• Journal of the Korea Society of Computer and Information
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• v.3 no.4
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• pp.78-85
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• 1998

In this paper, we analyzed the performances for FH/CPFSK system with limiter - discriminator detection and integrate-and-dump post-detection filtering under thermal noise and partial-band jamming noise. And, we considered intersymbol interference - related SNR and differential phase parameters for all eight of the possible adjacent bit data patterns, FM noise clicks for evaluating FH/CPFSK and CPFSK systems. In result, the optimum modulation index h was 0.7 and the optimum value of bandwidth-time product D was 1.0. Next, when we considered the thermal noise under the partial-band jamming, the thermal noise significantly influenced the error probability of system below 20dB approximately but could ignore above 20dB.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.195-201
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• 2014

In this study, face recognition system was designed based on polynomial Radial Basis Function Neural Networks(pRBFNNs) pattern classifier using 2-directional 2-dimensional principal component analysis algorithm. Existing one dimensional PCA leads to the reduction of dimension of image expressed by the multiplication of rows and columns. However $(2D)^2PCA$(2-Directional 2-Dimensional Principal Components Analysis) is conducted to reduce dimension to each row and column of image. and then the proposed intelligent pattern classifier evaluates performance using reduced images. The proposed pRBFNNs consist of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with the aid of fuzzy c-means clustering. In the conclusion part of rules. the connection weight of RBFNNs is represented as the linear type of polynomial. The essential design parameters (including the number of inputs and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. Using Yale and AT&T dataset widely used in face recognition, the recognition rate is obtained and evaluated. Additionally IC&CI Lab dataset is experimented with for performance evaluation.

• Korean Journal of Optics and Photonics
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• v.17 no.3
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• pp.231-239
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• 2006

In recent years, a hierarchical security architecture has been widely studied because it can efficiently protect information by allowing an authorized user access to the level of information. However, the conventional hierarchical decryption methods require several decryption keys for the high level information. In this paper, we propose a hierarchical image encryption using random phase masks and Walsh code having orthogonal characteristics. To decrypt the hierarchical level images by only one decryption key, we combine Walsh code into the hierarchical level system. For encryption process, we first perform a Fourier transform for the multiplication results of the original image and the random phase mask, and then expand the transformed pattern to be the same size and shape of Walsh code. The expanded pattern is finally encrypted by multiplying with the Walsh code image and the binary phase mask. We generate several encryption images as the same encryption process. The reconstruction image is detected on a CCD plane by a despread process and Fourier transform for the multiplication result of encryption image and hierarchical decryption keys which are generated by Walsh code and binary random phase image. Computer simulations demonstrate that the proposed technique can decrypt hierarchical information by using only one level decryption key image and it has a good robustness to the data loss such as random cropping.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.651-658
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• 2021

Because bolts with anti-loosening coatings are used mainly for joining safety-related components in automobiles, accurate automatic screening of these coatings is essential to detect defects efficiently. The performance of the convolutional neural network (CNN) used in a previous study [Identification of bolt coating defects using CNN and Grad-CAM] increased with increasing number of data for the analysis of image patterns and characteristics. On the other hand, obtaining the necessary amount of data for coated bolts is difficult, making training time-consuming. In this paper, resorting to the same VGG16 model as in a previous study, transfer learning was applied to decrease the training time and achieve the same or better accuracy with fewer data. The classifier was trained, considering the number of training data for this study and its similarity with ImageNet data. In conjunction with the fully connected layer, the highest accuracy was achieved (95%). To enhance the performance further, the last convolution layer and the classifier were fine-tuned, which resulted in a 2% increase in accuracy (97%). This shows that the learning time can be reduced by transfer learning and fine-tuning while maintaining a high screening accuracy.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.367-374
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• 2022

The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.3
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• pp.206-212
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• 2017

Induction motor torque is the reactive power is needed which corresponds to the exciting current to generate the magnetic flux as the product of current and flux. For use in the method of supplying the required reactive power to the induction motor power factor correction apparatus using a lot of ways to supply in place of the power supply side, when using a power factor compensation device can reduce the apparent power, the power factor can be improved. However, the distance to the emergency generator transformers or motors from the motor capacity is smaller but short and difficult to maneuver the theory and practice of the operating characteristics of the starting characteristics of the motor used a lot of large industrial plants were measured and analyzed. Therefore, this study investigated the motor starting Analysis and interpretation for the relationship with the large motor starting torque and speed during motor starting.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.1-6
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• 2016

Image rotation is one of main pre-processing step in image processing or image pattern recognition. It is implemented with rotation matrix multiplication. However it requires lots of floating point arithmetic operations and trigonometric function calculations, so it takes long execution time. We propose a new high speed image rotation algorithm without two major time-consuming operations. It use just 2 shear translation operations, so it is very fast. In addition, we apply a parallel computing technique with CUDA. CUDA is a massively parallel computing architecture using prevailed GPU recently. As GPU is a dedicated graphic processor, it is exellent for parallel processing of pixels. We compare the proposed algorithm with the conventional rotation one with various size images. Experimental results show that the proposed algorithm is superior to the conventional rotation ones.

• Korean Journal of Optics and Photonics
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• v.6 no.2
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• pp.142-147
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• 1995

Soliton pulse outputs are generated with figure '8' type erbium-doped fiber laser mode-locked by using a fiber loop mirror. The fiber loop mirror consists of an erbium-doped fiber amplifier at the one end of the loop, and 504 m-long dispersion-shifted fiber as a nonlinear medium. By pumping with a $1.48{\mu}m$ wavelength laser diode and adjusting the polarization controllers inside the loop, soliton pulses are generated with 1574 nm center wavelength and 1.2 nm linewidth. The soliton pulses are found randomly spaced within the fundamental period corresponding to cavity round trip time. The autocorrelation trace shows that the pulse width is 2.4 ps, which is in good agreement with the theoretical prediction. The pulsewidth- bandwidth product is found to be 0.348 which means that the pulses are nearly transform-limited.imited.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.71-79
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• 1997

In this paper, a codeword-dependent distance normalization(CDDN) and an instar-formed fuzzy smoothing of output distribution are proposed for robust estimation of output probabilities in the FVQ(fuzzy vector quantization)-DHMM(discrete hidden Markov model). The FVQ-DHMM is a variant of DHMM in which the state output probability is estimated by the sum oft he product of the output probability and its weighting factor for each codeword on an input vector. As the performance of the FVQ-DHMM is influenced by weighting factor and output distribution from a state, it is required to get a method to get robust estimation of weighting factors and output distribution for each state. From experimental results, the proposed CDDN method has reduced 24% of error rate over the conventional FVQ-DHMM, and also reduced 79% of error rate when the smoothing of output distribution is also applied to the computation of an output probability. These results indicate that the use of CDDN and the fuzzy smoothing of output distribution to the FVQ-DHMM lead to improved recognition, and therefore it may be used as an alternative to the robust estimation of output probabilities for HMMs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.3
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• pp.594-601
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• 2009

Shooting simulation systems not only reduce a great amount of expense and time for military exercises but also prevent accidents. In particular, the shooting simulation systems using laser beam have an advantage which is very similar to the shooting exercise that uses real bullets. However, real time technique for laser beam recognition in a target image is necessary. The method proposed in this paper takes a difference image from two adjacent image frames. Then a thresholding is applied on this difference image to discriminate laser beam from background. To decide the threshold value the intensity distribution of background points is modeled assuming normal distribution. Then a noise reduction and a region segmentation are applied on the binary image to find the position of a laser beam. The time complexity of this process depends on the size of an image multiplied by the size of a mask used in the noise reduction process. The experimental result showed that the accuracy of the system was 93.3%. Even in the inaccurate cases the beam was always found in the resultant region.