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arXiv 2023年

作者： Jung, Dahuin Lee, Hyungyu Yoon, Sungroh Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Electrical and Computer Engineering Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

Imitation learning, in which learning is performed by demonstration, has been studied and advanced for sequential decision-making tasks in which a reward function is not predefined. However, imitation learning methods still require numerous expert demonstration samples to successfully imitate an expert’s behavior. To improve sample efficiency, we utilize self-supervised representation learning, which can generate vast training signals from the given data. In this study, we propose a self-supervised representation-based adversarial imitation learning method to learn state and action representations that are robust to diverse distortions and temporally predictive, on non-image control tasks. In particular, in comparison with existing self-supervised learning methods for tabular data, we propose a different corruption method for state and action representations that is robust to diverse distortions. We theoretically and empirically observe that making an informative feature manifold with less sample complexity significantly improves the performance of imitation learning. The proposed method shows a 39% relative improvement over existing adversarial imitation learning methods on MuJoCo in a setting limited to 100 expert state-action pairs. Moreover, we conduct comprehensive ablations and additional experiments using demonstrations with varying optimality to provide insights into a range of factors. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Dimensions of Equity: Undergraduate Research through Vertically Integrated Projects at Five Institutions 53

Dimensions of Equity: Undergraduate Research through Vertica...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Sonnenberg-Klein, Julia Coyle, Edward J. Bringardner, Jack Kukta, Robert V. Llewellyn, Donna Ramirez, Nichole Zoltowski, Carla B. Georgia Institute of Technology Vip Program AtlantaGA United States Electrical & Computer Engineering Vip Program Georgia Institute of Technology AtlantaGA United States Colorado School of Mines Engineering Design & Society GoldenCO United States Stony Brook University Mechanical Engineering Stony BrookNY United States Institute for Inclusive & Transformative Scholarship Boise State University BoiseID United States Purdue University Vertically Integrated Projects West LafayetteIN United States School of Electrical & Computer Engineering Purdue University West LafayetteIN United States

ISBN: (纸本)9798350336429

In this innovative practice work-in-progress paper, enrollment data from five institutions was used to examine equity in undergraduate research through Vertically Integrated Projects (VIP) program.. VIP is a model for undergraduate research in which large student teams are embedded in faculty-driven projects. The American Association of Colleges and Universities recognizes undergraduate research as a high-impact experience, associated with higher graduation rates and greater learning gains in college. Participation in multiple high-impact experiences yields cumulative gains to students from all backgrounds, and compensatory gains for minoritized and marginalized students. Nationally however, minoritized students, first-generation college students, and transfer students participate in undergraduate research at lower rates than their peers. In this study, VIP enrollments at five institutions (N = 6,651 over two semesters) were compared to demographics of the institutions to determine the degree to which program. achieved equity among historically underserved minorities, transfer students, first-generation college students, and by gender. Analysis accounted for demographics and level of participation of the academic units involved, comparing enrollments with what would be expected under equitable enrollment. Analyses were done for each institution and across the pooled sample. By institution, equity across categories varied. Across the pooled sample, results show small effects sizes for status as a historically underserved minority, very small effect sizes for first-generation students and transfer students, and slightly higher participation among women than men. The large-scale nature of VIP teams enables institutions to scale-up their undergraduate research offerings. This paper begins answering the question of whether this scaling increases access for marginalized populations, and the results are encouraging. The paper is a work-in-progress, because data needs to be

关键词： higher education multidisciplinary project based learning team based learning transdisciplinary undergraduate Vertically Integrated Projects VIP

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Hyperspectral Image Classification Using Custom Spectral Convolutional Neural Networks (CSCNNs) 14

Hyperspectral Image Classification Using Custom Spectral Con...

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14th International Conference on Information Science and Technology, ICIST 2024

作者： Rouamba, Stephanie Zhang, Nian Mahmoud, Wagdy Thompson, Lara Denis, Max Deksissa, Tolessa University of the District of Columbia Department of Electrical and Computer Engineering WashingtonDC United States University of the District of Columbia Department of Mechanical Engineering Biomedical Engineering Program WashingtonDC United States University of the District of Columbia Department of Mechanical Engineering WashingtonDC United States Water Resources Research Institute University of the District of Columbia WashingtonDC United States

ISBN: (纸本)9798350353334

Custom spectral convolutional neural networks (CSCNNs) combine the strengths of convolutional neural networks with specialized spectral processing, resulting in improved classification accuracy by effectively capturing subtle variations in hyperspectral data. This paper proposes a CSCNNs based approach to classify hyperspectral images. The proposed method leverages the high-dimensional spectral data inherent in hyperspectral images, employing convolutional layers specifically designed to capture spectral-spatial features. By reducing dimensionality through principal component analysis and creating image patches, the model is trained to recognize complex patterns across different spectral bands. In addition, a comprehensive analysis of CSCNN performance is carried out, focusing on its architecture, key features, and benefits in computational efficiency and spectral representation. Experimental results on datasets such as Salinas-A, Pavia University (Pavia-U), and Indian Pines demonstrate that the CSCNN model surpasses traditional methods, achieving higher classification accuracy and more robust performance metrics like overall accuracy (OA), average accuracy (AA), and Kappa coefficient. The proposed model achieved an overall accuracy (OA) of 98.2%, 96.7%, and 92.5% for the Salinas-A, Pavia-U, and Indian Pines datasets, respectively. The model's average accuracy (AA) was 97.5%, 95.4%, and 89.8% for the same datasets. Furthermore, the model attained Kappa coefficients of 97.7%, 95.2%, and 89.1% for Salinas-A, Pavia-U, and Indian Pines, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Fully Geometric Panoramic Localization

Fully Geometric Panoramic Localization

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Junho Kim Jiwon Jeong Young Min Kim Dept. of Electrical and Computer Engineering Seoul National University Dept. of Electrical Engineering Stanford University Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce a lightweight and accurate localization method that only utilizes the geometry of 2D-3D lines. Given a pre-captured 3D map, our approach localizes a panorama image, taking advantage of the holistic 360° view. The system mitigates potential privacy breaches or domain discrepancies by avoiding trained or hand-crafted visual descriptors. However, as lines alone can be ambiguous, we express distinctive yet compact spatial contexts from relationships between lines, namely the dominant directions of parallel lines and the intersection between non-parallel lines. The resulting representations are efficient in processing time and memory compared to conventional visual descriptor-based methods. Given the groups of dominant line directions and their intersections, we accelerate the search process to test thousands of pose candidates in less than a millisecond without sacrificing accuracy. We empirically show that the proposed 2D-3D matching can localize panoramas for challenging scenes with similar structures, dramatic domain shifts or illumination changes. Our fully geometric approach does not involve extensive parameter tuning or neural network training, making it a practical algorithm that can be readily deployed in the real world. Project page including the code is available through this link: https://***/fgpl/.

关键词： Location awareness Geometry Training Visualization Accuracy Three-dimensional displays Pipelines

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Early Detection of Heart Rate Conditions in Autistic Children Using IoT-Based Systems

Early Detection of Heart Rate Conditions in Autistic Childre...

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International Conference on electrical engineering and computer Science (ICECOS)

作者： Nizam Albar Melinda Melinda Yuwaldi Away dept. Electrical Engineering Master of Electrical Engineering Study Program Universitas Syiah Kuala Banda Aceh Indonesia dept. Electrical Engineering and Computer Engineering Faculty Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368260

Early detection of heart rate conditions in autistic children is crucial for anticipating tantrum reactions and providing prompt medical intervention. Previous research designed a prototype for acquiring Electrocardiogram (ECG) signals using the AD8232 sensor, but it required a long time to identify heart rate conditions. This study aims to develop the previous prototype using Internet of Things (IoT) implementation to acquire and identify the heart rate conditions of autistic children in real time. The ECG signal data is analyzed using Butterworth Filter and Artificial Neural Network (ANN) integrated into Raspberry Pi and results are transmitted to the Blynk App. Testing over 20 minutes showed that in the first 10 minutes, the system identified a normal heart rate at 105.19 BPM with 67% accuracy, while in the second 10 minutes, the heart rate rose to 117.19 BPM with tantrum symptoms and 75% accuracy. This system helps medical personnel provide immediate intervention during tantrums.

关键词： Heart rate Pediatrics Accuracy Prototypes Artificial neural networks Electrocardiography Real-time systems Internet of Things Servers Testing

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External Lightning Protection System modeling on photovoltaic using ATP Draw

External Lightning Protection System modeling on photovoltai...

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International Conference on electrical engineering, computer Science and Informatics (EECSI)

作者： Rohana Rohana Surya Hardi Nasaruddin Nasaruddin Yuwaldi Away Doctoral Program. School of Engineering Universitas Syiah Kuala Banda Aceh Indonesia and Universitas Muhammadiyah Sumatera Utara Indonesia Dept. of Electrical Engineering Universitas Sumatera Utara Medan Indonesia Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Dept. of Electrical and Computer Universitas Syiah Kuala Banda Aceh Indonesia

Solar energy is environmentally friendly energy with a very large energy source. Indonesia is also exploring and implementing renewable energy options including solar panels as a viable and long-term alternative to green technology. On the other hand, to get sufficient solar radiation, solar panel systems are generally located in open areas such as hillsides, deserts, and water surfaces, making them more vulnerable to lightning strikes. Lightning is one of the main threats to PLTS, a lot of damage to solar panels is caused by lightning. Direct and indirect lightning strikes have great potential to affect the entire solar panel farm system. The main objective of this study is to evaluate Lightning Protection System (LPS) modeling for network-connected solar panel (PV) farm systems using the ATP-EMTP software. Field observations and simulation tests are used to determine the position of the LPS and its installation structure. The rolling ball method is used to see the installed LPS protection radius. The results show that it is necessary to add LPS to the PV field. It was found that there was one additional LPS needed on the north side of the PV field, because that position was not protected by the radius of the lightning rod. From the simulation results, it was found that the LPS installation structure was good, because the results from the modeling of the isolated LPS lightning protection system were better to use than without installing LPS.

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STEIN LATENT OPTIMIZATION FOR GENERATIVE ADVERSARIAL NETWORKS 10

STEIN LATENT OPTIMIZATION FOR GENERATIVE ADVERSARIAL NETWORK...

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10th International Conference on Learning Representations, ICLR 2022

作者： Hwang, Uiwon Kim, Heeseung Jung, Dahuin Jang, Hyemi Lee, Hyungyu Yoon, Sungroh Department of Electrical and Computer Engineering AIIS ASRI INMC ISRC NSI and Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

Generative adversarial networks (GANs) with clustered latent spaces can perform conditional generation in a completely unsupervised manner. In the real world, the salient attributes of unlabeled data can be imbalanced. However, most of existing unsupervised conditional GANs cannot cluster attributes of these data in their latent spaces properly because they assume uniform distributions of the attributes. To address this problem, we theoretically derive Stein latent optimization that provides reparameterizable gradient estimations of the latent distribution parameters assuming a Gaussian mixture prior in a continuous latent space. Structurally, we introduce an encoder network and novel unsupervised conditional contrastive loss to ensure that data generated from a single mixture component represent a single attribute. We confirm that the proposed method, named Stein Latent Optimization for GANs (SLOGAN), successfully learns balanced or imbalanced attributes and achieves state-of-the-art unsupervised conditional generation performance even in the absence of attribute information (e.g., the imbalance ratio). Moreover, we demonstrate that the attributes to be learned can be manipulated using a small amount of probe data. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Generative adversarial networks

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NFT-Based Hydroponic Automated Control Using Adaptive Network-Based Fuzzy Inference System 2

NFT-Based Hydroponic Automated Control Using Adaptive Networ...

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2nd International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2022

作者： Surantha, Nico Vincentdo, Vito Bina Nusantara University Binus Graduate Program - Computer Science Jakarta11480 Indonesia Tokyo City University Faculty of Scienc and Engineering Department of Electrical Electronics and Communication Engineering Japan

ISBN: (纸本)9781665459440

The rapid population growth and industrial development in developing countries harm the agricultural sector because many agricultural lands are converted into residential or industrial areas. Applying modern agriculture technologies such as hydroponic could help to overcome the problem. However, hydroponics requires special attention in adjusting the pH and nutrient levels to maximize plant growth, so an automated system is needed to manage the process. In this research, a smart hydroponic system is proposed by applying Adaptive Networkbased Fuzzy Inference System (ANFIS) and Internet-of-Things. The IoT system consists of sensor, actuator, and data processing layer is designed to monitor and control the condition of pH and nutrition of the observed plants. Then, the ANFIS algorithm is designed to control the level of pH and nutrition. The experiment results show that the system can automatically adjust the pH and nutrient levels to the expected range for growing plants, and the fuzzy controller made using ANFIS are more accurate and stable than the fuzzy controller made using Sugeno. This study shows that ANFIS has excellent performance when controlling multiple actuators, as long as the data set has great granularity and well defined. © 2022 IEEE.

关键词： Hydroponics

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An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge

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IEEE Transactions on Affective Computing 2023年第3期14卷 1857-1875页

作者： Xie, Hong-Xia Lo, Ling Shuai, Hong-Han Cheng, Wen-Huang National Yang Ming Chiao Tung Univresity Institute of Electronics Hsinchu300093 Taiwan National Yang Ming Chiao Tung Univresity Department of Electrical and Computer Engineering Hsinchu300093 Taiwan National Chung Hsing University Artificial Intelligence and Data Science Program Taichung40227 Taiwan

Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has drawn much attention because it can benefit a wide range of applications, e.g., police interrogation, clinical diagnosis, depression analysis, and business negotiation. In this survey, we offer a fresh overview to discuss new research directions and challenges these days for MER tasks. For example, we review MER approaches from three novel aspects: macro-to-micro adaptation, recognition based on key apex frames, and recognition based on facial action units. Moreover, to mitigate the problem of limited and biased ME data, synthetic data generation is surveyed for the diversity enrichment of micro-expression data. Since micro-expression spotting can boost micro-expression analysis, the state-of-the-art spotting works are also introduced in this paper. At last, we discuss the challenges in MER research and provide potential solutions as well as possible directions for further investigation. © 2010-2012 IEEE.

关键词： Surveys

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Design and Optimization of the Porous Metamaterial EM Absorbers in X-band

Design and Optimization of the Porous Metamaterial EM Absorb...

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2023 IEEE Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMC+SIPI 2023

作者： Anurakparadorn, Kanat Taub, Alan Michielssen, Eric University of Michigan Macromolecular Science and Engineering Program Department of Material Science and Engineering Ann ArborMI United States University of Michigan Department of Mechanical Engineering Ann ArborMI United States University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI United States

ISBN: (纸本)9798350309768

A strategy that combines experiment and simulation to design and optimize electromagnetic (EM) metamaterial absorbers containing a periodic porous structure is described. The approach provides the ability to produce absorbers that meet multiple user-specified objectives. Using the measured intrinsic properties of the baseline materials as an input to EM-field based computational modelling and optimization, absorption by the studied metamaterials measured by their reflection loss (RL) increases significantly. The resulting metamaterials have the potential for lower cost and lighter weight while providing greater protection than traditional metal gaskets and foams. © 2023 IEEE.

关键词： Metamaterials

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