• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.328-342
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• 2022

The Internet of Things (IoT) is a technology that offers lucrative services in various industries to facilitate human communities. Important information on people and their surroundings has been gathered to ensure the availability of these services. This data is vulnerable to cybersecurity since it is sent over the internet and kept in third-party databases. Implementation of data encryption is an integral approach for IoT device designers to protect IoT data. For a variety of reasons, IoT device designers have been unable to discover appropriate encryption to use. The static support provided by research and concerned organizations to assist designers in picking appropriate encryption costs a significant amount of time and effort. IoTES is a web app that uses machine language to address a lack of support from researchers and organizations, as ML has been shown to improve data-driven human decision-making. IoTES still has some weaknesses, which are highlighted in this research. To improve the support, these shortcomings must be addressed. This study proposes the "IoTES with Security" model by adding support for the security level provided by the encryption algorithm to the traditional IoTES model. We evaluated our technique for encryption algorithms with available security levels and compared the accuracy of our model with traditional IoTES. Our model improves IoTES by helping users make security-oriented decisions while choosing the appropriate algorithm for their IoT data.

• Journal of Korean Society of Rural Planning
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• v.29 no.4
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• pp.137-152
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• 2023

Data is indispensable for digital transformation of agriculture with the development of innovative information and communication technology (ICT). In order to devise and prioritize strategies for enhancing data competitiveness in the agricultural sector, we employed an Analytic Hierarchy Process (AHP) analysis. Drawing from existing research on data competitiveness indicators, we developed a three-tier decision-making structure reflecting unique characteristics of the agricultural sector such as farmers'awareness of the data industry or awareness of agriculture among data workers. AHP survey was administered to experts from both agricultural and non-agricultural sectors with a high understanding of data. The overall composite importance, derived from the respondents, was rated in the following order: 'Employment Support', 'Data Standardization', 'R&D Support', 'Start-up Ecosystem Support', 'Relaxation of Regulations', 'Legislation', and 'Data Analytics and Utilization Technology'. In the case of experts in the agricultural sector, 'Employment Support' was ranked as the top priorities, and 'Legislation', 'Undergrad and Grad Education', and 'In-house Training' were also regarded as highly important. On the other hand, experts in the non-agricultural sector perceived 'Data Standardization' and 'Relaxation of Regulations' as the top two priorities, and 'Data Center' and 'Open Public Data' were also highly rated.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.30-39
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• 2024

This study introduces a novel approach for identifying potential failure risks in missile manufacturing by leveraging Quality Inspection Management (QIM) data to address the challenges presented by a dataset comprising 666 variables and data imbalances. The utilization of the SMOTE for data augmentation and Lasso Regression for dimensionality reduction, followed by the application of a Random Forest model, results in a 99.40% accuracy rate in classifying missiles with a high likelihood of failure. Such measures enable the preemptive identification of missiles at a heightened risk of failure, thereby mitigating the risk of field failures and enhancing missile life. The integration of Lasso Regression and Random Forest is employed to pinpoint critical variables and test items that significantly impact failure, with a particular emphasis on variables related to performance and connection resistance. Moreover, the research highlights the potential for broadening the scope of data-driven decision-making within quality control systems, including the refinement of maintenance strategies and the adjustment of control limits for essential test items.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.373-379
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• 2019

Smart-farm has been spreading across Korea to improve the labor efficiency and productivity of greenhouse crops. Although notable improvements have been made in the monitoring technologies and environmental-controlling systems in greenhouses, only a few simple decision-support systems are available for predicting the optimum environmental conditions for crop growth. In this study, a tomato growth model (GreenTom), which was developed by Seoul National University in 1997, was calibrated and validated to examine if the model can be used as a decision-supporting system. The original GreenTom model was not able to simulate artificial defoliation, which resulted in overestimation of the leaf area index in the late growth. Thus, an algorithm for simulating the artificial defoliation was developed and added to the original model. The node development, leaf growth, stem growth, fruit growth, and leaf area index were generally well simulated by the modified model indicating that the model could be used effectively in the decision-making of smart greenhouse.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.541-547
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• 2010

Marine accidents do not correspond with another accidents because of a serious loss of lives and property. The many marine accidents can be attributed to human error like as carelessness and decision faults, and hence there is a strong need for decision-support tools for marine navigation. Much of researchers have introduced the techniques about the tools, but they hardly consider environmental factors (water depth, the width of waterway, a fishing ground, a current, the number of surrounding marine accidents, marine obstacles, etc), which are very important to the decision making of officers. In a previous research, we proposed the conceptual model of environmental risk assessment of ship navigation using fuzzy. This paper describes the detailed design of the environmental factors based on the opinion of navigation experts, and shows the validity of the conceptual model through a prototype system.

• Smart Media Journal
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• v.13 no.1
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• pp.9-17
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• 2024

This study aims to develop an integrated agricultural distribution network management system to improve the quality, profit, and decision-making efficiency of agricultural products. We adopt two key techniques: crop maturity detection based on the YOLOX target detection algorithm and market price prediction based on the Prophet model. By training the target detection model, it was possible to accurately identify crops of various maturity stages, thereby optimizing the shipment timing. At the same time, by collecting historical market price data and predicting prices using the Prophet model, we provided reliable price trend information to shipping decision makers. According to the results of the study, it was found that the performance of the model considering the holiday factor was significantly superior to that of the model that did not, proving that the effect of the holiday on the price was strong. The system provides strong tools and decision support to farmers and agricultural distribution managers, helping them make smart decisions during various seasons and holidays. In addition, it is possible to optimize the distribution network of agricultural products and improve the quality and profit of agricultural products.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.639-645
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• 2018

This paper is intended to develop a decision model that can be applied to autonomous vehicles and autonomous mobile vehicles. The developed module has an independent configuration for application in various driving environments and is based on a platform for organically operating them. Each module is studied for decision making on lane changes and for securing safety through reinforcement learning using a deep learning technique. The autonomous mobile moving body operating to change the driving state has a characteristic where the next operation of the mobile body can be determined only if the definition of the speed determination model (according to its functions) and the lane change decision are correctly preceded. Also, if all the moving bodies traveling on a general road are equipped with an autonomous driving function, it is difficult to consider the factors that may occur between each mobile unit from unexpected environmental changes. Considering these factors, we applied the decision model to the platform and studied the lane change decision system for implementation of the platform. We studied the decision model using a modular learning method to reduce system complexity, to reduce the learning time, and to consider model replacement.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.2057-2067
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• 2013

Traffic demand forecasting for pavement management in the present can be estimated using the past trends or subjective judgement of experts instead of objective methods. Also future road plans and local development plans of a target region, for example new road constructions and detour plans cannot be considered for the estimate of future traffic demands. This study, which is the fundamental research for developing objective and accurate decision-making support system of maintenance management for the national highway, proposed the methodology to predict future traffic demands according to 4-step traffic forecasting method using EMME in order to examine significance of future traffic demands affecting pavement deterioration trends and compare existing traffic demand forecasting methods. For the case study, this study conducted the comparison of traffic demand forecasting methods targeting Daejeon Regional Construction and Management Administration. Therefore, this study figured out that the differences of traffic demands and the level of agent costs as well as user costs between existing traffic demand forecasting methods and proposed traffic demand forecasting method with considering future road plans and local development plan.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.2
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• pp.81-89
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• 2002

The Battery Energy Storage System (BESS) has lots of advantages such as load leveling, quick response emergency power (spinning reserve), frequency and voltage control, improvement of reliability, and deferred generation and transmission construction. However, it is very critical that economic feasibility requires justification from the customer side of meter to promoting the dissemination of BESS in nation widely. In this paper, we proposed the economic assessment model of customer owned BESS which is complemented and improved the existing model. The proposed model is applied to the typical customer types, i.e. light industrial, commercial, and residential, which are taken from the statistical analysis on the load profile survey of Korea Electric Power COmpany (KEPCO). The economic viability performed for each customer load type to justifying their economic feasibility of BESS installation from the economic measures such as payback period, Net Present Worth (NPW), Rate Of Return (ROR). The results show that the BESS has economic benefits to the specific customer type, i.e. residential customer. Therefore, the government and the energy agency should be committing the support program, such as tax incentive, financial support, to disseminate the BESS nation widely. The results of this paper are useful to the customer investment decision-making and the national energy policy & strategy in Korea.

• Quality Improvement in Health Care
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• v.14 no.1
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• pp.49-54
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• 2008

Background : This study was to reduce incidence of falls by analyzing actual problem and drawing out improvement plan applicable to the clinical practice through operation of the staff nurses-centered fall peer review group. Method : The fall peer review group was composed of 8 nurses having patient nursing experience for over 5 years, and each of fall cases was reviewed and the root cause was analyzed. As a result, it was found that the patients and their families did not fully understandthe content of the education, and the staff nurses did not completely inspect the risk factors of falls and perform immediate intervention when patient's condition changed. Based on the above-mentioned results, improvement activity was conducted for the purposes of consolidating patients education method and supplementing computerized system to support nurses' decision making as well as devices and facilities. Result : As a result of conducting improvement activity in the aspects of education for patients, support of nurse's decision-making, and devices and facilities through operation of the staff nurses-centered fall peer review group, falls decreased by 9.5% compared to before improvement activity. Conclusion : It is concluded that operation of the clinical nurses-centered fall peer review group played a role of promoter to draw out practical and applicable improvement plan to the clinical practice and apply directions of the field-centered, and increased nurses' interest in falls and ultimately, reduced incidence of falls. Therefore the Center will continue to operate the staff nurses-centered peer review group, and recommends participation of nurses who actually take the charge of nursing patients in further analysis of patients' safety accidents.