• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.392-399
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• 2023

This paper proposes a deep learning model to determine the region and depth of cabbage cores for robotic automation of the cabbage core removal process during the kimchi manufacturing process. In addition, rather than predicting the depth of the measured cabbage, a model was presented that simultaneously detects and classifies the area by converting it into a discrete class. For deep learning model learning and verification, RGB images of the harvested cabbage 522 were obtained. The core region and depth labeling and data augmentation techniques from the acquired images was processed. MAP, IoU, acuity, sensitivity, specificity, and F1-score were selected to evaluate the performance of the proposed YOLO-v4 deep learning model-based cabbage core area detection and classification model. As a result, the mAP and IoU values were 0.97 and 0.91, respectively, and the acuity and F1-score values were 96.2% and 95.5% for depth classification, respectively. Through the results of this study, it was confirmed that the depth information of cabbage can be classified, and that it can be used in the development of a robot-automation system for the cabbage core removal process in the future.

• Knowledge Management Research
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• v.25 no.2
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• pp.19-47
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• 2024

Organizational ability to analyze and utilize data plays an important role in knowledge management and decision-making. This study aims to investigate the potential application of large language models in corporate data analysis. Focusing on the field of human resources, the research examines the data analysis capabilities of these models. Using the widely studied IBM HR dataset, the study reproduces machine learning-based employee turnover prediction analyses from previous research through ChatGPT and compares its predictive performance. Unlike past research methods that required advanced programming skills, ChatGPT-based machine learning data analysis, conducted through the analyst's natural language requests, offers the advantages of being much easier and faster. Moreover, its prediction accuracy was found to be competitive compared to previous studies. This suggests that large language models could serve as effective and practical alternatives in the field of corporate data analysis, which has traditionally demanded advanced programming capabilities. Furthermore, this approach is expected to contribute to the popularization of data analysis and the spread of data-driven decision-making (DDDM). The prompts used during the data analysis process and the program code generated by ChatGPT are also included in the appendix for verification, providing a foundation for future data analysis research using large language models.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.1-16
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• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• The Journal of Korean Society for Radiation Therapy
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• v.35
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• pp.15-21
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• 2023

Purpose: The purpose of this study is to compare the performance of the anisotropic analytical algorithm (AAA) and portal dose image prediction (PDIP) for patient-specific quality assurance based on electronic portal imaging device, and to evaluate the clinical feasibility of portal dosimetry using AAA. Subjects and methods: We retrospectively selected a total of 32 patients, including 15 lung cancer patients and 17 liver cancer patients. Verification plans were generated using PDIP and AAA. We obtained gamma passing rates by comparing the calculated distribution with the measured distribution and obtained MLC positional difference values. Results: The mean gamma passing rate for lung cancer patients was 99.5% ± 1.1% for 3%/3 mm using PDIP and 90.6% ± 5.8% for 1%/1 mm. Using AAA, the mean gamma passing rate was 98.9% ± 1.7% for 3%/3 mm and 87.8% ± 5.2% for 1%/1 mm. The mean gamma passing rate for liver cancer patients was 99.9% ± 0.3% for 3%/3 mm using PDIP and 96.6% ± 4.6% for 1%/1 mm. Using AAA, the mean gamma passing rate was 99.6% ± 0.5% for 3%/3 mm and 89.5% ± 6.4% for 1%/1 mm. The MLC positional difference was small at 0.013 mm ± 0.002 mm and showed no correlation with the gamma passing rate. Conclusion: The AAA algorithm can be clinically used as a portal dosimetry calculation algorithm for patientspecific quality assurance based on electronic portal imaging device.

• Smart Media Journal
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• v.13 no.4
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• pp.94-103
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• 2024

The eyeball input system is a text input system designed based on 'eye tracking technology' and 'virtual keyboard character-input technology'. The current virtual keyboard structure used is a rectangular QWERTY array optimized for a multi-input method that simultaneously utilizes all 10 fingers on both hands. However, since 'eye-tracking technology' is a single-input method that relies solely on eye movement, requiring only one focal point for input, problems arise when used in conjunction with a rectangular virtual keyboard structure designed for multi-input method. To solve this problem, first of all, previous studies on the shape, type, and movement of muscles connected to the eyeball were investigated. Through the investigation, it was identified that the principle of eye movement occurs in a circle rather than in a straight line. This study, therefore, proposes a new key arrangement wherein the keys are arranged in a circular structure suitable for rotational motion rather than the key arrangement of the current virtual keyboard which is arranged in a rectangular structure and optimized for both-hand input. In addition, compared to the existing rectangular key arrangement, a performance verification experiment was conducted on the circular key arrangement, and through the experiment, it was confirmed that the circular arrangement would be a good replacement for the rectangular arrangement for the virtual keyboard.

• Information Systems Review
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• v.20 no.4
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• pp.79-96
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• 2018

This study started by focusing on the internalization of the technology appraisal model into the credit rating model to increase the discriminative power of the credit rating model not only for SMEs but also for all companies, reflecting the items related to the financial stability of the enterprises among the technology appraisal items. Therefore, it is aimed to verify whether the technology appraisal model can be applied to identify high-stability SMEs in advance. We classified companies into industries (manufacturing vs. non-manufacturing) and the age of company (initial vs. non-initial), and defined as a high-stability company that has achieved an average debt ratio less than 1/2 of the group for three years. The C5.0 was applied to verify the discriminant power of the model. As a result of the analysis, there is a difference in importance according to the type of industry and the age of company at the sub-item level, but in the mid-item level the R&D capability was a key variable for discriminating high-stability SMEs. In the early stage of establishment, the funding capacity (diversification of funding methods, capital structure and capital cost which taking into account profitability) is an important variable in financial stability. However, we concluded that technology development infrastructure, which enables continuous performance as the age of company increase, becomes an important variable affecting financial stability. The classification accuracy of the model according to the age of company and industry is 71~91%, and it is confirmed that it is possible to identify high-stability SMEs by using technology appraisal items.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of Radiation Protection and Research
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• v.39 no.1
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• pp.54-60
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• 2014

IAEA has employed various types of radiation detectors - HPGe, NaI, CZT - for accountancy of nuclear material. Among them, HPGe has been mainly used in verification activities required for high accuracy. Due to its essential cooling component(a liquid-nitrogen cooling or a mechanical cooling system), it is large and heavy and needs long cooling time before use. New hand-held portable HPGe has been developed to address such problems. This paper deals with results of performance evaluation test of the new hand-held portable HPGe prototype which was used during IAEA's inspection activities. Radioactive spectra obtained with the new portable HPGe showed different characteristics depending on types and enrichments of nuclear materials inspected. Also, Gamma-rays from daughter radioisotopes in the decay series of $^{235}U$ and $^{238}U$ and characteristic x-rays from uranium were able to be remarkably separated from other peaks in the spectra. A relative error of enrichment measured by the new portable HPGe was in the range of 9 to 27%. The enrichment measurement results didn't meet partially requirement of IAEA because of a small size of a radiation sensing material. This problem might be solved through a further study. This paper discusses how to determine enrichment of nuclear material as well as how to apply the new hand-held portable HPGe to safeguard inspection. There have been few papers to deal with IAEA inspection activity in Korea to verify accountancy of nuclear material in national nuclear facilities. This paper would contribute to analyzing results of safeguards inspection. Also, it is expected that things discussed about further improvement of a radiation detector would make contribution to development of a radiation detector in the related field.

• Korean Security Journal
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• no.41
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• pp.37-66
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• 2014

In recent years, with the development of computers and electronics, electronics and communication technology in a growing and each part is dependent on the cross-referencing makes all electronic equipment is obsolete due to direct or indirect damage EMP. Korea and the impending standoff North Korea has a considerable level of technologies related to the EMP, EMP weapons you already have or in a few years, the development of EMP weapons will complete. North Korea launched a long-range missile and conducted a nuclear test on several occasions immediately after, when I saw the high-altitude nuclear blackmail has been strengthening the outright offensive nuclear EMP attacks at any time and practical significance for the EMP will need offensive skills would improve. At this point you can predict the damage situation of Korea's security reality that satisfy the need, more than anything else to build a protective system of the EMP. The scale of the damage that unforeseen but significant military damage and socio-economic damage and fatalities when I looked into the situation which started out as a satellite communications systems and equipment to attack military and security systems and transportation, finance, national emergency system, such as the damage elsewhere. In General, there is no direct casualties reported, but EMP medical devices that rely on lethal damage to people who can show up. In addition, the State power system failure due to a power supply interruption would not have thought the damage would bring State highly dependent on domestic power generation of nuclear plants is a serious nuclear power plant accident in the event of a blackout phenomenon can lead to the plant's internal problems should see a forecast. First of all, a special expert Committee of the EMP, the demand for protective facilities and equipment and conduct an investigation, he takes fits into your budget is under strict criteria by configuring the contractors should be sifting through. He then created the Agency for verification of performance EMP protection after you have verified the performance of maintenance, maintenance, safety and security management, design and construction company organized and systematic process Guard facilities or secret communications equipment and perfect for the EMP, such as protective equipment maneuver system should take.