• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.145-153
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• 2023

This study emphasizes the essential need in the military for effective measurement and monitoring of soldiers' physical fitness, health, and exercise capabilities to enhance both their overall fitness and combat effectiveness. The effective assessment of physical fitness is considered a core element of management, aligning with principles of modern management. Particularly, preparing soldiers with robust physical fitness is deemed crucial for adapting to dynamic changes on the battlefield. In this research, the RFM (Recency, Frequency, Monetary) customer analysis and clustering methods, validated in e-commerce, are introduced as a basis for applying an AI-driven customer analysis approach to assess military personnel fitness. To achieve this, the study explores the incorporation of the RSC (Reveal, Sustainable, Control) analysis model. This model aims to effectively categorize and monitor military personnel fitness. The application of the RFM technique in the RSC analysis model quantifies and models military fitness, fostering continuous improvement and seeking strategies to enhance the effectiveness of fitness management. Through these methods, the study develops an AI customer analysis technique applied to the RSC clustering analysis method for improving and sustaining military personnel fitness.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.377-383
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• 2023

Purpose: There is increasing attention to the application of transcranial direct current stimulation (tDCS) for enhancing cognitive functions in subjects to aging, mild cognitive impairment (MCI), and Alzheimer's disease (AD). Despite varying treatment outcomes in tDCS which depend on the amount of current reaching the brain, there is no general information on the impacts of anatomical features associated with AD on tDCS-induced electric field. Objective: The objective of this study is to examine how AD-related anatomical variation affects the tDCS-induced electric field using computational modeling. Methods: We collected 180 magnetic resonance images (MRI) of AD patients and healthy controls from a publicly available database (Alzheimer's Disease Neuroimaging Initiative; ADNI), and MRIs were divided into female-AD, male-AD, female-normal, and male-normal groups. For each group, segmented brain volumes (cerebrospinal fluid, gray matter, ventricle, rostral middle frontal (RMF), and hippocampus/amygdala complex) using MRI were measured, and tDCS-induced electric fields were simulated, targeting RMF. Results: For segmented brain volumes, significant sex differences were observed in the gray matter and RMF, and considerable disease differences were found in cerebrospinal fluid, ventricle, and hippocampus/amygdala complex. There were no differences in the tDCS-induced electric field among AD and normal groups; however, higher peak values of electric field were observed in the female group than the male group. Conclusions: Our findings demonstrated the presence of sex and disease differences in segmented brain volumes; however, this pattern differed in tDCS-induced electric field, resulting in significant sex differences only. Further studies, we will adjust the brain stimulation conditions to target the deep brain and examine the effects, because of significant differences in the ventricles and deep brain regions between AD and normal groups.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.17-22
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• 2023

Brain-Machine Interfaces(BMI) are interfaces that control machines by decoding brainwaves, which are electrical signals generated from neural activities. Although BMIs can be applied in various fields, their widespread usage is hindered by the low portability of the hardware required for brainwave measurement and decoding. To address this issue, previous research proposed a brain-machine interface system based on the Internet of Things (IoT) using cloud computing. In this study, we developed and tested an application that uses brainwaves to control the Pong game, demonstrating the real-time usability of the system. The results showed that users of the proposed BMI achieved scores comparable to optimal control artificial intelligence in real-time Pong game matches. Thus, this research suggests that IoT-based brain-machine interfaces can be utilized in a variety of real-time applications in everyday life.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.179-184
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• 2023

As many people went through Corona, they began to be interested in picture diaries that record their daily lives. However, existing applications have many paid services, and it is difficult to write a picture diary for those who have difficulty drawing themselves. In this paper, an application was developed to solve these problems. Basically, drawing diary writing function is provided, drawing is provided in the hand drawing function to increase convenience, and AI drawing function is added to make it possible to draw through keywords for those who have difficulty drawing. In addition, the emotional analysis function was added so that one could see one's past emotional statistics through the statistical function.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.177-182
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• 2023

When determining a meeting place, it is always a cumbersome problem to select an appropriate store with a short travel time for all participants. In this paper, to solve this problem, we propose an algorithm that recommends the best place and store based on the subway station and develop the system. This system provides a web-based store information registration function that allows self-employed people to register and promote their store, and provides an app-based function to recommend a meeting place to participants. The proposed algorithm reduces the travel time of all participants based on the subway map and improves fairness by using the standard deviation of the required time. In addition, this system presents a new way for self-employed people who have recently relied only on publicity through delivery apps.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.9
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• pp.273-282
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• 2023

Recently, various technologies such as logistics automation and port operations automation with ICT technology are being developed to build smart ports. However, there is a lack of technology development for port safety and safety accident prevention. This paper proposes an AI-based shipping container loading safety management system for the prevention of safety accidents at container loading fields in ports. The system consists of an AI-based shipping container safety accident risk classification and storage function and a real-time safety accident monitoring function. The system monitors the accident risk at the site in real-time and can prevent container collapse accidents. The proposed system is developed as a prototype, and the system is ecaluated by direct application in a port.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.1
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• pp.183-190
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• 2024

Purpose : This study aims to conduct a comprehensive comparison and analysis of intervention strategies utilized for school-aged children facing difficulties in writing, focusing on evaluating the effectiveness of various intervention approaches both domestically and internationally. The primary focus is on assessing the efficacy of each intervention approach and identifying gaps in the existing literature. Methods : Data for this study were gathered from the domestic database RISS from January 2013 to March 2020, and international databases Pubmed and Google Scholar were utilized. The keywords for domestic literature search included 'occupational therapy', 'handwriting', and 'school-aged', while for international literature search, the keywords were 'occupational therapy', 'handwriting', and 'children'. A total of 4 international and 2 domestic articles were selected for review based on predetermined inclusion and exclusion criteria. Results : The study findings present a thorough comparative analysis of intervention strategies, categorizing them into task-oriented intervention, sensory-motor intervention, and integrated intervention. All intervention methods demonstrated notable improvements in the legibility of handwriting. Comparison between domestic and international literature revealed a predominant use of task-oriented intervention in domestic studies, while international studies showcased a diverse range of intervention methods. Conclusion : Interventions were categorized into computer-based, task-oriented, sensory-motor, and integrated interventions. Task-oriented interventions were the most common in both domestic and international studies, while integrated interventions were the most effective. Based on these findings, it is necessary to increase awareness of the need for handwriting intervention research among occupational therapists in Korea. Additionally, there is a need for well-supported handwriting intervention research with larger sample sizes in both domestic and international occupational therapy. Finally, future research should actively investigate the application of tailored integrated interventions for school-aged children with handwriting difficulties.

• Journal of Periodontal and Implant Science
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• v.54 no.1
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• pp.3-12
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• 2024

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

• Convergence Security Journal
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• v.24 no.2
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• pp.141-150
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• 2024

With the increasing frequency of incidents related to personal information leaks, there is a growing concern about personal information protection. Moreover, with the emergence of blockchain technology, there is a heightened interest in self-sovereign identity models applied through blockchain, with ongoing research on Decentralized Identifiers (DID) to achieve this. However, despite universities storing and utilizing significant information such as personal data, their computer systems are operated and managed based on centralized systems, leading to annual occurrences of personal data breaches. Therefore, this paper proposes and implements a DID-based computing system applicable within universities. Additionally, it establishes and executes prominent services within the university context. The proposed system ensures users' self-sovereign identities through verifiable credentials, enabling the establishment of a secure integrated information system within the university, departing from traditional centralized systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.9-16
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• 2024

It is difficult to know the efficacy of pills if the pill bag or wrapper is lost after purchasing the pill. Many people do not classify the use of commercial pills when storing them after purchasing and taking them, so the inaccessibility of information on the side effects of pills leads to misuse of pills. Even with existing applications that search and provide information about pills, users have to select the details of the pills themselves. In this paper, we develope a pill recognition application by building a model that learns the formulation and colour of 22,000 photos of pills provided by a Pharmaceutical Information Institution to solve the above situation. We also develope a pill medication management function.