• Food Science of Animal Resources
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• v.41 no.2
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• pp.173-184
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• 2021

Clean labeling is emerging as an important issue in the food industry, particularly for meat products that contain many food additives. Among synthetic additives, nitrite is the most important additive in the meat processing industry and is related to the development of cured color and flavor, inhibition of oxidation, and control of microbial growth in processed meat products. As an alternative to synthetic nitrite, preconverted nitrite from natural microorganisms has been investigated, and the applications of pre-converted nitrite have been reported. Natural nitrate sources mainly include fruits and vegetables with high nitrate content. Celery juice or powder form have been used widely in various studies. Many types of commercial starter cultures have been developed. S. carnosus is used as a critical nitrate reducing microorganism and lactic acid bacteria or other Staphylococcus species also were used. Pre-converted nitrite has also been compared with synthetic nitrite and studies have been aimed at improving utilization by exploiting the strengths (positive consumer attitude and decreased residual nitrite content) and limiting the weaknesses (remained carcinogenic risk) of pre-converted nitrite. Moreover, as concerns regarding the use of synthetic nitrites increased, research was conducted to meet consumer demands for the use of natural nitrite from raw materials. In this report, we review and discuss various studies in which synthetic nitrite was replaced with natural materials and evaluate pre-converted nitrite technology as a natural curing approach from a clean label perspective in the manufacturing of processed meat products.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1762-1769
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• 2021

The final goal of this study is to develop a system that can analyze whether a wanted vehicle is a criminal vehicle from images collected from black boxes, smartphones, CCTVs, and so on. Data collection was collected using a self-developed black box. The used data in this study has used a total of 83,753 cases such as the eight vehicle types(truck, RV, passenger car, van, SUV, bus, sports car, electric vehicle) and 434 vehicle models. As a result of vehicle recognition using YOLO v5, mAP was found to be 80%. As a result of identifying the vehicle model with ReXNet using the self-developed black box, the accuracy was found to be 99%. The result was verified by surveying field police officers. These results suggest that improving the accuracy of data labeling helps to improve vehicle recognition performance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.381-392
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• 2021

In this study, we perform deep learning-based object detection analysis on eight types of buildings defined by the digital map topography standard code, leveraging images taken with UAV (Unmanned Aerial Vehicle). Image labeling was done for 509 images taken by UAVs and the YOLO (You Only Look Once) v5 model was applied to proceed with learning and inference. For experiments and analysis, data were analyzed by applying an open source-based analysis platform and algorithm, and as a result of the analysis, building objects were detected with a prediction probability of 88% to 98%. In addition, the learning method and model construction method necessary for the high accuracy of building object detection in the process of constructing and repetitive learning of training data were analyzed, and a method of applying the learned model to other images was sought. Through this study, a model in which high-efficiency deep neural networks and spatial information data are fused will be proposed, and the fusion of spatial information data and deep learning technology will provide a lot of help in improving the efficiency, analysis and prediction of spatial information data construction in the future.

• Journal of Food Hygiene and Safety
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• v.23 no.1
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• pp.51-61
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• 2008

This study was performed to improve of Health Functional Food(HFF) through investigating the label system. The logo on HFF to increase the reliance, understanding and quality warranty for consumers was developed through competition. In order to investigate the consumer's awareness about HFF label system a nationwide survey was conducted in metropolitan areas (6 cities) and middle-sizes cities (6 cities). The subjects was 2000(male 519, female 1481) adults aged 20 over, and information was collected by in-person interviews. The major results were as follows. 63.0% of consumer responded they need certification mark for HFF and trust function information of text/graphic format than text format. 85.3% of consumers chose the long claim including the mechanism because 38% reported that they could confide the information, 36% reported it is easy to understand and 26% reported that they thought it is more effective than short claim. As 58.8% of the total consumers answered that the manufacturers marked the nutrition function claim without the permit of the KFDA, a reliable certification mark developed by this research is expected to contribute in improving the label system of HFF, rising reliability and perception of consumer.

• The Journal of Fisheries Business Administration
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• v.42 no.1
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• pp.97-112
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• 2011

Today, the consumer is more careful in buying goods, invests more time in collecting relevant information to avoid any potential danger, and restricts from potential impulse buying. To react this consumer's carefulness, the seafood brands provide much information including the origin labeling system, the traceability, the food's safety & hygiene. Also the branding by region or company is pursued. Like that, a seafood brand's importance is increased, but there lack few researches dealing how current consumer's attitude influences on real purchase behavior, and how the attitude works consumer purchase decision. Therefore, this study researched the brand's influence on the consumer's attitude and purchase intention. For this purpose, this study targeted the salty mackerel and the dried yellow corvina because they are already branded and sold in some popularity, and researched how a brand's popularity, its image, and its recognized quality could effect on the consumer's attitude and purchase intention. As the result, it was appeared that a seafood brand's popularity didn't directly effect on the consumer's purchase intention, but indirectly influenced through the consumer's attitude as a parameter. From this result, improving a seafood brand's popularity needs some time to form the consumer's positive attitude and to lead to consumer purchase intention of seafood brand. So, it is thought that various promotion activities for seafood consumption must be continually performed rather than some temporary special events. Consumers showed more positive attitude on familiar seafood based on a product's original place and the freshness. Also they had better feeling about some seafood with their speciality images rather than the same kinds of products produced in other regions. This attitude temporarily led to purchase intention. Therefore, it is important that the branding strategy development should start from some seafood familiar to us in traditional food culture and food habit, but should delivery the reliance and the freshness in accurately indicating their origins, and should emphasize their differences as specialities. Consumers showed some positive attitudes on the seafood featuring the hygiene, the safety, continual good quality, and their attitudes led to their purchase intentions in temporary. The seafood product reflecting these results the best is the marketing activities on some Andong salty mackerel products acquired HACCP certification. it is thought that a seafood's branding strategy should be established on distinctive branding strategies using reliable certification mark like HACCP based on the hygiene, the safety, and the quality.

• The Korean Journal of Nuclear Medicine Technology
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• v.26 no.1
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• pp.15-26
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• 2022

Purpose Recently, in the process of examining the self-disposal of radioactive waste by the Korea Institute of Nuclear Safety, it is difficult to reach the final approval process for self-disposal. In connection with this, we intend to increase the processing efficiency of self-disposal and strengthen safety by analyzing cases of recent supplementary matters. Materials and Methods From 2018 to 2021, we compare and review a supplementary requests that preparing the procedures and plans for the self-disposal of radioactive waste by 20 institutions. In this regard, based on the provisions of the Atomic Energy Safety Act, we derive a detailed proposals for the self-disposal of radioactive waste by arranging the review processing period calculation and supplementary requests that occurred during the review process. Results The representative supplementary requests of the Korea Institute of Nuclear Safety are the calculation of the storage period by type and nuclide of radioactive waste, the contents of the packaging container, the RASIS reporting method, the planned storage method for self-disposal, confirmation of the final disposal company, and the storage period of the waste filter Calculation, radioactive labeling, etc. And it is emphasized as important. Conclusion The expected effects of the guidelines reflecting the latest supplements include reduction of the time required for document preparation and increase of work processing efficiency, improvement of storage efficiency in the radioactive waste storage room, and economic cost reduction. If the radioactive waste self-disposal guideline presented in this study is applied to the field, it is thought that it will be helpful in improving the work efficiency of those who are experiencing difficulties.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.166-172
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• 2022

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• The Journal of Bigdata
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• v.7 no.1
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• pp.125-137
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• 2022

The transportation industry is one of the important industries due to the geographical requirements surrounded by the sea on three sides of Korea and the problem of resource poverty, which relies on imports for most of its resource consumption. Among them, the proportion of the shipping industry is large enough to account for most of the transportation industry, and maintenance in the shipping industry is also important in improving the operational efficiency and reducing costs of ships. However, currently, inspections are conducted every certain period of time for maintenance of ships, resulting in time and cost, and the cause is not properly identified. Therefore, in this study, the proposed methodology, LSTM-AutoEncoder, is used to detect abnormalities that may cause ship failure by considering the time of actual ship operation data. In addition, clustering is performed through clustering, and the potential causes of ship main engine failure are identified by grouping outlier by factor. This enables faster monitoring of various information on the ship and identifies the degree of abnormality. In addition, the current ship's fault monitoring system will be equipped with a concrete alarm point setting and a fault diagnosis system, and it will be able to help find the maintenance time.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.358-366
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• 2023

This study aimed to provide a solution for improving ship collision alert of the 'accident vulnerable ship monitoring service' among the 'intelligent marine traffic information system' services of the Ministry of Oceans and Fisheries. The current ship collision alert uses a supervised learning (SL) model with survey labels based on large ship-oriented data and its operators. Consequently, the small ship data and the operator's opinion are not reflected in the current collision-supervised learning model, and the effect is insufficient because the alarm is provided from a longer distance than the small ship operator feels. In addition, the supervised learning (SL) method requires a large number of labeled data, and the labeling process requires a lot of resources and time. To overcome these limitations, in this paper, the classification model of collision alerts for small ships using unlabeled data with the semi-supervised learning (SSL) algorithms (Label Propagation and TabNet) was studied. Results of real-time experiments on small ship operators using the classification model of collision alerts showed that the satisfaction of operators increased.