• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.345-352
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• 2022

Recent LiDAR(Light Detection And Ranging) sensor is used for scanning object around in real-time. This sensor can detect movement of the object and how it has changed. As the production cost of the sensors has been decreased, LiDAR begins to be used for various industries such as facility guard, smart city and self-driving car. However, LiDAR has a large input data size due to its real-time scanning process. So another way for processing a large amount of data are needed in LiDAR system because it can cause a bottleneck. This paper proposes edge computing to compress massive point cloud for processing quickly. Since laser's reflection range of LiDAR sensor is limited, multiple LiDAR should be used to scan a large area. In this reason multiple LiDAR sensor's data should be processed at once to detect or recognize object in real-time. Edge computer compress point cloud efficiently to accelerate data processing and decompress every data in the main cloud in real-time. In this way user can control LiDAR sensor in the main system without any bottleneck. The system we suggest solves the bottleneck which was problem on the cloud based method by applying edge computing service.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.67-74
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• 2022

In recent, the number of real-time traffic information sources and providers has increased as increasing smartphone users and intelligent transportation system facilities installed at roadways including vehicle detection system (VDS), dedicated short-ranged communications (DSRC), and global positioning system (GPS) probe vehicle. The accuracy of such traffic information would vary with these heterogeneous information sources or spatiotemporal traffic conditions. Therefore, the purpose of this study is to propose an empirical strategy of heterogeneous information fusion to improve the accuracy of real-time traffic information. To carry out this purpose, travel speed data collection based on the floating car technique was conducted on 227 freeway links (or 892.2 km long) and 2,074 national highway links (or 937.0 km long). The average travel speed for 5 probe vehicles on a specific time period and a link was used as a ground truth measure to evaluate the accuracy of real-time heterogeneous traffic information for that time period and that link. From the statistical tests, it was found that the proposed fusion strategy improves the accuracy of real-time traffic information.

• Analytical Science and Technology
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• v.29 no.3
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• pp.114-125
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• 2016

In this study, 165 wastewater discharge facilities in 10 business types were investigated with regard to 24 specific hazardous substances that included heavy metals, VOCs, CN, and phenol in the Gwangju city. Cu in the range from from 0.008 to 35.420 mg/L was detected in all business types and the detection rate was 46.8 %. Other heavy metals, such as Cd, As, Hg, Pb, and Cr⁺⁶ were detected as well. However, their detection rates ranged between 0.6 and 1.8 %. CN and phenol were detected in one and five facilities, respectively. 12 species of VOCs were detected: chloroform 80.6 % (0.42 to 81.60 μg/L), benzene 16.4 % (1.49 to 3.31 μg/L), trichloroethylene 11.5 % (1.78 to 6.02 μg/L), 1,1-dichloroethylene 10.3 % (1.23 to 5.89 μg/L), and dichloromethane 8.5 % (0.28 to 968.86 μg/L) in the detection rate order. The concentration of VOCs was detected in trace amounts, except for dichloromethane that exceeded the effluent quality standard in three business types, namely, metal manufacturing, food industry, and car washing facility. Chloroform was detected in all business types, where 24.88 μg/L were detected in the laundry business and 53.41 μg/L in the water supply business; the mean concentration of chloroform in these two business types was higher than elsewhere. Therefore, for the disposal of non-degradable specific hazardous substances in industrial wastewater, it is necessary to introduce physical and chemical processes, such as activated carbon adsorption, fenton oxidation, ozone treatment, as well as photocatalyst and the UV radiation.

• The Journal of the Acoustical Society of Korea
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• v.21 no.8
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• pp.708-718
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• 2002

In this paper, the problems in identifying the noise sources by using the sound intensity technique are dealt with for the general radiated near-field from vibro-acoustic sources. For this purpose, a three-dimensional model structure resembling the engine room of a car or heavy equipment is considered. Similar to the practical situations, the model contains many mutually coherent and incoherent noise sources distributed on the complicated surfaces. The sources are located on the narrow, connected, reflecting planes constructed with rigid boxes, of which a small clearance exists between the whole box structure and the reflecting bottom. The acoustic boundary element method is employed to calculate the acoustic intensity at the near-field surfaces and interior spaces. The effects of relative source phases, frequencies, and locations are investigated, from which the results are illustrated by the contour map, vector plot, and energy streamlines. It is clearly observed that the application of sound intensity technique to the reactive or reverberant field, e.g., scanning over the upper engine room as is usually practiced, can yield the detection of fake sources. For the precise result for such a field, the field reactivity should be checked a priori and the proper effort should be directed to reduce or improve the reactivity of sound field.

• Transactions on Electrical and Electronic Materials
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• v.14 no.4
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• pp.211-215
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• 2013

Partial discharge diagnosis techniques using ultra high frequencies do not affect load movement, because there is no interruption of power. Consequently, these techniques are popular among the prevention diagnosis methods. For the first time, this measurement technique has been applied to the GIS, and has been tested by applying an extra high voltage switchboard. This particular technique makes it easy to measure in the live state, and is not affected by the noise generated by analyzing the causes of faults ? thereby making risk analysis possible. It is reported that the analysis data and the evaluation of the risk level are improved, especially for poor location, and that the measurement of Ultra high frequency (UHF) partial discharge of the real live wire in industrial switchgear is spectacular. Partial discharge diagnosis techniques by using the Ultra High Frequency sensor have been recently highlighted, and it is verified by applying them to the GIS. This has become one of the new and various power equipment techniques. Diagnosis using a UHF sensor is easy to measure, and waveform analysis is already standardized, due to numerous past case experiments. This technique is currently active in research and development, and commercialization is becoming a reality. Another aspect of this technique is that it can determine the occurrences and types of partial discharge, by the application diagnosis for live wire of ultra high voltage switchgear. Measured data by using the UHF partial discharge techniques for ultra high voltage switchgear was obtained from 200 places in Gumi, Yeosu, Taiwan and China's semiconductor plants, and also the partial discharge signals at 15 other places were found. It was confirmed that the partial discharge signal was destroyed by improving the work of junction bolt tightening check, and the cable head reinforcement insulation at 8 places with a possibility for preventing the interruption of service. Also, it was confirmed that the UHF partial discharge measurement techniques are also a prevention diagnosis method in actual industrial sites. The measured field data and the usage of the research for risk assessment techniques of the live wire status of power equipment make a valuable database for future improvements.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.5
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• pp.558-568
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• 2011

Hazardous volatile organic compounds (HVOCs) have been increasingly getting concern in urban air chemistry due to photochemical smog as well as its toxicity or potential hazards. In this study, we investigated their concentrations and the properties in tunnel, urban roadside and residential area. As a result, among 36HVOCs measured in this study, BTEX (benzene, toluene, ethylbenzene, xylene) and dichlorodifluoromethane, 1,2,4-trimethylbenzene, trichlorofluoromethane were detected above the concentration of $1{\mu}g/m^3$ in every sampling site and the most abundant compound was toluene. The other compounds were detected at trace level or below the detection limit. In addition, we found that three CFCs (chlorofluorocarbons), such as CFC-12, CFC-11, CFC-113, were persistently detected because of the emission in the past. Toluene to benzene ratio (T/B) at tunnel and roadside were calculated to be 4.3~5.3 and at residential area 15.4, suggesting that the residential area had several emission sources other than car exhaust. The ratio of X/E (m,p-xylene to ethylbenzene) ratio was calculated to be 1.8~2.1 at tunnel, 1.7 at roadside and 1.2 at residential area, which means this ratio reflected well the relative photochemical reactivity between these compounds. Good correlation between m,p-xylene and ethylbenzene ($r^2$ > 0.85) were shown in every study sites. This indicated that correlation between $C_2$-alkylbenzenes were not severely affected by 3-way catalytic converter. In this study, it was demonstrated that the concentration of benzene was very low, compared with national air quality standard (annual average of $5{\mu}g/m^3$). Its concentration were $2.52{\mu}g/m^3$ in roadside and $1.34{\mu}g/m^3$ in residential area. We thought this was the result of persistent policy implementation including the reduction of benzene content in gasoline enforced on January 1, 2009.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.468-469
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• 2018

We design CNN(convolutional neural network) which is applicable to steering control system of autonomous vehicle. CNN has been widely used in many fields, especially in image classifications. But CNN has not been applied much to the regression problem such as function approximation. This is because the input of CNN has a multidimensional data structure such as image data, which makes it is not applicable to general control systems. Recently, autonomous vehicles have been actively studied, and many techniques are required to implement autonomous vehicles. For this purpose, many researches have been studied to detect the lane by using the image through the black box mounted on the vehicle, and to get the vanishing point according to the detected lane for control the autonomous vehicle. However, in detecting the vanishing point, it is difficult to detect the vanishing point with stability due to various factors such as the external environment of the image, disappearance of the instant lane and detection of the opposite lane. In this study, we apply CNN for steering control of an autonomous vehicle using a black box image of a car.

• Proceedings of the PSK Conference
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• 2000.04a
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• pp.70-81
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• 2000

DA-5018 is a synthetic capsaicin derivative under development as a non-narcotic a analgesic ag$\varepsilon$nt. DA-50 18 showed a potent analgesic activity against acute and chronic pain m model(Tablel, 2.), but it had a narrow margin of safety. DA-5018 did not bind to opioid(${\kappa}, {\delta}, {\mu}$), NKl, CGRP receptors in vitro and its analgesic effect was not antagonized by naloxone, a and it did not develop analgesic tolerance. In addition DA-5018 had no inhibitory effects against c cyclooxygenase and 5-lipooxygenase activities. DA-5018 significantly increased the relcase of substance P from the slices of the rat spinal cord. These results suggest that DA-50 18 is not a narcotic nor aspirin-like analgesic and the release of substance P is one of analgesic mechanism of action of DA-5018. We found that DA-5018 was almost ten times more potent and was at l least IOO-times less irritable compared to capsaicin. Accordingly development of topical formula was adopted. Topical formula was desiged and screened by flux test of DA-5018 using hairless mouse skin and several formulas were selected. With these topical formulas we a assessed the analgesic efficacy and carried out the toxicity, skin irritation and pharmacokinetic studies. In streptozotocin-induced hyperalgesic rat and 50 % galactose-fed hyperalgesic rat as diabetic pain models, DA-5018 cream increased the pain thresh이ds up to 77.0% and 24.4% respectively, while Zostrix-HP(capsaicin cream) incr$\varepsilon$as cd by 65.9% and 21.0%. DA-5018 c cream showed a good analgesic effect as welI in FCA-induced arthritic rat. DA-5018 cream did not show any toxicological signs in acute and chronic toxicity test and had little skin irritation in car swclIing and scratching t$\varepsilon$st. Pharmacokinetics of DA-50 18 were studied after topical application of ${14}^C$-Iabelled or unlabelIed DA-5018 cream. Plasma and skin concentrations c except applied skin wcre below the dctection limit and after 7-day cummulative application, plasma concentrations were also below detection limit DA-50 18 may have an advantag$\varepsilon$ ov$\varepsilon$r c capsaicin and is now being developed as a topical agent for the treatment of pains. DA-50 18 cream was approved for Korean IND and is now under a Phase II clinical study for arthritic pain a after finising Phase I study. DA-50 18 was also liscensed out to Stiefel Company in America in

• Design & Manufacturing
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• v.14 no.1
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• pp.8-14
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• 2020

In general, auto parts production assembly line is assembled and produced by automatic mounting by an automated robot. In such a production site, quality problems such as misalignment of parts (doors, trunks, roofs, etc.) to be assembled with the vehicle body or collision between assembly robots and components are often caused. In order to solve such a problem, the quality of parts is manually inspected by using mechanical jig devices outside the automated production line. Automotive inspection technology is the most commonly used field of vision, which includes surface inspection such as mounting hole spacing and defect detection, body panel dents and bends. It is used for guiding, providing location information to the robot controller to adjust the robot's path to improve process productivity and manufacturing flexibility. The most difficult weighing and measuring technology is to calibrate the surface analysis and position and characteristics between parts by storing images of the part to be measured that enters the camera's field of view mounted on the side or top of the part. The problem of the machine vision device applied to the automobile production line is that the lighting conditions inside the factory are severely changed due to various weather changes such as morning-evening, rainy days and sunny days through the exterior window of the assembly production plant. In addition, since the material of the vehicle body parts is a steel sheet, the reflection of light is very severe, which causes a problem in that the quality of the captured image is greatly changed even with a small light change. In this study, the distance between the car body and the door part and the door are acquired by the measuring device combining the laser slit light source and the LED pattern light source. The result is transferred to the joint robot for assembling parts at the optimum position between parts, and the assembly is done at the optimal position by changing the angle and step.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.537-542
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• 2017

Aggressive driving is a major cause of car accidents. Previous studies have mainly analyzed young driver's aggressive driving tendency, yet they were only done through pure clustering or classification technique of machine learning. However, since elderly people have different driving habits due to their fragile physical conditions, it is necessary to develop a new method such as enhancing the characteristics of driving data to properly analyze aggressive driving of elderly drivers. In this study, acceleration data collected from a smartphone of a driving vehicle is analyzed by a newly proposed ECA(Enhanced Clustering method for Acceleration data) technique, coupled with a conventional clustering technique (K-means Clustering, Expectation-maximization algorithm). ECA selects high-intensity data among the data of the cluster group detected through K-means and EM in all of the subjects' data and models the characteristic data through the scaled value. Using this method, the aggressive driving data of all youth and elderly experiment participants were collected, unlike the pure clustering method. We further found that the K-means clustering has higher detection efficiency than EM method. Also, the results of K-means clustering demonstrate that a young driver has a driving strength 1.29 times higher than that of an elderly driver. In conclusion, the proposed method of our research is able to detect aggressive driving maneuvers from data of the elderly having low operating intensity. The proposed method is able to construct a customized safe driving system for the elderly driver. In the future, it will be possible to detect abnormal driving conditions and to use the collected data for early warning to drivers.