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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 |
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| ○ | ○ | Proceedings Paper | Deep Learning based Channel Estimation for Full-Duplex Backscatter Communication Systems | A novel deep learning (DL) based channel estimation method is proposed for full-duplex backscatter communication systems to realize the wireless-powered sensor networks (WPSN) for internet of things (IoT). We aim to minimize the power consumption at a sensor node by reflecting the supplied power signal from an access point (AP), which is called backscatter communication. Moreover, by adopting the frequency-shifted modulation technique during backscatter transmission, full-duplex communication is performed between the AP and the sensor node. However, this incurs a problem that the uplink and downlink channels are cascaded, which results in degrading the performance of beamforming. In order to overcome this problem, we propose a novel channel estimation method that extracts separate uplink and downlink channels from the cascaded channels. We formulate the problem for joint channel estimation and pilot optimization, and then design the DL based channel estimator, which is composed of feedforward neural network(FNN) and convolutional neural network(CNN), for compensating non-linearity and non-convexity. Finally, we analyze the performance of the proposed DL based channel estimator compared to the conventional channel estimator. | Jung, Chae Yoon; Kang, Jae Mo; Kim, Dong In | Sungkyunkwan Univ SKKU, Dept Elect & Comp Engn, Dept Superintelligence Engn, Suwon, South Korea; Kyungpook Natl Univ, Dept Artificial Intelligence, Daegu, South Korea | Kim, Dong/ADC-1101-2022 | 58175685700; 56024930400; 35476060100 | jennyjung97@skku.edu;jmkang@knu.ac.kr;dongin@skku.edu; | 2023 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION, ICAIIC | 2831-6991 | 0.97 | 2025-06-25 | 2 | 2 | Beamforming; channel estimation (CE); deep learning (DL); full-duplex backscatter communication; internet of things (IoT); wireless-powered sensor networks (WPSN) | MIMO SYSTEMS; DESIGN | Beamforming; channel estimation (CE); deep learning (DL); full-duplex backscatter communication; internet of things (IoT); wireless-powered sensor networks (WPSN) | Backscattering; Beamforming; Channel estimation; Feedforward neural networks; Internet of things; Learning systems; Sensor nodes; Channel estimation; Channel estimation method; Channel estimator; Communications systems; Deep learning; Full-duplex; Full-duplex backscatter communication; Internet of thing; Sensors network; Wireless-powered sensor network; Deep learning | English | 2023 | 2023 | 10.1109/icaiic57133.2023.10066967 | 바로가기 | 바로가기 | 바로가기 | |||||||||||
| ○ | Book chapter | Deep learning method for adult patients with neurological disorders under remote monitoring | Dementia is a disease that results in poor memory, overthinking, and poor decision-making that interferes with doing daily tasks rather than a particular illness. As far as dementia goes, Alzheimer's disease is the most prevalent. Even though dementia mostly affects older persons, it is not a natural aspect of aging. Around the world, there are currently more than 55 million dementia sufferers, and an estimated 10 million new cases are reported yearly. Dementia is a result of several conditions and trauma that either directly or indirectly impact the brain. The most prevalent type of Alzheimer's disease causes 60%–70% of instances of dementia. In the world today, dementia is one of the primary causes of impairment and reliance among older people and the seventh leading cause of mortality among all diseases. Nowadays, information technologies are employed more often than they were 10 years ago to educate and support dementia patients and the family members who care for them. Short-term memory loss can induce disorientation, which increases the risk of malnutrition, excessive eating, and dehydration in people with Alzheimer's disease. They require a caretaker to make sure they get their daily meals and stay hydrated. To help Alzheimer's patients reclaim some of their customary freedom and comfort, this project aims to create a deep learning-based artificial intelligence system prototype that is now being built. Artificial intelligence is used in the proposed approach to finding human activity in video and can distinguish when the individual being observed is eating or drinking, alerting them via voice messages when they have forgotten to eat or drink or have consumed too much. Additionally, it enables a caretaker to oversee and administer the nutrition program from a distance. © 2024 Elsevier Inc. All rights reserved including those for text and data mining AI training and similar technologies. | Kathiresan, K.; Preethi, T.; Yuvaraj, N.; Karthic, S.; Sri Preethaa, K.R. | Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Tamil Nadu, Coimbatore, India; Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Tamil Nadu, Coimbatore, India; Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Tamil Nadu, Coimbatore, India; Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Tamil Nadu, Coimbatore, India; Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Tamil Nadu, Coimbatore, India, Department of Robot and Smart System Engineering, Kyungpook National University, Daegu, South Korea | 58175397300; 59260298800; 57204528689; 57671929200; 57214320928 | Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications | 1.7 | 2025-06-25 | 2 | Alzheimer's disease; Artificial intelligence; Computer vision; Deep learning; Human activity recognition in video; Image classification; Object detection; Surveillance system | English | Final | 2023 | 10.1016/b978-0-443-13772-3.00008-x | 바로가기 | 바로가기 | ||||||||||||||||||||
| ○ | Conference paper | Deep learning photo-acoustic microscopy with three-dimensional under sampled data reconstruction | Photoacoustic microscopy (PAM) is a non-invasive, label-free functional imaging technique that provides high absorption contrast with high spatial resolution. Spatial sampling density and data size are key determinants of PAM imaging speed. Therefore, undersampling methods that reduce the number of scan points are usually employed to improve the imaging speed of PAM by increasing the scan step size. Because undersampling techniques sacrifice spatial sampling density, deep learning-based reconstruction techniques have been explored as alternatives. However, these methods have been applied to reconstruct two-dimensional PAM images related to spatial sampling density. Therefore, by considering the number of data points, the data size, and the characteristics of PAM to provide three-dimensional (3D) volume data, this study proposes a deep-learning-based complete reconstruction of undersampled 3D PAM data. newly reported to Obtained from real experiments (i.e. not manually generated). Quantitative analysis results show that the proposed method exhibits robustness and outperforms interpolation-based reconstruction methods at various undersampling ratios, resulting in 80x faster imaging speed and 800x smaller data. Improves PAM system performance with size. Furthermore, the applicability of this method is experimentally verified by enlarging a sparsely sampled test dataset. His proposed deep learning-based PAM data reconstruction has been demonstrated to be the closest model available under experimental conditions, significantly reducing the data size for processing and effectively reducing the imaging time. © 2023 SPIE. All rights reserved. | Seong, Daewoon; Lee, Euimin; Gu, Youngae; Park, Joome; Jeon, Mansik; Kim, Jeehyun | School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea | 57212512353; 57223052911; 58243822800; 58623942800; 24171094000; 7601373350 | Proceedings of SPIE - The International Society for Optical Engineering | 0277-786X | 12523 | 0 | 2025-06-25 | 0 | Deep learning; Photoacoustic microscopy; Sparse sampling; Three-dimensional reconstruction; Undersampled image | Data handling; Deep learning; Image reconstruction; Photoacoustic microscopy; Sampling; Statistical tests; Data size; Deep learning; Imaging speed; Sampling densities; Sparse sampling; Spatial sampling; Three-dimensional reconstruction; Under sampled; Under-sampling; Undersampled images; Compressed sensing | English | Final | 2023 | 10.1117/12.2664349 | 바로가기 | 바로가기 | |||||||||||||||||
| ○ | Conference paper | Deep learning photoacoustic microscopy with three-dimensional undersampled data reconsturction | Photoacoustic microscopy (PAM) is a non-invasive, label-free functional imaging technique that provides high absorption contrast with high spatial resolution. Spatial sampling density and data size are important determinants of the imaging speed of PAM. Therefore, undersampling methods that reduce the number of scanning points are typically adopted to enhance the imaging speed of PAM by increasing the scanning step size. For the reason that undersampling methods sacrifice spatial sampling density, deep learning-based reconstruction methods have been considered as an alternative; however, these methods have been applied to reconstruct the two-dimensional PAM images, which is related to the spatial sampling density. Therefore, by considering the number of data points, data size, and the characteristics of PAM that provides three-dimensional (3D) volume data, in this study, we newly reported deep learning-based fully reconstructing the undersampled 3D PAM data, which is obtained at the actual experiment (i.e., not manually generated). The results of quantitative analyses demonstrate that the proposed method exhibits robustness and outperforms interpolation-based reconstruction methods at various undersampling ratios, enhancing the PAM system performance with 80-times faster-imaging speed and 800-times lower data size. Moreover, the applicability of this method is experimentally verified by upscaling the sparsely sampled test dataset. The proposed deep learning-based PAM data reconstructing is demonstrated to be the closest model that can be used under experimental conditions, effectively shortening the imaging time with significantly reduced data size for processing. © 2023 SPIE. | Seong, Daewoon; Lee, Euimin; Kim, Yoonseok; Kim, Hayoung; Han, Sangyeob; Hong, Juyeon; Jeon, Hyungseo; Gu, Youngae; Kim, Shinheon; Jeon, Mansik; Kim, Jeehyun | School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehack-ro, Buk-gu, Daegu, 41566, South Korea | 57212512353; 57223052911; 57216828837; 57214326551; 57193695305; 58157239900; 58243338500; 58243822800; 57226603736; 24171094000; 7601373350 | Progress in Biomedical Optics and Imaging - Proceedings of SPIE | 1605-7422 | 12379 | 0 | 2025-06-25 | 0 | Deep learning; Photoacoustic microscopy; Sparse sampling; Three-dimensional reconstruction; Undersampled image | Data handling; Deep learning; Image reconstruction; Photoacoustic microscopy; Photons; Sampling; Statistical tests; Data size; Deep learning; Imaging speed; Sampling densities; Sparse sampling; Spatial sampling; Three-dimensional reconstruction; Under sampled; Under-sampling; Undersampled images; Compressed sensing | English | Final | 2023 | 10.1117/12.2649765 | 바로가기 | 바로가기 | |||||||||||||||||
| ○ | ○ | Proceedings Paper | Deep Learning-based Anomaly Detection in Radar Data with Radar-Camera Fusion | Sensors such as cameras, lidars, and radars are crucial to understanding driving situations in autonomous vehicles. These sensors are susceptible to external and internal abnormalities, potentially leading to severe traffic accidents. A radar sensor is inevitably affected by the obstruction caused by small objects, which can cause the system to malfunction. This paper presents a deep learning approach for detecting anomalies in radar data. The accuracy of anomaly detection is improved by using radar-camera fusion. Our proposed model detects the data anomaly by calculating the deviation from the standard radar cross section (RCS) range. The result demonstrates that the model is capable of identifying the normal range of radar signal and anomaly signal under several different obtained features situations. It enables the detection of potential hazards and warns of dangers to drivers and higher-level control systems, creating a more resilient environment for ensuring autonomous driving safety. | Ning, Dian; Han, Dong Seog | Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu, South Korea | Ning, Daoguan/IRZ-7360-2023 | 58175410800; 7403219442 | ningdian@knu.ac.kr;dshan@knu.ac.kr; | 2023 28TH ASIA PACIFIC CONFERENCE ON COMMUNICATIONS, APCC 2023 | 0 | 2025-06-25 | 0 | 0 | Anomaly Detection; Radar Cross Section (RCS); Sensor Fusion | Anomaly Detection; Radar Cross Section (RCS); Sensor Fusion | Anomaly detection; Autonomous vehicles; Deep learning; Radar cross section; Anomaly detection; Autonomous Vehicles; Driving situations; Learning approach; Radar cross section; Radar cross-sections; Radar data; Radar sensors; Sensor fusion; Small objects; Cameras | English | 2023 | 2023 | 10.1109/apcc60132.2023.10460729 | 바로가기 | 바로가기 | 바로가기 | |||||||||||||
| ○ | ○ | Proceedings Paper | Deep Learning-based Human Vehicle Interface for Smart Golf Cart | This paper proposes a system in which a golf cart recognizes and tracks a user using a deep learning algorithm. Existing tracking golf carts use image processing algorithms or wearable sensors. However, image processing algorithms have low user recognition and tracking capabilities. In addition, the recognition and tracking system using a wearable sensor has a problem that requires an additional wearable sensor. We propose a non-attached smart golf cart using a deep learning algorithm to solve this problem. Deep learning object detection and classification algorithms are used to detect people and hands and recognize gestures in the detected hands.The golf cart performs user recognition, tracking, and human vehicle interface(HVI) by using the box of people and hands and gesture information. This paper verifies the algorithm on the golf cart. | Yoo, Min Woo; Lee, Chae Hyun; Han, Dong Seog | Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu, South Korea; Kyungpook Natl Univ, Sch Elect Engn, Daegu, South Korea | 57216618843; 57563453700; 7403219442 | ydn7415@knu.ac.kr;dshan@knu.ac.kr; | 2023 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION, ICAIIC | 2831-6991 | 0 | 2025-06-25 | 0 | 0 | deep learning; object detection; classification | classification; deep learning; object detection | Classification (of information); Deep learning; Learning algorithms; Learning systems; Object recognition; Wearable sensors; Deep learning; Golf carts; Human-vehicle interface; Image processing algorithm; Learning objects; Object classification; Objects detection; Recognition systems; Tracking capability; Tracking system; Object detection | English | 2023 | 2023 | 10.1109/icaiic57133.2023.10067123 | 바로가기 | 바로가기 | 바로가기 | |||||||||||||
| ○ | Conference paper | Deep Learning-Based Multi-tasking System for Diabetic Retinopathy in UW-OCTA Images | Diabetic retinopathy causes various abnormality in retinal vessels. In addition, Detection and identification of vessel anomaly are challenging due to nature of complexity in retinal vessels. UW-OCTA provides high-resolution image of those vessels to diagnose lesions of vessels. However, the image suffers noise of image. We here propose a deep learning-based multi-tasking systems for DR in UW-OCTA images to deal with diagnosis and checking image quality. We segment three kinds of retinal lesions with data-adaptive U-Net architectures, i.e. nnUNet, grading images on image quality and DR severity grading by soft-voting outputs of fine-tuned multiple convolutional neural networks. For three tasks, we achieve Dice similarity coefficient of 0.5292, quadratic weighted Kappa of 0.7246, and 0.7157 for segmentation, image quality assessment, and grading DR for test set of DRAC2022 challenge. The performance of our proposed approach demonstrates that task-adaptive U-Net planning and soft ensemble of CNNs can provide enhancement of the performance of single baseline models for diagnosis and screening of UW-OCTA images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. | Cho, Jungrae; Shon, Byungeun; Jeong, Sungmoon | Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, South Korea; Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, South Korea, Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, South Korea; Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, South Korea, Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, South Korea | 57205507149; 58635954000; 23100090400 | jeongsm00@gmail.com; | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 0302-9743 | 13597 LNCS | 0 | 2025-06-25 | 0 | diabetic retinopathy; ensemble; semantic segmentation; SS-OCTA; UW-OCTA | Convolutional neural networks; Deep learning; Eye protection; Grading; Image quality; Learning systems; Multitasking; Ophthalmology; Semantic Segmentation; Semantics; Detection and identifications; Diabetic retinopathy; Ensemble; High-resolution images; Multi tasking; Performance; Retinal vessels; Semantic segmentation; SS-OCTA; UW-OCTA; Image enhancement | English | Final | 2023 | 10.1007/978-3-031-33658-4_9 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Conference paper | Deep-Learning based design and modeling for chiro-optical dielectric metasurfaces | Nanophotonics employ chiro-optical effects for a variety of applications, including advanced imaging and molecular detection and separation. Due to their outstanding qualities in light-matter interactions, planar metasurfaces comprised of subwavelength meta-atoms have attracted a lot of attention. Despite of the vast potential of metasurfaces, achievement of large chiro-optical effects compactly on-chip at the visible wavelengths is still hindered by its complex design and optimization procedure. Deep-learning (DL) based modelling techniques have been put out as an alternative to the time-consuming and computationally demanding traditional design and optimization procedure of metasurfaces during the past few years. In this work, we have employed deep-learning based forward and inverse models to design and optimize achiral nano-fins to achieve giant chiro-optical affects at the visible wavelengths. A regression based forward neural network is proposed, that takes all the structural dimensions of the achiral nano-fins as input and trained separately to predict three different types of asymmetric transmissions i.e., TLL, TLR and TRL and circular dichroism. An inverse design model is also demonstrated that simultaneously considers all the three target transmissions and optimizes the dimensions of the achiral nano-fins in such a way that they experience constructive and destructive interference, resulting in an average circular dichroism of more than 60% and 70% asymmetric transmission. With potential applications in chiral polarizers for optical displays, flat integrated polarization shifter’s exhibiting high efficiency, chiral-metasurface sensors and chiral beam splitters, the suggested DL-enabled design techniques ease the realization of op-chip giant chiro-optical response through planar metasurface. © 2023 SPIE. All rights reserved. | Noureen, Sadia; Khaliq, Hafiz Saad; Fizan, Muhammad; Zubair, Muhammad; Mehmood, Muhammad Qasim; Massoud, Yehia | MicroNano Lab, Department of Electrical Engineering, Information Technology University of the Punjab, Ferozepur Road, Lahore, 54600, Pakistan; School of Electronic and Electrical Engineering, Kyungpook National University (KNU), Daegu, 41566, South Korea; MicroNano Lab, Department of Electrical Engineering, Information Technology University of the Punjab, Ferozepur Road, Lahore, 54600, Pakistan; Innovative Technologies Laboratories (ITL), King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia; MicroNano Lab, Department of Electrical Engineering, Information Technology University of the Punjab, Ferozepur Road, Lahore, 54600, Pakistan; Innovative Technologies Laboratories (ITL), King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia | 57808658700; 56725698200; 58806526400; 56581448000; 56276474100; 14018366500 | Proceedings of SPIE - The International Society for Optical Engineering | 0277-786X | 12773 | 7.67 | 2025-06-25 | 7 | asymmetric-transmission; Chiro-optical; circular-dichroism; deep-learning; metasurfaces | Deep learning; Design; Fins (heat exchange); Light transmission; Optical instruments; Asymmetric transmissions; Chiro-optical; Deep-learning; Design and optimization; Design procedure; Metasurface; Optical effects; Optical-; Optimization procedures; Visible wavelengths; Dichroism | English | Final | 2023 | 10.1117/12.2685855 | 바로가기 | 바로가기 | |||||||||||||||||
| ○ | Conference paper | Derivation of Small-Signal Model of Boost-SEPIC Interleaved Converter Based on PWM Switch Model | Small-signal model is essential in ensuring the quality and performance of dc-dc converters. An insight into dynamics is a fundamental requirement to select and design the controller of converters correctly. The dc-dc boost-SEPIC interleaved (BSI) converter has the potential for wide application but the small-signal model has not been thoroughly studied. This paper presents a method to establish the small-signal model of the high-order BSI converter based on the circuit averaging and averaged switch techniques. The transfer function model obtained in factorized form with three complex poles, two complex zeros, and one real zero makes it convenient to analyze characteristics and evaluate the impact of components and working conditions on the dynamic of the converter. The obtained model is verified by simulation and experimental results on the frequency domain. © 2023 IEEE. | Tran, Thien-Dung; Cha, Honnyong; Bui, Van-Dai; Nguyen, Chan Viet | Kyungpook National University, School of Energy Engineering, Daegu, South Korea; Kyungpook National University, School of Energy Engineering, Daegu, South Korea; Kyungpook National University, School of Energy Engineering, Daegu, South Korea, Thuyloi University, Faculty of Electrical and Electronics Engineering, Hanoi, Viet Nam; Ho Chi Minh University of Technology, Faculty of Electrical & Electronics Engineering, Ho Chi Minh, Viet Nam | 58572374600; 24450248400; 57221961296; 57210827612 | tranthiendung@tnut.edu.vn; | Proceedings - 2023 International Symposium on Electrical and Electronics Engineering, ISEE 2023 | 1.72 | 2025-06-25 | 3 | average model; BSI converter; control; dc-dc converter; small-signal model | Boost converter; Frequency domain analysis; Power electronics; Average modeling; Boost-SEPIC interleaved converter; Circuit averaging; Dc/dc converters; High-order; Higher-order; Interleaved converters; Performance; Small signal model; Switch models; Pulse width modulation | English | Final | 2023 | 10.1109/isee59483.2023.10299870 | 바로가기 | 바로가기 | ||||||||||||||||||
| ○ | Conference paper | Design and Implementation of a Path Following Control System for Automated Driving | Automated driving, considered the centerpiece of future mobility technology, relies heavily on an accurate and reliable vehicle control system. In this paper, we present the design and implementation of a driving control system, emphasizing the realization of automated driving capabilities on a commercially available vehicle platform. By utilizing step motors as actuators, control is exerted over the steering wheel as well as the brake and accelerator pedals of the test vehicle. The high-level and low-level controllers are implemented to gain longitudinal and lateral vehicle control, enabling the test vehicle to reliably follow the planned driving path. Both low-speed and normal-speed driving scenarios with varying road curvatures are considered in the evaluation of the path-following control. The results from the performance evaluation demonstrate that the proposed approach achieves the necessary path-following control performance for automated driving. © 2023 Korean Society of Automotive Engineers. All rights reserved. | Kim, Ji Hoon; Baek, Minjin; Thu, Nguyen Thi Hoai; Han, Dong Seog | Center for ICT and Automobile Convergence, Kyungpook National University, Daegu, 41566, South Korea; Center for ICT and Automobile Convergence, Kyungpook National University, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, South Korea | 58691064500; 57848675100; 57216620557; 7403219442 | dshan@knu.ac.kr; | Transactions of the Korean Society of Automotive Engineers | 1225-6382 | 31 | 10 | 0 | 2025-06-25 | 0 | Automated driving; Driving control; Lateral control; Longitudinal control; Path following | Korean | Final | 2023 | 10.7467/ksae.2023.31.10.737 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | ○ | Article | Design and Implementation of a Planar MIMO Antenna for Spectrum-Sensing Applications | Spectrum sensing is an important aspect in cognitive radio (CR) networks as it involves the identification of unused frequency spectra, which saves both bandwidth and energy. The design of a compact super-wideband (SWB) multi-input multi-output (MIMO)/diversity antenna with triple-band-notched features is presented for spectrum sensing in CR systems. The MIMO antenna comprises four identical semi-elliptical-shaped monopole resonators, which are orthogonally positioned and excited individually via tapered coplanar waveguide feed lines. Also, a mirror-slot analogous to the radiator is etched in the ground conductor of each antenna element to achieve SWB characteristics. In order to avoid interference with the SWB, the antenna radiator is loaded with a staircase-shaped slit and a pair of concentric slits, arranged like a complementary split-ring resonator. The antenna resonates from 1.2 to 43 GHz, exhibiting a bandwidth ratio of 36:1. In the MIMO antenna, the antenna elements are located orthogonally, and the isolation > 18 dB and envelope correlation coefficient < 0.01 are realized in the resonating band. The antenna offers a peak gain of 4 dBi, and a sharp reduction in gain at notch frequencies (3.5 GHz, 5.5 GHz, and 8.5 GHz) is achieved. The size of the MIMO antenna is 52 mm x 52 mm. The proposed compact-size antenna features a high bandwidth ratio and straightforward design procedure, and can be simply integrated into contemporary RF equipment. The presented SWB MIMO antenna outperforms SWB antenna designs reported in the open literature, which featured one or two notched bands, whereas it has three notched bands. Also, the three notches in the SWB are achieved without the use of any filters, which simplifies the antenna development process. | Kumar, Sachin; Raheja, Dinesh Kumar; Palaniswamy, Sandeep Kumar; Kanaujia, Binod Kumar; Mostafa, Hala; Choi, Hyun Chul; Kim, Kang Wook | SRM Inst Sci & Technol, Dept Elect & Commun Engn, Kattankulathur 603203, India; Netaji Subhas Univ Technol, Dept Elect & Commun Engn, East Campus, Delhi 110031, India; Dr BR Ambedkar Natl Inst Technol, Dept Elect & Commun Engn, Jalandhar 144011, India; Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Technol, POB 84428, Riyadh 11671, Saudi Arabia; Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea | Kanaujia, Binod/L-6484-2019; kanaujia, Binod/L-6484-2019; Kumar, Sachin/W-2211-2019; Palaniswamy, Sandeep Kumar/AAF-2240-2021; PALANISWAMY, SANDEEP KUMAR/AAF-2240-2021; Mostafa, Hala/GQQ-7329-2022 | 56907994000; 57192106958; 56158830800; 56962785800; 56229063000; 57193342681; 57204432422 | kang_kim@ee.knu.ac.kr; | ELECTRONICS | 2079-9292 | 12 | 15 | 0.47 | 2025-06-25 | 4 | 5 | elliptical; MIMO; quad-port; SWB; triple-band elimination | SUPER-WIDE-BAND; SELF-COMPLEMENTARY ANTENNA; MULTIPLE-INPUT; NOTCH | elliptical; MIMO; quad-port; SWB; triple-band elimination | English | 2023 | 2023-08 | 10.3390/electronics12153311 | 바로가기 | 바로가기 | 바로가기 | ||||||||||
| ○ | ○ | Article | Design of a Common-Mode Rejection Filter Using Dumbbell-Shaped Defected Ground Structures Based on Equivalent Circuit Models | An efficient design method is proposed for a compact common-mode rejection (CMR) filter utilizing dumbbell-shaped defected ground (DS-DG) structures and gap-coupled stub (GCS) resonators. A CMR filter for differential lines helps to improve the signal integrity of high-speed digital signals on printed circuit boards. The proposed CMR filter design is based on the equivalent circuit models, while the previous designs depended heavily on the DS-DG structure optimization using the EM simulations. The proposed CMR filter effectively rejects the common-mode components while minimally affecting the differential signals. To prove the simplified design approach, a fifth-order Chebyshev band-stop filter is designed with three DS-DG structures and two GCS resonators. From the simulated and measured results, it is found that the proposed CMR filter provides similar to 90% fractional frequency bandwidth with more than 20 dB of common-mode rejection ratio and less than 0.6 dB of insertion loss of the differential signal. | Choi, Jeong-Sik; Min, Byung-Cheol; Kim, Mun-Ju; Kumar, Sachin; Choi, Hyun-Chul; Kim, Kang-Wook | Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea; SRM Inst Sci & Technol, Dept Elect & Commun Engn, Kattankulathur 603203, India | ; Kumar, Sachin/W-2211-2019 | 59419200600; 39161762500; 57478219300; 56907994000; 57193342681; 57204432422 | jeongsik2@knu.ac.kr;minbc4658@knu.ac.kr;dranswn@knu.ac.kr;sachinkr@srmist.edu.in;hcchoi@ee.knu.ac.kr;kang_kim@ee.knu.ac.kr; | ELECTRONICS | 2079-9292 | 12 | 15 | 0.23 | 2025-06-25 | 2 | 2 | defected ground structure; dumbbell-shaped; Chebyshev band-stop filter; common mode rejection filter; gap-coupled stub; electromagnetic interference; signal integrity | SUPPRESSION | Chebyshev band-stop filter; common mode rejection filter; defected ground structure; dumbbell-shaped; electromagnetic interference; gap-coupled stub; signal integrity | English | 2023 | 2023-08 | 10.3390/electronics12153230 | 바로가기 | 바로가기 | 바로가기 | ||||||||||
| ○ | Article | Design of Front-back Symmetric Four-wheel-steering Mobile Robot | Ackermann steering can reduce the turning radius of a vehicle to a limited extent, and individual steering for mobile robots increases cost owing to the need for more actuators. Therefore, in this paper, we propose a novel front-back-symmetric-steering mechanism that allows four-wheel steering using only two actuators. A robot prototype is fabricated to verify the basic performance of this design. The proposed steering system has no restriction on the change of the steering angle; therefore, the robot can rotate in place without lateral slip of the wheels. In addition, the four-wheel-drive function combined with the steering mechanism facilitates smooth driving even on somewhat uneven road surfaces without requiring a suspension system. As a result, the proposed steering mechanism is expected to apply to various mobile robots owing to its simplicity and the ability to achieve excellent rotation characteristics. © ICROS 2023. | Park, Songeun; Suh, Jungwook | Department of Robot and Smart System Engineering, Kyungpook National University, South Korea; Department of Robot and Smart System Engineering, Kyungpook National University, South Korea | 58627825100; 36606826500 | jwsuh@knu.ac.kr; | Journal of Institute of Control, Robotics and Systems | 1976-5622 | 29 | 9 | 0 | 2025-06-25 | 0 | four-wheel driving; four-wheel steering; steering system; wheeled mobile robot | Actuators; Automobile steering equipment; Four wheel steering; Machine design; Mobile robots; Suspensions (components); Ackermann steering; Four-wheel driving; Four-wheel steering; Performance; Robot prototypes; Steering mechanisms; Steering systems; Symmetrics; Turning radius; Wheeled mobile robot; Wheels | Korean | Final | 2023 | 10.5302/j.icros.2023.23.0077 | 바로가기 | 바로가기 | |||||||||||||||
| ○ | Article | Developing a hydrological model for evaluating the future flood risks in rural areas | Climate change is expected to amplify the future flooding risks in rural areas which could have devastating implications for the sustainability of the agricultural sector and food security in South Korea. In this study, spatially disaggregated and statistically bias-corrected outputs from three global circulation models (GCMs) archived in the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and 6) were used to project the future climate by 2100 under medium and extreme scenarios. A hydrological model was developed to simulate the flood phenomena at the Shindae experimental site located in the Chungcheongbuk Province, South Korea. Hourly rainfall, inundation depth, and discharge data collected during the two extreme events that occurred in 2021 and 2022 were used to calibrate and validate the hydrological model. Probability analysis of extreme rainfall data suggested a higher likelihood of intense and unprecedented extreme rainfall events, which would be particularly notable during 2051-2100. Consequently, the flooded area under an inundation depth of >700 mm increased by 13-36%, 54-74%, and 71-90% during 2015-2030, 2031-2050, and 2051-2100, respectively. Severe flooding probability was notably higher under extreme CMIP6 scenarios than under their CMIP5 counterparts. © 2023 Korea Water Resources Association. All rights reserved. | Adeyi, Qudus; Ahmad, Mirza Junaid; Adelodun, Bashir; Odey, Golden; Akinsoji, Adisa Hammed; Salau, Rahmon Abiodun; Choi, Kyung Sook | Department of Agricultural Civil Engineering, Kyungpook National University, Daegu, South Korea; Department of Agricultural Civil Engineering, Kyungpook National University, Daegu, South Korea; Department of Agricultural Civil Engineering, Kyungpook National University, Daegu, South Korea, Department of Agricultural and Biosystems Engineering, University of Ilorin, Ilorin, Nigeria; Department of Agricultural Civil Engineering, Kyungpook National University, Daegu, South Korea; Department of Agricultural Civil Engineering, Kyungpook National University, Daegu, South Korea; Department of Agricultural Civil Engineering, Kyungpook National University, Daegu, South Korea; Department of Agricultural Civil Engineering, Kyungpook National University, Daegu, South Korea | 58672628000; 57201479907; 57193774482; 57211444984; 57775999000; 58827632400; 54392662900 | ks.choi@knu.ac.kr; | Journal of Korea Water Resources Association | 2799-8746 | 56 | 12 | 0.28 | 2025-06-25 | 2 | Climate change; Extreme rainfall; Flood; Hydrological model | English | Final | 2023 | 10.3741/jkwra.2023.56.12.955 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Article | Developing a Prediction Model of Demolition-Waste Generation-Rate via Principal Component Analysis | Construction and demolition waste accounts for a sizable proportion of global waste and is harmful to the environment. Its management is therefore a key challenge in the construction industry. Many researchers have utilized waste generation data for waste management, and more accurate and efficient waste management plans have recently been prepared using artificial intelligence models. Here, we developed a hybrid model to forecast the demolition-waste-generation rate in redevelopment areas in South Korea by combining principal component analysis (PCA) with decision tree, k-nearest neighbors, and linear regression algorithms. Without PCA, the decision tree model exhibited the highest predictive performance (R2 = 0.872) and the k-nearest neighbors (Chebyshev distance) model exhibited the lowest (R2 = 0.627). The hybrid PCA–k-nearest neighbors (Euclidean uniform) model exhibited significantly better predictive performance (R2 = 0.897) than the non-hybrid k-nearest neighbors (Euclidean uniform) model (R2 = 0.664) and the decision tree model. The mean of the observed values, k-nearest neighbors (Euclidean uniform) and PCA–k-nearest neighbors (Euclidean uniform) models were 987.06 (kg·m−2), 993.54 (kg·m−2) and 991.80 (kg·m−2), respectively. Based on these findings, we propose the k-nearest neighbors (Euclidean uniform) model using PCA as a machine-learning model for demolition-waste-generation rate predictions. © 2023 by the authors. | Cha, Gi-Wook; Choi, Se-Hyu; Hong, Won-Hwa; Park, Choon-Wook | School of Science and Technology Acceleration Engineering, Kyungpook National University, Daegu, 41566, South Korea; School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu, 41566, South Korea; School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu, 41566, South Korea; Industry Academic Cooperation Foundation, Kyungpook National University, Daegu, 41566, South Korea | 55754413300; 7408119153; 7401527968; 56181530400 | shchoi@knu.ac.kr; | International Journal of Environmental Research and Public Health | 1661-7827 | 20 | 4 | 2.68 | 2025-06-25 | 20 | demolition-waste-generation rate; hybrid model; machine learning; principal component analysis; waste management | Artificial Intelligence; Construction Industry; Construction Materials; Principal Component Analysis; Waste Management; South Korea; artificial intelligence; demolition; machine learning; nearest neighbor analysis; prediction; principal component analysis; solid waste; waste management; article; construction and demolition waste; decision tree; k nearest neighbor; linear regression analysis; machine learning; prediction; principal component analysis; South Korea; waste management; artificial intelligence; building industry; building material; principal component analysis | English | Final | 2023 | 10.3390/ijerph20043159 | 바로가기 | 바로가기 |
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