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Mathematical evaluation of Medical Image computing by Application of Mathematical Computer-Aided Interventions to Soft Tissues 5

Mathematical Evaluation of Medical Image Computing by Applic...

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5th ieee international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Chinoy, Danesh Dinyar Mavi, Renu Shrivastav, A.K. Jothi, Naresh Kumar Utsab, Tuhin Rajput, Akshay D y Patil Deemed to Be University School of Physiotherapy Dept of Kinesio Therapeutics and Diagnosis Department of Mathematics Department of Chemistry Navi Mumbai India Swami Vivekananda Subharti University Department of Chemistry Uttar Pradesh Meerut India Mmec Maharshi Markandashwar Department of Mathematics Haryana Ambala India R&d Institute of Science and Technology Department of Mathematics India St. Xavier's University Kolkata India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350383522

Mini-invasive digestive surgery was seemingly one of the main disciplines where soft tissues were handled utilizing test systems, anatomical structure tracking, and specialized assistance robots. Yet, other therapeutic areas, such as urology, are concerned. Unquestionably, laparoscopic surgery, creative growth killing methods (e.g., HIFU, radiofrequency, or cryoablation), prior disease distinguishing proof, and the usage of interventional and diagnostic imaging modalities have of late acquainted new troubles with urologists and researchers keen on CAMI. This has driven in a significant flood in innovative work of computer-aided urology frameworks throughout recent years. In this study, we give a conversation of the main pressing issues related with computer-aided diagnostic and therapy of soft tissues, as well as an overview of the many kinds of help accessible to urologists: robotization, picture combination, and careful route. There is conversation of both exploration projects and functional modern frameworks. © 2024 ieee.

关键词： Computer Aided Medical Interventions Medical Image computing

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Half-Precision Scalar Support in Kokkos and Kokkos Kernels: An engineering Study and experience Report 18

Half-Precision Scalar Support in Kokkos and Kokkos Kernels: ...

引用

ieee 18th international conference on e-science (e-science)

作者： Harvey, evan Milewicz, Reed Trott, Christian Berger-Vergiat, Luc Rajamanickam, Siva Sandia Natl Labs Ctr Comp Res 1450 Innovat Pkwy SE Albuquerque NM 87123 USA

ISBN: (数字)9781665461245

ISBN: (纸本)9781665461245

To keep pace with the demand for innovation through scientific computing, modern scientific software development is increasingly reliant upon a rich and diverse ecosystem of software libraries and toolchains. Research software engineers (RSes) responsible for that infrastructure perform highly integrative work, acting as a bridge between the hardware, the needs of researchers, and the software layers situated between them;relatively little, however, has been written about the role played by RSes in that work and what support they need to thrive. To that end, we present a two-part report on the development of half-precision floating point support in the Kokkos ecosystem. Half-precision computation is a promising strategy for increasing performance in numerical computing and is particularly attractive for emerging application areas (e.g., machine learning), but developing practicable, portable, and user-friendly abstractions is a nontrivial task. In the first half of the paper, we conduct an engineering study on the technical implementation of the Kokkos half-precision scalar feature and showcase experimental results;in the second half, we offer an experience report on the challenges and lessons learned during feature development by the first author. We hope our study provides a holistic view on scientific library development and surfaces opportunities for future studies into effective strategies for RSes engaged in such work.

关键词： research software engineering scientific computing software libraries floating-point arithmetic

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A Generic Real-Time Simulation Modeling Method of Modular DC Chopper for VSC-Based Offshore Wind Farm Connection System 7

A Generic Real-Time Simulation Modeling Method of Modular DC...

引用

7th international conference on Power and energy engineering, ICPee 2023

作者： Kou, Longze Gao, Yuqin Gu, Huaiguang Gao, Yang Li, Jin Li, Fangyuan State Grid Smart Grid Research Institute Co. Ltd State Key Laboratory of Advanced Power Transmission Technology Beijing China State Grid Smart Grid Research Institute Co. Ltd Beijing Key Laboratory of DC Grid Technology & Simulation Beijing China

ISBN: (纸本)9798350381146

The DC chopper is a crucial equipment in VSC-based offshore wind farm connection system to dissipate the surplus power during fault scenarios and enhance the system fault ride through capability. The modular DC chopper technology has been used in real engineering project due to its good transient performance. It has numerous SMs stacked in arm bridge with different topologies and thus poses challenges to the electromagnetic modeling, simulation analysis and control system testing of the equipment. This paper proposes a generic electromagnetic modeling method of modular DC chopper, which is especially suitable for the real time simulation. It has both high simulation speed and accuracy and can be used to simulate modular DC chopper with different SM topology. The modeling method is validated and then implemented in real time simulation platform HYPeRSIM. Hardware in loop testing of VBC of DC chopper is carried out and the study cases demonstrates that the real time model of modular DC chopper works properly under transient operation conditions. © 2023 ieee.

关键词： Offshore wind farms

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Research on Prediction Model of Gas Concentration in Power Transformer Oil 2

Research on Prediction Model of Gas Concentration in Power T...

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2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Zhou, Chenyang Fu, Lidong Zhang, Shuangying Shenyang Ligong University School of Information Science and Engineering Shenyang China State Grid Liaoning Electric Power Company Limited Economic Research Institute Shenyang China

ISBN: (纸本)9798350368888

The research of gas concentration prediction model in power transformer oil is an important means to diagnose the fault type of power transformer. According to the gas concentration generated in the daily work of the power transformer, the gas concentration generated in the later power transformer oil can be predicted. According to the predicted gas concentration, the fault type of the power transformer can be better diagnosed in advance. Therefore, the prediction of gas concentration in power transformer oil is discussed and studied. For the gas prediction of power transformer oil, long short-term memory network prediction model, bidirectional long short-term memory network prediction model and gated recurrent unit prediction model are selected. Taking the hydrogen concentration generated by a power transformer as an example, experiments are carried out, and R2 and RMSe of the three models are compared and analyzed to evaluate which prediction model has better prediction effect. © 2024 ieee.

关键词： Prediction models

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Few-shot Biomedical NeR via Multi-task Learning and More Fine-grained grid-tagging Strategy

Few-shot Biomedical NER via Multi-task Learning and More Fin...

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2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Mu, Wenxuan Zhao, Di Meng, Jiana Liu, Shuang Lin, Hongfei Dalian Minzu University School of Computer Science and Engineering Dalian China Postdoctoral Workstation of Dalian Yongia Electronic Technology Co. Ltd China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350386226

Biomedical Named entity Recognition (NeR) serves as a crucial task in biomedical information extraction, aiming to identify and classify entities from unstructured biomedical texts. However, obtaining high-quality annotated biomedical texts is scarce due to their high privacy and the specialized expertise required. Therefore, more researchers are focusing on few-shot NeR in the biomedical field. Recent methods mainly fall into three categories: 1) transferring knowledge from high-resource data and fine-tuning with low-resource data, 2) modifying data using various methods to generate new samples, and 3) decompose the NeR task into two subtasks with multi-task learning. However, these methods either suffer from domain shift, generate low-quality synthetic data, or encounter error propagation issues. To address these limitations, we have investigated an few-shot NeR method, which firstly employs specific prompt templates to guide Large Language Models in generating high-quality new samples. Then, we propose a novel more fine-grained grid-tagging strategy and single-end sensitive multi-task learning framework. This method sets multiple losses to enable the model to learn from multiple task objectives: what constitutes a fully correct, partially correct, and incorrect entity. Additionally, through the new grid-tagging strategy, the model can decode entities based on more tagging clues. To better align with real-world scenarios, we trained in a few-shot scenario and evaluated on the full test set. extensive experimental results on the NCBI, BC5CDR, BioNLP11ePI, and BioNLP13Ge datasets confirm that our method outperforms previous state-of-the-art methods in most scenarios. © 2024 ieee.

关键词： Multi-task learning

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Research on Machine Learning-Based Fault Diagnosis and Prediction Techniques for Power Transformers 2

Research on Machine Learning-Based Fault Diagnosis and Predi...

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2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Zhang, Shuangying Fu, Lidong Wang, Jiaxing State Grid Liaoning Electric Power Company Limited Economic Research Institute Shenyang China Shenyang Ligong University School of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350368888

Due to the diversity and heterogeneity of electrical equipment, the operation monitoring systems for power transformers are undergoing diversified development. By utilizing machine learning methods, this study focuses on the prediction and diagnosis of heterogeneous terminal data from power transformers to address the challenges of online monitoring. This research is of significant importance for achieving the interconnection of all components in the power system and maintaining its operational stability. The study adopts machine learning techniques to comprehensively analyze the heterogeneous terminal data of power transformers, considering seven electrical energy parameters and four environmental parameters as the main input variables for the experimental analysis. A weighted least squares support vector machine algorithm based on chemical reaction optimization is proposed for parameter prediction in power transformers. Through a detailed comparative analysis during the simulation process, with a focus on evaluating regression algorithm performance metrics, the simulation results demonstrate that the proposed algorithm can rapidly and accurately predict parameter values for future time intervals. © 2024 ieee.

关键词： Distribution transformers

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Reproducible escience: The Data Containerization Challenge 19

Reproducible eScience: The Data Containerization Challenge

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19th ieee international conference on e-science, e-science 2023

作者： Malik, Tanu School of Computing DePaul University ChicagoIL United States

ISBN: (纸本)9798350322231

Computational reproducibility is the cornerstone of the scientific method. We are witnessing a surge of reproducible practices emerging in scientific disciplines. Use of containers is a common theme across several of these practices. Containers isolate applications and make it simple to share code, data, and experiment settings. This paper presents current escience infrastructures that use containers for reproducible research. We then present our view of the important research issues in data containerization for reproducible escience. Containerization requires considering packaging, sharing, security, and language choices to ensure reproducibility and preservation. Despite its benefits, containerization can be inefficient and impact experiment reproducibility when users stop using it or transition the experiment to different environments. We describe reproducible containers that in addition to containerization include services to ensure and maintain long-term reproducibility and lay forward a vision based on reproducible containers. © 2023 ieee.

关键词： Containers

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Prediction of Antiretroviral Therapy Treatment Failure in Malawi using a Soft computing Modelling approach

Prediction of Antiretroviral Therapy Treatment Failure in Ma...

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2024 international conference on Artificial Intelligence, Computer, Data sciences, and Applications, ACDSA 2024

作者： Ndhlovu, Dumisani Munthali, Kondwani University of Malawi Computer Science Department Lilongwe Malawi Lighthouse Trust M&e and Research Department Lilongwe Malawi University of Malawi Computer Science Department Zomba Malawi

ISBN: (纸本)9798350394528

As part of routine care in HIV programming, healthcare providers routinely assess patients for potential ART treatment failure. But, due to a shortage of experts and an increasing number of cases, manually analyzing patients for treatment failure is not practical. This study aimed to develop an efficient and effective model using Soft computing Modeling techniques to predict ART treatment failure. An artificial neural network binary classifier model was developed to predict treatment failure for both first- and second-line regimens. The research employed a qualitative and quantitative method design. ethnographic methods were utilized to address qualitative objectives, which involved identifying predictors of ART treatment failure and existing algorithms for determining treatment failure. The methodology adhered to the CRISP-DM framework and a backpropagation ANN model was constructed using Python, Scikit-learn library, Keras, and Tensorflow backend. The evaluation outcomes of the ANN model demonstrated an accuracy of 99.71%, indicating that the ANN model can effectively predict ART treatment failure outcomes. The findings in this research suggest that modeling treatment failure prediction using soft computing is a viable technique, and the research recommends the model for incorporation into the treatment failure review process. © 2024 ieee.

关键词： Neural networks

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Joint Resource Allocation and Task Offloading for Hybrid NOMA-assisted MeC Network with Network Slicing 25

Joint Resource Allocation and Task Offloading for Hybrid NOM...

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25th international conference on Advanced Communication Technology (ICACT) - New Cyber Security Risks for enterprise Amidst COVID-19 Pandemic

作者： Zhang, Yong Zhang, Hao Li, Yan Zhang, Yong Yuan, Siyu State Grid Shandong Elect Power Co Binzhou Shandong Peoples R China State Grid Shandong Elect Power Co Econ & Tech Res Inst Binzhou Shandong Peoples R China Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Work Safety Intelligent Monitorin Beijing Peoples R China

ISBN: (纸本)9791188428106

Mobile edge computing (MeC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies in fifth-generation (5G) networks. This paper proposes a hybrid NOMA-assisted MeC network slicing scenario. In one network slice, users are divided into some groups and use hybrid NOMA to occupy channel for communication. A two-step resource allocation and task offloading algorithm is proposed to minimize the total network energy consumption under the premise of maximum delay tolerance. The optimization problem is divided into two subproblems. One subproblem is the optimal resource allocation problem for users within a group, and the other is the user grouping problem. A two-layer deep reinforcement learning (DRL) algorithm based on double deep Q network (DDQN) is used to solve the resource allocation within a group, and a swap matching algorithm is used to solve the user grouping. The simulation results show that the proposed algorithm can effectively reduce the total energy consumption. Compared with DQN and Q-learning, the proposed DRL algorithm has faster convergence speed and better convergence value. Compared with exhaustive search, matching search has lower complexity and similar performance.

关键词： MeC hybrid NOMA network slicing DRL swap matching

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Metaverse Services: The Way of Services Towards the Future

Metaverse Services: The Way of Services Towards the Future

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2023 ieee international conference on Web Services, ICWS 2023

作者： Xu, Xiaofei Sheng, Quan Z. Benatallah, Boualem Chen, Zhong Gazda, Robert Saddik, Abdulmotableb ei Singh, Munindar P. Faculty of Computing Harbin Institute of Technology China Macquarie University School of Computing NSW2109 Australia Dublin City University School of Computing Ireland Peking University School of Computer Science China Wireless Networking Lab Interdigital United States University of Ottawa School of Electrical Engineering and Computer Science Canada North Carolina State University Department of Computer Science United States

ISBN: (纸本)9798350304855

With the emergence of new generation of digital technologies, e.g., artificial intelligence, blockchain, cloud computing, big data, edge computing, 5G/6G, VR/AR/MR, and the Internet of Things, an exciting era of metaverse is coming. Interacted and linked with the physical world, metaverse offers a platform of a new social ecosystem, dealing with digital twins and empowering virtual-reality symbiosis. In metaverse, social activities and business processes are performed based on the sequences of workflow or service processes. Bridging both the virtual space and the real world, such metaverse services are more complicated and present many new challenges and research topics. In this paper, the concept and characteristics of metaverse services are presented, the key technologies and typical use cases are reviewed, and the future challenges and opportunities of metaverse services are also discussed. © 2023 ieee.

关键词： Internet of things

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