• Food Industry
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• s.181
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• pp.10-37
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• 2004

식용유지는 식용으로 이용할 수 있는 가시적인 상태의 액상(oil) 또는 고체상(fat)의 식용기름을 말하지만 식품산업 측면에서는 가시적인 상태의 유지(가시지방)뿐만 아니라 유지가공 식품이나 기타의 식품에 함유되어 섭취되는 비가시적인 상태의 유지(비가지 지방)도 포함될 수 있다. 식용유지는 생명을 유지하는데 없어서는 안될 영양소로서 탄수화물 단백질과 함께 3대 영양소 중의 하나이며 1g당 9kcal를 내는 고도로 농축된 에너지원이면서 필수지방산의 공급원이고 비타민 A, D, E 등의 지용성 비타민과 기타 특수한 영양성분의 운반체로서 높은 영양적 가치를 갖는다. 지방성분은 음식물로부터 섭취가 불가능할 경우에는 체내에서 탄수화물이나 단백질을 기질로 하는 대사과정에서 합성되어 지질 또는 필수지방산과 지용성 비타민은 식품으로 지방질과 함께 흡수되어야 하므로 식품의 한 성분으로 지방의 중요성을 인정받고 있다. 1960년대 이후 국민소득의 증가와 함께 식생활도 다양화되고 서구화 간편화되면서 지방 함량이 높은 식품과 유지를 이용한 식품의 소비가 증가하였고 전체적인 유지 소비량도 늘어나는 추세에 있다. 60년대 초에는 총열량 중 지방이 차지하는 비율이 $6\%$ 정도였으나 최근에는 급격히 증가되어 2000년말 현재 $25.5\%$로 증가하였다. 97년까지 계속 증가하던 공급 에너지량은 98년 IMF의 영향으로 2,799 kcal까지 감소했으나 99년 들어 IMF 이전 수준으로 회복되었다. 또한 연간 일인당 순 식용유지류 공급량은 1990년대 까지 급격히 증가하였으나 이후로는 소폭 증가하는 추세이다. 2000년말 현재 식용유지 총 공급량은 76만2 천톤으로써 99년 73만5 천톤에 비해 2.7만톤이 증가하였다. 1998년 IMF 시기를 제외하고는 공급물량은 계속해서 증가 추세이다. 특히 1998년도에 비하여 2000년도에 식물성 유지는 2배 가까이 증가하였으나, 동물성 유지는 절반 이하로 감소되는 추세이며 공급물량으로 보면 대두유, 팜유가 주요 유종으로 식물유지 전체에서 차지하는 비중이 $72.3\%$로 압도적이다. 라면용 튀김기름과 마가린 쇼트닝의 원료로서 대부분 사용되는 팜유를 제외하고 가정용 식용유의 대부분을 차지하는 대두유가 이처럼 식용유지 시장의 대부분을 차지하게 된 이유는 첫째, 90년대 초반 수입 자유화 이후 타 유종 대비 낮은 가격을 형성하여 유지업체가 수입을 늘린 것이고 둘째로는 국내에서 대두유와 대두박을 생산하는 대두가공업체의 안정적 대두유 공급이 주요 원인이라고 할 수 있다. 식용유지의 시장규모는 식품공전상 식용유지로 분류된 제품들의 2000년도 출하액으로 보면 약 6,616 억원 정도이다. 한편 유종이 단순했던 가정용 식용유지 시장이 최근 새로워지고 있다. 주로 조리용으로 사용했던 것에서 건강을 생각하는 품목으로 바뀌는 경향이다. 아직은 가정용에서 대두유가 가장 많지만 옥배유나 채종유 더 나아가 건강이미지의 식용유인 홍화유 올리브유 해바라기유등이 증가하고 있는 추세이다. 한편 기존 식용유가 비만의 원인인 것 때문에 기피되던 것에서 새롭게 다이어트용 식용유 제품이 출시되어 시장에 새 바람이 불고 있다. 일본 식용유지 시장규모는 물량으로 172 만톤, 금액으로는 2,786 억엔(약 2조7860 억원) 수준이다. 지난 10년간 증가율은 물량 측면에서 약 $5\%$정도 신장하고 금액으로는 큰 증감이 없었다. 식용유는 조리의 기초 소재로서 안정한 수요를 갖고 있지만 주요 유종인 샐러드유의 가격이 하락하여, 전체적으로 시장이 물량이 증가하면서도 매출액이 신장하지 못하여 시장규모는 축소되었다고 할 수 있다. 일본 식용유지 업계의 제품동향을 보면 거의 모든 업체가 가정용 시장에서 건강 기능성 식용유지를 출시하고 있다는 점이다. 물론 일본은 건강기능성 식품에 대한 제도적인 뒷 받침이 되어 있어 기능성 식품의 시장출시가 용이한 점이 가정용 시장에 큰 영향을 미쳤다고 볼 수 있다. 유지 산업의 고부가가치화를 위해서는 전통적인 산업의 제품과 기술만으로는 달성하기 어렵다. 국내와 일본 식용유지 업계의 경향을 참고해 볼 때 차별화된 식용유지 제품과 기술의 개발이 절실히 필요하다. 앞서 언급한 고부가가치 제품과 기술 관련 사향을 바탕으로 국내 유지분야가 중점적으로 연구할 분야를 선정해 보면 리파제의 고정화 연구, 생물공학 기술을 활용한 효소의 개발과 이를 이용한 유지 가공 기술 연구, 유지성분의 유화 안정화 기술 연구, 식용유지의 선택적 수소경화 기술 연구 트랜스 지방산 저감기술연구, 효소 반응조 설계기술 연구, 지질 분자구조 분석기술 연구 분야 등이 유망하다. 상기와 같은 연구 분야를 육성하기 위해서는 유지 전문 인력의 육성, 해외 네트웍의 구축, 산학연 공동연구 추진, 국가적 차원의 정책 및 지원 등이 절실하다.

• Economic and Environmental Geology
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• v.54 no.2
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• pp.233-245
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• 2021

Many studies on the microstructures in rocks have been conducted using experimental methods with various equipment as well as natural rock studies to see the development of microstructures and understand their mechanisms. Grain boundary migration of mineral aggregates in rocks could cause grain growth or grain size changes during metamorphism or deformation as one of the main recrystallization mechanisms. This study suggests improved ways regarding the analog material experiments with reformed equipment to see sequential observations of these grain boundary migration. It can be more efficient than the existing techniques and carry out an appropriate microstructure analysis. This reformed equipment was implemented to enable optical manipulation by mounting polarizing plates capable of rotating operation on a stereoscopic microscope and a deformation rig capable of experimenting with analog materials. The equipment can automatically control the temperature and strain rate of the deformation rig by microcontrollers and programming and can take digital photomicrographs with constant time intervals during the experiment to observe any microstructure changes. The composite images synthesized using images by rotated polarizing plates enable us to see more accurate grain boundaries. As a rock analog material, norcamphor(C7H10O) was used, which has similar birefringence to quartz. Static grain growth and simple shear deformation experiments were performed using the norcamphor to verify the effectiveness of the equipment. The static grain growth experiments showed the characteristics of typical grain growth behavior. The number of grains decreases and the average grain size increases over time. These case experiments also showed a clear difference between the growth curves with three temperature conditions. The result of the simple shear deformation experiment under the medium temperature-low strain rate showed no significant change in the average grain size but presented the increased elongation of grain shapes in the direction of about 53° regarding the direction perpendicular to the shearing direction as the shear strain increases over time. These microstructures are interpreted as both the plastic deformation and the internal recovery process in grains are balanced by the deformation under the given experimental conditions. These experiments using the reformed equipment represent the ability to sequentially observe changing the microstructure during experiments as desired in the tests with the analog material during the entire process.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.