• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.6
• /
• pp.935-941
• /
• 2017

This paper proposes a method for estimating the distance betweeen vehicles in a moving situation using the image ratio of the distance between the tail lamps of a front vehicle. The actual distance between the tail lamps of a front vehicle was transmitted by LED tail lamps using visible light communication. As the distance between the front vehicle and the rear vehicle changes, it calculates the ratio of the pixel width between the tail lamps of the front vehicle projected on the image. The calculated values are used to derive a distance-mapping function through non-linear regression technique. Then, the distance between vehicles in the moving situation is estimated based on this function.

• Journal of Korea Multimedia Society
• /
• v.16 no.2
• /
• pp.131-137
• /
• 2013

Zoom motion estimation of video sequence is very complicated for implementation. In this paper, we propose a method to implement the zoom motion estimation using together the depth camera and color camera. Depth camera obtains the distance information between current block and reference block, then zoom ratio between both blocks is calculated from this distance information. As the reference block is appropriately zoomed by the zoom ratio, the motion estimated difference signal can be reduced. Therefore, the proposed method is possible to increase the accuracy of motion estimation with keeping zoom motion estimation complexity not greater. Simulation was to measure the motion estimation accuracy of the proposed method, we can see the motion estimation error was decreased significantly compared to conventional block matching method.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.3
• /
• pp.83-95
• /
• 2022

This paper proposes a system that performs object detection and distance estimation for application to autonomous vehicles. Object detection is performed by a network that adjusts the split grid to the input image ratio using the characteristics of the recently actively used deep learning model YOLOv4, and is trained to a custom dataset. The distance to the detected object is estimated using a bounding box and homography. As a result of the experiment, the proposed method improved in overall detection performance and processing speed close to real-time. Compared to the existing YOLOv4, the total mAP of the proposed method increased by 4.03%. The accuracy of object recognition such as pedestrians, vehicles, construction sites, and PE drums, which frequently occur when driving to the city center, has been improved. The processing speed is approximately 55 FPS. The average of the distance estimation error was 5.25m in the X coordinate and 0.97m in the Y coordinate.

• Proceedings of the KSME Conference
• /
• 2004.04a
• /
• pp.519-524
• /
• 2004

This study is concerned with the braking distance estimation using frictional energy rate. First, steady state rolling analysis is performed, and using this result, the braking distance is estimated. Dynamic rolling analysis during entire braking time period is impratical, so that this study divides the vehicle velocity by 10km/h to reduce the analysis time. The multiplication of the slip rate and the shear stress provides the frictional energy rate. Using frictional energy rate, total braking distance is estimated, In addition, ABS(Anti-lock Brake System) is considered, and two type of slip ratios are compared, One is 15% slip ratio for the ABS condition, and the other is 100% slip ratio which leads lo the almost same braking distance as the elementary kinematic theory. A slip ratio is controlled by angular velocity in ABAQUS/Explicit, A 15% slip ratio gives the real vehicle's braking distance when the frictional energy occurred al disk pad is included. Disk pad's frictional energy rate is calculated by the theoretical approach.

• Proceedings of the IEEK Conference
• /
• 2000.06d
• /
• pp.85-87
• /
• 2000

In pattern classification, the Bhattacharyya distance has been used as a class separability measure and provides useful information for feature selection and extraction. In this paper, we propose a method to predict the classification error for multimodal data based on the Bhattacharyya distance. In our approach, we first approximate the pdf of multimodal distribution with a Gaussian mixture model and find the bhattacharyya distance and classification error. Exprimental results showed that there is a strong relationship between the Bhattacharyya distance and the classification error for multimodal data.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.8 no.4
• /
• pp.344-350
• /
• 2003

In this paper, an estimation technique of initial rotor position at standstill in Switched Reluctance Motor(SRM) is proposed. In case search coils are used as a position sensor, it has many advantages like low cost, decrease in the volume, high robust characteristics and wide applications. However, the initial rotor position detection is very difficult because the search coil's EMF is not exist at a standstill. In this paper, a near estimation method of initial rotor position is suggested using squared Euclidean distance. The simulation and experiment for the proposed method are achieved. The validity of the proposed method is verified by experimental results.

• Journal of the Korea Society of Computer and Information
• /
• v.10 no.5 s.37
• /
• pp.209-216
• /
• 2005

In wireless sensor networks, an estimation method is proposed for distances between nodes within two hops. The method uses only proximity information of nodes without physiccal distance measurements. It drastically improves the performance of localization algorithms based on Proximity information. In addition, it is the first method that estimates distances between nodes exactly in two hops. The distances are estimated from the number of common neighbors under an assumption that the number of common neighbors is proportional to the intersection of two unit disks centered at the two nodes. Simulation analysis shows that the estimation error is roughly from 10 to 20 percent of real distances. Meanwhile, the number of messages required by a distributed algorithm realizing this method is only two times the number of nodes.

• Asian-Australasian Journal of Animal Sciences
• /
• v.15 no.5
• /
• pp.745-750
• /
• 2002

The effects of recording interval (1, 2, 3, 4, 5, 10, 15, 20 and 30 min) on the estimation of some grazing behavior variables in beef cows and calves (<4 months old) were investigated in a daytime grazing (7 h) system utilizing a bahiagrass (Paspalum notatum Flugge) pasture (a 1.1 ha paddock and a 0.4 ha resting area). Recording intervals of 10-30 min tended to underestimate the time spent grazing and ruminating and overestimate the time spent resting by animals, whereas intervals of 1-5 min resulted in almost constant estimates. In all grazing activities, the errors of estimation became larger when the recording interval exceeded 5 min. The accuracy of estimation was higher for grazing time>rumination time>resting time. An increase in recording interval always decreased estimates of the distance walked by animals. It was concluded that recording intervals of 1-5 min provide reliable estimates of the time spent grazing, ruminating and resting. It was also concluded that positioning of animals at 1 min intervals may provide estimates of walking distance with acceptable bias toward underestimation.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.6 no.3
• /
• pp.210-216
• /
• 2006

In the rule based modeling, data partitioning plays crucial role be cause partitioned sub data set implies particular information of the given data set or system. In this paper, we present an empirical study result of the data pattern estimation to find underlying data patterns of the given data. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). In each sequence, the average value of the sum of all inter-distance between centroid and data point. In the sequel, compute the derivation of the weighted average distance to observe a pattern distribution. For the final step, after overall clustering process is completed, weighted average distance value is applied to estimate range of the number of clusters in given dataset. The proposed estimation method and its result are considered with the use of FCM demo data set in MATLAB fuzzy logic toolbox and Box and Jenkins's gas furnace data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.180-182
• /
• 2021

This paper proposes a method for estimating the location of a user using a beacon to provide a service in an indoor environment. To estimate the location using the beacon, a Gaussian filter was applied to the RSSI value of the beacon, and the distance conversion function was obtained through the filtered RSSI value to estimate the tag location by trilateration. Then, in the indoor space where the beacons are installed, the location estimation accuracy of 8 places where 3 beacons are at a certain distance was confirmed. As a result, it was possible to confirm the position estimation accuracy of ±0.097 standard deviation and 0.242 distance error.