• 한국노년학
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• v.39 no.2
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• pp.213-229
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• 2019

The purpose of this study was to examine various factors influencing the needs of Seoul's newly implemented outreach community services for older adults, and to suggest the direction of the outreach community health services in Seoul. A multi-level regression was conducted using data collected by face-to-face interviews from 1,000 individuals aged 65 and 70 in 17 districts, where participated in the Seoul's outreach community services. The results demonstrated that socioeconomic status (higher income and living alone), health status (having multiple chronic conditions and depression, lower health literacy), limited experience of the outreach community services, and low government trust at the individual level were associated with higher unmet need for the community outreach services. In addition, shorter participation period of the outreach services and financial independency at the district level were associated with higher unmet need for the services. The findings from this study implies the need for improving the quality of services by focusing on vulnerable groups such as individuals with lower income and worse health status. In addition, the outreach community health services may need to target individuals aged 66 to increasing efficiency of the services through utilizing results of life-cycle health checkup by the National Health Insurance Corporation.

• Smart Media Journal
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• v.12 no.10
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• pp.55-62
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• 2023

Innovation and change are occurring rapidly in the agriculture and livestock industry, and new technologies such as smart bams are being introduced, and data that can be used to control equipment is being collected by utilizing various sensors. However, there are various challenges in the operation of bams, and virtual sensor technology is needed to solve these challenges. In this paper, we define various data items and sensor data types used in livestock farms, study cases that utilize virtual sensors in other fields, and implement and design a virtual sensor system for the final smart livestock farm. MBE and EVRMSE were used to evaluate the finalized system and analyze performance indicators. As a result of collecting and managing data using virtual sensors, there was no obvious difference in data values from physical sensors, showing satisfactory results. By utilizing the virtual sensor system in smart livestock farms, innovation and efficiency improvement can be expected in various areas such as livestock operation and livestock health status monitoring. This paper proposes an innovative method of data collection and management by utilizing virtual sensor technology in the field of smart livestock, and has obtained important results in verifying its performance. As a future research task, we would like to explore the connection of digital livestock using virtual sensors.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.123-132
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• 2023

Git's commit messages manage the history of source changes during project progress or operation. By utilizing this historical data, project risks and project status can be identified, thereby reducing costs and improving time efficiency. A lot of research related to this is in progress, and among these research areas, there is research that classifies commit messages as a type of software maintenance. Among published studies, the maximum classification accuracy is reported to be 95%. In this paper, we began research with the purpose of utilizing solutions using the commit classification model, and conducted research to remove the limitation that the model with the highest accuracy among existing studies can only be applied to programs written in the JAVA language. To this end, we designed and implemented an additional step to standardize source change data into natural language using GPT. This text explains the process of extracting commit messages and source change data from Git, standardizing the source change data with GPT, and the learning process using the DistilBERT model. As a result of verification, an accuracy of 91% was measured. The proposed model was implemented and verified to ensure accuracy and to be able to classify without being dependent on a specific program. In the future, we plan to study a classification model using Bard and a management tool model helpful to the project using the proposed classification model.

• Journal of Venture Innovation
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• v.5 no.3
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• pp.19-40
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• 2022

The purpose of this study is to analyze and evaluate the role and merits and demerits of insurance agencies, which are the main sales channels of the domestic insurance market, from a neutral standpoint, and to present a roadmap by finding ways and tasks for the sustainable development of the insurance agency industry. Recently, criticisms and regulations on independent distribution channels have been strengthened due to deterioration in growth potential and profitability, and increase in civil complaints of insurance companies. In the 2000s, as the center of insurance sales channels shifted to GA and bancassurance, the supervisory authorities regarded the irrationality of project costs and recruitment fees as the root causes and announced a regulatory improvement plan. In view of these circumstance, the operating status, problems of domestic insurance agencies and their contribution to the insurance industry were reviewed. In addition, we tried to find a mid-to-long term development plan by analyzing the case of insurance agency operation in Japan. This study identified the operating status and contribution of insurance agencies in South Korea, and focused on the major status of the Japanese insurance agency industry, ways to improve management efficiency, and the status and role of self-regulatory organizations for insurance agencies. Based on this, it was proposed to improve the professionalism of the sales organization, introduce a company specializing in insurance sales, and to strengthen the status of the association of the insurance agency as for the development plan of the insurance agency industry in South Korea.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.21-27
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• 2023

As a means of helping ships navigate safely, navigational aids in operation in the maritime envirionment require periodic management, and due to the nature of the environment, it is difficult to visually check the exact state. As a result, the smart navigation aid system, which improves route safety and operational efficiency, utillizes expertise including sensors, communications, and information technology, unlike general route markings. The communication environment of the smart navigation aid system, which aims to ensure the safety of the navigators operating the ship and the safety of the ship, uses a wireless communication network in accordance with the marine environment. The ship collects the information necessary for the maritime environment on the land and operates. In this process, there is a need to consider the wireless communication security guideline. Basically, based on IHO S-100 a standard for facilitating data exchange and SECOM, which provides an interface for safe communication. This paper research a security system for safe communication in a maritime environment. The security system for the basic interface based on the document was presented, and there were some vulnerabillties to data exchange due to the wireless communication characteristics of the maritime environment, and the user authetication part was added considering the vulnerability that unauthorized users can access the service.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.166-171
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• 2023

Efficient energy management is a very important factor in sensor networks with limited resources, and cluster techniques have been studied a lot in this respect. However, a problem may occur in which energy use of the cluster header is concentrated, and when the cluster header is not evenly distributed over the entire area but concentrated in a specific area, the transmission distance of the cluster members may be large or very uneven. The transmission distance can be directly related to the problem of energy consumption. Since the energy of a specific node is quickly exhausted, the lifetime of the sensor network is shortened, and the efficiency of the entire sensor network is reduced. Thus, balanced energy consumption of sensor nodes is a very important research task. In this study, factors for balanced energy consumption by cluster headers and sensor nodes are analyzed, and a balancing distribution clustering method in which cluster headers are balanced distributed throughout the sensor network is proposed. The proposed cluster method uses multi-hop routing to reduce energy consumption of sensor nodes due to long-distance transmission. Existing multi-hop cluster studies sets up a multi-hop cluster path through a two-step process of cluster setup and routing path setup, whereas the proposed method establishes a hierarchical cluster routing path in the process of selecting cluster headers to minimize the overhead of control messages.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.153-166
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• 2024

The digitalization of tasks for smart construction requires the smooth exchange of digital data among stakeholders to be effective, but there is a lack of digital data standardization and utilization methods. This paper proposes a digital construction information structure to transform information from road construction sites into digital formats. The study targets include significant tasks, such as work planning, scheduling, safety management, and quality control. The key to the construction information structure is separating construction information into objects and activities, defining unit works by combining these two types of information to ensure flexibility in representing and modifying construction information. The objects and activities have their respective hierarchical structures, which are defined flexibly to match the actual content. This structure achieves both efficiency and detail. The pilot structure was applied to highway construction projects and implemented digitally using general formats. This study enables the digitalization of road construction processes that closely resemble reality, accelerating the digital transformation of the civil engineering industry by developing a digital twin of the entire road construction lifecycle.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.1-12
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• 2024

In this paper, we fine-tuned KoAlpaca, a large-scale Korean language model, and implemented a blog text generation system utilizing it. Blogs on social media platforms are widely used as a marketing tool for businesses. We constructed training data of positive reviews through emotion analysis and refinement of collected sponsored blog texts and applied QLoRA for the lightweight training of KoAlpaca. QLoRA is a fine-tuning approach that significantly reduces the memory usage required for training, with experiments in an environment with a parameter size of 12.8B showing up to a 58.8% decrease in memory usage compared to LoRA. To evaluate the generative performance of the fine-tuned model, texts generated from 100 inputs not included in the training data produced on average more than twice the number of words compared to the pre-trained model, with texts of positive sentiment also appearing more than twice as often. In a survey conducted for qualitative evaluation of generative performance, responses indicated that the fine-tuned model's generated outputs were more relevant to the given topics on average 77.5% of the time. This demonstrates that the positive review generation language model for sponsored content in this paper can enhance the efficiency of time management for content creation and ensure consistent marketing effects. However, to reduce the generation of content that deviates from the category of positive reviews due to elements of the pre-trained model, we plan to proceed with fine-tuning using the augmentation of training data.