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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Nu-Ri Park Min-Joong Kim Jongsu Lee Minah Seo Yong-Sang Ryu Myung-Ki Kim KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA School of Biomedical Engineering Korea University Seoul Republic of Korea Interdisciplinary Program in Precision Public Health Korea University Seoul Republic of Korea

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen (<3%... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Electric potential Accuracy Hydrogen Lasers and electrooptics Optical sensors Electro-optical waveguides

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An Architecture of Real-World Data Database 1

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5th International Conference on Innovative Computing, IC 2022

作者： Lee, Hsiu-An Hsu, Chien-Yeh Department of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan Department of Information Management National Taipei University of Nursing and Health Sciences Taipei Taiwan Master Program in Global Health and Development Taipei Medical University Taipei Taiwan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

In the analysis of medical data, it is now important to use "Real-World Data (RWD)". In the past, clinical research in the medical field was often completed by clinical trials. Clinical trials are a kind of use of listed drugs and comfort according to research objectives. The process of comparing drugs or other medical treatments on subjects as a control group. The Real-world data research database is essential to precision health. In order to develop the value of clinical data, improve the medical care service, and promote precision medicine, the goal of this research is to design a Real-World Data Research Database, aiming at the wide range and standardization of data, the detail component including: *** standard Health Level 7-Fast Health Interoperability Resources (HL-7 FHIR).*** sorting and cleaning ***-based database research ***-based technology for identity verification and database authorization recognition mechanism. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clinical research

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Using AI Algorithm to Establish the CVD Risk Assessment Model 1

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5th International Conference on Innovative Computing, IC 2022

作者： Chen, Yin-Chen Lee, Hsiu-An Hsu, Chien-Yeh Department of Information Management National Taipei University of Nursing and Health Sciences Taipei Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan Master Program in Global Health and Development Taipei Medical University Taipei Taiwan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

In Taiwan, diseases with cardiovascular include heart disease and cerebrovascular disease among the top ten causes of death. With the development of data mining in the medical field, it can be used to establish the risk prediction models of disease as a tool to assist physicians in decision-making in diagnosis. The purpose of this study is taking Taiwanese health examination data as an example, to identify the significant risk factors of cardiovascular disease and to establish the risk assessment model of cardiovascular disease using data mining. Using Chi-square test and Information Gain identify the correlation between various factors and cardiovascular diseases. Using eight algorithms such as decision tree, random forest, XGBoost, neural network, logistic regression, support vector machine, K nearest neighbor algorithm and voting algorithm to establish the risk assessment model of cardiovascular disease and using confusion matrix and AUC as model evaluation. Compare model performance with different factor combinations. Experiment result shows that it finds 22 questionnaires and biochemical variable risk factors affecting cardiovascular disease and 10 questionnaire factors. ANN and VOTE with 22 factors of Information Gain (threshold > 0.01) are the best models. Both models have the same accuracy (0.88) and AUC (0.90). The best model of questionnaire variable is ANN with the accuracy rate is 0.89, and the AUC is 0.91. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Air-Ground Integrated Federated Learning: An Experimental Implementation

Air-Ground Integrated Federated Learning: An Experimental Im...

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2021 IEEE International Conference on Space-Air-Ground Computing, SAGC 2021

作者： Jing, Yuqian Dong, Chao Qu, Yuben Zhou, Fuhui Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9781665400114

With the envision of sixth generation (6G) networks technology, diverse artificial intelligence (AI) services are gradually developed from the network center to the edge, which enables unmanned aerial vehicle (UAV) as a hot spot to provide auxiliary services of machine learning (ML). Due to the sensitive privacy and limited resources, traditional centralized ML may not be used directly in such networks. As a promising distributed collaborative ML, federated learning (FL) is more suitable. However, the current research on air-ground integrated federal learning (AGIFL) mostly focuses on architecture design and network simulation. In this paper, we mainly verify the feasibility of deploying FL between UAV and terrestrial users through practical platform. Extensive experiments demonstrate that the training accuracy is higher when the training nodes is close to the server, and the accuracy is improved from 69.48% to 83.52% when distance is reduced from 20 meters to 5 meters. © 2021 IEEE.

关键词： Antennas

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Deep and shallow data science for multi-scale optical neuroscience

arXiv

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arXiv 2024年

作者： Mishne, Gal Charles, Adam Halıcıoğlu Data Science Institute Department of Electrical and Computer Engineering the Neurosciences Graduate Program UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Biomedical Engineering Kavli Neuroscience Discovery Institute Center for Imaging Science Department of Neuroscience Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21287 United States

Optical imaging of the brain has expanded dramatically in the past two decades. New optics, indicators, and experimental paradigms are now enabling in-vivo imaging from the synaptic to the cortex-wide scales. To match the resulting flood of data across scales, computational methods are continuously being developed to meet the need of extracting biologically relevant information. In this pursuit, challenges arise in some domains (e.g., SNR and resolution limits in micron-scale data) that require specialized algorithms. These algorithms can, for example, make use of state-of-the-art machine learning to maximally learn the details of a given scale to optimize the processing pipeline. In contrast, other methods, however, such as graph signal processing, seek to abstract away from some of the details that are scale-specific to provide solutions to specific sub-problems common across scales of neuroimaging. Here we discuss limitations and tradeoffs in algorithmic design with the goal of identifying how data quality and variability can hamper algorithm use and dissemination. © 2024, CC BY.

关键词： Neuroimaging

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A Cascade Deep Neural Network Approach for Oil Spills Classification and Detection

A Cascade Deep Neural Network Approach for Oil Spills Classi...

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International Conference on Electric Power and Energy Conversion Systems (EPECS)

作者： Mohammad Rashwan Mohammed Alhadi Mohammad A. Jaradat Mohammad I. Daoud Mechatronics Graduate Program American University of Sharjah Sharjah United Arab Emirates Mechanical Engineering Department American University of Sharjah Sharjah United Arab Emirates Computer Science and Engineering Department American University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331516963

ISBN: (纸本)9798331516970

Oil spills represent a growing environmental challenge that poses a significant threat to living organisms. Moreover, the treatment of oil spills, especially in severe cases, has serious economic repercussions and requires substantial labor and time. Therefore, the effective detection of oil spills has become an important research problem. Traditional methods for detecting oil spills, such as manual patrolling and dynamic sensors, are often limited in accuracy and coverage. As a result, the automation of oil spills detection has emerged as a critical global imperative in scientific research. The aim of this paper is to employ deep learning technology to achieve effective detection of oil spills based on aerial images. Our approach is composed of two phases. In the first phase, a Deep Convolutional Neural Network (DCNN), namely ResNet50, is trained on a large dataset containing images showing oil spills at a seaport. The trained DCNN is used to classify the input image as "Oil Spill" or "No Oil Spill". In the second phase, the images classified as "Oil Spill" are analyzed using a deep learning detection model, namely You-Only-Look-Once (YOLOv4), to localize the oil spills. The results indicate the capability of the proposed method to achieve effective oil spill detection. In particular, the classification accuracy obtained by the ResNet50 model is equal to 98%. Moreover, the YOLOv4 model was able to obtain effective localization of the oil spills with mean-average precision of 62%.

关键词： Deep learning Accuracy Oils Manuals Seaports Organisms Sensors Power systems Convolutional neural networks Residual neural networks

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Orchard: building large cancer phylogenies using stochastic combinatorial search

arXiv

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arXiv 2023年

作者： Kulman, Ethan Kuang, Rui Morris, Quaid Computational and Systems Biology Program Sloan Kettering Institute New YorkNY United States Department of Computer Science and Engineering University of Minnesota MinneapolisMN United States

Phylogenies depicting the evolutionary history of genetically heterogeneous subpopulations of cells from the same cancer, i.e., cancer phylogenies, offer valuable insights about cancer development and guide treatment strategies. Many methods exist that reconstruct cancer phylogenies using point mutations detected with bulk DNA sequencing. However, these methods become inaccurate when reconstructing phylogenies with more than 30 mutations, or, in some cases, fail to recover a phylogeny altogether. Here, we introduce Orchard, a cancer phylogeny reconstruction algorithm that is fast and accurate using up to 1000 mutations. Orchard samples without replacement from a factorized approximation of the posterior distribution over phylogenies, a novel result derived in this paper. Each factor in this approximate posterior corresponds to a conditional distribution for adding a new mutation to a partially built phylogeny. Orchard optimizes each factor sequentially, generating a sequence of incrementally larger phylogenies that ultimately culminate in a complete tree containing all mutations. Our evaluations demonstrate that Orchard outperforms state-of-the-art cancer phylogeny reconstruction methods in reconstructing more plausible phylogenies across 90 simulated cancers and 14 B-progenitor acute lymphoblastic leukemias (B-ALLs). Remarkably, Orchard accurately reconstructs cancer phylogenies using up to 1,000 mutations. Additionally, we demonstrate that the large and accurate phylogenies reconstructed by Orchard are useful for identifying patterns of somatic mutations and genetic variations among distinct cancer cell subpopulations. © 2023, CC BY.

关键词： Plant diseases

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Camera-Only Perception System for Traffic Jam Assistance

Camera-Only Perception System for Traffic Jam Assistance

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International Automatic Control Conference

作者： Xiao-Wei Huang Xiu-Zhi Chen Yen-Lin Chen Master Program in Artificial Intelligence Technology National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

Traditional perception systems for TJA (Traffic Jam Assistance) are mostly implemented by fusing images with radar or lidar. As computer vision techniques become more powerful, cameras can almost replace the need for radar and lidar in perception tasks, which reduces the hardware cost of the system. In this research, we propose a camera-only perception system for TJA, which is able to provide the information of the vehicles ahead and the drivable area. The proposed system has been evaluated through real-world scenario sequences, and proved that it achieves high robustness, which is highly possible to be adopted for TJA development.

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Robust Object Detection Model for UAV Application

Robust Object Detection Model for UAV Application

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International Automatic Control Conference

作者： Chun Ku Xiu-Zhi Chen Yen-Lin Chen Master Program in Artificial Intelligence Technology National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

Not only common issues on object detection task need to be deal with, for Unmanned Aerial Vehicle (UAV) applications, small object is one of the critical problems that needs to be solved. YOLOv7 is a powerful network architecture that provides high efficiency and accuracy object detection results. This paper adopts YOLOv7 as an object detection model for two different kinds of targets, one is vehicle, and the other is ocean flotsam. By training the model with open datasets and fine-tuning the model with self-collected datasets, we prove through sequences collected from real-world scenarios that YOLOv7 is able to provide robust and accurate object detection results, including vehicles and ocean flotsam, with real-time efficiency. Based on such experimental result, we confirmed that YOLOv7 can be the baseline for object detection model development.

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Data Analytics and Visualization in Bimanual Rehabilitation Monitoring Systems: A User-Centered Design Approach to Healthcare Professionals Decision Support

Data Analytics and Visualization in Bimanual Rehabilitation ...

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International Conference on Electrical engineering and Informatics (ICELTICs)

作者： Parkpoom Wisedsri Chutiporn Anutariya Aekavute Sujarae Roongtiwa Vachalathiti Sunee Bovonsunthonchai Computer Science Program School of Engineering and Technology Asian Institute of Technology Pathum Thani Thailand Punsarn Asia Co. Ltd Thailand Faculty of Physical Therapy Mahidol University Nakhon Pathom (MU) Thailand

ISBN: (数字)9798350366822

ISBN: (纸本)9798350366839

This study presents the design of a user-centered data visualization dashboard for stroke rehabilitation, which integrates the principles of visualization data analytics techniques in the field of healthcare. Addressing the complexities of visualizing for robotic bimanual rehabilitation, the design introduces novel methods to enhance data visualization, broadening monitoring capabilities, and improving the clarity of torque data. These enhancements lead to a clearer understanding of patient robot interactions. A prototype has been developed to visualize transparent torque data from a bimanual master slave robotic system, enabling healthcare professionals to monitor patient progress effectively. The dashboard features intuitive visual representations of master, slave, and assistive torques, simulating elbow joint movements through line and sunburst charts. Feedback from rehabilitation therapists during the design process ensures that the visualization approach is aligned with clinical needs. The findings suggest that the system can assist physiotherapists in devising personalized treatment strategies by providing actionable insights from the recorded data. However, additional research is needed to integrate real clinical data into the system for further enhancement and validation. This innovation has the potential to significantly improve stroke rehabilitation effectiveness and patient outcomes through a bimanual rehabilitation visualization and decision support system.

关键词： Data analysis Torque User centered design Data visualization Prototypes Medical services Stroke (medical condition) Main-secondary Monitoring Robots

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