• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.121-126
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• 2019

Despite the trend of decreasing accidents, falling accidents in the construction industry have not decreased and are even rising. Most major accidents are falling accidents caused by hanging scaffolding and are mostly serious. We conducted a review of the literature, institutions, and regulations, which showed that domestic standards are not unified because they are drawn up by referring to overseas standards for hanging scaffolding. New regulations or standards should be established, which should reflect the safety plans for equipment, work advance plans, education, and management. If facility improvement plan for Hanging Scaffolding and implementation of revision of the current work use, regulations and guides are implemented, it will be recognized in advance of the causes of the accident statistics and the classification system and will be the basis for the implementation of joint efforts by workers, managers, supervisors and owners to reduce accidents.

• Journal of Environmental Health Sciences
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• v.45 no.4
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• pp.358-369
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• 2019

Objective: This study aimed to introduce cases of exposure to humidifier disinfectant (HD) in hospitals and to present their exposure characteristics. Methods: We used data from 4,393 subjects who participated in the fourth assessment survey of environmental exposure to HD conducted by the Korea Environmental Industry & Technology Institute. In this study, we selected 301 subjects who reported their place of use of HD as a hospital. Then, we classified cases as 'Hospital-provided'. 'Probably hospital-provided', 'Individual purchased', and 'Unknown' according to the supply sources of HD. Also, we introduced detailed exposure characteristics for the selected cases. Results: Of the 4,393 subjects, 301 (6.9%) reported the use of HD in 392 hospitals (including duplicate answers for the use in ${\geq}2$ hospitals). The 301 hospital-user subjects included 139 survivors and 162 non-survivors. When we classified the 392 cases by supply sources, 'Hospital-provided' was 12.2% (48 cases), 'Probably hospital-provided' was 25.5% (100 cases), 'Individual purchased' was 59.7% (234 cases), and 'Unknown' was 2.6% (10 cases). Among the 'Hospital-provided' cases, we selected six cases and provided a detailed description of the HD use in this study. Additionally, we reported details for six cases that had purchased HD upon a doctor or nurse's recommendation and for three cases that had purchased it at hospital stores. Conclusion: This study presents various cases of HD exposure in hospitals. Because there may be a considerable burden of HD exposure in public spaces, including hospitals, further studies are necessary to assess HD exposure in hospitals and public places.

• Fashion & Textile Research Journal
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• v.21 no.5
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• pp.627-639
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• 2019

This study analyzes differences between the results of 3D direct measurements and automated measurements for Korean elderly females according to age groups, side somatotype, and BMI groups. This study compares the measurement differences of the direct and the 3D automated measurements for women between the ages of 70 to 85, according to age group, BMI group, and side somatotype. A comparison of the results of the direct measurement and the 3D automated measurements for elderly women show that a meaningful discrepancy exists for 29 items out of 33 items. Furthermore, the results of comparing the average error tolerance recommended by ISO20685 shows that 30 items out of 33 items exceeded ISO recommendations. The results of the automated measurement program shows a higher degree of accuracy for straight postures; however, this unsuitable for postures of elderly women with a changed somatotype. The analysis results of the measurement difference indicate the suitability of the automatic measurement programs is found to be high for stood postures, while problems seem to exist on several items along with an automated program is not appropriately used due to posture and part of body changes for elderly women. Therefore, it is recommended to develop an algorithm, that reflects the body changes of elderly women first and then upgrade the automated program equipped with a measurement size method. It is hoped that the study results can be utilized as base data for improving the automated measurement program.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.959-968
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• 2019

In the agricultural industry, wireless sensor network technology has being applied by utilizing various sensors and embedded systems. In particular, a lot of researches are being conducted to diagnose diseases of crops early by using sensor network. There are some difficulties on traditional research how to diagnose crop diseases is not practical for agriculture. This paper proposes the algorithm which enables to investigate and analyze the crop leaf image taken by image camera and detect the infected area within the image. We applied the enhanced k-means clustering method to the images captured at horticulture facility and categorized the areas in the image. Then we used the edge detection and edge tracking scheme to decide whether the extracted areas are located in inside of leaf or not. The performance was evaluated using the images capturing tomato leaves. The results of performance evaluation shows that the proposed algorithm outperforms the traditional algorithms in terms of classification capability.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.11
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• pp.95-104
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• 2018

Healthcare technology has been growing and fostering cooperation between industry, university and hospitals as growth engines in korea. So, the medibio research institutes in hospital have been constructed to promote research and industrialization centering on healthcare technology. The purpose of this study is to investigate the cases of research institutes in hospitals, and search the characteristics of building organization of medibio research laboratory facilities. Case study is investigated by floor plan, homepage and site visits about five research institutes selected in research-driven hospitals. The facility title and size of research laboratory is originated from site area and research building location. The building function include not only the research lab and business office reflecting on the development platform, and but assembly and meeting room in the ground level. Laboratory floor plans have three types, rectangular, rectangular+linear and linear type, one is traditional and efficient, the others are people and friendly. And building core types are correlated with lab space unit modules, single and double side core are shown in rectangular type. All the laboratories are open lab, composed with laboratory bench and research note writing desk facing the lab service and enclosed lab-support area. And they have communication space looking as warm and cozy common area for the innovation, convergence and collaboration. As the high risk of contamination and high standard for safety and security, equipment and facilities are well managed with biological environment including BSC, fume hood, PCR classification, eye washing and emergency shower.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1595-1603
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• 2020

Online defamation incidents such as Internet news comments on portal sites, SNS, and community sites are increasing in recent years. Bias and hate expressions threaten online service users in various forms, such as invasion of privacy and personal attacks, and defamation issues. In the past few years, academia and industry have been approaching in various ways to solve this problem The purpose of this study is to build a dataset and experiment with deep learning classification modeling for detecting various bias expressions as well as hate expressions. The dataset was annotated 7 labels that 10 personnel cross-checked. In this study, each of the 7 classes in a dataset of about 137,111 Korean internet news comments is binary classified and analyzed through deep learning techniques. The Proposed technique used in this study is multi-channel CNN model with attention. As a result of the experiment, the weighted average f1 score was 70.32% of performance.

• Tunnel and Underground Space
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• v.31 no.1
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• pp.25-40
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• 2021

As the era of Industry 4.0 arrives, the researches using artificial intelligence in the field of rock engineering as well have increased. For a better understanding and availability of AI, this paper analyzed the types of algorithms and how to apply them to the research papers where AI is applied among domestic and international studies related to tunnels, blasting and mines that are major objects in which rock engineering techniques are applied. The analysis results show that the main specific fields in which AI is applied are rock mass classification and prediction of TBM advance rate as well as geological condition ahead of TBM in a tunnel field, prediction of fragmentation and flyrock in a blasting field, and the evaluation of subsidence risk in abandoned mines. Of various AI algorithms, an artificial neural network is overwhelmingly applied among investigated fields. To enhance the credibility and accuracy of a study result, an accurate and thorough understanding on AI algorithms that a researcher wants to use is essential, and it is expected that to solve various problems in the rock engineering fields which have difficulty in approaching or analyzing at present, research ideas using not only machine learning but also deep learning such as CNN or RNN will increase.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.687-699
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• 2020

The government-led top-down development approach for weapons system faces the problem of technological obsolescence now that technology has rapidly grown. As a result, the government has gradually expanded the corporate-led bottom-up project implementation method to the defense industry. The key success factor of the bottom-up project implementation is the ability of defense companies to plan their technologies. This paper presented a method of analyzing patent data through data mining technique so that domestic defense companies can utilize it for technology planning activities. The main content is to propose corporate selection techniques corresponding to the defense communication-electronics sectors and conduct principal component analysis and cluster analysis for the International Patent Classification. Through this, the technology was classified into four groups based on the patents of nine companies and the representative enterprises of each group were derived.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.20-29
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• 2021

In the Industry 4.0 era, artificial intelligence has attracted considerable interest for learning mass data to improve the accuracy of forecasting and classification. On the other hand, the current method of detecting anomalies relies on traditional statistical methods for a limited amount of data, making it difficult to detect accurate anomalies. Therefore, this paper proposes an artificial intelligence-based anomaly detection methodology to improve the prediction accuracy and identify new data patterns. In particular, data were collected and analyzed from the point of view that sensor data collected at vehicle idle could be used to detect abnormalities. To this end, a sensor was designed to determine the appropriate time length of the data entered into the forecast model, compare the results of idling data with the overall driving data utilization, and make optimal predictions through a combination of various sensor data. In addition, the predictive accuracy of artificial intelligence techniques was presented by comparing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) as the predictive methodologies. According to the analysis, using idle data, using 1.5 times of the data for the idling periods, and using CNN over LSTM showed better prediction results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1046-1052
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• 2021

Artificial intelligence is being applied in various industrial fields to the development of the fourth industry and the construction of high-performance computing environments. In the medical field, deep learning learning such as cancer, COVID-19, and bone age measurement was performed using medical images such as X-Ray, MRI, and PET and clinical data. In addition, ICT medical fusion technology is being researched by applying smart medical devices, IoT devices and deep learning algorithms. Among these techniques, medical image-based deep learning learning requires accurate finding of medical image biomarkers, minimal loss rate and high accuracy. Therefore, in this paper, we would like to compare and analyze the performance of the Cross-Entropy function used in the image classification algorithm of the loss function that derives the loss rate in the chest X-Ray image-based deep learning learning process.