• Journal of the Korean Society of Radiology
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• v.10 no.2
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• pp.81-87
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• 2016

In this study we proposed a texture feature analysis algorithm that distinguishes between a normal image and a diseased image using CT images of some fatty liver patients, and generates both Eigen images and test images which can be applied to the proposed computer aided diagnosis system in order to perform a quantitative analysis for 6 parameters. And through the analysis, we derived and evaluated the recognition rate of CT images of fatty liver. As the results of examining over 30 example CT images of fatty liver, the recognition rates representing a specific texture feature-value are as follows: some appeared to be as high as 100% including Average Gray Level, Entropy 96.67%, Skewness 93.33%, and Smoothness while others showed a little low disease recognition rate: 83.33% for Uniformity 86.67% and for Average Contrast 80%. Consequently, based on this research result, if a software that enables a computer aided diagnosis system for medical images is developed, it will lead to the availability for the automatic detection of a diseased spot in CT images of fatty liver and quantitative analysis. And they can be used as computer aided diagnosis data, resulting in the increased accuracy and the shortened time in the stage of final reading.

• Science of Emotion and Sensibility
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• v.23 no.2
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• pp.3-12
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• 2020

The purpose of this work is to suggest the optimal color temperature, which induces a sense of comfort for autonomous vehicle users through the analysis of biosignal using electroencephalography (EEG) and photoplethysmography (PPG). To achieve this purpose, we applied lighting with a color temperature of 3000 K, 4000 K, 5000 K, and 6000 K to the autonomous driving environment. We experimented in a laboratory equipped with a graphic driving simulator. The experimental procedure is as follows: 1) stabilization (5 min). 2) Uchida-Kraepelin test (3 min). 3) Automatic driving + lighting (3 min). This procedure was repeated four times under different color temperatures. We performed frequency analysis on a collected time-series data and calculated the power value for each frequency band through power spectrum analysis. In the case of EEG, we analyzed α- and β-waves, which are indicators of stability and arousal, respectively. In the case of PPG, we analyzed the sympathetic nervous system activity. To reduce deviations between the subjects, we normalized the data before analysis. The result of the first analysis revealed that α-wave increased only at 5000 K, while the β-wave increased at almost all color temperatures. In addition, in the case of PPG, sympathetic nervous system activity (SNSA) increased under driving conditions. The result of the second analysis revealed that the difference between β-wave and SNSA is insignificant. In conclusion, the increase in α-waves showed that EEG was most stable at 5000 K. The results of this study can be applied to the upcoming autonomous driving era to induce high driver satisfaction. Furthermore, this approach could eventually lead to the acceptance of autonomous vehicles by suggesting a positive effect of autonomous driving.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.5
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• pp.8-14
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• 2009

In the research field of speech recognition, pinpointing the endpoints of speech utterance even with the presence of background noise is of great importance. These noise present during recording introduce disturbances which complicates matters since what we just want is to get the stationary parameters corresponding to each speech section. One major cause of error in automatic recognition of isolated words is the inaccurate detection of the beginning and end boundaries of the test and reference templates, thus the necessity to find an effective method in removing the unnecessary regions of a speech signal. The conventional methods for speech endpoint detection are based on two linear time-domain measurements: the short-time energy, and short-time zero-crossing rate. They perform well for clean speech but their precision is not guaranteed if there is noise present, since the high energy and zero-crossing rate of the noise is mistaken as a part of the speech uttered. This paper proposes a novel approach in finding an apparent threshold between noise and speech based on Lyapunov Exponents (LEs). This proposed method adopts the nonlinear features to analyze the chaos characteristics of the speech signal instead of depending on the unreliable factor-energy. The excellent performance of this approach compared with the conventional methods lies in the fact that it detects the endpoints as a nonlinearity of speech signal, which we believe is an important characteristic and has been neglected by the conventional methods. The proposed method extracts the features based only on the time-domain waveform of the speech signal illustrating its low complexity. Simulations done showed the effective performance of the Proposed method in a noisy environment with an average recognition rate of up 92.85% for unspecified person.

• Journal of Bio-Environment Control
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• v.26 no.3
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• pp.215-220
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• 2017

This study was conducted to improve the insufficiency of fruit vegetable grafting system developed by National Institute of Agricultural Sciences, Rural Development Administration. When the rotary blade cut the stem of scions and rootstocks, the grafting failure at curved cutting surfaces happened. The cutting depth of a tomato seedling by a rotated cutter was calculated 0.11 mm even when the cutting arm length and the maximum stem diameter were 50 mm and 5 mm, respectively. Mathematical analysis and high-speed photography showed that there was no problem by cutting in straight the stem of scions and rootstocks. The compression test of seedling stems to design the optimal shape of gripper showed that stems were not completely restored when they were compressed above 0.8 mm and 0.6 mm in case of rootstocks and scion, respectively. This study found that the bending angle of stem of tomato seedlings at the grafting period was 10 degree on average. The optimal gripper finger was the edge finger type which could be precisely set center point by adjusting the distance between fingers. In addition, it was found that most of seedling could be grasped without damage when the finger-to-finger distances is set to 2.5 mm for scion and 3.0 mm for rootstocks and finger are coated by 1 mm-thick flexible material.

• Journal of the Korean Society of Radiology
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• v.9 no.6
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• pp.369-374
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• 2015

In this study we proposed a texture feature analysis algorithm that distinguishes between a normal image and a diseased image using CT images of some brain hemorrhage patients, and generates both Eigen images and test images which can be applied to the proposed computer aided diagnosis system in order to perform a quantitative analysis for 6 parameters. And through the analysis, we derived and evaluated the recognition rate of CT images of brain hemorrhage. As the results of examining over 40 example CT images of brain hemorrhage, the recognition rates representing a specific texture feature-value are as follows: some appeared to be as high as 100% including average gray level, average contrast, smoothness, and Skewness while others showed a little low disease recognition rate: 95% for uniformity and 87.5% for entropy. Consequently, based on this research result, if a software that enables a computer aided diagnosis system for medical images is developed, it will lead to the availability for the automatic detection of a diseased spot in CT images of brain hemorrhage and quantitative analysis. And they can be used as computer aided diagnosis data, resulting in the increased accuracy and the shortened time in the stage of final reading.

• Economic and Environmental Geology
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• v.29 no.6
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• pp.713-724
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• 1996

The time-series resident solute concentrations, monitored at two field plots using the automated 144-channel TDR system by Kim (this issue), are used to investigate the dominant transport mechanism at field scale. Two models, based on contradictory assumptions for describing the solute transport in the vadose zone, are fitted to the measured mean breakthrough curves (BTCs): the deterministic one-dimensional convection-dispersion model (CDE) and the stochastic-convective lognormal transfer function model (CLT). In addition, moment analysis has been performed using the probability density functions (pdfs) of the travel time of resident concentration. Results of moment analysis have shown that the first and second time moments of resident pdf are larger than those of flux pdf. Based on the time moments, expressed in function of model parameters, variance and dispersion of resident solute travel times are derived. The relationship between variance or dispersion of solute travel time and depth has been found to be identical for both the time-series flux and resident concentrations. Based on these relationships, the two models have been tested. However, due to the significant variations of transport properties across depth, the test has led to unreliable results. Consequently, the model performance has been evaluated based on predictability of the time-series resident BTCs at other depths after calibration at the first depth. The evaluation of model predictability has resulted in a clear conclusion that for both experimental sites the CLT model gives more accurate prediction than the CDE model. This suggests that solute transport at natural field soils is more likely governed by a stream tube model concept with correlated flow than a complete mixing model. Poor prediction of CDE model is attributed to the underestimation of solute spreading and thus resulting in an overprediction of peak concentration.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.663-672
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• 2002

In this paper, Using an automatic tracking system, behavior of an aquatic insect, Chironomus sp. (Chironomidae), was observed in semi-natural conditions in response to sub-lethal treament of a carbamate insecticide, carbofuran. The fourth instar larvae were placed in an observation cage $(6cm\times{7cm}\times{2.5cm)}$ at temperature of $18^\circ{C}$ and the light condition of 10 time (light) : 14 time (dark). The tracking system was devised to detect the instant, partial movement of the insect body. Individual movement was traced after the treatment of carbofuran (0.1ppm) for four days 2days : before treatment, 2 days : after treatment). Along with the other irregular behaviors, "ventilation activity", appearing as a shape of "compressed zig-zag", was more frequently observed after the treatment of the insecticide. The activity of the test individuals was also generally depressed after the chemical treatment. In order to detect behavioral changes of the treated specimens, wavelet analysis was implemented to characterize different movement patterns. The extracted parameters based on Discrete Wavelet Transforms (DWT) were subsequently provided to artificial neural networks to be trained to represent different patterns of the movement tracks before and after treatments of the insecticide. This combined model of wavelets and artificial neural networks was able to point out the occurrence of characteristic movement patterns, and could be an alternative tool for automatically detecting presences of toxic chemicals for water quality monitoring. quality monitoring.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.1-7
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• 2021

Detect and avoid (DAA) system, which is essential for the operation of UAS, detects intruding aircraft and offers the ranges of turn and climb/descent maneuver that are required to avoid the intruder. This paper uses detect and avoid alerting logic for unmanned systems (DAIDALUS) developed at NASA as a DAA algorithm. Since DAIDALUS offers ranges of avoidance maneuvers, the actual avoidance maneuver must be decided by the UAS pilot as well as the timing and method of returning to the original route. It can be readily used in real-time human-in-the-loop (HiTL) simulations where a human pilot is making the decision, but a pilot decision model is required in fast-time simulations that proceed without human pilot intervention. This paper proposes a pilot decision model that maneuvers the aircraft based on the DAIDALUS avoidance maneuver range. A series of tests were conducted using test vectors from radio technical commission for aeronautics (RTCA) minimum operational performance standards (MOPS). The alert levels differed by the types of encounters, but loss of well clear (LoWC) was avoided. This model will be useful in fast-time simulation of high-volume traffic involving UAS.

• Journal of radiological science and technology
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• v.37 no.3
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• pp.177-185
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• 2014

This study focused on effects of patient exposure dose reduction with AEC (Auto Exposure Control) marker that is designed for showing location of AEC in X-ray Chest radiography. It included 880 adults who have to use Chest X-ray Digital Radiography system (DRS, LISTEM, Korea). AEC (Ion chambers are posited in top of both sides) are used to every adult and set X-ray system as Field size $17{\times}17inch$, 120kVp, FFD 180cm. 440 people of control group are posited on detector to include both sides of lung field and the other 440 people of experimental group are set to contact their lung directly to Ion chamber (making marker to shows location). Then, measured every DAP and, estimated patient effective dose by using PCXMC 2.0. The average age of control group (M:F=245:195) is 53.9 and the average BMI is 23.4. BMI ranges from under weight: 35, normal range: 279, over weight: 106 to obese: 20 and average DAP is 223.56mGycm2, Mean effective dose is 0.045mSv. The average age of experimental group (M:F=197:243) is 53.7 and the average BMI is 22.7. BMI ranges from under weight: 34, normal range: 315, over weight: 85 to obese: 6 and average DAP is 207.36mGycm2, Mean effective dose is 0.041mSv. Experimental group shows less Mean effective dose as 0.004mSv (9.7%) than control group. Also, patient numbers who got over exposure more than 0.056mSv (limit point to know efficiency of AEC marker) is 65 in control group (14.7%), 19 in experimental group (4.3%) and take statistics with t-Test. The statistical difference between two groups is 0.006. In order to use proper amount of X-ray in auto exposure controlled chest X-ray system, matching location between ion chamber and body part is needed, and using AEC marker (designed for showing location of ion chamber) is a way to reduce unnecessary patient exposure dose.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.2
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• pp.93-98
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• 2016

For accurate precipitation forecasts the choice of weather factors and prediction method is very important. Recently, machine learning has been widely used for forecasting precipitation, and artificial neural network, one of machine learning techniques, showed good performance. In this paper, we suggest a new method for forecasting precipitation using DBN, one of deep learning techniques. DBN has an advantage that initial weights are set by unsupervised learning, so this compensates for the defects of artificial neural networks. We used past precipitation, temperature, and the parameters of the sun and moon's motion as features for forecasting precipitation. The dataset consists of observation data which had been measured for 40 years from AWS in Seoul. Experiments were based on 8-fold cross validation. As a result of estimation, we got probabilities of test dataset, so threshold was used for the decision of precipitation. CSI and Bias were used for indicating the precision of precipitation. Our experimental results showed that DBN performed better than MLP.