• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1319-1325
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• 2006

Urine analysis is basic test in clinical medicine using visual examination by expert nurse. Recently, this test is measured by automatic urine analysis system. But, this system has different results by each instrument. So, a new classification algorithm is required for accurate classify and urine color collection. In this paper, a intelligent color classifier of urine analysis system was designed using neural network algorithm. The input parameters are three stimulus(RGB) after preprocessing using normalization. The fuzzy inference and neural network ware constructed for classify class according to 9 urine test items and $3{\sim}7$ classes. The experiment material to be used a standard sample of medicine. The possibility to adapt classifier designed for urine analysis system was verified as classifying measured standard samples and observing classified result. Of many test items, experimental results showed a satisfactory agreement with test results of reference system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.261-269
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• 2014

There are many researches on fast exponentiation algorithm which is used to implement a public key cryptosystem such as RSA. On the other hand, the malicious attacker has tried various side-channel attacks to extract the secret key. In these attacks, an attacker uses the power consumption or electromagnetic radiation of cryptographic devices which is measured during computation of exponentiation algorithm. In this paper, we propose a novel simple power analysis attack on m-ary exponentiation implementation. The core idea of our attack on m-ary exponentiation with pre-computation process is that an attacker controls the input message to identify the power consumption patterns which are related with secret key. Furthermore, we implement the m-ary exponentiation on evaluation board and apply our simple power analysis attack to it. As a result, we verify that the secret key can be revealed in experimental environment.

• Journal of Korea Multimedia Society
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• v.8 no.10
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• pp.1337-1346
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• 2005

In this paper, we describe an algorithm which can automatically recognize human gesture for Human-Robot interaction. In early works, many systems for recognizing human gestures work under many restricted conditions. To eliminate these restrictions, we have proposed the method that can represent 3D and 2D gesture information simultaneously, APM. This method is less sensitive to noise or appearance characteristic. First, the feature vectors are extracted using APM. The next step is constructing a gesture space by analyzing the statistical information of training images with PCA. And then, input images are compared to the model and individually symbolized to one portion of the model space. In the last step, the symbolized images are recognized with HMM as one of model gestures. The experimental results indicate that the proposed algorithm is efficient on gesture recognition, and it is very convenient to apply to humanoid robot or intelligent interface systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2304-2310
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• 2009

This paper presents WSK(wavelet shift keying) that can be improved to bit transmission rate in the digital communication. An algorithm of the conventional modulation is carried out that the scaling function and wavelet are encoded to 1(mark) and 0(space) for the input binary data, respectively. A new modulation technique that uses four carrier frequencies is proposed. Four carrier frequencies are defined as scaling function, inversed scaling function, wavelet, and inversed wavelet, which are encoded to 10, 11, 00 and 01 respectively. An algorithm of the proposed demodulation is decode to the original data using four correlation. As a results of simulation, we confirmed that the proposed method was improved to the performance at twice for the bit transmission rate.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1550-1556
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• 1997

In this paper, we apply the evolutionary algorithm to the combinatorial optimization problem. Evolutionary algorithm useful for the optimization of the large space problem. This paper propose a method for the reuse of wastes of light water in atomic reactor system. These wastes contain several reusable elements, and they should be carefully selected and blended to satisfy requirements as an input material to the heavy water atomic reactor system. This problem belongs to an NP-hard like the 0/1 knapsack problem. Two evolutionary strategies are used as approximation algorithms in the highly constrained combinatorial optimization problem. One is the traditional strategy, using random operator with evaluation function, and the other is heuristic based search that uses the vector operator reducing between goal and current status. We also show the method which perform the feasible test and solution evaluation by using the vectored knowledge in problem domain. Finally, We compare the simulation results of using random operator and vector operator for such combinatorial optimization problems.

• Journal of the Korea Society of Computer and Information
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• v.19 no.7
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• pp.17-25
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• 2014

In the devices that leave video records, it is an important issue to distinguish whether the input image is a real object or a photo when securing an identifying image. Using a single image and sensor, which is a simple way to distinguish the target from distance measurement has many weaknesses. Thus, this paper proposes a way to distinguish a simple photo and a real object by using stereo images. It is not only measures the distance to the target, but also checks a three-dimensional effect by making the depth map of the face area. They take pictures of the photos and the real faces, and the measured value of the depth map is applied to the learning algorithm. Exactly through iterative learning to distinguish between the real faces and the photos looked for patterns. The usefulness of the proposed algorithm was verified experimentally.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.1
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• pp.121-131
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• 2015

Passive emitter localization is preferred to use a small number of receivers as possible for the efficiency of strategic management in the field of modern electronic warfare support. Accurate emitter localization can be expected when utilizing continuous measurable parameters and a appropriate combination of theirs. For this reason, we compare CRLB (Cramer-Rao lower bound) of two moving platform with various measurable parameters to choose a appropriate combination of parameters for a better localization performance. And we propose the passive emitter localization method based on Levenberg-Marquardt algorithm with combined TDOA/FDOA and DOA to achieve better accuracy of emitter localization which is located on the ground and stationary. In addition, we present a method for determining the initial emitter position for LM algorithm's input to avoid the divergence of estimation and local minimum.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2492-2498
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• 2013

In this paper, A lane tracking algoritm is proposed for lane departure warning system. To eliminate perspective effect, input image is converted into Bird's View by inverse perspective mapping. Next, suitable features are extracted for lane detection. Using clustering and lane similarity function with noise suppression features are extracted. Finally, lane model is calculated using RANSAC and lane model is tracked using Kalman Filter. Experimental results show that the proposed algorithm can be processed within 20ms and its detection rate approximately 90% on the highway in a variety of environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.421-422
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• 2015

Deep learning, a deepen neural network technology that demonstrates the enhanced performance of neural network analysis, has been getting the spotlight in recent years. The present study proposed a process to test the error rates of certain variables and predict big data by using R, a analysis visualization tool based on deep learning, applying the RBM(Restricted Boltzmann Machine) algorithm to deep learning. The weighted value of each dependent variable was also applied after the classification of dependent variables. The investigator tested input data with the RBM algorithm and designed a process to detect error rates with the application of R.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4A
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• pp.434-439
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• 2008

This paper proposes an efficient coarse frequency synchronizer for digital video broadcasting - second generation (DVB-S2). The input signal requirement of acquisition range for coarse frequency estimator in the DVB-S2 is around ${\pm}1.5625Mhz$, which corresponds to 6.25% of the symbol rate at 25Mbaud. At the process of analyzing the robust algorithm among data-aided approaches, we find that the Luise & Reggiannini (L&R) algorithm is the most promising one for coarse frequency estimation with respect to estimation performance and complexity. However, it requires many multipliers and adders to compute output values of correlators. We propose an efficient architecture identifying the serial correlator with the buffer and multiplexers. The proposed coarse frequency synchronizer can reduce the hardware complexity about 92% of the direct implementation. The proposed architecture has been implemented and verified on the Xilinx Virtex II FPGA.