• Korean Journal of Fisheries and Aquatic Sciences
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• v.56 no.2
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• pp.162-173
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• 2023

Four roe protein isolates (RPIs) from olive flounder Paralichthys olivaceus roes (OFR) were recovered by isoelectric solubilization (pH 11 and 12) and precipitation (pH 4.5 and 5.5) and investigated for their food characteristics. RPIs contained 4.5-9.6% moisture, 64.1-69.5% protein, 16.1-19.8% lipid, and 1.0-3.9% ash. The protein yields of RPIs ranged from 50.1 to 56.8%, which was significantly different depending on the recovery conditions. A difference was observed in the SDS-PAGE protein band (25-100 kDa) between the alkaline solubilization at pH 11 (RPI-1 and 2) and pH 12 (RPI-3 and 4). The major amino acids of RPIs were Leu, Lys, Asp, Glu and Ala and major mineral components were sulfur, sodium, phosphorus, and magnesium, which were significantly different from OFR (P<0.05). Additionally, the lead and cadmium content was below the chemical hazard standard of the Korean food standards code. The Hunter color and whiteness of RPIs also showed significant differences according to the treatment conditions of the ISP process (P<0.05). This suggests that protein isolates recovered from olive flounder roes have high potential as nutritional supplements.

• Smart Media Journal
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• v.12 no.7
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• pp.43-51
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• 2023

As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.

• Analytical Science and Technology
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• v.19 no.4
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• pp.290-300
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• 2006

Disinfection by-products(DBPs), such as volatile trihalomethanes and the nonvolatile organochlorine acids, created by chlorination have been extensively studied. However MX which contributes 20-50% of the mutagenic activity in drinking water began to people's attention since 1990. Its chemical name is 3-chloro-4-dichloromethyl-5-hydroxy-2(5H)-furanone. According to WHO guidelines its concentration should be controlled, but its value has not been set up. Due to analytical difficulties in measuring this compound at such a low concentrations and lack of information on toxicity to human. Because concentration (ng/L) of MX in drinking water is low traditional testing methods are ineffective. Therefore this study compared LLE and SPE and have chosen SPE to improve preconcentration. MX has been identified in chlorinated drinking water samples in several countries but not in korea Therefore this study analyzed concentration of MX in different water sources and in spring water. This study examined the causes of changing MX content. Chlorine dosage, seasons, water temperature and distance from the source was all discoverd to be relavant. MX was analyzed in various treatment to find optimum disinfection methods. The outcome was that the concentration of MX was minimized when using biological activated carbon-O3 and granular activated carbon.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.50-50
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• 2022

한국농어촌공사 산하의 농업용저수지 중 3786개소에 대한 수질조사를 '19년도에 실시한 결과, TOC 기준 4등급 초과 저수지 비율은 약 20%로써, 도심 근교 저수지에서 녹조현상 빈발로 인해 수질, 악취, 미관 등의 환경문제 개선 민원이 다수 발생하고 있다. 현재 녹조 발생 사후관리를 위해 주로 사용되고 있는 대형 조류제거선은 저수심 수변부에서의 적용성에 한계가 있고, Al 기반의 응집제를 사용하여 조류를 수거해서 폐기하고 있는 실정이다. (주)이엔이티는 농어촌연구원, (주)코레드, (주)삼호인넷과 함께 호소나 정체하천의 수변지역에 적용될 수 있는 저에너지형 유해조류 포집시스템 개발과, 수거된 조류부산물을 무독화하여 농업재료로 재활용하는 방안을 연구하고 있다. 저수지나 정체수역의 녹조는 바람, 수면유동 등에 의해 수변에 집적되는 특성이 있어, 인공지능 기술로 녹조현상을 감시하여 조류 밀집구간에 접근할 수 있는 자율이동식 수상이동장치를 개발 중이다. 수상이동장치는 조류포집장치를 탑재하기 위한 부력체, 원격 운전이 가능한 무인항법장치, 수변식생대 및 저수심지역 이동을 고려한 수차방식 추진체, 전체 장치의 전원 공급을 위한 고성능 배터리 등으로 구성하여 상세 도면 설계를 진행하고 있다. 조류포집장치에는 표층에 주로 분포하는 남조류를 선택 흡입하는 포집 부표를 적용하였고, Al계 응집제 사용을 배제한 분리막 실험을 통해 침지형 막분리조 및 가압형 농축조를 설계하였다. 유해조류 포집 및 농축은 수상에서 이동체에 탑재하여 이뤄지고, 육상에서는 자원 회수가 가능하도록 회분식 응집공정으로 구분하였다. 조류 밀집지역에서 수거된 조류의 무독화 및 농업재료 자원화 타당성 평가를 위해 특용 버섯균주를 활용한 시료별 분석항목을 선정하고 실험 매트릭스에 따라 실증실험을 수행하였다. 수거조류를 전처리하여 성분 및 발열량을 분석하고 버섯재배 전후의 마이크로시스틴 독소(LR, RR, LR)를 포함한 성분 분석을 수행하여, 고체연료, 비료 및 사료로 활용방안을 검토하였다. 무인자율이동 조류포집장치는 실증화 규모로 제작하여 기선정된 테스트베드에서 현장적용성 평가를 수행할 예정이다. 본 연구를 통해 개발된 유해조류 포집 시스템은 기존의 녹조제거 방안을 보완하여 정체수역의 생태계 복원 및 친수공간의 환경개선 등에 적용되며, 무독화가 입증된 유해조류의 농업재료 자원화 기술은 고부가 상품 개발 및 환경폐기물 감축에 활용될 것이다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.841-849
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• 2023

Construction projects have risks due to various factors such as construction delays and construction accidents. Based on these construction risks, the method of calculating the construction period of the construction project is mainly made by subjective judgment that relies on supervisor experience. In addition, unreasonable shortening construction to meet construction project schedules delayed by construction delays and construction disasters causes negative consequences such as poor construction, and economic losses are caused by the absence of infrastructure due to delayed schedules. Data-based scientific approaches and statistical analysis are needed to solve the risks of such construction projects. Data collected in actual construction projects is stored in unstructured text, so to apply data-based risks, data pre-processing involves a lot of manpower and cost, so basic data through a data classification model using text mining is required. Therefore, in this study, a document-based data generation classification model for risk management was developed through a data classification model based on SVM (Support Vector Machine) by collecting construction project documents and utilizing text mining. Through quantitative analysis through future research results, it is expected that risk management will be possible by being used as efficient and objective basic data for construction project process management.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.255-266
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• 2022

The quality control of coarse aggregate among aggregates, which are the main ingredients of concrete, is currently carried out by SPC(Statistical Process Control) method through sampling. We construct a smart factory for manufacturing innovation by changing the quality control of coarse aggregates to inspect the coarse aggregates based on this image by acquired images through the camera instead of the current sieve analysis. First, obtained images were preprocessed, and HED(Hollistically-nested Edge Detection) which is the filter learned by deep learning segment each object. After analyzing each aggregate by image processing the segmentation result, fineness modulus and the aggregate shape rate are determined by analyzing result. The quality of aggregate obtained through the video was examined by calculate fineness modulus and aggregate shape rate and the accuracy of the algorithm was more than 90% accurate compared to that of aggregates through the sieve analysis. Furthermore, the aggregate shape rate could not be examined by conventional methods, but the content of this paper also allowed the measurement of the aggregate shape rate. For the aggregate shape rate, it was verified with the length of models, which showed a difference of ±4.5%. In the case of measuring the length of the aggregate, the algorithm result and actual length of the aggregate showed a ±6% difference. Analyzing the actual three-dimensional data in a two-dimensional video made a difference from the actual data, which requires further research.

• Food Science and Preservation
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• v.30 no.5
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• pp.896-904
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• 2023

The texturization characteristics of textured vegetable protein (TVP) were investigated based on the extent of soybean decoating during the pretreatment of defatted soybean flour used for TVP. The raw materials for TVP consisted of 50% defatted soybean flour, 30% gluten, and 20% corn starch. The weight ratios of soybean seed coat to soybean flour were 9%, 6%, 3%, and zero. Extrusion was performed using an extruder equipped with a cooling die, maintaining a barrel temperature of 190℃ and screw speed of 250 rpm, Water was injected at a rate of 9 rpm using a metering pump. Regarding the textures of the extruded TVPs produced from defatted soybean flour, an increase in the soybean seed coat content led to a decrease in the apparent fibrous structural layer and an increase in hardness. However, there were no significant changes in elasticity and cohesion. Moreover, as the soybean seed coat content increased, the pH of TVPs decreased. A higher soybean seed coat content also tended to lower the moisture content, increasing water absorption, solids elution, and turbidity. These results suggest that an increased seed coat content reduces the proportion of protein, and the fibers present in the seed coats prevent texturization.

• Informatization Policy
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• v.31 no.1
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• pp.3-31
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• 2024

This study aimed to examine public concerns in South Korea considering the country's unique context, triggered by the advent of generative artificial intelligence such as ChatGPT. To achieve this, comments from 102 YouTube video news related to ethical issues were collected using a Python scraper, and morphological analysis and preprocessing were carried out using Textom on 15,735 comments. These comments were then analyzed using a Correlated Topic Model (CTM). The analysis identified six primary topics within the comments: "Legal and Ethical Considerations"; "Intellectual Property and Technology"; "Technological Advancement and the Future of Humanity"; "Potential of AI in Information Processing"; "Emotional Intelligence and Ethical Regulations in AI"; and "Human Imitation."Structuring these topics based on a correlation coefficient value of over 10% revealed 3 main categories: "Legal and Ethical Considerations"; "Issues Related to Data Generation by ChatGPT (Intellectual Property and Technology, Potential of AI in Information Processing, and Human Imitation)"; and "Fear for the Future of Humanity (Technological Advancement and the Future of Humanity, Emotional Intelligence, and Ethical Regulations in AI)."The study confirmed the coexistence of various concerns along with the growing interest in generative AI like ChatGPT, including worries specific to the historical and social context of South Korea. These findings suggest the need for national-level efforts to ensure data fairness.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.205-212
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• 2024

This study developed a deep learning model that distinguishes the front (with garnish) and the back (without garnish) surface of the dried semi-finished product (dried bukak) for screening operation before transfter the dried bukak to oil heater using robot's vacuum gripper. For deep learning model training and verification, RGB images for the front and back surfaces of 400 dry bukak that treated by data preproccessing were obtained. YOLO-v5 was used as a base structure of deep learning model. The area, surface information labeling, and data augmentation techniques were applied from the acquired image. Parameters including mAP, mIoU, accumulation, recall, decision, and F1-score were selected to evaluate the performance of the developed YOLO-v5 deep learning model-based surface detection model. The mAP and mIoU on the front surface were 0.98 and 0.96, respectively, and on the back surface, they were 1.00 and 0.95, respectively. The results of binary classification for the two front and back classes were average 98.5%, recall 98.3%, decision 98.6%, and F1-score 98.4%. As a result, the developed model can classify the surface information of the dried bukak using RGB images, and it can be used to develop a robot-automated system for the surface detection process of the dried bukak before deep frying.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.250-255
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• 2004

To analyze delay, Seoul - Daegu line of Korea High Speed Railway was divided into three sections and analyzed independently by the business characteristics. The analysis on the project delay reasons was performed on macro and micro scales. This analytic method was named as 'Macro-Micro Delay Analysis Method (MMDAM)'. The macro scale analysis has three approaches, which are (1) scheduling, (3) structural characteristic, (3) and responsibility of project administrative works. Micro analysis also has three, methodologies which are (1) As Planned Method, (2) As Built method, (3) Modified Time Impact Analysis for analyzing the most influential section which the largest delay occurred. Using elicited project delay reasons from above analysis, the questionnaire was carried out for analyzing the influence of project delay reason. The reasons of the delay were driven from two different aspects (1) structural characteristic and (2) responsibility of the people involved in the project. The reasons that were identified from aforementioned three sections are the factors of the delay of the large-scale government driven projects. Finally, the author suggested the methodology of identifying the project delaying factors. The author also analyzed delay reasons in both the overseas and domestic cases of high rapid railway construction and has elicited some benchmarks for the future projects.