• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2702-2719
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• 2013

A new optimal scheme based on curvelet transform is proposed for retinal image enhancement (RIE) using real-coded quantum genetic algorithm. Curvelet transform has better performance in representing edges than classical wavelet transform for its anisotropy and directional decomposition capabilities. For more precise reconstruction and better visualization, curvelet coefficients in corresponding subbands are modified by using a nonlinear enhancement mapping function. An automatic method is presented for selecting optimal parameter settings of the nonlinear mapping function via quantum genetic search strategy. The performance measures used in this paper provide some quantitative comparison among different RIE methods. The proposed method is tested on the DRIVE and STARE retinal databases and compared with some popular image enhancement methods. The experimental results demonstrate that proposed method can provide superior enhanced retinal image in terms of several image quantitative evaluation indexes.

• Journal of Biomedical Engineering Research
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• v.18 no.3
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• pp.315-323
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• 1997

Driving is a complex vigilance task that includes improper lookout, excessive speed and inattention. The primary objective of this research is to detect driver drowsiness so that the driver can be alerted to an impending traffic accident in performance. We developed the automatic detection and warning system during drowsy driving. A drowsiness detection system must be able to monitor driver status and detect the detrimental changes of a driver performance. Eyeblink has been found to be a reliable factor of drowsiness detection in earlier studies. As an additional parameter, we also considered the yawning which often occurs in a low vigilance state and predicts the drowsy state. We used a computer vision method to extract the eyeblink and yawning in the face image sequences. When the drowsy state was detected, the driver was refreshed by alarming device and menthol scent generator after deciding the warning level by fuzzy logic. For the evaluation of our system, we measured the physiological parameters such as EOG and EEG. The results indicated that it is possible to detect and alert the driver drowsiness temporarily or continuously by using our system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.371-377
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• 2003

In this paper, comparison of feature parameters for emotion recognition using speech signal is studied. For this purpose, a corpus of emotional speech data recorded and classified according to the emotion using the subjective evaluation were used to make statical feature vectors such as average, standard deviation and maximum value of pitch and energy and phonetic feature such as MFCC parameters. In order to evaluate the performance of feature parameters speaker and context independent emotion recognition system was constructed to make experiment. In the experiments, pitch, energy parameters and their derivatives were used as a prosodic information and MFCC parameters and its derivative were used as phonetic information. Experimental results using vector quantization based emotion recognition system showed that recognition system using MFCC parameter and its derivative showed better performance than that using the pitch and energy parameters.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.493-505
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• 2017

Maximum likelihood estimation (MLE) of the generalized extreme value distribution (GEVD) is known to sometimes over-estimate the positive value of the shape parameter for the small sample size. The maximum penalized likelihood estimation (MPLE) with Beta penalty function was proposed by some researchers to overcome this problem. But the determination of the hyperparameters (HP) in Beta penalty function is still an issue. This paper presents some data adaptive methods to select the HP of Beta penalty function in the MPLE framework. The idea is to let the data tell us what HP to use. For given data, the optimal HP is obtained from the minimum distance between the MLE and MPLE. A bootstrap-based method is also proposed. These methods are compared with existing approaches. The performance evaluation experiments for GEVD by Monte Carlo simulation show that the proposed methods work well for bias and mean squared error. The methods are applied to Blackstone river data and Korean heavy rainfall data to show better performance over MLE, the method of L-moments estimator, and existing MPLEs.

• Journal of Advanced Navigation Technology
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• v.12 no.6
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• pp.634-639
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• 2008

In mobile communication networks, the main power consumption is due to the actual transmissions power. Therefore, power efficiency network structures have gained considerable importance in mobile multi-hop systems and networks in recent years. In this paper, the performance of mobile multi-hop wireless system with M-QAM signal and forward error control (FEC) technique are analyzed The FEC technique uses extra processing power related to encoding and decoding, it is need complex functions to be built into the communication node. The probability of receiving a correct bit and codeword for relaying a data frame over h hop relay station to the final station is evaluated as a function of channel parameter and number of hops, and the distance between the different station.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.3
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• pp.380-393
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• 2015

This study presents how we can evaluate stereo camera systems for the structural deformation monitoring. A stereo camera system, consisting of a set of stereo cameras and reflective markers attached on the structure, is introduced for the measurement and the stereo pattern recognition (SPR) method is utilized for the full-field structural deformation estimation. Performance of this measurement system depends on many parameters including types and specifications of the cameras, locations and orientations of them, and sizes and positions of markers; it is difficult to experimentally identify the effects of each parameter on the measurement performance. In this study, a simulation framework for evaluating performance of the stereo camera systems with various parameters has been developed. The maximum normalized root-mean-square (RMS) error is defined as a representative index of stereo camera system performance. A plate structure is chosen for an introductory example. Its several modal harmonic vibrations are generated and estimated in the simulation framework. Two cases of simulations are conducted to see the effects of camera locations and the resolutions of the cameras. An experimental validation is carried out for a few selected cases from the simulations. Using the simultaneous laser displacement sensor (LDS) measurements as the reference, the measurement errors are obtained and compared with the simulations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.50-58
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• 2018

Intermittent demand is a demand with a pattern in which zero demands occur frequently and non-zero demands occur sporadically. This type of demand mainly appears in spare parts with very low demand. Croston's method, which is an initiative intermittent demand forecasting method, estimates the average demand by separately estimating the size of non-zero demands and the interval between non-zero demands. Such smoothing type of forecasting methods can be suitable for mid-term or long-term demand forecasting because those provides the same demand forecasts during the forecasting horizon. However, the smoothing type of forecasting methods aims at short-term forecasting, so the estimated average forecast is a factor to decrease accuracy. In this paper, we propose a forecasting method to improve short-term accuracy by improving Croston's method for intermittent demand forecasting. The proposed forecasting method estimates both the non-zero demand size and the zero demands' interval separately, as in Croston's method, but the forecast at a future period adjusted by binomial weight according to occurrence probability. This serves to improve the accuracy of short-term forecasts. In this paper, we first prove the unbiasedness of the proposed method as an important attribute in forecasting. The performance of the proposed method is compared with those of five existing forecasting methods via eight evaluation criteria. The simulation results show that the proposed forecasting method is superior to other methods in terms of all evaluation criteria in short-term forecasting regardless of average size and dispersion parameter of demands. However, the larger the average demand size and dispersion are, that is, the closer to continuous demand, the less the performance gap with other forecasting methods.

• The Journal of the Acoustical Society of Korea
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• v.25 no.8
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• pp.383-388
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• 2006

This Paper presents a novel approach to single channel microphone speech enhancement in noisy environments. Widely used noise reduction techniques based on the spectral subtraction are generally expressed as a spectral gam depending on the signal-to-noise ratio (SNR). The well-known decision-directed(DD) estimator of Ephraim and Malah efficiently reduces musical noise under the background noise conditions, but generates the delay of the a prioiri SNR because the DD weights the speech spectrum component of the Previous frame in the speech signal. Therefore, the noise suppression gain which is affected by the delay of the a priori SNR, which is estimated by the DD matches the previous frame rather than the current one, so after noise suppression. this degrades the noise reduction performance during speech transient periods. We propose a computationally simple but effective speech enhancement technique based on the sigmoid type function for the weight Parameter of the DD. The proposed approach solves the delay problem about the main parameter, the a priori SNR of the DD while maintaining the benefits of the DD. Performances of the proposed enhancement algorithm are evaluated by ITU-T p.862 Perceptual Evaluation of Speech duality (PESQ). the Mean Opinion Score (MOS) and the speech spectrogram under various noise environments and yields better results compared with the fixed weight parameter of the DD.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.4
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• pp.30-39
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• 2003

In this paper, we carried out the performance evaluation of HM-Net(Hidden Markov Network) speech recognition system for Korean speech databases. We adopted to construct acoustic models using the HM-Nets modified by HMMs(Hidden Markov Models), which are widely used as the statistical modeling methods. HM-Nets are carried out the state splitting for contextual and temporal domain by PDT-SSS(Phonetic Decision Tree-based Successive State Splitting) algorithm, which is modified the original SSS algorithm. Especially it adopted the phonetic decision tree to effectively express the context information not appear in training speech data on contextual domain state splitting. In case of temporal domain state splitting, to effectively represent information of each phoneme maintenance in the state splitting is carried out, and then the optimal model network of triphone types are constructed by in the parameter. Speech recognition was performed using the one-pass Viterbi beam search algorithm with phone-pair/word-pair grammar for phoneme/word recognition, respectively and using the multi-pass search algorithm with n-gram language models for sentence recognition. The tree-structured lexicon was used in order to decrease the number of nodes by sharing the same prefixes among words. In this paper, the performance evaluation of HM-Net speech recognition system is carried out for various recognition conditions. Through the experiments, we verified that it has very superior recognition performance compared with the previous introduced recognition system.

• Journal of radiological science and technology
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• v.44 no.5
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• pp.459-464
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• 2021

The detector performance is important role in acquiring the gamma rays from patients. Among parameters of detector performances, there is density, which relates to respond to gamma rays. Therefore, we confirm the detection efficiency according to various detector materials based on the density parameter using GATE (geant4 application for emission tomography) simulation tool. The NaI (density: 3.67 g/cm³), CZT (Cadimium Zinc Telluride) (density: 5.80 g/cm³), CdTe (Cadmium Telluride) (5.85 g/cm³), and GAGG (Gadoinium Aluminum Gallium Garnet) (density g/cm³) were used as detector materials. In addition, the point source and quadrant bar phantom, which is modeled for 0.5, 1.0, 1.5, and 2.0 mm thicknesses, were modeled to confirm the quatitative analysis using sensitivity (cps/MBq) and the full width at half maximum (FWHM, mm) at the 2.0 mm bar thickness containing visual evaluation. Based on the results, the sensitivity for NaI, CZT, CdTe, and GAGG detector materials were 0.12, 0.15, 0.16, and 0.18 cps/MBq. In addition, the FWHM for quadrant bar phantom in the 2.0 mm bar thickness is 3.72, 3.69, 3.70, and 3.73 mm for NaI, CZT, CdTe, and GAGG materials, respectively. Compared with performance of detector materials according to density, the high density can improve detection efficiency in terms of sensitivity and mean count. Among these detector materials, the GAGG material is efficient for detection of gamma rays.