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A review and taxonomy of wind and solar energy forecasting methods based on deep learning

Energy and AI 2021年第2期4卷 136-160页

作者： Ghadah Alkhayat Rashid Mehmood Department of Computer Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia High Performance Computing Centre King Abdulaziz UniversityJeddah 21589Saudi Arabia

Renewable energy is essential for planet *** energy output forecasting has a significant impact on making decisions related to operating and managing power *** prediction of renewable energy output is vital to ensure grid reliability and permanency and reduce the risk and cost of the energy market and *** learning’s recent success in many applications has attracted researchers to this field and its promising potential is manifested in the richness of the proposed methods and the increasing number of *** facilitate further research and development in this area,this paper provides a review of deep learning-based solar and wind energy forecasting research published during the last five years discussing extensively the data and datasets used in the reviewed works,the data pre-processing methods,deterministic and probabilistic methods,and evaluation and comparison *** core characteristics of all the reviewed works are summarised in tabular forms to enable methodological *** current challenges in the field and future research directions are *** trends show that hybrid forecasting models are the most used in this field followed by Recurrent Neural Network models including Long Short-Term Memory and Gated Recurrent Unit,and in the third place Convolutional Neural *** also find that probabilistic and multistep ahead forecasting methods are gaining more ***,we devise a broad taxonomy of the research using the key insights gained from this extensive review,the taxonomy we believe will be vital in understanding the cutting-edge and accelerating innovation in this field.

关键词： Deep learning Renewable energy forecasting Solar energy Wind energy Taxonomy Hybrid methods

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Dynamic AI-Driven Network Slicing with O-RAN for Continuous Connectivity in Connected Vehicles and Onboard Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 720-733页

作者： Shah, Syed Danial Ali Bashir, Ali Kashif Al-Otaibi, Yasser D. Dabel, Maryam M. Al Ali, Farman University of Leeds School of Electronic and Electrical Engineering LeedsLS2 9JT United Kingdom Manchester Metropolitan University Department of Computing and Mathematics M15 6BH United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Jeddah21589 Saudi Arabia University of Hafr Al Department of Computer Science and Engineering Batin Saudi Arabia Sungkyunkwan University School of Convergence Seoul Korea Republic of

The rise of connected and autonomous vehicles signifies an era of intelligent transportation systems, where robust and continued network connectivity is essential for critical applications and enhanced in-vehicle Consumer Electronics (CE) experiences. Slicing at the network's edge offers tailored and dedicated logical networks for diverse and low-latency vehicular demands, including Advanced Driver Assistance systems (ADAS) and in-car infotainment. However, seamless migration of network slices as vehicles traverse coverage areas of different network operators presents formidable challenges, such as ensuring continuous connectivity and uninterrupted service for both safety-critical systems and consumer-oriented services. In this paper, we introduced dynamic network slicing for continuous connectivity in connected vehicles and onboard CE using the Open Radio Access Network (O-RAN) framework in a highly dynamic and mobile environment. We implemented an xAPP within O-RAN that enables Deep Reinforcement Learning (DRL) agent to learn optimal policies through interaction with the network, guiding intelligent decisions on slice migration, resource allocation, and handover optimization. We conducted simulations and evaluations to demonstrate the effectiveness of the proposed xAPP in maintaining optimal Quality of Service (QoS), ensuring efficient RAN resource utilization, minimizing service interruptions, and prioritizing safety-critical slices, all while supporting seamless operation of CE within vehicles during mobility. © 1975-2011 IEEE.

关键词： Advanced driver assistance systems

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Sleep Monitoring with Wearable Sensor Data in an eCoach Recommendation System: A Conceptual Study with Machine Learning Approach 7th

Sleep Monitoring with Wearable Sensor Data in an eCoach Reco...

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7th International Conference on Emerging Applications of information technology, EAIT 2022

作者： Chatterjee, Ayan Prinz, Andreas Pahari, Nibedita Das, Jishnu Riegler, Michael Alexander Department of Information Technology Center for eHealth University of Agder Kristiansand Norway Department of Software Engineering Knowit AS Oslo Norway Department of Library and Information Science University of Calcutta Calcutta India Department of Holistic Systems Simula Research Laboratory Oslo Norway

ISBN: (纸本)9789811951909

The collective effects of sleep loss and sleep disorders are correlated with many adverse health consequences, including increased risk of high blood pressure, obesity, diabetes, depressive state, and cardiovascular symptoms. Research in eHealth can provide methods to enrich personal health care with information and communication technologies (ICTs). An eCoach system may allow people to manage a healthy lifestyle with extended health state monitoring (e.g., sleep) and tailored recommendation generation. Using supervised machine learning (ML) techniques, this study investigated the possibility of classifying sleep stages at night for adults on hourly and daily basis. The daily total sleep minutes and hourly total sleep minutes for defined sleeping period served as input for the classification models. We first used publicly available Fitbit dataset to build the initial classification models. Second, using the transfer learning approach, we re-used the top five best-performing models on a real dataset as collected from the MOX2-5 wearable medical-grade activity device. We found that support vector classifier (SVC) with "linear" kernel outdated other classifiers with a mean accuracy score of 99.92% for hourly sleep classification and a K-nearest neighbor (KNN) outpaced other classifiers with a mean accuracy score of 99.47% for daily sleep classification, for the public Fitbit datasets. Moreover, to determine the practical efficacy of the classifier models, we conceptualized to use the classifier models in an eCoach prototype system to attain tailored sleep goals (e.g., a weekly goal of 49–63 h of sleeping). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Collaborative Fair-is-Better Filtering for Implicit Feedback

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Procedia Computer Science 2024年 246卷 1498-1507页

作者： Hoang V. Dong Huu-Quang Nguyen Hoang D. Nguyen Duc-Trong Le School of Computing and Information Systems Singapore Management University Singapore University of Engineering and Technology Vietnam National University Hanoi University College Cork Cork Ireland

With the wide adoption of recommender systems, fairness has increasingly become a critical topic in many applications, such as e-commerce, job search, and online entertainment. Collaborative filtering is susceptible to unfair recommendations for users from sensitive groups due to the non-negligible presence of biases. Whereas recent work mostly concerned fairness with the explicit ratings, we propose fairness-awareness recommendation models, referred to as CFBF, for implicit feedback (e.g., clicks, views, or purchases), which are ubiquitous in today’s context. This paper considers sensitive attributes, such as gender and age, in both disjoined and combined manners to investigate the models’ unfairness. We discuss several fairness metrics for implicit feedback recommendations based on Spearman’s rank correlation and Kendal Tau. Comprehensive experiments on Movielens and LastFM show that CFBF significantly improves user groups’ fairness with comparable and even better ranking performance.

关键词： Collaborative filtering fairness implicit feedback

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An Efficient Color and Geometric Feature Fusion Module for 6D Object Pose Estiamtion 12

An Efficient Color and Geometric Feature Fusion Module for 6...

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12th International Conference on CYBER technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Li, Jiangeng Liu, Hong Huang, Gao Zuo, Guoyu Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Faculty of Information Beijing University of Technology Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

6D pose estimation is widely used in robot tasks such as sorting and grasping. RGB-D-based methods have recently attained brilliant success, but they are still susceptible to heavy occlusion. Our critical insight is that color and geometry information in RGBD images are two complementary data, and the crux of the pose estimation problem under occlusion is fully leveraging them. Towards this end, we propose a new color and geometry feature fusion module that can efficiently leverage two complementary data sources from RGB-D images. Unlike prior fusion methods, we conduct a two-stage fusion strategy to do color-depth fusion and local-global fusion successively. Specifically, we fuse the color features extracted from RGB images into the point cloud in the first stage. In the second stage, we extract local and global features from the fused point cloud using an ASSANet-like network and splice them together to obtain the final fusion features. We conducted experiments on the widely used LineMod and YCB-Video datasets, which shows that our method improves the prediction accuracy while reducing the training time. © 2022 IEEE.

关键词： Color

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An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques

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Computers, Materials & Continua 2023年第2期74卷 3315-3332页

作者： Mesfer Al Duhayyim Saud S.Alotaibi Shaha Al-Otaibi Fahd N.Al-Wesabi Mahmoud Othman Ishfaq Yaseen Mohammed Rizwanullah Abdelwahed Motwakel Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science and Arts at MuhayelKing Khalid UniversityMuhayel AseerSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing ***,waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management *** study concentrates on the design of hazardous waste detection and classification using ensemble learning(HWDC-EL)technique to reduce toxicity and improve human *** goal of the HWDC-EL technique is to detect the multiple classes of wastes,particularly hazardous and non-hazardous *** HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns(DLBP),EfficientNet,and *** addition,the flower pollination algorithm(FPA)based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 ***,a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine(SVM),extreme learning machine(ELM),and gradient boosting tree(GBT).The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%.

关键词： Hazardous waste image classification ensemble learning deep learning intelligent models human health weighted voting model

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Efficient Hierarchical Exploration with An Active Subgoal Generation Strategy

Efficient Hierarchical Exploration with An Active Subgoal Ge...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Xu, Xin Zuo, Guoyu Li, Jiangeng Huang, Gao The Faculty of Information Technology Beijing University of Technology Beijing100124 China The Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Faculty of Information Beijing University of Technology Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (纸本)9781665481090

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach in long-horizon and complex tasks. It has been successfully used by decomposing tasks via subgoals. However, the existing goal-conditioned HRL methods often suffer from training inefficiency in the long-horizon tasks with sparse external rewards. For this problem, this paper proposes a novel framework that can train high-level policy efficiently with an effective sub goal generation method. The key component of the framework is an active subgoal generation strategy which consists of (a) an efficient high-level policy learning mechanism and (b) two measures of novelty and coverage for filtering effective subgoals. We also introduce the HER mechanism to the framework to solve the problem of sparse reward in the environment. Building upon the proposed framework, this paper develops an active hierarchical exploration strategy with an efficient subgoal generation method. We test our method on a variety of continuous control tasks. Experimental results demonstrate that the strategy outperforms the state-of-the-art HRL approaches. © 2022 IEEE.

关键词： Reinforcement learning

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An Exemplifying Study of the E-Learning Adoption during Covid-19 Pandemic in Oman

An Exemplifying Study of the E-Learning Adoption during Covi...

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2022 Applied Informatics International Conference, AiIC 2022

作者： Al Maashri, Issaa Mohamed Hassan Ghosh, Shounak Bukar, Umar Ali University of Technology and Applied Sciences Academic Electronic Systems Center Muscat Oman School of Computing and Technology Malaysia Universiti Putra Malaysia Faculty of Computer Science and Information Technology Malaysia

ISBN: (数字)9781728184289

ISBN: (纸本)9781728184289

As a consequence of the Covid-19 outbreak, students are required to stay at home to prevent the virus from spreading. This has resulted in a quick acceptance of online learning, as students and faculty cannot participate in in-class activities. This study examines the determinants affecting E-Learning uptake in Oman during the Covid-19 outbreak, using 223 student responses and structural equation modeling (SEM). Four hypotheses were supported, while two were rejected. Specifically, perceived ease of use, perceived usefulness, and institutional support affect behavioral intention to adopt E-Learning. However, infrastructure support and privacy risk, on the other hand, were not. Thus, this research serves as a springboard for higher education officials to increase the use of E-Learning and adjust to student hurdles for students to comprehend and effectively use the system. © 2022 IEEE.

关键词： E-learning

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A Cascaded Broad Learning System for Manipulator Motion Control

A Cascaded Broad Learning System for Manipulator Motion Cont...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Guoyu Zuo Shuaifeng Dong Jiyong Zhou Shuangyue Yu Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Intelligent control methods have led to a significant simplification of the robotic arm modeling and control tuning process, and thus they have been widely used. To further improve the precision of robotic arm motion control, this paper proposes a robotic arm motion control strategy based on a cascaded feature-enhanced elastic-net broad learning system (CFE-EN-BLS). This will fully extract data features to improve motion control accuracy. Moreover, ElasticNet regression is introduced to reduce feature redundancy. Finally, Lyapunov stability theory is introduced to constrain the learning parameters of the proposed learning method to enhance the convergence of the control strategy. The simulation and experiment show that the proposed control strategy can realize high-precision trajectory tracking control of the robotic arm.

关键词： Learning systems Accuracy Manipulators Feature extraction Stability analysis Motion control Robots Tuning Intelligent control Convergence

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Continuous American Sign Language Recognition Using Computer Vision And Deep Learning Technologies

Continuous American Sign Language Recognition Using Computer...

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2022 IEEE Region 10 Symposium, TENSYMP 2022

作者： Senanayaka, S.A.M.A.S. Perera, R.A.D.B.S. Rankothge, W. Usgalhewa, S.S. Hettihewa, H.D. Abeygunawardhana, P.K.W. Sri Lanka Institute of Information Technology Department of Computer Science & Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (数字)9781665466585

ISBN: (纸本)9781665466585

Sign language is a non-verbal communication method used to communicate between hard of hearing or deaf and ordinary people. Automatic Sign language detection is a complex computer vision problem due to the diversity of modern sign languages and variations in gesture positions, hand and finger form, and body part placements. This research paper aims to conduct a systematic experimental evaluation of computer vision-based approaches for sign language recognition. The present research focuses on mapping non-segmented video streams to glosses to gain insights into sign language recognition. The proposed machine learning model consists of Recurrent Neural Network (RNN) layers such as Long Short-Term Memory (LSTM). The model is implemented using current deep learning frameworks such as Google TensorFlow and Keras API. © 2022 IEEE.

关键词： Deep neural networks

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