• Korean Journal of Applied Biomechanics
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• v.32 no.3
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• pp.69-79
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• 2022

Objective: The purpose of this study is to analyze trends related to sports and artificial intelligence (AI) to understand the trends and how they change according to time, and to establish methods to apply AI in sports. Both macro and micro perspectives related to sports utilization of AI were analyzed. Method: In this study, after analyzing and discussing various information related to the use of artificial intelligence in the sports through a search of academic journals, papers, books, and websites published recently at nationally and internationally, the application plan of artificial intelligence in the sports field was presented. Results: 1) Motion analysis technology using artificial intelligence is effective in sports where posture is important, and if it provides systematic feedback and training methods, it can help improve performance. 2) The introduction of a sports referee judgment system using artificial intelligence is expected to improve performance by restoring factual judgment and objective fairness in sports games. 3) Artificial intelligence will provide coaching staff and players with a variety of information to help improve performance through systematic coaching and improving feedback and enhanced training methods. 4) It is judged that artificial intelligence-related to sports ethics, sports ICT, sports marketing, sports prediction, etc. We think that based on the current AI research trends will have a positive impact on all sports-related areas, helping to revitalize sports. Conclusion: Motion analysis technology using artificial intelligence, sports referee judgment system, coaching using artificial intelligence, and artificial intelligence are judged to have a positive effect on all sports-related areas and help revitalize sports.

• Journal of Platform Technology
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• v.11 no.5
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• pp.126-135
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• 2023

Demand for NFT is expanding along with Blockchain. And cyber security threats are also increasing. Therefore, this study derives security level inspection items by analyzing status related to NFT security such as NFT features, security threats, and compliance for the purpose of strengthening NFT security. Based on this, the relative importance was confirmed by applying it to the AHP model. As a result of the empirical analysis, the priority order of importance was found in the order of Security management system establishment and operation, encryption, and risk management, etc. The significance of this study is to reduce NFT security incidents and improve the NFT security management level of related companies by deriving NFT-related security level check items and demonstrating the research model. And If you perform considering relative importance of the NFT check items, the security level can be identified early.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.3
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• pp.151-174
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• 2017

The diffusion of the internet, the advancement of ICT technology, and digital diffusion have facilitated the streamlining and acceleration of scholarly communication and speeding up research, and the paradigm of scholarly information dissemination is changing. This study introduces the ORCID, a unique author identifier, and examines the ORCID organization's activities, the advantages given to researchers and research institutes, and the membership status. In addition, this paper examines adoptions and utilizations of ORCID in major countries including USA, UK, Italy, and China. Based on this, this paper suggests the necessary considerations for utilizing ORCID in terms of governance, system elements, policy and institutional aspects in an effort to identify authors at national level.

• Asia pacific journal of information systems
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• v.19 no.4
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• pp.1-28
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• 2009

Banking industries have continuously innovated through technology-enhanced products and services. Many studies have recognized the importance of the Internet in banking industries, arguing that it has been widely adopted. Many studies published on the Internet banking in specific countries are mostly related with such issues as internet banking adoption and acceptance, security and risks of online banking system, and interface design. Several studies have been done to examine the differences and similarities between other banking channels and the Internet banking. However, to the best of our knowledge, only a limited number of studies has examined the differences and similarities between two specific countries in order to create a new customer satisfaction model. In this research, we studied the internet banking satisfaction model by comparing two countries: South Korea and Indonesia. We conducted an empirical study based on the data collected in both two countries. In this research, we found that countries which have adopted electric banking services, particularly between a country with high ICT adoption and a country with low ICT adoption, show different satisfaction trends. Based on the study results, herein we provide discussion, managerial, and practical implications.

• Journal of the Korea Society for Simulation
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• v.26 no.3
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• pp.125-139
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• 2017

Recently, interest in improving the quality of EMS(emergency medical services) has been increasing. Much effort is being made to innovate the EMS process. The rapid progress of ICT technology has accelerated the automation or intelligence of EMS processes. This study suggests an emergency room management method based on real-time data considering resource utilization optimization, minimization of human error and enhancement of predictability of medical care. Emergency room operation indices - Emergency care index, Short stay index, Human error inducing index, Waiting patience index - are developed. And emergency room operation rules based on these indices are presented. Simulation was performed on a virtual emergency room to verify the effectiveness of the proposed operating rule. Simulation results showed excellent performance in terms of length of stay.

• ETRI Journal
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• v.45 no.1
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• pp.105-118
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• 2023

We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross-validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.268-276
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• 2023

The representative branch of ICT education is Arduino. However, there are various problems when teaching using Arduino. Arduino requires a complex understanding of hardware and software, and this can be perceived as a difficult course, especially for beginners who are not familiar with programming or electronics. Additionally, the process of connecting the pins of the Arduino board and components must be accurate, and even small mistakes can lead to project failure, which can reduce the learner's concentration and interest in learning Arduino. Existing Arduino learning content consists of text and images in 2D format, which has limitations in increasing student understanding and immersion. Therefore, in this paper analyzes the necessary conditions for sprouting 'growing kidney beans' in the first semester of the fourth grade of elementary school, and builds an automated experimental environment using Arduino. Augmented reality of the pin connection process was designed and produced to solve the difficulties when building an automation system using Arduino. After 3D modeling Arduino and components using 3D Max, animation was set, and augmented reality (AR) content was produced using Unity to provide learners with more intuitive and immersive learning content when learning Arduino. Augmented reality (AR)-based Arduino learning content production is expected to increase educational effects by improving the understanding and immersion of classes in ICT education using Arduino and inducing fun and interest in physical computing coding education.

• Journal of Platform Technology
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• v.11 no.6
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• pp.13-20
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• 2023

Regularly inspecting vehicle tires' condition is imperative for driving safety and comfort. Poorly maintained tires can pose fatal risks, leading to accidents. Unfortunately, manual tire visual inspections are often considered no less laborious than employing an automatic tire inspection system. Nevertheless, an automated tire inspection method can significantly enhance driver compliance and awareness, encouraging routine checks. Therefore, there is an urgency for automated tire inspection solutions. Here, we focus on developing a deep learning (DL) model to predict cracked tires. The main idea of this study is to demonstrate the comparative analysis of DenseNet121, VGG-19 and EfficientNet Convolution Neural Network-based (CNN) Transfer Learning (TL) and suggest which model is more recommended for cracked tire classification tasks. To measure the model's effectiveness, we experimented using a publicly accessible dataset of 1028 images categorized into two classes. Our experimental results obtain good performance in terms of accuracy, with 0.9515. This shows that the model is reliable even though it works on a dataset of tire images which are characterized by homogeneous color intensity.

• Journal of Appropriate Technology
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• v.7 no.1
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• pp.115-124
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• 2021

Due to the successful accomplishments of the first-stage base center project, a road-map for the second-stage, global base center 2030 project has recently been proposed. The vision of the base center is to build a technology centered, cooperation based platform for a sustainable global community. The global base center 2030 project is based on three core strategies as well as three key strategies. The main goal of the core strategy is to establish an interdisciplinary smart platform, as well as a global tech-coordination facility to implement sustainable, inclusive, and innovative science and technology based ODA projects. To achieve such goals, the global center will focus on developing a global living lab, interdisciplinary smart linkage systems, and a global operating platform. The main goals for the key strategies are to solve issues at the base centers while establishing an international relationship through sustainable technology. To achieve such goals, key projects are centered in establishing a ICT package, and a global living lab based on smart interconnected system. With this, a global inter-connected business platform will also be established to support technical and operational issues.

• Smart Media Journal
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• v.12 no.1
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• pp.9-16
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• 2023

Recently, a convolutional neural network (CNN) based system is being developed to overcome the limitations of human resources in the apple quality classification of farmhouse. However, since convolutional neural networks receive only images of the same size, preprocessing such as sampling may be required, and in the case of oversampling, information loss of the original image such as image quality degradation and blurring occurs. In this paper, in order to minimize the above problem, to generate a image patch based graph of an original image and propose a random walk-based positional encoding method to apply the graph transformer model. The above method continuously learns the position embedding information of patches which don't have a positional information based on the random walk algorithm, and finds the optimal graph structure by aggregating useful node information through the self-attention technique of graph transformer model. Therefore, it is robust and shows good performance even in a new graph structure of random node order and an arbitrary graph structure according to the location of an object in an image. As a result, when experimented with 5 apple quality datasets, the learning accuracy was higher than other GNN models by a minimum of 1.3% to a maximum of 4.7%, and the number of parameters was 3.59M, which was about 15% less than the 23.52M of the ResNet18 model. Therefore, it shows fast reasoning speed according to the reduction of the amount of computation and proves the effect.