• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.90-100
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• 2011

We present a parallel bi-conjugate gradient (Bi-CG) matrix solver for large scale Bio-FET simulations based on recent graphics processing units (GPUs) which can realize a large-scale parallel processing with very low cost. The proposed method is focused on solving the Poisson equation in a parallel way, which requires massive computational resources in not only semiconductor simulation, but also other various fields including computational fluid dynamics and heat transfer simulations. As a result, our solver is around 30 times faster than those with traditional methods based on single core CPU systems in solving the Possion equation in a 3D FDM (Finite Difference Method) scheme. The proposed method is implemented and tested based on NVIDIA's CUDA (Compute Unified Device Architecture) environment which enables general purpose parallel processing in GPUs. Unlike other similar GPU-based approaches which apply usually 32-bit single-precision floating point arithmetics, we use 64-bit double-precision operations for better convergence. Applications on the CUDA platform are rather easy to implement but very hard to get optimized performances. In this regard, we also discuss the optimization strategy of the proposed method.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.423-435
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• 2010

The number of professionals in the science and engineering fields decreases all over the world. Especially in Korea, the declining rate of both the laborable and economically active population, aging of the population at the fastest level, and the declining birth rate make it tougher to secure the core brains of the future. After speculation of all programs above, some common factors have been derived and every program appeared to have the support for each level of inducing, caring, and utilizing. And the means of support could be categorized into the financial, legal, and social aspects. Lastly, a logical tool called Systems Thinking has been applied to the FLS Conditions and the Brain Internalization Process to assure the efficacy and applicability of the models. This is to minimize any de facto side effects by analyzing all 'feedback loops' stemming from the models. And the 'causal loop diagrams' have been utilized to come with the complementary measures. Such series of verification could convince the virtue of the models. Governments and universities can make use of the FLS Conditions and the Brain Internalization Process so the policies or plans about the foreign brains can be built in a uniformized and consistent framework. I hope, as a result, the international competency of Korea to induce and utilize the foreign brains be raised with the constant and standardized formality.

• Journal of Advanced Navigation Technology
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• v.14 no.5
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• pp.684-690
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• 2010

Recently, the NG(Next Generation) system is studied for supporting convergence of various services and multi mode of single terminal. And a demand of user for taking the various services is getting increased, for supporting these services, many systems being able to transmit a large message have been appeared. In the NG system, it has to be supporting the CDMA and WCDMA besides the tele communication systems using OFDM method with single terminal An intergrated system can be improved with adopting of SoC technique. For adopting SoC technique on the intergrated terminal, we have to solve the non linear problem of HPA(High Power Amplifier). Nonlinear characteristic of HPA distorts both amplitude and phase of transmit signal, this distortion cause deep adjacent channel interference. We adopt a polynomial pre-distortion technique for this problem. In this paper, a noble modem design for NG mobile communication service and a method using polynomial pre-distorter with PAPR technique for counterbalancing nonlinear characteristic of the HPA are proposed.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.15-26
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• 2003

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This paper describes an alternative approach which uses the least mean square (LMS) method and expectation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximization (SAGE) algorithm In conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.1
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• pp.27-35
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• 2005

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature spare. This paper describes an alternative approach which uses the least mean square (LMS) method and exportation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximiBation (SAGE) algorithm ill conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor. Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7C
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• pp.421-427
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• 2011

The importance of the real-time object recognition and tracking field has been growing steadily due to rapid advancement in the computer vision applications industry. As is well known, the mean-shift algorithm is widely used in robust real-time object tracking systems. Since the mentioned algorithm is easy to implement and efficient in object tracking computation, many say it is suitable to be applied to real-time object tracking systems. However, one of the major drawbacks of this algorithm is that it always converges to a local mode, failing to perform well in a cluttered environment. In this paper, an Optical Flow-based algorithm which fits for real-time recognition of multiple moving objects is proposed. Also in the tests, the newly proposed method contributed to raising the similarity of multiple moving objects, the similarity was as high as 0.96, up 13.4% over that of the mean-shift algorithm. Meanwhile, the level of pixel errors from using the new method keenly decreased by more than 50% over that from applying the mean-shift algorithm. If the data processing speed in the video surveillance systems can be reduced further, owing to improved algorithms for faster moving object recognition and tracking functions, we will be able to expect much more efficient intelligent systems in this industrial arena.

• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.30-36
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• 2000

The hidden node plays a role of the functional units that classifies the features of input pattern in the given question. Therefore, a neural network that consists of the number of a suitable optimum hidden node has be on the rise as a factor that has an important effect upon a result. However there is a problem that decides the number of hidden nodes based on back-propagation learning algorithm. If the number of hidden nodes is designated very small perfect learning is not done because the input pattern given cannot be classified enough. On the other hand, if designated a lot, overfitting occurs due to the unnecessary execution of operation and extravagance of memory point. So, the recognition rate is been law and the generality is fallen. Therefore, this paper suggests a method that decides the number of neural network node with feature information consisted of the parameter of learning algorithm. It excludes a node in the Pruning target, that has a maximum value among the feature value obtained and compares the average of the rest of hidden node feature value with the feature value of each hidden node, and then would like to improve the learning speed of neural network deciding the optimum structure of the multi-layer neural network as pruning the hidden node that has the feature value smaller than the average.

• Geophysics and Geophysical Exploration
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• v.2 no.4
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• pp.167-173
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• 1999

Since the resolution of the 2-D hole-to-hole seismic traveltime tomography is affected by the limited ray transmission angle, various methods were used to improve the resolution. Linear traveltime interpolation(LTI) ray tracing method was chosen for forward-modeling method. Inversion results using the LTI method were compared with those using the other ray tracing methods. As an inversion algorithm, SIRT method was used. In the iterative non-linear inversion method, the cost of ray tracing is quite expensive. To reduce the cost, each raypath was stored and the inversion was performed from this information. Using the proposed method, fast convergence was achieved. Inversion results are likely to be affected by the initial velocity guess, especially when the ray transmission angle was limited. To provide a good initial guess for the inversion, generalized regression neural network(GRNN) method was used. When the transmitted raypath angle is not limited or the geological model is very complex, the inversion results are not affected by initial velocity model very much. Since the raypath angles, however, are limited in most geophysical tomographic problems, the enhancement of resolution in tomography can be achieved by providing a proper initial velocity model by another inversion algorithm such as GRNN.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.101-106
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• 2016

This paper proposes the SU-SE-MMA algorithm which applying the concept of selective updaing to the SE-MMA that is possible to reduce the intersymbol interference due to distortion occurred at the channel when transmit the nonconstant modulus 16-QAM signal. The SE-MMA emerged for the simplifying the computational operation from the current MMA adaptation algorithm, then it's has the fast convergence speed and has a problem of increase the residual component in the steady state. The SU-SE-MMA performs the selectively tap updating when the distance of equalizer output and specified transmit signal point is greater than the given threshold value and tap updaing does not occurred in the small distance. By this selective updating process, it is possible to more reduction in the computational operation in the propose algorithm. The improved adaptive equalization performance of SU-SE-MMA like as the equalizer output signal constellation, residual isi, MD, SER were confirmed by computer simulation compared to SE-MMA. As a result of simulation, the AV-SE-MMA has better performance in output signal constellation, residual isi and MD compared to the SE-MMA, but it was confirmed that the AV-SE-MMA has similar in the SER performance that means the robustness to the noise.

• Polymer(Korea)
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• v.38 no.3
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• pp.320-326
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• 2014

The effects of high shear rate processing on the thermal properties of PC/ABS blends were studied. It was executed by the high shear processing machine (NHSS2-28) at the varied conditions of screw speeds and loaded duration. After the samples were processed with NHSS2-28, the $T_gs$ were shifted from 143 to $133^{\circ}C$, and the behavior of degradation determined by TGA showed two distinct steps before high shear rate processing, while it showed a straight line after the processing. In order to provide the reasons of the properties, it was showen by SEM and UTM that the droplet sizes morphologically decreased after the processing, and the elongations decreased slightly until 1000 rpm of screw speed and then sharply decreased, according to the conditions of high shear rate processing. Therefore, it can be confirmed that $T_g,s$ of PC/ABS blends were considerably shifted under an appropriate high shear rate condition, and rapidly dropped, so that blends degraded above the condition, due to stress-induced degradation.