• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.21-26
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• 2018

Thermoplastic starch (TPS) was prepared by mixing starch with glycerol as a plasticizer. The glycerol content ranged from 20 to 35 wt. % and TPS was prepared in a twin screw extruder. The shear viscosity, thermal and mechanical properties of the TPS were investigated. The viscosity of TPS exhibited typical shear thinning behavior: decreasing viscosity with increasing shear rate. The power index, n, increased with increasing glycerol content. This is because as the content of glycerol, a Newtonian fluid, increases, the viscosity behavior of the TPS becomes closer to that of a Newtonian fluid. The thermal behavior of TPS showed that starch and glycerol are miscible. In addition, when TPS was aged for more than one day at room temperature, TPS showed a partially miscible phase structure. The moisture absorbed into the TPS was assumed to change the phase behavior. The mechanical properties of TPS were found to be strongly dependent on the content of the plasticizer. Both the toughness and stiffness increased with increasing plasticizer content. DSC showed that this unusual result was due to the combined effect of humidity and the high amylose content in starch.

• Food Science and Preservation
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• v.9 no.3
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• pp.315-320
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• 2002

In order to improve some problems such as contamination of undesirable mold, mycotoxin production and excessive drying on the surface of traditional meju. Control meju without koji and capsule type meju(CM) coated with soybean mixture containing 0.5%, 1% and 2%(w/w) R. oligosporus koji were dried at room temperature (10∼15$^{\circ}C$) for 3 days. Control meju I was fermented in outdoor for 27 days. Control meju IIand CMs were fermented in Korean yellow clay room at 25$^{\circ}C$ for 7 days under 80% relative humidity as first step, and then fermented in outdoor (average temp. 2.7$^{\circ}C$, December) for 20 days as second step. The moisture content of CMs were higher than that of control meju I to the range of 2.88∼7.55%(w/w). pH and titratable acidity in CMs were similar to control group. Amino type nitrogen content in CMs(800.80, 816.0, 901.60 mg%) were 2.2∼2.6 times higher than that in control meju I (347.2 mg%). Reducing sugar content in CMs(2.78∼3.13%) was similar to control meiu I (2.10%) and control meju H(2.31%). Lightness(L) value of control meju I was higher than that of control meju IIand CMs.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.527-536
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• 2017

With the recent development of smart grid industry, the necessity for efficient EMS(Energy Management System) has been increased. In particular, in order to reduce electric load and energy cost, sophisticated electric load forecasting and efficient smart grid operation strategy are required. In this paper, for more accurate electric load forecasting, we extend the data collected at demand time into high time resolution and construct an artificial neural network-based forecasting model appropriate for the high time resolution data. Furthermore, to improve the accuracy of electric load forecasting, time series data of sequence form are transformed into continuous data of two-dimensional space to solve that problem that machine learning methods cannot reflect the periodicity of time series data. In addition, to consider external factors such as temperature and humidity in accordance with the time resolution, we estimate their value at the time resolution using linear interpolation method. Finally, we apply the PCA(Principal Component Analysis) algorithm to the feature vector composed of external factors to remove data which have little correlation with the power data. Finally, we perform the evaluation of our model through 5-fold cross-validation. The results show that forecasting based on higher time resolution improve the accuracy and the best error rate of 3.71% was achieved at the 3-min resolution.

• Food Science and Industry
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• v.55 no.2
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• pp.188-202
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• 2022

In the insect industry, as the scope of application of insects is expanded from pet insects and natural enemies to feed, edible and medicinal insects, the demand for quality control of insect raw materials is increasing, and interest in securing the safety of insect products is increasing. In the process of expanding the industrial scale, controlling the temperature and humidity and air quality in the insect breeding room and preventing the spread of pathogens and other pollutants are important success factors. It requires a controlled environment under the operating system. European commercial insect breeding facilities have attracted considerable investor interest, and insect companies are building large-scale production facilities, which became possible after the EU approved the use of insect protein as feedstock for fish farming in July 2017. Other fields, such as food and medicine, have also accelerated the application of cutting-edge technology. In the future, the global insect industry will purchase eggs or small larvae from suppliers and a system that focuses on the larval fattening, i.e., production raw material, until the insects mature, and a system that handles the entire production process from egg laying, harvesting, and initial pre-treatment of larvae., increasingly subdivided into large-scale production systems that cover all stages of insect larvae production and further processing steps such as milling, fat removal and protein or fat fractionation. In Korea, research and development of insect smart factory farms using artificial intelligence and ICT is accelerating, so insects can be used as carbon-free materials in secondary industries such as natural plastics or natural molding materials as well as existing feed and food. A Korean-style customized breeding system for shortening the breeding period or enhancing functionality is expected to be developed soon.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.