs in providing simultaneously a set of alternative solutions and the superiority of HAMP compared to the state-of-the-art hardware-aware network pruning method.

关键词： Multi-objective optimization Evolutionary algorithm Neural network pruning Hardware-awaremachine learning Hardware efficiency

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FedCDR:Privacy-preserving federated cross-domain recommendation

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Digital Communications and Networks 2022年第4期8卷 552-560页

作者： Dengcheng Yan Yuchuan Zhao Zhongxiu Yang Ying Jin Yiwen Zhang School of Computer Science and Technology Anhui UniversityHefeiChina Weifang Key Laboratory of Blockchain on Agricultural Vegetables Weifang University of Science and TechnologyWeifangChina Department of Management Hefei UniversityHefeiChina

Cross-Domain Recommendation(CDR)aims to solve data sparsity and cold-start problems by utilizing a relatively information-rich source domain to improve the recommendation performance of the data-sparse target ***,most existing approaches rely on the assumption of centralized storage of user data,which undoubtedly poses a significant risk of user privacy leakage because user data are highly *** this end,we propose a privacy-preserving Federated framework for Cross-Domain Recommendation,called *** our method,to avoid leakage of user privacy,a general recommendation model is trained on each user's personal device to obtain embeddings of users and items,and each client uploads weights to the central *** central server then aggregates the weights and distributes them to each client for ***,because the weights implicitly contain private information about the user,local differential privacy is adopted for the gradients before uploading them to the server for better protection of user *** distill the relationship of user embedding between two domains,an embedding transformation mechanism is used on the server side to learn the cross-domain embedding transformation *** experiments on real-world datasets demonstrate that ourmethod achieves performance comparable with that of existing data-centralized methods and effectively protects user privacy.

关键词： Cross-domain recommendation Federated learning Privacy preserving

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Dynamic Emotion-Dependent Network With Relational Subgraph Interaction for Multimodal Emotion Recognition

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IEEE Transactions on Affective Computing 2025年第2期16卷 712-725页

作者： Wang, Ye Zhang, Wei Liu, Ke Wu, Wei Hu, Feng Yu, Hong Wang, Guoyin Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Department of Computer Science and Technology Chongqing400065 China Shanghai Jiao Tong University Shanghai Key Laboratory of Emotions and Affective Disorders School of Medicine Shanghai201600 China Chongqing Normal University Chongqing401333 China

Multimodal Emotion Recognition in Conversations (MERC) is an important topic in human-computer interaction. In the MERC task, conversations exhibit dynamic emotional dependency, including inter-speaker and intra-speaker emotional dependency, both are vital in understanding the content. However, current research primarily integrates these two emotional dependencies into one unified module, limiting the accuracy of MERC. In this paper, we propose a dynamic emotion-dependent network with relational subgraph interaction named DEDNet. DEDNet introduces relational subgraphs to separately model two emotional dependencies, enabling structured learning paths for utterances based on distinct emotional dependency types. Specifically, nodes indicate the utterances at different moments in the conversation, while edges define the emotional dependency and temporal relationships between nodes. To explicitly capture the differences between these two emotional dependencies, distinct subgraphs are designed for comprehensive representations. Furthermore, we propose an incremental interactive strategy, sequentially leveraging two emotional dependencies to learn the changes in dependency relationships. We find that modeling inter-speaker emotional dependency can better identify negative emotions and modeling intra-speaker emotional dependency can better recognize positive emotions. Experimental results demonstrate that our model outperforms current state-of-the-art methods on three benchmark datasets, IEMOCAP, MELD and DailyDialog. © 2010-2012 IEEE.

关键词： HTTP

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High-dimensional nonlinear variable separation solutions and novel wave excitations for the(4+1)-dimensional Boiti-Leon-Manna-Pempinelli equation

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Communications in Theoretical Physics 2024年第11期76卷 1-11页

作者： Zu-feng Liang Xiao-yan Tang Wei Ding Department of Physics Hangzhou Normal UniversityHangzhou 310036China School of Mathematical Sciences Key Laboratory of Mathematics and Engineering Applications(Ministry of Education)&Shanghai Key Laboratory of PMMPEast China Normal UniversityShanghai 200241China School of Computer Science and Technology Shanghai Normal UniversityShanghai 200234China

Considering the importance of higher-dimensional equations that are widely applied to real nonlinear problems,many(4+1)-dimensional integrable systems have been established by uplifting the dimensions of their corresponding lower-dimensional integrable ***,an integrable(4+1)-dimensional extension of the Boiti-Leon-Manna-Pempinelli(4DBLMP)equation has been proposed,which can also be considered as an extension of the famous Korteweg-de Vries equation that is applicable in fluids,plasma physics and so *** is shown that new higher-dimensional variable separation solutions with several arbitrary lowerdimensional functions can also be obtained using the multilinear variable separation approach for the 4DBLMP *** addition,by taking advantage of the explicit expressions of the new solutions,versatile(4+1)-dimensional nonlinear wave excitations can be *** an illustration,periodic breathing lumps,multi-dromion-ring-type instantons,and hybrid waves on a doubly periodic wave background are discovered to reveal abundant nonlinear structures and dynamics in higher dimensions.

关键词： (4+1)-dimensional Boiti-Leon-Manna-Pempinelli equation variable separation solution periodic breathing lumps multi-dromion-ring-type instanton hybrid waves on a doubly periodic wave background

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