• The Journal of the Korea institute of electronic communication sciences
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• v.3 no.3
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• pp.188-194
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• 2008

In this paper, we propose for improving QoS in Hospital wireless network using Cellular-IP/PRC(Paging Route Cache) with Paging Cache and Route Cache in Cellular-IP and propose for performance of realtime and non-real time handoff service using Handoff state machine Paging Route Cache. Although the Cellular-IP/PRC technology is devised for mobile internet communication, it has its vulnerability in frequent handoff environment. This handoff state machine using differentiated handoff improves quality of services in Cellular-IP/PRC Suggested algorithm shows better performance than existing technology in wireless mobile internet communication environment. When speech quality is secured considering increment of interference to receive in case of suppose that proposed acceptance method grooves base radio station capacity of transfer node is plenty, and moat of contiguity cell transfer node was accepted at groove base radio station with a blow, groove base radio station new trench lake acceptance method based on transmission of a message electric power estimate of transfer node be. Do it so that may apply composing PC(Paging Cache) and RC(Routing Cache) that was used to manage paging and router in radio Internet network in integral management and all nodes as one PRC(Paging Router Cache), and add hand off state machine in transfer node so that can manage hand off of transfer node and Roaming state efficiently, and studies so that achieve connection function at node. Analyze benevolent person who influence on telephone traffic in system environment and forecasts each link currency rank and imbalance degree, forecast most close and important lake interception probability and lake falling off probability, GoS(Grade of Service), efficiency of cell capacity in QoS because applies algorithm proposing based on algorithm use gun send-receive electric power that judge by looking downward link whether currency book was limited and accepts or intercept lake and handles and displays QoS performance improvement.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Journal of the korean Society of Automotive Engineers
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• v.18 no.5
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• pp.53-68
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• 1996

마그네슘이 자동차 경량화에 관심이 되는 이유는 근본적으로 CAFE 규제와 같이 경량화를 통한 화석연료의 소모를 크게 억제해야 한다는 사회적인 규제이나, 지난 10여년간의 기술발전으로 내식성이 나쁘다거나 취급이 위험한 금속이라는 인식이 크게 개선된 데에도 있다. 다른 경량금속에 대한 Mg 지금 가격의 비교조건이 호전되었고 향후 원소 재공급의 다변화가 추진되고 있는 것도 환경을 변화시킨 중요한 요인이다. 그간 중요한 경량화 대체 재료로 연구투자가 많았던 유기고분자 재료 및 FRP 등과 같은 복합재료는 폐기부품의 재활용이 어려움 때문에 호나경친화적인 단점이 부각되어, 이 소재의 증가가 주춤해 있다. 마그네슘의 경우에는 재활용이 가능하고, 진동흡수효과가 매우 커서 소음발생을 크게 줄일 뿐만 아니라, 주행 및 내구성시험에서 치수안정성이 좋고 많은 종류의 전자기기 사용에 의한 전자파 차폐효과도 큰 장점을 가지고 있다. 본 고에서는 Mg 다이캐스팅으로 자동차부품의 경량화 현황과 선진국에서 보는 전망을 미국을 중심으로 정리하고, 이와 관련한 Mg 다이캐스팅으로 자동차부품의 경량화 현황과 선진국에서 보는 전망을 미국을 중심으로 정리하고, 이와 관련한 Mg 기술적인 이슈와 시장전망도 서술하였다. 그리고 현재 우리나라의 연구계와 부품업계에서 추진하고 있는 연구개발 동향을 자동차 업계에 소개하는 의미도 있다. 이처럼 우리나라의 현황을 정리해 보는 것은 국내 자동차 산업이 국제적인 경쟁을 하고 있고 Mg기술과 원료확보에서 일본의 견제를 받고 있는 우리의 현실에서도 필요한 작업으로 생각된다.값들로 구성되는 형상을 내구 성능, 성형성등을 고려하여 최종 형상으로 결정한다. 내구성능의 예측은 금속부품의 내구수명 예측에 널리 이용되고 있는 방법이 방진 고무부품의 경우에도 적용 가능한지를 검토하고, 방진 고무부품에도 일반적으로 적용될수 있는 내구수명 예측방안의 개발 가능성을 타진해 보았다. 본 연구의 목표는 시제품을 제작하기 이전에 설계된 부품에 대한 스프링 상수 및 내구특성을 체계적으로 규명하여 제품 시험의 횟수를 줄이고, 보다 정밀한 제품을 제작할 수 있도록 하기 위한 것이다.세포수는 초기 배반포기배에서 팽윤 배반포기배로 진행됨에 따라 두배에서 세배 정도 증가되었음을 알 수 있었다. 또한, differential labelling과 bisbenzimide기법에서 얻어진 각각의 총세포수를 비교하였을 때 총세포수는 발달의 진행 정도에 따라 증가되며 그와 동시에 동일한 군 간의 세포수도 거의 유사함을 알 수 있었다. 따라서, ICM과 TE를 differential labelling하는 기법은 수정란의 quality를 평가하는데 매우 유용한 기법으로서 착상전 embryo 발달을 연구하는데 효과적으로 이용될 수 있다는 것을 시사한다. 고도의 유의차를 나타낸 반면 비수구, 초생수로구 및 Bromegrass 목초구 간에는 아무런 유의차가 인정되지 않았다. 7. 농지보전 처리구인 배수구와 초생수로구는 비처리구에 비해 낮은 침두 유출량과 낮은 토양유실량을 나타내었다.구보다 14% 절감되는 것으로 나타났다.작용하는 것으로 사료된다.된다.정량 분석한 결과이다. 시편의 조성은 33.6 at%

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.1-6
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• 2021

In the field of speech recognition, as the DNN is applied, the use of speech recognition is increasing, but the amount of calculation for parallel training needs to be larger than that of the conventional GMM, and if the amount of data is small, overfitting occurs. To solve this problem, we propose an efficient method for robust voice feature extraction and voice signal noise removal even when the amount of data is small. Speech feature extraction efficiently extracts speech energy by applying the difference in frame energy for speech and the zero-crossing ratio and level-crossing ratio that are affected by the speech signal. In addition, in order to remove noise, the noise of the speech signal is removed by removing the noise of the speech signal with an average predictive improved LMS filter with little loss of speech information while maintaining the intrinsic characteristics of speech in detection of the speech signal. The improved LMS filter uses a method of processing noise on the input speech signal by adjusting the active parameter threshold for the input signal. As a result of comparing the method proposed in this paper with the conventional frame energy method, it was confirmed that the error rate at the start point of speech is 7% and the error rate at the end point is improved by 11%.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.37-52
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• 2015

The recent global trends display expansion and growing solidity in both cooperative collaboration between industry, education, and research and R&D network systems. A greater support for the network and cooperative research sector would open greater possibilities for the evolution of new scholar and industrial fields and the development of new theories evoked from synergized educational research. Similarly, the national need for a strategy that can most efficiently and effectively support R&D network that are established through the government's R&D project research is on the rise. Despite the growing urgency, due to the habitual dependency on simple individual personal information data regarding R&D industry participants and generalized statistical data references, the policies concerning network system are disappointing and inadequate. Accordingly, analyses of the relationships involved for each subject who is participating in the R&D industry was conducted and on the foundation of an educational-industrial-research network system, possible changes within and of the network that may arise were predicted. To predict the R&D network transitions, Common Neighbor and Jaccard's Coefficient models were designated as the basic foundational models, upon which a new prediction model was proposed to address the limitations of the two aforementioned former models and to increase the accuracy of Link Prediction, with which a comparative analysis was made between the two models. Through the effective predictions regarding R&D network changes and transitions, such study result serves as a stepping-stone for an establishment of a prospective strategy that supports a desirable educational-industrial-research network and proposes a measure to promote the national policy to one that can effectively and efficiently sponsor integrated R&D industries. Though both weighted applications of Common Neighbor and Jaccard's Coefficient models provided positive outcomes, improved accuracy was comparatively more prevalent in the weighted Common Neighbor. An un-weighted Common Neighbor model predicted 650 out of 4,136 whereas a weighted Common Neighbor model predicted 50 more results at a total of 700 predictions. While the Jaccard's model demonstrated slight performance improvements in numeric terms, the differences were found to be insignificant.

• Journal of the Korean Society of Propulsion Engineers
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• v.2 no.2
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• pp.14-23
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• 1998

The present study investigates numerically particle laden flow through compressor cascade. In general, a lot of turbine engines are affected by various particles which are suspending in the atmosphere. Especially in the case of aircraft aviating in volcanic, industrial and desert region including many particles, each components of engine system are damaged severely. That damage modes are erosion of compressor binding and rotor path components, partial or total blockage of cooling passage and engine control system degradation.. Initial damages can not be serious but cumulation of damages influences on safety of aircraft control and economical maintenance cost of engine system can be increased. When dust, materials and volcanic particles in the atmosphere flow in the compressor, it is necessary to predict damaged and deposited region of compressor blades. To the various flow inlet angle, predictions of particles trajectory in compressor cascade by Lagrangian method are presented and impulses by impaction of particles at blade surface are calculated. By the definition of particle deposition efficiency, characteristics of particles impact are considered quantitatively. With these prediction and experimental data, erosion rates are predicted for two materials - ceramic, soft metal - on compressor blade surface. Improvements like coating of blade surface could be found, by above prediction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.521-529
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• 2020

Forest fires are one of the most important environmental risks that have adverse effects on many aspects of life, such as the economy, environment, and health. The early detection, quick prediction, and rapid response of forest fires can play an essential role in saving property and life from forest fire risks. For the rapid discovery of forest fires, there is a method using meteorological data obtained from local sensors installed in each area by the Meteorological Agency. Meteorological conditions (e.g., temperature, wind) influence forest fires. This study evaluated a Data Mining (DM) approach to predict the burned area of forest fires. Five DM models, e.g., Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Decision Tree (DT), Random Forests (RF), and Deep Neural Network (DNN), and four feature selection setups (using spatial, temporal, and weather attributes), were tested on recent real-world data collected from Gyeonggi-do area over the last five years. As a result of the experiment, a DNN model using only meteorological data showed the best performance. The proposed model was more effective in predicting the burned area of small forest fires, which are more frequent. This knowledge derived from the proposed prediction model is particularly useful for improving firefighting resource management.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.4
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• pp.274-284
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• 2022

To use kenaf (Hibiscus cannabinus L.) as a fiber and livestock feed, a high-yielding variety needs to be identified. For this, accurate phenotyping of plant height is required for this breeding purpose due to the strong relationship between plant height and yield. Plant height can be estimated using RGB images from unmanned aerial vehicles (UAV-RGB) and photogrammetry based on Structure from Motion (SfM) algorithms. In kenaf, accurate measurement of height is limited because kenaf stems have high flexibility and its height is easily affected by wind, growing up to 3 ~ 4 m. Therefore, we aimed to identify a method suitable for the accurate estimation of plant height of kenaf and investigate the feasibility of using the UAV-RGB-derived plant height map. Height estimation derived from UAV-RGB was improved using multi-point calibration against the five different wooden structures with known heights (30, 60, 90, 120, and 150 cm). Using the proposed method, we analyzed the variation in temporal height of 23 kenaf cultivars. Our results demontrated that the actual and estimated heights were reliably comparable with the coefficient of determination (R²) of 0.80 and a slope of 0.94. This method enabled the effective identification of cultivars with significantly different heights at each growth stages.

• The Journal of the Acoustical Society of Korea
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• v.34 no.5
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• pp.385-393
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• 2015

The speed of train has rapidly been increased in accordance with the developed railway technology. Nowadays, high-speed trains were introduced which has the speed faster than 400 km/h. In Korea, a lots of efforts were undertaken to increase the speed of train faster than 350 km/h, however noise and vibration are still the main problems to solve for realization of the high-speed train. In the case of operation speed faster than 350 km/h, it can be easily presumed that the noise and vibration damages could be increased in the train stations which are close to the passing railway tracks. Thus, the noise in the five different types of high-speed train stations were analyzed including stations built on the ground, underground, under rail, and two types on rail. The present paper predicts noises inside the stations depending on the speed of the passing trains and analyze the noise comparing with noise criteria (NC). Sound insulation performance of each part of buildings was calculated using the transmission noise formula and computer modeling, Finally, a series of processes were introduced to satisfy the aural environment with the optimum interior noise criteria by changing interior finishing materials.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3653-3659
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• 2009

Inter-prediction is always the main bottleneck in H.264/AVC baseline profile. This paper describes an efficient inter-prediction hardware architecture design. H.264/AVC decoder supports various block types but reference software considers only the $4{\times}4$ block when the reference block is being fetched. This causes duplicated pixels which needs extra fetch cycles. In order to eliminate some of the duplicated pixels, the $8{\times}8$ and $4{\times}4$ blocks were considered in the previous design. If the block size is larger than or equal to the $8{\times}8$ block, it will be decomposed into several $8{\times}8$ blocks and if the block size is smaller than the $8{\times}8$ block it will be decomposed into several $4{\times}4$ blocks. Comparing with the reference software, the maximum and minimum cycle reduction of the previous design are 41.5% and 28.2% respectively. For further reduction of the fetch cycles, the various block types are considered in this paper. As a result, the maximum cycle reduction is 18.6% comparing with the previous design.