• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.56-62
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• 2010

Auto-exposure control automatically calculates and adjusts the exposure for consecutive input image. Recently, this is usually controlled by the sensor gain, however, unsuitable control causes oscillation of luminance for sonsecutive input images, called as flickering. Also, in mobile phone cameras, only simple information, such as the average luminance value, can be utilized due to coarse performance. Therefore, this paper presents a new real-time AE control method using a Look Up Table(LUT) based on Scene-Luminance curves to avoid the generation of flickering. Prior to the AE control, a LUT is constructed, which illustrates the characteristic of outputs for input patches corresponding to sensor gains. The AE control is first performed by estimating a current scene as a patch using the proposed LUT. A new sensor gain is then estimated using also LUT with previously estimated patch. The entire estimation process is performed using linear interpolation to achieve real-time execution. Based on experimental results, the proposed AE control is demonstrated with real-time, flicker-free.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1195-1200
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• 2013

In this paper, we proposed an enhanced Max-Min neural network by auto-tuning of learning rate using fuzzy control method. For the reduction of training time required in the competition stage, the method was proposed that arbitrates dynamically the learning rate by applying the numbers of the accuracy and the inaccuracy to the input of the fuzzy control system. The experiments using real concrete crack images showed that the enhanced Max-Min neural network was effective in the recognition of direction of the extracted cracks.

• Microbiology and Biotechnology Letters
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• v.21 no.4
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• pp.354-365
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• 1993

A novel control method with automatic tuning of PID controller parameters has been developed for efficient regulation of dissolved oxygen concentration in fed-batch fermentations of Escherichia coli. Agitation speed and oxygen partial pressure in the inlet gas stream were chosen to be the manipulated variables. A heuristic reasoning allowed improved tuning decisions from the supervision of control performance indices and it coule obviate the needs for process assumptions or disturbance patterns. The control input consisted of feedback and feedforword parts. The feedback part was determined by PID control and the feedforward part is determined from the feed rate. The proportional gain was updated on-line by a set of heuristics rules based on the supervision of three performance indices. These indices were output error covariance, the average value of output error, and input covariance, which were calculated on-line using a moving window. The integral and derivative time constants were determined from the period of output response. The specific growth rate was maintained at a low level to avoid acetic acid accumulation and thus to achieve a high cell density. The specific growthe rate was estimated from the carbon dioxide evolution rate. In fed-batch fermentation, the simutaneous control of dissolved oxygen concentration (at 0.2; fraction of saturated value) and specific growth rate (at 0.25$hr^{-1}$) was satisfactory for the entire culture period in spite of the changes in the feed rate and the switching of control input.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.2 s.344
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• pp.39-43
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• 2006

This paper deals with the AR Identification of unknown system using cumulant, which is the 3rd order statistics of output signal in the presence of the noise signal. The algorithms for identification of unknown system we applies to the AR identification method using the cumulant which is possible to the guarantees of global convergence and the representation of amplitude and phase information of system among with the method of parametric modeling. In the process of identification, we considered unknown system to the one of AR system. After the generation of input signal, it was being passed through the system then We use the its output signal that the noise is added. As a result of identification of AR system by changing the signal to noise ratio, we get the fairly good results compared to original system output values and confirmed that the pole was located in the unit circle of z transform.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.505-508
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• 1997

In this paper, an auto-tuning method for fuzzy controller based on the neural network is presented. The backpropagated error of neural emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and used for speed control of induction motor. For the torque control method, an indirect vector control scheme with slip calculation is used because of its stable characteristics regardless of speed. Motor input current is regulated by a current controlled voltage source PWM inverter using space voltage vector technique. Also, the scheme of current control fuzzy controller is synchronous reference frame with decoupling term. DSP(TMS320C31) is used to achieve the high speed calculation of the space voltage vector PWM and to build the self-learning fuzz. control algorithm. An IPM is used to simplify hardware design.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2388-2396
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• 2012

This paper is on an simplified orthogonal projection method which cancel the acoustic echo signals in the stereo acoustic echo canceller. 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 for signals with the high auto-correlation, and has small computational quantities. To verify the convergence characteristics of the proposed algorithm, we simulated about various input signals. And we compared the results of simulation for this algorithm with the ones for the NLMS algorithm. By these works, it was proved that the stereo acoustic echo canceller adopting the proposed algorithm shows about 3dB more high ERLE than the NLMS algorithm for the white noise signals, and 5dB for the colored voice signals.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1166-1171
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• 2001

The method for output disturbance estimation is proposed. In this method, output disturbance is estimated from the closed loop system dynamics using the output and control input signals. In the closed-loop output-disturbance estimator, precise system identification is required to reduce estimation error. The realization of estimator was done by the DSP board (DSPl103), and disturbance estimation in various environments was performed： change of rotation speed, media feature and spindle motor with (or without) auto-ball balancing system (ABS). From these experiments, the disturbance characteristics of ODD under various conditions are analyzed, and the desirable servo loop configuration based these results is proposed.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.74-83
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• 2023

In this paper, we propose an image compression scheme which uses a demosaicking network and a channel-wise decoder in the decoding network. For the demosaicing network, we use as the input a colored mosaiced pattern rather than the well-known Bayer pattern. The use of a colored mosaiced pattern results in the mosaiced image containing a greater amount of information pertaining to the original image. Therefore, it contributes to result in a better color reconstruction. The channel-wise decoder is composed of multiple decoders where each decoder is responsible for each channel in the color image, i.e., the R, G, and B channels. The encoder and decoder are both implemented by wavelet based auto-encoders for better performance. Experimental results verify that the separated channel-wise decoders and the colored mosaic pattern produce a better reconstructed color image than a single decoder. When combining the colored CFA with the multi-decoder, the PSNR metric exhibits an increase of over 2dB for three-times compression and approximately 0.6dB for twelve-times compression compared to the Bayer CFA with a single decoder. Therefore, the compression rate is also increased with the proposed method than with the method using a single decoder on the Bayer patterned mosaic image.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.17-22
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• 2021

In this paper, a machine learning-based automatic infographic recommendation system is proposed to improve the existing infographic production method. This system consists of a part that machine learning multiple infographic images and a part that automatically recommends infographics with artificial intelligence only by inputting basic data from the user. The recommended infographics are provided in the form of a library, and additional data can be input by drag & drop method. In addition, the infographic image is designed to be dynamically adjusted according to the size of the input data. As a result of analyzing the machine learning-based automatic infographic recommendation process, the matching success rate for layout and keyword was very high, and the matching success rate for type was rather low. In the future, a study to improve the matching success rate for the image type for each part of the infographic will be needed.