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An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

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Building simulation 2024年第5期17卷 739-770页

作者： Davide Coraci silvio Brandi Tianzhen Hong Alfonso Capozzoli Politecnico di Torino Department of EnergyTEBE research groupBAEDA Lab.Corso Duca degli Abruzzi 24Torino10129Italy Building Technology and Urban Systems Division Lawrence Berkeley National LaboratoryOne Cyclotron RoadBerkeleyCA 94720USA

Deep Reinforcement learning(DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-based Controllers(RBCs),but it still lacks scalability and generalisation due to the necessity of using tailored models for the training *** learning(TL)is a potential solution to address this ***,existing TL applications in building control have been mostly tested among buildings with similar features,not addressing the need to scale up advanced control in real-world scenarios with diverse energy *** paper assesses the performance of an online heterogeneous TL strategy,comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and *** study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy storage(TEs).The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems,different building envelope features,occupancy schedule and boundary conditions(e.g.,weather and price signal).The TL approach incorporates model slicing,imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target *** show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL ***,online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to ***,online TL achieves worse performance compared to offline DRL in either transductive or inductive ***,offline Deep Reinforcement learning(DRL)agents should be trained at least for 15 episodes to reach the same level of performance as the online

关键词： transfer learning reinforcement learning building control building energy management

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A novel agile based framework for employee promotion

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International Journal of Information Technology (singapore) 2024年第7期16卷 4481-4487页

作者： Agrawal, Poonam Goyal, shikha Jandwani, Aman Research Scholar Lovely Professional University Mittal School of Business Research Task

The delayed existing assessment practices in organizations created a setback for the progress of the advanced employees into their new jobs. In this manner, to avoid delay, the given paper proposes a predictive framework in the context of human resource (HR) analytics with the assistance of agile methodology and machine learning (ML) classifiers. The proposed structure is utilized with agile methodology to recognize a couple of variables that could be properties in the predictive system for concluding which employee will get promoted in view of their past experiences. This methodology involves phases such as requirements planning, design, development, testing, and deployment. It covers the end-to-end pipeline in a machine learning framework, which includes the pre-processing of data, feature selection, classification, and validation. The performance of the proposed framework is validated based on metrics such as accuracy, sensitivity, and specificity by taking an open-source Kaggle dataset (‘Employee’s performance for HR analytics’) into consideration. The results show that the proposed framework efficiently outperforms the recent techniques, thereby contributing to the productivity, collaboration, and well-being of employees in an organization. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Agile methodology Data mining HR analytics Key performance indicators (KPI’s) Machine learning

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Deep Reinforcement learning based Resource Allocation for Fault Detection with Cloud Edge Collaboration in smart Grid

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CsEE Journal of Power and Energy systems 2024年第3期10卷 1220-1230页

作者： Qiyue Li Yadong Zhu Jinjin Ding Weitao Li Wei sun Lijian Ding Hefei University of Technology Engineering Technology Research Center of Industrial Automation Hefei 230009China Electric Power Research Institute of State Grid Anhui Electric Power Co. Ltd.Hefei 230000China Hefei University of Technology Hefei 230009China IEEE CSEE

Real-time fault detection is important for operation of smart *** has become a trend of future development to design an anomaly detection system based on deep learning by using the powerful computing power of the ***,delay of Internet transmission is large,which may make the delay time of detection and transmission go beyond the ***,the edge-based scheme may not be able to undertake all data detection tasks due to limited computing resources of edge ***,we propose a cloud-edge collaborative smart grid fault detection system,next to which edge devices are placed,and equipped with a lightweight neural network with different precision for fault *** addition,a sub-optimal and realtime communication and computing resource allocation method is proposed based on deep reinforcement *** method greatly speeds up solution time,which can meet the requirements of data transmission delay,maximize the system throughput,and improve communication *** results show the scheme is superior in transmission delay and improves real-time performance of the smart grid detection system.

关键词： Cloud-edge collaboration communication delay deep reinforcement learning fault detection smart grid

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Unleashing the Power of Multi-Agent Reinforcement learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information systems

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computers, Materials & Continua 2024年第8期80卷 3123-3138页

作者： saket sarin sunil K.singh sudhakar Kumar shivam Goyal Brij Bhooshan Gupta Wadee Alhalabi Varsha Arya Chandigarh College of Engineering and Technology Chandigarh160019India Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Department of Computer Science Immersive Virtual Reality Research GroupKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut11022801Lebanon UCRD Chandigarh UniversityChandigarh140413India

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic *** in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading *** meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading *** AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital *** research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital ***,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

关键词： Neurodynamic Fintech multi-agent reinforcement learning algorithmic trading digital financial frontier

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Geographic-scale Coffee Cherry Counting with smartphones and Deep learning

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Plant Phenomics 2024年第2期6卷 390-400页

作者： Juan Camilo Rivera-Palacio Christian Bunn Eric Rahn Daisy Little-savage Paul Günter schmidt Masahiro Ryo Leibniz Centre for Agricultural Landscape Research(ZALF) Müncheberg15374Germany Alliance of Bioversity International and CIAT Rome00153Italy Brandenburg University of Technology Cottbus-Senftenberg Cottbus03046Germany Producers Direct LondonE28EXUK

Deep learning and computer vision,using remote sensing and drones,are 2 promising nondestructive methods for plant monitoring and ***,their applications are infeasible for many crop systems under tree canopies,such as coffee crops,making it challenging to perform plant monitoring and phenotyping at a large spatial scale at a low *** study aims to develop a geographic-scale monitoring method for coffee cherry counting,supported by an artificial intelligence(AI)-powered citizen science *** approach uses basic smartphones to take a few pictures of coffee trees;2,968 trees were investigated with 8,904 pictures in Junin and Piura(Peru),Cauca,and Quindio(Colombia)in 2022,with the help of nearly 1,000 smallholder coffee ***,we trained and validated YOLO(You Only Look Once)v8 for detecting cherries in the dataset in *** average number of cherries per picture was multiplied by the number of branches to estimate the total number of cherries per *** model's performance in Peru showed an R^(2)of *** the model was tested in Colombia,where different varieties are grown in different biogeoclimatic conditions,the model showed an R^(2)of *** overall performance in both countries reached an R^(2)of *** results suggest that the method can be applied to much broader scales and is transferable to other varieties,countries,and *** our knowledge,this is the first AI-powered method for counting coffee cherries and has the potential for a geographic-scale,multiyear,photo-based phenotypic monitoring for coffee crops in low-income countries worldwide.

关键词： geographic learning scale coffee deep with cherry counting smartphones

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A Participatory research Approach Applying Video stories and sketches 8th

A Participatory Research Approach Applying Video Stories and...

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8th EAI International Conference on Design, learning and Innovation, DLI 2023

作者： Henningsen, Birgitte Ørngreen, Rikke Research Group Department of Culture and Learning Aalborg University Copenhagen Campus Copenhagen Denmark

ISBN: (纸本)9783031673061

This empirical research study explores the use of video stories and video sketches applied in participatory and design-based research. The aim is to explore whether and how the two methods can provide a space of inquiry and broaden the insight into a phenomenon from a research perspective. The empirical data are derived from a research and development project exploring the transition processes of young people leaving their childhood homes. The theoretical framework is inspired by inquiry-based learning. Workshops were conducted with young participants aiming to create a time, space, and process for them to explore, frame, and communicate their perspectives. The analysis reveals alternative perspectives when the young participants used video stories and video sketches to investigate the data collected earlier in the research project. This paper illustrates how researchers can employ these methods and how video stories and video sketches can be analyzed. Furthermore, it addresses the challenging aspects that require consideration when using video stories and video sketches. © ICsT Institute for computer sciences, social Informatics and Telecommunications Engineering 2024.

关键词： Design-based Participatory research sketching storytelling Video

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Machine learning model based on non-convex penalized huberized-sVM

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Journal of Electronic science and Technology 2024年第1期22卷 81-94页

作者： Peng Wang Ji Guo Lin-Feng Li School of Finance and Economics Xizang Minzu UniversityXianyang712082China Research Center for Quality Development of Xizang Special Industries Xianyang712082China School of Computer Science and Technology Xinjiang UniversityUrumqi830017China

The support vector machine(sVM)is a classical machine learning *** the hinge loss and least absolute shrinkage and selection operator(LAssO)penalty are usually used in traditional ***,the hinge loss is not differentiable,and the LAssO penalty does not have the Oracle *** this paper,the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property,contributing to higher accuracy than traditional *** is experimentally demonstrated that the two non-convex huberized-sVM methods,smoothly clipped absolute deviation huberized-sVM(sCAD-HsVM)and minimax concave penalty huberized-sVM(MCP-HsVM),outperform the traditional sVM method in terms of the prediction accuracy and classifier *** are also superior in terms of variable selection,especially when there is a high linear correlation between the *** they are applied to the prediction of listed companies,the variables that can affect and predict financial distress are accurately filtered *** all the indicators,the indicators per share have the greatest influence while those of solvency have the weakest *** companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.

关键词： Huberized loss Machine learning Non-convex penalties support vector machine(sVM)

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Machine learning applied to species occurrence and interactions:the missing link in biodiversity assessment and modelling of Antarctic plankton distribution

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Ecological Processes 2024年第3期13卷 192-209页

作者： Marco Grillo stefano schiaparelli Tiziana Durazzano Letterio Guglielmo Antonia Granata Falk Huettmann Department of Physical Sciences Earth and Environment(DSFTA)University of SienaSienaItaly Italian National Antarctic Museum(MNA Section of Genoa)University of GenoaGenoaItaly Department of Earth Environmental and Life Sciences(DISTAV)University of GenoaGenoaItaly Department of Arctic and Marine Biology UiT The Arctic University of NorwayTromsøNorway Integrative Marine Ecology Department Stazione Zoologica Anton DohrnNaplesItaly Institute of Polar Sciences National Research Council(CNR)MessinaItaly Department of Biological ChemicalPharmaceuticaland Environmental Sciences(ChiBioFarAm)University of MessinaMessinaItaly -EWHALE Lab- Biology&Wildlife DepartmentInstitute of Arctic BiologyUniversity of Alaska FairbanksAlaskaUSA

Background Plankton is the essential ecological category that occupies the lower levels of aquatic trophic networks,representing a good indicator of environmental ***,most studies deal with distribution of single species or taxa and do not take into account the complex of biological interactions of the real world that rule the ecological *** This study focused on analyzing Antarctic marine phytoplankton,mesozooplankton,and microzooplankton,examining their biological interactions and *** data yielded 1053 biological interaction values,762 coexistence values,and 15 zero *** phytoplankton assemblages and six copepod species were selected based on their abundance and ecological *** 23 environmental descriptors,we modelled the distribution of taxa to accurately represent their *** was conducted during the 2016–2017 Italian National Antarctic Programme(PNRA)‘P-ROsE’project in the East Ross *** learning techniques were applied to the occurrence data to generate 48 predictive species distribution maps(sDMs),producing 3D maps for the entire Ross sea *** models quantitatively predicted the occurrences of each copepod and phytoplankton assemblage,providing crucial insights into potential variations in biotic and trophic interactions,with signifcant implications for the management and conservation of Antarctic marine *** Receiver Operating Characteristic(ROC)results indicated the highest model efciency,for Cyanophyta(74%)among phytoplankton assemblages and Paralabidocera antarctica(83%)among copepod *** sDMs revealed distinct spatial heterogeneity in the Ross sea area,with an average Relative Index of Occurrence values of 0.28(min:0;max:0.65)for phytoplankton assemblages and 0.39(min:0;max:0.71)for *** The results of this study are essential for a science-based management for one of the world’s most pristine ecosystems and addressing potential climate-in

关键词： Copepoda Phytoplankton Ross sea Terra Nova Bay species Distribution Model Marine trophic web

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IoT Technology and Digital Upskilling Framework for Farmers in the Northern Rural Area of Thailand

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Journal of Mobile Multimedia 2023年第5期19卷 1129-1152页

作者： Yooyativong, Thongchai Kamyod, Chayapol School of Information Technology Mae Fah Luang University 57100 Thailand Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Mae Fah Luang University 57100 Thailand

One-third of Thailand’s workers are in agriculture, but the country’s agricultural GDP is still less than 10% of its total GDP. Most Thai farmers are smallholders with limited land and low incomes. To improve the agricultural GDP and the economic situation of smallholder farmers, the Thai Government has been trying for decades to encourage and support smallholder farmers to adopt modern farming methods and smart farming equipment, including digital technologies. However, the improvement is still sluggish due to a lack of an effective approach to delivering essential digital knowledge and skills, as well as investment support for smart farming equipment. These have hindered smallholder farmers’ digital farming skill progress. To address this issue, the Broadcasting and Telecommunications research and Development Fund for Public Interest has funded a project to develop the Digital Farmer Development Framework. This framework provides essential digital knowledge, training, coaching, and fundamental resources to upgrade smallholder digital-farming literacy to become digital farmers using problem- or project-based learning approaches and collaborative blended learning theories. Bloom’s taxonomy is used as a guideline for evaluating the framework’s effectiveness. Implementation of the Digital Farmer Development Framework has shown that farmers can significantly improve their digital farming literacy and are capable of using digital technology to improve farm management and productivity. based on Bloom classification guidelines, 100% of the farms in the project can apply digital skills and utilize fundamental smart farming equipment as well as able to evaluate and analyze data from IoT devices. Moreover, 66% can create their own smart-system solution from fundamental smart farming tools for their farm. The project has also created a digital farmer community that shares knowledge and resources with others. © 2023 River Publishers

关键词： blended learning collaborative learning digital farmer IoT technology learning framework problem-based learning project-based learning smart farmer

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A Machine learning based Funding project Evaluation Decision Prediction

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computer systems science & Engineering 2023年第5期45卷 2111-2124页

作者： Chuqing Zhang Jiangyuan Yao Guangwu Hu Xingcan Cao Economic Research Institute Beijing Language and Culture UniversityBeijing100083China School of Computer Science and Technology Hainan UniversityHaikou570228China School of Computer Science Shenzhen Institute of Information TechnologyShenzhen518172China Faculty of Arts University of British ColumbiaVancouverCV6T 1Z1Canada

Traditional linear statistical methods cannot provide effective prediction results due to the complexity of human *** this paper,we apply machine learning to the field of funding allocation decision making,and try to explore whether personal characteristics of evaluators help predict the outcome of the evaluation decision?and how to improve the accuracy rate of machine learning methods on the imbalanced dataset of grant funding?since funding data is characterized by imbalanced data distribution,we propose a slacked weighted entropy decision tree(sWE-DT).We assign weight to each class with the help of slacked *** experimental results show that the sWE decision tree performs well with sensitivity of 0.87,specificity of 0.85 and average accuracy of *** also provides a satisfied classification accuracy with Area Under Curve(AUC)=*** implies that the proposed method accurately classified minority class instances and suitable to imbalanced *** adding evaluator factors into the model,sensitivity is improved by over 9%,specificity improved by nearly 8%and the average accuracy also increased by 7%.It proves the feasibility of using evaluators’characteristics as *** by innovatively using machine learning method to predict evaluation decisions based on the personal characteristics of evaluators,it enriches the literature in the field of decision making and machine learning field.

关键词： Expert evaluation prediction machine learning grant allocation

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