• Journal of the Korean Solar Energy Society
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• v.32 no.6
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• pp.29-36
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• 2012

The existing solar tracking sensor for 2 axial control system to trace latitude and longitude is made of four phototransistor. The phototransistor-making is difficult and it's manufacturing is more high-priced than a wide use phototransistor because they have to the same characteristics of each phototransistor output signal. This paper described the algorithm for supplement these weakness. The algorithm applied to signal normalizing method and vector decomposition law. The deviations of each a wide use phototransistor output signal are resolved by signal normalizing method and it is able to make a solar tracking sensor using three phototransistor by vector decomposition law. Therefore, in this paper, it is reduced the number of phototransistor that is composed of solar tracking sensor and possible to use a wide use phototransistor by the proposed algorithm.

• Journal of Biosystems Engineering
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• v.40 no.4
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• pp.417-427
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• 2015

Purpose: This study investigated different band selection methods to classify spectrally similar data - obtained from aerial images of healthy citrus canopies and citrus greening disease (Huanglongbing or HLB) infected canopies - using small differences without unmixing endmember components and therefore without the need for an endmember library. However, large number of hyperspectral bands has high redundancy which had to be reduced through band selection. The objective, therefore, was to first select the best set of bands and then detect citrus Huanglongbing infected canopies using these bands in aerial hyperspectral images. Methods: The forward feature selection algorithm (FFSA) was chosen for band selection. The selected bands were used for identifying HLB infected pixels using various classifiers such as K nearest neighbor (KNN), support vector machine (SVM), naïve Bayesian classifier (NBC), and generalized local discriminant bases (LDB). All bands were also utilized to compare results. Results: It was determined that a few well-chosen bands yielded much better results than when all bands were chosen, and brought the classification results on par with standard hyperspectral classification techniques such as spectral angle mapper (SAM) and mixture tuned matched filtering (MTMF). Median detection accuracies ranged from 66-80%, which showed great potential toward rapid detection of the disease. Conclusions: Among the methods investigated, a support vector machine classifier combined with the forward feature selection algorithm yielded the best results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.7
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• pp.950-955
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• 2013

This paper proposes a new low-cost current detection method to implement vector-controlled inverter of 3-phase induction motor or permanent-magnet synchronous motor using 2 shunt resistors instead of expensive Hall current sensors. The proposed method can detect perfect phase currents without current-immeasurable area in all operating conditions of motor. This method uses 2 shunt resistors in Hall current sensor positions conventionally used to detect phase currents. Therefore, it requires accurate analog differential amplifiers to detect voltages across shunt resistors at high electric potential to ground. We show the good solutions which are implemented by voltage-divider resistors networks and the instrumentation amplifiers using several Op Amps at cheap price. Computer simulations and experiments were performed to confirm the effectiveness of proposed method. These results show that proposed method can perfectly detect phase currents without current-immeasurable area in all operating conditions of motor.

• Journal of Korea Multimedia Society
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• v.8 no.6
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• pp.827-835
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• 2005

In this paper, self-networking and replaceable structure in vector graphics contents are presented for wireless internet service. The wireless networks over 2G or 3G are limited in the sense of the speed and the cost. Considering these characteristics of wireless network, self-networking method and replaceable structure in downloaded contents are introduced in order to save the amount of data and provide variations for contents. During the display of contents, a certain data for the contents is downloaded from the server and it is managed appropriately for the operation of the contents. The downloaded materials are reflected to the original contents using replaceable structure. Also, the downloading and modification are independent of the play. In this implementation, the data consists of control data for control and resource data for image, sound or text. Comparing to the conventional methods which download the whole data, the amount of the transmitted data is very small since only the difference is downloaded. Also, during the play of the contents, the changes are adopted immediately. The whole functions are implemented in wireless handset and the various applications are discussed.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.1_2
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• pp.135-143
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• 2004

3D Graphics accelerator is usually composed of two parts, geometry engine and rasterizer. In this paper, VGE(Vector Geometry Engine) which exploits vertex-level parallelism is proposed. In VGE, Common Floating-Point Unit by adding four-FADD, four-FMUL unit and 128-vector register accelerates geometry calculation. In comparison with SH4, Performance result show that the VGE can achieve performance gain over 4.7 times. To evaluate VGE performance, we make simulator to rebuild Simple-Scalar, general purpose processor simulator. In simulator model, we use Viewperf-benchmark.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1411-1418
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• 1999

In this paper, we propose a fast motion estimation algorithm using motion prediction and neural network. Considering that the motion vectors have high spatial correlation, the motion vector of current block is predicted by those of neighboring blocks. The codebook of motion vector is designed by Kohonen self-organizing feature map(KSFM) learning algorithm which has a fast learning speed and 2-D adaptive chararteristics. Since the similar codevectors are closely located in the 2-D codebook the motion is progressively estimated from the predicted codevector in the codebook. Computer simulation results show that the proposed method has a good performance with reduced computational complexity.

• Journal of the Korean Society of Propulsion Engineers
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• v.9 no.4
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• pp.73-80
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• 2005

In general, the performance of movable nozzle used for thrust vector control in solid rocket motor is estimated on the basis of the effective pivot of nozzle. However, it is nearly impossible to define the exact effective pivot by the mathematical model or experimental technique owing to pivot dynamics. In this paper, pivot dynamic properties were investigated by ADAMS simulation technique and trajectory of the exact effective pivot was modelled by the artificial neural network. Comparison of the proposed method was made with the virtual movable nozzle (computer simulation) to verify the method, and showed good agreement. Therefore, the proposed method will be applicable to predict the effective pivot of movable nozzle during bench or ground test.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.436-454
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• 2016

This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.871-886
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• 2011

This paper proposes a powerful SVM-ASM filter, the adaptive switching median(ASM) filter based on support vector machines(SVMs), to effectively reduce impulse noise in corrupted images while preserving image details and features. The proposed SVM-ASM filter is composed of two stages: SVM impulse detection and ASM filtering. SVM impulse detection determines whether the pixels are corrupted by noise or not according to an optimal discrimination function. ASM filtering implements the image filtering with a variable window size to effectively remove the noisy pixels determined by the SVM impulse detection. Experimental results show that the SVM-ASM filter performs significantly better than many other existing filters for denoising impulse noise even in highly corrupted images with regard to noise suppression and detail preservation. The SVM-ASM filter is also extremely robust with respect to various test images and various percentages of image noise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2785-2799
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• 2020

We propose a novel speaker adaptation method using acoustic model clustering. The similarity of different speakers is defined by the cosine distance between their i-vectors (intermediate vectors), and various efficient clustering algorithms are applied to obtain a number of speaker subsets with different characteristics. The speaker-independent model is then retrained with the training data of the individual speaker subsets grouped by the clustering results, and an unknown speech is recognized by the retrained model of the closest cluster. The proposed method is applied to a large-scale speech recognition system implemented by a hybrid hidden Markov model and deep neural network framework. An experiment was conducted to evaluate the word error rates using Resource Management database. When the proposed speaker adaptation method using i-vector based clustering was applied, the performance, as compared to that of the conventional speaker-independent speech recognition model, was improved relatively by as much as 12.2% for the conventional fully neural network, and by as much as 10.5% for the bidirectional long short-term memory.