• Journal of Creative Information Culture
• /
• v.7 no.3
• /
• pp.177-185
• /
• 2021

In future education, the paradigm of education is changing due to changes in learner-led and assessment methods. In addition, AI-based learning infrastructure and software education are increasingly needed. Thus, this study aims to examine the effectiveness of AI-based evaluation in future education by combining it with learner-led assessment. Using AI education and evaluation literature and Step 7 of the Learner-Driven Software Assessment Method, we sought to extract evaluation elements tailored to elementary school level in conjunction with the 2015 revised elementary practical course content elements, software understanding, procedural problem solving, and structural evaluation elements. In the future, we will develop a grading system that applies AI-based learner-led evaluation elements in software education and continuously demonstrate its effectiveness, and help the school site prepare for future education independently through AI-based learner-led assessment in software education.

• Journal of Technology Innovation
• /
• v.29 no.4
• /
• pp.161-196
• /
• 2021

This study presents a new methodology for developing AI-based products. It identifies the distinctive attributes of AI innovation that are different from existing methods, and presents a product design process and methodology reflecting these attributes. This study emphasizes that AI product development should be oriented toward an ambidexterity approach. This study proposes a design process and specific development method for AI-based products that including steps such as technology push oriented idea generation with morphological approach, market pull oriented consumer requirements analysis, product design refinement, etc. In order to verify the practical applicability of this methodology, an AI-based car infotainment system development strategy is derived as a case study. 13 innovative ideas were generated by the morphological approach and expert review based on technological possibility, and a total of 6 quality requirements were derived as new product development strategies through the analysis of consumer requirements by combining Kano and TOPSIS. The methodology proposed in this research paper can be usefully utilized for companies to pioneer new markets through AI-based products or to expand the market by upgrading existing products or services.

• The Journal of Korean Institute of Information Technology
• /
• v.17 no.12
• /
• pp.125-134
• /
• 2019

As the limitations of the passive recognition domain, which is not guaranteed transparency of the operation process, AI technology has a vulnerability that depends on the data. Human error is inherent because raw data for artificial intelligence learning must be processed and inspected manually to secure data quality for the advancement of AI learning. In this study, we examine the necessity of learning data management before machine learning by analyzing inaccurate cases of AI learning data and cyber security attack method through the approach from cyber security perspective. In order to verify the learning data integrity, this paper presents the direction of data-preserving artificial intelligence system, a blockchain-based learning data environment model. The proposed method is expected to prevent the threats such as cyber attack and data corruption in providing and using data in the open network for data processing and raw data collection.

• Journal of the Korean Geosynthetics Society
• /
• v.23 no.2
• /
• pp.43-52
• /
• 2024

Recent abnormal climate conditions have increased the risk of slope collapses, which frequently result in significant loss of life and property due to the absence of early prediction and warning dissemination. In this paper, we develop a slope condition analysis system using IoT sensors and AI-based camera to assess the condition of slopes. To develop the system, we conducted hardware and firmware design for measurement sensors considering the ground conditions of slopes, designed AI-based image analysis algorithms, and developed prediction and warning solutions and systems. We aimed to minimize errors in sensor data through the integration of IoT sensor data and AI camera image analysis, ultimately enhancing the reliability of the data. Additionally, we evaluated the accuracy (reliability) by applying it to actual slopes. As a result, sensor measurement errors were maintained within 0.1°, and the data transmission rate exceeded 95%. Moreover, the AI-based image analysis system demonstrated nighttime partial recognition rates of over 99%, indicating excellent performance even in low-light conditions. Through this research, it is anticipated that the analysis of slope conditions and smart maintenance management in various fields of Social Overhead Capital (SOC) facilities can be applied.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.25 no.5
• /
• pp.513-521
• /
• 2022

This study deals with an AI architecture model for collecting battlefield data using the tactical C4I system. Based on this model, the artificial staff can be utilized in tactical echelon. In the current structure of the Army's tactical C4I system, Servers are operated by brigade level and above and divided into an active and a standby server. In this C4I system structure, the AI server must also be installed in each unit and must be switched when the C4I server is switched. The tactical C4I system operates a server(DB) for each unit, so data matching is partially delayed or some data is not matched in the inter-working process between servers. To solve these issues, this study presents an operation concept so that all of alternate server can be integrated based on virtualization technology, which is used as an source data for AI Meta DB. In doing so, this study can provide criteria for the AI architectural model of the ground tactical echelon.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.2
• /
• pp.168-175
• /
• 2023

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.3
• /
• pp.633-640
• /
• 2024

This study develops AI-based 3D body shape modeling technology that can be utilized in the healthcare sector, proposing a system that enables monitoring of users' body shape changes and health status. Utilizing data from Size Korea, the study developed a model to generate 3D body shape images from 2D images, and compared various models to select the one with the best performance. Ultimately, by proposing a system process through the developed technology, including personalized health management, exercise recommendations, and dietary suggestions, the study aims to contribute to disease prevention and health promotion.

• Journal of Aerospace System Engineering
• /
• v.17 no.2
• /
• pp.56-61
• /
• 2023

Recently, advancement of AI-enabled autonomous MUM-T combat system and industrial revitalization are rapidly emerging as global issues. However, the Defense Business Act of the Ministry of National Defense in Korea is judged to be somewhat insufficient compared to NATO leading countries in advancement of operation part of a weapon system as MUM-T is centered on a weapon system's own device. We established the concept of AI-enabled autonomous MUM-T to strengthen international competitiveness of complex combat systems such as future global UGV, UAV, and UMS. In addition, NATO and US-centered autonomy, interoperability, and data standardization-based defense AI MUM-T top-level platform construction and operation plan, establishment of a national defense innovation committee such as the National Science and Technology Advisory Council, review and advisory function reinforcement, and additional governance measures are proposed.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.1
• /
• pp.27-38
• /
• 2023

In this paper, we propose a conversational AI agent based on continual learning that can continuously learn and grow with new data over time. A continual learning-based conversational AI agent consists of three main components: Task manager, User attribute extraction, and Auto-growing knowledge graph. When a task manager finds new data during a conversation with a user, it creates a new task with previously learned knowledge. The user attribute extraction model extracts the user's characteristics from the new task, and the auto-growing knowledge graph continuously learns the new external knowledge. Unlike the existing conversational AI agents that learned based on a limited dataset, our proposed method enables conversations based on continuous user attribute learning and knowledge learning. A conversational AI agent with continual learning technology can respond personally as conversations with users accumulate. And it can respond to new knowledge continuously. This paper validate the possibility of our proposed method through experiments on performance changes in dialogue generation models over time.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.47 no.2
• /
• pp.21-29
• /
• 2024

Fueled by international efforts towards AI standardization, including those by the European Commission, the United States, and international organizations, this study introduces a AI-driven framework for analyzing advancements in drone technology. Utilizing project data retrieved from the NTIS DB via the "drone" keyword, the framework employs a diverse toolkit of supervised learning methods (Keras MLP, XGboost, LightGBM, and CatBoost) enhanced by BERTopic (natural language analysis tool). This multifaceted approach ensures both comprehensive data quality evaluation and in-depth structural analysis of documents. Furthermore, a 6T-based classification method refines non-applicable data for year-on-year AI analysis, demonstrably improving accuracy as measured by accuracy metric. Utilizing AI's power, including GPT-4, this research unveils year-on-year trends in emerging keywords and employs them to generate detailed summaries, enabling efficient processing of large text datasets and offering an AI analysis system applicable to policy domains. Notably, this study not only advances methodologies aligned with AI Act standards but also lays the groundwork for responsible AI implementation through analysis of government research and development investments.