• Analytical Science and Technology
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• v.34 no.2
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• pp.68-77
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• 2021

Cannabis is one of the most abused drugs in Korea. The main psychoactive component in cannabis, Δ9-tetrahydrocannabinol, is metabolized to 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THCCOOH) and THCCOOH-glucuronide (THCCOOH-glu) in the human liver, whereby the amount of THCCOOH-glu found in urine is twice as high as that of THCCOOH. The analytical process adapted by the majority of urine drug-testing programs involves a two-step method consisting of an initial immunoassay-based screening test followed by a confirmatory test if the screening test result is positive. In this study, a qualitative gas chromatography-mass spectrometry (GC-MS) method was developed and validated for the detection of THCCOOH in human urine, where THCCOOH-glu was converted into THCCOOH by alkaline hydrolysis. For purification of the urine extract prior to instrumental analysis, high-speed centrifugation was used to minimize interference. In addition, an injection-port derivatization method using ethyl acetate and N,O-bis(trimethylsilyl)-trifluoroacetamide containing 1 % trimethylchlorosilane was employed to reduce the time required for derivatization, and an aliquot of the final solution was injected into the GC-MS. The method was validated by measuring the selectivity, limit of detection (LOD), and repeatability. The sensitivity, specificity, precision, accuracy, Kappa, F-measure, false positive, and false negative rate were determined by comparing the GC-MS results with those obtained using the immunoassay. The LOD was determined to be 0.32 ng/mL, while the repeatability was within 9.1 % for THCCOOH. Furthermore, a comparison study was carried out, whereby the screening immunoassay exhibited a sensitivity of 86.4 % and a specificity of 100 % compared to GC-MS. The applicability of the developed method was examined by analyzing spiked urine and forensic urine samples obtained from suspected cannabis abusers (n = 221).

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.551-567
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• 2021

Purpose: The purpose of this paper is to present implications by analyzing research trends on smart factories by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on smart factories. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "SMART FACTORY" and "Smart Factory" as search terms, and the titles and Korean abstracts were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, 739 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; Smart factory research slowed down from 2005 to 2014, but until 2019, research increased rapidly. According to the analysis by fields, smart factories were studied in the order of engineering, social science, and complex science. There were many 'engineering' fields in the early stages of smart factories, and research was expanded to 'social science'. In particular, since 2015, it has been studied in various disciplines such as 'complex studies'. Overall, in keyword analysis, the keywords such as 'technology', 'data', and 'analysis' are most likely to appear, and it was analyzed that there were some differences by fields and years. Conclusion: Government support and expert support for smart factories should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to smart factories. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.618-620
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• 2019

Smart Farm, which combines agriculture and ICT convergence technology, is at a lower stage than other industries in Korea, but it is also one of the most active research and development fields. Smart Farm aims to improve the efficiency of each step by collecting, processing and analyzing various information of agriculture sector through convergence between agriculture and ICT technology. In this study, we studied the image processing method that can distinguish strawberry which can be harvested at harvest time by color for smart farm composition of strawberry which is a horticultural crop. Strawberry harvesting requires a lot of labor in the process of growing strawberries. In this study, we aim to collect information necessary for labor saving in strawberry harvester. As a precedent study, we plan to implement a form in which the color temperature changes according to the light direction and brightness value through OpenCV color detection using Python. In the future, it is planned to study strawberry color value suitable for harvest by applying compensation value to color temperature change.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.859-864
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• 2022

Drowsy driving, one of the biggest causes of traffic accidents every year, is accompanied by various factors. As a general method to check whether or not there is drowsiness, a method of identifying a driver's expression and driving pattern, and a method of analyzing bio-signals are being studied. This paper proposes a driver fatigue detection system using deep learning technology and bio-signal measurement technology. As the first step in the proposed method, deep learning is used to detect the driver's eye shape, yawning presence, and body movement to detect drowsiness. In the second stage, it was designed to increase the accuracy of the system by identifying the driver's fatigue state using the pulse wave signal and body temperature. As a result of the experiment, it was possible to reliably determine the driver's drowsiness and fatigue in real-time images.

• Korean Journal of Construction Engineering and Management
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• v.23 no.5
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• pp.98-107
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• 2022

The purpose of this study is to suggest a model for evaluation of a risk based value index for modular design alternatives in plant construction projects. Accordingly, 1) Setting the basic project cost and the scope to apply the module, 2) Evaluating the importance, easiness, and effectiveness index for Engineering, Procurement, Fabrication, transportation, and construction work, 3) Estimating the total project cost by analyzing the risk reserve Step, 4) Comparing the effectiveness index and total project cost for each modular design alternative, it was composed of the steps of deriving RVI. To verify such a model, Plan-A, which applied a module to one process, and Plan-B, which applied a module to three processes, were composed to evaluate RVI.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.751-776
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• 2022

Purpose: The purpose of this paper is to present implications by analyzing research trends on CSR, CSV and ESG by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on CSR, CSV and ESG. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "CSR", "CSV" and "ESG" as search terms, and the Korean abstracts and keyword were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, CSR 2,847 papers, CSV 395 papers, ESG 555 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; CSR, CSV, and ESG studies showed that research slowed down somewhat before 2010, but research increased rapidly until recently in 2019. Research have been found to be heavily researched in the fields of social science, art and physical education, and engineering. As a result of the study, there were many keyword of 'corporate', 'social', and 'responsibility', which were similar in the word cloud analysis. Looking at the frequent keyword and word cloud analysis by field and year, overall keyword were derived similar to all keyword by year. However, some differences appeared in each field. Conclusion: Government support and expert support for CSR, CSV and ESG should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to them. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.

• The korean journal of orthodontics
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• v.52 no.2
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• pp.112-122
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• 2022

Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses. Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

• Publications of The Korean Astronomical Society
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• v.39 no.1
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• pp.13-26
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• 2024

The armillary sphere, an astronomical observation device embodying the Orbital Heaven Theory of the Later Han Dynasty in China, holds both historical and scientific significance. It has been produced in various forms by many individuals since its inception in the era of King Sejong in the Joseon Dynasty. A prominent figure in this field was Nam Byeong-cheol (南秉哲, 1817-1863), known for his work 'Uigijipseol' (儀器輯說), published in 1859, which detailed the history, production methods, and usage of the armillary sphere. This text particularly highlights 21 applications of the armillary sphere, divided into 33 measurements, covering aspects like installation, time, and positional measurements, supplemented with explanations of spherical trigonometry. Despite numerous records of the armillary sphere's design during the Joseon Dynasty, detailed usage information remains scarce. In this study, the 33 measurements described in 'Uigijipseol' (儀器輯說) were systematically classified into six for installation, nineteen for position measurement, seven for time measurement, and one for other purposes. Additionally, the measurement methods were analyzed and organized by dividing them into the ecliptic ring, moving equatorial ring, and fixed equatorial ring of the armillary sphere. In other words, from a modern astronomical perspective, the results of schematization for each step were presented by analyzing it from the viewpoint of longitude, right ascension, and solar time. Through the analysis of Nam's armillary sphere, this study not only aims to validate the restoration model of the armillary sphere but also suggests the potential for its use in basic astronomical education based on the understanding of the 19th-century Joseon armillary sphere.

• Korean Journal of Applied Biomechanics
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• v.31 no.1
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• pp.72-78
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• 2021

Objective: The purpose of this study was to investigate relations and effectiveness about mountain climbling exercise with different level of support surfaces by analyzing heart rate and EMG data. A total of 10 male college students with no musculoskeltal disorder were recruited for this study. Method: The biomechanical analysis was performed using heart rate monitor (Polar V800, Polar Electro Oy, Finland), step-box, exercise mat, and EMG device (QEMG8, Laxtha Inc. Korea, sampling frequency = 1,024 Hz, gain = 1,000, input impedance > 1012 Ω, CMRR > 100 dB). In this research, step-box were used to create different surface levels on the upper body (flat surface, 10% of subject's height, 20% of subject's height, and 30% of subject's hight). Based on these different conditions, data was collected by performing mountain climbing exercise during 30 seconds. Subjects were given 5 minutes of break to prevent muscular fatigue after each exercise. For each dependent variable, a one-way analysis of variance with repeated measures was conducted to find significant differences and Bonferroni post-hoc test was performed. Results: The results of this study showed that exercise intensity was reduced statistically as increased surface level on the upper body. Muscle activity of the upper rectus abdominis and biceps femoris for 30% of surface level was significantly higher than the corresponding values for flat surface. However, the opposite was found in the rectus femoris. In general, muscle activity of the lower rectus abdominis, erector spinae, external oblique abdominis, and gluteus maximus increased when surface level increased, but the differences were not significant. Conclusion: As a result, the increase in surface level of the body would change muscle activity of the upper body, indicating that different surface level of the upper body may cause significant effect on particular muscles to be more active during mountain climbing exercise. Based on results of this study, it is suggested to set up an appropriate surface level to target particular muscle to expect an effective training. It is also important to set adequate surface levels to create an effective training condition for preventing exercise injuries.