• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.823-826
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• 2003

FPGA-based logic emulator with lane gate capacity generally comprises a large number of FPGAs connected in mesh or crossbar topology. However, gate utilization of FPGAs and speed of emulation are limited by the number of signal pins among FPGAs and the interconnection architecture of the logic emulator. The time-multiplexing of interconnection wires is required for multi-FPGA system incorporating several state-of-the-art FPGAs. This paper proposes a circuit partitioning algorithm called SCATOMi(SCheduling driven Algorithm for TOMi)for multi-FPGA system incorporating four to eight FPGAs where FPGAs are interconnected through TOMi(Time-multiplexed, Off-chip, Multicasting interconnection). SCATOMi improves the performance of TOMi architecture by limiting the number of inter-FPGA signal transfers on the critical path and considering the scheduling of inter-FPGA signal transfers. The performance of the partitioning result of SCATOMi is 5.5 times faster than traditional partitioning algorithms. Architecture comparison show that the pin count is reduced to 15.2%-81.3% while the critical path delay is reduced to 46.1%-67.6% compared to traditional architectures.

• International Journal of Highway Engineering
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• v.17 no.4
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• pp.117-126
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• 2015

PURPOSES : This study aims to investigate the direct and indirect influence areas from incidents on urban interrupted roadways and to develop traffic management strategies for each influence area. METHODS : Based on a literature review, various traffic management strategies for certain incidents were collected. In addition, the relationship between the measure of effectiveness and the characteristics of incidents was explored using an extensive simulation study. RESULTS : From the simulation studies, traffic delays increased as the number of lane closures increased, and the impact of lane closures was reduced to the direction upstream from the incident site. However, the magnitude of the delay change depended on the degree of saturation. Using these characteristics, the direct and indirect influence areas resulting from incidents were defined, and traffic management strategies were established for each direct and indirect influence area and for each level of incident. CONCLUSIONS: The results of this study will contribute to the improvement of national traffic safety by preventing secondary incidents and by effective adaptation to incident events.

• The KIPS Transactions:PartA
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• v.14A no.7
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• pp.391-398
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• 2007

In this paper, we design a future mobile camera standard interface based on the MIPI CSI-2 and D-PHY specification. The proposed CSI-2 have the efficient multi-lane management layer, which the independent buffer on the each lane are merged into single buffer. This scheme can flexibly manage data on multi lanes though the number of supported lanes are mismatched in a camera processor transmitter and a host processor. The proposed CSI-2 & D-PHY are verified under test bench. We make an experiment on CSI-2 & D-PHY with FPGA type test-bed and implement them onto a mobile handset. The proposed CSI-2 & D-PHY module are used as both the bridge type and the future camera processor IP for SoC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.146-151
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• 2021

Currently, the demand for vehicles from one family is increasing in many countries at home and abroad, reducing the number of people on the vehicle and increasing the number of vehicles on the road. The multi-passenger lane system, which is available to solve the problem of traffic congestion, is being implemented. The system allows police to monitor fast-moving vehicles with their own eyes to crack down on illegal vehicles, which is less accurate and accompanied by the risk of accidents. To address these problems, applying deep learning object recognition techniques using images from road sites will solve the aforementioned problems. Therefore, in this paper, we compare and analyze the performance of existing deep learning models, select a deep learning model that can identify real-time vehicle occupants through video, and propose a vehicle occupancy detection algorithm that complements the object-ident model's problems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.120-122
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• 2021

Currently, as demand for cars at home and abroad increases, the number of vehicles is decreasing and the number of vehicles is increasing. This is the main cause of the traffic jam. To solve this problem, it operates a high-ocompancy vehicle (HOV) lane, a multi-passenger vehicle, but many people ignore the conditions of use and use it illegally. Since the police visually judge and crack down on such illegal activities, the accuracy of the crackdown is low and inefficient. In this paper, we propose a system design that enables more efficient detection using imaging techniques using computer vision to solve such problems. By improving the existing vehicle detection method that was studied, the trigger was set in the image so that the detection object can be selected and the image analysis can be conducted intensively on the target. Using the YOLO model, a deep learning object recognition model, we propose a method to utilize the shift amount of the center point rather than judging by the bounding box in the image to obtain real-time object detection and accurate signals.

• Journal of Korean Society of Transportation
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• v.30 no.5
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• pp.3-10
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• 2012

This study dealt with traffic accidents occurring within roundabouts. The objective was to develop roundabout accident models for different driving types by using simultaneous equations. In pursuing the above, this study used a statistical program SPSS 17.0 to accommodate data from 148 accidents occurred within 39 roundabouts of Korea. In addition, the 2SLS(2 stage least square) estimation method was adopted to calibrate the models. The main results are as follows. First, both the number of accidents and the EPDO were evaluated to have bilateral relationships. Second, all 6 different simultaneous equation models according to driving types were found to be statistically significant. Third, the developed models were compared to each other with respect to either common or specific variables. Finally, variables such as ADT, conflicting rate, heavy vehicle ratio, circulatory roadway width, number of circulatory roadway lane, approach lane width, average approach lanes, parking places, and bus stops were selected as independent variables for the models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.4
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• pp.1279-1287
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• 2014

The purpose of this study is to develop the U-turn accident model at signalized intersections in urban areas. The characteristics of the accidents which are associated with U-turn operation at 3 and 4-legged signalized intersections was analyzed and the U-turn accident model was developed by regression analysis in Changwon city. First, in order to analyze the effectiveness on traffic accidents by U-turn installation, the difference of mean of traffic accident number are measured between two groups which are composed by whether or not U-turn installation the groups by Mann-Whitney U test. The result of significance test showed that intergroup comparison on mean by accident types made difference except rear-end accident type and by accident locations exit section only showed difference in significance level at 4-legged intersections, so the accident number have more where the U-turn is permitted than not. Response measures about the number of accidents were classified by whether accidents occurred and accident model were constructed using binomial logistic regression analysis method. The developed models show that the variables of conflict traffic, number of opposing lane are adopted as independent variable for both intersections. The variables of longitudinal grade for 3-legged signalized intersection and number of crosswalk for 4-legged signalized intersection at which the U-turn is permitted is adopted as independent variable only. These study results suggest that U-turn would be permitted at the intersection where the number of opposing lane is more than 3.5 each, the longitudinal grade of opposing road is upward flow and there is need to establish the U-turn traffic sign at signalized intersections.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.11
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• pp.24-35
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• 2012

In this paper, we propose a dynamic lane control scheme with a traffic predictor module and a rate controller for reconciling with commercial optical PHY modules in energy efficient optical Ethernet systems. The commercial high speed optical Ethernet system capable of 40/100Gbps employs 4 or 10 multiple optical transceivers over WDM or multiple optical links. Each of the transceivers is always turned on even if the link is idle. To save energy, we propose the dynamic lane control scheme. It allows that several links may be entirely turned off in a low traffic load and frames are handled on the remaining active links. To preserve the byte order even if the number of active links may be changed, we propose a rate controller to be sat on the reconciliation sublayer. The main role of the controller is to insert null byte streams into the xGMII of inactive lanes. For the PHY module, the null input streams corresponding to inactive lanes will be disregarded on inactive PMDs. It is very handy to implement the rate controller module with MAC in FPGA without any modification of commercial PHYs. It is very crucial to determine the number of active links based on the fluctuated traffic load, we provide a simple traffic predictor based on both the current transmission buffer size and the past one with different weighting factors for adapting to the traffic load fluctuation. Using the OMNET++ simulation framework, we provide several performance results in terms of the energy consumption.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1107-1109
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• 1996

In this paper, we discuss the automatic recognition system of vehicle types and licence plate numbers using artificial neural networks, which will be used as vehicle identifier. We confine to expose the vehicle licence number for violating bus lane and stolen cars. Therefore, the vehicle height, width and distribution profile are used as the feature parameters of vehicle type. This system is composed of two parts: one is an image preprocessor of vehicle images and the other one is a pattern classifier by neural networks. The experimental results show that our method has good results for the recognition of vehicle types and numbers.

• Journal of the Korean Society of Safety
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• v.29 no.6
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• pp.151-157
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• 2014

This study deals with the rear-end collision at roundabouts. The purpose of this study is to develop the accident models of rear-end collision in Korea. In pursuing the above, this study gives particular attention to developing the appropriate models using Poisson, negative binomial model, ZAM, multiple linear and nonlinear regression models, and statistical analysis tools. The main results are as follows. First, the Vuong statistics and overdispersion parameters indicate that ZIP is the most appropriate model among count data models. Second, RMSE, MPB, MAD and correlation coefficient tests show that the multiple nonlinear model is the most suitable to the rear-end collision data. Finally, such the independent variables as traffic volume, ratio of heavy vehicle, number of circulatory roadway lane, number of crosswalk and stop line are adopted in the optimal model.