• Journal of the Korea Convergence Society
• /
• v.10 no.5
• /
• pp.35-42
• /
• 2019

Recently, as the Internet and various wearable devices have appeared, Internet technology has contributed to obtaining more convenient information and doing business. However, as the internet is used in various parts, the attack surface points that are exposed to attacks are increasing, Attempts to invade networks aimed at taking unfair advantage, such as cyber terrorism, are also increasing. In this paper, we propose a feature selection method to improve the classification performance of the class to classify the abnormal behavior in the network traffic. The UNSW-NB15 dataset has a rare class imbalance problem with relatively few instances compared to other classes, and an undersampling method is used to eliminate it. We use the SVM, k-NN, and decision tree algorithms and extract a subset of combinations with superior detection accuracy and RMSE through training and verification. The subset has recall values of more than 98% through the wrapper based experiments and the DT_PSO showed the best performance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.1
• /
• pp.240-263
• /
• 2021

One of the biggest challenges that the software industry is facing today is to create highly efficient applications without affecting the quality of healthcare system software. The demand for the provision of software with high quality protection has seen a rapid increase in the software business market. Moreover, it is worthless to offer extremely user-friendly software applications with no ideal security. Therefore a need to find optimal solutions and bridge the difference between accessibility and protection by offering accessible software services for defense has become an imminent prerequisite. Several research endeavours on usable security assessments have been performed to fill the gap between functionality and security. In this context, several Multi-Criteria Decision Making (MCDM) approaches have been implemented on different usability and security attributes so as to assess the usable-security of software systems. However, only a few specific studies are based on using the integrated approach of fuzzy Analytic Network Process (FANP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique for assessing the significant usable-security of hospital management software. Therefore, in this research study, the authors have employed an integrated methodology of fuzzy logic, ANP and TOPSIS to estimate the usable - security of Hospital Management System Software. For the intended objective, the study has taken into account 5 usable-security factors at first tier and 16 sub-factors at second tier with 6 hospital management system softwares as alternative solutions. To measure the weights of parameters and their relation with each other, Fuzzy ANP is implemented. Thereafter, Fuzzy TOPSIS methodology was employed and the rating of alternatives was calculated on the foundation of the proximity to the positive ideal solution.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.3
• /
• pp.571-576
• /
• 2021

This study was carried out to overcome the problems of the existing fitness business and to build a fitness system that can meet the increased demand in the Corona situation. As a platform technology for non-face-to-face fitness edutainment service, it is a next-generation fitness exercise device that can use various body parts and synchronize network-type information. By synchronizing the exercise information of the fitness equipment, it was composed of learning contents through MR-based avatars. A quantified result was derived from examining the applicability of the customized evaluation system through momentum analysis with A.I analysis applying the LSTM-based algorithm according to the cumulative exercise effect of the user. It is a motion capture and 3D visualization fitness program for the application of systematic exercise techniques through academic experts, and it is judged that it will contribute to the improvement of the user's fitness knowledge and exercise ability.

• Journal of Korean Society for Quality Management
• /
• v.51 no.2
• /
• pp.283-296
• /
• 2023

Purpose: The purpose of this study is to actually implement and verify whether welding defects can be detected in real time by utilizing deep learning AI solutions in the welding process of electric vehicle hairpin winding motors. Methods: AI's function and technological elements using synthetic neural network were applied to existing electric vehicle hairpin winding motor laser welding process by making special hardware for detecting electric vehicle hairpin motor laser welding defect. Results: As a result of the test applied to the welding process of the electric vehicle hairpin winding motor, it was confirmed that defects in the welding part were detected in real time. The accuracy of detection of welds was achieved at 0.99 based on mAP@95, and the accuracy of detection of defective parts was 1.18 based on FB-Score 1.5, which fell short of the target, so it will be supplemented by introducing additional lighting and camera settings and enhancement techniques in the future. Conclusion: This study is significant in that it improves the welding quality of hairpin winding motors of electric vehicles by applying domestic artificial intelligence solutions to laser welding operations of hairpin winding motors of electric vehicles. Defects of a manufacturing line can be corrected immediately through automatic welding inspection after laser welding of an electric vehicle hairpin winding motor, thus reducing waste throughput caused by welding failure in the final stage, reducing input costs and increasing product production.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.5
• /
• pp.347-353
• /
• 2023

Measuring the growth of fish in fish farms still uses a laborious method. This method requires a lot of labor and causes stress to the fish, which has a negative impact on mortality. To solve this problem, we propose the Fish Growth Regression System (FGRS), a system to automate the growth of fish. FGRS consists of two modules. The first is a module that detects fish based on Yolo v8, and the second consists of a module that predicts the growth of fish using fish image data and a CNN-based neural network model. As a result of the simulation, the average prediction error before learning was 134.2 days, but after learning, the average error decreased to 39.8 days. It is expected that the system proposed in this paper can be used to predict the growing date and use the growth prediction of fish to contribute to automation in fish farms, resulting in a significant reduction in labor and cost savings.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.7
• /
• pp.81-88
• /
• 2024

Despite the rapid growth of the generative AI market and significant interest from domestic companies and institutions, concerns about the provision of inaccurate information and potential information leaks have emerged as major factors hindering the adoption of generative AI. To address these issues, this paper designs and implements a question-answering system based on the Retrieval-Augmented Generation (RAG) architecture. The proposed method constructs a knowledge database using Korean sentence embeddings and retrieves information relevant to queries through optimized searches, which is then provided to the generative language model. Additionally, it allows users to directly manage the knowledge database to efficiently update changing business information, and it is designed to operate in a private network to reduce the risk of corporate confidential information leakage. This study aims to serve as a useful reference for domestic companies seeking to adopt and utilize generative AI.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.5
• /
• pp.165-175
• /
• 2024

In this paper, the purpose of this study was to analyze various information related to the government's youth entrepreneurship support policy, propose a customized entrepreneurship promotion strategy for college students majoring in sports, and increase their entrepreneurship efficacy. Through this, the aim was to increase the entrepreneurship efficacy of sports majors. Firstly, a literature study was conducted, and secondly, alternatives for promoting entrepreneurship among college students majoring in sports were analyzed based on expert interviews. Therefore, the following suggestions were made. 1. Recognizing the added value and individuality of the sports field in startup policy. 2. Creation of a start-up network and platform for sports majors to start a business. 3. Sports start-up space composition. 4. Organizing and supporting start-up clubs. 5. Activating entrepreneurship education and competitions in capstone design and employment seminar subjects. 6. Increasing start-up efficacy through start-up mentoring.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.7
• /
• pp.1968-1985
• /
• 2024

At present, the world is witnessing a rapid change in all the fields of human civilization business interests and goals of all the sectors are changing very fast. Global changes are taking place quickly in all fields - manufacturing, service, agriculture, and external sectors. There are plenty of hurdles in the emerging technologies in agriculture in the modern days. While adopting such technologies as transparency and trust issues among stakeholders, there arises a pressurized necessity on food suppliers because it has to create sustainable systems not only addressing demand-supply disparities but also ensuring food authenticity. Recent studies have attempted to explore the potential of technologies like blockchain and practices for smart and sustainable agriculture. Besides, this well-researched work investigates how a scientific cum technological blockchain architecture addresses supply chain challenges in Precision Agriculture to take up challenges related to transparency traceability, and security. A robust registration phase, efficient authentication mechanisms, and optimized data management strategies are the key components of the proposed architecture. Through secured key exchange mechanisms and encryption techniques, client's identities are verified with inevitable complexity. The confluence of IoT and blockchain technologies that set up modern farms amplify control within supply chain networks. The practical manifestation of the researchers' novel blockchain architecture that has been executed on the Hyperledger network, exposes a clear validation using corroboration of concept. Through exhaustive experimental analyses that encompass, transaction confirmation time and scalability metrics, the proposed architecture not only demonstrates efficiency but also underscores its usability to meet the demands of contemporary Precision Agriculture systems. However, the scholarly paper based upon a comprehensive overview resolves a solution as a fruitful and impactful contribution to blockchain applications in agriculture supply chains.

• Dementia and Neurocognitive Disorders
• /
• v.23 no.3
• /
• pp.146-160
• /
• 2024

Background and Purpose: The emotions of people at various stages of dementia need to be effectively utilized for prevention, early intervention, and care planning. With technology available for understanding and addressing the emotional needs of people, this study aims to develop speech emotion recognition (SER) technology to classify emotions for people at high risk of dementia. Methods: Speech samples from people at high risk of dementia were categorized into distinct emotions via human auditory assessment, the outcomes of which were annotated for guided deep-learning method. The architecture incorporated convolutional neural network, long short-term memory, attention layers, and Wav2Vec2, a novel feature extractor to develop automated speech-emotion recognition. Results: Twenty-seven kinds of Emotions were found in the speech of the participants. These emotions were grouped into 6 detailed emotions: happiness, interest, sadness, frustration, anger, and neutrality, and further into 3 basic emotions: positive, negative, and neutral. To improve algorithmic performance, multiple learning approaches were applied using different data sources-voice and text-and varying the number of emotions. Ultimately, a 2-stage algorithm-initial text-based classification followed by voice-based analysis-achieved the highest accuracy, reaching 70%. Conclusions: The diverse emotions identified in this study were attributed to the characteristics of the participants and the method of data collection. The speech of people at high risk of dementia to companion robots also explains the relatively low performance of the SER algorithm. Accordingly, this study suggests the systematic and comprehensive construction of a dataset from people with dementia.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.15 no.1
• /
• pp.71-94
• /
• 2020

Korea's innovation cluster policy has been implemented since 2005 with the goal of balanced regional development. The purpose of this study is to investigate the factors affecting the sustainability of innovative cluster tenants by using Pangyo Techno Valley as an example. Pangyo Techno Valley was established under the leadership of the local government (Gyeonggi-do) rather than the central government and it is called "Silicon Valley of Korea" and "Asia Silicon Valley" and is becoming more representative. The growing number of companies in Pangyo Techno Valley decreased in 2017 compared to 2016. This is because Pangyo Techno Valley's business ecosystem will change from 2019. In this paper, quantitative and qualitative studies were conducted to investigate the influencing factors. Quantitative research was conducted based on the survey and qualitative research was applied through interviews. The quantitative research examined the factors affecting the sustainability of Pangyo Techno Valley, and the qualitative research examined the specific reasons and additional factors for the quantitative research results. The quantitative results showed that factors affecting sustainability in terms of changes in corporate internal conditions, human and physical infrastructure, cooperation and synergy, and occupancy patterns. The specific reason for the impact appeared in the qualitative research process. The support category of local governments did not show any significant factors in quantitative research. In addition, qualitative research suggested 'Good image of Pangyo Techno Valley' as the category that has the greatest impact on sustainability. It is shown that companies are passive and expect the role of local governments in activating cooperation network in Pangyo Techno Valley. In this paper, based on the results of the study, Pangyo Techno Valley is presented with a realistic plan based on real estate issues and an ideal plan with a long-term perspective.