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2024 IEEE Conference on Electrical Insulation and Dielectric Phenomena, CEIDP 2024

作者： Saha, Anoy Ghassemi, Mona The University of Texas at Dallas ZEROES lab Department of Electrical and Computer Science Engineering RichardsonTX United States

ISBN: (纸本)9798350366679

The increasing need for power on the lunar surface, expected to reach megawatt levels, aligns with NASA's goal of establishing a sustainable human presence on the Moon, able to support over 100 personnel indefinitely. As humanity explores sustained lunar habitation and aims to establish bases, efficient and reliable power transmission becomes crucial. Power lines with high power density are needed, focusing on weight and volume while ensuring safety and cost-effectiveness in the harsh lunar environment. There are three possible locations for power lines on the lunar surface: above, on, or below the surface. Due to harsh lunar conditions like temperature fluctuations, micro meteoroid impacts, and radiation exposure, the initial two methods have limited effectiveness in transferring power on the moon. Underground deployment is considered safe and convenient;however, low soil thermal conductivity could pose a challenge. However, a temperature of -23.15 °C at a depth of 30 cm proves beneficial for waste heat. This study employs COMSOL Multiphysics to propose a novel method for developing and analyzing bipolar MVDC cables for power transmission beneath the lunar surface. Designed for ±10 kV voltage and 2 MW power, these cables offer an efficient solution for lunar power transmission and are resilient to harsh conditions. © 2024 IEEE.

关键词： Waste heat

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TOWARDS IMITATION LEARNING TO BRANCH FOR MIP: A HYBRID REINFORCEMENT LEARNING BASED SAMPLE AUGMENTATION APPROACH 12

TOWARDS IMITATION LEARNING TO BRANCH FOR MIP: A HYBRID REINF...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Changwen Ouyang, Wenli Yuan, Hao Gong, Liming Sun, Yong Guo, Ziao Dong, Zhichen Yan, Junchi AI Lab Lenovo Research China Department of Computer Science and Engineering Shanghai Jiao Tong University China

Branch-and-bound (B&B) has long been favored for tackling complex Mixed Integer Programming (MIP) problems, where the choice of branching strategy plays a pivotal role. Recently, Imitation Learning (IL)-based policies have emerged as potent alternatives to traditional rule-based approaches. However, it is nontrivial to acquire high-quality training samples, and IL often converges to suboptimal variable choices for branching, restricting the overall performance. In response to these challenges, we propose a novel hybrid online and offline reinforcement learning (RL) approach to enhance the branching policy by cost-effective training sample augmentation. In the online phase, we train an online RL agent to dynamically decide the sample generation processes, drawing from either the learning-based policy or the expert policy. The objective is to strike a balance between exploration and exploitation of the sample generation process. In the offline phase, a value function is trained to fit each decision's cumulative reward and filter the samples with high cumulative returns. This dual-purpose function not only reduces training complexity but also enhances the quality of the samples. To assess the efficacy of our data augmentation mechanism, we conduct comprehensive evaluations across a range of MIP problems. The results consistently show that it excels in making superior branching decisions compared to state-of-the-art learning-based models and the open-source solver SCIP. Notably, it even often outperforms Gurobi. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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Disentangling Prosody Representations With Unsupervised Speech Reconstruction

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 39-54页

作者： Qu, Leyuan Li, Taihao Weber, Cornelius Pekarek-Rosin, Theresa Ren, Fuji Wermter, Stefan Institute of Artificial Intelligence Zhejiang Lab Hangzhou311121 China Department of Informatics University of Hamburg Hamburg20148 Germany School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information. Significant progress has been made in disentangling representations for semantic content and speaker identity in speech recognition and speaker verification tasks respectively. However, it is still an open challenging question to extract prosodic information because of the intrinsic association of different attributes, such as timbre and rhythm, and because of the need for supervised training schemes to achieve robust speech recognition. The aim of this article is to address the disentanglement of emotional prosody based on unsupervised reconstruction. Specifically, we identify, design, implement and integrate three crucial components in our proposed model Prosody2Vec: (1) a unit encoder that transforms speech signals into discrete units for semantic content, (2) a pretrained speaker verification model to generate speaker identity embeddings, and (3) a trainable prosody encoder to learn prosody representations. We first pretrain Prosody2Vec on unlabelled emotional speech corpora, then fine-tune the model on specific datasets to perform Speech Emotion Recognition (SER) and Emotional Voice Conversion (EVC) tasks. Both objective and subjective evaluations on the EVC task suggest that Prosody2Vec effectively captures general prosodic features that can be smoothly transferred to other emotional speech. In addition, our SER experiments on the IEMOCAP dataset reveal that the prosody features learned by Prosody2Vec are complementary and beneficial for the performance of widely used speech pretraining models and surpass the state-of-the-art methods when combining Prosody2Vec with HuBERT representations. Audio samples can be found on our demo website. © 2023 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

关键词： Speech recognition

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Calculation of Television Interference (TVI) for Unconventional High Surge Impedance Loading (HSIL) Transmission Lines

Calculation of Television Interference (TVI) for Unconventio...

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2024 IEEE Conference on Electrical Insulation and Dielectric Phenomena, CEIDP 2024

作者： Khan, Mushfiqul Abedin Ghassemi, Mona The University of Texas at Dallas ZEROES lab Department of Electrical and Computer Science Engineering RichardsonTX United States

ISBN: (纸本)9798350366679

Television Interference (TVI) is a pivotal factor in high voltage transmission line design. TVI occurs because of corona discharges along the transmission line, causing signal deterioration and power loss. Conventional lines have the bundled conductors symmetrically placed on a circle, and the bundle radius is chosen such that the corona discharge effects are reduced. High Surge Impedance Loading (HSIL) transmission lines consist of a higher number of subconductors and a larger bundle radius, increasing line loadability. Conductor costs are reduced by using subconductors with smaller radii. The novel 500 kV unconventional HSIL line designed in this paper exhibits a potential to enhance the natural power of the line and differ from the conventional HSIL and the conventional lines in having asymmetrical bundled conductor configurations. For this unconventional HSIL line, TVI produced due to corona discharges on and around the line are calculated and compared to the conventional line. As a result of this study, we are taking a step forward toward the development of unconventional HSIL lines for modern power systems that will revolutionize power delivery. © 2024 IEEE.

关键词： Television interference

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An Ambiguous Edge Detection Method for Computed Tomography Scans of Coronavirus Disease 2019 Cases

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2024年第1期54卷 352-364页

作者： Singh, Pritpal Huang, Yo-Ping Central University of Rajasthan Quantum Optimization Research Lab Department of Data Science and Analytics Ajmer305817 India Department of Electrical Engineering National Penghu University of Science and Technology Magong88046 Taiwan The Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan The Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan The Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan

Recently, the coronavirus disease of 2019 (COVID-19), as named by the World Health Organization (WHO), has spread to over 200 countries. The WHO has declared this disease as a worldwide public health emergency. One of the most difficult tasks in combating this epidemic is to identify and segregate the afflicted people. The reverse transcription-polymerase chain reaction test (RT-PCR) is the most common pathology test used to diagnose this infection. Studies show that the RT-PCR test has a low-positive rate and sometimes becomes ineffective in diagnosing infection. In some cases, computed tomography (CT) scans reveal acute pneumonia and pulmonary anomalies. Therefore, CT scans are used together with RT-PCR tests to confirm infected people. Existing artificial intelligence and machine learning techniques require a large number of CT scans for training, which is a time-consuming process. Visual inspection shows that most CT scans of COVID-19 cases have broken, blurred, and ambiguous edges for infectious areas. Another major issue with these images is the heterogeneous intensity of the pixels, high noise, and low resolution. As a result of all these issues, the problem of effective edges/boundaries of various areas of CT scans of COVID-19 cases cannot be resolved by the current edge detection approach. Indeed, improper selection of edges can lead to an incorrect diagnosis of diseases through CT scans of COVID-19 cases. Therefore, there is an urgent need for a diagnostic method in addition to the RT-PCR test that can extract useful information from the minimum number of chest CT scans of suspected COVID-19 cases. This study introduces a new ambiguous edge detection method (AEDM) for identifying the edges/boundaries of different regions in CT scans of COVID-19 cases. The proposed AEDM is developed on the basis of ambiguous set (AS) theory, which is highly efficient in processing ambiguous pixel information. For simulation purposes, various CT scans of COVID-19 cases are c

关键词： Coronavirus

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Development of a CNN Model for Efficient Leaf Disease Detection

Development of a CNN Model for Efficient Leaf Disease Detect...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Dagar, Ishant Sharma, Harsh Gusain, Raj Vidyarthi, Anurag Prakash, Rishi Gowri, R. Propagation Research Lab Department of Electronics & Communication Engineering Dehradun India Graphic Era Hill University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350373783

In order to mitigate financial losses for producers and safeguard crops against diseases, the proposed solution presents a CNN model that enables early detection of leaf issues. In the agriculturally dominant nation of India, foliage maladies annually reduce agricultural output by 35%. The process of manually identifying diseases in foliage is time-consuming due to the lack of apparatus and expertise in laboratories required to identify early indicators of such conditions. The following are examples of such diseases: late blight, early blight, black root, bacterial mark, infected leaf, healthy leaf, and so forth. The importance of developing self-governing algorithms to diagnose diseases in leaves through the analysis of visual symptoms has been underscored by recent developments in deep learning and computer vision models. The main aim of the proposed model is to address the challenge of leaf disease diagnosis through the implementation of a straightforward method that minimizes the demand for computational resources. We implemented the multi-layered architecture of convolutional neural networks (CNN) in order to identify and eradicate leaf diseases. A model for image classification is trained utilizing our proposed approach, and it attains an accuracy of 91.5%, surpassing the performance of existing methodologies. © 2024 IEEE.

关键词： Multilayer neural networks

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Automated Segmentation of COVID-19 Infected Lungs via Modified U-Net Model

Automated Segmentation of COVID-19 Infected Lungs via Modifi...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sunil Kumar Biswajit Bhowmik Department of Computer Science and Engineering Maharshi Patanjali CPS Lab BRICS Laboratory National Institute of Technology Surathkal Mangalore Karnataka Bharat

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The COVID-19 pandemic has led to significant outbreaks in more than 220 countries worldwide, profoundly impacting the public health and lives. As of February 2024, over 774 million cases have been reported, with more than 7,035,337 deaths recorded. Therefore, there is a significant need for automated image segmentation to serve as clinical decision support. This paper presents a novel automated segmentation framework that dynamically generates distinct and randomized image patches for training using preprocessing techniques and extensive data augmentation. The proposed architecture employs a semantic segmentation approach, ensuring accuracy despite limited data availability. Experimental assessment comprises a visual inspection of the predicted segmentation outcomes. Quantitative evaluation of segmentation includes standards performance metrics such as precision, recall, Dice score, and Intersection over Union (IoU). The results exhibit a remarkable Dice coefficient score of 98.3% and an IoU rate surpassing 96.8%, demonstrating the model’s robustness in identifying COVID-19-infected lung regions.

关键词： COVID-19 Measurement Training Visualization Three-dimensional displays Convolution Computed tomography Semantic segmentation Lungs Decoding

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Lung and Colon Cancer Classification of Histopathology Images Using Convolutional Neural Network

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SN computer science 2024年第2期5卷 223页

作者： Singh, Onkar Kashyap, Kanchan Lata Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India Computing Science Engineering Indian Institute of Information Technology Kottayam Kerala India

The greatest significant contributor to cancer-related morbidity and mortality worldwide is malignant lung tumors. Lung cancer frequency has been seen to be on the rise recently. Lung cancer histopathology diagnosis is a crucial part of the patient’s treatment. The current study aims to demonstrate the efficiency of convolutional neural networks for the identification of squamous cell carcinoma and adenocarcinoma of the lung and colon by investigating the diagnosis of histopathology images. Five state-of-the-art pre-trained (ImageNet) convolutional neural network architectures, VGG-19, InceptionResNetV2, DenseNet201, EfficientNetB6, and MobileNetV2, are employed in this investigation to tri-categorize lung cancer images (normal, adenocarcinoma, and squamous cell carcinoma), together with colon cancer images (normal and adenocarcinoma). Regularization strategies have been applied to fine-tune the learning rate for improving accuracy. The LC25000 dataset has been used to validate the proposed method. EfficientNetB6, VGG19, InceptionResNetV2, DenseNet201, and MobileNetV2 accuracy on test data is reported to be 93.12, 98.00, 97.92, 99.12, and 99.32 percent respectively. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： DenseNet201 EfficientNetB6 Histopathology images InceptionResNetV2 Lung cancer MobileNetV2 VGG19

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Application of a 3D Sensor as a Vehicle Detector

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IEEJ Transactions on Industry Applications 2023年第6期143卷 464-472页

作者： Teng, Lin Watanabe, Nobuhiro Matsuzaki, Hiroaki Izumi, Takashi Lu, Xiaofeng Izumi & Teng Lab. Department of Computer Engineering College of Science and Technology Nihon University 7-24-1 Narashinodai Funabashi Chiba274-8501 Japan School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an710048 China

Vehicle detectors are required to operate in various environments. Current detectors employ various types of sensors including ultrasonic, loop coil, and image sensors. However, there exist some problems such as temperature and wind effects, maintenance problems, and accuracy degradation during nighttime. To solve these problems, we propose a vehicle detector that utilizes a 3D sensor based on near infrared laser and Time-of-Flight. This sensor can acquire a three-dimensional image that includes the distance data, unaffected by the external factors mentioned earlier. Therefore, our proposed vehicle detector design can be applied to traffic volume measurement, vehicle classification, multiple lane detection, etc. This paper discusses the application of the proposed vehicle detector in several scenarios. © 2023 The Institute of Electrical Engineers of Japan.

关键词： Volume measurement

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Navigating Data Imbalances in Credit Risk Management: A One-Sided Selection Approach

Navigating Data Imbalances in Credit Risk Management: A One-...

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Control Instrumentation System Conference (CISCON)

作者： Shashank J Bennehalli Sharan Vakkund Anusha Hegde H Biswajit Bhowmik Department of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory National Institute of Technology Karnataka Surathkal Mangalore Bharat

ISBN: (数字)9798350375480

ISBN: (纸本)9798350375497

Credit scoring plays a vital role in mitigating the information asymmetry that is pervasive on platforms for peer-to-peer (P2P) lending. A considerable challenge stems from the disparity in loan repayment outcomes: a significant minority of loan applicants defaulting on their loans, while the majority fulfilling their repayment obligations. The presence of imbalance in the dataset has the potential to incorporate bias into predictive model, which could lower its performance. In order to address this issue, data balancing techniques are often employed to enhance the performance of credit scoring models through the generation of datasets that are more balanced. This work constructs a robust credit scoring model capable of precisely assessing the creditworthiness of individuals seeking P2P lending. Four distinct classifiers - Logistic Regression, Random Forest, LightGBM, and Support Vector Machine (SVM) are employed. In doing so, it effectively mitigates the distortions that can result from unbalanced data distributions. This work achieves data balance with One-Sided Selection methodology along with Information gain and Pearson correlation which mainly determine the features to include. The proposed model thus works on both balanced and unbalanced datasets. Experimental results show that the standard metrics like accuracy, precision, recall, and F1-Score achieves upto 90.41%, 89.51%, 90.40%, and 89.96%, respectively.

关键词： Support vector machines Measurement Analytical models Correlation Accuracy Predictive models Data models Peer-to-peer computing Standards Random forests

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