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WoS SCOPUS Document Type Document Title Abstract Authors Affiliation ResearcherID (WoS) AuthorsID (SCOPUS) Author Email(s) Journal Name JCR Abbreviation ISSN eISSN Volume Issue WoS Edition WoS Category JCR Year IF JCR (%) FWCI FWCI Update Date WoS Citation SCOPUS Citation Keywords (WoS) KeywordsPlus (WoS) Keywords (SCOPUS) KeywordsPlus (SCOPUS) Language Publication Stage Publication Year Publication Date DOI JCR Link DOI Link WOS Link SCOPUS Link
Conference paper Lg= 60 nm In0.53Ga0.47As MBCFETs: From gm-max= 13.7 mS/μm and Q = 180 to virtual-source modeling In this paper, we report scalable 5 -level stacked gate-all-around (GAA) In 0.53 Ga0.47As multi-bridge channel FETs (MBCFETs), with careful attention paid to fluorine migration. At its heart, we maintained temperature of all the unit process steps below 300 °C and inserted an n-InP ledge into a top In0.52Al0.48As sacrificial layer to suppress F- -induced donor passivation. In addition, we used a selectively regrown n+In0.53Ga0.47 As contact formation by MOCVD and precision dry etching. The dry etching process resulted in a highly vertical etching slope along both the S/D and Wg directions. The fabricated Lg=60nm MBCFET showed a record combination of S=76mV/dec, gm-max=13.7 mS/mum, ION=2.24mA/mum and Q=180 at VDS=0.5V. © 2023 JSAP. Yoo, J.-H.; Jo, H.-B.; Lee, I.-G.; Choi, S.-M.; Baek, J.-M.; Lee, S.T.; Jang, H.; Kong, M.W.; Kim, H.H.; Lee, H.J.; Kim, H.-J.; Jeong, H.-S.; Park, W.-S.; Ko, D.H.; Shin, S.H.; Kwon, H.-M.; Kim, S.K.; Kim, J.G.; Yun, J.; Kim, T.; Shin, K.-Y.; Kim, T.-W.; Shin, J.-K.; Lee, J.-H.; Shin, C.-S.; Seo, K.-S.; Kim, D.-H. Kyungpook National University, South Korea; Kyungpook National University, South Korea; Kyungpook National University, South Korea; Kyungpook National University, South Korea; Kyungpook National University, South Korea; Korea Advanced Nano Fab Center, South Korea; Korea Advanced Nano Fab Center, South Korea; Korea Advanced Nano Fab Center, South Korea; Kyungpook National University, South Korea; Kyungpook National University, South Korea; Kyungpook National University, South Korea; Kyungpook National University, South Korea; Kyungpook National University, South Korea; Yonsei University, South Korea; Korea Polytechnics, South Korea; Korea Polytechnics, South Korea; Qsi; Qsi; Qsi; Qsi; University of Ulsan, South Korea; University of Ulsan, South Korea; Kyungpook National University, South Korea; Kyungpook National University, South Korea; Korea Advanced Nano Fab Center, South Korea; Korea Advanced Nano Fab Center, South Korea; Kyungpook National University, South Korea 57545572700; 57202871742; 37016357200; 57825819100; 57189694750; 57221766125; 55860474200; 59109130700; 58530978900; 59106441600; 57202516002; 57200366272; 57222957219; 16240618000; 57217467919; 55549386600; 57221604960; 59087926000; 57221602607; 59603995600; 57221120602; 57203495132; 7402723873; 55690077600; 57201540732; 58711653100; 57212363794 Digest of Technical Papers - Symposium on VLSI Technology 0743-1562 2023-June 0.61 2025-06-25 1 Dry etching; Indium phosphide; Semiconducting indium phosphide; Semiconductor alloys; 5-level; Contact formation; Dry etching process; Gate-all-around; Process steps; Sacrificial layer; Source models; Stacked gate; Vertical etching; Virtual sources; III-V semiconductors English Final 2023 10.23919/vlsitechnologyandcir57934.2023.10185250 바로가기 바로가기
Conference paper Lightweighted FPGA Implementation of Even-Odd-Buffered Active Noise Canceller with On-Chip Convolution Acceleration Units To make the acoustic signal that was processed by the noise canceller sound more natural for users [1]-[3], the delay in anti-noise generation should be reduced. For a single buffer, processing delay occurrs because it is impossible to write input signals while the processor is processing the data. when interfering with anti-noise and output signal, this processing delay creates additional buffering overhead to match the phase. The processing delay can be minimized using an Even-/Odd-buffer Structure to alternately read and write operations. In addition, the differences between the two methods of noise cancellation (FFT-based noise cancellation and adaptive algorithm) are compared in terms of output signal quality, processing time, and power consumption. As a result, using an Even-/Odd-buffer, reduced the processing delay of a single buffer. The FFT-based noise canceling method experienced fewer errors than the adaptive noise canceling method. © 2023 IEEE. Park, Seunghyun; Park, Daejin Kyungpook National University, School of Electronic and Electrical Engineering, South Korea; Kyungpook National University, School of Electronic and Electrical Engineering, South Korea 57903951400; 55463943600 boltanut@knu.ac.kr; 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 1.58 2025-06-25 3 Adaptive algorithm; Even-Odd-buffer; FFT; Noise cancelling Acoustic noise; Data handling; Fast Fourier transforms; Field programmable gate arrays (FPGA); Spurious signal noise; Active noise; Antinoise; Even-odd-buffer; FPGA implementations; FPGAs implementation; Noise cancellation; Noise canceller; Noise cancelling; Processing delay; Single buffers; Adaptive algorithms English Final 2023 10.1109/iceic57457.2023.10049949 바로가기 바로가기
Conference paper Lightweighted Shallow CTS Techniques for Checking Clock Tree Synthesizable Paths and Optimizing Clock Tree in RTL Design Time The microcontroller unit (MCU) are mainly used in low power devices, which use limited energy sources such as batteries, energy harvesting, and wireless communications. Therefore, reducing the operating power of the MCU is important to improve energy efficiency by extending battery life or minimizing energy consumption. The MCU is one of the chip designs composed of digital integrated circuits. The clock signal is important element to the MCU. The clock tree, which consists of the clock signal, is directly related to MCU low power operation and performance improvement. Also, chip verification process is important role to improve performance of overall system and reliability to the MCU. However, as the degree of integation of chips, the chip verification process increases complexity and time-comsumption to process many data. Currently, many users dependent on licensed electronic design automation (EDA) tools to ensure high accuracy, minimizing errors in circuit design and improving reliability. The use of licensed EDA tool puts a burden on users including high costs, limited license, difficulty in customization, slow speed, etc. An effective approach to avoid problems by using licensed EDA tools proceeds verification that is unrestricted license and customization for possible using only a register transfer level (RTL) source. In this paper, we propose to predict roughly pre-estimated CTS results using an RTL source in which temporary logic using random buffer insertion is placed before the route process. This paper contributes to reducing MCU operating power and hardware area by performing optimized CTS and minimizing resources according to the RTL structure to be designed. © 2023 IEEE. Kwon, Nayoung; Park, Daejin Kyungpook National University, School of Electronic and Electrical Engineering, South Korea; Kyungpook National University, School of Electronic and Electrical Engineering, South Korea 57638935700; 55463943600 boltanut@knu.ac.kr; International Conference on ICT Convergence 2162-1233 0 2025-06-25 0 clock tree synthesis (CTS); licensed EDA tool; low power; micro controller unit (MCU); placement and route (P&R); register transfer level (RTL); shallow CTS; synthesizable Clocks; Computer aided software engineering; Controllers; Electronic design automation; Energy harvesting; Energy utilization; Integrated circuit design; Integrated circuit manufacture; Microcontrollers; Clock tree synthesis; Electronic design automation tools; Licensed electronic design automation tool; Low Power; Micro controller unit; Micro controller units; Placement and route; Register transfer level; Register-transfer level; Shallow clock tree synthesis; Synthesizable; Energy efficiency English Final 2023 10.1109/ictc58733.2023.10393374 바로가기 바로가기
Conference paper Liquid-Crystalline Lens Array-Based 3D/2D Switchable Augmented Reality Display System Using One-Shot Learning Model An advanced three-dimensional/two-dimensional switchable augmented-reality system is proposed. A camera captures the images of real objects, and it is displayed as it is or the elemental image array generated from a captured image and estimated depth information via a one-shot learning model, is reconstructed by a liquid-crystalline lens array. © 2023 The Author (s). Erdenebat, Munkh-Uchral; Khuderchuluun, Anar; Darkhanbaatar, Nyamsuren; Nam, Oh-Seung; Kim, Joon Hyun; Imtiaz, Shariar Md.; Kwon, Ki-Chul; Gil, Sang-Keun; Kim, Hak-Rin; Kim, Nam School of Information and Communication Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu. Cheongju, Chungbuk, 28644, South Korea; School of Information and Communication Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu. Cheongju, Chungbuk, 28644, South Korea; School of Information and Communication Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu. Cheongju, Chungbuk, 28644, South Korea; School of Information and Communication Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu. Cheongju, Chungbuk, 28644, South Korea; School of Information and Communication Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu. Cheongju, Chungbuk, 28644, South Korea; School of Information and Communication Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu. Cheongju, Chungbuk, 28644, South Korea; School of Information and Communication Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu. Cheongju, Chungbuk, 28644, South Korea; Department of Electronics Engineering, Suwon University, 17 Wauan-gil, Bongdam-eup, Gyeonggi, Hwaseong, 18323, South Korea; School of Electronics Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 41566, South Korea; School of Information and Communication Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu. Cheongju, Chungbuk, 28644, South Korea 36166588400; 57203638261; 57203635381; 57344811300; 57347716000; 57213601979; 7201503212; 55663924400; 7410124944; 35494120000 namkim@chungbuk.ac.kr; 3D Image Acquisition and Display: Technology, Perception and Applications in Proceedings Optica Imaging Congress, 3D, COSI, DH, FLatOptics, IS, pcAOP 2023 0 2025-06-25 0 Learning systems; Liquid crystals; Three dimensional displays; Augmented reality systems; Crystalline lens; Display system; Learning models; Lens array; Liquid crystalline; One-shot learning; Real objects; Switchable; Two-dimensional; Augmented reality English Final 2023 10.1364/3d.2023.dw4a.5 바로가기 바로가기
Article Local Region-Based Hand Segmentation Using Deep Learning Segmenting objects through the Digital Image Processing makes it difficult to draw high accuracy in a complex background. In this paper, we propose a method of using deep-learning segmentation to segment objects in complex environments, providing ROI(Region of Interest) of images as a pre-processing, and Thresholding in the post-processing. The proposed method is to detect the position of the hand in a complex image using a deep learning-based object recognition algorithm employing the YOLOv4 model; to expand the ROI so that the deep learning-based segmentation techniques using the U-Net model can be applied locally, not on the entire image; and to process by Thresholding through Otsu's Binarization method. We applied the proposed algorithm to hand images with complex backgrounds and verified the effectiveness of the algorithm by measuring the IoU values of the masks of correct answers and results. © 2023, Korean Institute of Communications and Information Sciences. All rights reserved. Jeon, Junhyun; Kim, Tae-Hun; Jeong, Yoosoo; Park, Kil-Houm Department of Electronic and Electrical Engineering, Kyungpook National University, South Korea; DIPVISION, Poland; Electronics and Telecommunications Research Institute(ETRI), South Korea; Department of Electronic and Electrical Engineering, Kyungpook National University, South Korea 58965765300; 55696523100; 57193450818; 35776805000 khpark@ee.knu.ac.kr; Journal of Korean Institute of Communications and Information Sciences 1226-4717 48 9 0 2025-06-25 0 Deep Learning; Hand; Object Detection; Object Segmentation Korean Final 2023 10.7840/kics.2023.48.9.1135 바로가기 바로가기
Book chapter Localization with Wi-Fi Ranging and Built-in Sensors: Self-Learning Techniques Securing precise distance measurements from nearby reference nodes is a critical task in determining the performance of range-based positioning solutions. However, the multipath propagation characteristics of wireless channels render it difficult to obtain precise ranging results. In this context, this chapter utilizes machine learning techniques that extract useful features from Wi-Fi measurements to identify channel conditions and thus produce enhanced ranging results. Specifically, two neural networks (NN) are designed to perform ranging procedures for signal strength-based and round-trip time-based ranging scenarios. Furthermore, self-learning techniques that train the proposed NN-based ranging models with unlabeled training data are discussed in detail. The effectiveness of the proposed ranging models and self-learning techniques is extensively verified using a real-time positioning application. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. Choi, Jeongsik; Choi, Yang-Seok; Talwar, Shilpa School of Electronics Engineering, Kyungpook National University, Daegu, South Korea; Intel Labs, Intel Corporation, Hillsboro, OR, United States; Intel Labs, Intel Corporation, Santa Clara, CA, United States 58534394200; 37058389600; 57216999310 jeongsik.choi@knu.ac.kr; Machine Learning for Indoor Localization and Navigation 0.7 2025-06-25 1 Inertial sensors; Received signal strength; Round-trip time; Unsupervised learning; Wi-Fi ranging English Final 2023 10.1007/978-3-031-26712-3_5 바로가기 바로가기
Article Locking Screw Insertion Path Guidance System in Minimally Invasive Fracture Reduction Surgery In this paper, we propose a system to detect the location and direction of locking screw insertion using only a fluoroscopic image and guide screw insertion path, with a device that combines a SCARA robot arm and a conical remote center-of-motion (RCM) mechanism. In the proposed system, the target location and direction for screw insertion were derived from the fluoroscopic image of an intramedullary nail. The insertion guidance device comprise the SCARA robot arm and the RCM mechanism. The insertion guidance device calculated the current indicated path through forward kinematics analysis, and the target path and the current path were transmitted to the orthopedic surgeon through the control program. Moreover, the insertion guidance device set the insertion position and direction independently. The orthopedic surgeon first adjusted the SCARA robot arm to match the insertion position value to the target value and then sequentially adjusted the RCM mechanism so that the insertion direction matched the target value. After determining the insertion position and direction, the orthopedic surgeon inserted the surgical instrument through the path guided by the insertion guidance device and fastened the screw to the bone fragments and the intramedullary nail. When the proposed screw insertion guidance system was applied to fracture surgery, the locking screw was fastened with a minimum number fluoroscopic image, thus reducing surgery time and radiation exposure. The validity of the proposed method was confirmed through simulations and experiments using a fracture model. © ICROS 2023. Choi, Youn-Ho; Lee, Suk-Joong; Oh, Chang-Wug; Bang, Hyun Hee; Lee, Hyun Woo; Park, Chul Woo Institute of Medical Device and Robot, Kyungpook National University, South Korea; Department of Orthopaedic Surgery, Gyeongsang National University College of Medicine and Gyeongsang National University Changwon Hospital, South Korea; Department of Orthopaedic Surgery, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, South Korea; Institute of Medical Device and Robot, Kyungpook National University, South Korea; Institute of Medical Device and Robot, Kyungpook National University, South Korea; Institute of Medical Device and Robot, Kyungpook National University, South Korea 58249710900; 57078230800; 22135834200; 57216785452; 59400287600; 23995581000 pcw.mdrip@gmail.com; Journal of Institute of Control, Robotics and Systems 1976-5622 29 5 0 2025-06-25 0 C-arm fluoroscopy; intramedullary nailing; locking screw guide; minimal invasive fracture surgery Bone; Locks (fasteners); Remote control; Robotic arms; Screws; Surgery; Surgical equipment; C-arm fluoroscopy; Fluoroscopic images; Intramedullary nailing; Locking screw guide; Locking screws; Minimal invasive; Minimal invasive fracture surgery; Robot arms; SCARA robot; Screw insertions; Fracture Korean Final 2023 10.5302/j.icros.2023.23.0014 바로가기 바로가기
Book chapter Locust attacks on crop plants and control strategies to minimize the extent of the problem Locusts' invasion has been a major threat to crop plants and overall food security across the globe. Crop damage by locusts is even more threatening and devastating due to global pandemics such as COVID-19. Recently, the locust's attack has caused major damage to the agricultural and food crops and the value of this damage is estimated to cross billions of dollars. Although these locusts and grasshoppers are major feeders of crop plants, at the same time their populations are also important to maintain ecosystem functioning and balance. However, their overpopulation in certain seasons can cause major destruction of growing crops. Therefore, it is significantly important to check their populations, feeding activities, habitats, and crop damage in various regions so that effective management measures and strategies can be devised as per requirements and extent of the problem. The control and management measures mentioned in this chapter can greatly help the farmers and researchers to minimize the locust's invasion to achieve food security on a sustainable basis. © 2024 Apple Academic Press, Inc. Athar, Tabinda; Fatima, Hina; Waris, Aisha A.; Aqsa; Kanwal, Nafisa; Majid, Farah Institute of Soil and Environmental Sciences, University of Agriculture Faisalabad, Faisalabad, Pakistan; School of Applied Biosciences, Kyungpook National University, Daegu, South Korea; Institute of Soil and Environmental Sciences, University of Agriculture Faisalabad, Faisalabad, Pakistan; Department of Soil Science, University of Agriculture Faisalabad, Sub Campus Burewala, Vehari, Pakistan; Department of Soil Science, University of Agriculture Faisalabad, Sub Campus Burewala, Vehari, Pakistan; Department of Entomology, University of Agriculture, Faisalabad, Faisalabad, Pakistan 57215817227; 59800323300; 57204141168; 58342359900; 58510900500; 58511597700 Locust Outbreaks: Management and the World Economy 1.92 2025-06-25 2 Climate change; Cotton; Desert locust; Management tools; Red locust; Sustainability English Final 2023 바로가기
Article Low-Power FPGA Realization of Lightweight Active Noise Cancellation with CNN Noise Classification Active noise cancellation (ANC) is the most important function in an audio device because it removes unwanted ambient noise. As many audio devices are increasingly equipped with digital signal processing (DSP) circuits, the need for low-power and high-performance processors has arisen because of hardware resource restrictions. Low-power design is essential because wireless audio devices have limited batteries. Noise cancellers process the noise in real time, but they have a short secondary path delay in conventional least mean square (LMS) algorithms, which makes implementing high-quality ANC difficult. To solve these problems, we propose a fixed-filter noise cancelling system with a convolutional neural network (CNN) classification algorithm to accommodate short secondary path delay and reduce the noise ratio. The signal-to-noise ratio (SNR) improved by 2.3 dB with CNN noise cancellation compared to the adaptive LMS algorithm. A frequency-domain noise classification and coefficient selection algorithm is introduced to cancel the noise for time-varying systems. Additionally, our proposed ANC architecture includes an even-odd buffer that efficiently computes the fast Fourier transform (FFT) and overlap-save (OLS) convolution. The simulation results demonstrate that the proposed even-odd buffer reduces processing time by 20.3% and dynamic power consumption by 53% compared to the single buffer. Park, Seunghyun; Park, Daejin Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea 57903951400; 55463943600 ijjh0435@knu.ac.kr;boltanut@knu.ac.kr; ELECTRONICS 2079-9292 12 11 0.47 2025-06-25 3 5 active noise cancellation; convolutional neural networks; even-odd buffer; low-power system design; digital signal processing active noise cancellation; convolutional neural networks; digital signal processing; even–odd buffer; low-power system design English 2023 2023-06-02 10.3390/electronics12112511 바로가기 바로가기 바로가기
Conference paper Low-to-mid spatial frequency wavefront error control for off-axis freeform three-mirror KASI-Deep Rolling Imaging Fast Telescope We have developed the KASI-Deep Rolling Imaging Fast Telescope (K-DRIFT), adopting a 300 mm aperture off-axis freeform three-mirror design to detect faint and diffuse low-surface-brightness structures. By conducting the on-sky test observations and performing a series of simulations to analyze the performance of the K-DRIFT, we confirmed three main error sources causing optical performance degradation. The imaging performance of the K-DRIFT has successfully improved by correcting low-to-mid spatial frequency wavefront errors based on performance analysis results. This paper presents the K-DRIFT’s optical performance analysis algorithm and the optical performance improvement. © 2023 SPIE. Lee, Gayoung; Kim, Yunjong; Kim, Daewook; Byun, Woowon; Choi, Changsu; Lee, Yongseok; Kim, Dohoon; Chang, Seunghyuk; Ko, Jongwan Department of Astronomy, Kyungpook National University, Daegu, 41566, South Korea, Korea Astronomy and Space Science Institute, Daejeon, 34055, South Korea; Korea Astronomy and Space Science Institute, Daejeon, 34055, South Korea; Wyant College of Optical Sciences, University of Arizona, Tucson, 85721, AZ, United States; Korea Astronomy and Space Science Institute, Daejeon, 34055, South Korea; Korea Astronomy and Space Science Institute, Daejeon, 34055, South Korea; Korea Astronomy and Space Science Institute, Daejeon, 34055, South Korea, School of Space Research, Kyung Hee University, Yongin, 17104, South Korea; Green Optics Co., Ltd., Cheongju, 28126, South Korea; Center for Integrated Smart Sensors, Daejeon, 34141, South Korea; Korea Astronomy and Space Science Institute, Daejeon, 34055, South Korea, University of Science and Technology, Daejeon, 34055, South Korea 57846551600; 57203310827; 57219213285; 57216758314; 18433745100; 36068040300; 57295662700; 12759772300; 18434193800 gylee9097@kasi.re.kr; Proceedings of SPIE - The International Society for Optical Engineering 0277-786X 12677 0 2025-06-25 0 freeform; K-DRIFT; mid-spatial frequency error; misalignment; off-axis; optical performance analysis; telescope; three-mirror system Mirrors; Telescopes; Wavefronts; Deep rolling; Freeforms; Frequency errors; KASI-deep rolling imaging fast telescope; Mid-spatial frequency error; Mirror systems; Misalignment; Off-axis; Optical performance analysis; Spatial frequency; Three-mirror system; Errors English Final 2023 10.1117/12.2676309 바로가기 바로가기
Article LSTM-Based Imitation Learning of Robot Manipulator Using Impedance Control This paper proposes an imitation learning method based on long short-term memory (LSTM) to demonstrate robot manipulators using impedance control. An impedance controller controls the force and position of the robot manipulator. In this study, direct demonstrated position and force data for imitation learning of the robot were designed to be the reference input of the impedance controller. LSTM-based imitation learning methods enabled the robot to function as intended, even when its initial position was changed or other contact forces were applied according to the environment. The proposed method was verified by applying the writing task of the actual industrial robot manipulator that functions as the expert’s intention. © ICROS 2023. Park, Sejun; Jo, Seonghyeon; Lee, Sangmoon Department of Electronic and Electrical Engineering, Kyungpook National University, South Korea; Department of Electronic and Electrical Engineering, Kyungpook National University, South Korea; Department of Electronic and Electrical Engineering, Kyungpook National University, South Korea 58097901300; 57220154785; 59510733500 moony@knu.ac.kr; Journal of Institute of Control, Robotics and Systems 1976-5622 29 2 0.14 2025-06-25 1 Character Writing Task; Imitation Learning; Impedance Control; LSTM; Robot Manipulator Controllers; Flexible manipulators; Industrial robots; Learning systems; Modular robots; Robot applications; Character writing task; Contact forces; Force data; Imitation learning; Impedance control; Impedance controllers; Learning methods; Position data; Reference inputs; Robots manipulators; Long short-term memory Korean Final 2023 10.5302/j.icros.2023.22.0218 바로가기 바로가기
Proceedings Paper Lunar Landing Site Selection using Machine Learning The value of a planetary landing mission is critically dependent on the choice of landing sites which are generally influenced by several scientific and engineering constraints. The conventional method for Landing Site (LS) selection requires the manual analyses of individual sites involving a large talent force of multi-domain experts hence rendering the process cumbersome and expensive. Simultaneously, the currently employed methodology of recycling previously selected LSs for future missions would not be realized in the case of hitherto unexplored planets. As a consequence, machine learning algorithms are being actively explored in aiding effective space exploration. However, their application to selection of LSs that satisfy the scientific and engineering requirements of a mission have not yet been explored. In this paper, we propose an end-to-end Landing Site selection methodology using Moon as a case study and employing Hierarchical clustering of regions based on their altitude, expandable to other planets. Furthermore, we enforce commonly used constraints on the potential sites and select final sites for landing based on the user provided scientific and engineering constraints. With this approach, the LS selection process is simplified and the temporal requirement is reduced. Darlan, Daison; Ajani, Oladayo S.; Mallipeddi, Rammohan Kyungpook Natl Univ, Dept Artificial Intelligence, Daegu, South Korea Darlan, Daison/KQA-9542-2024; AJANI, Oladayo/HIR-9607-2022; Mallipeddi, Rammohan/AAL-5306-2020 58164208500; 57465126000; 25639919900 daisondarlan33@gmail.com;oladayosolomon@gmail.com;mallipeddi.ram@gmail.com; 2023 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE FOR GEOANALYTICS AND REMOTE SENSING, MIGARS 2.61 2025-06-25 1 3 Landing Site Selection; Clustering Clustering; Landing Site Selection Interplanetary flight; Landing; Learning algorithms; Lunar missions; Machine learning; Planets; Space research; Clusterings; Conventional methods; Engineering constraints; Landing mission; Landing site; Landing site selection; Machine-learning; Manual analysis; Multi-domains; Planetary landing; Site selection English 2023 2023 10.1109/migars57353.2023.10064571 바로가기 바로가기 바로가기
Book chapter Machine learning for renewable energy applications [No abstract available] Arumugam, Dhanasekaran; Stephen, Christopher; Parmar, Richa; Jegadeesan, Vishnupriyan; Paul, Ajay John; Kariyama, Ibrahim Denka Center for Energy Research, Department of Mechanical Engineering, Chennai Institute of Technology, Chennai, Tamil Nadu, India; Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala, R & D Institute of Science and Technology, Chennai, Tamil Nadu, India, Teaching Associateship for Research, Excellence (TARE) Fellow under SERB, National Institute of Solar Energy (NISE), Gurugram, Haryana, India; Solar Water Pump Laboratory, National Institute of Solar Energy, Gurugram, Haryana, India; Center for Energy Research, Department of Electrical and Electronics Engineering, Chennai Institute of Technology, Chennai, Tamil Nadu, India; School of Mechanical Engineering, Kyungpook National University, Daegu, Gyeongbuk Province, South Korea; Department of Agricultural Engineering, Dr. Hilla Limann Technical University, Wa, Upper West, Ghana 57212452016; 58336427100; 57216739022; 56422530100; 58338427000; 48561539000 Renewable Energy and AI for Sustainable Development 0 2025-06-25 0 English Final 2023 10.1201/9781003369554-8 바로가기 바로가기
Proceedings Paper Machine Learning-Based Batch Processing for Calibration of Model and Noise Parameters Non-Gaussian or non-whiteness of noise sources often occurs in many digital avionics systems. Incorrect modeling of the system degrades the performance of parametric model-based estimators and controllers. To calibrate the model and noise parameters, this paper proposes a machine learning-based batch processing approach. We first mathematically formulate a state augmentation system containing three types of noise: color noise, state-dependent noise, and correlation noise. Next, we define accessible process and measurement residuals to create the training data set. Finally, we propose offline batch processing that recursively utilizes a machine learning technique to calibrate the model and noise parameters. Simulation results under various conditions validate the calibration performance of the proposed approach. Lee, Kyuman Kyungpook Natl Univ, Dept Robot & Smart Syst Engn, Daegu, South Korea lee, kyuman/AAM-6979-2020 57193932345 klee400@knu.ac.kr; 2023 IEEE/AIAA 42ND DIGITAL AVIONICS SYSTEMS CONFERENCE, DASC 2155-7195 0 2025-06-25 0 0 non-whiteness noise; modeling error; calibration; batch processing; machine learning batch processing; calibration; machine learning; modeling error; non-whiteness noise Batch data processing; Digital avionics; Machine learning; Avionic systems; Batch processing; Machine-learning; Model errors; Modeling parameters; Noise parameters; Noise source; Non-Gaussian; Non-whiteness noise; Performance; Calibration English 2023 2023 10.1109/dasc58513.2023.10311101 바로가기 바로가기 바로가기
Proceedings Paper MAFD: A Federated Distillation Approach with Multi-head Attention for Recommendation Tasks The key challenges that recommendation systems must overcome are data isolation and privacy protection issues. Federated learning can efficiently train global models using decentralized data while preserving privacy. In real-world applications, however, it is difficult to achieve high prediction accuracy due to the heterogeneity of devices, the lack of data, and the limited generalization capacity of models. In this research, we introduce a personalized federated knowledge distillation model for a recommendation system based on a multi-head attention mechanism for recommendation systems. Specifically, we first employ federated distillation to improve the performance of student models and introduce a multi-head attention mechanism to enhance user encoding information. Next, we incorporate Wasserstein distance into the objective function of combined distillation to reduce the distribution gap between teacher and student networks and also use an adaptive learning rate technique to enhance convergence. We show that the proposed approach achieves better effectiveness and robustness through benchmarks. Wu, Aming; Kwon, Young-Woo Kyungpook Natl Univ, Daegu, South Korea Kwon, Young-Woo/HGE-6607-2022 58262125900; 57208480210 wuaming@knu.ac.kr;ywkwon@knu.ac.kr; 38TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2023 0 2025-06-25 1 0 Federated learning; Multi-head attention; Wasserstein distance; Recommendation systems federated learning; multi-head attention; recommendation systems; wasserstein distance Distillation; Learning systems; Privacy-preserving techniques; Attention mechanisms; Decentralised; Federated learning; Generalization capacity; Global models; Multi-head attention; Prediction accuracy; Privacy protection; Real-world; Wasserstein distance; Recommender systems English 2023 2023 10.1145/3555776.3577849 바로가기 바로가기 바로가기
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WoS Web of Science. Clarivate Analytics에서 제공하는 학술 데이터베이스입니다. 해당 논문이 WoS에 수록되어 있는지 여부를 표시합니다 (○: 수록됨).
SCOPUS Elsevier에서 제공하는 세계 최대 규모의 초록 및 인용 데이터베이스입니다. 해당 논문이 SCOPUS에 수록되어 있는지 여부를 표시합니다 (○: 수록됨).
Document Type 문헌의 유형을 나타냅니다. Article(원저), Review(리뷰), Proceeding Paper(학회논문), Editorial Material(편집자료), Letter(레터) 등으로 분류됩니다.
Title 논문의 제목입니다.
Abstract 논문의 초록(요약)입니다. 연구의 목적, 방법, 결과, 결론을 간략히 요약한 내용입니다.
Authors 논문의 저자 목록입니다. 공동 저자가 여러 명인 경우 세미콜론(;)으로 구분됩니다.
Affiliation 저자들의 소속 기관 정보입니다. 대학, 연구소, 기업 등 저자가 소속된 기관명이 표시됩니다.
ResearcherID (WoS) Web of Science의 고유 연구자 식별번호입니다. 동명이인을 구분하고 연구자의 업적을 정확하게 추적할 수 있습니다.
AuthorsID (SCOPUS) SCOPUS의 고유 저자 식별번호입니다. 연구자의 모든 출판물을 추적하고 관리하는 데 사용됩니다.
Journal 논문이 게재된 학술지의 정식 명칭입니다.
JCR Abbreviation Journal Citation Reports에서 사용하는 저널의 공식 약어입니다. 저널을 간략하게 표기할 때 사용됩니다.
ISSN International Standard Serial Number. 국제표준연속간행물번호로, 인쇄본 저널에 부여되는 고유 식별번호입니다.
eISSN Electronic ISSN. 전자 버전 저널에 부여되는 고유 식별번호입니다.
Volume 저널의 권(Volume) 번호입니다. 보통 연도별로 하나의 권이 부여됩니다.
Issue 저널의 호(Issue) 번호입니다. 한 권 내에서 여러 호로 나누어 출판되는 경우가 많습니다.
WoS Edition Web of Science의 에디션입니다. SCIE(Science Citation Index Expanded), SSCI(Social Sciences Citation Index), AHCI(Arts & Humanities Citation Index) 등으로 구분됩니다.
WoS Category Web of Science의 주제 분류 카테고리입니다. 저널과 논문이 속한 학문 분야를 나타냅니다.
JCR Year 해당 저널의 JCR(Journal Citation Reports) 지표가 산출된 연도입니다.
IF (Impact Factor) 저널 영향력 지수. 최근 2년간 발표된 논문이 해당 연도에 평균적으로 인용된 횟수를 나타냅니다. 저널의 학술적 영향력을 나타내는 대표적인 지표입니다.
JCR (%) 해당 카테고리에서 저널이 위치하는 상위 백분율입니다. 값이 낮을수록 우수한 저널임을 의미합니다 (예: 5%는 상위 5%를 의미).
FWCI Field-Weighted Citation Impact. 분야별 가중 인용 영향력 지수입니다. 논문이 받은 인용을 동일 분야, 동일 연도, 동일 문헌 유형의 평균과 비교한 값입니다. 1.0이 평균이며, 1.0보다 높으면 평균 이상의 인용을 받았음을 의미합니다.
FWCI UpdateDate FWCI 값이 마지막으로 업데이트된 날짜입니다. FWCI는 인용이 누적됨에 따라 주기적으로 업데이트됩니다.
WOS Citation Web of Science에서 집계된 해당 논문의 총 인용 횟수입니다.
SCOPUS Citation SCOPUS에서 집계된 해당 논문의 총 인용 횟수입니다.
Keywords (WoS) 저자가 논문에서 직접 지정한 키워드입니다. Web of Science에 등록된 저자 키워드 목록입니다.
KeywordsPlus (WoS) Web of Science에서 자동으로 추출한 추가 키워드입니다. 논문의 참고문헌 제목에서 자주 등장하는 단어들로 생성됩니다.
Keywords (SCOPUS) 저자가 논문에서 직접 지정한 키워드입니다. SCOPUS에 등록된 저자 키워드 목록입니다.
KeywordsPlus (SCOPUS) SCOPUS에서 자동으로 추출하거나 추가한 색인 키워드입니다.
Language 논문이 작성된 언어입니다. 대부분 English이며, 그 외 다양한 언어로 작성된 논문이 포함될 수 있습니다.
Publication Year 논문이 출판된 연도입니다.
Publication Date 논문의 정확한 출판 날짜입니다 (년-월-일 형식).
DOI Digital Object Identifier. 디지털 객체 식별자로, 논문을 고유하게 식별하는 영구적인 식별번호입니다. 이를 통해 논문의 온라인 위치를 찾을 수 있습니다.