• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1457-1467
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• 2015

Depending on the type of Tx-Rx pairs, VHF Data Exchange System (VDES) for maritime communication is expected to employ the different frequency channels. Load imbalance between the different channels turns out to be a critical problem for the multi-hop communication using Ad-hoc Self-Organizing TDMA (ASO-TDMA) MAC protocol, which has been proposed to provide the connectivity between land station and remote ship stations. In order to handle the inter-channel load imbalance problem, we consider a model of the stochastic geomety in this paper. After analyzing the spatial reuse efficiency in each hop region by the given model, we show that the resource utility can be maximized by balancing the inter-channel traffic load with optimal resource allocation in each hop region.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.70
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• pp.135-157
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• 2016

Globalization of consumption, expansion of cross border e-trade, increase use of internet and mobile have led to rapid growth of world e-commerce particularly in Asia and emerging markets. Impacted by Korean wave, online export is continuously increasing, yet Korea is experiencing severe e-commerce trade imbalance. Export growth rate and ratio of Korean small companies are relatively low from OECD member countries. Therefore, Korean government is currently emphasizing on vitalization of online export to China to resolve trade imbalance and to increase export of small companies. To propose detail measures to vitalize online export to China, this study is focused on export customs clearance procedure of Korea and import customs clearance procedure of China in view of online export company. Also suggested countermeasure plan and analysis for the new tax revision plan related to e-commerce which implemented on April 8th 2016. This study have grouped countermeasure plan by short term plan of firms and long term plan of the government. As for the short-term countermeasure plan for firms, first, comparison analysis of tax rate on products is need to decide type of e-commerce strategy; second, if planning to start e-commerce business to China, sales possibility and certification check is necessary; third, through preparation of customs clearance document is needed; last in order to obtain price competitiveness, new logistics strategy and packing development is required. As for the long-term countermeasure plan for the government, I have suggested cooperated bonded logistics service for small businesses and operation plan of show room for promising Korean products.

• Journal of the Korean Institute of Educational Facilities
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• v.18 no.3
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• pp.3-11
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• 2011

This study aims at proposing some reform measures for the middle school grouping system in Gwangju Metropolitan City, which is divided 86 middle schools into 10 clusters and 3 school districts. In doing so, it analyzes the present status of educational environment and student walking distance in each school district such as the number of student per teacher, the student density, the school size and the gender ratio in class. And it conducts a survey of 5,363 middle school students, 3,966 parents and 1,007 teachers, also evaluates their satisfaction levels and needs with the student allocation system. As the result of the survey and data analysis, it finds out some problems in some school districts which are gender imbalance in class, the preference for private middle schools and inconvenience in commuting to school. To solve these problems, the study suggests the better alternatives to replace the current system. Firstly, to set up the basic fundamental principles detailed in 3 action plan, which emphasize the adherence to a close-range allocation, the appropriate size of school and class, and the equalization of educational environment. Secondly, to establish the information system for managing the school district in order to be more objective and transparent. Finally, it gives a concrete proposal which divides the 10th school grouping system into the 11th. The result would be expected to ease the gender imbalance and the concentration of private middle schools, to improve the student walking condition to school.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.64-69
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• 2022

Commercialization of public technology is the transfer of government-led scientific and technological innovation and R&D results to the private sector, and is recognized as a key achievement driving economic growth. Therefore, in order to activate technology transfer, various machine learning methods are being studied to identify success factors or to match public technology with high commercialization potential and demanding companies. However, public technology commercialization data is in the form of a table and has a problem that machine learning performance is not high because it is in an imbalanced state with a large difference in success-failure ratio. In this paper, we present a method of utilizing CTGAN to resolve imbalances in public technology data in tabular form. In addition, to verify the effectiveness of the proposed method, a comparative experiment with SMOTE, a statistical approach, was performed using actual public technology commercialization data. In many experimental cases, it was confirmed that CTGAN reliably predicts public technology commercialization success cases.

• The Journal of Bigdata
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• v.6 no.2
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• pp.85-98
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• 2021

The pandemic of COVID-19 further promoted the imbalance in the volume of imports and exports among countries using containers, which worsened the shortage of empty containers. Since it is important to secure as many empty containers as the appropriate demand for stable and efficient port operation, measures to predict demand for empty containers using various techniques have been studied so far. However, it was based on long-term forecasts on a monthly or annual basis rather than demand forecasts that could be used directly by ports and shipping companies. In this study, a daily and weekly prediction method using an actual artificial neural network is presented. In details, the demand forecasting model has been developed using multi-layer perceptron and multiple linear regression model. In order to overcome the limitation from the lack of data, it was manipulated considering the business process between the loaded container and empty container, which the fully-loaded container is converted to the empty container. From the result of numerical experiment, it has been developed the practically applicable forecasting model, even though it could not show the perfect accuracy.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.47-55
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• 2024

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.

• Applied Biological Chemistry
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• v.43 no.4
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• pp.236-240
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• 2000

Correlations between mitochondrial respiration, glycolysis activity and overall growth activity of rice (Oryza sativa: cv. Dasan) seedlings during low temperature exposure were studied in order to provide insights into the underlying mechanism for the primary phase of chilling injury in plants. Among cellular membranes involved in energy metabolism, only the mitochondrial inner membrane showed not only physical phase transition at ca. $13^{\circ}C$, as monitored by ESR spin label, but also functional phase transition at the same temperature, as assessed by cytochrome c oxidase activity. The main regulatory enzyme of glycolysis, phosphofructokinase, in situ did not suffer phase transition of its activity at least in the $4{\sim}27^{\circ}C$ range. Low temperature caused a significant accumulation of glucose 6-phosphate (G6P) and fructose 6-phosphate (F6P), which disappeared almost completely on rewarming of the seedlings. Temperature profiles of the steady state levels of G6P and F6P revealed the inflection point appearing at around phase transition temperature of the mitochondrial membrane. The results conform to our previous proposition on the mechanism for the early stage events of chilling injury that the accumulation of glycolytic metabolites in cells due to metabolic imbalance at low temperature gives rise to an excess supply of electrons during rewarming period, which, in turn, results in overproduction of active oxygen in mitochondria.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.287-294
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• 2007

Many classification algorithms for real world data suffer from a data class imbalance problem. To solve this problem, various methods have been proposed such as altering the training balance and designing better sampling strategies. The previous methods are not satisfy in the distribution of the input data and the constraint. In this paper, we propose a focused sampling method which is more superior than previous methods. To solve the problem, we must select some useful data set from all training sets. To get useful data set, the proposed method devide the region according to scores which are computed based on the distribution of SOM over the input data. The scores are sorted in ascending order. They represent the distribution or the input data, which may in turn represent the characteristics or the whole data. A new training dataset is obtained by eliminating unuseful data which are located in the region between an upper bound and a lower bound. The proposed method gives a better or at least similar performance compare to classification accuracy of previous approaches. Besides, it also gives several benefits : ratio reduction of class imbalance; size reduction of training sets; prevention of over-fitting. The proposed method has been tested with kNN classifier. An experimental result in ecoli data set shows that this method achieves the precision up to 2.27 times than the other methods.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.217-228
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• 2023

Video instance segmentation is an intelligent visual task with high complexity because it not only requires object instance segmentation for each image frame constituting a video, but also requires accurate tracking of instances throughout the frame sequence of the video. In special, human instance segmentation in drama videos has an unique characteristic that requires accurate tracking of several main characters interacting in various places and times. Also, it is also characterized by a kind of the class imbalance problem because there is a significant difference between the frequency of main characters and that of supporting or auxiliary characters in drama videos. In this paper, we introduce a new human instance datatset called MHIS, which is built upon drama videos, Miseang, and then propose a novel video data augmentation method, CDVA, in order to overcome the data imbalance problem between character classes. Different from the previous video data augmentation methods, the proposed CDVA generates more realistic augmented videos by deciding the optimal location within the background clip for a target human instance to be inserted with taking rich spatio-temporal context embedded in videos into account. Therefore, the proposed augmentation method, CDVA, can improve the performance of a deep neural network model for video instance segmentation. Conducting both quantitative and qualitative experiments using the MHIS dataset, we prove the usefulness and effectiveness of the proposed video data augmentation method.

• Information Systems Review
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• v.24 no.4
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• pp.97-118
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• 2022

As generative adversarial network (GAN) based oversampling techniques have achieved impressive results in class imbalance of unstructured dataset such as image, many studies have begun to apply it to solving the problem of imbalance in structured dataset. However, these studies have failed to reflect the characteristics of structured data due to changing the data structure into an unstructured data format. In order to overcome the limitation, this study adapted CycleGAN to reflect the characteristics of structured data, and proposed hybridization of synthetic minority oversampling technique (SMOTE) and the adapted CycleGAN. In particular, this study tried to overcome the limitations of existing studies by using a one-dimensional convolutional neural network unlike previous studies that used two-dimensional convolutional neural network. Oversampling based on the method proposed have been experimented using various datasets and compared the performance of the method with existing oversampling methods such as SMOTE and adaptive synthetic sampling (ADASYN). The results indicated the proposed hybrid oversampling method showed superior performance compared to the existing methods when data have more dimensions or higher degree of imbalance. This study implied that the classification performance of oversampling structured data can be improved using the proposed hybrid oversampling method that considers the characteristic of structured data.