• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.138-144
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• 2020

Currently, an autonomous vehicle studies are working to develop a four-level autonomous vehicle that can cope with emergencies. In order to flexibly respond to an emergency, the autonomous vehicle must move in a direction to minimize the damage, which must be conducted by judging all the states of the road, such as the surrounding pedestrians, road conditions, and surrounding vehicle conditions. Therefore, in this paper, we suggest a passenger detection and sharing system to detect the passenger situation inside the autonomous vehicle and share it with V2V to the surrounding vehicles to assist in the operation of the autonomous vehicle. Passenger detection and sharing system improve the weighting method that recognizes passengers in the current vehicle to identify the passenger's position accurately inside the vehicle, and shares the passenger's position of each vehicle with other vehicles around it in case of emergency. So, it can help determine the driving of a vehicle. As a result of the experiment, the body pressure sensor applied to the passenger recognition sub-module showed about 8% higher accuracy than the conventional resonant sensor and about 17% higher than the piezoelectric sensor.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.531-540
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• 2007

In this research, we proposed a two-phase hybrid stock price forecasting model with cointegration tests and artificial neural networks. Using not only the related stocks to the target stock but also the past information as input features in neural networks, the new model showed an improved performance in forecasting than that of the usual neural networks. Firstly in order to extract stocks which have long run relationships with the target stock, we made use of Johansen's cointegration test. In stock market, some stocks are apt to vary similarly and these phenomenon can be very informative to forecast the target stock. Johansen's cointegration test provides whether variables are related and whether the relationship is statistically significant. Secondly, we learned the model which includes lagged variables of the target and related stocks in addition to other characteristics of them. Although former research usually did not incorporate those variables, it is well known that most economic time series data are depend on its past value. Also, it is common in econometric literatures to consider lagged values as dependent variables. We implemented a price direction forecasting system for KOSPI index to examine the performance of the proposed model. As the result, our model had 11.29% higher forecasting accuracy on average than the model learned without cointegration test and also showed 10.59% higher on average than the model which randomly selected stocks to make the size of the feature set same as that of the proposed model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.827-835
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• 2017

With the rapid progress of ageing several issues concurrently occur, and one important social issue that must be resolved is accidents involving Elderly drivers. Efforts to reduce the frequency of such accidents is a must in order to be prepared to face a superaged society. Currently people aged 65 or older are prescribed as an "Elderly person." Therefore, various studies concerning accidents involving Elderly drivers apply this age criteria to separate regular drivers and Elderly drivers. However, there is no criteria to practically discern Elderly drivers with certain physical features as vulnerable road users based on a level of acceptable accuracy. Therefore, this studies intends to compare the possibility of accidents by age group of Elderly drivers by correlation analysis to analyze the accident characteristics by age group. Results showed that for drivers aged 75 and older, their influence on major accident characteristics by vehicle type increased with higher age groups. In particular, passenger cars had a relatively low accident frequency rate for drivers aged between 70 and 80, but for drivers aged 75 to 84, they had higher influence on accidents for the same vehicle type. This demonstrates that as ageing progresses and the average life expectancy increases, the age span of elders continues to increase, meaning that characteristics differ by age group among the aged. This study confirmed that the influence on the possibility of accidents differs by age group among the aged.

• Journal of Broadcast Engineering
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• v.13 no.5
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• pp.732-743
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• 2008

In H.264/AVC, 4$\times$4 block transform is used for intra and inter prediction instead of 8$\times$8 block transform. Using small block size coding, H.264/AVC obtains high temporal prediction efficiency, however, it has limitation in utilizing spatial redundancy. Motivated on these points, we propose a multi-dimensional transform which achieves both the accuracy of temporal prediction as well as effective use of spatial redundancy. From preliminary experiments, the proposed multi-dimensional transform achieves higher energy compaction than 2-D DCT used in H.264. We designed an integer-based transform and quantization coder for multi-dimensional coder. Moreover, several additional methods for multi-dimensional coder are proposed, which are cube forming, scan order, mode decision and updating parameters. The Context-based Adaptive Variable-Length Coding (CAVLC) used in H.264 was employed for the entropy coder. Simulation results show that the performance of the multi-dimensional codec appears similar to that of H.264 in lower bit rates although the rate-distortion curves of the multi-dimensional DCT measured by entropy and the number of non-zero coefficients show remarkably higher performance than those of H.264/AVC. This implies that more efficient entropy coder optimized to the statistics of multi-dimensional DCT coefficients and rate-distortion operation are needed to take full advantage of the multi-dimensional DCT. There remains many issues and future works about multi-dimensional coder to improve coding efficiency over H.264/AVC.

• Journal of Animal Environmental Science
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• v.7 no.3
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• pp.155-164
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• 2001

Environmental factors such as $NH_3,\;H_2S,\;CO_2$, dust, temperature, and humidity in the animal house are a potential health hazard to humans and animals. Until now, most of measurement methods can only provide periodic results with low accuracy. A data acquisition system which can measure continuously and simultaneously $NH_3,\;H_2S,\;CO_2$, temperature, and humidity was developed and installed in two pig houses. Daily changes of environment for the pig-houses were investigated by the data acquisition system. In order to evaluate NH$_3$sensor, gas samples were obtained and NH$_3$concentrations were measured at nine positions; combinations of three positions(inlet, middle, and outlet) and three heights(0 cm, 40 cm, 150 cm). Ammonia concentration of 14.0 ~37.1 ppm for slurry pig-house is higher than that of 8.4~29.7 ppm for scraper pig-house, and there were no statistical differences among the positions. However, the concentration of $NH_3$at 150 cm was higher than thats of 0 cm and 40 cm.

• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.313-322
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• 2021

Purpose: To find diagnostic image features, to compare diagnostic performance of multiphase CT versus gadoxetic acid disodium-enhanced MRI (GAD-MRI), and to evaluate the impact of analyzing Liver Imaging Reporting and Data System (LI-RADS) imaging features, for distinguishing combined hepatocellular-cholangiocarcinoma (CHC) from hepatocellular carcinoma (HCC). Materials and Methods: Ninety-six patients with pathologically proven CHC (n = 48) or HCC (n = 48), diagnosed June 2008 to May 2018 were retrospectively analyzed in random order by three radiologists with different experience levels. In the first analysis, the readers independently determined the probability of CHC based on their own knowledge and experiences. In the second analysis, they evaluated imaging features defined in LI-RADS 2018. Area under the curve (AUC) values for CHC diagnosis were compared between CT and MRI, and between the first and second analyses. Interobserver agreement was assessed using Cohen's weighted κ values. Results: Targetoid LR-M image features showed better specificities and positive predictive values (PPV) than the others. Among them, rim arterial phase hyperenhancement had the highest specificity and PPV. Average sensitivity, specificity, and AUC values were higher for MRI than for CT in both the first (P = 0.008, 0.005, 0.002, respectively) and second (P = 0.017, 0.026, 0.036) analyses. Interobserver agreements were higher for MRI in both analyses (κ = 0.307 for CT, κ = 0.332 for MRI in the first analysis; κ = 0.467 for CT, κ = 0.531 for MRI in the second analysis), with greater agreement in the second analysis for both CT (P = 0.001) and MRI (P < 0.001). Conclusion: Rim arterial phase hyperenhancement on GAD-MRI can be a good indicator suggesting CHC more than HCC. GAD-MRI may provide greater accuracy than CT for distinguishing CHC from HCC. Interobserver agreement can be improved for both CT and MRI by analyzing LI-RADS imaging features.

• Journal of the Korean Geotechnical Society
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• v.24 no.9
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• pp.41-48
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• 2008

The aim of this study is to present a graphical method in order to evaluate stages in shrinkage cracking. Firstly, the distribution of crack openings is established by sorting the openings of individual cracks in the soil cracking system. Secondly, it is normalized in a range of 0 to 1 to obtain the normalized crack opening distribution. Thirdly, three S-shape curve models introduced by Brooks and Corey(1964), Fredlund and Xing(1994) and van Genuchten(1980) are chosen to fit the normalized crack opening distribution using a curve fitting method. The accuracy of fitting which is described through fitting parameters by the van Genuchten equation is much higher than that by the Brooks and Corey equation and slightly higher than that by the Fredlund and Xing equation; thus the van Genuchten model is used. Finally, the stages of shrinkage cracking are graphically evaluated by drawing three separate straight lines corresponding to three linear parts of the fitted normalized crack opening distribution. The proposed method is tested with different sample thicknesses. The measured data are fitted by the selected model with the fairly high regression coefficient and small root mean square error. The results show graphically that shrinkage cracking comprises three stages; namely, primary, secondary and residual stages. Subsequently, the ranges of evaluated crack opening for each of these stages are presented.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.229-240
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• 2019

3D geo-spatial model have been widely used in the field of Civil Engineering, Medical, Computer Graphics, Urban Management and many other. Especially, the demand for high quality 3D spatial information such as precise road map construction has explosively increased, MMS and UAV techniques have been actively used to acquire them more easily and conveniently in surveying and geo-spatial field. However, in order to perform 3D modeling by integrating the two data set from MMS and UAV, its so needed an proper registration method is required to efficiently correct the difference between the raw data acquisition sensor, the point cloud data generation method, and the observation accuracy occurred when the two techniques are applied. In this study, we obtained UAV point colud data in Yeouido area as the study area in order to determine the automatic registration performance between MMS and UAV point cloud data using ICP(Iterative Closet Point) method. MMS observations was then performed in the study area by dividing 4 zones according to the level of overlap ratio and observation noise with based on UAV data. After we manually registered the MMS data to the UAV data, then compared the results which automatic registered using ICP method. In conclusion, the higher the overlap ratio and the lower the noise level, can bring the more accurate results in the automatic registration using ICP method.

• Science of Emotion and Sensibility
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• v.18 no.2
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• pp.45-54
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• 2015

This study carried out an experiment to identify subject's work effectiveness and energy saving effect using LED light. Towards this end, this study configured nine various lighting environments in order to control PWM (Pulse Width Modulation) and illuminance (lux), which are the characteristics of LED light. The PWM ratio of LED light was set as R:G:B=1:1:1, R:G:B=4:1:5, and R:G:B=8:7:7, respectively, and illuminance (lux) was set as 400 lx, 700 lx, and 1000 lx, respectively. In addition, the indoor environment was set temperature $20-24^{\circ}C$, humidity 50%-60%, and the amount of clothing 1. This study analyzed work effectiveness and energy consumption in nine lighting environments, each. Error correction was performed for work effectiveness analysis, and cumulative power consumption was measured in each lighting environment for energy consumption analysis. According to experiment results through the lighting environments suggested in this study, accuracy and spent time effectiveness were good in 700lux and higher than 400lux. For spent time, the best effectiveness was revealed in the suggested PWM ratio, R:G:B=8:7:7. The lowest power consumption on each illuminance (lux) was revealed in the order of R:G:B=8:7:7, RGB=1:1:1, and R:G:B=4:1:5. Therefore, pulse-width modulation effect is proposed in this paper was found to affect the efficiency and energy saving.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.4
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• pp.90-101
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• 2012

The landmark selection is crucial to successful perform in SLAM(Simultaneous Localization and Mapping) with a mono camera. Especially, in unknown environment, automatic landmark selection is needed since there is no advance information about landmark. In this paper, proposed visual attention system which modeled human's vision system will be used in order to select landmark automatically. The edge feature is one of the most important element for attention in previous visual attention system. However, when the edge feature is used in complicated indoor area, the response of complicated area disappears, and between flat surfaces are getting higher. Also, computation cost increases occurs due to the growth of the dimensionality since it uses the responses for 4 directions. This paper suggests to use a corner feature in order to solve or prevent the problems mentioned above. Using a corner feature can also increase the accuracy of data association by concentrating on area which is more complicated and informative in indoor environments. Finally, this paper will prove that visual attention system based on corner feature can be more effective in SLAM compared to previous method by experiment.