• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.9 s.102
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• pp.1070-1076
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• 2005

Vibration test using the MAST(multi axial simulation table) provide a more reliable testing environment than any conventional one. The multi axial simulation could be possible with a advanced control algorithm and hardware supports so that most of the operation is automatically conducted by MAST system itself except the input information that is synthesized by the measured response signals. That means the reliability of the vibration test is highly depended on the quality of the input profile. In this paper, the optimal algorithm based on the energy method is introduced to construct a best combination of candidated input PSD data could be constructed. Since the optimal algorithm renders time information, the nitration fatigue test is completely possible for any measured signals one wants. The proposed method is explained with representing acquired road signals from the candidate input PSD obtained from a proving ground.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.08a
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• pp.142-145
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• 2008

This paper proposes a computationally efficient algorithm for color image segmentation using self-organizing map(SOM) and grassfire algorithm. We reduce a computation time by decreasing the number of input neuron and input data which is used for learning at SOM. First converting input image to CIE $L^*u^*v^*$ color space and run the learning stage with the SOM-input neuron size is three and output neuron structure is 4by4 or 5by5. After learning, compute output value correspondent with input pixel and merge adjacent pixels which have same output value into segment using grassfire algorithm. The experimental results with various images show that proposed method lead to a good segmentation results than others.

• ETRI Journal
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• v.40 no.3
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• pp.337-346
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• 2018

In this paper, we propose a grant-aware (GA) scheduling algorithm that can provide higher throughput and lower latency than a conventional dual round-robin matching (DRRM) method. In our proposed GA algorithm, when an output receives requests from different inputs, the output not only sends a grant to the selected input, but also sends a grant indicator to all the other inputs to share the grant information. This allows the inputs to skip the granted outputs in their input arbiters in the next iteration. Simulation results using OPNET show that the proposed algorithm provides a maximum 3% higher throughput with approximately 31% less queuing delay than DRRM.

• Proceedings of the IEEK Conference
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• 2002.06b
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• pp.101-104
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• 2002

This paper propose that is algorithm of power dissipation reduction in the high level synthesis design for DSP(Digital Signal Processor), as the portable terminal system recently demand high power dissipation. This paper obtain effect of power dissipation reduction and switching activity that increase correlation of operands as input data of function unit. The algorithm search loop or repeatedly data to the input operands of function unit. That can be reduce the power dissipation using the new low power high level synthesis algorithm. In this Paper, scheduling operation search same nodes from input DFG(Data Flow Graph) with correlation coefficient of first input node and among nodes. Function units consist a multiplier, an adder and a register. The power estimation method is added switching activity for each bits of nodes. The power estimation have good efficient using proposed algorithm. This paper result obtain more Power reduction of fifty percents after using a new low power algorithm in a function unit as multiplier.

• Journal of Information Technology Applications and Management
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• v.9 no.2
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• pp.1-16
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• 2002

Recently, neural network methods have been studied to find out more valuable information in data bases. But the supervised learning methods of neural networks have an overfitting problem, which leads to errors of target patterns. And the unsupervised learning methods can distort important information in the process of regularizing data. Thus they can't efficiently classify data, To solve the problems, this paper introduces a hybrid neural networks HACAB(Hybrid Algorithm combining a Competition learning model And BP Algorithm) combining a competition learning model and 8P algorithm. HACAB is designed for cases which there is no target patterns. HACAB makes target patterns by adopting a competition learning model and classifies input patterns using the target patterns by BP algorithm. HACAB is evaluated with random input patterns and Iris data In cases of no target patterns, HACAB can classify data more effectively than BP algorithm does.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.5
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• pp.1172-1179
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• 1996

General adaptive noise canceller(ANC) suffers from the misadjustment of adaptive filter weights, because of the gradient-estimate noise at steady state. In this paper, an adaptive noise cancellation algorithm with speech detector which is distinguishing speech from silence and adaptation-transient region is proposed. The speech detector uses property of adaptive prediction-error filter which can filter the highly correlated speech. To detect speech region, estimation error which is the output of the adaptive filter is applied to the adaptive prediction-error filter. When speech signal apears at the input of the adaptive prediction-error filter. The ratio of input and output energy of adaptive prediction-error filter becomes relatively lower. The ratio becomes large when the white noise appears at the input. So the region of speech is detected by the ratio. Sign algorithm is applied at speech region to prevent the weights from perturbing by output speech of ANC. As results of computer simulation, the proposed algorithm improves segmental SNR and SNR up to about 4 dBand 11 dB, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.7
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• pp.1028-1035
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• 1993

The signed regressor (SR) algorithm employs one bit quantization on the input regressor (or tap input) in such a way that the quantized input sequences become +1 or -1. The algorithm is computationally more efficient by nature than the popular least mean square (LMS) algorithm. The behavior of the SR algorithm unfortunately is heavily dependent on the characteristics of the input signal, and there are some Inputs for which the SR algorithm becomes unstable. It is known, however, that such a stability problem does not take place with the SR algorithm when the input signal is Gaussian, such as in the case of speech processing. In this paper, we explore a statistical analysis of the SR algorithm. Under the assumption that signals involved are zero-mean and Gaussian, and further employing the commonly used independence assumption, we derive a set of nonlinear evolution equations that characterizes the mean and mean-squared behavior of the SR algorithm. Experimental results that show very good agreement with our theoretical derivations are also presented.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.49-58
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• 2012

This paper is on an improved orthogonal projection method which can cancel the acoustic feedback signals in the digital hearing aids. Comparing with the NLMS algorithm which is widely used for simplicity and stability, it shows that this method has the improvement of the convergence performances, and has small computational quantities, for signals with the large auto-correlation as speech signals. This uses the improved orthogonal projection algorithm which reduces the correlation of signals. To verify the convergence characteristics of the proposed algorithm, we simulated about various input signals. The acoustic feedback canceller has a 12-bit resolution with 64-tap adaptive FIR filter. And we compared the results of simulation for this algorithm with the ones for the NLMS algorithm. By these works, it is proved that the feedback canceller adopting the proposed algorithm shows about 3.5dB more high SNR than the NLMS algorithm in the colored input signals.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.792-798
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• 2021

In this paper, an algorithm for determining the coagulant input rate in the drug-injection tank during the process of the water purification plant was derived through big data analysis and prediction based on artificial intelligence. In addition, analysis of big data technology and AI algorithm application methods and existing academic and technical data were reviewed to analyze and review application cases in similar fields. Through this, the goal was to develop an algorithm for determining the coagulant input rate and to present the optimal input rate through autonomous driving simulator and pilot operation of the coagulant input process. Through this study, the coagulant injection rate, which is an output variable, is determined based on various input variables, and it is developed to simulate the relationship pattern between the input variable and the output variable and apply the learned pattern to the decision-making pattern of water plant operating workers.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.176-184
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• 2023

Points of interest (POIs) are widely used in tourism recommendations and to provide information about areas of interest. Currently, situation judgement using POI and GPS data is mainly rule-based. However, this approach has the limitation that inferences can only be made using predefined POI information. In this study, we propose an algorithm that uses POI data, GPS data, and schedule information to calculate the current speed, location, schedule matching, movement trajectory, and POI coverage, and uses machine learning to determine whether to stay or go. Based on the input data, the clustered information is labelled by k-means algorithm as unsupervised learning. This result is trained as the input vector of the SVM model to calculate the probability of moving and staying. Therefore, in this study, we implemented an algorithm that can adjust the schedule using the travel schedule, POI data, and GPS information. The results show that the algorithm does not rely on predefined information, but can make judgements using GPS data and POI data in real time, which is more flexible and reliable than traditional rule-based approaches. Therefore, this study can optimize tourism scheduling. Therefore, the stay detection algorithm using GPS movement trajectories and POIs developed in this study provides important information for tourism schedule planning and is expected to provide much value for tourism services.