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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 | POSTER: Seccomp profiling with Dynamic Analysis via ChatGPT-assisted Test Code Generation | The effectiveness of Seccomp kernel feature depends on how tightly and accurately the necessary system calls are specified in the seccomp policy. Static code analysis may miss out or over-approximate required system calls. With dynamic analysis, it is difficult to cover all possible execution paths. In this work, we aim to advance the state-of-the-art dynamic analysis approach by enabling it to increase the coverage of the target application's functionalities. Our approach takes as input the application's online documentation and leverages ChatGPT to generate a large number of test codes for functionalities in the documentation. This automated process eliminates the barrier to manually writing a large number of test codes for conducting dynamic analysis. Through our preliminary evaluation, we confirmed that ChatGPT can be used effectively to automatically generate a large number of test codes. Also, we observed early evidence that the seccomp policy generated from running the test codes could be more sound than the ones generated by static analysis. | Song, Somin; Kundu, Ashish; Tak, Byungchul | Cisco Res, San Jose, CA USA; Kyungpook Natl Univ, Daegu, South Korea | 59857087200; 23482383700; 6506911621 | somsong@cisco.com;ashkundu@cisco.com;bctak@knu.ac.kr; | PROCEEDINGS OF THE 19TH ACM ASIA CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, ACM ASIACCS 2024 | 4.08 | 2025-05-07 | 2 | 2 | Seccomp; ChatGPT; Static analysis; Dynamic analysis; Test code | ChatGPT; Dynamic analysis; Seccomp; Static analysis; Test code | Automation; Analysis approach; ChatGPT; Codegeneration; Dynamics analysis; Execution paths; Seccomp; State of the art; Static code analysis; System calls; Test code; Static analysis | English | 2024 | 2024 | 10.1145/3634737.3659426 | 바로가기 | 바로가기 | 바로가기 | ||||||||||||||
| ○ | Book chapter | Potential Applications of Microalgae and Its Derivatives in Various Industries: A Future Bioeconomy | Algae has been in use for 2000 years, however, microalgal commercial applications have only been known to people for the past three decades. Due to their high nutritional value, red, green, and brown algae are mass-cultured to produce dietary supplements. Polyunsaturated fatty acids (PUFA), pigments like carotenoids and phycobiliproteins, and bioactive substances are helpful as nutraceuticals, medicines, and industrial products. Microalgae, for example, can be utilized to improve the nutritional content of food and animal feed, play a key role in aquaculture, and even be used in cosmetics. Future research should focus on photobioreactors, open pond systems, and strain genetic alteration. Microalgal products would thus become more diverse and competitive in the economy. © 2024 selection and editorial matter, Nivedita Sahu and S. Sridhar; individual chapters, the contributors. | Yadavalli, Rajasri; Reddy, C. Nagendranatha; Mishra, Bishwambhar; Chandrasekhar, K.; Vineetha, Y.; Shalini, A. | Department of Biotechnology, Chaitanya Bharathi Institute of Technology (A), Telangana, Hyderabad, India; Department of Biotechnology, Chaitanya Bharathi Institute of Technology (A), Telangana, Hyderabad, India; Department of Biotechnology, Chaitanya Bharathi Institute of Technology (A), Telangana, Hyderabad, India; Kyungpook National University, Daegu, South Korea; Department of Biotechnology, Chaitanya Bharathi Institute of Technology (A), Telangana, Hyderabad, India; Department of Biotechnology, Chaitanya Bharathi Institute of Technology (A), Telangana, Hyderabad, India | 55601798800; 55847980300; 55223869300; 8690011100; 57890797100; 57890797200 | Algal Biotechnology: Applications for Industrial Development and Human Welfare | 0 | 2025-05-07 | 0 | English | Final | 2024 | 10.1201/9781003219194-18 | 바로가기 | 바로가기 | |||||||||||||||||||||
| ○ | Review | Potential of Freshwater Cyanobacterial Harmful Algal Bloom Biomass for Biomethane Production via Anaerobic Digestion | The increasing incidence of freshwater cyanobacterial harmful algal blooms (cyanoHABs), driven by anthropogenic activities, poses significant environmental challenges and risks to water quality management. However, these blooms also represent an untapped resource for renewable energy production, particularly through the conversion of biomass to biomethane via anaerobic digestion (AD). AD not only converts cyanoHAB biomass into renewable energy but also degrades harmful microcystins, transforming environmental hazards into energy resources. This review explores the potential of cyanobacterial biomass as a substrate for biomethane production highlighting the dual benefits of alleviating environmental impacts and contributing to the renewable energy sector. It discusses the composition and characteristics of cyanobacterial biomass, the process and efficiency of anaerobic digestion, and the practical challenges and opportunities in integrating this approach into existing waste management and energy systems. This paper aims to bridge the gap between managing environmental hazards and generating renewable energy offering insights into research advancements, commercial scalability, and policy implications. © 2024, The Korean Society for Microbiology and Biotechnology. | Park, Jong Myong; You, Young-Hyun; Kang, Nam Seon; Cho, Eunsue; Back, Chang-Gi; Hong, Ji Won | Water Quality Research Institute, Waterworks Headquarters Incheon Metropolitan City, Incheon, South Korea; Species Diversity Research Division, National Institute of Biological Resources, Incheon, 22689, South Korea; Department of Taxonomy and Systematics, National Marine Biodiversity Institute of Korea, Seocheon, 33662, South Korea; Department of Hydrogen and Renewable Energy, Kyungpook National University, Daegu, 41566, South Korea, Daesung Eco-Energy, Daegu, 42926, South Korea; Department of Environmental Horticulture and Landscape Architecture, Environmental Horticulture, Dankook University, Cheonan, 31116, South Korea; Department of Hydrogen and Renewable Energy, Kyungpook National University, Daegu, 41566, South Korea, Advanced Bio-Resource Research Center, Kyungpook National University, Daegu, 41566, South Korea | 54382161000; 53868615500; 7102653317; 59308999200; 36144957400; 57201579963 | jwhong@knu.ac.kr; | Microbiology and Biotechnology Letters | 1598-642X | 52 | 4 | 0 | 2025-05-07 | 0 | anaerobic digestion; biomethane; Cyanobacterial bloom; environmental management; microcystin; renewable energy | microcystin; algal bloom; anaerobic digestion; biomass; degradation; energy resource; energy yield; environmental impact; fresh water; nonhuman; pharmaceutics; renewable energy; review; waste management; water quality | English | Final | 2024 | 10.48022/mbl.2408.08003 | 바로가기 | 바로가기 | |||||||||||||||
| ○ | Conference paper | Practical Waveform Design for ISAC Systems: An Instrumental Variable Approach | With the advancement of hardware and signal processing technologies, next-generation wireless networks are expected to be multi-functional, offering various radio-based services such as communications and sensing. In this context, integrated sensing and communications (ISAC) is a promising solution for improving spectral efficiency, but its implementation is challenging because of practical constraints such as complexity. In this paper, we propose a novel waveform design principle for ISAC systems, which can decompose a complex ISAC waveform design problem into more tractable subproblems. The proposed method enables superimposing a sensing signal onto the communication signal without affecting the communication channel via the instrumental variable (IV) method. We apply the proposed waveform structure to a beampattern matching problem and develop the corresponding solution. We provide an in-depth analysis with respect to degrees of freedom (DoF), channel mismatch, and so forth. We evaluate the performance of the proposed waveform design algorithm through simulations. © 2024 IEEE. | Lee, Byunghyun; Kim, Hwanjin; Love, David J.; Krogmeier, James V. | Elmore Family School of Electrical Engineering, Purdue University, United States; School of Electronics Engineering, Kyungpook National University, Daegu, South Korea; Elmore Family School of Electrical Engineering, Purdue University, United States; Elmore Family School of Electrical Engineering, Purdue University, United States | 58621502400; 57204105066; 7202200691; 7004262427 | lee4093@purdue.edu;djlove@purdue.edu;jvk@purdue.edu; | Conference Record - Asilomar Conference on Signals, Systems and Computers | 1058-6393 | 0 | 2025-05-07 | 0 | Image coding; Integrated circuit design; Communications systems; Design Principles; Design problems; Instrumental variables; Integrated sensing; Multi-functional; Sensing systems; Signal processing technologies; Spectral efficiencies; Waveform designs; Radio communication | English | Final | 2024 | 10.1109/ieeeconf60004.2024.10943020 | 바로가기 | 바로가기 | ||||||||||||||||||
| ○ | ○ | Proceedings Paper | Pre-LogMGAE: Identification of Log Anomalies Using a Pre-trained Masked Graph Autoencoder | Log-based anomaly detection in software systems is becoming increasingly crucial for monitoring network operations and ensuring system security. Deep learning-based methods are widely used for large-scale log anomaly detection due to their capacity to learn complex features. However, current research predominantly treats original logs as simple sequences, ignoring their complex structure and dynamic dependency relationships. Additionally, these methods often rely on extensive labeled data or domain-specific vectors to represent logs for model training, which can be labor-intensive to label manually and ineffective across various domains within a system. To address these challenges, this paper proposes Pre-LogMGAE, a universal masked graph autoencoder (GAE) framework with contrastive learning for self-supervised pre-training for log anomaly detection. In contrast to graph or link reconstruction, Pre-LogMGAE focuses on node feature reconstruction using a masking strategy to reduce the impact of excessive redundant information. Furthermore, we introduce Graph Attention Networks (GAT) with the Gated Recurrent Unit (GRU) to incorporate sequence modeling, allowing for capturing long-term and short-term dependencies in log events. We include contrastive learning objectives in fine-tuning to extract diverse features and enhance the algorithm's robustness. Through an extensive evaluation of three real-world datasets and specific case studies with configuration error, Pre-LogMGAE demonstrates superior performance compared to the six baselines, including PCA, IM, DeepLog, LogRobust, LogBERT, and DeepTraLog. This superiority is evident in terms of precision, recall, F1 score, and time efficiency, highlighting Pre-LogMGAE's stability and reliability in anomaly detection. The study aims to improve anomaly detection capabilities in multi-source system logs, offering innovative technical support to enhance system security and reliability. | Wu, Aming; Kwon, Young-Woo | Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu, South Korea | Kwon, Young-Woo/HGE-6607-2022 | 58262125900; 57208480210 | wuaming@knu.ac.kr;ywkwon@knu.ac.kr; | 2024 43RD INTERNATIONAL SYMPOSIUM ON RELIABLE DISTRIBUTED SYSTEMS, SRDS 2024 | 1060-9857 | 2575-8462 | 0 | 2025-05-07 | 0 | 0 | System Security; Graph Autoencoder; Self-Supervised Learning; Contrastive Learning; Anomaly Detection | Anomaly Detection; Contrastive Learning; Graph Autoencoder; Self-Supervised Learning; System Security | Anomaly detection; Deep learning; Software reliability; Supervised learning; Anomaly detection; Auto encoders; Graph autoencoder; Large-scales; Learn+; Learning-based methods; Monitoring network; Network operations; Software-systems; System security; Self-supervised learning | English | 2024 | 2024 | 10.1109/srds64841.2024.00036 | 바로가기 | 바로가기 | 바로가기 | |||||||||||
| ○ | ○ | Proceedings Paper | Precise ischemic stroke model inducing method with minimal tissue damage using localized photoacoustic microscopy | The ischemic stroke animal model has gained increasing popularity to elucidate the pathophysiology and evaluate the efficacy of reperfusion and neuroprotective strategies for ischemic injuries. Various conventional methods to induce the ischemic models have been reported, however, it is difficult to control specific neurological deficits, mortality rates, and the extent of the infarction since the size of the affected region is precisely controlled, which limits the closeness of animal model to human stroke. In this study, we report a novel creation method of the target ischemic stroke model by simultaneous vessel monitoring and photothrombosis induction using localization photoacoustic microscopy (L-PAM), which minimizes infarct size at a precise location with high reproducibility. By utilizing the proposed L-PAM system, we resolve the occurred position error of the scanner for high-speed imaging caused by external resistance, which enables the precise localization up to a single micro-vasculature. The reproducibility and validity of the suggested target ischemic stroke model-inducing method have been successfully proven through repeated experiments and histological analyses. These results demonstrate that the proposed method is able to induce the closest ischemic stroke model to the clinical pathology for brain ischemia research from inducement dynamics, occurrence mechanisms to the recovery process. | Seong, Daewoon; Han, Sangyeob; Kim, Yoonseok; Hong, Juyeon; Kim, Jeehyun; Jeon, Mansik | Kyungpook Natl Univ, Sch Elect & Elect Engn, 80 Daehak Ro, Daegu 41566, South Korea | 57212512353; 57193695305; 57216828837; 58157239900; 7601373350; 24171094000 | NEURAL IMAGING AND SENSING 2024 | 1605-7422 | 12828 | 0 | 2025-04-16 | 0 | 0 | Ischemic stroke model; photoacoustic microscopy; blood vessel monitoring; photothrombosis induction; minimal invasive model | RESOLUTION | blood vessel monitoring; Ischemic stroke model; minimal invasive model; photoacoustic microscopy; photothrombosis induction | Animals; Clinical research; Photoacoustic effect; Photoacoustic microscopy; Photons; Animal model; Blood vessel monitoring; Ischemic stroke model; Ischemic strokes; Localisation; Localised; Minimal invasive; Minimal invasive model; Photothrombose induction; Tissue damage; Blood vessels | English | 2024 | 2024 | 10.1117/12.3002016 | 바로가기 | 바로가기 | 바로가기 | ||||||||||||
| ○ | Article | Predicting factors for social oocyte cryopreservation intention among unmarried Korean women | In 2023, the average age of Korean women for the first marriage and giving birth was reported to 31.45 and 33.6, respectively. Due to the late marriage, the number of patients undergoing infertility treatment per 100,000 in Korea increased by 16.9% over the past five years to 27.3. Recently, interest in social oocyte cryopreservation (SOC) has been growing in Korea as a strategy to address the low birth rate. This study is descriptive research to determine the influence of unmarried women's knowledge of fertility preservation, tendency toward late marriage, and awareness of fertility preservation on intention to SOC. This study aims to identify predictors of unmarried women's intention to SOC. Data were collected from July 20 to 26, 2024, through a online self-reported survey from unmarried women in their 20s to 40s. 234 copies were collected, but 211 copies were used for the analysis after excluding 23 insincere responses. The data were analyzed using descriptive statistical analysis and logistic regression analysis using the SPSS/PC 29.0 program. In this study, 41.7% of unmarried women intended to SOC, while 58.3% did not. In addition, the intention to SOC was higher when they wanted children (p=.041, exp(B)=2.35), were aware of SOC (p=.015, exp(B)=2.98), and had a high awareness of fertility preservation (p<0.001, exp(B)=1.47). But the higher the tendency to marry late (p=.044, exp(B)=.92), the lower the intention to SOC. The findings can serve as foundational data to increase social awareness of SOC for unmarried women and to help develop supportive policies. © 2024 by the authors. | Lee, Sung Hee; Baek, Ji Woo; Lee, Yeong Ju; Choi, Ki Hoon; Choi, Yu Jin; Ha, Si Eun; Choi, Hyo Won; Kim, Jung A. | College of Nursing, Kyungpook National University, South Korea; College of Nursing, Kyungpook National University, South Korea; College of Nursing, Kyungpook National University, South Korea; College of Nursing, Kyungpook National University, South Korea; College of Nursing, Kyungpook National University, South Korea; College of Nursing, Kyungpook National University, South Korea; College of Nursing, Kyungpook National University, South Korea; College of Nursing, Kyungpook National University, South Korea | 56824569300; 59718930100; 59718971300; 59718971400; 59718930200; 59718971500; 59718957100; 57204148294 | kimjunga2008@gmail.com; | Edelweiss Applied Science and Technology | 2576-8484 | 8 | 6 | 0 | 2025-05-07 | 0 | Cryopreservation; Fertility preservation; Intention; Oocyte; Unmarried | English | Final | 2024 | 10.55214/25768484.v8i6.2721 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | ○ | Proceedings Paper | Predicting Rough Error Causes in Novice Programmers Using Cognitive Level | Novice programmers face various errors during the learning of a programming language. Most of them need help from instructors since they lack error resolution skills. On the other side, instructors spend a lot of time figuring out students' error causes. Long error detection times result in delayed and failed feedback, leading to a loss of student motivation. To support instructor's fast feedback, a detection method of error cause is needed. Existing detection methods, which are code-based, detect common and specific errors that can be identified by analyzing source code. These methods do not cover the diverse error patterns of novice programmers sufficiently, such as logical defects. To resolve this issue, it may be beneficial to detect rough and correct error causes of diverse error patterns. In this paper, a prediction method of rough error cause is proposed by considering not only source code, but also problem information, execution results, and the cognitive level indicating programming skills. We assume that different programming skills lead to different error patterns, which can help roughly but precisely predict error causes of runtime and logic errors in novice programmers. For performance evaluation, data from two introductory programming courses are used to validate the effectiveness of the cognitive level. Additionally, the usability for fast feedback is validated by comparing the error causes detection times of the instructors in each case. | Kim, Deok Yeop; Lee, Woo Jin | Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu, South Korea | 57211058397; 55682653854 | woojin@knu.ac.kr; | GENERATIVE INTELLIGENCE AND INTELLIGENT TUTORING SYSTEMS, PT I, ITS 2024 | 0302-9743 | 1611-3349 | 14798 | 2.07 | 2025-04-16 | 1 | 1 | Error Detection; Cognitive Level; Programming Error; Introductory Programming Course; Learning Taxonomy | Cognitive Level; Error Detection; Introductory Programming Course; Learning Taxonomy; Programming Error | Codes (symbols); Education computing; Forecasting; Students; Cognitive levels; Detection methods; Detection time; Error patterns; Introductory programming course; Learning taxonomy; Novice programmer; Programming errors; Programming skills; Source codes; Error detection | English | 2024 | 2024 | 10.1007/978-3-031-63028-6_28 | 바로가기 | 바로가기 | 바로가기 | |||||||||||
| ○ | Article | Predicting the transmission trends of COVID-19: an interpretable machine learning approach based on daily, death, and imported cases | COVID-19 is caused by the SARS-CoV-2 virus, which has produced variants and increasing concerns about a potential resurgence since the pandemic outbreak in 2019. Predicting infectious disease outbreaks is crucial for effective prevention and control. This study aims to predict the transmission patterns of COVID-19 using machine learning, such as support vector machine, random forest, and XGBoost, using confirmed cases, death cases, and imported cases, respectively. The study categorizes the transmission trends into the three groups: L0 (decrease), L1 (maintain), and L2 (increase). We develop the risk index function to quantify changes in the transmission trends, which is applied to the classification of machine learning. A high accuracy is achieved when estimating the transmission trends for the confirmed cases (91.5-95.5%), death cases (85.6-91.8%), and imported cases (77.7-89.4%). Notably, the confirmed cases exhibit a higher level of accuracy compared to the data on the deaths and imported cases. L2 predictions outperformed L0 and L1 in all cases. Predicting L2 is important because it can lead to new outbreaks. Thus, this robust L2 prediction is crucial for the timely implementation of control policies for the management of transmission dynamics. © 2024 the Author(s). | Ahn, Hyeonjeong; Lee, Hyojung | Department of Statistics, Kyungpook National University, Daegu, 41566, South Korea; Department of Statistics, Kyungpook National University, Daegu, 41566, South Korea | 58029969000; 57196021198 | hjlee@knu.ac.kr; | Mathematical Biosciences and Engineering | 1547-1063 | 21 | 5 | 0 | 2025-04-16 | 0 | classification; COVID-19; machine learning; prediction; transmission | Algorithms; COVID-19; Disease Outbreaks; Humans; Machine Learning; Pandemics; SARS-CoV-2; Support Vector Machine; Disease control; Forecasting; Forestry; Learning systems; Support vector machines; Transmissions; Viruses; Control policy; High-accuracy; Index functions; Infectious disease outbreaks; Machine learning approaches; Machine-learning; Prevention and controls; Random forests; Risk indices; Support vectors machine; algorithm; coronavirus disease 2019; epidemic; epidemiology; human; machine learning; mortality; pandemic; Severe acute respiratory syndrome coronavirus 2; support vector machine; COVID-19 | English | Final | 2024 | 10.3934/mbe.2024270 | 바로가기 | 바로가기 | |||||||||||||||
| ○ | Conference paper | Prediction Accuracy and Adversarial Robustness of Error-Based Input Perturbation Learning | Error backpropagation algorithms are essential for training deep neural networks, but they have several problems due to sequential feedback calculation to propagate error signals. Recently, a method using only two consecutive forward calculation with input perturbation has been proposed as an alternative, which is called PEPITA. Although PEPITA has shown the possibility of successful learning without backward computation, it is still in its early stages and needs further investigation on its properties. In this study, we analyze the characteristics of PEPITA and propose a new method for generating modulated input, specifically for the second forward computation. In particular, we show that the adversarial perturbation used to generate attack samples is closely related to the input perturbation process of PEPITA, and propose to use the adversarial perturbation in combination with PEPITA learning. The potential of the existing PEPITA and the proposed modification is analyzed through experiments using different activation functions under various attack conditions. From the experiments, we confirm that a proper combination of input modulation and activation function can improve the prediction accuracy and adversarial robustness. This work extends the applicability of PEPITA and lays the foundation for the analysis of alternative learning algorithms. © 2024 IEEE. | Lee, Soha; Yang, Heesung; Park, Hyeyoung | 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 | 58175908900; 58175679600; 55713613500 | hypark@knu.ac.kr; | 6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024 | 0 | 2025-04-16 | 0 | Adversarial Attack; Biological plausibility; Error backpropagation; Feedback alignment; Two forward Learning; Weight transport problem | Backpropagation; Bioinformatics; Chemical activation; Deep neural networks; Errors; Activation functions; Adversarial attack; Biological plausibility; Error back-propagation; Feedback alignment; Input perturbation; Prediction accuracy; Transport problems; Two forward learning; Weight transport problem; Feedback | English | Final | 2024 | 10.1109/icaiic60209.2024.10463228 | 바로가기 | 바로가기 | ||||||||||||||||||
| ○ | Article | Prediction of form demolding strength of concrete mixed with non-sintered hwangto using ultrasonic pulse velocity method | This study presented a trend equation derived from regression analysis of compressive strength and ultrasonic velocity to predict the form demolding time for concrete mixed with Non-sintered Hwangto (NHT) at various replacement rates. The experimental results showed that compressive strength decreased as the NHT substitution ratio increased. Regarding ultrasonic speed, the speed difference widened as age increased until one day old but decreased when measured up to 28 days. Regression analysis revealed that plain concrete had a lower ultrasonic velocity than HC at the same compressive strength. Additionally, plain concrete reached the time for vertical, single-layer, and multi-layer horizontal form demolding faster than HC. An error test comparing the trend formula from this study with previous studies showed that this empirical formula had an average error value close to zero, indicating high reliability for predicting demolding time. © 2024 Architectural Institute of Korea. | Young-Jin, Nam; Won-Chang, Kim; Hyeong-Gil, Choi; Tae-Gyu, Lee | Department of Fire and Disaster Prevention, Semyung University, Jecheon, South Korea; Department of Fire and Disaster Prevention, Semyung University, Jecheon, South Korea; School of Architecture, Kyungpook National University, Daegu, South Korea; Department of Fire and Disaster Prevention, Semyung University, Jecheon, South Korea | 59308197100; 59308348900; 59308197200; 59307276400 | ltg777@semyung.ac.kr; | Journal of the Architectural Institute of Korea | 2733-6239 | 40 | 6 | 0 | 2025-05-07 | 0 | Compressive strength; Concrete; Form demolding strength; Non-sintered Hwangto; Ultrasonic pulse velocity method | Korean | Final | 2024 | 10.5659/jaik.2024.40.6.217 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Conference paper | Prediction of Liquefaction-Induced Settlement Using Artificial Neural Network | This study aims to propose a machine-learning algorithm for predicting the ground settlement caused by liquefaction. An artificial neural network (ANN) approach was used. The properties of soil layers, namely unit weight (γ), soil layer depth (d), standard penetration test blow count (N1(60) ), cyclic stress ratio (CSR), and corresponding settlements were selected to train, validate, and test the proposed model. Using the R-squared, the proposed model was compared to other machine learning models like linear regression, elastic net regression, polynomial regression, and support vector machine. For the comparison between the real and predicted settlements, the experimental results show that while the lowest R2 value of 0.322 was found from elastic net regression, the highest accuracy of 0.871 was obtained from the proposed ANN model. It concluded the effectiveness of the machine learning method, particularly in the ANN model, in predicting the soil characteristics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. | Hoang, Dung V.; Bui, Phuoc T. H.; Phan, An T. T.; Nguyen, Tan-No | Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, Viet Nam; Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, Viet Nam; Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, Viet Nam; Department of Civil Engineering, Kyungpook National University, Daegu, South Korea | 59745249800; 58528768100; 59745249900; 57862912800 | tannonguyen.ce@gmail.com; | Lecture Notes in Civil Engineering | 2366-2557 | 482 | 0 | 2025-05-07 | 1 | artificial neural network; Liquefaction; machine learning; settlement | Contrastive Learning; Prediction models; Support vector regression; Artificial neural network approach; Artificial neural network modeling; Elastic net; Ground settlement; Machine learning algorithms; Machine-learning; Neural-networks; Property; Settlement; Soil layer; Adversarial machine learning | English | Final | 2024 | 10.1007/978-981-97-1972-3_100 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Conference paper | Prediction of Liquified Soil Settlement Based on Artificial Neural Network | Severe seismic movements caused certain special types of soil, such as loose sands or poorly gravelly soil, to liquefaction, which is referred to as soil liquefaction and led to ground settlements. Over the past few decades, laboratory or in-situ testing approaches have mainly been applied to investigate these settlements. The aim of this study was to use an artificial neural network (ANN) to predict ground settlement due to the Pohang earthquake. An ANN algorithm was implemented to the soil dataset for this purpose. Various variables of soil characteristics were considered as input parameters namely unit weight, soil layer depth, standard penetration test blow counts, and cyclic stress ratio. Furthermore, different prediction errors of mean average error (MAE), mean squared error (MSE), root mean squared error (RMSE) and the coefficient of determination or R-squared were employed in this study to evaluate the model performance. The testing results revealed that the difference between the original and predicted values using MAE, MSE, and RMSE was 0.209, 0.106 and 0.325, respectively. Besides, the R-squared value of 0.868 was achieved by predicted results and actual values of ground settlements. It concluded the feasibility of the proposed ANN model with the high R-squared value for predicting liquefaction-induced settlements. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. | Nguyen, Tan-No; Tran, Luc V.; Cuong, Phan Viet; Tran, Thanh Danh | Department of Civil Engineering, Kyungpook National University, Daegu, South Korea; Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Viet Nam; Research Development Center for Radiation Technology, Vietnam Atomic Energy Institute, Hanoi, Viet Nam; Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, Viet Nam | 57862912800; 58771645800; 14053421600; 57226534956 | danh.tt@ou.edu.vn; | Lecture Notes in Civil Engineering | 2366-2557 | 442 | 0 | 2025-04-16 | 0 | Artificial Neural Network; Liquefaction; Loose Sand; Settlement | Errors; Forecasting; Mean square error; Settlement of structures; Soil liquefaction; Soils; AS-soils; Average errors; Gravelly soils; Ground settlement; Liquified soils; Loose sands; Mean squared error; Root mean squared errors; Settlement; Soil settlements; Neural networks | English | Final | 2024 | 10.1007/978-981-99-7434-4_128 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Editorial | Preface | [No abstract available] | Choudhury, Tanupriya; Koley, Bappaditya; Nath, Anindita; Um, Jung-Sup; Patidar, Atul Kumar | CSE Department Graphic Era (Deemed to be University), Uttarakhand, Dehradun, India; Department of Geography, Bankim Sardar College, South 24 Parganas, West Bengal, India; Department of Geography, Bankim Sardar College, South 24 Parganas, West Bengal, India; Department of Geography, Kyungpook National University, College of Social Sciences, Daegu, South Korea; Petroleum Engineering and Earth Sciences, UPES, Dehradun, India | 57193140084; 57219158700; 57219157935; 35173565000; 8434240100 | Advances in Geographic Information Science | 1867-2434 | Part F2902 | 0 | 2025-05-07 | 0 | English | Final | 2024 | 바로가기 | |||||||||||||||||||||
| ○ | Article | Preparation and Properties of Silane-Modified Cellulose Nanofiber and Its Blended Bio-Polyurethane Based Composites Film | This study focuses on enhancing nanocellulose dispersion in bio-polyurethane (B-PU) films through silane modification using methyltrimethoxysilane (MTMS). Analyzing wetting, chemical, thermal, morphological, and dispersion properties of modified and unmodified cellulose nanofiber (CNF), the study demonstrates controlled MTMS conjugation, controlling diameter between 50–100 nm. Zeta potential values indicate improved Si-CNF dispersion stability in dimethylformamide. Scanning electron microscopy confirms enhanced dispersion in B-PU composites. Tensile strength increases by 23.52% at 1–2% Si-CNF but decreases (23.80–40.47%) at 4–8%. Si-CNF increases elongation by up to 4%, decreasing slightly at 8%. X-ray diffraction shows B-PU/Si-CNF 1wt.% has the highest crystallinity. Composite films exhibit improved thermal stability. This interaction offers a unique way to enhance Si-CNF and B-PU compatibility, promising applications in furniture coating, biomedical, and food packaging industries. © 2024 Korean Technical Assoc. of the Pulp and Paper Industry. All rights reserved. | Kim, Mikyung; Yoon, Songhyun; Hyun, Jae Min; Lee, Jungeon; Jung, Jae Hoon; Yang, Seong Baek; Yeasmin, Sabina; Kwon, Oh Kyung; Yeum, Jeong Hyun | DYETEC Institute, Daegu, 41706, South Korea; DYETEC Institute, Daegu, 41706, South Korea; DYETEC Institute, Daegu, 41706, South Korea; Department of Biofibers and Biomaterials Science, Kyungpook National University, Daegu, 41566, South Korea; Department of Biofibers and Biomaterials Science, Kyungpook National University, Daegu, 41566, South Korea; Research Institute for Green Energy Convergence Technology, Gyeongsang National University, Jinju, 52828, South Korea; Department of Biofibers and Biomaterials Science, Kyungpook National University, Daegu, 41566, South Korea; Research Director, BSG Co. Ltd., Daegu, 41494, South Korea; Department of Biofibers and Biomaterials Science, Kyungpook National University, Daegu, 41566, South Korea | 59500566900; 59500963800; 59500963900; 57559595700; 58476748000; 56258526300; 57216565706; 59500831500; 6602257098 | sbyang@gnu.ac.kr;jhyuem@knu.ac.kr; | Palpu Chongi Gisul/Journal of Korea Technical Association of the Pulp and Paper Industry | 0253-3200 | 56 | 6 | 0 | 2025-05-07 | 0 | bio-polyurethane; Cellulose nanofiber; composite film; silanization; surface hydrophobic modification | Cellulose Derivatives; Composites; Crystallinity; Diameter; Dispersions; Packaging; Thermal Stability; Wetting; Cellulose derivatives; Cellulose films; Concrete construction; Nanocomposite films; Paper and pulp industry; Wetting; Bio-polyurethane; Cellulose nanofibers; Methyltrimethoxysilane; Modified cellulose; Nano-cellulose; Polyurethane films; Property; Silanizations; Surface hydrophobic modification; Thermal; Dispersions | English | Final | 2024 | 10.7584/jktappi.2024.12.56.6.40 | 바로가기 | 바로가기 |
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