• Journal of KIISE:Software and Applications
• /
• v.32 no.11
• /
• pp.1021-1028
• /
• 2005

The general solution for classification and regression problems can be found by matching and modifying matrices with the information in real world and then these matrices are teaming in neural networks. This paper treats primary space as a real world, and dual space that Primary space matches matrices using kernel. In practical study, there are two kinds of problems, complete system which can get an answer using inverse matrix and ill-posed system or singular system which cannot get an answer directly from inverse of the given matrix. Further more the problems are often given by the latter condition; therefore, it is necessary to find regularization parameter to change ill-posed or singular problems into complete system. This paper compares each performance under both classification and regression problems among GCV, L-Curve, which are well known for getting regularization parameter, and kernel methods. Both GCV and L-Curve have excellent performance to get regularization parameters, and the performances are similar although they show little bit different results from the different condition of problems. However, these methods are two-step solution because both have to calculate the regularization parameters to solve given problems, and then those problems can be applied to other solving methods. Compared with UV and L-Curve, kernel methods are one-step solution which is simultaneously teaming a regularization parameter within the teaming process of pattern weights. This paper also suggests dynamic momentum which is leaning under the limited proportional condition between learning epoch and the performance of given problems to increase performance and precision for regularization. Finally, this paper shows the results that suggested solution can get better or equivalent results compared with GCV and L-Curve through the experiments using Iris data which are used to consider standard data in classification, Gaussian data which are typical data for singular system, and Shaw data which is an one-dimension image restoration problems.

• Journal of Intelligence and Information Systems
• /
• v.18 no.3
• /
• pp.53-77
• /
• 2012

This study analyses the difference of contents and tones of arguments among three Korean major newspapers, the Kyunghyang Shinmoon, the HanKyoreh, and the Dong-A Ilbo. It is commonly accepted that newspapers in Korea explicitly deliver their own tone of arguments when they talk about some sensitive issues and topics. It could be controversial if readers of newspapers read the news without being aware of the type of tones of arguments because the contents and the tones of arguments can affect readers easily. Thus it is very desirable to have a new tool that can inform the readers of what tone of argument a newspaper has. This study presents the results of clustering and classification techniques as part of text mining analysis. We focus on six main subjects such as Culture, Politics, International, Editorial-opinion, Eco-business and National issues in newspapers, and attempt to identify differences and similarities among the newspapers. The basic unit of text mining analysis is a paragraph of news articles. This study uses a keyword-network analysis tool and visualizes relationships among keywords to make it easier to see the differences. Newspaper articles were gathered from KINDS, the Korean integrated news database system. KINDS preserves news articles of the Kyunghyang Shinmun, the HanKyoreh and the Dong-A Ilbo and these are open to the public. This study used these three Korean major newspapers from KINDS. About 3,030 articles from 2008 to 2012 were used. International, national issues and politics sections were gathered with some specific issues. The International section was collected with the keyword of 'Nuclear weapon of North Korea.' The National issues section was collected with the keyword of '4-major-river.' The Politics section was collected with the keyword of 'Tonghap-Jinbo Dang.' All of the articles from April 2012 to May 2012 of Eco-business, Culture and Editorial-opinion sections were also collected. All of the collected data were handled and edited into paragraphs. We got rid of stop-words using the Lucene Korean Module. We calculated keyword co-occurrence counts from the paired co-occurrence list of keywords in a paragraph. We made a co-occurrence matrix from the list. Once the co-occurrence matrix was built, we used the Cosine coefficient matrix as input for PFNet(Pathfinder Network). In order to analyze these three newspapers and find out the significant keywords in each paper, we analyzed the list of 10 highest frequency keywords and keyword-networks of 20 highest ranking frequency keywords to closely examine the relationships and show the detailed network map among keywords. We used NodeXL software to visualize the PFNet. After drawing all the networks, we compared the results with the classification results. Classification was firstly handled to identify how the tone of argument of a newspaper is different from others. Then, to analyze tones of arguments, all the paragraphs were divided into two types of tones, Positive tone and Negative tone. To identify and classify all of the tones of paragraphs and articles we had collected, supervised learning technique was used. The Na$\ddot{i}$ve Bayesian classifier algorithm provided in the MALLET package was used to classify all the paragraphs in articles. After classification, Precision, Recall and F-value were used to evaluate the results of classification. Based on the results of this study, three subjects such as Culture, Eco-business and Politics showed some differences in contents and tones of arguments among these three newspapers. In addition, for the National issues, tones of arguments on 4-major-rivers project were different from each other. It seems three newspapers have their own specific tone of argument in those sections. And keyword-networks showed different shapes with each other in the same period in the same section. It means that frequently appeared keywords in articles are different and their contents are comprised with different keywords. And the Positive-Negative classification showed the possibility of classifying newspapers' tones of arguments compared to others. These results indicate that the approach in this study is promising to be extended as a new tool to identify the different tones of arguments of newspapers.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.13 no.3
• /
• pp.24-34
• /
• 2005

The automobile seat must satisfy various safety regulations for the passenger's safety. In many design practices, each component is independently designed by concentrating on a single related regulation. However, since multiple regulations can be involved in a seat component, there may be design confliction among the various safety regulations. Therefore, a new design methodology is required to effectively design an automobile seat. The axiomatic approach is employed for considering multiple regulations. The Independence Axiom is used to define the overall flow of the seat design. Functional requirements (FRs) are defined by safety regulations and components of the seat are classified into groups which yield design Parameters (DPs). The classification is carried out to have independence in the FR-DP relationship. Components in a DP group are determined by using orthogonal away of the design of experiments (DOE). Numerical analyses are utilized to evaluate the safety levels by using a commercial software system for nonlinear transient finite element analysis.

• Journal of KIISE:Software and Applications
• /
• v.27 no.5
• /
• pp.563-574
• /
• 2000

Recognition of numeral fields is a very important task for many document automation applications. Conventional methods are based on the two-steps process, segmentation of touching numerals and recognition of the individual numerals. However, due to a large variation of touching types this approach has not produced a robust result. In this paper, we present a new segmentation-free method for recognizing the two touching numerals. In this approach, two touching numerals are regarded as a single pattern coming from 100 classes ('00', '01', '02', ..., '98', '99'). For the test set, we manually extract two touching numerals from the data set of NIST numeral fields. Due to the limitation of conventional neural network in case of large-set classification, we use a modular neural network and Drove its superiority through recognition experimen.

• Journal of Environmental Impact Assessment
• /
• v.22 no.2
• /
• pp.147-156
• /
• 2013

Myanmar(Burma) has been preserved valuable environmental resources because of its political isolation. But recently, Myanmar has moved a capital city(Naypyidaw) at central forest area and it has been urbanized radically since 2005. In this paper, we built multi-temporal land cover map from Landsat images of 1970s to 2012 with ENVI 4.5 software. For a broad approach, administrative district Yamethin which includes Naypyidaw is classified into 3 classes and with only Naypyidaw region is classified with 4-5 classes to analyse specific changes. And with forest cover extracted by Object Oriented Classification, we evaluated forest fragmentation before and after the development using Patch Analyst(FRAGSTATs 3.3) at Yamethin area. For Yamethin area, there were significant forest cover change, 51% in 1999 to 48% in 2012, and for Naypyidaw area, 67% in 1999 to 57% in 2012 respectively. Also landscape indices resulted from Patch Analyst concluded that the total edge, edge density and mean shaped index of forest patches increased and total core area is decreased. It is attributed from land cover change with urbanization and agricultural land expansion.

• Journal of Korea Multimedia Society
• /
• v.21 no.2
• /
• pp.211-223
• /
• 2018

To understand the meaning of data is a common goal of research on unstructured data. Among these unstructured data, there are difficulties in analyzing the meaning of unstructured data related to corpus and sentences. In the existing researches, the researchers used LSA to select sentences with the most similar meaning to specific words of the sentences. However, it is problematic to examine many sentences continuously. In order to solve unstructured data classification problem, several search sites are available to classify the frequency of words and to serve to users. In this paper, we propose a method of classifying documents by using the frequency of similar words, and the frequency of non-relevant words to be applied as weights, and storing them in terms of a multi-pattern storage. We use Tensorflow's Softmax to the nearby sentences for machine learning, and utilize it for unstructured data analysis and the inference of traffic information.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.2
• /
• pp.988-1001
• /
• 2018

Arrhythmia has recently emerged as one of the major causes of death in Koreans. Premature Ventricular Contraction (PVC) is the most common arrhythmia that can be found in clinical practice, and it may be a precursor to dangerous arrhythmias, such as paroxysmal insomnia, ventricular fibrillation, and coronary artery disease. Therefore, we need for a method that can detect an abnormal heart beat and diagnose arrhythmia early. We extracted the features corresponding to the QRS pattern from the subject's ECG signal and classify the premature ventricular contraction waveform using the features. We modified the weighting and bias values based on the error back-propagation algorithm through learning data. We classify the normal signal and the premature ventricular contraction signal through the modified weights and deflection values. MIT-BIH arrhythmia data sets were used for performance tests. We used RR interval, QS interval, QR amplitude and RS amplitude features. And the hidden layer with two nodes is composed of two layers to form a total three layers (input layer 0, output layer 3).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.5
• /
• pp.423-430
• /
• 2017

Recently, data analysis research has been carried out using the deep learning technique in various fields such as image interpretation and/or classification. Various types of algorithms are being developed for many applications. In this paper, we propose a precipitation prediction algorithm based on deep learning with high accuracy in order to take care of the possible severe damage caused by climate change. Since the geographical and seasonal characteristics of Korea are clearly distinct, the meteorological factors have repetitive patterns in a time series. Since the LSTM (Long Short-Term Memory) is a powerful algorithm for consecutive data, it was used to predict precipitation in this study. For the numerical test, we calculated the PWV (Precipitable Water Vapor) based on the tropospheric delay of the GNSS (Global Navigation Satellite System) signals, and then applied the deep learning technique to the precipitation prediction. The GNSS data was processed by scientific software with the troposphere model of Saastamoinen and the Niell mapping function. The RMSE (Root Mean Squared Error) of the precipitation prediction based on LSTM performs better than that of ANN (Artificial Neural Network). By adding GNSS-based PWV as a feature, the over-fitting that is a latent problem of deep learning was prevented considerably as discussed in this study.

• Journal of Korean Institute of Industrial Engineers
• /
• v.25 no.1
• /
• pp.87-99
• /
• 1999

A shop floor control system(SFCS) performs the production activities required to fill orders. In order to effectively control these activities, the autonomous agent-based heterarchical shop floor control architecture is adopted where a supervisor does not exist. In this paper, we define functional perspective of the heterarchical shop floor control using planning, scheduling, and execution modules. In particular, we focus on an execution module that can coordinate the planning and scheduling modules and a general execution module that easily can be modified to execute the other equipment. The execution module can be defined informally as a module that downloads and performs a set of scheduled tasks. The execution module is also responsible for identifying and resolving various errors whether they come from hardware or software. The purpose of this research is to identify all the execution activities and solving techniques under the assumptions of the heterarchical control architecture. And we model the execution module in object-oriented modelling technique for generalization. The execution module modeled in object-oriented concept can be adopted to the other execution module easily. This paper also proposes a classification scheme for execution activities of the heterarchical control architecture. Petri-nets are used as a unified framework for modeling and controlling execution activities. For solving the nonexistence of a supervisor, a negotiation-based solution technique is utilized.

• Journal of the Korea Computer Industry Society
• /
• v.2 no.12
• /
• pp.1505-1516
• /
• 2001

The goal of Medical multimedia server is to develop computer hardware and software which would enable electronic access, storage, transmission, and display of patient data and images. Since the current network only provides so called "best-effort" services, it is impossible to satisfy QoS guarantee that is required for real time application services for emergency room, operating room etc. Accordingly, world-wide research is being made for a variety of services to provide QoS. he goal of DiffServ is to offer scalable differentiated service in Internet which are made possible by traffic classification and conditioning only performed at an edge(or a boundary) node. In case DiffServ was deployed in the Medical multimedia network, it is difficult to estimate how the QoS mechanism would affect totally the network performance. Therefore, we need to verify by simulation the design of algorithm which provide a variety of differentiated services. In QoS for Medical multimedia network, a simulator is designed and implemented using OPNET to investigate the performance of DiffServ QoS support mechanism. The developed DiffServ simulator may generate packets according to random, and bursty traffic models in order to incorporate diverse traffic conditions in the Medical multimedia network environment. Based on our simulation results, we confirmed that service differentiation is possible by using the EF(Expedited Forwarding) class in DiffServ networks.