• Journal of KIISE
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• v.42 no.6
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• pp.707-712
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• 2015

Mobile cloud computing is a technology that uses cloud services to overcome resource constrains of a mobile device, and it applies the computation offloading scheme to transfer a portion of a task which should be executed from a mobile device to the cloud. If the communication cost of the computation offloading is less than the computation cost of a mobile device, the mobile device commits a certain task to the cloud. The previous cost analysis models, which were used for separating functions running on a mobile device and functions transferring to the cloud, only considered the amount of data transfer and response time as the offloading cost. In this paper, we proposed a new task partitioning scheme that considers the frequency of function calls and data synchronization, during the cost estimation of the computation offloading. We also verified the energy efficiency of the proposed scheme by using experimental results.

• Ocean and Polar Research
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• v.33 no.1
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• pp.1-11
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• 2011

This study describes measurements of fast ice recorded on May 23, 2009, in Kongsfjorden (translated as 'Kongs Fjord'), an inlet on the west coast of Spitsbergen in the Svalbard Archipelago. Seasonal fast ice is an important feature for Svalbard fjords, both in relation to their physical environment and also the local ecosystem, since it grows seaward from the coast and remains in place throughout the winter. Ice thickness, snow, ice properties, and wind speed were measured, while SAR (Synthetic Aperture Radar) data was observed simultaneously observed two times from ALOS-PALSAR (L-band). Measured ice thickness was about 25-35 cm while the thickness of ice floe broken from fast ice was measured as 10-15 cm. Average salinity was 1.9-2.0 ppt during the melting period. Polarimetric data was used to extract H/A/alpha-angle parameters of fast ice, ice floe, snow and glacier, which was classified into 18 classes based on these parameters. It was established that the area of fast ice represents surface scattering which indicates low and medium entropy surface scatters such as Bragg and random surfaces, while fast ice covered with snow belongs to a zone of low entropy surface scattering similar to snow-covered land surfaces. The results of this study will contribute to various interpretations of interrelationships between H/A/alpha parameters and the wave scattering Phenomenon of sea ice.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.178-186
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• 2018

Subject classification of thesis units is essential to serve scholarly information deliverables. However, to date, there is a journal-based topic classification, and there are not many article-level subject classification services. In the case of academic papers among domestic works, subject classification can be a more important information because it can cover a larger area of service and can provide service by setting a range. However, the problem of classifying themes by field requires the hands of experts in various fields, and various methods of verification are needed to increase accuracy. In this paper, we try to classify topics using the unsupervised learning algorithm to find the correct answer in the unknown state and compare the results of the subject classification algorithms using the coherence and perplexity. The unsupervised learning algorithms are a well-known Hierarchical Dirichlet Process (HDP), Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) algorithm.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.825-835
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• 2009

The advance in medical technology has decreased death rates from diseases such as tubercle, pneumonia, malnutrition, and hepatitis. However, death rates from cardiac diseases are still increasing. To prevent cardiac diseases and quantify cardiac function, magnetic resonance imaging not harmful to the body is used for calculating blood volumes and ejection fraction(EF) on routine clinics. In this paper, automatic left ventricle(LV) segmentation is presented to segment LV and calculate blood volume and EF, which can replace labor intensive and time consuming manual contouring. Radial threshold determination is designed to segment LV and blood volume and EF are calculated. Especially, basal slices which were difficult to segment in previous researches are segmented automatically almost without user intervention. On short axis cardiac MRI of 36 subjects, the presented algorithm is compared with manual contouring and General Electronic MASS software. The results show that the presented algorithm performs in similar to the manual contouring and outperforms the MASS software in accuracy.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.322-327
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• 2017

The classification problem in the field of Natural Language Processing has been studied for a long time. Continuing forward with our previous research, which classifies large-scale text using Convolutional Neural Networks (CNN), we implemented Recurrent Neural Networks (RNN), Long-Short Term Memory (LSTM) and Gated Recurrent Units (GRU). The experiment's result revealed that the performance of classification algorithms was Multinomial Naïve Bayesian Classifier < Support Vector Machine (SVM) < LSTM < CNN < GRU, in order. The result can be interpreted as follows: First, the result of CNN was better than LSTM. Therefore, the text classification problem might be related more to feature extraction problem than to natural language understanding problems. Second, judging from the results the GRU showed better performance in feature extraction than LSTM. Finally, the result that the GRU was better than CNN implies that text classification algorithms should consider feature extraction and sequential information. We presented the results of fine-tuning in deep neural networks to provide some intuition regard natural language processing to future researchers.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.1
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• pp.19-27
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• 2007

Entering the 21st century, the demand on information service via mobile devices is skyrocketing along with the popularization of computers and mobile communication devices and the rapid development of wireless communication technology. In particular, as mobile device service such as LBS and Telematics becomes highlighted, the management of mobile devices is ever more drawing attention. However, since there is no fixed standard on geographical space data, many commercialized monitoring systems do not use common geographical space data but independent geographic information. Furthermore, as it is impossible to save location information of each mobile device by integrating such information after acquiring them, it is difficult to trace management. Therefore, in this paper, geographic data with DXF. DWG and SHP format, which are commonly used files, were created and visualized by GML format, OGC standard advice. And then, TMS(Trace Monitoring System of Mobile Device) that can trace and manage information after acquiring and saving space information that show the movement of users was implemented.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.7
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• pp.276-281
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• 2007

In this paper, we use Support Vector Machine to predict the susceptibility of chronic hepatitis from single nucleotide polymorphism data. Our data set consists of SNP data for 328 patients based on 28 SNPs and patients classes(chronic hepatitis, healthy). We use leave-one-out cross validation method for estimation of the accuracy. The experimental results show that SVM with SNP is capable of classifying the SNP data successfully for chronic hepatitis susceptibility with accuracy value of 67.1%. The accuracy of all SNPs with health related feature(sex, age) is improved more than 7%(accuracy 74.9%). This result shows that the accuracy of predicting susceptibility can be improved with health related features. With more SNPs and other health related features, SVM prediction of SNP data is a potential tool for chronic hepatitis susceptibility.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.606-610
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• 2010

In order to provide effective studies in accordance with the shifts in learning paradigms, an LD Publication engine, which is the former engine from the Learning Design (LD) based learning management system, was designed and implemented. The LD Publication engine analyzes the learning contents packages that have been prepared based on the LD and analyzes the constructions and meanings of the information files that describe learning activities in order to model them. The modeled data are fragmentized into effective and accessible forms from in the learning management systems and are then put into the database. LD based learning management systems provide learning effects and learner convenience designed to provide learners with a high performance learning platform. In addition, they will activate the development of content through the reproduction, reuse and sharing of the learning content, which will contribute to the expansion of infrastructures. These systems are also designed to enable linkages among learners' competences, preferences and portfolio, and thus the systems can be easily expanded.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.1
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• pp.19-26
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• 2010

In this paper, we adopt Storage Class Memory, which is next-generation non-volatile RAM technology, as part of main memory parallel to DRAM, and exploit the SCM+DRAM main memory system from the dependability perspective. Our system provides instant system on/off without bootstrapping, dynamic selection of process persistence or non-persistence, and fast recovery from power and/or software failure. The advantages of our system are that it does not cause the problems of checkpointing, i.e., heavy overhead and recovery delay. Furthermore, as the system enables full application transparency, our system is easily applicable to real-world environments. As proof of the concept, we implemented a system based on a commodity Linux kernel 2.6.21 operating system. We verify that the persistence enabled processes continue to execute instantly at system off-on without any state and/or data loss. Therefore, we conclude that our system can improve availability and reliability.

• Journal of KIISE
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• v.41 no.9
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• pp.715-727
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• 2014

The number of subscribers in 4th generation mobile system has been increased rapidly. Along with that, preserving subscribers' privacy has become a hot issue. To prevent users' location from being revealed publicly is important more than ever. In this paper, we first show that the privacy-related problem exists in user authentication procedure in 4th generation mobile system, especially LTE. Then, we suggest an attack model which allows an adversary to trace a user, i.e. he has an ability to determine whether the user is in his observation area. Such collecting subscribers' location by an unauthorized third party may yield severe privacy problem. To keep users' privacy intact, we propose a modified authentication protocol in LTE. Our scheme has low computational overhead and strong secrecy so that both the security and efficiency are achieved. Finally, we prove that our scheme is secure by using the automatic verification tool ProVerif.