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SSRN 2023年

作者： Li, Yunlong Tan, Tao Xie, Yinlei Duan, Chao Lewis, Elfed Tian, Ye Zhang, Jianzhong Key Lab of In-Fiber Integrated Optics of Ministry of Education Harbin Engineering University Harbin150001 China Fiber Optical Sensing Center for Excellence Yantai Research Institute & Graduate School Harbin Engineering University Yantai264000 China Optical Fibre Sensors Research Centre Department of Electronic and Computer Engineering University of Limerick Limerick Ireland

A self-decoupling six-dimensional force/torque sensor based on Fiber Bragg grating is described, which is designed based on a spherical rotor and a double-layer beam. 14 FBGs are attached in a symmetric fashion to the elastic structure to enable differential measurement of force/torque-induced strains. The sensing characteristics are obtained through simulation and experiment. Calibration experiments demonstrate low cross coupling errors of 2.26%, 2.37%, 1.49%, 2.89%, 1.79% and 1.66% within the range of ±5.72 N, ±5.72 N, ±5.72 N, ±0.206 Nm, ±0.206 Nm and ±0.147 Nm, for the detection of Fx, Fy, Fz, Mx, My and Mz, respectively. Compared with the existing six-dimensional force/torque sensor, the designed sensor adopts a spherical sliding structure to reduce the coupling between various dimensions, which simplifies the calibration and maintenance of the sensor. This sensor can be widely used in six-dimensional force/torque measurement and has significance for widespread use in the robotics, medicine, and aerospace fields. © 2023, The Authors. All rights reserved.

关键词： Fiber Bragg gratings

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ISLab at ROCLING 2023 MultiNER-Health Task: A Three-Stage NER Model Combining Textual Content and Label Semantics 35

ISLab at ROCLING 2023 MultiNER-Health Task: A Three-Stage NE...

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35th Conference on Computational Linguistics and Speech Processing, ROCLING 2023

作者： Wu, Jun-Jie Chang, Tao-Hsing Hsu, Fu-Yuan Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Institute for Research Excellence in Learning Sciences Research Center for Psychological and Educational Testing National Taiwan Normal University Taipei Taiwan

ISBN: (纸本)9789869576963

ROCLING 2023 MultiNER-Health Task is to identify named entities in the healthcare domain. These recognized named entities need to be further classified into one of ten categories. This study proposes a threestage framework. The first stage of the framework is to recognize and categorize named entities using Label Semantics Model. This model employs both of textual content and label semantics to identify name entities and their categories. In the second stage, the Label Correction Model is used to correct abnormal labels. In the third stage, a rule-based approach is employed to revise the abnormal labels that cannot be corrected in the second stage. The proposed method is evaluated on three test sets provided by the task and its average F1score is 0.6838. It is the best performing model for SM test set among all participating task models. © 2023 ROCLING 2023 - Proceedings of the 35th Conference on Computational Linguistics and Speech Processing. All rights reserved.

关键词： Semantics

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Coalition Formation of Microgrids with Distributed Energy Resources and Energy Storage in Energy Market

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Journal of Modern Power Systems and Clean Energy 2020年第5期8卷 906-918页

作者： Jaber Valinejad Mousa Marzband Mert Korkali Yijun Xu Ameena Saad Al-Sumaiti IEEE Bradley Department of Electrical and Computer Engineering Virginia TechNorthern Virginia CenterGreater Washington D.C.VA 22043USA Department of Computer Science Virginia TechNorthern Virginia CenterGreater Washington D.C.VA 22043USA Department of Mathematics Physicsand Electrical EngineeringNorthumbria UniversityNewcastleUK Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21589Saudi Arabia Computational Engineering Division Lawrence Livermore National LaboratoryLivermoreCA 94550USA Advanced Power and Energy Center Electrical Engineering and Computer ScienceKhalifa UniversityAbu DhabiP.O.Box 127788UAE

Power grids include entities such as home-microgrids(H-MGs),consumers,and retailers,each of which has a unique and sometimes contradictory objective compared with others while exchanging electricity and heat with other ***,there is the need for a smart structure to handle the new *** paper proposes a bilevel hierarchical structure for designing and planning distributed energy resources(DERs)and energy storage in H-MGs by considering the demand response(DR).In general,the upper-level structure is based on H-MG generation competition to maximize their individual and/or group income in the process of forming a coalition with other *** upper-level problem is decomposed into a set of low-level market clearing *** electricity and heat markets are simultaneously modeled in this ***,including wind turbines(WTs),combined heat and power(CHP)systems,electric boilers(EBs),electric heat pumps(EHPs),and electric energy storage systems,participate in the electricity *** addition,CHP systems,gas boilers(GBs),EBs,EHPs,solar thermal panels,and thermal energy storage systems participate in the heat *** show that the formation of a coalition among H-MGs present in one grid will not only have a significant effect on programming and regulating the value of the power generated by the generation resources,but also impact the demand consumption and behavior of consumers participating in the DR program with a cheaper market clearing price.

关键词： Microgrids distributed energy resource(DER) electricity market heat market demand response(DR) coalition formation energy storage

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Unsupervised Unlearning of Concept Drift with Autoencoders

arXiv

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arXiv 2022年

作者： Artelt, André Malialis, Kleanthis Panayiotou, Christos G. Polycarpou, Marios M. Hammer, Barbara Faculty of Technology Bielefeld University Bielefeld Germany KIOS Research Innovation Center of Excellence Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

Concept drift refers to a change in the data distribution affecting the data stream of future samples. Consequently, learning models operating on the data stream might become obsolete, and need costly and difficult adjustments such as retraining or adaptation. Existing methods usually implement a local concept drift adaptation scheme, where either incremental learning of the models is used, or the models are completely retrained when a drift detection mechanism triggers an alarm. This paper proposes an alternative approach in which an unsupervised and model-agnostic concept drift adaptation method at the global level is introduced, based on autoencoders. Specifically, the proposed method aims to "unlearn" the concept drift without having to retrain or adapt any of the learning models operating on the data. An extensive experimental evaluation is conducted in two application domains. We consider a realistic water distribution network with more than 30 models in-place, from which we create 200 simulated data sets/scenarios. We further consider an image-related task to demonstrate the effectiveness of our method. © 2022, CC BY.

关键词： Learning systems

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Image Acquisition and Deep Convolutional Neural Network Incorporated Cost-Effective Automated Nursery Disease Detection Method for the Tea Industry

Image Acquisition and Deep Convolutional Neural Network Inco...

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Moratuwa engineering Research Conference (MERCon)

作者： Dinithi Bhagya Weerasekara Nipun Shantha Kahatapitiya Nuwan Sanjeewa Vimukthi Madushan Wathudura Nuwan Prasanna Deshan Kalupahana Udaya Wijenayake Bhagya Nathali Silva Akila Wijethunge Ruchire Eranga Wijesinghe Department of Materials and Mechanical Technology Faculty of Technology University of Sri Jayewardenepura Pitipana Sri Lanka Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka IOT Solutions Pvt Ltd Homagama Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Excellence in Informatics Electronics & Transmission (CIET) Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331529048

ISBN: (纸本)9798331529055

Tea cultivation, a globally significant industry across over 60 countries, requires effective management of tea nurseries for robust plant growth and disease control. Implementing automated disease detection in tea nurseries using advanced technologies enhances efficiency, precision, and sustainability compared to manual inspection methods. An image acquisition system incorporating a Deep Convolutional Neural Network (DCNN) was developed as a cost-effective automated solution for nursery disease detection. First, images of Blister Blight (BB) disease in tea plants were collected primarily from various estates. These images were then preprocessed, labeled, and augmented to create a robust dataset. Two models based on the YOLOv5 architecture were trained, with the second model benefiting from dataset augmentation and enhanced labeling. The Improved Model achieved a mean Average Precision (mAP) of 80.6% at a 0.5 threshold, demonstrating its high accuracy in BB detection in tea nurseries. It was integrated with the image acquisition system to enable continuous monitoring of tea leaves in nursery plantations and facilitate the immediate detection of BB diseases. This system holds significant potential for further enhancement in disease management across the tea industry, promoting healthier crops, reducing costs, and minimizing environmental impact.

关键词： Industries YOLO Costs Accuracy Plantations Crops Real-time systems Convolutional neural networks Monitoring Diseases

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Quality of Service (QoS)-driven Edge Computing and Smart Hospitals: A Vision, Architectural Elements, and Future Directions

arXiv

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arXiv 2023年

作者： Buyya, Rajkumar Srirama, Satish N. Mahmud, Redowan Goudarzi, Mohammad Ismail, Leila Kostakos, Vassilis Lab School of Computing and Information Systems The University of Melbourne Australia School of Computer and Information Sciences The University of Hyderabad India School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University United Arab Emirates

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This paradigm opens the door to new innovations for creating novel types of interactions among objects, services, and people in smarter ways to enhance the quality of patient services and the efficient utilisation of resources. However, the realisation of real-time IoT applications in healthcare and, ultimately, the development of Smart Hospitals are constrained by their current Cloud-based computing environment. Edge computing emerged as a new computing model that harnesses edge-based resources alongside Clouds for real-time IoT applications. It helps to capitalise on the potential economic impact of the IoT paradigm of $11 trillion per year, with a trillion IoT devices deployed by 2025 to sense, manage and monitor the hospital systems in real-time. This vision paper proposes new algorithms and software systems to tackle important challenges in Edge computing-enabled Smart Hospitals, including how to manage and execute diverse real-time IoT applications and how to meet their diverse and strict Quality of Service (QoS) requirements in hospital settings. The vision we outline can help tackle timely challenges that hospitals increasingly face. © 2023, CC BY.

关键词： Internet of things

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Server configuration optimization in mobile edge computing: A cost-performance tradeoff perspective

Server configuration optimization in mobile edge computing: ...

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作者： He, Zhenli Li, Kenli Li, Keqin Zhou, Wei College of Computer Science and Electronic Engineering & National Supercomputing Center in Changsha Hunan University Changsha China College of Software Yunnan University Kunming China Department of Computer Science State University of New York New PaltzNY United States

Before service providers build up an mobile edge computing (MEC) platform, an important issue that needs to be considered is the configuration of computing resources on edge servers. Since the computing resources on an edge server are limited compared with a cloud server and the service provider's deployment budget is limited, it would be unrealistic to equip all edge servers with abundant computing resources. In addition, the edge servers have different computation demands due to their different geographies. Therefore, this article investigates the problem of server configuration optimization in an MEC environment based on a given computation demand statistics of the selected deployment locations. Our strategy is to treat each edge server as an M/M/m queueing model, and then establish the performance and cost models for the system. Two optimization problems, including cost constrained performance optimization, and performance constrained cost optimization are formulated based on our models and solved by a series of fast numerical algorithms. We also conduct extensive numerical simulation examples to show the effectiveness of the proposed algorithms. MEC service providers can use our strategy to get the appropriate type of processor and obtain the optimal processor number for each edge server to achieve two different goals: (1) deliver the highest-quality services with a given cost constraint;(2) minimize the investment cost with a service-quality guarantee. Our research is of great significance for service providers to control the tradeoff between investment cost and service quality. © 2021 John Wiley & Sons, Ltd.

关键词： Mobile edge computing

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High-Resolution Optical Imaging Incorporated Non-Destructive Morphological Boundary Detection of Fruit Disorders: A Pilot Study

High-Resolution Optical Imaging Incorporated Non-Destructive...

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Advancements in Computing (ICAC), International Conference on

作者： Nethmi Tharushi Hakmana Nipun Shantha Kahatapitiya Sasmitha Weerasinghe Deshan Kalupahana Bhagya Nathali Silva Naresh Kumar Ravichandran Daewoon Seong Sangyeob Han Seung Yeol Lee Hee-Young June Mansik Jeon Jeehyun Kim Udaya Wijenayake Ruchire Eranga Wijesinghe Department of Mechanical Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Excellence in Informatics Electronics & Transmission (CIET) Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Scientific Instrumentation Korea Basic Science Institute Daejeon Republic of Korea School of Electronic and Electrical Engineering College of IT Engineering Kyungpook National University Daegu Republic of Korea ICT Convergence Research Center Kyungpook National University Daegu Republic of Korea Department of Plant Medicine Kyungpook National University Daegu Republic of Korea Institute of Plant Medicine Kyungpook National University Daegu Republic of Korea Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Plant diseases caused by various pathogens, such as fungi, bacteria, and viruses, lead to significant reductions in crop yield and quality. Early detection of these diseases is critical for preventing their spread and optimizing crop yield. Traditional methods of disease detection, while effective, often involve invasive or time-consuming procedures that may not provide real-time insights. This study investigates the use of Swept-Source Optical Coherence Tomography (SS-OCT) for non-destructive, high-resolution imaging to identify morphological abnormalities in fruit specimens affected by diseases. The research focuses on differentiating healthy regions, healthy boundaries, infected boundaries, and fully infected regions in apples. Using a 1310 nm center wavelength laser, the SS-OCT system captured detailed cross-sectional images of fruit tissue, revealing microstructural changes associated with disease progression. A qualitative assessment of the two-dimensional (2D) OCT images highlighted distinct layers in healthy tissues, noticeable structural deformities, and the merging of microstructural layers in the boundary and infected regions. Quantitative depth-profile analysis of axial-direction A-scan profiles further demonstrated the reduction and merging of intensity peaks correlating with tissue abnormalities in infected fruit specimens. These findings underscore the potential of SS-OCT as an effective tool for early disease detection in agricultural products, providing a means for timely intervention and optimized postharvest management.

关键词： Plant diseases Pathogens Statistical analysis Two-dimensional displays Merging High-resolution imaging Optical imaging Real-time systems Crop yield Diseases

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Info-Gap-Based Optimization of Microgrids Integrated with Power, Cooling and Hydrogen Generation Units

Info-Gap-Based Optimization of Microgrids Integrated with Po...

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Compatibility in Power Electronics (CPE)

作者： Fayezeh Mahmoudnezhad Mohammad Amin Mirzaei Freddy Plaum Roya Ahmadiahangar Jako Kilter Argo Rosin Smart City Center of Excellence (Finest Twins) Tallinn Estonia Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

ISBN: (数字)9781665496780

ISBN: (纸本)9781665496797

The emerging concept of integrated demand response (IDR) is a promising platform for promoting the utilization of renewable energy sources (RES) and improving energy efficiency in multi-energy systems. In this paper, a robust scheduling model for a power, cool, and hydrogen-based islanded microgrid (MG) in the presence of hydrogen fueling stations (HFSs) and EV parking lots (EVPLs) is proposed. Additionally, the impact of power and cooling-based IDR is investigated in reducing the operating cost of the proposed system. The proposed multi-energy MG is equipped with power generation, wind turbine, electrical, cooling and hydrogen storage systems, power to hydrogen (P2H) facility and an electrical chiller to meet power, cooling, and hydrogen demands simultaneously. In order to deal with wind power generation uncertainty, a robust optimization approach is employed without the need for probability density function (PDF) or scenario generation, which strengthens the optimal operation of the proposed multi-energy MG against the wind power uncertainty and allows the operator to apply a risk-averse approach. Numerical results demonstrate that the establishment of IDR in the presence of storage systems reduces the total operating costs by 5.6%.

关键词： Costs Uncertainty Hydrogen storage Cooling Hydrogen Optimal scheduling Microgrids

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Ensemble meta machine learning for predicting the adsorption of anionic and cationic dyes from aqueous solutions using Polymer/graphene/clay/MgFeAl-LTH nanocomposite

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Chemosphere 2024年 349卷 140861页

作者： BinMakhashen, Galal M. Bahadi, Salem A. Al-Jamimi, Hamdi A. Onaizi, Sagheer A. Computer Science and Engineering King Fahd University of Petroleum and Minerals Dhahran31216 Saudi Arabia Research Excellence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Department of Chemical Engineering King Fahd University of Petroleum and Minerals Dhahran31216 Saudi Arabia Interdisciplinary Research Center for Hydrogen and Energy Storage King Fahd University of Petroleum and Minerals Dhahran31216 Saudi Arabia

Adsorption is one of the most promising wastewater treatment methods due to its simplicity and efficacy at ambient temperature and pressure. However, the technical and economic feasibility of this process largely depends on the performance of the utilized adsorbents. In this study, a promising adsorbent made of polyethyleneimine, graphene oxide (GO), bentonite, and MgFeAl-layered triple hydroxide (MgFeAl-LTH) has been synthesized and characterized. The results revealed that the synthesized nanocomposite (abbreviated as PGB-LTH) possesses good porosity and crystallinity. The adsorption performance of the PGB-LTH nanocomposite towards two harmful water pollutants (i.e., methyl orange (MO) and crystal violet (CV)) was investigated, and the results revealed that the nanocomposite outperforms its parental materials (i.e., GO, bentonite, and MgFeAl-LTH). The maximum adsorption capacity (qmax) of MO and CV onto the nanocomposite could reach 1666.7 and 1250.0 mg/g, respectively, as predicted using the Langmuir adsorption isotherm. Additionally, the PGB-LTH nanocomposite is highly reusable with an insignificant decline in performance upon repetitive use. In terms of thermodynamics, MO adsorption onto the nanocomposite is exothermic while CV adsorption is endothermic despite that both dyes adsorb spontaneously as revealed by the negative values of the Gibbs free energy change at all the examined temperatures. The generated adsorption data were utilized for constructing and assessing ensemble meta-machine learning techniques aimed at cost-effective simulation and prediction of the proposed adsorption method. Bagging and boosting methods were developed and evaluated intensively using the obtained adsorption data. The Extra Trees model achieved promising results as evidenced by the high correlation coefficient of 99% as well as low computed RMSE and MAE errors of 11.42 and 5.11, respectively, during the testing phase. These results demonstrate the model strong capability to effe

关键词： Nanocomposites

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