• The Journal of the Acoustical Society of Korea
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• v.32 no.6
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• pp.472-483
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• 2013

Actual maritime exercise for improving the capability of submarine sonar operator leads to a lot of cost and constraints. Sonar simulator maximizes the capability of sonar operator and training effect by solving these problems and simulating a realistic battlefield environment. In this study, a passive sonar simulator algorithm is suggested, where the simulator is divided into three modules: maneuvering module, noise source module, and sound propagation module. Maneuvering module is implemented in three-dimensional coordinate system and time interval is set as the rate of vessel changing course. Noise source module consists of target noise, ocean ambient noise, and self noise. Target noise is divided into modulated/unmodulated and narrowband/broadband signals as their frequency characteristics, and they are applied to ship radiated noise level depending on the vessel tonnage and velocity. Ocean ambient noise is simulated depending on the wind noise considering the waveguide effect and other ambient noise. Self noise is also simulated for flow noise and insertion loss of sonar-dome. The sound propagation module is based on ray propagation, where summation of amplitude, phase, and time delay for each eigen-ray is multiplied by target noise in the frequency domain. Finally, simulated results based on various scenarios are in good agreement with generated noise in the real ocean.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.6
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• pp.709-717
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• 2002

A new multi constant modulus algorithm (MCMA) for a blind decision feedback equalizer is proposed. In order to avoid the error propagation problem in the conventional DFE structure, Feed-Back Filter coefficients are updated only after Feed-Forward Filter coefficients are sufficiently converged to the steady state. Therefore, it has the problem of slow convergence speed characteristics. To overcome this drawback, the proposed MCMA algorithm uses not only new cost function considering the minimum distance between the received signal and the representative value containing the statistical characteristics of the transmitted signal, but also adaptive step-size according to the equalizer outputs to fast convergence speed of FBF. Simulations were carried out under the certified communication channel environment to evaluate a performance of the proposed equalizer. The simulation results show that the proposed equalizer has an improved convergence and SER performance compared with previous methods. The proposed techniques offer the possibility of practical equalization for cable modem and terrestrial HDTV broadcast (using 8-VSB or 64-QAM) applications.

• Journal of Korean Society of Steel Construction
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• v.17 no.6 s.79
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• pp.661-672
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• 2005

For the efficient stochastic optimization of steel structures for which a large number of analyses is required, artificial neural networks,which have emerged as a powerful tool that could have been used to replace time-consuming procedures in many scientific or engineering applications, are applied. They are utilized for the solution of the equilibrium equations resulting from the application of the finite element method in connection with the reanalysis type of problem, for which a large number of finite element analyses are required in this study. As such, the use of artificial neural networks to predict finite element analysis outputs simplifies and facilitates the performance of the stochastic optimal design of structural systems where a trained neural network is used to replace the structural reanalysis phase. Moreover, to improve efficiency of used artificial neural networks, genetic algorithm is utilized. The stochastic optimizer used in this study is an algorithm based on the evolution theory. The efficiency of the proposed procedure is examined in problems with both volume (weight) functions and real-world cost functions

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.679-690
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• 2015

The need to remove the cause of traffic accidents by improving the engineering system for a vehicle and the road in order to minimize the accident hazard. This is likely to cause traffic accident continue to take a large and significant social cost and time to improve the reliability and efficiency of this generally poor road, thereby generating a lot of damage to the national traffic accident caused by improper environmental factors. In order to minimize damage from traffic accidents, the cause of accidents must be eliminated through technological improvements of vehicles and road systems. Generally, it is highly probable that traffic accident occurs more often on roads that lack safety measures, and can only be improved with tremendous time and costs. In particular, traffic accidents at intersections are on the rise due to inappropriate environmental factors, and are causing great losses for the nation as a whole. This study aims to present safety countermeasures against the cause of accidents by developing an intersection Traffic safety evaluation model. It will also diagnose vulnerable traffic points through BPA (Back -propagation algorithm) among artificial neural networks recently investigated in the area of artificial intelligence. Furthermore, it aims to pursue a more efficient traffic safety improvement project in terms of operating signalized intersections and establishing traffic safety policies. As a result of conducting this study, the mean square error approximate between the predicted values and actual measured values of traffic accidents derived from the BPA is estimated to be 3.89. It appeared that the BPA appeared to have excellent traffic safety evaluating abilities compared to the multiple regression model. In other words, The BPA can be effectively utilized in diagnosing and practical establishing transportation policy in the safety of actual signalized intersections.

• Journal of IKEEE
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• v.11 no.4
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• pp.322-330
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• 2007

Recent advances in wireless networks and low-cost, low-power design have led to active research in large-scale networks of small, wireless, low power sensors and actuators, In large-scale networks, lots of timing-synchronization protocols already exist (such as NTP, GPS), In ad-hoc networks, especially wireless sensor networks, it is hard to synchronize all nodes in networks because it has no infrastructure. In addition, sensor nodes have low-power CPU (it cannot perform the complex computation), low batteries, and even they have to have active and inactive section by periods. Therefore, new approach to time synchronization is needed for wireless sensor networks, In this paper, I propose Energy-Efficient Time Synchronization (EETS) protocol providing network-wide time synchronization in wireless sensor networks, The algorithm is organized two phase, In first phase, I make a hierarchical tree with sensor nodes by broadcasting "Level Discovery" packet. In second phase, I synchronize them by exchanging time stamp packets, And I also consider send time, access time and propagation time. I have shown the performance of EETS comparing Timing-sync Protocol for Sensor Networks (TPSN) and Reference Broadcast Synchronization (RBS) about energy efficiency and time synchronization accuracy using NESLsim.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2818-2820
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• 2002

In this paper, we design neurofuzzy networks architecture by means of linear fuzzy inference. The proposed neurofuzzy networks are equivalent to linear fuzzy rules, and the structure of these networks is composed of two main substructures, namely premise part and consequence part. The premise part of neurofuzzy networks use fuzzy space partitioning in terms of all variables for considering correlation between input variables. The consequence part is networks constituted as first-order linear form. The consequence part of neurofuzzy networks in general structure(for instance ANFIS networks) consists of nodes with a function that is a linear combination of input variables. But that of the proposed neurofuzzy networks consists of not nodes but networks that are constructed by connection weight and itself correspond to a linear combination of input variables functionally. The connection weights in consequence part are learned by back-propagation algorithm. For the evaluation of proposed neurofuzzy networks. The experimental results include a well-known NASA dataset concerning software cost estimation.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.3
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• pp.74-82
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• 2011

This paper describes a control system for positioning the real-time locations of the autonomous warfare vehicles and infantry, and for avoiding collisions between them. The control system utilizes the low-cost RSSI (Received Signal Strength Indication) for positioning the locations of the wireless devices. The mathematical mean filtering processes are applied to the calculation of the RSS matrix to improve the performance for positioning the wireless devices in the multi-path propagation environment. A fuzzy rule is proposed to recover and replace the broken packets occurring in the wireless communication. The gradient and geometric triangulation algorithms are proposed to trace the real-time locations of wireless devices, based on the distances between them. The estimated location results of the geometric triangulation algorithm are compared with the results of the GPS and the gradient algorithm.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.3
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• pp.113-120
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• 2017

In recent years, indoor localization has been researched for the improvement of its localization accuracy capability in Wi-Fi environment. The fingerprint and RF propagation models has been the main approach in determining indoor positioning. With the use of fingerprint, a low-cost, versatile localization system can be achieved without the use of external hardware. However, only a few research have been made on virtual access points (VAPs) among indoor localization models. In this paper, the idea of indoor localization system using fingerprint with the addition of VAP in Wi-Fi environment is discussed. The idea is to virtually add APs in the existing indoor Wi-Fi system, this would mean additional virtually APs in the network. The experiments of the proposed algorithm shows the positive results when 2VAPs are used compared with only APs. A combination of 3APs and 2VAPs in the 3rd case had the lowest average error of 3.99 among its 4 scenarios.

• Journal of the Society of Naval Architects of Korea
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• v.36 no.4
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• pp.137-146
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• 1999

Inspection becomes to be important in the safety of structure and economical viewpoint, because structural damage accompanies lots of economical cost and social problems. Especially ship structure is composed of a lot of members and it is impossible to inspect all members continuously. The purpose of this paper is to get optimal inspection plan containing inspection time and method. Crack is one of major modes on the structural failure and can lead to collapse of structure. In this paper, the deteriorating process, which contains inspection to detect the crack before the propagation to large crack, is idealized as Markov chain model. Genetic algorithm is also used to accomplish the optimization of inspection plan. Especially, the probabilistic characteristics of cracks are changed, because ship is operating in corrosive environments and the scantling of structural members is reduced due to corrosion. Non-stationary Markov chain model is used to represent the process of corrosion in structural members. In this paper, the characteristics of indivisual inspection plan are compared by numerical examples for the change of corrosion rate, the cost due to scheduled system down and target failure probability. From the numerical example, it can be seen that the improvement of fatigue life for the members with short fatigue life is the most effective way in order to reduce total maintenance cost.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.6
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• pp.21-28
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• 2016

This paper presents a cooperative transmission scheme for underwater acoustic sensor networks to improve packet transmission rate and reduce energy consumption. Source node transmits duplicated information relayed by distributed antennas called a virtual antenna array. Destination node combines that information to reduce packet error rate. The suggested cooperative scheme enhances the reliability by providing high diversity gains through intermediate relay nodes to overcome the distinct characteristics of the underwater channel, such as high transmission loss, propagation delay, and ambient noises. It is suggested that the algorithm select destinations and potential relays from a set of neighboring nodes that utilize distance cost, the residual energy of each node and local measurement of the channel conditions into calculation. Simulation results show that the proposed scheme reduces average energy consumption, response time, and increases packet delivery ratio compared with the SPF(Shortest Path First) and non-cooperative scheme using OPNET Moduler.