• Journal of the Korean Society of Food Culture
• /
• v.22 no.5
• /
• pp.597-602
• /
• 2007

For the hotel industry, the situations having difficulties in management are becoming we planed by the rises of the cost and labor costs, the imbalance between supply and demand, stiffening competitions between the hotels. Therefore, there has been a plan for a great change to attract customers, escaping from the existing form of management in order to secure competitive powers in the food and beverage field. For that purpose, we plan to investigate into the preference of buffet restaurants in ten 5star hotels in Seoul. By the analysis, we also plan to present the menu concepts that stand out and are preferred by the customers in managing semi-buffet restaurants. Therefore, the linear and planar coordinate values of the H Hotels and I Hotels came out both positive(+) as results of a similarity analysis using MOS, we can predict that they would be positioning on the same dimension. Furthermore we can predict that the menu of antipasto, sushi, sashimi and desserts would be positioning on the same dimension as a result of analysis of the most preferred menu by customers for each station in managing a semi-buffet restaurant. Based on these results, there must be continuous supervision over the menu of buffet restaurants.

• Journal of Intelligence and Information Systems
• /
• v.28 no.3
• /
• pp.139-159
• /
• 2022

As interest in health functional foods has increased since COVID-19, the importance of imported food safety inspections is growing. However, in contrast to the annual increase in imports of health functional foods, the budget and manpower required for inspections for import and export are reaching their limit. Hence, the purpose of this study is to propose a machine learning model that efficiently detects unsuitable food suitable for the characteristics of data possessed by government offices on imported food. First, the components of food import/export inspections data that affect the judgment of nonconformity were examined and derived variables were newly created. Second, in order to select features for the machine learning, class imbalance and nonlinearity were considered when performing exploratory analysis on imported food-related data. Third, we try to compare the performance and interpretability of each model by applying various machine learning techniques. In particular, the ensemble model was the best, and it was confirmed that the derived variables and models proposed in this study can be helpful to the system used in import/export inspections.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.5
• /
• pp.331-337
• /
• 2023

In this paper, we propose a deep learning model to detect lumbar 3 (L3) CT images to determine the occurrence and degree of sarcopenia. In addition, we would like to propose an optimization technique that uses oversampling ratio and class weight as design parameters to address the problem of performance degradation due to data imbalance between L3 level and non-L3 level portions of CT data. In order to train and test the model, a total of 150 whole-body CT images of 104 prostate cancer patients and 46 bladder cancer patients who visited Gangneung Asan Medical Center were used. The deep learning model used ResNet50, and the design parameters of the optimization technique were selected as six types of model hyperparameters, data augmentation ratio, and class weight. It was confirmed that the proposed optimization-based L3 level extraction model reduced the median L3 error by about 1.0 slices compared to the control model (a model that optimized only 5 types of hyperparameters). Through the results of this study, accurate L3 slice detection was possible, and additionally, we were able to present the possibility of effectively solving the data imbalance problem through oversampling through data augmentation and class weight adjustment.

• Information Systems Review
• /
• v.22 no.1
• /
• pp.59-72
• /
• 2020

Artificial Intelligence is establishing itself as a familiar tool from an intractable concept. In this trend, financial sector is also looking to improve the problem of existing system which includes Fraud Detection System (FDS). It is being difficult to detect sophisticated cyber financial fraud using original rule-based FDS. This is because diversification of payment environment and increasing number of electronic financial transactions has been emerged. In order to overcome present FDS, this paper suggests 3 types of artificial intelligence models, Generative Adversarial Network (GAN), Deep Neural Network (DNN), and Convolutional Neural Network (CNN). GAN proves how data imbalance problem can be developed while DNN and CNN show how abnormal financial trading patterns can be precisely detected. In conclusion, among the experiments on this paper, WGAN has the highest improvement effects on data imbalance problem. DNN model reflects more effects on fraud classification comparatively.

• Management & Information Systems Review
• /
• v.38 no.2
• /
• pp.83-103
• /
• 2019

Supply chain management(SCM) requires efforts to search for methods for mutual growth with partner companies and to maintain continuous cooperative relations in order to gain a competitive edge. Because information competencies play a big role within the supply chain, it is essential to examine the relationship of information sharing and partnership quality that can affect information competency. In order to maintain continuous business relations between partner companies, it is necessary to identify the obstacles with partner companies resulting from the imbalance of power within a supply chain and to take on a strategic approach for effectively managing such obstacles. Therefore, there is a significant need to discuss strategic approach methods to enable the logic of mutual growth through the CSV that is worth learning from the partner company and the attributes of non-mediated power. CSV will be reviewed from various aspects as a new management paradigm in the future. This study aims at suggesting a continuous growth model for companies by solving social problems through the integration of CSV and the concept of non-mediated power to advance the information competencies of SCM. A total of 142 copies of survey forms for SCM Implementation Companies were using the PLS structural equation modeling for an analysis, and the following are the findings. Results of this study showed that both CSV and non-mediated power had significant impact on information sharing and partnership qualities, and the conclusion that it is possible to enhance information competency through information sharing and partnership quality. Based on this, this study proposes the implication that it is necessary to elevate awareness of CSV and non-mediated power as variables for the coexistence of SCM participating companies.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.2
• /
• pp.201-211
• /
• 2022

According to the recent change in the cybersecurity paradigm, research on anomaly detection methods using machine learning and deep learning techniques, which are AI implementation technologies, is increasing. In this study, a comparative study on data preprocessing techniques that can improve the anomaly detection performance of a GRU (Gated Recurrent Unit) neural network-based intrusion detection model using NGIDS-DS (Next Generation IDS Dataset), an open dataset, was conducted. In addition, in order to solve the class imbalance problem according to the ratio of normal data and attack data, the detection performance according to the oversampling ratio was compared and analyzed using the oversampling technique applied with DCGAN (Deep Convolutional Generative Adversarial Networks). As a result of the experiment, the method preprocessed using the Doc2Vec algorithm for system call feature and process execution path feature showed good performance, and in the case of oversampling performance, when DCGAN was used, improved detection performance was shown.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.6
• /
• pp.1530-1544
• /
• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.

• Journal of Genetic Medicine
• /
• v.7 no.2
• /
• pp.156-159
• /
• 2010

Inversion, one of the balanced rearrangements, usually does not lead to phenotypic abnormalities; all genetic information exists in the proper amount, merely in a different order or in an abnormal location. However, offspring of an inversion carrier is at risk of chromosomal imbalance because an inversion loop can be formed during crossing-over of the paternal and the maternal chromosomes in meiosis. We report a 38-year-old woman with inversion and balanced translocation and her fetus with unusual rearrangement causing chromosomal imbalance. We performed conventional cytogenetic analysis, MLPA, and subtelomeric FISH in the cells of the embryo. The results showed that the distal portion of chromosome 13q was added to the terminal portion of chromosome 9p during crossing-over. Therefore, the final karyotype of the fetus was 46,XY,rec(9)t(9;13)(p22;q32)inv(9)(p12q13)mat, confirmed using molecular-cytogenetic analyzing tools.

• International Commerce and Information Review
• /
• v.11 no.2
• /
• pp.173-201
• /
• 2009

As a result of overemphasizing the goods sector in trade structure, Korea does not meet properly the global trend which has the key role of 'trade in services' as the service economy have been expanded. Hereafter, it is easily forecasted that trade in services will be one of the main factors for Korea's competitiveness and engine of growth. Nevertheless, because Korea does not equip the concreteness of governance for trade in services, it is possible that the efficiency deterioration of trade volume, confusion of Korea's trade policy, conflict among trading countries, and discordance between the interested parties may be occurred. This paper analyzes the governance system of Korea for trade in services in order to enhance the competitiveness reflecting the importance of trade in services and to draw some strategies for remodeling the service governance system. It is expected to raise the efficiency of Korea's trade policy by constructing the systematic governance for trade in services, and to remove lots of latent risks during global transactions by improving the imbalance between manufacturing and service part for the development of trade in services in Korea. Analysis revealed itself the result that Korea is weak enough to can not identify the governance system about trade in services. Except 'Extent of Services' article of the Foreign Trade Act, Korea has not prepared the governance system for trade in services so that governance system have been scattered overly or decentralized. Problems about trade in services are not limited to enterprise's side, but extended to all the players including government agency whole, academic world and research institute. Therefore, the governance of trade in services should be strengthened and systematized by making the model law for trade in services(provisional name : Master Law for Trade in Services or Promotion Law for Trade in Services) by formatting type of fundamental law or separate legislation. If the bill legislation does not meet the conditions, the Foreign Trade Act should be totally reformed to Omnibus Trade Act concept including trade in services.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.16 no.3
• /
• pp.1-13
• /
• 2014

This study investigated body type among women in their 20s and the development of prototypes for tailored jackets by body type in order to design clothes in consideration of an imbalance in body type caused by the popularity of portable devices such as smartphones and Netbooks. This study aims to create a design of jacket patterns by body type through both actual and virtual wear testing among women in their 20s, who are the major consumer of ready-made clothes and are very sensitive to size fit. This study will provide pattern information for the manufacture of jackets with a goal of securing the latest scientific body type information and establish the grounds for a research method in the manufacture of clothes. According to actual and virtual wear tests of four different body types, a significant difference was found in armhole circumference in most types because it was scanned with the arms slightly apart to prevent the armhole area from being missed during the 3D scanning. This has resulted in a slight distortion in measurements. To correct this problem, it is necessary to verify the precision of the body scanner and its program. In categories in which a large significant difference was found, it is necessary to test them against many subjects. In addition, it would be required to perform a further study on the 3D virtual wear system, which could be useful in the clothing industry.