• 한국컴퓨터정보학회논문지
• /
• 제21권1호
• /
• pp.115-123
• /
• 2016

In this study, we collect various side effect pairs which are appeared frequently at many drugs, and select side effect pairs that have higher severity. For every selected side effect pair, we extract common genetic networks which are shared by side effects' genes and drugs' target genes based on PPI(Protein-Protein Interaction) network. For this work, firstly, we gather drug related data, side effect data and PPI data. Secondly, for extracting common genetic network, we find shortest paths between drug target genes and side effect genes based on PPI network, and integrate these shortest paths. Thirdly, we develop a classification model which uses this common genetic network as a classifier. We calculate similarity score between the common genetic network and genetic network of a drug for classifying the drug. Lastly, we validate our classification model by means of AUC(Area Under the Curve) value.

• 대한전기학회논문지
• /
• 제43권5호
• /
• pp.838-847
• /
• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2010년도 춘계학술대회
• /
• pp.523-526
• /
• 2010

FM 무전기는 반이중통신(Half-duplex)방식을 사용한다, FM 무전기를 DCE(Data Circuitterminating Equipment)로 사용하고 다수의 가입자가 정보교환을 수행하는 통신망에서 데이터 전송이 일어나고 있는지를 검출하고 데이터 전송 시 충돌을 방지하기 위해 Network Access Control을 사용한다. 본 논문에서는 현재 우리 군에서 운용하고 있는 FM무전기를 사용하는 통신망에서 MIL-STD-188-220C의 Network Access Control 방법 중 R-NAD(Random Network Access Delay)를 적용한 MPC8260 Power QUICC 기반의 디지털 메시지 전송장비에 대하여 연구한다.

• 한국컴퓨터정보학회논문지
• /
• 제6권4호
• /
• pp.68-73
• /
• 2001

MPLS(Multi protocol Label Switching)는 Network Traffic 흐름의 속도를 높이고 관리하기 쉽게 하기 위한 표준 기술이다. MPLS는 정해진 Pack 열에 특정 경로를 설정하는 것에 관여하고, 각 Pack 내에는 라벨이 있어 Router 입장에서는 그 Pack을 전달할 노드의 주소를 확인하여 소요시간을 절약한다. MPLS는 IP, ATM및 프레임 릴레이 Network protocol 등과 같이 작동한다. MPLS는 Network OSI 참조모델과 함께 3Layer가 아닌 Switching을 하는 2Layer에서 대부분의 Pack이 전달되게 한다. MPLS는 Traffic을 빠르게 움직이게 하며, QoS를 위한 Network관리를 쉽게 한다. 이런 이유에 MPLS 기술은 더 많고 특정한 Traffic을 전송하기 시작한 Network로 채택될 유망한 기술로 기대되고 있다.

• International Journal of Computer Science & Network Security
• /
• 제21권7호
• /
• pp.159-168
• /
• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• 한국측량학회지
• /
• 제37권2호
• /
• pp.91-97
• /
• 2019

In automatic map generalization, the formalization of cartographic principles is important. This study proposes and evaluates the selection method for road network generalization that analyzes existing maps using reverse engineering and formalizes the selection rules for the road network. Existing maps with a 1:5,000 scale and a 1:25,000 scale are compared, and the criteria for selection of the road network data and the relative importance of each network object are determined and analyzed using $T{\ddot{o}}pfer^{\prime}s$ Radical Law as well as the logistic regression model. The selection model derived from the analysis result is applied to the test data, and road network data for the 1:25,000 scale map are generated from the digital topographic map on a 1:5,000 scale. The selected road network is compared with the existing road network data on the 1:25,000 scale for a qualitative and quantitative evaluation. The result indicates that more than 80% of road objects are matched to existing data.

• 스마트미디어저널
• /
• 제11권5호
• /
• pp.82-90
• /
• 2022

Along with the growing popularity of 5G technology, providing flexible and personalized network services suitable for requirements of customers has also become a lucrative venture and business key for network service providers. Therefore, dynamic network provisioning is needed to help network service providers. Moreover, increasing user demand for network services meets specific requirements of users, including location, usage duration, and QoS. In this paper, a routing algorithm, which makes routing decisions using Reinforcement Learning (RL) based on the information about link stability, is proposed and called Link Stability aware Reinforcement Learning (LSRL) routing. To evaluate this algorithm, several mininet-based experiments with various network settings were conducted. As a result, it was observed that the proposed method accepts more requests through the evaluation than the past link annotated shorted path algorithm and it was demonstrated that the proposed approach is an appealing solution for dynamic network provisioning routing.

• Journal of information and communication convergence engineering
• /
• 제19권4호
• /
• pp.241-247
• /
• 2021

Particulate matter has emerged as a serious global problem, necessitating highly reliable information on the matter. Therefore, various algorithms have been used in studies to predict particulate matter. In this study, we compared the prediction performance of neural network models that have been actively studied for particulate matter prediction. Among the neural network algorithms, a deep neural network (DNN), a recurrent neural network, and long short-term memory were used to design the optimal prediction model using a hyper-parameter search. In the comparative analysis of the prediction performance of each model, the DNN model showed a lower root mean square error (RMSE) than the other algorithms in the performance comparison using the RMSE and the level of accuracy as metrics for evaluation. The stability of the recurrent neural network was slightly lower than that of the other algorithms, although the accuracy was higher.

• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 2002년도 가을 학술발표논문집 Vol.29 No.2 (3)
• /
• pp.76-78
• /
• 2002

Multicasting over mobile IP network becomes more important with the increasing needs of supporting multimedia services in mobile network. The IETF has suggested two approaches which are remote subscription and bidirectional tunneling for supporting mobility management in multicasting over mobile IP. But these protocols have problems - the frequent reconstruction of multicast delivery tree, packet less during handoff, convergence problem, and so on. In this paper, we propose to use preconfiguration of multicast delivery tree when mobile host enters the foreign network. It will decrease the frequency of multicast delivery tree reconstruction, and reduce the packet loss during handoff, Also the multicast delivery tree maintained by Keep Alive messages makes the signaling overload of networks diminished.

• ETRI Journal
• /
• 제46권3호
• /
• pp.461-472
• /
• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.