• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.409-416
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• 2017

In this paper, we consider a framework of compressed sensing over finite fields. One measurement sample is obtained by an inner product of a row of a sensing matrix and a sparse signal vector. A recovery algorithm proposed in this study for sparse signals based probabilistic decoding is used to find a solution of compressed sensing. Until now compressed sensing theory has dealt with real-valued or complex-valued systems, but for the processing of the original real or complex signals, the loss of the information occurs from the discretization. The motivation of this work can be found in efforts to solve inverse problems for discrete signals. The framework proposed in this paper uses a parity-check matrix of low-density parity-check (LDPC) codes developed in coding theory as a sensing matrix. We develop a stochastic algorithm to reconstruct sparse signals over finite field. Unlike LDPC decoding, which is published in existing coding theory, we design an iterative algorithm using probability distribution of sparse signals. Through the proposed recovery algorithm, we achieve better reconstruction performance as the size of finite fields increases. Since the sensing matrix of compressed sensing shows good performance even in the low density matrix such as the parity-check matrix, it is expected to be actively used in applications considering discrete signals.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.29-34
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• 2015

Brain-computer interface (BCI) is being studied for convenient life in various application fields. The purpose of this study is to investigate a changing electroencephalography (EEG) for precise motion of a robot or an artificial arm. Three subjects who participated in this experiment performed three-task: Grip, Move, Relax. Acquired EEG data was extracted feature data using two feature extraction algorithm (power spectrum analysis and multi-common spatial pattern). Support vector machine (SVM) were applied the extracted feature data for classification. The classification accuracy was the highest at Grip class of two subjects. The results of this research are expected to be useful for patients required prosthetic limb using EEG.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.1-9
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• 2017

With the Development of Wireless Network Technology and Wireless Link Technology, Wireless Mesh Network (WMN) is Attracting Attention as a Key Technology to Construct the Wireless Transit Network. The WMN has been Studied for a Long Time in Various Fields, however there are still many Problems that have not been solved yet. One of them is the Routing Problem to find an Optimal path in a Multi-hop Network Composed of Wireless Links. In the Hybrid-WMN, which is one of the Three Types of WMN, Optimal Path Selection Requires Research on Path Search Protocols that Effectively use the Infrastructure Mesh as a Transit Network, Together with Research for a Routing Metric with Excellent Performance. Therefore, this Paper Proposes a Dual Mode-AODV(Ad hoc On-demand Distance Vector) for Hybrid-WMN. Simulation result shows that the Path Selection Delay was Reduced by 52% than AODV when the Proposed Dual Mode-AODV was applied.

• Knowledge Management Research
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• v.16 no.4
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• pp.109-132
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• 2015

We examine offline and online channel sales of experience goods, and compare and contrast the sales patterns of existing products and new products between channels. To this end, we obtain the channel-specific time-series sales data from the leading company selling beauty products, both offline and online. By applying the Vector Autoregressive Model, we empirically find out how the relationship between existing products and new products changes between the shopping channels. Our empirical findings are as follows. First, the sales effects from existing products to new products are significantly positive at both offline and online channels, and this positive effect is greater in the offline channel than in the online channel. Second, the influence of new products on existing products is more positive in the offline channel than in the online channel. Third, the impact of existing products sales on new products sales is greater than that of new products on existing products. Lastly, the inertia effect, the effect within the same shopping channel and the same selling product, is significantly positive in the offline channel but not in the online channel, and this asymmetric inertia effect emerges as we focus on experience goods. Moreover, the impulse response function analysis provides the three important implications. First, companies should pay attention to the same channel but different types of products. Second, the offline channel is more vulnerable to market shock than the online channel. Third, new products sales vary by existing products sales to the greater extent, compared to the opposite relationship. We believe our study contributes theoretically and practically to the fields of marketing and knowledge management.

• Journal of the Korean Geographical Society
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• v.45 no.3
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• pp.319-341
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• 2010

The purposes of this study are to classify heavy snowfall types in the Republic of Korea based on fresh snowfall data and atmospheric circulation data during the last 36(1973/74-2008/09) snow seasons and to identify typical surface synoptic climate patterns that characterize each heavy snowfall type. Four synoptic climate categories and seventeen regional heavy snowfall types are classified based on sea level pressure/surface wind vector patterns in East Asia and frequent spatial clustering patterns of heavy snowfall in the Republic of Korea, respectively. Composite analyses of multiple surface synoptic weather charts demonstrate that the locations and intensity of pressure/wind vector mean and anomaly cores in East Asia differentiate each regional heavy snowfall type in Korea. These differences in synoptic climatic fields are primarily associated with the surge of the Siberian high pressure system and the appearance of low pressure systems over the Korean Peninsula. In terms of hemispheric atmospheric circulation, synoptic climatic patterns in the negative mode of winter Arctic Oscillation (AO) are also associated with frequent heavy snowfall in the Republic of Korea at seasonal scales. These results from long-term synoptic climatic data could contribute to improvement of short-range or seasonal prediction of regional heavy snowfall.

• Korean Journal Plant Pathology
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• v.2 no.1
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• pp.17-21
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• 1986

The viruliferous vectors of the rice dwarf virus, nymphs of Nephotettix cincticeps did not overwinter in Uljin, although the disease occurred in fields. When considered the relationship between seasonal changes of vector and disease occurrence, there were 5 and 3 peaks in a year in occurrence of vector and disease, respectively. The over­wintered adults and the nymphs of the 2nd and 3rd generation served as the major transmittor of the. virus. In a field where the disease has been a problem for years, the ratoon hills rice cultivar Milyang No. 30 was infected $22.4\~26.8\%$ with the rice dwarf virus. When nonviruliferous nymphs were fed on the infected ratoon hills for 11 to 30 days, viruliferous nymphs overwintered, increased to $13.0\~18.2\%$. The winter barley infected with rice dwarf virus did not survive in winter, suggesting that infected barley may not serve as a inoculum source.

• Progress in Superconductivity
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• v.5 no.2
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• pp.98-104
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• 2004

We analyzed a noise-sensitivity profile of a specific SQUID sensor system for the localization of brain activity. The location of a neuromagnetic current source is estimated from the recording of spatially distributed SQUID sensors. According to the specific arrangement of the sensors, each site in the source space has different sensitivity, that is, the difference in the lead field vectors. Conversely, channel noises on each sensor will give a different amount of the estimation error to each of the source sites. e.g., a distant source site from the sensor system has a small lead-field vector in magnitude and low sensitivity. However, when we solve the inverse problem from the recorded sensor data, we use the inverse of the lead-field vector that is rather large, which results in an overestimated noise power on the site. Especially, the spatial sensitivity profile of a gradiometer system measuring tangential fields is much more complex than a radial magnetometer system. This is one of the causes to make the solutions of inverse problems unstable on intervening of the sensor noise. In this study, in order to improve the localization accuracy, we calculated the noise-sensitivity profile of our 40-channel planar SQUID gradiometer system, and applied it as a normalization weight factor to the source localization using synthetic aperture magnetometry.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1909-1916
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• 2017

Recently, deep learning algorithms have good performance in various fields, but they are not actively applied to sonar systems. In this study, we carried out experiments to classify active sonar returns into a metal object such as a mine and a rock using a convolutional neural network which is one of the deep learning algorithms. Data augmentation is applied on this paper to avoid overfitting and increase performance. And we analyzed performance variation depending on hyperparameter value and change of the number of training data through data augmentation. The experiments are performed with two training data; an aspect-angle independent and an aspect-angle dependent. As a result, the performances are 88.9% and 94.9% in aspect-angle independent and dependent, respectively. These are up to 4.5% point higher than the performance obtained by applying artificial neural network and support vector machine algorithm in the previous study.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.227-232
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• 2016

3D body scan data are used widely in various fields to make products and living spaces for superior human body fitness. Based on the 3D measurements of human bodies for teens in Size Korea 2013, this research provides a way of finding the representative body sizes and 3D body scan data. First, a multi-dimensional vector space consisting of many measurement items was projected onto a 2D vector space with circumference and length components via factor analysis. The representative body sizes and 3D scan data close to these values were obtained via the Mahalanobis distance in 2D space. Considering the adolescent growth pattern shown on this 2D space, males were divided into 4 age groups and females were divided into 3 age groups. Using the eigenbodies corresponding to the column vectors of the component score coefficient matrix, the representative body sizes of 13 measurement items (male) and 14 measurement items (female) for each age group were calculated. The representative body sizes and 3D scan data are very useful for modeling representative 3D human figures.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.