• The Journal of the Acoustical Society of Korea
• /
• v.17 no.1E
• /
• pp.54-65
• /
• 1998

This study attempts to test the classifying performance of a neural network and thereby examine its applicability to the signals distorted in a shallow water environment. Linear frequency modulated(LFM) signals are simulated by using an acoustic model and also measured through sea experiment. The network is constructed to have three layers and trained on both data sets. To get normalized power spectra as feature vectors, the study considers the three transforms : shot-time Fourier transform (STFT), wavelet transform (WT) and pseudo Wigner-Ville distribution (PWVD). After trained on the simulated signals over water depth, the network gives over 95% performance with the signal to noise ratio (SNR) being up to-10 dB. Among the transforms, the PWVD presents the best performance particularly in a highly noisy condition. The network performs worse with the summer sound speed profile than with the winter profile. It is also expected to present much different performance by the variation of bottom property. When the network is trained on the measured signals, it gives a little better results than that trained on the simulated data. In conclusion, the simulated signals are successfully applied to training a network, and the trained network performs well in classifying the signals distorted by a surrounding environment and corrupted by noise.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.8
• /
• pp.1683-1690
• /
• 2005

This study examines dynamic flow control method in UDP, analyzes packet loss which is frequently used element in measuring existing dynamic flow control and characteristics of round trip time, and proposes a new method of measurement, RSST. The algorithm that uses the proposed RSST enables accurate measurement of network status by considering both the currently measured network status and the past history of network status in controlling the transmission rate. For comparison study, a network status measurement software program that displays detailed information about volume of transmission generation of network status, and flow pattern of network was developed. The performance test shows that the proposed algorithm can better adjust to network condition in terms of low pack loss rate over existing algorithms.

• Journal of Biosystems Engineering
• /
• v.25 no.5
• /
• pp.409-414
• /
• 2000

A machine vision system was built to obtain single stationary image from an egg. This system includes a CCD camera, an image processing board and a lighting system. A computer program was written to acquire, enhance and get histogram from an image. To minimize the evaluation time, the artificial neural network with the histogram of the image was used for eggshell evaluation. Various artificial neural networks with different parameters were trained and tested. The best network(64-50-1 and 128-10-1) showed an accuracy of 87.5% in evaluating eggshell. The comparison test for the elapsed processing time per an egg spent by this method(image processing and artificial neural network) and by the processing time per an egg spent by this method(image processing and artificial neural network) and by the previous method(image processing only) revealed that it was reduced to about a half(5.5s from 10.6s) in case of cracked eggs and was reduced to about one-fifth(5.5s from 21.1s) in case of normal eggs. This indicates that a fast eggshell evaluation system can be developed by using machine vision and artificial neural network.

• 한국정보통신설비학회:학술대회논문집
• /
• 2008.08a
• /
• pp.45-50
• /
• 2008

Many applications of sensor network require connection to the Internet. The transmission protocol of traditional sensor network was designed within the sensor network itself. However, based on 6LoWPAN which can be accessed using IPv6, direct connection is possible between the sensor network and the TCP/IP network outside. Transmission of data in applications of sensor network falls into two main categories. One is a small packet that is periodically produced such as packet related to temperature and humidity. The other is a relatively large packet that brings about network overheads such as images. We investigated the conformance test and pros and cons of application data over the transmission protocol of Zigbee and 6LoWPAN. As a result, both Zigbee and 6LoWPAN have shown low rate of loss for periodic data and have in creased reliability of data transfer. When transmitting streaming image data, both ACK, non ACK mode of Zigbee and UDP of 6LoWPAN minimized transmission time but suffered the consequences of high packet loss. Even though TCP of 6LoWPAN required a long transmission time, we were able to confirm that no loss has occurred.

• Nuclear Engineering and Technology
• /
• v.39 no.4
• /
• pp.337-348
• /
• 2007

A fuzzy neural network model is presented to predict residual stress for dissimilar metal welding under various welding conditions. The fuzzy neural network model, which consists of a fuzzy inference system and a neuronal training system, is optimized by a hybrid learning method that combines a genetic algorithm to optimize the membership function parameters and a least squares method to solve the consequent parameters. The data of finite element analysis are divided into four data groups, which are split according to two end-section constraints and two prediction paths. Four fuzzy neural network models were therefore applied to the numerical data obtained from the finite element analysis for the two end-section constraints and the two prediction paths. The fuzzy neural network models were trained with the aid of a data set prepared for training (training data), optimized by means of an optimization data set and verified by means of a test data set that was different (independent) from the training data and the optimization data. The accuracy of fuzzy neural network models is known to be sufficiently accurate for use in an integrity evaluation by predicting the residual stress of dissimilar metal welding zones.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.1
• /
• pp.240-259
• /
• 2020

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.

• Knowledge Management Research
• /
• v.13 no.4
• /
• pp.71-82
• /
• 2012

The purpose of this study is to investigate the effects of employees who attend graduate school on the expansion of the knowledge sharing network in their company. For this purpose, the researchers chose 10 worker-graduate students and 75 members of company 'A' that they belong to and 107 members of university 'B' that they belong to, 172 members in total. 10 overlapped employee-students were excluded. The results of this study are summarized as follow: First, the personal relations of the employee-students enhanced after they have entered the graduate school. The score for the question was 3.85 out of 5 points. Second, the employee-students played the role of the knowledge bridge between company's co-worker network and graduate school's classmate network. It was confirmed that the density of the company's network was higher than the density of the connected network of the company and the graduate school. The analysis result confirmed that the difference of the two groups was significant. This means that the company carried out exchange with more members and therefore gained various kinds of knowledge. Also, in all types of network, the structural hole of the company network was lower than that of the connected network of the company and graduate school. The ANOVA test using QAP procedure confirmed that the difference of two groups was significant (friendship network F=1.2856, p<0.05; information network F=1.278, p<0.05; and trust network F=1.23, p<0.05). It means that the company not only gained the newly acquired knowledge by the knowledge bridge of the employee-students, but also was able to share it more effectively with members. Third and lastly, the employee-students share various information related to the organization, duties and roles rest in the company throughout break time, working hours and direct inquiries. This means that the employee-students contributed to the innovation of knowledge sharing in the company by sharing knowledge that they gained from the graduate school within the company.

• Journal of the Korean Geotechnical Society
• /
• v.17 no.6
• /
• pp.25-36
• /
• 2001

The purpose of this paper is to discuss the effects of soil properties such as liquid limit, water content, etc. on the compression index and to propose the empirical equation of compression index far regional clay and to verify the application Back Propagation Neural Network(BPNN). The compression index values obtained from laboratory tests are in the range of 0.01 to 3.06 for clay soils sampled in eleven regions. As the compare with the results of laboratory test and the predicted compression index value from the proposed empirical equations, the results of empirical equations including single soil parameter have a possibility to be overestimated. Also, the results of empirical equations including multiple soil parameters closed to the measured value more than that of empirical equations including single soil parameter, but the standard error for measured value obtained larger than 0.05. For these reasons, the empirical equations including single or multiple soil parameters proposed base on the results of laboratory test and the determination coefficient is up to 0.89. The result of BPNN shows that correlation coefficient and standard error between test and neural network result is larger than 0.925 and smaller than 0.0196, which means high correlativity, respectively. Especially, the estimated result by neural network, using only three parameters such as natural water content, dry unit weight and in-situ void ratio among various factors is available to the estimation of compression index and the correlation coefficient is 0.974. This result verified the possibility that if BPNN use, the compression index can be predicted by the parameters, which obtained from simplex field test.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2169-2173
• /
• 2003

The development project of Korean High Speed Train (KHST) was started in 1996. As a national research project, the KHST project aims for a development of the next generation prototype train that has a maximum speed of 350 km/h. The development process of prototype KHST including 7 vehicles was completed last year and currently the prototype train is on its way of test running over the test track with gradually increased speed. The prototype KHST uses the real time network called TCN (Train Communication Network) for exchanging information between various onboard control equipments. After 10 years of development and modification period, TCN was confirmed as international standard (IEC61375-1) for the electrical railway equipment train bus. In the prototype KHST, all major control devices are connected by TCN and exchange their information. Such devices include SCU (Supervisory Control Unit), ATC (Automatic Train Control), TCU (Traction Control Unit), and so forth. For each device that sends and receives data using TCN, a device has to find out whether TCN is in normal or failure state before its data exchange. And also a device must have a proper method of data validation that was received in a normal TCN state. This is a one of the major important factors for devices using network. Some misleading information can lead the entire system to a catastrophic condition. This paper briefly explains how TCN was implemented in the prototype KHST train, and also shows what kind of the fault diagnosis method was adopted for a fail safe operation of TCN system

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.9
• /
• pp.1710-1716
• /
• 2016

In general, software upgrade technique is needed to add new features or fix bug of software on a network devices. However, the problem is that the software must be upgraded after the termination of networking service to replace new package. An ISSU(In-Service Software Upgrade) technique is used to solve such the problem. ISSU is a technology to upgrade the software without interrupting the network service or an offline network equipment. In this paper, to provide a seamless networking service, we design and implement an architecture to apply ISSU technique to a network operating system. In this paper, we use high-availability feature in N2OS which has been developed by ETRI. In addition, in order to verify that the implemented ISSU function is operation properly, we proceed to test using a test environment based on a virtual machine.