• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.1-8
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• 2019

This paper carried out a study to reduce damage from jellyfish whose population has increased due to global warming. The emergence of jellyfish on the beach could result in casualties from jellyfish stings and economic losses from closures. This paper confirmed from the preceding studies that the pattern of jellyfish's appearance is predictable through machine learning. This paper is an extension of The prediction model of emergence of Busan coastal jellyfish using SVM. In this paper, we used deep neural network to expand from the existing methods of predicting the existence of jellyfish to the classification by index. Due to the limitations of the small amount of data collected, the 84.57% prediction accuracy limit was sought to be resolved through data expansion using bootstraping. The expanded data showed about 7% higher performance than the original data, and about 6% better performance compared to the transfer learning. Finally, we used the test data to confirm the prediction performance of jellyfish appearance. As a result, although it has been confirmed that jellyfish emergence binary classification can be predicted with high accuracy, predictions through indexation have not produced meaningful results.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.471-476
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• 2021

Soft magnetic composite (SMC) materials based on powder metallurgy have a number of advantages over the conventional electrical steel sheets commonly used in electric machines. Thus, technologies related to these materials have shown significant improvement in recent years. In general, SMCs are magnetically isotropic owing to the shape of the powder, which makes them suitable for the construction of electric machines with three-dimensional flux and complex structures. However, the materials with isotropic magnetic properties (such as SMCs) have complex vector hysteresis; thus, it is very difficult to predict accurate loss properties. Therefore, we manufactured ring-type specimens of electrical steel sheets and SMC, which analyzed their magnetic properties according to the specimen size, and performed the electromagnetic field analysis of a high-speed permanent magnet (PM) motor driven at 800 Hz or higher using the measured magnetic information to compare the core loss of the motor. The reliability of this paper has been verified by measuring the efficiency after manufacturing the motor.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.77-90
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• 2023

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.459-463
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• 2024

Frequent closures among self-employed individuals lead to national economic losses. Given the high closure rates in the restaurant industry, predicting changes in this sector is crucial for business survival. While research on factors affecting restaurant industry survival is active, studies predicting commercial district changes are lacking. Thus, this study focuses on forecasting such alterations, designing a deep learning model for Seoul's administrative district commercial district changes. It collects 2023 and 2022 second-quarter variables related to these changes, converting yearly fluctuations into percentages for augmentation. The proposed deep learning model aims to predict commercial district changes. Future policies, considering this study, could support restaurant industry growth and economic development.

• Korean Journal of Weed Science
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• v.31 no.3
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• pp.289-293
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• 2011

Field experiments were conducted to predict rice yield losses caused by Echinochloa crus-galli (L.)P. Beauv., Bidens frondosa L. and Aeschynomeme indica L. at a range of plant densities under machine transplanted rice cultivation in different regions of Korea in 2006, and to determine their economic threshold levels (ET). All data were fitted to Cousens' rectangular hyperbola to estimate parameters for predicting rice yield loss. The rice yield loss models of Bidens frondosa L. was predicted as y=5.43/(1+0.0113x), $R^2$=0.963, A. indica was y=5.47/(1+0.0332x), $R^2$=0.976 and E. crus-galli y=5.43/(1+0.01552x), $R^2$=0.950. The mean competitivities represented by the parameter, whose reciprocal ($1/{\beta}$) is a weed density reducing crop yield by 50%. Those of E. crus-galli, B. frondosa and A. indica were 0.01552, 0.01113 and 0.0332 in normal-season machine transplanting of Korea, respectively. Single year mean economic thresholds (ET) of A. indica were 0.5, 0.6 and 0.7 plant $m^{-2}$ with the application of flucetosulfuron, flucetosulfuron+imazosulfuron GR and flucetosulfuron+imazosulfuron+carfentrazone GR herbicides, respectively. Meanwhile ET values of 1.6, 1.9 and 1.9 plants $m^{-2}$ for B. frondosa, 1.2, 1.4, and 1.4 plants $m^{-2}$ for E. crus-galli.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• The Journal of Korean Academy of Prosthodontics
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• v.57 no.2
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• pp.110-117
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• 2019

Purpose: This study was to evaluate the wear resistance of 3D printed, milled, and conventionally cured provisional resin materials. Materials and methods: Four types of resin materials made with different methods were examined: Stereolithography apparatus (SLA) 3D printed resin (S3P), digital light processing (DLP) 3D printed resin (D3P), milled resin (MIL), conventionally self-cured resin (CON). In the 3D printed resin specimens, the build orientation and layer thickness were set to $0^{\circ}$ and $100{\mu}m$, respectively. The specimens were tested in a 2-axis chewing simulator with the steatite as the antagonist under thermocycling condition (5 kg, 30,000 cycles, 0.8 Hz, $5^{\circ}C/55^{\circ}C$). Wear losses of the specimens were calculated using CAD software and scanning electron microscope (SEM) was used to investigate wear surface of the specimens. Statistical significance was determined using One-way ANOVA and Dunnett T3 analysis (${\alpha}=.05$). Results: Wear losses of the S3P, D3P, and MIL groups significantly smaller than those of the CON group (P < .05). There was no significant difference among S3P, D3P, and MIL group (P > .05). In the SEM observations, in the S3P and D3P groups, vertical cracks were observed in the sliding direction of the antagonist. In the MIL group, there was an overall uniform wear surface, whereas in the CON group, a distinct wear track and numerous bubbles were observed. Conclusion: Within the limits of this study, provisional resin materials made with 3D printing show adequate wear resistance for applications in dentistry.

• Research in Plant Disease
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• v.18 no.4
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• pp.298-303
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• 2012

Tomato yellow leaf curl virus (TYLCV), transmitted exclusively by the whitefly (Bemisia tabaci) in a circulative manner is one of the most important virus in tomato. Since the first report of TYLCV incidence in Korea in 2008, the virus has rapidly spread nationwide. TYLCV currently causes serious economic losses in tomato production in Korea. Early detection of TYLCV is one of the most important methods to allow rouging of infected tomato plants to minimize the spread of TYLCV disease. We have developed an ultra-rapid and sensitive real-time polymerase chain reaction (PCR) using a new designed real-time PCR system, GenSpectorTM TMC-1000 that is a small and portable real-time PCR machine requiring only a $5{\mu}l$ reaction volume on microchips. The new system provides ultra-high speed reaction (30 cycles in less than 15 minutes) and melting curve analysis for amplified TYLCV products. These results suggest that the short reaction time and ultra sensitivity of the GenSpector$^{TM}$-based real-time PCR technique is suitable for monitoring epidemics and pre-pandemic TYLCV disease. This is the first report for plant virus detection using an ultra-rapid real-time PCR system.

• Food Science and Preservation
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• v.13 no.4
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• pp.438-444
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• 2006

This study was carried out to obtain fundamental data such as peeling efficiency and quality of potatoes and sweet potatoes peeled by hand, machine and alkali(NaOH). The weight loss by peeling was influenced by peeling methods. Weight losses by rotational brushing-type peeler showed the lowest value, 7.9% in potato, and 7.3% in sweet potato. Any significant differences in moisture content were not found in potatoes and sweet potatoes by peeling methods. The pH of potatoes and sweet potatoes just after peeling were 5.8-6.8 and 6.23-6.63, and decreased somewhat until 3 hrs after peeling. Hardness of potatoes and sweet potatoes peeled by hand with fruit knife were better than that of others. Depending upon the peeling method used the color and color differences undergo some changes in their color and browning. Color difference value of peeled potatoes by hand with a technical tools, and by mechanical peeler such as rotational cutting-type peeler and rotational brushing-type peeler showed just slightly. In particular, changes of rotor differences value of potatoes and sweet potatoes peeled by dipping with 10% NaOH solution at $100^{\circ}C$ was the highest in the samples peeled by NaOH.