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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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| ○ | Article | Altitude Standardization Method to Improve Altitude Data Accuracy in On-road Driving | In a vehicle driving test, altitude data is used to determine elevation gain and calculate driving resistance. However, altitude data acquired from GPS has the disadvantage of low accuracy and missing data. This study proposed altitude standardization in order to increase the accuracy and interpolate the missing data. The altitude was recorded during 20 driving tests on the same route. All the data were accumulated based on latitude and longitude, and the missing data was interpolated by using the smoothing spline method. Positive cumulative elevation gain with high reliability was obtained by using only the altitude data from three driving tests. The accuracy of the standardized altitude was confirmed through driving resistance analysis. Meanwhile, driving resistance was used to calculate engine efficiency. The results showed that the engine efficiency calculated using the individual altitude was unrealistic, whereas the engine efficiency calculated using the standard altitude was constant at 35 %. This indicates that the standard altitude was correctly interpolated. Copyright © 2022 KSAE / 199-01 | Song, Jingeun | School of Automotive Engineering, Kyungpook National University, Gyeongbuk, 37224, South Korea | 56714139600 | sjg@knu.ac.kr; | Transactions of the Korean Society of Automotive Engineers | 1225-6382 | 30 | 6 | 0.21 | 2025-06-25 | 3 | Driving resistance; Positive cumulative elevation gain; Real driving emission; Road gradient; Standardization of altitude | Korean | Final | 2022 | 10.7467/ksae.2022.30.6.447 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Book chapter | Ambient Mass Spectrometry Imaging of Small Molecules from Cells and Tissues | New methods to analyze cells and tissues in ambient condition without any harsh chemical fixation or physical freezing and drying are summarized in this report. The first approach, an atmospheric pressure mass spectrometry imaging method, is based on laser ablation in atmospheric pressure assisted by atmospheric plasma and nanomaterials such as nanoparticles and graphene to enhance laser ablation. The second one is based on secondary ion mass spectrometry (SIMS) imaging of live cells in solution capped with single-layer graphene to preserve intact and hydrated biological samples even under ultrahigh vacuum for SIMS bio-imaging in solution. © 2022, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature. | Kim, Jae Young; Lim, Heejin; Moon, Dae Won | School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea; Department of New Biology, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, South Korea; Department of New Biology, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, South Korea, School of Undergraduate Studies, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, South Korea | 57205191453; 57200994073; 16433239500 | dwmoon@dgist.ac.kr; | Methods in Molecular Biology | 1064-3745 | 2437 | 0.65 | 2025-06-25 | 2 | Ambient mass spectrometric imaging; Atmospheric pressure mass spectrometry imaging; Laser ablation; Nanomaterials; Secondary ion mass spectrometry; Single-layer graphene | Atmospheric Pressure; Graphite; Laser Therapy; Molecular Imaging; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Spectrometry, Mass, Secondary Ion; graphene; nanoparticle; graphite; adult; airflow; animal tissue; atmospheric pressure; atmospheric pressure mass spectrometry imaging; caudal fin; cell culture; electrospray; high content imaging; hippocampal slice; image analysis; imaging; laser desorption ionization mass spectrometry; laser surgery; live cell imaging; mass spectrometry; methodology; mouse; nonhuman; plasma enhanced chemical vapor deposition; secondary ion mass spectrometry; spin coating; vacuum; workflow; zebra fish; low level laser therapy; matrix-assisted laser desorption-ionization mass spectrometry; molecular imaging; secondary ion mass spectrometry | English | Final | 2022 | 10.1007/978-1-0716-2030-4_3 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | ○ | Proceedings Paper | An Accurate and Efficient Stochastic Computing Adder Exploiting Bit Shuffle Control Scheme | Stochastic computing is an emerging computing paradigm. With the use of a simple logic gate, SC can perform arithmetic operations, resulting in a small and low-power design. In this paper, we present an SC adder with one RNG and shuffling operation to improve the accuracy of the operation results. With a 65-nm CMOS technology, the proposed SC adder improves in terms of area, power, and energy by 25.5%, 27.62%, and 23.67%, respectively compared to the conventional SC adder. Additionally, the proposed SC adder improves the MAE and MSE by 1.8 ' and 3.3 ', respectively, compared to the conventional counterpart. | Lee, Donghui; Baik, Junhyuk; Kim, Yongtae | Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 41566, South Korea | 57266466900; 57995712600; 55699627900 | yongtae@knu.ac.kr; | 2022 19TH INTERNATIONAL SOC DESIGN CONFERENCE (ISOCC) | 2163-9612 | 1.34 | 2025-06-25 | 4 | 6 | stochastic computing; shuffle; stochastic adder | shuffle; stochastic adder; stochastic computing | Computation theory; Computing power; Electric power supplies to apparatus; Stochastic systems; 65 nm CMOS technologies; Arithmetic operations; Control schemes; Emerging computing paradigm; Low-power design; Shuffle; Simple++; Stochastic adder; Stochastic computing; Stochastics; Adders | English | 2022 | 2022 | 10.1109/isocc56007.2022.10031549 | 바로가기 | 바로가기 | 바로가기 | |||||||||||||
| ○ | Conference paper | An Adaptive Handover Scheme to support UE with various movement speeds in 5G network | The 5G network is infrastructure that can be supported by extending the mobile speed of UE supported from 350 km/h in the existing 4G network environment to 500 km/h. The 5G network has to support the fast movement speed of UE. 3GPP-based handover has a problem in that it cannot guarantee QoS for the mobility of some UEs as it provides UE based handover in a single environment that does not consider the network service environment with various movement speeds of UE. In this paper, an adaptive handover scheme is proposed to the cell located to the moving direction by determining and triggering the handover time according to the individual speed of UE to ensure the QoS according to the UE's seamless service and movement between cells. In simulation test to verify the proposed scheme compared with existing handover scheme based on 3GPP, the proposed scheme shows excellent performance in handover failure and data transmission for UE with a fast movement speed. © 2022 IEEE. | Yoon, Mahnsuk; Park, Jeaseok; Park, Taeuk; Seo, Jihun; Yun, Jang-Kyu; Cho, Keuchul | Geri (Gumi Electronics and Information Technology Research Institute), Future Mobile Communications Research Center, Gumi-si, South Korea; Geri (Gumi Electronics and Information Technology Research Institute), Future Mobile Communications Research Center, Gumi-si, South Korea; Geri (Gumi Electronics and Information Technology Research Institute), Future Mobile Communications Research Center, Gumi-si, South Korea; Korea Transport Institute, Center for Connected and Automated Driving Research, Department of the Fourth Industrial Revolution and Transport, Sejong-si, South Korea; Gyeongbuk Institute of It Convergence Industry Technology, Vehicle Research Team, Gyeongsan-si, South Korea; Kyungpook National University, School of Computer Secience and Engineering, Software Education Center, Daegu-si, South Korea | 57203640624; 57991598000; 57210919157; 58000906600; 26432460900; 26031217700 | 2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022 | 1.54 | 2025-06-25 | 4 | 5G network; Handover control; High speed device; Mobility management | Adaptive control systems; Queueing networks; 350 km/h; 4G networks; Fast movement; Hand over; Handover control; Handover schemes; High-speed devices; Mobile speed; Mobility management; Movement speed; 5G mobile communication systems | English | Final | 2022 | 10.1109/icce-asia57006.2022.9954717 | 바로가기 | 바로가기 | |||||||||||||||||||
| ○ | Conference paper | An Assessment of Graph Neural Networks for Detecting Pointer and Type Errors | As the number of reported vulnerabilities continues to rise, detecting vulnerabilities remains a significant research challenge. Due to the complexity of current software systems, it becomes more difficult to detect vulnerabilities quickly and precisely using conventional methods such as static program analysis or text-based pattern matching. Recently, graph neural networks (GNN) have attracted much attention in vulnerability identification because they can manage graph structures that can represent different information flows and their relationships within a program. Due to the varying types of vulnerabilities and graph topologies of a program, it is challenging to select an appropriate graph neural network model for vulnerability identification. In this paper, we compare four GNN models to detect vulnerabilities including GCN, GraphSAGE, FastGCN, and AS-GCN. To train these models, we employ eight CWEs that can be categorized into two groups pertaining to pointer and type errors. Based on our experimental results, GraphSAGE shows better performance than other models in most cases, however GCN-based models show better results for some CWEs. As a result, our comparative study can be used as an indicator to select an appropriate model in vulnerability detection using graph neural networks. © 2022 IEEE. | Choi, Yoola; Kwon, Young-Woo | School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea; School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea | 57419121500; 57208480210 | International Conference on ICT Convergence | 2162-1233 | 2022-October | 0 | 2025-06-25 | 0 | Flow graphs; Information management; Network security; Neural network models; Pattern matching; 'current; Conventional methods; Graph neural networks; Graph structures; Neural network model; Pattern-matching; Research challenges; Software-systems; Static program analysis; Type errors; Graph neural networks | English | Final | 2022 | 10.1109/ictc55196.2022.9952665 | 바로가기 | 바로가기 | ||||||||||||||||||
| ○ | Conference paper | An Autonomous Maximum Speed Control Considering Boarding Weight For Safe E-scooter Driving | Accident cases are also increasing in proportion to the recent increase in the number of users of shared e-scooters. In order to reduce the accidents, the shared e-scooter platform applies the same maximum speed. We propose an effective maximum speed control to increase safety in e-scooter driving on straight and curved roads according to the boarding weight using a pressure sensor. © 2022 IEEE. | Ha, Junwoo; Jung, Im Y. | Kyungpook National University, School of Electronics Engineering, Daegu, 41566, South Korea; Kyungpook National University, School of Electronics Engineering, Daegu, 41566, South Korea | 58101295400; 18037522200 | gkwnsdn0402@naver.com; | Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 | 0.33 | 2025-06-25 | 1 | Autonomous maximum speed control; Boarding weight; Road condition; Safe e-scooter driving | Accidents; Speed regulators; Vehicles; Accident case; Autonomous maximum speed control; Boarding weight; Maximum speed; Road condition; Safe e-scooter driving; Speed control | English | Final | 2022 | 10.1109/bigdata55660.2022.10020552 | 바로가기 | 바로가기 | ||||||||||||||||||
| ○ | ○ | Proceedings Paper | An Effective Supplementation of Insufficient Data by Generative Adversarial Networks | Generative Adversarial Networks (GANs) can be used for data augmentation in order to improve the outcome and performance of machine learning models for automatic information retrieval. We looked into the challenge faced with limited blurry and distorted digit images from expiry dates datasets, which is required to improve digit recognition tasks on medicine, consumables, cosmetic products and tube-type ointments. For our dataset, Wasserstein GAN with a gradient norm penalty (WGAN-GP) was effective for data augmentation among the state-of-the-art GANs by visible inspection and Frechet Inception Distance (FID) value comparison. | Abdulraheem, Abdulkabir; Jung, Im Y. | Kyungpook Natl Univ, Sch Elect & Elect Engn, 80 Daehakro Bukgu, Daegu 41566, South Korea | 57929177700; 18037522200 | aaoabdul@gmail.com;iyjung@ee.knu.ac.kr; | 2022 IEEE/ACM INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING, APPLICATIONS AND TECHNOLOGIES, BDCAT | 0.27 | 2025-06-25 | 1 | 1 | Data Augmentation; Generative Adversarial Networks; Automatic Information Retrieval; Machine learning | Automatic Information Retrieval; Data Augmentation; Generative Adversarial Networks; Machine learning | Image enhancement; Information retrieval; Automatic information; Automatic information retrieval; Consumables; Cosmetic products; Data augmentation; Digit recognition; Machine learning models; Machine-learning; Performance; Product types; Generative adversarial networks | English | 2022 | 2022 | 10.1109/bdcat56447.2022.00030 | 바로가기 | 바로가기 | 바로가기 | ||||||||||||||
| ○ | Conference paper | An Integrated Bus Routing Control Platform for Autonomous Bus Driving based on Traffic-Demand Trade-off | Data conversion of the current system is essential for the commercialization of autonomous buses. The biggest advantage of autonomous buses is that they can follow flexible routes according to traffic demand. If a program automatically finds the optimal route depending on the time of day or traffic demand, then it will be very helpful for the commercialization of autonomous buses. Because it is most important to gather data such as the locations of bus stops, this paper proposes a method of creating a platform based on the bus stop data provided by Daegu City. Currently, Daegu City provides all locations and demand for bus stops. Visual Studio was used to classify the data according to Daegu's districts, and a platform to find the optimal route was created using the modified Dijkstra algorithm, which made it possible to simulate flexibly according to changes in data. © 2022 IEEE. | Park, Soeun; Park, Daejin | School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea | 57656360700; 55463943600 | boltanut@knu.ac.kr; | LifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies | 0 | 2025-06-25 | 0 | automation; autonomous driving; data structure; Dijkstra algorithm | Data handling; Economic and social effects; Routing algorithms; Autonomous driving; Bus Routing; Bus stop; Commercialisation; Data conversion; Dijkstra's algorithms; Optimal routes; Routing control platforms; Trade off; Traffic demands; Autonomous vehicles | English | Final | 2022 | 10.1109/lifetech53646.2022.9754830 | 바로가기 | 바로가기 | ||||||||||||||||||
| ○ | Conference paper | An Investigation on Deep Learning-Based Activity Recognition Using IMUs and Stretch Sensors | With the advancement and ubiquitousness of wearable devices, wearable sensor-based human activity recognition (HAR) has become a prominent research area in the healthcare domain and human-computer interaction. Inertial measurement unit (IMU) which can provide a wide range of information such as acceleration, angular velocity has become one of the most commonly used sensors in HAR. Recently, with the growing demand for soft and flexible wearable devices, mountable stretch sensors have become a new promising modality in wearable sensor-based HAR. In this paper, we propose a deep learning-based multi-modality HAR framework which consists of three IMUs and two fabric stretch sensors in order to evaluate the potential of stretch sensors independently and in combination with IMU sensors for the activity recognition task. Three different deep learning algorithms: long short-term memory (LSTM), convolutional neural network (CNN) and hybrid CNN-LSTM are deployed to the sensor data for automatically extracting deep features and performing activity classification. The impact of sensor type on recognition accuracy of different activities is also examined in this study. A dataset collected from the proposed framework, namely iSPL IMU-Stretch and a public dataset called w-HAR are used for experiments and performance evaluation. © 2022 IEEE. | Hoai Thu, Nguyen Thi; Han, Dong Seog | Kyungpook National University, School of Electronic and Electrical Engineering, Daegu, South Korea; Kyungpook National University, School of Electronic and Electrical Engineering, Daegu, South Korea | 57221708543; 7403219442 | 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings | 1.37 | 2025-06-25 | 5 | activity recognition; deep learning; IMUs; stretch sensors; wearable sensors | Human computer interaction; Learning algorithms; Long short-term memory; Pattern recognition; Activity recognition; Convolutional neural network; Deep learning; Healthcare domains; Human activity recognition; IMU; Inertial measurements units; Research areas; Stretch sensor; Wearable devices; Wearable sensors | English | Final | 2022 | 10.1109/icaiic54071.2022.9722621 | 바로가기 | 바로가기 | |||||||||||||||||||
| ○ | Conference paper | An Open Dataset for Deep Learning-based Earthquake Detection using MEMS Sensors | Due to the high population density and economic value of contemporary cities, earthquakes inflict greater damage on these cities. Consequently, the importance of quick earthquake early warning (EEW) is expanding, yet it is challenging to create a dense seismic monitoring network due to high installation and management costs. In order to overcome such limitations, MEMS sensors to monitor earthquakes and artificial intelligence (AI) technologies to analyze massive earthquake monitoring data are widely used today. In AI-based earthquake detection, the key to accurate detection is the use of sufficient data that accurately represents the various earthquake patterns. Unfortunately, how-ever, there is no publicly accessible database containing IoT-based seismic data. This is the result of relatively short research efforts. During the last two years of operation of CrowdQuake, a MEMS-based earthquake detection system, we collected earthquake and non-earthquake events, as well as normal noise data, which was greatly useful to improve the accuracy of AI models. As a result, we present an open dataset that is publicly available for MEMS-based earthquake detection research. © 2022 IEEE. | Lee, Jangsoo; Sim, Jae-Heon; Ahn, Jae-Kwang; Kwon, Young-Woo | Kyungpook National University, Daegu, South Korea; Kyungpook National University, Daegu, South Korea; Korea Meteorological Administration, Seoul, South Korea; Kyungpook National University, Daegu, South Korea | 57208408850; 58100916400; 57214806947; 57208480210 | dellhart@knu.ac.kr; | Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 | 0.99 | 2025-06-25 | 3 | Accelerometer; Dataset; Deep Learning; Earthquake; Earthquake Early Warning | Deep learning; Population statistics; Dataset; Deep learning; Density value; Earthquake detection; Earthquake early warning; Economic values; High population density; MEMS sensors; Monitoring network; Seismic monitoring; Earthquakes | English | Final | 2022 | 10.1109/bigdata55660.2022.10020481 | 바로가기 | 바로가기 | ||||||||||||||||||
| ○ | ○ | Article | An Optimized Data Analysis on a Real-Time Application of PEM Fuel Cell Design by Using Machine Learning Algorithms | In recent years, machine learning algorithms have been applied in many real-time applications. Crises in the energy sector are the primary challenges experienced today among all countries across the globe, regardless of their economic status. There is a huge demand to acquire and produce environmentally friendly renewable energy and to distribute and utilize it efficiently because of its huge production cost. PEMFC are known for their energy efficiency and comparatively low cost, and can be an alternative energy source. The efficiency of these PEMFC can still be enhanced with the help of advanced technologies like machine learning and artificial intelligence, as they provide an optimal solution to explore the hidden knowledge from the generated data. The proposed model attempts to compare several design techniques with varied humidity levels. To enhance the performance of PEMFC, the various humidification processes were considered during the experimental study. The humidification reduces the heat during energy generation and increases the performance of PEM fuel cell. The humidity levels such as 100%, 50%, and 10% were considered to be tested with the machine learning models. The SVMR, LR, and KNN algorithms were tested and observed with the RMSE value as the evaluation parameters. The observed results show that SVMR has an RMSE rate of 0.0046, the LR method has an RMSE rate of 0.0034, and KNN has an RMSE rate of 0.004. The analysis shows that the LR model provides better accuracy than other models. The LR model enhances the PEMFC performance. | Saco, Arun; Sundari, P. Shanmuga; Karthikeyan, J.; Paul, Anand | Sri Venkateswara Coll Engn & Technol, Dept Mech Engn, Chitoor 517127, India; Sri Venkateswara Coll Engn & Technol, Dept Comp Sci & Engn, Chittoor 517127, India; Sri Venkateswara Coll Engn & Technol, Humanities & Sci, Chittoor 517127, India; Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 37224, South Korea | Puthisigamani, Shanmuga Sundari/ISA-0429-2023; Paul, Anand/V-6724-2017; S, Arunsaco/ADM-5499-2022; J, KARTHIKEYAN/D-6610-2019 | 57191041503; 57191675522; 57196619583; 56650522400 | sigashanmu@gmail.com; | ALGORITHMS | ALGORITHMS | 1999-4893 | 15 | 10 | ESCI | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE;COMPUTER SCIENCE, THEORY & METHODS | 2022 | 2.3 | 2.65 | 2025-06-25 | 10 | 23 | PEM fuel cells; machine learning; SVMR; LR; KNN | FLOW-FIELD; POLARIZATION CURVES; GAS CHANNEL; PERFORMANCE; SIMULATION; DYNAMICS | KNN; LR; machine learning; PEM fuel cells; SVMR | Costs; Design; Energy efficiency; Energy policy; Learning algorithms; Machine learning; Humidity levels; KNN; LR; Machine learning algorithms; Machine-learning; P.E.M.F.C; PEM fuel cell; Performance; Real-time application; SVMR; Proton exchange membrane fuel cells (PEMFC) | English | 2022 | 2022-10 | 10.3390/a15100346 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |||
| ○ | Conference paper | Analysis and Application of Sensor Data Collected from Strawberry Greenhouse | Attempts to contribute to increasing the production of crops by using AI and IoT technologies are increasing. In particular, since strawberry is an environmentally sensitive crop, it is necessary to apply smart agriculture. Therefore, in this paper, to optimize the strawberry cultivation environment, the cultivation environment was analyzed based on sensor data collected from a specific strawberry cultivation farm. We generated a predictive model based on a multivariate time-series data set collected by temperature-humidity, CO2, soil, and light sensors. Also, we applied a deep learning model called StemGNN that considers the correlation between variables by using a graph neural network. In the future, we are going to conduct research such as environmental prediction, abnormality detection, and growth prediction, and plan to increase strawberry cultivation efficiency by accumulating more diverse data and creating optimal scenarios by using multiple models. © 2022 IEEE. | Oh, Seungtaek; Moon, Jaewon; Jo, Jungsu; Kum, Seungwoo | Information Media Research Center, Korea Electronics Technology Institute, Seoul, South Korea; Information Media Research Center, Korea Electronics Technology Institute, Seoul, South Korea; Institute of Agricultural Science and Technology, Kyungpook National University, Daegu, South Korea; Information Media Research Center, Korea Electronics Technology Institute, Seoul, South Korea | 57414318900; 37041654800; 57197337268; 35113505800 | International Conference on ICT Convergence | 2162-1233 | 2022-October | 0.22 | 2025-06-25 | 1 | Deep Learning; IoT; Sensor Data; Smart Farm; Time-series Data; Time-series Data Prediction | Cultivation; Deep learning; Forecasting; Fruits; Graph neural networks; Internet of things; Time series; Time series analysis; Data prediction; Deep learning; Environmentally sensitive; IoT; Sensors data; Smart agricultures; Smart farm; Time-series data; Time-series data prediction; Crops | English | Final | 2022 | 10.1109/ictc55196.2022.9952640 | 바로가기 | 바로가기 | |||||||||||||||||
| ○ | Article | Analysis of differences in variables related to health and safety according to the employment type of Korean workers | Introduction: The purpose of this study was to understand the differences in variables related to health and safety according to the employment type of Korean workers, specifically to identify the differences by employment type on in health status, the likelihood of wearing protective gear when working, access to manuals on emotional expression, and access to information on risk factors related to health and safety. Methods: The secondary data of four items on employment type, health type of workers and safety among the 5th Korean Working Condition Survey conducted in 2017 in Korea was used in this study. The data of workers were processed by using SPSS/WIN 23.0 Program and R 3.1.2, and demographic characteristics were quantified as frequency and percentage. Results: A total of 30,300 employed people were surveyed. The result shows that part-time workers have poorer health than full-time workers (c2 = 540.7155, p < 0.05), insufficiently wore protective gear (c2 = 24.8702, p < 0.05), had insufficient access to manuals on emotional expression (c2 = 27.7612, p < 0.05) and lacked information about risk factors (c2 = 185.0082, p < 0.05). Conclusion: Health and safety manager will need to have education and consultation, development of manual and perform an early intervention to improve safety environment as primary health care providers by understanding factors related to health and safety of part-time workers. © 2022 The authors. All right reserved. | Jung, H.; Han, S. | College of Nursing, Kyungpook National University, 680 Gukchaebosang-ro Jung-gu, Daegu, 41944, South Korea; Department of Emergency Medical Technology, Kyungil University, Gamasilgil Hayangeup, Gyeongbuk, 38428, South Korea | 57224449640; 57210797256 | swhan@kiu.ac.kr; | International Journal of Occupational Safety and Health | 2738-9707 | 12 | 1 | 0.41 | 2025-06-25 | 3 | Employment; Health and Safety; Korea; Work | English | Final | 2022 | 10.3126/ijosh.v12i1.41030 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | ○ | Article | Analysis of electro-chemical RAM synaptic array for energy-efficient weight update | While electro-chemical RAM (ECRAM)-based cross-point synaptic arrays are considered to be promising candidates for energy-efficient neural network computational hardware, array-level analyses to achieve energy-efficient update operations have not yet been performed. In this work, we fabricated CuOx/HfOx/WOx ECRAM arrays and demonstrated linear and symmetrical weight update capabilities in both fully parallel and sequential update operations. Based on the experimental measurements, we showed that the source-drain leakage current (I-SD) through the unselected ECRAM cells and resultant energy consumption-which had been neglected thus far-contributed a large portion to the total update energy. We showed that both device engineering to reduce I-SD and the selection of an update scheme-for example, column-by-column-that avoided I-SD intervention via unselected cells were key to enable energy-efficient neuromorphic computing. | Kang, Heebum; Kim, Nayeon; Jeon, Seonuk; Kim, Hyun Wook; Hong, Eunryeong; Kim, Seyoung; Woo, Jiyong | Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu, South Korea; Pohang Univ Sci & Technol, Dept Mat Sci & Engn, Pohang, South Korea | 57232405900; 59884547500; 57955098300; 57557016000; 57556070800; 57211871375; 53985749100 | jiyong.woo@knu.ac.kr; | FRONTIERS IN NANOTECHNOLOGY | 2673-3013 | 4 | 0.34 | 2025-06-25 | 4 | 4 | neuromorphic system; synaptic device; ECRAM array; weight update; energy consumption | ECRAM array; energy consumption; neuromorphic system; synaptic device; weight update | English | 2022 | 2022-10-25 | 10.3389/fnano.2022.1034357 | 바로가기 | 바로가기 | 바로가기 | |||||||||||||
| ○ | Article | Analysis of Ink Used in Ancient Documents Based on Printing Method | This study evaluates the ink type suitable for document restoration by analyzing ink used in woodblock, metal type-printed, and ancient handwritten documents. Among the commercially available inks, lampblack soot ink (vs. charcoal soot ink) exhibited relatively low hydrogen, oxygen, and nitrogen content and a high spreading ratio to Hanji. Moreover, compared to solid type inks, liquid type inks have a higher absorption ratio into Hanji at the same concentration; thus, drying time after printing is expected to be reduced. Furthermore, different types of ink were used depending on the printing method used in the ancient documents. Therefore, in the future, production efficiency can be improved by classifying ink types based on printing method and using liquid ink rather than solid ink to restore or reproduce histroical documents. © 2022 Korean Technical Assoc. of the Pulp and Paper Industry. All rights reserved. | Kim, Kang-Jae; Hwang, In-Seo; Eom, Tae-Jin | Major in Wood Science and Technology, School of Forestry, Science and Landscape Architecture, Agricultural Science and Technology Research Institute, Kyungpook National University, South Korea; Major in Wood Science and Technology, School of Forestry, Science and Landscape Architecture, Kyungpook National University, South Korea; Major in Wood Science and Technology, School of Forestry, Science and Landscape Architecture, Agricultural Science and Technology Research Institute, Kyungpook National University, South Korea | 35733947500; 58041609000; 13410809400 | tjeom@knu.ac.kr; | Palpu Chongi Gisul/Journal of Korea Technical Association of the Pulp and Paper Industry | 0253-3200 | 54 | 4 | 0.07 | 2025-06-25 | 1 | handwriting; Ink stick; metal type printing; particle size; woodblock printing | Charcoal; Documents; Ink; Liquids; Particle Size; Printing Machines; Restoration; Type; Charcoal; Ink; Particle size analysis; Printing presses; Restoration; Ancient documents; Document restoration; Document-based; Handwriting; Ink stick; Metal type printing; Metal types; Particles sizes; Printing method; Woodblock printings; Particle size | Korean | Final | 2022 | 10.7584/jktappi.2022.08.54.4.11 | 바로가기 | 바로가기 |
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