• Asia pacific journal of information systems
• /
• v.19 no.2
• /
• pp.139-155
• /
• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Journal of Biomedical Engineering Research
• /
• v.28 no.2
• /
• pp.258-264
• /
• 2007

Spatiotemporal changes of brain rhythmic activity at a certain frequency have been usually monitored in real time using scalp potential maps of multi-channel electroencephalography(EEG) or magnetic field maps of magnetoencephalography(MEG). In the present study, we investigate if it is possible to implement a real-time brain activity monitoring system which can monitor spatiotemporal changes of cortical rhythmic activity on a subject's cortical surface, neither on a sensor plane nor on a standard brain model, with a high temporal resolution. In the suggested system, a frequency domain inverse operator is preliminarily constructed, considering the individual subject's anatomical information, noise level, and sensor configurations. Spectral current power at each cortical vertex is then calculated for the Fourier transforms of successive sections of continuous data, when a single frequency or particular frequency band is given. An offline study which perfectly simulated the suggested system demonstrates that cortical rhythmic source changes can be monitored at the cortical level with a maximal delay time of about 200 ms, when 18 channel EEG data are analyzed under Pentium4 3.4GHz environment. Two sets of artifact-free, eye closed, resting EEG data acquired from a dementia patient and a normal male subject were used to show the feasibility of the suggested system. Factors influencing the computational delay are investigated and possible applications of the system are discussed as well.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.7
• /
• pp.59-70
• /
• 2014

This paper propose a new wireless multi-hop routing protocol that takes the hierarchical mesh of the hybrid WMN into account. WMN that is possible to provide various applications of wireless networks still has many open issues that should be solved despite the studies carried out over a decade. Especially, in routing protocol area, a problem degrading the routing efficiency by applying one of the routing protocols, which are designed for the MANET, to the hybrid WMN be solved above all. For the improvement of the routing performance, both good routing protocol and metric are essential. However, the recent studies are only concentrated in routing metric by use of the cross-layer design. Therefore, this paper is dedicated to the routing protocol that is essential for the performance of the routing but needed more studies. The proposed protocol in this paper is reactive, and designed to reorganize the hybrid WMN with several pseudo domains, and carry out domain-based route decision. By the simulation result for the performance analysis of the proposed protocol, the average delay for the route decision was decreased by 43% compared to AODV that is the typical reactive protocol.

• Convergence Security Journal
• /
• v.19 no.4
• /
• pp.87-96
• /
• 2019

Recently, with the rapid development of video equipment and technology, tremendous video information is produced and utilized in military domain to acquire battlefield information or for effective command control. Note that the video playback devices currently used in the military domain ranges from low-performance tactical multi-functional terminals (TMFT) to high-performance video servers and the networks where the video information is transmitted also range from the low speed tactical information and communication network (TICN) to ultra-high speed defense broadband converged networks such as M-BcN. Therefore, there is a need for an efficient streaming technique that can efficiently transmit defense video information in heterogeneous communication equipment and network environments. To solve the problem, this paper proposes a Scalable Video Coding (SVC) and balanced selection algorithm based Peer-to-Peer (P2P) streaming technique and the feasibility of the proposed technique is verified by simulations. The simulation results based on our BitTorrent simulator show that the proposed balanced selection scheme outperforms the sequential or rarest selection algorithm.

• Convergence Security Journal
• /
• v.14 no.7
• /
• pp.53-58
• /
• 2014

MANET can quickly build a network because it is configured with only the mobile node and it is very popular today due to its various application range. However, MANET should solve vulnerable security problem that dynamic topology, limited resources of each nodes, and wireless communication by the frequent movement of nodes have. In this paper, we propose a domain-based distributed cooperative intrusion detection techniques that can perform accurate intrusion detection by reducing overhead. In the proposed intrusion detection techniques, the local detection and global detection is performed after network is divided into certain size. The local detection performs on all the nodes to detect abnormal behavior of the nodes and the global detection performs signature-based attack detection on gateway node. Signature DB managed by the gateway node accomplishes periodic update by configuring neighboring gateway node and honeynet and maintains the reliability of nodes in the domain by the trust management module. The excellent performance is confirmed through comparative experiments of a multi-layer cluster technique and proposed technique in order to confirm intrusion detection performance of the proposed technique.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.13 no.2
• /
• pp.231-244
• /
• 2000

This paper presents a dynamic analysis technique for a 2-D soil-structure interaction problem in the frequency domain, which can directly be applied as an analysis tool for seismic response analyses of underground structures, tunnels, embankments, and so on. In this method, the structure and near-field soil is modeled by the standard finite elements, while the unbounded far-field soil is represented using the dynamic infinite elements in the frequency domain. The earthquake-input motion is regarded as traveling P and SV waves which are incident vertically from the far-field of underlying half-space to the near-field of layered medium. The equivalent earthquake forces are then calculated utilizing so-called fixed-exterior-boundary-method and the free-field responses including displacements and tractions. For the verification of the present study, seismic response analyses are carried out for a multi-layered half-space free-field soil medium and a cylindrical cavity embedded in a homogeneous half-space. Comparisons of the present results with solutions by other approaches indicate that the proposed methodology gives accurate estimates. Finally, an application example of seismic response analysis for a subway station is presented, which demonstrates the applicability of the present study.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.175-197
• /
• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.4
• /
• pp.303-307
• /
• 2002

Virtual hosting is a typical service to connect each directory of site and domain name. If traffic amounts may increase at one site present in the server, then it affects traffic amounts of other sites as well (including the sites which have flew requests). To overcome this problem, we suggest a simple feedback-control concept for the system by periodically monitoring the traffic and properly actuating traffic dispersions by investigating the log file. Specifically, large files are to be served in a backup server (to reduce the workload of the main server) by changing their own URL's in html format. In other words, it automatically redistributes the workload by using the URL. Furthermore, we also use the redirecting method by just adding html tags to html header. This method efficiently handles the workload and maintains the capability of the server effectively to the varying workload.

• The Research Journal of the Costume Culture
• /
• v.2 no.1
• /
• pp.93-104
• /
• 1994

Nowadays computer technology is being applied in various areas of apparel design. In particular, since the task of pattern making is to be performed by a set of predefined drawing rules, the effect of computer application in pattern making will be significant, There have been a large number of studies on pattern making program. For instance, the previous studies have developed computer programs for pattern making of women's wear, men's wear, children's wear, Han-Bok, etc. Most of them have focused on the development of computer program for a particular kind of apparel only and, however, have disregarded the feasibility of developing a multi-purposed computer program so that is just can be modified to adopt for various styles. For example, by widening the hem-wide of the basic H-Line skirt and then connecting its waist line and widened hem-wide, we can draw the A-Line skirt. Therefore, we have developed a program which can make a pattern for the basic skirt and can mae, with a slight change of he program, other patterns for various style as well. The objective of this paper is to identify and organize modules which will be used for developing a general pattern making computer system. This general pattern making system is a computer program by which we can draw a variety of apparel styles. This system is restricted to skirt pattern making only. there presentation scheme used in organizing these modules is an AND-OR tree, the one being often used in representing a complex problem in artificial intelligence domain.

• Journal of Ocean Engineering and Technology
• /
• v.15 no.2
• /
• pp.11-21
• /
• 2001

The focus of this paper is on the numerical investigation of obliquely incident wav interactions with a system composed of fully submerged and floating dual buoy/vertical-flexible-membrane breakwaters placed in parallel with spacing between two systems. The fully submerged two systems allow surface and bottom gaps to enable wave transmission over and under the system. The problem is formulated based on the two-dimensional multi-domain hydro-elastic linear wave-body interaction theory. The hydrodynamic interaction of oblique incident waves with the combination of the rigid and flexible bodies was solved by the distribution of the simple sources (modified Bessel function of the second kind) that satisfy the Helmholz governing equation in fluid domains. A boundary element program for three fluid domains based on a discrete membrane dynamic model and simple source distribution method is developed. Using this developed computer program, the performance of various dual systems varying buoy radiuses and drafts, membrane lengths, gaps, spacing, mooring-lines stiffness, mooring types, water depth, and wave characteristics is thoroughly examined. It is found that the fully submerged and floating dual buoy/membrane breakwaters can, if it is properly tuned to the coming waves, have good performances in reflecting the obliquely incident waves over a wide range of wave frequency and headings.