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10th International Conference on optimization and Applications, ICOA 2024

作者： Benmamoun, Zoubida Elkhechafi, Mariam Abdo, Ahmad Abu Jebbor, Ikhlef Liwa College of technology Faculty of Engineering Abu Dhabi United Arab Emirates Iscae Larem Laboratory Groupe Casablanca Morocco Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9798350387353

This paper investigates the optimization of carbon emissions in asphalt pavement construction by deep Q-learning. The study developed a model to identify optimal hybrid systems that are environmentally and cost-effective. The optimized system resulted in a 5% reduction in CO2 emissions compared to the classical methods, significant improvements in energy consumption, water use and hazardous waste generation in Besides, the inclusion of plastic waste in asphalt mix not only increased the sustainability but also saved costs by 0.8%. The results demonstrate the effectiveness of deep Q-learning to overcome challenging optimization challenges in pavement construction, to promote environmental and economic benefits. Despite the computational demands of the model, the findings highlighted the potential of advanced Deep learning techniques to increase product sustainability and efficiency. The integration of deep Q-learning provides a new approach to construction optimization, significantly reducing carbon emissions and operating costs while promoting sustainable development. © 2024 IEEE.

关键词： Zero-carbon

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Landslide Prediction methods: A Comparative Overview of Traditional and AI-Based Decision-Making Approaches

Landslide Prediction Methods: A Comparative Overview of Trad...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ajraoui, Abdelrhani Chakiri, Said Mansouri, Hammou Benhaddou, Mohamed Laassilia, Oussama Saouabe, Abdelkrim IbnTofail University Laboratory of Geosciences of Natural Resources Faculty of Sciences Kénitra Morocco Casablanca20110 Morocco Salé Morocco Chouaib Doukkali University El Jadida Morocco IbnTofail University Computer Science Research Laboratory Faculty of Sciences Kénitra Morocco

ISBN: (纸本)9798350369106

Landslides pose significant global hazards, and traditional methods for risk assessment, such as expert systems and geotechnical models, are resource-intensive and limited by human subjectivity. With the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL) methods have become prominent in landslide detection, offering advanced capabilities for modelling complex geotechnical data. Despite initial reliance on visual interpretation of remote sensing data, AI-driven methods have shown potential to improve accuracy and objectivity. ML models have been used to detect landslides based on pixel data and geological features, while DL models, especially CNNs like U-net and ResNet, have gradually replaced ML. However, DL methods are still in their early stages and face challenges due to the complexity of landslide characteristics across regions. © 2024 IEEE.

关键词： Contrastive Learning

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Unreliability Tracing of Power systems for Identifying the Most Critical Risk Factors Considering Mixed Uncertainties in Wind Power Output

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Protection and Control of Modern Power systems 2024年第5期9卷 96-111页

作者： Siying Zhao Changzheng Shao Jinfeng Ding Bo Hu Kaigui Xie Xueying Yu Zihan Zhu Hubei Electric Power Planning Design and Research Institute Co.Ltd.Wuhan 430040China State Key Laboratory of Power Transmission Equipment Technology School of Electrical EngineeringChongqing UniversityChongqing 400044China State Grid Chengdu Electric Power Supply Company Chengdu 610000China School of Electrical Engineering Si-chuan UniversityChengdu 610065China

For conventional power systems,the forced outage of components is the major cause of load *** tracing is utilized to allocate the total system load-shedding risk among individual components in accordance with their different ***,critical components are identified and pertinent measures can be taken to improve system *** integration of wind power introduces additional risk factors into power systems,causing previous unreliability tracing methods to become *** this paper,a novel unreliability tracing method is proposed that considers both aleatory and epistemic uncertainties in wind power output and their impacts on power system load-shedding ***,modelling methods for wind power output considering aleatory and epistemic uncertainties and component outages are ***,a variance-based index is proposed to measure the contributions of individual risk factors to the system load-shedding ***,a novel unreliability tracing framework is developed to identify the critical factors that affect power system *** studies verify the ability of the proposed method to accurately allocate load-shedding risk to individual risk factors,thus providing decision support for reliability enhancement.

关键词： Unreliability tracing epistemic uncer-tainty wind power multisource heterogeneous risk factors

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Machine learning for membrane bioreactor research:principles,methods,applications,and a tutorial

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Frontiers of Environmental Science & Engineering 2025年第3期19卷 119-144页

作者： Yizhe Lai Kang Xiao Yifan He Xian Liu Jihua Tan Wenchao Xue Aiqian Zhang Xia Huang Beijing Yanshan Earth Critical Zone National Research Station College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101408China Key Laboratory of Earth System Numerical Modeling and Application University of Chinese Academy of SciencesBeijing 101408China Environmental Chemistry Faculty College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101408China State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Department of Energy Environmentand Climate ChangeSchool of EnvironmentResourcesand DevelopmentAsian Institute of TechnologyP.O.Box 4Klong LuangPathumthani 12120Thailand State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China

Membrane fouling poses a significant challenge to the sustainable development of membrane bioreactor(MBR)technologies for wastewater *** accurate prediction of the membrane filtration process is of great importance for identifying and controlling *** learning methods address the limitations of traditional statistical approaches,such as low accuracy,poor generalization ability,and slow convergence,particularly in predicting complex filtration and fouling processes within the realm of big *** article provides an in-depth exposition of machine learning *** study then reviews advances in MBRs that utilize machine learning methods,including artificial neural networks(ANN),support vector machines(SVM),decision trees,and ensemble *** on current literature,this study summarizes and compares the model input and output characteristics(including foulant characteristics,solution environments,filtration conditions,operating conditions,and time factors),as well as the selection of models and optimization *** modeling procedures of SVM,random forest(RF),back propagation neural network(BPNN),long short-term memory(LSTM),and genetic algorithm-back propagation(GA-BP)methods are elucidated through a tutorial *** simulation results demonstrated that all five methods yielded accurate predictions with R2>***,the existing challenges in the implementation of machine learning models in MBRs were *** is notable that integration of deep learning,automated machine learning(AutoML)and explainable artificial intelligence(XAI)may facilitate the deployment of models in practical engineering *** insights presented here are expected to facilitate the establishment of an intelligent control framework for MBR processes in future endeavors.

关键词： Membrane bioreactor Machine learning Pollutant removal Membrane fouling Model prediction

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A Summary of Rotational Speed Measurement Techniques for Bearing and Rotor systems—Part I

A Summary of Rotational Speed Measurement Techniques for Bea...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Ren, Zhijun Lin, Tantao Zhu, Yongsheng Hong, Jun Feng, Ke Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong University Xi’an710049 China School of Engineering The University of British Columbia KelownaV1V1V7 Canada

ISBN: (纸本)9783031494208

Rotational speed measurement is a fundamental technology used in various industries to monitor and control the speed of rotating equipment. Accurate speed measurement is crucial for optimizing performance, ensuring safety, and monitoring equipment health. This summary explores different techniques used for measuring rotational speed, which are categorized into analog, digital, and hybrid analog-digital methods. Analog methods convert rotation into proportional signals in other media, while digital methods convert rotation into a series of electrical pulses with the number of pulses proportional to the speed. hybrid analog-digital methods convert rotation into proportional signals and calculate the frequency of the signal to obtain speed. This summary provides guidance for the design, optimization, and application of rotational speed measurement solutions in the industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Speed regulators

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A situation awareness assessment method based on fuzzy cognitive maps

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Journal of systems Engineering and Electronics 2022年第5期33卷 1108-1122页

作者： CHEN Jun GAO Xudong RONG Jia GAO Xiaoguang School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710072China Chongqing Institute for Brain and Intelligence Guangyang Bay LaboratoryChongqing 400064China Department of Data Science and AI Monash UniversityClayton VIC3800Australia

The status of an operator’s situation awareness is one of the critical factors that influence the quality of the *** the measurement method of the situation awareness status is an important topic to *** far,there are lots of methods designed for the measurement of situation awareness status,but there is no model that can measure it accurately in real-time,so this work is conducted to deal with such a ***,collect the relevant physiological data of operators while they are performing a specific mission,simultaneously,measure their status of situation awareness by using the situation awareness global assessment technique(SAGAT),which is known for accuracy but cannot be used in *** then,after the preprocessing of the raw data,use the physiological data as features,the SAGAT’s results as a label to train a fuzzy cognitive map(FCM),which is an explainable and powerful intelligent ***,a hybrid learning algorithm of particle swarm optimization(PSO)and gradient descent is proposed for the FCM *** final results show that the learned FCM can assess the status of situation awareness accurately in real-time,and the proposed hybrid learning algorithm has better efficiency and accuracy.

关键词： situation awareness(SA) fuzzy cognitive map(FCM) particle swarm optimization(PSO) gradient descent

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Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

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Chinese Journal of Chemical Engineering 2024年第2期66卷 167-179页

作者： Zhiqiang Wang Dakuo He Haotian Nie College of Information Science and Engineering Northeastern UniversityShenyang 110004China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110004China

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial *** paper addresses the fluctuation problem of CCG through an operational optimization ***,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore ***,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the *** on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be *** ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution ***,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

关键词： Weighted Gaussian process regression Index-oriented adaptive differential evolution Operational optimization Copper flotation process

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Design optimization of a multi-source renewable energy system using a novel method based on selective ensemble learning

Design optimization of a multi-source renewable energy syste...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Bouaouda, Anas Sayouti, Yassine LSIB Laboratory FST Mohammedia Hassan II University of Casablanca Morocco

This study conducts a comparative assessment to optimize the design of a hybrid Renewable Energy System (HRES) consisting of PV panels, wind turbines (WTs), and hydrogen storage (PV/WT/FC). The study focuses on Dakhla City, leveraging its favorable weather conditions to evaluate the potential of renewable energy sources. A novel approach, Selective Ensemble Marine Predators (SEMPA), is introduced. SEMPA utilizes a selective ensemble learning strategy to enhance the performance of the Marine Predators Algorithm (MPA) and determine the optimal system size with the lowest Total Net Present Cost (TNPC) and improved reliability. To benchmark SEMPA's performance, we compare it against established methods, including MPA, Particle Swarm optimization (PSO), and Harmony Search (HS). Results demonstrate that the integrated PV/WT/FC system provides a reliable and cost-effective energy solution for the southwest region of Morocco, particularly in Dakhla. SEMPA outperforms HS, PSO, and traditional MPA, achieving stable convergence after 30 iterations. The system exhibits a superior Cost of Energy (COE) at 0.3127 $/kWh, a TNPC of 0.9689 M$, and a low Loss of Power Supply Probability (LPSP) at 0.04747. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： hybrid systems

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Stochastic normalized gradient descent with momentum for large-batch training

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Science China(Information Sciences) 2024年第11期67卷 77-91页

作者： Shen-Yi ZHAO Chang-Wei SHI Yin-Peng XIE Wu-Jun LI National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing University

Stochastic gradient descent(SGD) and its variants have been the dominating optimization methods in machine learning. Compared with SGD with small-batch training, SGD with large-batch training can better utilize the computational power of current multi-core systems such as graphics processing units(GPUs)and can reduce the number of communication rounds in distributed training settings. Thus, SGD with large-batch training has attracted considerable attention. However, existing empirical results showed that large-batch training typically leads to a drop in generalization accuracy. Hence, how to guarantee the generalization ability in large-batch training becomes a challenging task. In this paper, we propose a simple yet effective method, called stochastic normalized gradient descent with momentum(SNGM), for large-batch training. We prove that with the same number of gradient computations, SNGM can adopt a larger batch size than momentum SGD(MSGD), which is one of the most widely used variants of SGD, to converge to an?-stationary point. Empirical results on deep learning verify that when adopting the same large batch size,SNGM can achieve better test accuracy than MSGD and other state-of-the-art large-batch training methods.

关键词： non-convex problems large-batch training stochastic normalized gradient descent momentum

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Adaptive blind control strategy based on iterative optimization to minimize cooling and lighting demands in office space

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Building Simulation 2025年第1期18卷 207-223页

作者： Shen Zhang Zichuan Nie Sisi Chen Lihua Zhao State Key Laboratory of Subtropical Building and Urban Science School of ArchitectureSouth China University of TechnologyGuangzhou510640China

The emergence of adaptive facades offers a new approach for buildings to enhance their resilience against external weather conditions while responding to occupants’demands,thereby improving both indoor environmental quality and energy *** control methods are crucial to achieving these ***,most existing studies for automatic control of blinds have focused on visual comfort,leaving potential for further energy savings by reducing cooling and artificial lighting ***,current optimization methods for slat angles are mostly simplified as a discrete process,neglecting the impact of thermal mass in building ***,this paper aims to explore the energy reduction potential of window blinds by developing an iterative optimization method for devising hourly adaptive control *** this end,a co-simulation platform between EnergyPlus and Python was established for the optimization and a case study in a subtropical city was *** proposed strategies effectively balanced lighting and cooling demands to achieve an overall energy reduction of 7.3%–12.5%compared to reference cases while also ensuring visual comfort by mitigating glare risk and excessive *** advantages were also compared with several simpler control scenarios,with analyses tailored to various glazing types and ***,the optimal window configurations with blind control strategies for different orientations were *** findings also indicated that glass properties markedly impact the performance of control strategies,underscoring the necessity of holistically considering shading components and glazing types in the optimization to achieve optimal performance.

关键词： adaptive control iterative optimization window blinds building energy efficiency visual comfort

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