• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.1
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• pp.73-80
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• 2016

The Car License Plate Recognition(: CLPR) is required in searching the hit-and-run car, measuring the traffic density, investigating the traffic accidents as well as in pursuing vehicle crimes according to the increasing in number of vehicles. The captured images on the real environment of the CLPR is contaminated not only by snow and rain, illumination changes, but also by the geometrical distortion due to the pose changes between camera and car at the moment of image capturing. We propose homographic transformation and intensity histogram of vertical image projection so as to transform the distorted input to the original image and cluster the character and number, respectively. Especially, in this paper, the Multilayer Perceptron Algorithm(: MLP) in the CLPR is used to not only recognize the charcters and car license plate, but also determine the optimized ratio among the number of input, hidden and output layers by the real experimental result.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2095-2100
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• 2012

In this paper, we propose the channel sounding scheme which is made for ideal communication between some application as well as the short distance of high speed data transmission in MIMO-OFDM system for Wireless PAN. This method is able to perceive the duration of the impulse response through the delaying of power delay profile, modeled a power delay profile which has an attenuate characteristic, and obtained the coefficient of channel response by ML (maximum likelihood). Through the amplitudes, phases and delays associated with each multipath component which were acquired from this channel sounding scheme, we can describe the wave propagation characteristics of channels between the transmitter and receiver so that the receiver could enhance not only the reliability but also the ability of communication link. Multi agent system models can be used to analyze the path of the system within any time frame. Further, parameter values can be perturbed to examine how the path of the system changes in response to exogenous shocks.

• Journal of the Korean Geotechnical Society
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• v.16 no.1
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• pp.75-81
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• 2000

The anisotropy of soils has an important effect on stress-strain behavior. In this study, an attempt has been made to implement artificial neural network model for modeling the stress-strain relationship and predicting the undrained shear strength of normally consolidated clay with varying consolidation pressure ratios. The multi-layer neural network model, adopted in this study, utilizes the error back-propagation loaming algorithm. The artificial neural networks use the results of undrained triaxial test with various consolidation pressure ratios and different effective vertical consolidation pressure fur learning and testing data. After learning from a set of actual laboratory testing data, the neural network model predictions of the undrained shear strength of the normally consolidated clay are found to agree well with actual measurements. The predicted values by the artificial neural network model have a determination coefficient$(r^2)$ above 0.973 compared with the measured data. Therefore, this results show a positive potential for the applications of well-trained neural network model in predicting the undrained shear strength of cohesive soils.

• Journal of Ocean Engineering and Technology
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• v.21 no.6
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• pp.72-80
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• 2007

The tsunami that resulted from the Central East sea Earthquake, which registered 7.7 on the Richter scale, that occurred over the entire water region in Akita on May. 26, 1983 and the tsunami that was triggered by the Southwest off Hokkaido Earthquake (7.8 on the Richter scale) that occurred in Southwest off Hokkaido on July 12, 1993 are representative cases that led to considerable damage in life and property, not only in Japan but also in Korea. In this study, multi-grid method was used in order to reproduce sufficiently the shoaling effect that occurs as water depth becomes shallow in the shallow water region and moving boundary condition was introduced to consider the runup in the coastal region. For the tsunamis that exerted considerable effect on the East Sea coast of Korea that were caused by the Central East Sea Earthquake in 1983 and the Southwest off Hokkaido Earthquake in 1993, characteristics like water level rise and propagation in the East Sea coast will be examined using numerical simulations. At the same time, these values will be compared with observed values. In addition, maximum water level rise and change in the water level with respect to time that were caused by the tsunamis were examined at each location along the East sea coast. Usefulness of numerical analysis was verified by comparing with observed values.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.50-58
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• 2013

In this paper, parallel decimal fixed-point multiplier which uses the limited range of Singed-Digit number encoding and the reduction step is proposed. The partial products are generated without carry propagation delay by encoding a multiplicand and a multiplier to the limited range of SD number. With the limited range of SD number, the proposed multiplier can improve the partial product reduction step by increasing the number of possible operands for multi-operand SD addition. In order to estimate the proposed parallel decimal multiplier, synthesis is implemented using Design Compiler with SMIC 180nm CMOS technology library. Synthesis results show that the delay of proposed parallel decimal multiplier is reduced by 4.3% and the area by 5.3%, compared to the existing SD parallel decimal multiplier. Despite of the slightly increased delay and area of partial product generation step, the total delay and area are reduced since the partial product reduction step takes the most proportion.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.3
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• pp.26-34
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• 1999

A new silicon-based IC interconnect transmission line parameter extraction methodology is presented and experimentally examined. Unlike the PCB or MCM interconnects, a dominant energy propagation mode in the silicon-based IC interconnects is not quasi-TEM but slow wave mode(SWM). The transmission line parameters are extracted taking the silicon substrate effect (i.e., slow wave mode) into account. The capacitances are calculated considering silicon substrate surface as a ground. Whereas the inductances are calculated by using an effective dielectric constant. In order to verify the proposed method, test patterns were designed. Experimental data have agreement within 10%. Further, crosstalk noise simulation shows excellent agreements with the measurements which are performed with high-speed time domain measurement ( i.e., TDR/TDT measurements) for test pattern, while RC model or RLC model without silicon substrate effect show about 20~25% underestimation error.

• Structural Engineering and Mechanics
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• v.57 no.4
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• pp.657-680
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• 2016

Reinforced concrete (RC) deep beams are structural members that predominantly fail in shear. Therefore, determining the shear strength of these types of beams is very important. The strut-and-tie method is commonly used to design deep beams, and this method has been adopted in many building codes (ACI318-14, Eurocode 2-2004, CSA A23.3-2004). In this study, the efficiency of artificial neural networks (ANNs) in predicting the shear strength of RC deep beams is investigated as a different approach to the strut-and-tie method. An ANN model was developed using experimental data for 214 normal and high-strength concrete deep beams from an existing literature database. Seven different input parameters affecting the shear strength of the RC deep beams were selected to create the ANN structure. Each parameter was arranged as an input vector and a corresponding output vector that includes the shear strength of the RC deep beam. The ANN model was trained and tested using a multi-layered back-propagation method. The most convenient ANN algorithm was determined as trainGDX. Additionally, the results in the existing literature and the accuracy of the strut-and-tie model in ACI318-14 in predicting the shear strength of the RC deep beams were investigated using the same test data. The study shows that the ANN model provides acceptable predictions of the ultimate shear strength of RC deep beams (maximum $R^2{\approx}0.97$). Additionally, the ANN model is shown to provide more accurate predictions of the shear capacity than all the other computed methods in this study. The ACI318-14-STM method was very conservative, as expected. Moreover, the study shows that the proposed ANN model predicts the shear strengths of RC deep beams better than does the strut-and-tie model approaches.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.181-189
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• 2013

In this paper, we introduce the fuzzy neural network based on the individual input space to design the pattern recognizer. The proposed networks configure the network by individually dividing each input space. The premise part of the networks is independently composed of the fuzzy partition of individual input spaces and the consequence part of the networks is represented by polynomial functions. The learning of fuzzy neural networks is realized by adjusting connection weights of the neurons in the consequent part of the fuzzy rules and it follows a back-propagation algorithm. In addition, in order to optimize the parameters of the proposed network, we use real-coded genetic algorithms. Finally, we design the optimized pattern recognizer using the experimental data for pattern recognition.

• Journal of the Korean Society for Nondestructive Testing
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• v.13 no.4
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• pp.9-17
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• 1994

It is well recognized recently that ultrasonic technique is one of the most widely used methods of nondestructive evaluation to characterize material properties of nonconventional engineering materials. Therefore it is very important to understand physical phenomenon on propagation behavior of elastic wave in these materials, which is directly associated with ultrasonic signals in the test. In this study, the theoretical analysis on multi-scattering of harmonic elastic wave due to the particulate with interface between matrix and fiber in metal matrix composites(MMCs) was done on the basis of Lax's quasi-crystalline approximation and extinction theorem. SiC particulate (SiCp) reinforced A16061-T6 composite material was chosen for this analysis. From this analysis, frequency dependences of phase velocity and amplitude attenuation of effective plane wave due to the change of volume fraction of SiC particulate were clearly found. It was also shown that the interface condition between matrix and fiber in MMCs gives a direct effect on the variation of phase velocity of plane wave in MMCs.