• Journal of Service Research and Studies
• /
• v.13 no.1
• /
• pp.127-143
• /
• 2023

Currently, the domestic traditional market has not escaped the swamp of stagnation that began in the early 2000s despite various projects promoted by many related players such as the central government and local governments. In order to overcome the crisis faced by the traditional market, various R&Ds have recently been conducted on how to build a smart traditional market that combines information and communication technologies such as big data analysis, artificial intelligence, and the Internet of Things. This study analyzes various previous studies, users of traditional markets, and application cases of ICT technology in foreign traditional markets since 2012 and proposes a model to build a smart traditional market using ICT technology based on the analysis. The model proposed in this study includes building a traditional market metaverse that can interact with visitors, certifying visits to traditional markets through digital signage with NFC technology, improving accuracy of fire detection functions using IoT and AI technology, developing smartphone apps for market launch information and event notification, and an e-commerce system. If a smart traditional market platform is implemented and operated based on the smart traditional market platform model presented in this study, it will not only draw interest in the traditional market to MZ generation and foreigners, but also contribute to revitalizing the traditional market in the future.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.10
• /
• pp.445-454
• /
• 2023

Recently, various fault diagnosis studies are being conducted utilizing data from collaborative robots. Existing studies performing fault diagnosis on collaborative robots use static data collected based on the assumed operation of predefined devices. Therefore, the fault diagnosis model has a limitation of increasing dependency on the learned data patterns. Additionally, there is a limitation in that a diagnosis reflecting the characteristics of collaborative robots operating with multiple joints could not be conducted due to experiments using a single motor. This paper proposes an LSTM diagnostic model that can overcome these two limitations. The proposed method selects representative normal patterns using the correlation analysis of vibration and current data in single-axis and multi-axis work environments, and generates residual patterns through differences from the normal representative patterns. An LSTM model that can perform gear wear diagnosis for each axis is created using the generated residual patterns as inputs. This fault diagnosis model can not only reduce the dependence on the model's learning data patterns through representative patterns for each operation, but also diagnose faults occurring during multi-axis operation. Finally, reflecting both internal and external data characteristics, the fault diagnosis performance was improved, showing a high diagnostic performance of 98.57%.

• Journal of Korea Water Resources Association
• /
• v.57 no.2
• /
• pp.127-137
• /
• 2024

Physical models, which are often used for water resource management, are difficult to build and operate with input data and may involve the subjective views of users. In recent years, research using data-driven models such as machine learning has been actively conducted to compensate for these problems in the field of water resources, and in this study, an artificial neural network was used to simulate long-term rainfall runoff in the Osipcheon watershed in Samcheok-si, Gangwon-do. For this purpose, three input data groups (meteorological observations, daily precipitation and potential evapotranspiration, and daily precipitation - potential evapotranspiration) were constructed from meteorological data, and the results of training the LSTM (Long Short-term Memory) artificial neural network model were compared and analyzed. As a result, the performance of LSTM-Model 1 using only meteorological observations was the highest, and six LSTM-MLP ensemble models with MLP artificial neural networks were built to simulate long-term runoff in the Fifty Thousand Watershed. The comparison between the LSTM and LSTM-MLP models showed that both models had generally similar results, but the MAE, MSE, and RMSE of LSTM-MLP were reduced compared to LSTM, especially in the low-flow part. As the results of LSTM-MLP show an improvement in the low-flow part, it is judged that in the future, in addition to the LSTM-MLP model, various ensemble models such as CNN can be used to build physical models and create sulfur curves in large basins that take a long time to run and unmeasured basins that lack input data.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.1
• /
• pp.25-32
• /
• 2020

In the context-aware system, rule-based AI technology has been used in the abstraction process for getting context information. However, the rules are complicated by the diversification of user requirements for the service and also data usage is increased. Therefore, there are some technical limitations to maintain rule-based models and to process unstructured data. To overcome these limitations, many studies have applied machine learning techniques to Context-aware systems. In order to utilize this machine learning-based model in the context-aware system, a management process of periodically injecting training data is required. In the previous study on the machine learning based context awareness system, a series of management processes such as the generation and provision of learning data for operating several machine learning models were considered, but the method was limited to the applied system. In this paper, we propose a training data generating method of a machine learning model to extend the machine learning based context-aware system. The proposed method define the training data generating model that can reflect the requirements of the machine learning models and generate the training data for each machine learning model. In the experiment, the training data generating model is defined based on the training data generating schema of the cardiac status analysis model for older in health status notification service, and the training data is generated by applying the model defined in the real environment of the software. In addition, it shows the process of comparing the accuracy by learning the training data generated in the machine learning model, and applied to verify the validity of the generated learning data.

• Journal of Internet Computing and Services
• /
• v.22 no.2
• /
• pp.77-87
• /
• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• Journal of Internet Computing and Services
• /
• v.22 no.4
• /
• pp.1-11
• /
• 2021

As the Internet developed, various and complex cyber attacks began to emerge. Various detection systems were used outside the network to defend against attacks, but systems and studies to detect attackers inside were remarkably rare, causing great problems because they could not detect attackers inside. To solve this problem, studies on the lateral movement detection system that tracks and detects the attacker's movements have begun to emerge. Especially, the method of using the Remote Desktop Protocol (RDP) is simple but shows very good results. Nevertheless, previous studies did not consider the effects and relationships of each logon host itself, and the features presented also provided very low results in some models. There was also a problem that the model could not explain why it predicts that way, which resulted in reliability and robustness problems of the model. To address this problem, this study proposes an interpretable RDP-based lateral movement detection system using page rank algorithm and SHAP(Shapley Additive Explanations). Using page rank algorithms and various statistical techniques, we create features that can be used in various models and we provide explanations for model prediction using SHAP. In this study, we generated features that show higher performance in most models than previous studies and explained them using SHAP.

• Phonetics and Speech Sciences
• /
• v.14 no.4
• /
• pp.57-66
• /
• 2022

In this study, the definition of "digital therapeutics" was clarified by examining related studies, and the development trend of digital therapeutics at the domestic level was investigated. Further, research data and technologies applied to various communication disorders since 2015 were analyzed. From all these, digital therapeutics can be defined as software that can support evidence-based treatment when used on patients to prevent, manage, and treat disorders With huge investments and research efforts increasingly made in the field of digital therapeutics, 17 of the 22 studies examined were on digital therapeutics applied in the treatment of language disorders. In the research papers examined, the technologies applied were virtual reality and augmented reality, with augmented reality used in most cases. The effects of applying digital treatment were positive, and most studies focused on content development in relation to the development of digital treatment, although one study was conducted for app development. Future studies could examine the application of digital therapeutics to more diverse communication disorder subjects. Active government support is expected in developing more sophisticated software that can be applied using a wider range of technologies in the field of digital therapeutics to treat more communication disorders.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.36 no.6
• /
• pp.381-389
• /
• 2023

One of the crucial components in building information modeling (BIM) is data. To systematically manage these data, various research studies have focused on the creation of object breakdown structures and property sets. Specifically, crucial data for managing programs and payments involves work breakdown structures (WBSs) and cost breakdown structures (CBSs), which are indispensable for mapping BIM objects. Achieving this requires disassembling CBS quantities based on 3D objects and WBS. However, this task is highly tedious owing to the large volume of CBS and divergent coding practices employed by different organizations. Manual processes, such as those based on Excel, become nearly impossible for such extensive tasks. In response to the challenge of computing quantities that are difficult to derive from BIM objects, this study presents methods for disassembling length-based quantities, incorporating significant portions of the bill of quantities (BOQs). The proposed approach recommends suitable CBS by leveraging the accumulated history of WBS-CBS mapping databases. Additionally, it establishes a unified CBS code, facilitating the effective operation of CBS databases.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.6
• /
• pp.525-530
• /
• 2023

Digital drawing subjects require the subdivision of evaluation elements and the graduality of evaluation according to the recent movement of the innovative curriculum. The purpose of this paper is to present the criteria for evaluating the drawing and to propose it as a rubric evaluation. In the text, criteria for beginner evaluation were technical skills such as the accuracy and consistency of the line, the ratio and balance of the picture, and the ability to effectively utilize various brushes and tools at the intermediate levels. In the advanced evaluation section, it is a part of a new perspective or originality centered on creativity and originality, and a unique perspective or interpretation of a given subject. In addition, as an understanding of design principles, the evaluation of completeness was derived focusing on the ability to actively utilize various functions of digital drawing software through design principles such as placement, color, and shape. The importance of introducing rubric evaluation is to allow instructors to make objective and consistent evaluations, and the key to research in rubric evaluation in these art subjects is to help learners clearly grasp their strengths and weaknesses, and learners can identify what needs to be improved and develop better drawing skills accordingly through feedback on each item.

• International Journal of Internet, Broadcasting and Communication
• /
• v.12 no.1
• /
• pp.67-72
• /
• 2020

The fifth generation (5G) mobile communication has an impact on the human life over the whole world, nowadays, through the artificial intelligence (AI) and the internet of things (IoT). The low latency of the 5G new radio (NR) access is implemented by the state-of-the art technologies, such as non-orthogonal multiple access (NOMA). This paper investigates a practical issue that in NOMA, for the practical channel models, such as fading channel environments, the successive interference cancellation (SIC) should be performed on the stronger channel users with low power allocation. Only if the SIC is performed on the user with the stronger channel gain, NOMA performs better than orthogonal multiple access (OMA). Otherwise, NOMA performs worse than OMA. Such the superiority requirement can be easily implemented for the channel being static or slow varying, compared to the block interval time. However, most mobile channels experience fading. And symbol by symbol channel estimations and in turn each symbol time, selections of the SIC-performing user look infeasible in the practical environments. Then practically the block of symbols uses the single channel estimation, which is obtained by the training sequence at the head of the block. In this case, not all the symbol times the SIC is performed on the stronger channel user. Sometimes, we do perform the SIC on the weaker channel user; such cases, NOMA performs worse than OMA. Thus, we can say that by what percent NOMA is better than OMA. This paper calculates analytically the percentage by which NOMA performs better than OMA in the practical mobile communication systems. We show analytically that the percentage for NOMA being better than OMA is only the function of the ratio of the stronger channel gain variance to weaker. In result, not always, but almost time, NOMA could perform better than OMA.