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Image-based Korean Cattle Weight Estimation System using Top View Image

Transactions of the Korean Institute of Electrical Engineers 2022年第1期71卷 246-252页

作者： Lee, Chang-Bok Park, Yeon-Soo Cho, Hyun-Chong Dept. of Electronics Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Gangwon-do Livestock Research Institute Korea Republic of

Weight information is important to understand the growth of cattle and to calculate an appropriate daily feed amount. However, since the method of measuring weight using a scale consumes a lot of labor and time, weight information is not actively used in Korean cattle farms. Image-based weight estimation can be a new weight estimation method that does not cause stress to cattle and does not require labor. In this study, top-view data that is easy to apply to actual Korean cattle farms was used. A segmentation mask was obtained by dividing the cow and the background, and weight was estimated by extracting weight correlation factors from the segmentation mask. The weight estimation of the method of proposing area, body length, and body width, which are verified factors based on related studies, reached an error rate of 8.5%, and when the extracted grid elements were added to improve performance, it reached 7.5%. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Image segmentation

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Goral Detection System using YOLOv4 Object Detection Algorithm

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Transactions of the Korean Institute of Electrical Engineers 2022年第9期71卷 1308-1314页

作者： Lee, Han-Sung Oh, Yu-Jeong Park, Yung-Chul Lim, Sang Jin Cho, Hyun-Chong Dept. of Electronics Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Dept. of Electronics Engineering Kangwon National University Korea Republic of Dept. of Forest Science Kangwon National University Korea Republic of Dept. of Forest Science and Institute of Forest Science Kangwon National University Korea Republic of

Global industrialization has made human life comfortable, while causing environmental pollution. Environmental pollution has destroyed wildlife habitats, and as a result, several animals are endangered. In particular, long-tailed goral (Naemorhaedus caudatus) is an international endangered species that must be restored. A huge amount of photographic data has been accumulated through camera trap research for the restoration of endangered species. Until now, analysis of such photo data has only relied on experts, so it took a lot of time to analyze camera trap data. The goral detection system developed in this study, based on goral camera trap data, can enable goral data analysis at a faster than conventional methods. YOLOv4 was used as an object detection algorithm for goral detection. YOLOv4 is a network based on CSPDarknet53 and has improved performance through Bos (Bag of Specials) and BoF (Bag of Freebies). As a result, goral detection was possible with high accuracy and high speed. The performance of the goral detection system can be decreased by luminance at night. In this work, we propose a light invariant goral detection system by appropriately distributing day and night datasets for training YOLOv4. Test results on the night dataset showed an mAP of 0.907 and test results on the day dataset showed an mAP of 0.927. As a result, the goral detection system showed robust detection performance at night. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Cameras

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Continual Vision-and-Language Navigation

arXiv

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arXiv 2024年

作者： Jeong, Seongjun Kang, Gi-Cheon Choi, Seongho Kim, Joochan Zhang, Byoung-Tak Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Dept. of Computer Science and Engineering Seoul National University Korea Republic of AI Institute of Seoul National University Korea Republic of

In developing Vision-and-Language Navigation (VLN) agents that navigate to a destination using natural language instructions and visual cues, current studies largely assume a train-once-deploy-once strategy. We argue that this kind of strategy is less realistic, as deployed VLN agents are expected to encounter novel environments continuously through their lifetime. To facilitate more realistic setting for VLN agents, we propose Continual Vision- and-Language Navigation (CVLN) paradigm for agents to continually learn and adapt to changing environments. In CVLN, the agents are trained and evaluated incrementally across multiple scene domains (i.e., environments). We present two CVLN learning setups to consider diverse forms of natural language instructions: Initial-instruction based CVLN, focused on navigation via initial-instruction interpretation, and dialogue-based CVLN, designed for navigation through dialogue with other agents. We introduce two simple yet effective baseline methods, tailored to the sequential decision-making needs of CVLN: Perplexity Replay (PerpR) and Episodic Self-Replay (ESR), both employing a rehearsal mechanism. PerpR selects replay episodes based on episode difficulty, while ESR stores and revisits action logits from individual episode steps during training to refine learning. Experimental results indicate that while existing continual learning methods are insufficient for CVLN, PerpR and ESR outperform the comparison methods by effectively utilizing replay memory. © 2024, CC BY-NC-ND.

关键词： Visual languages

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A Precision Public Health Study on the Divergence of Life Expectancies over Time in United States Counties

A Precision Public Health Study on the Divergence of Life Ex...

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2023 IEEE Conference on Technologies for Sustainability, SusTech 2023

作者： Sheth, Suraj Bettencourt, Luís Pritzker School of Medicine and Interdisciplinary Scientist Training Program United States University of Chicago Biomedical Informatics Precision Medicine and Global Health Mansueto Institute for Urban Innovation ChicagoIL United States University of Chicago Mansueto Institute for Urban Innovation Ecology and Evolution Faculty Dept. of Sociology ChicagoIL United States Santa Fe Institute External Complex Systems United States

ISBN: (纸本)9798350345483

Ensuring healthy lifespans and promoting equity in health outcomes is a critical component of the United Nations Sustainable Development Goals, and progress has largely been measured using national life expectancy averages. While national averages reveal broad trends over time, they also largely obscure differences and inequalities at the subnational level. In studies of sustainability and public health, divergence refers to the increasing difference between two values over time. A major topic of interest is how subnational units, such as counties, deviate from the national average over time. In this work, we examined how three factors critical to evaluating the health of communities and quality of life, namely life expectancy, education, and income, have deviated in the United States at the county level in comparison to the national average. We found that while deviations in income and education have remained largely constant, the life expectancies of some counties have increased significantly over the national average, while others have stalled or even decreased in comparison. There has been a systematic divergence in life expectancies in US counties over a 34-year period leading to increasing disparities between counties. Gains in life expectancy were primarily concentrated in metropolitan areas while rural counties lagged, increasing urban-rural disparities over time. Identifying the upward or downward trends in life expectancy at the county level and understanding the root causes and risk factors driving those trends is critical to formulating customized Precision Public Health interventions and policies to improve health outcomes and create sustainable communities. © 2023 IEEE.

关键词： Risk management

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Cascade AOA Estimation Technique Based on The Combined Flexible Massive Array Antenna

Cascade AOA Estimation Technique Based on The Combined Flexi...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Tae-Yun Kim Heui-Seon Park Seul-Bi Jeon Tae-Ho Jo Suk-Seung Hwang Institute of AI Convergence Chosun University Gwangju Korea Dept. of Electronic Engineering Interdisciplinary Program in IT-Bio Convergence System Chosun University Gwangju Korea Dept. of Electronic Engineering Interdisciplinary Program in IT-Bio Convergence System Chosun University Gwangju Republic of Korea Interdisciplinary Program in IT-Bio Convergence System School of Electronic Engineering Chosun University Gwangju Korea

In the next-generation communication system, various communication services employs beamforming and massive array antenna techniques for reliable and efficient wireless communications. Based on the massive array antenna with a huge number of elements, the beamforming technique such as the estimation of Angle of Arrival (AOA) may not operate in real time, because the computational complexity for this situation should be extremely high. In order to enhance this problem, in this paper, we propose the cascade AOA estimation algorithm based on the Flexible Massive Combined Array (FMCA) antenna. The proposed algorithm finds AOA groups including multiple signal AOAs employing Capon based on some elements of the FMCA antenna, and it estimates the detail signal AOAs in the found AOA groups employing Beamspace Multiple Signal Classification (MUSIC) based on entire elements of the FMCA antenna. In addition, we provide the received signal model for applying the FMCA antenna and the proposed cascade AOA estimation algorithm.

关键词： Wireless communication Array signal processing Estimation Receiving antennas Real-time systems Multiple signal classification Classification algorithms

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EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving Object

arXiv

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arXiv 2023年

作者： Kim, Hyunseo Yoon, Hye Jung Kim, Minji Han, Dong-Sig Zhang, Byoung-Tak Interdisciplinary Program in Neuroscience Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Dept. of Computer Science and Engineering Seoul National University Korea Republic of AIIS Seoul National University Korea Republic of

Current robotic hand manipulation narrowly operates with objects in predictable positions in limited environments. Thus, when the location of the target object deviates severely from the expected location, a robot sometimes responds in an unexpected way, especially when it operates with a human. For safe robot operation, we propose the EXit-aware Object Tracker (EXOT) on a robot hand camera that recognizes an object's absence during manipulation. The robot decides whether to proceed by examining the tracker's bounding box output containing the target object. We adopt an out-of-distribution classifier for more accurate object recognition since trackers can mistrack a background as a target object. To the best of our knowledge, our method is the first approach of applying an out-of-distribution classification technique to a tracker output. We evaluate our method on the first-person video benchmark dataset, TREK-150, and on the custom dataset, RMOT-223, that we collect from the UR5e robot. Then we test our tracker on the UR5e robot in real-time with a conveyor-belt sushi task, to examine the tracker's ability to track target dishes and to determine the exit status. Our tracker shows 38% higher exit-aware performance than a baseline method. The dataset and the code will be released at https://***/hskAlena/EXOT. Copyright © 2023, The Authors. All rights reserved.

关键词： Belt conveyors

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Foot hemodynamics using optical tomography: A potential biomarker of peripheral arterial disease

Foot hemodynamics using optical tomography: A potential biom...

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Advances in Science and engineering Technology International Conferences (ASET)

作者： Rabah M. Al Abdi Amer Zakaria Hasan Al Nashash Tasnim Zalak Nandu Goswami Vishwa Jeet Dept. of Biomedical Engineering Jordan Univ. Of Science and Tech. Irbid Jordan Dept. of Electrical Engineering American University of Sharjah Sharjah UAE Master of Science in Biomedical Engineering Program American University of Sharjah Sharjah UAE College of Medicine Mohammed Bin Rashed University of Medicine and Health Sciences Dubai UAE

Peripheral arterial disease (PAD) is the narrowing of the arteries within the lower extremities, which inhibits blood flow to the foot. We present the use of dynamic diffuse optical tomography (DDOT) system to measure blood flow in the foot during venous and arterial occlusions. DDOT is a non-ionizing, contrast-free, and fast imaging modality. The DDOT system consists of 4 sensors, each containing four laser diodes (at wavelengths of 670, 780, 808, and 850 nm) and two silicon photodiodes. We examined 11 healthy subjects with a protocol consisting of five consecutive measurements. Biomarkers were extracted from changes in hemoglobin concentrations during the measurements. The results from a pilot study are presented in this report. The average increase in total hemoglobin was $\mathbf{17.2\pm 5.8}\ \mu \mathbf{M}$ during a measurement with 60 mmHg occlusion and $\mathbf{10.4\pm 6.7} \ \mu \mathbf{M}$ during a measurement with 180 mmHg occlusion. The biomarkers can quantify blood perfusion and oxygen consumption in the foot, and they have the potential to detect and monitor PAD.

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High performance gamma-ray spectrometer based on Cs3Cu2I5 single crystal grown by room-temperature vacuum solvent-evaporation method

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Journal of Alloys and Compounds 2025年 1027卷

作者： Seo, Jiwon Song, Seungho Park, Minhwan Byun, Jangwon Park, Beomjun Park, Chansun Jang, Mee Yeom, Jung-Yeol Dept. of Health and Safety Convergence Science Korea University Seoul02841 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02841 Korea Republic of Dept. of Bioengineering Korea University Seoul02841 Korea Republic of Dept. of Materials Science and Engineering Korea University Seoul02841 Korea Republic of The Institute for High Technology Materials and Devices Korea University Seoul02841 Korea Republic of Global Health Technology Center Korea University Seoul02841 Korea Republic of Daejeon34057 Korea Republic of School of Biomedical Engineering Korea University Seoul02841 Korea Republic of

In this study, the properties of Cs3Cu2I5 (CCI) crystals grown using the Slow Vacuum Evaporation Technique (SVET) were examined. Crystals were grown at room temperature (300 K, 27 °C) under vacuum conditions, producing single crystals with maximum diameter of 13 mm over 23 days. We confirmed excellent crystallinity via X-ray diffraction (XRD), compositional uniformity via X-ray photoelectron spectroscopy (XPS), and homogeneity via electron probe micro analyzer (EPMA). UV-Vis measurements indicated a high transmittance of 83 % and a bandgap of 3.64 eV. The Stokes shift of 110 nm, smaller than previously reported values, contributed to faster decay times and improved low-energy detection feasibility. Time-resolved photoluminescence (TR-PL) showed a rapid decay component (4.1 ns), accounting for 95.11 % of the total decay, demonstrating superior properties compared to existing reports. The gamma-ray detection capabilities of the CCI crystals were evaluated using six radioactive sources (Am-241, Ba-133, Co-57, Na-22, Cs-137, and Mn-54), achieving excellent energy resolution, including 5.2 % at 662 keV. The crystals displayed a highly linear response to gamma radiation (R² = 0.99998) with a light yield of 55,613 photons/MeV. Moreover, assessments of multiple crystals grown under identical conditions showed consistent performance with a coefficient of variation (CV) of 1.23 %. These findings confirm that SVET is a highly effective and cost-efficient method for producing high-quality CCI crystals, offering significant advantages over other growth techniques. © 2025 Elsevier B.V.

关键词： Gamma ray spectrometers

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DDD: A PERCEPTUALLY SUPERIOR LOW-RESPONSE-TIME DNN-BASED DECLIPPER

arXiv

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arXiv 2024年

作者： Yi, Jayeon Koo, Junghyun Lee, Kyogu Dept. of Electrical Engineering and Computer Science University of Michigan United States Dept. of Intelligence and Information Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Artificial Intelligence Institute Seoul National University Korea Republic of

Clipping is a common nonlinear distortion that occurs whenever the input or output of an audio system exceeds the supported range. This phenomenon undermines not only the perception of speech quality but also downstream processes utilizing the disrupted signal. Therefore, a real-time-capable, robust, and low-response-time method for speech declipping (SD) is desired. In this work, we introduce DDD (Demucs-Discriminator-Declipper), a real-time-capable speech-declipping deep neural network (DNN) that requires less response time by design. We first observe that a previously untested real-time-capable DNN model, Demucs, exhibits a reasonable declipping performance. Then we utilize adversarial learning objectives to increase the perceptual quality of output speech without additional inference overhead. Subjective evaluations on harshly clipped speech shows that DDD outperforms the baselines by a wide margin in terms of speech quality. We perform detailed waveform and spectral analyses to gain an insight into the output behavior of DDD in comparison to the baselines. Finally, our streaming simulations also show that DDD is capable of sub-decisecond mean response times, outperforming the state-of-the-art DNN approach by a factor of six. © 2024, CC BY-NC-SA.

关键词： Deep neural networks

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A Reduced Spiking Neural Network Architecture for Energy Efficient Context-Dependent Reinforcement Learning Tasks

A Reduced Spiking Neural Network Architecture for Energy Eff...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Hira Rasheed Peyman Mirtaheri Ali Muhtaroglu Dept. of Machines Electronics & Chemistry Faculty of Technology Art and Design (TKD) Oslo Metropolitan University Oslo Norway Biomedical Engineering MS Program Advanced Health Intelligence and Brain-Inspired Technologies (ADEPT)

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Neuromorphic circuits and systems involving spiking neural networks (SNN) have resulted in disruptive advances in performance/joule for relevant applications. A novel reinforcement learning (RL) digital hardware architecture is presented in this work that achieves energy consumption improvements through three fundamental techniques: The first two techniques comprise reduction in the complexity of the arithmetic unit for the optimization of recurring synapse and neuron cores in the network array, which is inspired by "crude" nature of the building blocks in the biological neurons that are tolerant to inaccuracy and noise. As the third technique, the RL SNN middle (hippocampus) layer is equipped with a simple scratchpad to facilitate temporal hysteresis in synaptic plasticity during the RL processes of long-term potentiation/depression (LTP / LTD). This feature is inspired by the non-temperamental behavior of biological synapses. The intelligent allocation significantly reduces learning time in a given task. Implementation on Intel Cyclone IV FPGA demonstrated significant advantages in cost, power dissipation and execution time, resulting in more than two orders of magnitude benefit in energy consumption for a context-dependent learning task on a 16-node 3-layer RL network presented in the literature.

关键词： Energy consumption Circuits and systems Neurons Reinforcement learning Biology Power dissipation Resource management

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