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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
Proceedings Paper Deep Learning based Human Detection using Thermal-RGB Data Fusion for Safe Automotive Guided-Driving Every year, the number of drivers increases, resulting in a corresponding increase in traffic fatalities. In Korea, pedestrian accidents constituted 35.5% of all traffic accidents for the last 2 years, with the number of child accidents rising annually. Autonomous vehicles currently rely on a lidar, which is insufficient in preventing accidents as it only recognizes obstacles that are far away. To mitigate these accidents, we propose selective thermal imaging data to identify people beyond the limited field of view. First, RGB camera image data for object recognition is performed. When vehicles or obstacles are present, the optional use of thermal data is applied. Thermal data can only identify a person, and it is used to prevent unforeseen incidents. The RGB images are divided into thirds and each section is assessed for obstacles, prioritizing the area with the most obstacles for integration with thermal data. Using the described algorithm, the level of accuracy increased by 2.07 times, from 40.43% to 83.91%. Additionally, experiments performed on a personal computer demonstrate that the algorithm is capable of functioning in real-time at a rate of 2.7 frames per second, utilizing 175.95 megabytes of memory at 0.36 seconds per image. When executed on a lightweight board such as the Jetson Nano, the algorithm runs at a rate of 0.75 frames per second, utilizing 140.08 megabytes of memory, at 1.33 seconds per image. Heuijee, Yun; Park, Daejin Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu, South Korea 58966829000; 55463943600 boltanut@knu.ac.kr; 2024 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS AND OTHER AFFILIATED EVENTS, PERCOM WORKSHOPS 2836-5348 3.06 2025-04-16 1 1 Deep Learning; Thermal data; RGB image data; ADAS(Advanced Driver Assistance System) ADAS(Advanced Driver Assistance System); Deep Learning; RGB image data; Thermal data Accidents; Automobile drivers; Deep learning; Infrared imaging; Learning systems; Object detection; Object recognition; Personal computers; Automotives; Deep learning; Frames per seconds; Human detection; Image data; RGB image data; RGB images; Thermal; Thermal data; Traffic fatalities; Advanced driver assistance systems English 2024 2024 10.1109/percomworkshops59983.2024.10503400 바로가기 바로가기 바로가기
Proceedings Paper Deep Learning-Based Calibration Method for An Augmented Reality Surgical Navigation System without Head-mounted Optical Markers 2D medical visualization techniques often fall short in adequately representing complex 3D anatomical structures. 2D surgical navigation lacks depth information, which is a significant drawback. Additionally, displaying medical data on a 2D screen during surgery is suboptimal because it necessitates the surgeon to constantly shift their focus. Augmented Reality (AR) compensates for the significant drawback of 2D surgical navigation, which lacks depth information. AR is a technology that overlays computer-generated information onto the real world, providing users with an enhanced visual experience. By integrating digital information with the physical environment in real-time, AR offers more intuitive and useful information. Currently, research on surgical navigation using AR is actively progressing. This innovative technology is being explored and developed to enhance the precision, efficiency, and safety of surgical procedures. In this paper, we utilize a snapshot from the built-in forward camera of the OST-HMDs, capturing both virtual points and real marker balls, to automatically calculate the transformation matrix between the virtual and real world. This method requires precise positions of both the virtual points and the real markers to successfully overlay anatomical information onto the real world. we use the YOLOv8 model and Virtual Aruco Marker to precisely determine the positions of both real and virtual points, and to automatically identify their points, ensuring an enhanced AR Navigation System. Kim, Seong Kyeong; Kim, Min Young Kyungpook Natl Univ, Dept Elect & Elect Engn, Daegu 41566, South Korea; Kyungpook Natl Univ, Res Ctr Neurosurg Robot Syst, Daegu 41566, South Korea tjdrud9915@knu.ac.kr;minykim@knu.ac.kr; 2024 24TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS, ICCAS 2024 2093-7121 0 Yolov8; Aruco Marker; Augmented Reality English 2024 2024 바로가기
Conference paper Deep Learning-Based Calibration Method for An Augmented Reality Surgical Navigation System without Head-mounted Optical Markers 2D medical visualization techniques often fall short in adequately representing complex 3D anatomical structures. 2D surgical navigation lacks depth information, which is a significant drawback. Additionally, displaying medical data on a 2D screen during surgery is suboptimal because it necessitates the surgeon to constantly shift their focus. Augmented Reality (AR) compensates for the significant drawback of 2D surgical navigation, which lacks depth information. AR is a technology that overlays computer-generated information onto the real world, providing users with an enhanced visual experience. By integrating digital information with the physical environment in real-time, AR offers more intuitive and useful information. Currently, research on surgical navigation using AR is actively progressing. This innovative technology is being explored and developed to enhance the precision, efficiency, and safety of surgical procedures. In this paper, we utilize a snapshot from the built-in forward camera of the OST-HMDs, capturing both virtual points and real marker balls, to automatically calculate the transformation matrix between the virtual and real world. This method requires precise positions of both the virtual points and the real markers to successfully overlay anatomical information onto the real world. we use the YOLOv8 model and Virtual Aruco Marker to precisely determine the positions of both real and virtual points, and to automatically identify their points, ensuring an enhanced AR Navigation System. © 2024 ICROS. Kim, Seong Kyeong; Kim, Min Young Kyungpook National University, Department of Electronic and Electrical Engineering, Daegu, 41566, South Korea; Kyungpook National University, Department of Electronic and Electrical Engineering, Daegu, 41566, South Korea, Kyungpook National University, Research Center for Neurosurgical Robotic System, Daegu, 41566, South Korea 59504421000; 56739349100 minykim@knu.ac.kr; International Conference on Control, Automation and Systems 1598-7833 0 2025-05-07 0 Aruco Marker; Augmented Reality; Yolov8 Electrotherapeutics; Helmet mounted displays; Metadata; Transplantation (surgical); Virtual environments; Aruco marker; Calibration method; Depth information; Medical visualization; Optical markers; Real-world; Surgical navigation; Surgical navigation systems; Virtual points; Yolov8; Augmented reality English Final 2024 10.23919/iccas63016.2024.10773365 바로가기 바로가기
Conference paper Deep Learning-Based Metasurface Design Platform with Self-Data Generation Design parameters of metasurfaces representing the desired color were extracted through a deep learning based design platform. In addition, design accuracy was increased with self-generated data during the validation process. © 2024 The Author(s) Jeong, Ki Won; Do, Yun Seon School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu, South Korea 58672002300; 24338060500 yuns.do@knu.ac.kr; Conference on Lasers and Electro-Optics/Pacific Rim, CLEO-PR 2024 in Proceedings 2024 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR) 0 2025-05-07 0 Data generation; Design parameters; Design platform; Metasurface; Validation process English Final 2024 바로가기
Conference paper Deep Learning-Based Metasurface Design Platform with Self-Data Generation Design parameters of metasurfaces representing the desired color were extracted through a deep learning based design platform. In addition, design accuracy was increased with self-generated data during the validation process. © 2024 IEEE. Jeong, Ki Won; Do, Yun Seon School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu, South Korea 58672002300; 24338060500 yuns.do@knu.ac.kr; 16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024 0 2025-05-07 0 Data generation; Design parameters; Design platform; Metasurface; Validation process English Final 2024 10.1109/cleo-pr60912.2024.10676933 바로가기 바로가기
Conference paper Deep Learning-Based QoS Prediction for Optimization of Robotic Communication The robustness of quality of service (QoS) in robotic communications is essential for operational efficiency and reliability. This paper presents an innovative deep learningbased methodology specifically designed for QoS prediction in robotic networks. A predictive model was developed by extensively analyzing communication data, including aspects such as latency and bandwidth, along with environmental factors. This model accurately predicts important QoS parameters. The results show a significant improvement in QoS prediction accuracy and overall network performance over traditional machine learning methods. The implications of this study are important for the development of autonomous robot operations and provide scalable and efficient solutions for realtime communication coordination that are pivotal to managing the complexity of adaptive robot systems. © 2024 IEEE. Kim, Tae Hyun; Lee, Jong Hyuk; Lee, Jin Hyuk; Kim, Min Young Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea; Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea; Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea; Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea 58978891300; 57208132362; 58072935700; 56739349100 6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024 0 2025-04-16 1 adaptive systems; Attention; autonomous robots; CNN; GNN; LSTM; predictive modeling; QoS; robotic communication Forecasting; Learning systems; Long short-term memory; Robots; Attention; Efficiency and reliability; GNN; LSTM; Operational efficiencies; Operational reliability; Optimisations; Predictive models; Quality-of-service; Robotic communication; Quality of service English Final 2024 10.1109/icaiic60209.2024.10463315 바로가기 바로가기
Conference paper Deep Neural Network NMPC for Computationally Tractable Optimal Power Management of Hybrid Electric Vehicles This study presents a method for deep neural network nonlinear model predictive control (DNN-MPC) to reduce computational complexity, and we show its practical utility through its application in optimizing the energy management of hybrid electric vehicles (HEVs). For optimal power management of HEVs, we first design the online NMPC to collect the data set, and the deep neural network is trained to approximate the NMPC solutions. We assess the effectiveness of our approach by conducting comparative simulations with rule and online NMPC-based power management strategies for HEV, evaluating both fuel consumption and computational complexity. Lastly, we verify the real-time feasibility of our approach through process-in-the-loop (PIL) testing. The test results demonstrate that the proposed method closely approximates the NMPC performance while substantially reducing the computational burden. © 2024 AACC. Park, Suyong; Nguyen, Duc Giap; Park, Jinrak; Kim, Dohee; Eo, Jeong Soo; Han, Kyoungseok University of Kyungpook National, Mechanical Engineering, Daegu, Buk-gu, 41566, South Korea; University of Kyungpook National, Mechanical Engineering, Daegu, Buk-gu, 41566, South Korea; Hyundai Motor Company, Namyang-eup, Hwaseong-si, 18278, South Korea; Hyundai Motor Company, Namyang-eup, Hwaseong-si, 18278, South Korea; Hyundai Motor Company, Namyang-eup, Hwaseong-si, 18278, South Korea; University of Kyungpook National, Mechanical Engineering, Daegu, Buk-gu, 41566, South Korea 57560254700; 57221496576; 57202138375; 57198638320; 36650692500; 56465294700 Proceedings of the American Control Conference 0743-1619 0 2025-05-07 0 Deep neural network; Energy management; Hybrid electric vehicle; Model predictive control; Process-in-the-loop Hybrid vehicles; Predictive control systems; Comparative simulation; Data set; Energy; First designs; ITS applications; Model-predictive control; Neural-networks; Nonlinear model predictive control; Optimal power; Process-in-the-loop; Hybrid power English Final 2024 10.23919/acc60939.2024.10644283 바로가기 바로가기
Proceedings Paper Deep Reinforcement Learning-based Edge Discovery within the 3GPP Framework for C-ITS With the evolution of edge computing, addressing challenges within the framework of the Third Generation Partnership Project (3GPP) standard has garnered attention. In particular, challenges such as edge discovery and relocation, session management function (SMF) selection, and edge lifecycle management pose significant hurdles in providing seamless services, especially in advanced Cooperative Intelligent Transportation Systems (C-ITS). This paper proposes an intelligent solution for edge discovery tailored to continuously provision C-ITS services to users within the 3GPP framework. Leveraging deep reinforcement learning (DRL), our proposed algorithm facilitates optimal edge discovery based on specific user requirements. We demonstrate the compatibility of our approach with 3GPP standard operations and address the critical challenge of edge discovery by employing an intelligent DRL-based methodology. Saad, Malik Muhammad; Tariq, Muhammad Ashar; Ajmal, Mahnoor; Jeon, Donghyun; Kim, Jinhong; Lim, Kil-Taek; Baek, Jang Woon; Kim, Dongkyun Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu, South Korea; Elect & Telecommun Res Inst, Daejeon, South Korea Saad, Malik/ABF-9433-2021; Baek, JangWoon/ABE-2176-2020; 마, 누르아즈말/NRY-5736-2025 57220715290; 57219865336; 57238144300; 59308006000; 57195433331; 7403175725; 57219860188; 35753648800 maliksaad@knu.ac.kr;tariqashar@knu.ac.kr;mahnoor.ajmal@knu.ac.kr;jdh0830@knu.ac.kr;jinhong@etri.re.kr;ktl@etri.re.kr;jwbaek98@etri.re.kr;dongkyun@knu.ac.kr; 2024 FIFTEENTH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS, ICUFN 2024 2165-8528 2165-8536 0 2025-05-07 0 0 Edge Computing; Edge Discovery; Cooperative Intelligent Transportation Systems (C-ITS) Cooperative Intelligent Transportation Systems (C-ITS); Edge Computing; Edge Discovery Reinforcement learning; 3GPP standard; Cooperative intelligent transportation system; Edge computing; Edge discovery; Intelligent transportation systems; Management functions; Reinforcement learnings; Seamless services; Session management; Third generation; Deep reinforcement learning English 2024 2024 10.1109/icufn61752.2024.10624860 바로가기 바로가기 바로가기
Article Deformed Zero-Sequence Flux Model of Open End Winding IPMSM; [권선 매입형 영구자석 동기전동기의 변형된 영상분 자속 모델] This paper proposes a deformed zero-sequence flux model of open-end winding (OEW) Interior Permanent Magnet Synchronous Motor (IPMSM), which takes into account non-linearity and structural features of IPMSM. The flux and phase voltage of the IPMSM are investigated using finite element analysis (FEA). This analysis confirmed the existence of the zero sequence voltage (ZSV) that causes the zero sequence current (ZSC) when utilizing an OEW inverter. Additionally, the presence of coupled components between the zero-sequence and DQ axes due to the winding structure and core saturation is identified. The proposed model effectively expresses the properties of zero-sequence components in IPMSM. © The Korean Institute of Electrical Engineers. Kim, Ji-Heon; Gu, Bon-Gwan; Lim, Jong-Kyong; Kim, Rae-Young School of Energy Engineering, Kyungpook National University, Daegu, South Korea; School of Energy Engineering, Kyungpook National University, Daegu, South Korea; Dept. of Electrical Engineering, Hanyang University, South Korea; Dept. of Electrical Engineering, Hanyang University, South Korea 59221545500; 50061273700; 57716104400; 7202933100 bggu@knu.ac.kr; Transactions of the Korean Institute of Electrical Engineers 1975-8359 73 7 0 2025-05-07 0 finite element analysis; flux model; IPMSM; Open-end winding (OEW); zero-sequence voltage Permanent magnets; Synchronous motors; Winding; Finite element analyse; Flux modeling; Interior permanent magnet synchronous motor; Open-end winding; Open-end windings; Phase voltage; Structural feature; Zero sequence current; Zero sequence voltage; Zero sequences; Finite element method Korean Final 2024 10.5370/kiee.2024.73.7.1162 바로가기 바로가기
Proceedings Paper Derivation and Analysis of New Small-Signal Model for Active Clamp Forward Converter Active clamp forward converter (ACFC) is widely used for low-voltage and high-current applications. Despite its extensive usage, the open-loop power stage characteristics of the converter have not been fully investigated and require further analyses. This paper reveals that the power stage dynamics of ACFC are different from those of either other buck-derived isolated converters or forward converters with different reset schemes. This paper presents a new small-signal model of the ACFC and provides the factorized expressions of the power stage transfer functions, which carry the useful information on the control design and performance evaluation. The accuracy of the small-signal model and the validity of the factorized transfer functions are verified both SIMPLIS simulations and experimental measurements. Lee, Dongheon; Kang, Yonghan; Choi, Byungcho; Cha, Honnyong Kyungpook Natl Univ, Sch Energy Engn, Daegu, South Korea; Cisco Syst Inc, San Jose, CA USA; Kyungpook Natl Univ, Sch Elect Engn, Daegu, South Korea 2024 IEEE APPLIED POWER ELECTRONICS CONFERENCE AND EXPOSITION, APEC 1048-2334 0 active clamp reset; forward converter; powerstage; transfer functions; small-signal modeling. English 2024 2024 10.1109/apec48139.2024.10509100 바로가기 바로가기
Conference paper Design and Implementation of a 5G Security Testbed Based on Zero Trust Architecture This paper presents the design and implementation of a 5G security testbed based on Zero Trust Architecture (ZTA). As 5G networks offer ultra-high speeds, low latency, and massive connectivity, ensuring their security has become a critical challenge, especially with the distributed nature and numerous connected devices. Traditional perimeter-based security models are no longer sufficient. Therefore, Zero Trust Architecture, which continuously verifies and monitors all resources and users regardless of their location, has been proposed as a solution. In this study, we propose a comprehensive security management system by integrating ZTA into a 5G testbed, focusing on real-time monitoring and dynamic policy enforcement using data collected from the Access and Mobility Management Function (AMF) and User Plane Function (UPF). The testbed's effectiveness is demonstrated through the deployment of Nokia's network equipment, allowing the collection and analysis of high-resolution traffic data, which aids in early threat detection and response. The proposed system enhances 5G network security and offers significant potential for application across various industries. © 2024 IEEE. Yoon, Mahnsuk; Seo, Jihun; Lee, Jonghoon; Cho, Keuchul Future Mobile Communication Research Center, GERI(Gumi Electronics & Information Technology Research Institute), Gumi, South Korea; Center for Connected & Automated Driving Research, Korea Transport Institute, Department of The Fourth Industrial Revolution & Transport, Sejong, South Korea; ETRI(Electronics and Telecommunications Research Institute), Cyber Security Research Division, Daejeon, South Korea; School of Computer Secience and Engineering, Software Education Center, Kyungpook National University, Daegu, South Korea 57203640624; 58000906600; 57195728430; 26031217700 International Conference on ICT Convergence 2162-1233 0 2025-05-07 0 5G Security; Dynamic Policy Enforcement; Real-time Monitoring; Zero Trust Architecture Information management; Network security; Queueing networks; Security systems; Testbeds; 5g security; Critical challenges; Design and implementations; Dynamic policy enforcement; Low latency; Real time monitoring; Security management systems; Security modeling; Ultra high speed; Zero trust architecture; 5G mobile communication systems English Final 2024 10.1109/ictc62082.2024.10826685 바로가기 바로가기
Conference paper Design and Implementation of a 5G Security Testbed for AI Solution Validation The growing adoption of 5G private networks has significantly enhanced industries such as smart factories, autonomous vehicles, and smart homes, thanks to their high-speed data transmission, low latency, and support for large-scale device connectivity. These networks have driven advancements in real-time monitoring and process automation, boosting productivity and efficiency while reducing costs. However, the deployment of 5G private networks has also introduced various security challenges, particularly with the emergence of AI-driven technologies. This paper discusses these challenges, focusing on the design and implementation of a 5G security testbed to validate AI solutions within such environments. Key security threats associated with 5G private networks include the exploitation of AI and generative AI for sophisticated phishing and spoofing attacks, the hacking of autonomous vehicles and smart homes that can compromise both safety and privacy, and security breaches in AI-based smart factory systems, which could lead to operational disruptions and economic losses. These risks emphasize the need for proactive countermeasures to ensure the integrity and security of 5G private networks. This paper verifies the security of AI-based applications implemented through a testbed using Nokia's 5G network system and provides the necessary configurations for the stable deployment of AI-based service solutions. © 2024 IEEE. Yoon, Mahnsuk; Kwon, Jaeuk; Seo, Jihun; Cho, Kyucheol GERI(Gumi Electronics & Information Technology Research Institute), Future Mobile Communication Research Center, Gumi, South Korea; GERI(Gumi Electronics & Information Technology Research Institute), Future Mobile Communication Research Center, Gumi, South Korea; Korea Transport Institute, Center for Connected & Automated Driving Research, Department of The Fourth Industrial Revolution & Transport, Sejong, South Korea; School of Computer Secience and Engineering, Kyungpook National University, Software Education Center, Daegu, South Korea 57203640624; 57991576500; 58000906600; 26031217700 International Conference on ICT Convergence 2162-1233 0 2025-05-07 0 5G Security; 5G Security testbed architecture; AI solution testbed Cost reduction; Network security; Phishing; Smart manufacturing; Testbeds; 5g security; 5g security testbed architecture; AI solution testbed; Autonomous Vehicles; Design and implementations; High-speed data transmission; Large-scales; Low latency; Private networks; Smart homes; 5G mobile communication systems English Final 2024 10.1109/ictc62082.2024.10827562 바로가기 바로가기
Conference paper Design of a Low-Cost Stochastic Computing-based Median Filter for Digital Image Processing Stochastic computing (SC) is an approximate computation method that enables the design of low-cost and error-tolerant systems. The median filter (MF) is one of the widely used filters in image processing and is particularly effective in removing salt-and-pepper noise. In this paper, we propose an energy-efficient SC-based MF. The proposed SC-based MF increases hardware efficiency while maintaining performance quality by reducing the number of comparators. The proposed SC-based MF was implemented using Verilog HDL and synthesized using 65-nm CMOS technology. The proposed SC-based MF achieves up to 63.4% and 75.2% savings in area and power, respectively, compared to the binary implementation of MF. Furthermore, compared to the conventional SC-based MF, the proposed design improves by 13.2% and 10.2%, respectively. Despite a slight decrease in peak signal-to-noise ratio (PSNR), the proposed design exhibits excellent overall hardware performance. © 2024 IEEE. Lee, Donghui; Kim, Yongtae Kyungpook National University, School of Computer Science and Engineering, Daegu, South Korea; Kyungpook National University, School of Computer Science and Engineering, Daegu, South Korea 57266466900; 55699627900 yongtae@knu.ac.kr; Proceedings - International SoC Design Conference 2024, ISOCC 2024 0 2025-05-07 1 comparator; median filter; stochastic computing Comparator circuits; Computer circuits; Wiener filtering; Approximate computation; Computation methods; Digital image; Energy efficient; Error-tolerant; Images processing; Low-costs; Median-Filter; Salt-and-pepper noise; Stochastic computing; Median filters English Final 2024 10.1109/isocc62682.2024.10762709 바로가기 바로가기
Conference paper Design of an Efficient Parallel Random Number Generator Using a Single LFSR for Stochastic Computing This paper proposes a parallel random number generator (RNG) using a single linear feedback shift register (LFSR) to generate two distinct random numbers, achieving twice the operational speed of a traditional serial RNG. The proposed RNG generates two distinct random numbers utilizing an LFSR. When implemented in a 65-nm CMOS technology, the proposed design leads to a 15.6% improvement in area and a 14.8 % improvement in power efficiency, addressing the trade-off between accuracy and energy efficiency in stochastic computing (SC). Furthermore, the proposed design not only matches but surpasses the performance of serial SC in an edge-detection digital image processing application. Therefore, for enhanced hardware efficiency and improved accuracy, the proposed parallel RNG architecture can be effectively employed. © 2024 IEEE. Lee, Donghui; Seo, Hyoju; Kim, Yongtae School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea; School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea; School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea 57266466900; 57215662369; 55699627900 yongtae@knu.ac.kr; 6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024 0 2025-04-16 1 linear feedback shift register (LFSR); parallel random num-ber generator (RNG); stochastic computing (SC) Economic and social effects; Energy efficiency; Feedback; Number theory; Random number generation; Shift registers; Stochastic systems; 65 nm CMOS technologies; Design leds; Linear feedback shift register; Linear feedback shift registers; Parallel random num-ber generator; Parallel random number generator; Power-efficiency; Random Numbers; Stochastic computing; Image processing English Final 2024 10.1109/icaiic60209.2024.10463230 바로가기 바로가기
Proceedings Paper Design of an Ultra-High-Speed Digital Interface Based on a Coplanar Stripline A design for an ultra-high-speed digital interface, providing autonomous signal integrity improvement, is proposed. The proposed interface is on a coplanar stripline and verified to perform from DC to 30 GHz for 5G/6G communications. Kim, Mun-Ju; Min, Byung-Cheol; Choi, Hyun-Chul; Kim, Kang-Wook Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu, South Korea 57478219300; 39161762500; 57193342681; 57204432422 dranswn@knu.ac.kr;minbc4658@knu.ac.kr;hcchoi@ee.knu.ac.kr;kang_kim@ee.knu.ae.kr; 2024 IEEE 33RD CONFERENCE ON ELECTRICAL PERFORMANCE OF ELECTRONIC PACKAGING AND SYSTEMS, EPEPS 2024 2165-4107 0 2025-05-07 0 0 ultra-high-speed interface; vertical transition; ultra-high; speed digital transmission ultra-high-speed digital transmission; ultra-high-speed interface; vertical transition Coplanar striplines; Digital interfaces; High-speed digital transmission; High-speed interfaces; Signal Integrity; Ultra high speed; Ultra-high-speed digital transmission; Ultra-high-speed interface; Vertical transitions; 5G mobile communication systems English 2024 2024 10.1109/epeps61853.2024.10754372 바로가기 바로가기 바로가기
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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.