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共 1561 条记录，以下是401-410 订阅

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Single-Domain Generalization Combining Geometric Context Toward Instance Segmentation of Track Components

Single-Domain Generalization Combining Geometric Context Tow...

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international Joint conference on Neural Networks (IJCNN)

作者： Su, Shixiang Du, Songlin Wang, Dezhou Tong, Shuzhen Lu, Xiaobo Southeast Univ Sch Automat Nanjing 210096 Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing 210096 Peoples R China

ISBN: (纸本)9798350359329;9798350359312

A stable track components segmentation model should have consistent performance across a broad spectrum of railroad conditions, particularly in unfamiliar locations. Despite this, satisfying this requirement proves challenging when working with a limited track dataset, as there is a substantial domain shift between the given dataset and unobserved distributions. The goal of this paper is to improve the generalization ability of track component segmentation in situations where single-domain training data is available. Toward this end, a novel track component instance segmentation method combining the geometric context is proposed. First, we design an initial mask prediction head (IMPH) that utilizes predicted box output from the object detector to generate initial masks by merging the geometric priors of track components. Meanwhile, a multiscale feature fusion structure is introduced to encourage IMPH to better capture the geometric context. Then, a final mask refinement head (FMRH) is introduced to get higher quality masks with a geometry-gated aggregation strategy. On the basis of experiments on track datasets, it was determined that the heads can be incorporated with several types of detection frameworks and have demonstrated consistent generalization enhancements across multiple object detectors. Furthermore, our method substantially enhances segmentation performance on various unobserved domains.

关键词： Domain generalization geometric context instance segmentation track component.

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Blood Glucose data Analysis based EMD-LSTM 9

Blood Glucose Data Analysis based EMD-LSTM

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9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Liang, Junjian Zhao, Weiren Ling, Bingo W. K. Sun, Quan Guangdong University of Technology School of Information Engineering Guangzhou510006 China Guangdong University of Technology School of Physics and Optoeletronic Engineering School of Information Engineering Guangzhou510006 China

ISBN: (纸本)9798350376548

In modern hospitals, people can check their blood glucose data through minimally invasive instruments, and the data will be transmitted to the central mainframe of the hospital through the Internet of Things. Through minimally invasive instruments, blood glucose information of the human body can be measured. The blood glucose information data measured is a highly complex nonlinear system, and the signal does not advance in a uniform time process but has its own advance. To enhance prediction accuracy, the Empirical Mode Decomposition (EMD) algorithm is employed. Combined with the characteristics of LSTM (Long Short-Term Memory) recursive neural network, data were normalized and then pushed to LSTM network models with different LSTM layers and different numbers of hidden neurons at the same layer for training and testing. The appropriate number of LSTM layers and hidden neurons was found by comparing the evaluation index and prediction effect, resulting in an improvement in prediction accuracy by about 30%. The test results show that the model prediction accuracy improved significantly. This model can complete the patient's individual blood glucose data modeling in the Internet of Things in big data, providing a beneficial reference to forecast movements in patients with blood glucose. It can also help people in understanding their actual blood glucose levels and building appropriate health maintenance strategies. © 2024 ieee.

关键词： Long short-term memory

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SLEEPWISE: A Continuous Sleep Monitoring and cognitive Evaluation Platform in Ambient Residential Environment 46

SLEEPWISE: A Continuous Sleep Monitoring and Cognitive Evalu...

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46th Annual international conference of the ieee Engineering in Medicine and Biology Society, EMBC 2024

作者： Jin, Shuwen Wang, Danqiong Ma, Xiao Li, Tao Lin, Feng Yu, Lei Zhang, Qing Zhejiang Lab Research Center for Frontier Fundamental Studies Hangzhou China Third Hospital of Shanxi Medical University Shanxi Bethune Hospital Shanxi Academy of Medical Sciences Tongji Shanxi Hospital Taiyuan China Huazhong University of Science and Technology Tongji Hospital Tongji Medical College Wuhan China Complex Systems Research Center Shanxi University Key Laboratory of Complex Systems and Data Science of Ministry of Education Shanxi China

ISBN: (纸本)9798350371499

Age-related cognitive decline presents a significant health challenge for older adults, with no specific medication available to counteract it. Early detection of cognitive decline is crucial for timely intervention and effective management. Sleep status plays a vital role in evaluating cognitive function, as disrupted sleep patterns are associated with cognitive decline and increased risk of neurodegenerative conditions. Existing methods for assessing sleep status, such as self-report questionnaires, actigraphy, and polysomnography, lack the ability to consistently monitor sleep status over a long period. In this paper we introduced SLEEPWISE, an AI-powered platform deployed in residential environments for continuous ambient sleep monitoring to assess the cognitive well-being of older adults. SLEEPWISE utilizes non-contact pads with highly sensitive piezoelectric sensors to monitor sleep parameters such as duration and efficiency, which are transmitted daily to the server. Moreover, we have also developed a gamified module for SLEEPWISE, allowing an objective assessment of daily cognitive functions. This module aids SLEEPWISE in establishing the correlation models between sleep and cognitive health. To evaluate the effectiveness of SLEEP-WISE, a pilot study was conducted with participants recruited from a local hospital. During the 30-days trials, SLEEPWISE identified a negative impact on congnitive performance associated with increased nocturnal body movements and getting out of bad. These findings emphasize the potential of SLEEPWISE as an early screening tool for evaluating daily cognitive health and well-being. © 2024 ieee.

关键词： Sleep research

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Empirical Study of Molecular Dynamics Workflow data Movement: DYAD vs. Traditional I/O Systems

Empirical Study of Molecular Dynamics Workflow Data Movement...

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1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Lumsden, Ian Devarajan, Hariharan Marquez, Jack Brink, Stephanie Boehme, David Pearce, Olga Yeom, Jae-Seung Taufer, Michela Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Lawrence Livermore Natl Lab Livermore CA 94550 USA

ISBN: (纸本)9798350364613;9798350364606

This experimental work examines data movement in molecular dynamics (MD) workflows, comparing the Dynamic and Asynchronous data Streamliner (DYAD) middleware with traditional, industry-standard I/O systems such as XFS and Lustre. DYAD moves MD simulation frames to analytics processes, providing enhanced flexibility and efficiency for dynamic data transfers and in situ analytics. At the same time, traditional I/O storage systems provide durability and scalability for high-performance computing (HPC) systems. The study integrates MD workflows with common simulation codes, facilitating immediate capture and transfer of MD frames to a staging area. It explores various molecular models, from simple to complex, assessing data management performance and scalability. Different producer-consumer pairs, molecular models, and data transaction frequency enable testing across small to large-scale HPC scenarios, from single-node configurations to large, distributed environments. The findings reveal that adaptive mechanisms for minimizing synchronization, direct network communication between producer and consumer processes, and optimizations of both data movement and synchronization are crucial for performance and scalability in MD workflows.

关键词： Producer-Consumer Paradigm Molecular Dynamics Workflows Dynamic data Management High-Performance computing In Situ Analytics

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Comparative Analysis of NoSQL and Time Series databases for Fast data Acquisition System of Indus-2 3

Comparative Analysis of NoSQL and Time Series Databases for ...

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3rd international conference on Power, Control and computing Technologies, ICPC2T 2024

作者： Nayak, Abhisek Merh, Bhavna N. Chaudhari, Swati Agrawal, Rajesh Kumar Accelerator Controls and Beam Diagnostics Division Raja Ramanna Centre for Advanced Technology Department of Atomic Energy Govt. of India MP Indore452013 India Homi Bhabha National Institute Training School Complex Mumbai400094 India Computer Division Raja Ramanna Centre for Advanced Technology Department of Atomic Energy Govt. of India MP Indore452013 India

ISBN: (纸本)9798350349207

Relational databases have been the go-To data logging solution in the scientific community for years. But due to the rapidly changing nature of scientific instruments and the data generated by them, relational databases have begun to fall short of the requirement. In recent years NoSQL and time series databases have come up. These databases have a flexible data model, have horizontal scalability, and are distributed in nature, which helps them handle large volumes of data. This paper provides a comparative study of performance of Cassandra, MongoDB and InfluxDB databases that are used for logging fast data from the data acquisition system of Synchrotron Radiation Source Indus-2. The study uses both the open source synthetic benchmarks and Indus-2 machine parameter data to ascertain the relative performance of the three databases. © 2024 ieee.

关键词： Time series

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Modified Shuffled complex Evolution Algorithm for Energy Efficiency in WSNs 15

Modified Shuffled Complex Evolution Algorithm for Energy Eff...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Lipare, Amruta Kongara, Mahesh Chowdary Shewale, Amit IIIT Pune Maharashtra Pune411041 India Mahindra University Hyderabad Telangana Hyderabad500043 India IIIT Nagpur Maharashtra Nagpur441108 India

ISBN: (纸本)9798350370249

Energy consumption stands as a pivotal factor in Wireless Sensor Networks (WSNs), drawing significant attention across multiple domains. Recent studies find that, the limited availability of vital resource has been one of the challenges posed by energy consumption in WSNs. The operational functionality of sensor nodes depends on the energy reserved in WSN. Maximizing the network's lifespan necessitates effective mitigation of energy consumption. In clustered WSNs, the cluster head, serving as the leader, undertakes various tasks including data collection from member nodes, data accumulation, and communication with the base station. Consequently, ensuring the load balancing within WSNs emerges as a crucial task for extending network longevity. To tackle this challenge, this paper employs the modifications in the Shuffled complex Evolution (MSCE) algorithm. Additionally, a novel fitness function is designed to assess the effectiveness of solutions generated by the SCE algorithm. Through simulation, the proposed MSCE algorithm is compared against existing state-of-the-art algorithms. Experimental results shows the out performance of the proposed MSCE in terms of energy efficiency, and stability of the network. © 2024 ieee.

关键词： Sensor nodes

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Leveraging reinforcement learning for advanced financial planning for effective personalization in economic forecasting and savings strategies 15

Leveraging reinforcement learning for advanced financial pla...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Avacharmal, Rajiv Balakrishnan, Anandaganesh Ranjan, Piyush Vandanapu, Manoj Kumar Mulukuntla, Sarika Preethi, P. University of Connecticut United States American Water Data Engineering United States AVP/Software Eng Ieee United States Ubs Dallas TX United States Kongunadu College of Engineering and Technology Department of Cse Trichy India

ISBN: (纸本)9798350370249

There is rarely a single response that can be considered 'right' when it comes to the domain of financial guidance and planning. Traditional algorithms have been successful in addressing linear issues;but, their performance is strongly dependent on selecting the 'right' features from a dataset, which can be difficult to accomplish in complex financial settings. Machine learning (ML) is investigated in this research for its potential applications in financial forecasting, the prediction of economic indicators, and the development of strategies for personal savings. In order to assist customers in attaining their financial goals, the machine learning algorithm that Vanguard uses, which is based on deep reinforcement learning, determines the optimal savings rates across a variety of goals and income sources. These algorithms are designed to identify market indications and behaviors that are too complex to be captured by standard formulae and rules. They do this by modeling the financial success trajectories of investors as a Markov method of decision making. According to the findings of the study, reinforcement learning has the potential to significantly increase the value that financial advisors and end-investors receive by increasing efficiency, personalizing financial planning, and providing solutions that are data-driven and tailored. © 2024 ieee.

关键词： Economic Forecasting Financial Planning Machine Learning and Markov Decision Process Reinforcement Learning Savings Strategy

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Feature Interaction Detection in Big data Through a New Choquet Integral based Deep Neural Network

Feature Interaction Detection in Big Data Through a New Choq...

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2024 ieee international conference on Big data, Bigdata 2024

作者： Fried, Matthew Wang, Honggang Fang, Hua Yeshiva University New York United States University of Massachusetts Dartmouth Chan Medical School Dartmouth United States

ISBN: (纸本)9798350362480

Learning from massive amounts of domain-specific information requires new algorithms and models for parsing the ever-expanding field of big data. Such algorithms for exploring and identifying key features in vast databases require analysis of complex interactions to uncover critical features under a variety of circumstances. We study a comprehensive collection of health-related data, showing that our novel Choquet Integral activation function for deep neural networks transforms high-dimensional data into simpler sub-feature sets that better model complex interactions. While standard methods account for unitary feature tracking, they do not extend to multiple feature subsets, an impactful and necessary knowledge base. To this end, our novel activation function creates a sub-additive tool that better considers the weighted compilation of features within a robust set of standard benchmarks, advancing the synergistic and antagonistic relationships among features, capturing non-linear dependencies. We present the theoretical underpinnings, highlighting balanced fuzzy measures and sub-additivity for an optimized model based on real-world health data targeting weight loss. We further test different model settings, akin to hyper-parameter optimization. Despite computational time consumption, which could be improved via nowadays more powerful computing units, this novel method can be implemented as a pre-trained model using big data to identify heretofore unknown sub-additive feature interactions in a variety of fields such as biomedicine, fraud detection, cyber-security, and finance. © 2024 ieee.

关键词： Choquet integral

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Design of Gadget Usage Tracker using data Visualization, Analytics and ThingSpeak 5

Design of Gadget Usage Tracker using Data Visualization, Ana...

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5th international conference on data Intelligence and cognitive Informatics, ICDICI 2024

作者： Krithiga, S. Prasath, S. Gyana Guru Kanna, R.P. Sujit Srm Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350389609

This research study presents a Gadget Usage Tracker designed with Python and integrated with the ThingSpeak platform to monitor and analyze gadget usage patterns. The system collects real-time data from various gadgets, which is transmitted to ThingSpeak for storage and preliminary processing. Python interfaces with Thing Speak to fetch this data and perform detailed analytics using statistical methods and machine learning algorithms to identify usage patterns, anomalies, and trends. data visualization is achieved using Python libraries such as Matplotlib and Seaborn, transforming raw data into comprehensible visual formats like graphs, charts, and dashboards. These visualizations highlight key metrics, peak usage times, and overall trends, making complex data accessible for end-users. The integration of ThingSpeak with Python ensures seamless data flow from collection to analysis and visualization. This approach provides insights into user behavior and recommendations for optimizing gadget usage, leading to improved gadget management, better user habits, and enhanced efficiency in technology utilization. The study demonstrates the effective use of data visualization, analytics, and IoT platforms in monitoring and optimizing gadget usage. Key words: data Visualization, Analytics, ThingSpeak, Python, Real-time data, Machine learning algorithms, Statistical methods, Usage patterns, Anomalies, Trends, IoT platforms, Technology utilization. © 2024 ieee.

关键词： Visual analytics

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Optimization of Container Cloud Elastic Scaling Integrating ARIMA Forecasting and HPA Response 4

Optimization of Container Cloud Elastic Scaling Integrating ...

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4th international conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Zhang, Liangbin Wang, Feng Wang, Yuanming Liu, Xinwei Zhejiang Wanli University College of Big Data and Software Engineering Ningbo China

ISBN: (纸本)9798331528973

Load changes of cloud computing resources show more and more complex characteristics. Load forecasting is crucial for elastic scaling of cloud computing resources. Kubernetes is a mainstream container cloud platform. Its built-in HPA policy automatically adjusts the number of Pod replicas according to load changes to achieve elastic scaling, but it has the problems of single load metric and user response delay during peak periods. This paper proposes a container cloud elastic scaling optimization method based on load forecasting and response, which dynamically monitors and calculates the combined load of CPU and Memory in the Pod set, uses ARIMA model to complete the combined load forecasting value based on historical data, and HPA responsive algorithm to make up for the lack of forecasting. Experimental results show that compared with the HPA strategy, our method can execute the corresponding scaling operations in advance, and can effectively reduce the response delay time and improve the Qos quality. © 2024 ieee.

关键词： Cloud platforms

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