• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.27-32
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• 2020

Along with the aging of the population, the number of dementia patients is increasing, and the social and economic burden is also increasing. Currently, the effective way to manage dementia patients is to identify patients with dementia early. However, in rural and island areas where medical staff are scarce, there is a problem that it is difficult to visit a hospital and get an early examination. Therefore, we propose a remote early detection system for dementia to solve the problems. The remote dementia early diagnosis system is a system that allows a patient to receive examination and treatment from a remote dementia expert using remote medical technology based on real-time image communication. The remote early diagnosis system for dementia consists of a local client system used by medical staff at health centers in the island, an image server that transmits, stores and manages images, and an expert client used by remote dementia experts. The local client subsystem satisfies the current medical law's remote collaboration by allowing the patient to use it with the health center's medical staff. In addition, expert clients are used by dementia experts, and can store/manage patient information, analyze patient history information, and predict the degree of dementia progression in the future.

• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.15-21
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• 2020

Although IoT systems are used in a variety of heterogeneous environments as cloud environments develop, all IoT devices are not provided with reliable protocols and services. This paper proposes an IoT data management technique that can extend the IoT cloud environment to an n-layer multi-level structure so that information collected from different heterogeneous IoT devices can be efficiently sorted and processed. The proposed technique aims to classify and process IoT information by transmitting routing information and weight information through wireless data link data collected from heterogeneous IoT devices. The proposed technique not only delivers information classified from IoT devices to the corresponding routing path but also improves the efficiency of IoT data processing by assigning priority according to weight information. The IoT devices used in the proposed technique use each other's reliable protocols, and queries for other IoT devices locally through a local cloud composed of hierarchical structures have features that ensure scalability because they maintain a certain cost.y channels of IoT information in order to make the most of the multiple antenna technology.

• 동북아역사논총
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• no.60
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• pp.196-227
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• 2018

The issues of this study are as follows. First, I will examine the overall situation and transition trends of Tibetan research in Taiwan since the modern period, and examine the development and trends of Tibetan history research in Taiwan. Secondly, in order to satisfy the above, we will analyze trends of Taiwan's major Tibetan research institutes and scholars, and trace their trends and their trajectories. Third, the trend of Tibetan research in Taiwan may be a useful indicator for us to analyze research methods and trends of Taiwanese scholars. If there is a flow of features and transitions, the text will explore the reason. Fourth, one of the implications of this study is that it can trigger an understanding of locality in the structure of the central region, the Han Chinese minority, and the possession and distribution of academic reasoning. In other words, it should be noted that even though the same Tibetan research is conducted, China is in the position of the vested right to distribute 226 | 동북아역사논총 60호the central or ownership, while Taiwan has historical and territorial characteristics that deviate from such a gaze and attitude. Taiwan may be sensitive to the vertical concept understood as a change in the relationship between the state and the center, or whether it is applicable to Tibetan research. If there is such an academic climate, I would like to consider suggestions for us. This may provide a direction to view the academic issues of a few scholars, or even the domestic academic world as an independent object of more specific academic research.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.1
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• pp.443-467
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• 2021

The purpose of this study was to conduct interviews with librarians who have experience in establishing local authority data by participating in the national authority sharing system of the National Library of Korea and to understand librarians' recognition and support for the joint utilization of national authority data. For this purpose, a total of 10 librarians who participated in the national authority sharing system project were interviewed by telephone using semi-structured questionnaires. Through this, it was possible to investigate the benefits, difficulties, utilization plans, revision plans of headings, and opinions on necessary support. The results of this study showed that the participants recognized that the joint utilization of national authority data provides the basis for the authority work of the local library and brings about the efficiency of the authority work, but they recognized the difficulty of modifying, selecting, creating new data, lacking knowledge, and lacking support system. The necessary support for the joint utilization of national authority data was provided with education and manuals related to authority, provision of rules related to authority that fully consider the position of the institution, budget and manpower support for system development and maintenance, establishment of communication channel and council, system and data advancement, and incentive to participating libraries. Based on the results of the study, the method and direction for the future operation of the joint utilization of national authority data were presented.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.323-332
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• 2022

As various services using AI technology are being developed, much attention is being paid to AI service production. Recently, AI technology is acknowledged as one of ICT services, a lot of research is being conducted for general-purpose AI service production. In this paper, I describe the research results in terms of systems for AI service production, focusing on the distribution and production of machine learning models, which are the final steps of general machine learning development procedures. Three different Ubuntu systems were built, and experiments were conducted on the system, using data from 2017 validation COCO dataset in combination of different AI models (RFCN, SSD-Mobilenet) and different communication methods (gRPC, REST) to request and perform AI services through Tensorflow serving. Through various experiments, it was found that the type of AI model has a greater influence on AI service inference time than AI machine communication method, and in the case of object detection AI service, the number and complexity of objects in the image are more affected than the file size of the image to be detected. In addition, it was confirmed that if the AI service is performed remotely rather than locally, even if it is a machine with good performance, it takes more time to infer the AI service than if it is performed locally. Through the results of this study, it is expected that system design suitable for service goals, AI model development, and efficient AI service production will be possible.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.403-410
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• 2022

Non-Fungible Tokens (NFTs) created on the basis of blockchain have their own unique value, so they cannot be forged or exchanged with other tokens or coins. Using these characteristics, NFTs can be issued to digital assets such as images, videos, artworks, game characters, and items to claim ownership of digital assets among many users and objects in cyberspace, as well as proving the original. However, interest in NFTs exploded from the beginning of 2020, causing a lot of load on the blockchain network, and as a result, users are experiencing problems such as delays in computational processing or very large fees in the mining process. Additionally, all actions of users are stored in the blockchain, and digital assets are stored in a blockchain-based distributed file storage system, which may unnecessarily expose the personal information of users who do not want to identify themselves on the Internet. In this paper, we propose an NFT platform using cloud computing, access gate, conversion table, and cloud ID to improve usability and privacy problems that occur in existing system. For performance comparison between local and cloud systems, we measured the gas used for smart contract deployment and NFT-issued transaction. As a result, even though the cloud system used the same experimental environment and parameters, it saved about 3.75% of gas for smart contract deployment and about 4.6% for NFT-generated transaction, confirming that the cloud system can handle computations more efficiently than the local system.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.139-152
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• 2023

Recently, there has been an increasing interest in the migration of young people to local areas. These young people are discovering and reinterpreting local resources to open local businesses while generating new value in local activities and businesses. This study was initiated with the recognition that fundamental solutions are needed for these young people to settle and sustain residence in the local area. Edward Relph stated that 'place' is a fundamental attribute that influences human existence in the world and is the source of stability and identity formation for individuals. This is deeply related to the psychology of young people who have migrated to local areas. These young people accept the unfamiliar 'space' as a 'place' to form stability and personal identity. Therefore, this study utilized PhotoVoice methodology to examine the process and key factors of place identity formation among migrant youths. As a result, the study identified factors that enable young people to settle in local areas and sustain residence while recognizing elements the local government should focus on to support and address the influx of young people. The results of this study can serve as a foundation for addressing the declining population in local areas through the formation of a relationship population and spur the inflow of young people to local cities in the future.

• Journal of Practical Agriculture & Fisheries Research
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• v.18 no.1
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• pp.101-112
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• 2016

Monitoring and control of the greenhouse environment play a decisive role in greenhouse crop production processes. The network system for greenhouse control was developed by using recent technologies of networking and wireless communications. In this paper, a remote monitoring and control system for greenhouse using a smartphone and a computer with internet has been developed. The system provides real-time remote greenhouse integrated management service which collects greenhouse environment information and controls greenhouse facilities based on sensors and equipments network. Graphical user interface for an integrated management system was designed with bases on the HMI and the experimental results showed that a sensor data and device status were collected by integrated management in real-time. Because the sensor data and device status can be displayed on a web page, transmitted using the server program to remote computer and mobile smartphone at the same time. The monitored-data can be downloaded, analyzed and saved from server program in real-time via mobile phone or internet at a remote place. Performance test results of the greenhouse control system has confirmed that all work successfully in accordance with the operating conditions. And data collections and display conditions, event actions, crops and equipments monitoring showed reliable results.

• The Journal of Bigdata
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• v.9 no.1
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• pp.101-113
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• 2024

Federated learning has garnered attention as an efficient method for training machine learning models in a distributed environment while maintaining data privacy and security. This study proposes a novel FedRFBagging algorithm to optimize the performance of random forest models in such federated learning environments. By dynamically adjusting the trees of local random forest models based on client-specific data characteristics, the proposed approach reduces communication costs and achieves high prediction accuracy even in environments with numerous clients. This method adapts to various data conditions, significantly enhancing model stability and training speed. While random forest models consist of multiple decision trees, transmitting all trees to the server in a federated learning environment results in exponentially increasing communication overhead, making their use impractical. Additionally, differences in data distribution among clients can lead to quality imbalances in the trees. To address this, the FedRFBagging algorithm selects only the highest-performing trees from each client for transmission to the server, which then reselects trees based on impurity values to construct the optimal global model. This reduces communication overhead and maintains high prediction performance across diverse data distributions. Although the global model reflects data from various clients, the data characteristics of each client may differ. To compensate for this, clients further train additional trees on the global model to perform local optimizations tailored to their data. This improves the overall model's prediction accuracy and adapts to changing data distributions. Our study demonstrates that the FedRFBagging algorithm effectively addresses the communication cost and performance issues associated with random forest models in federated learning environments, suggesting its applicability in such settings.