• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.4008-4023
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• 2022

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.359-373
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• 2022

The new crown pneumonia (COVID-19) has become a global epidemic. The disease has spread to most countries and poses a challenge to the healthcare system. Contact tracing technology is an effective way for public health to deal with diseases. Many experts have studied traditional contact tracing and developed digital contact tracking. In order to better understand the field of contact tracking, it is necessary to analyze the development of contact tracking in the field of computer science by bibliometrics. The purpose of this research is to use literature statistics and topic analysis to characterize the research literature of contact tracking in the field of computer science, to gain an in-depth understanding of the literature development status of contact tracking and the trend of hot topics over the past decade. In order to achieve the aforementioned goals, we conducted a bibliometric study in this paper. The study uses data collected from the Scopus database. Which contains more than 10,000 articles, including more than 2,000 in the field of computer science. For popular trends, we use VOSviewer for visual analysis. The number of contact tracking documents published annually in the computer field is increasing. At present, there are 200 to 300 papers published in the field of computer science each year, and the number of uncited papers is relatively small. Through the visual analysis of the paper, we found that the hot topic of contact tracking has changed from the past "mathematical model," "biological model," and "algorithm" to the current "digital contact tracking," "privacy," and "mobile application" and other topics. Contact tracking is currently a hot research topic. By selecting the most cited papers, we can display high-quality literature in contact tracking and characterize the development trend of the entire field through topic analysis. This is useful for students and researchers new to field of contact tracking ai well as for presenting our results to other subjects. Especially when comprehensive research cannot be conducted due to time constraints or lack of precise research questions, our research analysis can provide value for it.

• 한국산업융합학회 논문집
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• 제26권4_2호
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• pp.705-713
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• 2023

The phenomenon of abnormal climate conditions resulting from greenhouse gas-induced global warming is increasingly prevalent. To address this challenge, global initiatives are underway to adopt environmentally friendly, zero-emission fuels. In this study, we investigate the hydrogen embrittlement characteristics of materials used for eco-friendly hydrogen storage systems. The effects of hydrogen embrittlement on austenitic stainless steels of the FCC series and structural steel of the BCC series were examined. Initially, test samples of three different steel types were prepared in 2t and 3t sizes, and hydrogen was injected into the specimens using an electrochemical method over a 24-hour period. Subsequently, a universal material testing machine (UTM) was employed to monitor changes in mechanical strength and elongation. The FCC series stainless steels exhibited a tendency for elongation to decrease, indicating low sensitivity to hydrogen. In contrast, the mechanical strength and elongation of the BCC series steel changed significantly upon hydrogen charging, posing challenges for prediction. The results of the present study are expected to serve as a fundamental database for analyzing the impact of hydrogen embrittlement on both FCC and BCC series steel materials.

• Imaging Science in Dentistry
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• 제53권3호
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• pp.199-207
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• 2023

Purpose: The objective of this study was to evaluate the accuracy and effectiveness of an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs(PRs), as well as to assess the appropriateness of its treatment recommendations. Materials and Methods: PRs from 100 patients(representing 4497 teeth) with known clinical examination findings were randomly selected from a university database. Three dentomaxillofacial radiologists and the Diagnocat AI software evaluated these PRs. The evaluations were focused on various dental conditions and treatments, including canal filling, caries, cast post and core, dental calculus, fillings, furcation lesions, implants, lack of interproximal tooth contact, open margins, overhangs, periapical lesions, periodontal bone loss, short fillings, voids in root fillings, overfillings, pontics, root fragments, impacted teeth, artificial crowns, missing teeth, and healthy teeth. Results: The AI demonstrated almost perfect agreement (exceeding 0.81) in most of the assessments when compared to the ground truth. The sensitivity was very high (above 0.8) for the evaluation of healthy teeth, artificial crowns, dental calculus, missing teeth, fillings, lack of interproximal contact, periodontal bone loss, and implants. However, the sensitivity was low for the assessment of caries, periapical lesions, pontic voids in the root canal, and overhangs. Conclusion: Despite the limitations of this study, the synthesized data suggest that AI-based decision support systems can serve as a valuable tool in detecting dental conditions, when used with PR for clinical dental applications.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.185-185
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• 2023

Agricultural water demand is considered as the major sector of water withdrawal due to irrigation. The majority part of the global agricultural field depends on various irrigation techniques. Therefore, a timely and sufficient supply of water is the most important requirement for agriculture. Irrigation is implemented in different ways in various land surface models, it can be modeled empirically based on observed irrigation rates or by calculating water supply and demand. Certain models can also calculate the irrigation demand as per the soil water deficit. In these implementations, irrigation is typically applied uniformly over the irrigated land regardless of crop types or irrigation techniques. Whereas, the latest version of Community Land Model (CLM) in the Community Terrestrial Systems Model (CTSM) uses a global distribution map of irrigation with 64 crop functional types (CFTs) to simulate the irrigation water demand. It can estimate irrigation water withdrawal from different sources and the amount or the areas irrigated with different irrigation techniques. Hence, we set up the model for the simulation period of 16 years from 2000 to 2015 to analyze the global irrigation demand at a spatial resolution of 1.9° × 2.5°. The simulated irrigation water demand is evaluated with the available observation data from FAO AQUASTAT database at the country scale. With the evaluated model, this study aims to suggest new sustainable scenarios for the ratios of irrigation water withdrawal, high depending on the withdrawal sources e.g. surface water and groundwater. With such scenarios, the CFT maps are considered as the determining factor for selecting the areas where the crop pattern can be altered for a sustainable irrigation water management depending on the available withdrawal sources. Overall, our study demonstrate that the scenarios for the future sustainable water resources management in terms of irrigation water withdrawal from the both the surface water and groundwater sources may overcome the excessive stress on exploiting the groundwater in major river basins globally.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Smart Structures and Systems
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• 제31권2호
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• pp.101-111
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• 2023

Structural health monitoring (SHM) covers a range of damage detection strategies for buildings. In real-time, SHM provides a basis for rapid decision making to optimise the speed and economic efficiency of post-event response. Previous work introduced an SHM method based on identifying structural nonlinear hysteretic parameters and their evolution from structural force-deformation hysteresis loops in real-time. This research extends and generalises this method to investigate the impact of a wide range of flag-shaped or pinching shape nonlinear hysteretic response and its impact on the SHM accuracy. A particular focus is plastic stiffness (K_p), where accurate identification of this parameter enables accurate identification of net and total plastic deformation and plastic energy dissipated, all of which are directly related to damage and infrequently assessed in SHM. A sensitivity study using a realistic seismic case study with known ground truth values investigates the impact of hysteresis loop shape, as well as added noise, on SHM accuracy using a suite of 20 ground motions from the PEER database. Monte Carlo analysis over 22,000 simulations with different hysteresis loops and added noise resulted in absolute percentage identification error (median, (IQR)) in K_p of 1.88% (0.79, 4.94)%. Errors were larger where five events (Earthquakes #1, 6, 9, 14) have very large errors over 100% for resulted K_p as an almost entirely linear response yielded only negligible plastic response, increasing identification error. The sensitivity analysis shows accuracy is reduces to within 3% when plastic drift is induced. This method shows clear potential to provide accurate, real-time metrics of non-linear stiffness and deformation to assist rapid damage assessment and decision making, utilising algorithms significantly simpler than previous non-linear structural model-based parameter identification SHM methods.

• 한국전자통신학회논문지
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• 제18권3호
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• pp.413-420
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• 2023

오래전부터 연구자들은 CBIR에 대한 많은 연구로 인해 이미지 검색 분야에 우수한 결과를 제시하였다. 그러나 이미지에 대한 이러한 검색 결과와 사람이 인식하는 결과 사이에 의미적 격차는 여전히 존재한다. 적은 수의 이미지를 사용하여 사람이 인식하는 수준의 이미지를 분류하는 것은 아직까지 어려운 문제이다. 따라서 본 논문은 이미지 검색에서 사람과 검색 시스템의 이미지의 의미적 격차를 최소화하기 위해 딥 러닝 기반의 전이 학습을 이용한 이미지 분류 모델을 제안한다. 실험 결과, 학습 모델의 손실률은 0.2451%, 정확도는 0.8922%로 제안한 이미지 분류 방법의 구현은 원하는 목표를 달성할 수 있었다. 그리고 딥 러닝에서 CNN의 전이 학습 모델 방법이 새로운 데이터를 추가하여 이미지 데이터베이스를 구축하는데 효과적인 결과를 확인할 수 있었다.

• 산업융합연구
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• 제21권5호
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• pp.133-142
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• 2023

최근 학력 위변조 문제는 단순히 교육기관에서 생성된 증명서 조작, 재학 중에 수료를 졸업으로 속여 기록하는 등 자동화된 관계 검증과 검증 자체를 신뢰하기 어려운 문제가 존재한다. 이는 대학 기관들의 학력 데이터베이스 공유 부재와 학력 추적이 어려운 독립된 시스템을 구축/운영하고 있기 때문이다. 본 연구는 대학 기관을 중심으로 학력 검증을 위한 E-AV 모델을 설계 및 구현한다. 기존 학력에 대한 연계 정보들을 요약하여 암호화된 데이터베이스로 저장하고, 기존 시스템의 호환성과 확장성을 고려하여 웹 표준 기술로 구현했다. 샘플 데이터 검증 결과 위변조에 안전성을 개선하고, 저장 공간과 실행성능의 준수한다는 것을 확인했다. 본 연구는 향후 국내 대학 교육기관 학력 관리 등 온라인 검증 서비스 개선에 이바지하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권5호
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• pp.1339-1355
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• 2023

Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.