• Proceedings of the Korea Information Processing Society Conference
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• 2002.11b
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• pp.1531-1534
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• 2002

주로 음성 트래픽을 처리하는 2G CDMA/PCS 시스템과 달리 CDMA2000 및 IxEV-DO 와 같은 차세대 이동통신시스템은 음성 트래픽뿐 만 아니라 패킷형태의 데이터 트래픽도 처리해야 하므로 차세대 이동통신망의 설계 및 디멘져닝을 위해서는 무엇보다도 데이터 트래픽의 주요 특성인 버스트성(Burstiness)과 자기유사성(Self-similarity)이 반영된 트래픽 모델이 요구된다. 이러한 관점에서 본 논문에서는 데이터 트래픽 특성이 서로 다른 다수의 데이터 트래픽의 통합되어 망에 입력되는 경우의 성능을 시뮬레이션 하였고, 그 결과를 해석적 모델인 Norros의 Effective Bandwidth Formula및 Z. FAN의 Bahadur-Rao Theorem을 적용한 Formula와도 비교하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.2
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• pp.364-369
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• 2009

Today, as various access technologies appear, for example WiMax, WiBro, the demand for spectrum is increasing rapidly. Spectrums are a national resource with scarcity value so that we need to a]locate them effectively for the national economy and industry. Accordingly, In the paper, we proposed several factors and new model for estimating spectrum requirements of a next generation mobile network with multimedia data services. These are self-similarity characteristics of data traffic, engineered capacity considering QoS, structure of FA increase, asymmetry of data traffic between uplink and downlink, handoff traffic, and uneven traffic pattern among base stations. It can applied mobile WiMax or other wireless broadband systems in general.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.545-550
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• 2022

In Deep Learning method, it is well known that it requires large amount of data to train the deep neural network. And it also requires the labeling of each data to fully train the neural network, which means that experts should spend lots of time to provide the labeling. To alleviate the problem of time-consuming labeling process, some methods have been suggested such as weak-supervised method, one-shot learning, self-supervised, suggestive learning, and so on. In this manuscript, those methods are analyzed and its possible future direction of the research is suggested.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.83-89
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• 2016

The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.27-34
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• 2010

Recently, lots of internet service companies provide on-line health diagnosis systems. But general persons not having expert knowledge are difficult to use, because most of the health diagnosis systems present prescriptions or dietetic treatments for diseases based on western medicine. In this paper, a self health diagnosis system of oriental medicine coinciding with physical characteristics of Korean using fuzzy ART algorithm, is proposed. In the proposed system, three high rank of diseases having high similarity values are derived by comparing symptoms presented by a user with learned symptoms of specific diseases based on treatment records using neural networks. And also the proposed system shows overall symptoms and folk remedies for the three high rank of diseases. Database on diseases and symptoms is built by several oriental medicine books and then verified by a medical specialist of oriental medicine. The proposed self health diagnosis system of oriental medicine showed better performance than conventional health diagnosis systems by means of learning diseases and symptoms using treatment records.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1305-1311
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• 2016

The smart factory, a combination of ICT technology to the entire production process of a product, means can you intelligent factory is to achieve such reduction and process improvement of the production cost. To implement the smart factory, inevitably must have an internal equipment connections to the external network, this is by equipment which is operated by the existing closure network is exposed to the outside network, the security vulnerability so that gender is increased. In order to solve this problem, it is possible to apply security solutions that are used in normal environments. However, it is impossible to have just completely blocking security threats that can occur in a smart factory network. Further, considering the economic damage that can occur during security breach accident, which cannot be not a serious problem. Therefore, in this paper, a look to know the security measures that can be applied to smart factory, to introduce the main fusion security technology necessary to smart factory dedicated security gateway.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.117-135
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• 2003

This study is to propose a procedure solving scale problem of traditional collaborative filtering (CF) approach. The CF approach generally uses some similarity measures like correlation coefficient. So, as the user of the Website increases, the complexity of computation increases exponentially. To solve the scale problem, this study suggests a clustering model-based approach using Self-Organizing Map (SOM) and RFM (Recency, Frequency, Momentary) method. SOM clusters users into some user groups. The preference score of each item in a group is computed using RFM method. The items are sorted and stored in their preference score order. If an active user logins in the system, SOM determines a user group according to the user's characteristics. And the system recommends items to the user using the stored information for the group. If the user evaluates the recommended items, the system determines whether it will be updated or not. Experimental results applied to MovieLens dataset show that the proposed method outperforms than the traditional CF method comparatively in the recommendation performance and the computation complexity.

• Journal of radiological science and technology
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• v.44 no.6
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• pp.655-661
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• 2021

Positron emission tomography (PET) images is affected by acquisition time, short acquisition times results in low gamma counts leading to degradation of image quality by statistical noise. Noise2Void(N2V) is self supervised denoising model that is convolutional neural network (CNN) based deep learning. The purpose of this study is to evaluate denoising performance of N2V for PET image with a short acquisition time. The phantom was scanned as a list mode for 10 min using Biograph mCT40 of PET/CT (Siemens Healthcare, Erlangen, Germany). We compared PET images using NEMA image-quality phantom for standard acquisition time (10 min), short acquisition time (2min) and simulated PET image (S2 min). To evaluate performance of N2V, the peak signal to noise ratio (PSNR), normalized root mean square error (NRMSE), structural similarity index (SSIM) and radio-activity recovery coefficient (RC) were used. The PSNR, NRMSE and SSIM for 2 min and S2 min PET images compared to 10min PET image were 30.983, 33.936, 9.954, 7.609 and 0.916, 0.934 respectively. The RC for spheres with S2 min PET image also met European Association of Nuclear Medicine Research Ltd. (EARL) FDG PET accreditation program. We confirmed generated S2 min PET image from N2V deep learning showed improvement results compared to 2 min PET image and The PET images on visual analysis were also comparable between 10 min and S2 min PET images. In conclusion, noisy PET image by means of short acquisition time using N2V denoising network model can be improved image quality without underestimation of radioactivity.

• The Journal of the Korea Contents Association
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• v.14 no.4
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• pp.97-109
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• 2014

Social media is a general term for online services by which users share opinions, perspectives, and experiences. It supports interactions between users in sharing contents on it and weak ties among them play an important role in the process. This exploratory study attempts to identify crucial factors of establishing weak ties between social media users in the perspective of social network theory and uncertainty reduction theory. We collected data through diary study and in-depth interview and analyzed it following grounded theory approach. As a result, social media users more actively interacted each other or shared contents based on weak ties, compared to strong ties. In addition, similarity, self-disclosure, and relevance appeared to facilitate establishment of weak ties, by reducing psychological distance between social media users and perceived uncertainty of them.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.109-125
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• 2020

The Ministry of National Defense is pushing for the Defense Acquisition Program to build strong defense capabilities, and it spends more than 10 trillion won annually on defense improvement. As the Defense Acquisition Program is directly related to the security of the nation as well as the lives and property of the people, it must be carried out very transparently and efficiently by experts. However, the excessive diversification of laws and regulations related to the Defense Acquisition Program has made it challenging for many working-level officials to carry out the Defense Acquisition Program smoothly. It is even known that many people realize that there are related regulations that they were unaware of until they push ahead with their work. In addition, the statutory statements related to the Defense Acquisition Program have the tendency to cause serious issues even if only a single expression is wrong within the sentence. Despite this, efforts to establish a sentence comparison system to correct this issue in real time have been minimal. Therefore, this paper tries to propose a "Comparison System between the Statement of Military Reports and Related Laws" implementation plan that uses the Siamese Network-based artificial neural network, a model in the field of natural language processing (NLP), to observe the similarity between sentences that are likely to appear in the Defense Acquisition Program related documents and those from related statutory provisions to determine and classify the risk of illegality and to make users aware of the consequences. Various artificial neural network models (Bi-LSTM, Self-Attention, D_Bi-LSTM) were studied using 3,442 pairs of "Original Sentence"(described in actual statutes) and "Edited Sentence"(edited sentences derived from "Original Sentence"). Among many Defense Acquisition Program related statutes, DEFENSE ACQUISITION PROGRAM ACT, ENFORCEMENT RULE OF THE DEFENSE ACQUISITION PROGRAM ACT, and ENFORCEMENT DECREE OF THE DEFENSE ACQUISITION PROGRAM ACT were selected. Furthermore, "Original Sentence" has the 83 provisions that actually appear in the Act. "Original Sentence" has the main 83 clauses most accessible to working-level officials in their work. "Edited Sentence" is comprised of 30 to 50 similar sentences that are likely to appear modified in the county report for each clause("Original Sentence"). During the creation of the edited sentences, the original sentences were modified using 12 certain rules, and these sentences were produced in proportion to the number of such rules, as it was the case for the original sentences. After conducting 1 : 1 sentence similarity performance evaluation experiments, it was possible to classify each "Edited Sentence" as legal or illegal with considerable accuracy. In addition, the "Edited Sentence" dataset used to train the neural network models contains a variety of actual statutory statements("Original Sentence"), which are characterized by the 12 rules. On the other hand, the models are not able to effectively classify other sentences, which appear in actual military reports, when only the "Original Sentence" and "Edited Sentence" dataset have been fed to them. The dataset is not ample enough for the model to recognize other incoming new sentences. Hence, the performance of the model was reassessed by writing an additional 120 new sentences that have better resemblance to those in the actual military report and still have association with the original sentences. Thereafter, we were able to check that the models' performances surpassed a certain level even when they were trained merely with "Original Sentence" and "Edited Sentence" data. If sufficient model learning is achieved through the improvement and expansion of the full set of learning data with the addition of the actual report appearance sentences, the models will be able to better classify other sentences coming from military reports as legal or illegal. Based on the experimental results, this study confirms the possibility and value of building "Real-Time Automated Comparison System Between Military Documents and Related Laws". The research conducted in this experiment can verify which specific clause, of several that appear in related law clause is most similar to the sentence that appears in the Defense Acquisition Program-related military reports. This helps determine whether the contents in the military report sentences are at the risk of illegality when they are compared with those in the law clauses.