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Prognostics and System Health Management Conference (PHM)

作者： Peng Su Yeqi Wang Conglei Xiang Erik Wendel Madhav Mishra DeJiu Chen Department of Engineering Design Mechatronics Division KTH Royal Institute of Technology Stockholm Sweden Embedded Systems Master Program of Electrical Engineering and Computer Science (EECS) School KTH Royal Institute of Technology Stockholm Sweden Digital Systems Division RISE Research Institutes of Sweden Gothenburg Sweden

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Addressing the challenges in prognostics and health management for modern intelligent systems, especially automated driving systems, is complex due to the contextual nature of faults. This complexity necessitates a thorough understanding of spatial, and temporal conditions, and relationships within operational scenarios and life-cycle stages. This paper introduces a framework designed to automatically recognize driving scenarios in automated driving systems using graph neural networks (GNNs). The framework extracts relational data from image frames, constructing graph-based models and transforming unstructured sensory data into structured data with diverse node types and relationships. A specific graph neural network processes the graph model to reveal and detect operational conditions and relationships. The proposed framework is evaluated using the KITTI dataset, demonstrating superior performance compared to conventional feed-forward networks such as MLP, particularly in handling relational data.

关键词： Graph neural networks Data models Safety Complexity theory Data mining Prognostics and health management Intelligent systems

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Integrating subvolume dose and myocardial perfusion imaging parameters to assess the impact of radiation therapy on heart function in breast cancer patients: A comparative analysis between left- and right-sided breast cancer

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Thoracic Cancer 2023年第26期14卷 2696-2706页

作者： Wang, Shan-Ying Tsai, Wei-Ta Lin, Kuan-Heng Yu, Chih-Wei Yang, Shu-Ya Shueng, Pei-Wei Wu, Yen-Wen Hsu, Chen-Xiong Wu, Tung-Hsin Department of Biomedical Imaging and Radiological Sciences National Yang Ming Chiao Tung University Taipei Taiwan Department of Nuclear Medicine Far Eastern Memorial Hospital New Taipei City Taiwan Department of Radiation Oncology Dalin Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation Chiayi Taiwan Division of Radiation Oncology Department of Radiology Far Eastern Memorial Hospital New Taipei City Taiwan Industrial Ph.D. Program of Biomedical Science and Engineering National Yang Ming Chiao Tung University Taipei Taiwan Department of Radiation Oncology China Medical University Hsinchu Hospital Zhubei City Taiwan Institute of Nuclear Engineering and Science National Tsing Hua University Hsinchu City Taiwan Department of Nuclear Medicine Cheng Hsin General Hospital Taipei Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Division of Cardiology Cardiovascular Medical Center Far Eastern Memorial Hospital New Taipei City Taiwan

Background: This study aimed to utilize an innovative method of integrating the 20 subvolume dose of left ventricle and the Tl-201 single photon emission computed tomography (SPECT) with myocardial perfusion imaging (MPI) parameters in patients with left- and right-sided breast cancer after radiation therapy. Methods: Female patients with breast cancer underwent SPECT MPI before commencing radiotherapy and 12 months later were enrolled from January 2014 to December 2018. The images of CT simulation and SPECT MPI were integrated into the treatment planning system. The differences of doses and parameters of MPI in all cardiac subvolumes between left- and right-sided breast cancer patients were analyzed. Results: Patients with left-sided breast cancer (n = 61) received a higher radiation dose to the heart, left ventricular, and its territories and subvolumes, compared to patients with right-sided breast cancer (n = 19). The 20-segment analysis also showed statistically significant disparities in the average radiation doses received by the two groups. In different coronary artery territories, the end-diastolic perfusion and end-systolic perfusion showed a decrease in both sides, with no significant differences. However, the wall motion and wall thickening showed a significant decline in subregions within the left- and right-sided coronary artery territories. Conclusion: This study demonstrates an innovative integrated method combining the left ventricular 20 regional doses with SPECT MPI which shows that left-sided breast cancer patients receive a higher subvolume dose than right-sided breast cancer patients. Further research is needed to confirm the potential impact on heart function after radiotherapy on both sides. © 2023 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.

关键词： myocardial perfusion imaging radiation therapy subvolume dose

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NeuroScope: An Ultrahigh Resolution Clinical Brain Scanner with hardwareImplemented Super Resolution Sampling Strategies

NeuroScope: An Ultrahigh Resolution Clinical Brain Scanner w...

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IEEE Symposium on nuclear science (NSS/MIC)

作者： S. Mandot E.M. Zannoni C. Yang S. Zhu L.-J. Meng Department of Nuclear Plasma and Radiological Engineering University of Illinois at Urbana-Champaign Champaign Illinois United States of America University of Texas at Austin Nuclear and Radiation Engineering Program Austin Texas United States of America Beckman Institute for Advance Science and Technology University of Illinois at Urbana-Champaign Champaign Illinois United States of America

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

Brain-dedicated imaging systems with high sensitivity and high spatial resolution have become an urgent requirement to facilitate improved understanding of several neurological disorders like Parkinson’s Disease, epilepsy, Alzheimer’s Disease and so on. In this paper, we present the design and preliminary simulation studies of our ultrahigh resolution brain Single Photon Emission Computed Tomography (SPECT) scanner with a hardware-implemented sampling enhancement strategies. The system is designed with 3202 -mm thick cadmium zinc telluride (CZT) detectors, arranged in a cylindrical geometry with dual field of view capability of 20 cm and 5 cm diameter. The whole brain imaging will be performed by coupling the detectors with a 2-mm diameter pinhole collimator, whereas, the microscopic imaging will be facilitated by using a nonconventional micro-ring aperture design with 0.15-mm ring opening. The micro-ring design offers an ultrahigh intrinsic spatial resolution with a reasonably adequate sensitivity. The collimator apertures are designed to be mounted with a precise single linear motor to shift the apertures slightly to improve the non-redundant angular sampling and thereby enhancing the spatial resolution of the system. The preliminary simulation study yields a peak sensitivity of $\sim 0.13 \%$ for larger FOV and $\sim 0.19 \%$ for the smaller FOV using micro-ring apertures.

关键词： Temperature sensors Collimators Sensitivity Semiconductor detectors Superresolution Apertures Spatial resolution

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Machine Learning for Analyzing Atomic Force Microscopy (AFM) Images Generated from Polymer Blends

arXiv

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arXiv 2024年

作者： Paruchuri, Aanish Wang, Yunfei Gu, Xiaodan Jayaraman, Arthi Master of Science in Data Science Program University of Delaware NewarkDE19713 United States School of Polymer Science and Engineering University of Southern Mississippi 118 College Drive #5050 HattiesburgMS39406 United States Department of Chemical and Biomolecular Engineering University of Delaware 150 Academy St NewarkDE19713 United States Department of Materials Science and Engineering University of Delaware NewarkDE19713 United States Data Science Institute University of Delaware NewarkDE19713 United States

In this paper we present a new machine learning (ML) workflow with unsupervised learning techniques to identify domains within atomic force microscopy (AFM) images obtained from polymer films. The goal of the workflow is to identify the spatial location of the two types of polymer domains with little to no manual intervention (Task 1) and calculate the domain size distributions which in turn can help qualify the phase separated state of the material as macrophase or microphase ordered/disordered domains (Task 2). We briefly review existing approaches used in other fields - computer vision and signal processing - that can be applicable for the above tasks that happen frequently in the field of polymer science and engineering. We then test these approaches from computer vision and signal processing on the AFM image dataset to identify the strengths and limitations of each of these approaches for our first task. For our first domain segmentation task, we found that the workflow using discrete Fourier transform (DFT) or discrete cosine transform (DCT) with variance statistics as the feature works the best. The popular ResNet50 deep learning approach from computer vision field exhibited relatively poorer performance in the domain segmentation task for our AFM images as compared to the DFT and DCT based workflows. For the second task, for each of 144 input AFM images, we then used an existing porespy python package to calculate the domain size distribution from the output of that image from DFT-based workflow. The information and open-source codes we share in this paper can serve as a guide for researchers in the polymer and soft materials fields who need ML modeling and workflows for automated analyses of AFM images from polymer samples that may have crystalline/amorphous domains, sharp/rough interfaces between domains, or micro or macro- phase separated domains. © 2024, CC BY.

关键词： Unsupervised learning

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Research on Traffic Sign Detection and Recognition System Using Deep Ensemble Learning

Research on Traffic Sign Detection and Recognition System Us...

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IEEE International Conference on Computer Communication and the Internet (ICCCI)

作者： Lung-Jen Wang Taweelap Suwattanapunkul Jarinya Thalauy Parinya Jansengrat Department of Computer Science and Information Engineering National Pingtung University Pingtung Taiwan International Master Program in Information Technology and Applications International College National Pingtung University Pingtung Taiwan Department of Big Data Management and Analytics Rajamangala University of Technology Thanyaburi Pathum Thani Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

There is a rise in car accidents due to human errors on the road. A critical task of self-driving cars that can reduce accidents on the road is traffic sign detection and recognition (TSDR), which is vital in alerting drivers to the presence of traffic signs in advance. This research will separate the proposed deep ensemble learning algorithm into two methods. First, after the traffic scene process, the algorithm will detect the traffic sign as two categories with the YOLOv5s network. Then, process the traffic sign to recognize the traffic sign into seven classes with the MobileNet network. The detection model was trained with the Taiwan Traffic Sign Detection (TTSD) dataset collected from Taiwan roads. The recognition model was trained with the Taiwan Traffic Sign Recognition (TTSR) dataset. The result of the proposed algorithm showed high performance when experimenting with 95.83% accuracy, 87.34% true prediction, and 191.3 milliseconds (ms) of inference time.

关键词： YOLO Roads Prediction algorithms Inference algorithms Internet Autonomous automobiles Ensemble learning

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Human Activity Classification: A LSTM and MediaPipe Approach for Intelligent Systems Application

Human Activity Classification: A LSTM and MediaPipe Approach...

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International Conference on Control and Robots (ICCR)

作者： Yi-An Chen Pei-Ching Yang Master Degree Program in Information and Design Chang Jung Christian University Tainan Taiwan Department of Computer Science and Information Engineering Chang Jung Christian University Tainan Taiwan Artificial Intelligence Research Center Chang Jung Christian University Tainan Taiwan

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

With advancements in artificial intelligence, computer vision technologies have seen significant progress, enabling applications like facial recognition, autonomous driving, and medical imaging. This study leverages the Multi-Camera Action Dataset (MCAD) and MediaPipe Pose to create a skeletal dataset for training a Long Short-Term Memory (LSTM) model to recognize 18 types of daily actions. Evaluation using confusion matrix and F1 score reveals that Action 16 (Object Carry) is often misclassified as Action 7 (Walk) due to similar leg movements, and Action 9 (Cell to Ear) as Action 1 (Wave) due to similar hand movements. Future work aims to enhance model accuracy and explore broader applications in physical activity and exercise analysis.

关键词： Training Legged locomotion Hands Computer vision Computational modeling Face recognition Ear Intelligent systems Long short term memory Biomedical imaging

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900 MHz RF Power Rectifier Based on Ultra-low Turn-on Voltage Quasi-vertical GaN Schottky Diode

900 MHz RF Power Rectifier Based on Ultra-low Turn-on Voltag...

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Wireless Power Week (WPW)

作者： Xiaochen Yu Ya-Xun Lin Ivona Z. Mitrovic Steve Hall Der-Sheng Chao Jenq-Horng Liang Yi Huang Ta-Jen Yen Jiafeng Zhou International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom Nuclear Science and Technology Development Center National Tsing Hua University Hsinchu Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu Taiwan Department of Materials Science and Engineering National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9798350349139

ISBN: (纸本)9798350349146

This paper presents a 20 W high-power RF-to-DC rectifier that can be applied to wireless power transfer (WPT) systems. To address the power limitations of conventional diodes, a rectifier is designed based on a gallium nitride (GaN) integrated circuit (IC) quasi-vertical Schottky barrier diode (SBD). Leveraging the advantages of high mobility, wide bandgap, and saturation velocity of the GaN, significant benefits of the diode have been derived, with an ultra-low turn-on voltage of 0.23 ± 0.005 V (at 1 A/cm 2 ) and a high breakdown voltage of 180 V, to improve the rectification performance. Using this diode, a high-power rectifier with a wide input power range and high efficiency is developed. The proposed rectifier structure consists of a single-shunt self-developed diode and a topology with class-F harmonics control networks. It achieves a peak power conversion efficiency of 72.9% with an input power of 43 dBm at 900 MHz.

关键词： Wireless communication Integrated circuits Schottky diodes Wireless sensor networks Breakdown voltage Rectifiers Wireless power transfer

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Study on Fluid Descends Vertically on Static Fluid Using Moving Particle Semi-Implicit Method 2

Study on Fluid Descends Vertically on Static Fluid Using Mov...

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2nd International Conference on Energy sciences 2018, ICES 2018

作者： Hidayati, A.N. Mustari, A.P.A. Waris, A. Yulianto, Y. Ilham, M. Master Program of Physics Department Faculty of Mathematics and Natural Science Bandung Institute of Technology Jalan Ganesha 10 Bandung40132 Indonesia Nuclear Physics and Biophysics Research Division Physics Department Faculty of Mathematics and Natural Science Bandung Institute of Technology Jalan Ganesha 10 Bandung40132 Indonesia

The process of fluid descend vertically on static fluid has been studied through experiment and simulation by using Moving Particle Semi-Implicit (MPS) method. MPS has been developed by Koshizuka and Oka (1996). MPS method is a particle method based on the Lagrangian calculation for incompressible medium and does not rely on grid system. This study has been done by doing simulation first then validated by experiment. Water and cooking oil were used in the experiment. This study was carried out to analyze distance and density influence in the height of static fluid bursting process. The experiments were conducted in an acrylic box with the dimension 150 mm x 40 mm x 30 mm and a bottle with height of 100 mm and the nozzle of bottle was 26 mm. The distance between nozzle and surface of fluid inside box is varied for 100 mm and 200 mm. The results show that in the same height, water will be more difficult to be moved than cooking oil because water has bigger density. The distance between nozzle and fluid surface inside the box will affect at the pressure which is received by the fluid. Higher distance will create higher pressure so that fluid inside the box will be moved easier and more fluid will split out to the box. It also gives some influence in the final condition of remaining fluid in the acrylic box. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Bottles

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Classification of Acute Intracerebral Hemorrhage Using Radiomics on Brain Computed Tomography Images

Classification of Acute Intracerebral Hemorrhage Using Radio...

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International Conference on Information Technology and Electrical engineering (ICITEE)

作者： Phattanun Thabarsa Salita Angkurawaranon Chakri Madla Withawat Vuthiwong Kittisak Unsrisong Papangkorn Inkeaw Master's Degree Program in Data Science Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Radiology Faculty of Medicine Chiang Mai University Chiang Mai Thailand Department of Computer Science Data Science Research Center Faculty of Science Chiang Mai University Chiang Mai Thailand

Acute intracerebral hemorrhage (ICH) entity accounts for 10 to 15% of all strokes and is associated with a higher mortality rate ischemic stroke or subarachnoid hemorrhage. Causes of ICH are divided into primary, and secondary, including vascular malformation and tumorous. Primary ICH accounts for approximately 80% of all ICH cases. Vascular anomalies rank as the second most common cause of spontaneous ICH overall. Furthermore, hemorrhage resulting from brain tumors can occur in up to 10% of all primary or metastatic tumors. Early recognizing of these three causes of bleeding is critical for clinicians in precise diagnosis, effective treatment management, and helps avoid delayed diagnosis. We proposed a radiomics approach for classifying multiple causes of acute ICH as vascular malformation, tumorous, and primary-related hematoma. Non-contrast brain computed tomography with clinical features was used as input. The regions of both hematoma and perihematomal edema were delineated by using manual segmentation approach. Four feature selection methods were adopted. Also, three classification models were investigated in this study. The results showed that using the features selected by F-value applied with SVM classifier outperformed the other models, achieving weighted average accuracy (± SD) of 0.84 (± 0.07). Additionally, the model demonstrated average sensitivity and positive predictive value of 0.84 (± 0.06) and 0.86 (± 0.05), respectively. We also evaluate the overall performance of discriminating each class from the rest using AUC. The result suggested that our proposed model achieved the weighted average AUC of 0.90. Our proposed method highlights the potential in identifying multiple causes of acute and nontraumatic ICH, which has not been previously explored.

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Elemental changes in heart and coronaries after breast cancer radiotherapy assessed by synchrotron radiation soft X-ray spectromicroscopy 44

Elemental changes in heart and coronaries after breast cance...

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44th Brazilian Workshop on nuclear Physics

作者： Pickler, A. Mendes, G. Tanure, T. Serqueira, L. Sena, G. Fidalgo, G. Colaço, M. Muñoz, E. Ferreira-Machado, S. Lau, C.C. Salata, C. Mantuano, A. Mota, C.L. Nascimento, A. de Almeida, C.E. Barroso, R.C. Physics Institute State University of Rio de Janeiro Brazil Radiological Science Department State University of Rio de Janeiro Brazil Laboratory of Cardiovascular Investigation Oswaldo Cruz Institute Brazil Nuclear Engineering Program Federal University of Rio de Janeiro Brazil

Radiotherapy (RT) plays a pivotal role in the treatment of breast cancer (BC) and various thoracic malignancies. Radiation induced heart disease (RIHD) is one such long term toxicity which can offset the improvement in cancer specific mortality. Long term normal tissue toxicity is becoming a bigger concern, as early diagnosis and the improvement in the treatment of these cancers has led to patients surviving longer. Our research group on Physics applied to biomedical sciences has been investigating the side effects of BC treatment (RT and chemotherapy) for more than ten years. The cardiac regeneration has been studied to better understand the damage that occurs following radiation procedures in the heart tissue after many thoracic cancer treatments. One possible complication is coronary artery disease induced by irradiation after radiotherapy in thoracic area. Studies on the structures of cardiac tissue and the distribution of low atomic weight element can help to understand mechanisms associated with damage to healthy tissue, as these are of fundamental importance to metabolism in biological systems. The present study aimed to elucidate how radiotherapy in the thoracic area causes damage in the coronary artery, and to verify the potential use of losartan in reducing, or even preventing, the side effects of irradiation in this artery. To assess elemental and morphological differences in aortic and coronary samples, the Low Energy X-Ray Fluorescence (LEXRF) technique using Synchrotron Radiation was employed. SR- LEXRF and scanning transmission X-ray microscopy measurements were carried out at the beamline TwinMic at Elettra Sincrotrone Triste, Italy. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Radiotherapy

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