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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Hannan Shi, Jun Zhao, Minfan Qiao, Liang Wang, Zhaohui Dai, Yue Ma, Yuchen An, Hong University of Science and Technology of China School of Computer Science and Technology Hefei China University of Virginia School of Engineering & Applied Science Charlottesville United States

ISBN: (纸本)9798350371499

Recently, automatic segmentation algorithms based on deep learning have achieved promising results on various pathological image segmentation tasks. However, the inherent data-hungry nature of these methods and the high cost of annotating pathological images make them difficult to apply to practical clinical tasks. To address this problem, many studies have explored interactive segmentation methods to reduce the cost of annotating pathological images, which replace pixel-by-pixel annotation operations with clicks or other user instructions. Despite their effectiveness, most of these methods only utilize small-scale data resulting in poor generalization ability for different tasks or segmentation objects. In this paper, we propose a robust Click-based Interactive Segmentation framework for low-cost and interactive annotation of Pathological images, named Clisp. To improve the generalization ability, we construct a multi-source pathological image annotation dataset including around 79,000 images for supervised training of the proposed method. Besides, we adopt a heavy-parameter vision transformer as the image encoder to learn feature representations for large-scale data. We evaluate the proposed method on 7 open-source datasets, and the experimental results show that Clisp is superior to the baseline method in segmentation performance on most datasets and has outstanding generalization ability. © 2024 IEEE.

关键词： Image segmentation

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A DC-Link Hybrid Active Discharge Scheme for Traction Inverters

A DC-Link Hybrid Active Discharge Scheme for Traction Invert...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Kulkarni, Badrinath Saadat, Nima Bierhoff, Michael Tailor, Vivek Einhorn, Markus Hochschule Stralsund -University of Applied Science Renewable Energy and E-Mobility Stralsund Germany Stuttgart Germany Hochschule Stralsund-University of Applied Science Faculty of Electrical Engineering and Computer Science Stralsund Germany

ISBN: (纸本)9798350364446

when an Electrical Vehicle (EV) encounters an accident or the vehicle is taken to a service station, the DC-link capacitor in the inverter must be discharged to ensure safety of both the passengers and the operator. This paper examines the limitations of traditional discharge techniques and proposes a novel hybrid discharge solution that combines the existing winding-based discharge method with a flyback converter. The proposed technique enhances redundancy, decreases the discharge time and minimize the risk of DC-link voltage overshoot during the regeneration action caused by quadrature axis current injection. The study introduces a low-voltage discharge circuit enabled by a flyback converter using MOSFET in linear mode, presenting two distinct approaches. The paper includes a simulation comparison of winding-based discharge with the proposed Hybrid discharge technique. The proposed solution has a higher discharge rate and reduces the voltage overshoot on the DC-Link capacitor. The proposed hardware is verified using the simulation and experiments conducted on a high voltage supply test rig. © 2024 IEEE.

关键词： Capacitor bank

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Asset Management Strategies for District Heating Networks 8

Asset Management Strategies for District Heating Networks

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8th International Conference on System Reliability and Safety, ICSRS 2024

作者： Grønberg, Jens Tahavori, Maryamsadat Bacher, Peder Dtu Engineering Technology Ballerup Denmark Dtu Department of Applied Mathematics and Computer Science Kongens Lyngby Denmark

ISBN: (纸本)9798350354508

District Heating (DH) became widely adopted in European cities during the 20th century to meet demand for heating from a growing urban population. Today, the desire to decarbonize Europe's heat consumption has lead to an renewed interest in increasing the adoption of DH. To meet this end, DH has to be a price competitive alternative to other heating technologies. Over time the underground heat distribution network that DH companies operate suffers from faults that require replacement of pipes. On-demand replacement is more expensive than planned large scale replacement projects. The only question is, when is the optimal time to renew the pipes? In this article this question will be formulated using Reliability Theory. Our approach is to use historical records of time to failure and a bounding result to estimate the optimal time for replacement. The method can be used by DH companies with historical fault records to form an Asset Management strategy on the question of when to renew which pipes. © 2024 IEEE.

关键词： Asset management

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Estimating Risk-aware Flexibility Areas for Electric Vehicle Charging Pools via AC Stochastic Optimal Power Flow

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1247-1256页

作者： Juan S.Giraldo Nataly Bañol Arias Pedro P.Vergara Maria Vlasiou Gerwin Hoogsteen Johann L.Hurink Energy Transition Studies Group Netherlands Organisation for Applied Scientific Research(TNO)Amsterdam1043 NTThe Netherlands Department of Electrical Engineering Mathematics and Computer ScienceUniversity of TwenteEnschede7522 NBThe Netherlands Intelligent Electrical Power Grids Group Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft2628 CDThe Netherlands

This paper introduces an AC stochastic optimal power flow(SOPF)for the flexibility management of electric vehicle(EV)charging pools in distribution networks under *** AC SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging *** application of the AC SOPF is described where a distribution system operator(DSO)requires flexibility to each charging pool in a day-ahead time frame,minimizing the cost for flexibility while guaranteeing technical *** areas are defined for each charging pool and calculated as a function of a risk parameter involving the uncertainty of the *** show that all players can benefit from this approach,i.e.,the DSO obtains a riskaware solution,while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.

关键词： Electric vehicle flexibility management stochastic optimal power flow(SOPF) risk awareness compensation mechanism

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Efficacious Routing Approaches in Vehicular Ad hoc Network: An Empirical Study 1

Efficacious Routing Approaches in Vehicular Ad hoc Network: ...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Janet Kumari, J Jesy Thangam, S. Raja, A. Saleem Amrita Vishwa Vidhyapeetham Amrita School of Computing Bengaluru Department of Computer Science and Engineering India University of Technology and Applied Science-Shinas Department of Computer Science and Engineering Oman

ISBN: (纸本)9798350342796

VANET, or Vehicular Ad hoc Network is a transient wireless structure made up of different kinds of vehicles serving as network hubs and the links between them acting as links. VANET is an improving mechanism that has received an amount of growth in the fields of research, academia, and business. A VANET is a mobile network that uses vehicles as terminals. VANETs, similar to spontaneous systems, possess all the main features of independent connectivity, including processing and preservation constraints. To improve safety while driving and other services, a network of VANETs can be established. The effectiveness of VANETs has been demonstrated in numerous real-world applications, such as traffic management and driver-assist systems. Information exchanged over VANETs can undoubtedly help users experience a better driving experience and prevent accidents. academia, and government. VANET is an acronym used to describe the Vehicular ad hoc network that forms over-moving vehicles on the road. Vehicle networks are rapidly emerging as a platform for creating and implementing new and classic applications. VANETs are distinguished by their high mobility, frequently changing topology, and ephemeral, one-time connections. Vehicle interruptions are common due to how dynamic and moving vehicles are at present. Clustering techniques for vehicles that arrange to increase network scalability and connection dependability as VANET introduces vehicles in groups. Subsequently, other clustering strategies and techniques also exist, and it is necessary for a wide range of situations, including information transfer, routing, and detection of accidents. The methods used for vehicle clustering techniques are examined in this study in terms of networking techniques. © 2023 IEEE.

关键词： ant colony optimization hybrid protocol proactive reactive VANET

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An Online Scheduler for Reconfigurable Time-Sensitive Networks 29

An Online Scheduler for Reconfigurable Time-Sensitive Networ...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Kummer, Fabian Golatowski, Frank Brekenfelder, Willi Parzyjegla, Helge Danielis, Peter Mühl, Gero Institute of Applied Microelectronics and Computer Engineering University of Rostock Rostock18051 Germany Institute of Computer Science University of Rostock Rostock18051 Germany

ISBN: (纸本)9798350361230

Time-Sensitive Networking (TSN) facilitates the implementation of realtime data traffic with deterministic delay and jitter in Ethernet networks. In its time-triggered communication variant, network paths as well as transmission time slots along the paths need to be planned and reserved for all data streams, respectively. Conventionally, such a feasible (i. e., realtime compliant) network schedule is computed and optimized offline, installed upon deployment, and cannot be changed anymore at runtime without stopping the communication. The latter becomes unsuitable for future industrial use cases with a growing demand for flexibility including schedule adjustments. In this paper, we present an online scheduler capable to incrementally integrate new streams at runtime and to adapt the existing schedule, if necessary, while retaining the realtime guarantees given to active streams. For this purpose, the scheduling heuristic identifies streams whose adjustment requires minimal reconfiguration efforts. In a thorough evaluation, we analyze the scheduler's trade-off between resource usage and schedule quality showing its suitability for diverse application scenarios. © 2024 IEEE.

关键词： Delay tolerant networks

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YARN Schedulers for Hadoop MapReduce Jobs: Design Goals, Issues and Taxonomy

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Recent Advances in computer science and Communications 2023年第6期16卷 44-55页

作者： Kotikam, Gnanendra Selvaraj, Lokesh Department of Information and Communication Engineering Anna University Tamil Nadu Chennai India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore641062 India

Objective: Big Data processing is a demanding task, and several big data processing frameworks have emerged in recent decades. The performance of these frameworks is greatly dependent on resource management models. Methods: YARN is one of such models which acts as a resource management layer and provides computational resources for execution engines (Spark, MapReduce, storm, etc.) through its schedulers. The most important aspect of resource management is job scheduling. Results: In this paper, we first present the design goal of YARN real-life schedulers (FIFO, Capacity, and Fair) for the MapReduce engine. Later, we discuss the scheduling issues of the Hadoop MapReduce cluster. Conclusion: Many efforts have been carried out in the literature to address issues of data locality, heterogeneity, straggling, skew mitigation, stragglers and fairness in Hadoop MapReduce scheduling. Lastly, we present the taxonomy of different scheduling algorithms available in the literature based on some factors like environment, scope, approach, objective and addressed issues. © 2023 Bentham science Publishers.

关键词： Energy utilization

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Analysis of various Classification Techniques using CNN models for the Detection of Alzheimer's Disease using MRI images 14

Analysis of various Classification Techniques using CNN mode...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Hemkiran, S. Sudha Sadasivam, G. Pradeepa, T. Samukthaa, R.S. Ajay Raj, R. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Psg College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335095

Alzheimer's disease (AD) is a brain disorder that is associated with memory loss and is typically observed in elderly and aging individuals. This condition is irreversible in nature. Neural networks have shown better performance than traditional machine learning algorithms when it comes to analyzing high-dimensional data from Magnetic Resonance Images (MRI) brain images. This is an approach for processing such data. Detection of Alzheimer's disease is essential because the early treatment can control the progression of the disease in a slower pace. In this work, the proposed model used different models of convolutional neural networks (CNN) techniques to process the MRI images of the brain and classified them into different classes. The proposed model detects AD using neural network feature extraction and classification methods based on the pre-processed datasets. Here, in the proposed model, various algorithms such as Resnet-50, DenseNet-169 and Hyperparameter tuning of Convolutional Neural Network are utilized to find the better performing model in terms of accuracy when compared with other traditional techniques. © 2023 IEEE.

关键词： Image classification

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A survey on textual emotion cause extraction in social networks

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Digital Communications and Networks 2025年第2期11卷 524-536页

作者： Sancheng Peng Lihong Cao Guojun Wang Zhouhao Ouyang Yongmei Zhou Shui Yu Laboratory of Language Engineering and Computing Center for Linguistics and Applied LinguisticsGuangdong University of Foreign StudiesGuangzhou510006China School of English Education Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China South China University of Technology Guangzhou510006China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science University of Technology SydneySydneyNSW 2007Australia

With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their *** and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing *** Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language *** is different from the previous tasks of emotion recognition and emotion *** addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion *** this paper,we provide a survey for ***,we introduce the development process and classification of ***,we discuss the existing methods and key factors for ***,we enumerate the challenges and developing trend for TECE.

关键词： Text Emotion Emotion cause Machine learning Deep learning

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Cardiac Arrest Prediction Model Using Ensemble Technique 4

Cardiac Arrest Prediction Model Using Ensemble Technique

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Sinha, Dibakar More, Sarla Jeyakrishnan, V. Mishra, Durgesh Manipal University Jaipur Department of Computer Science Engineering Rajasthan Jaipur India Symbiosis University of Applied Sciences School of Computer Science and Information Technology MP Indore India

ISBN: (纸本)9798331518981

The physician employs a diverse range of laboratory diagnostics, physical examinations, and on occasion, even invasive procedures to ascertain the presence of diseases. It is necessary to have a physician that possesses exceptional training, expertise and skill in the specific field, for detection of the disease in its initial phases. Despite the widespread use of ensemble techniques in disease prediction, here is a notable absence of comprehensive evaluation of regularly utilized ensemble algorithms for highly studied diseases. Consumers typically resort to seeking medical assistance from a doctor or other medical expert only after exhausting all other home remedies without finding relief for their ailments. This is probably a result of insufficient comprehension, as well as other factors related to behavior. This paper introduces a conventional ensemble LSTM model that incorporates data preparation and feature engineering. The model proposed in this research represents major gains in numerous statistical parameters like precision, f score, accuracy and ROC curve in comparison to the other models. © 2024 IEEE.

关键词： Diseases

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