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A practical and low cost Blind Spot Monitoring system is proposed. By using a single camera, the range and azimuth position of a vehicle in a blind spot are measured. The algorithm is based on the proposed RWA (Range Window Algorithm). The camera is installed on the door mirror and monitoring the side and rear of the host vehicle. The algorithm processes the image and identifies range and azimuth angle of the vehicle in the adjacent lane. This algorithm is applied to real situations. The 388 images including several kinds of vehicles are analyzed. The detection rate is 86% and the range accuracy is 1.6[m]. The maximum detection range is about 30[m].

Estimation and prediction of residual life and reliability are serious concerns in life cycle management for aging structures. Laboratory testing replicating fatigue loading for a typical military aircraft wing skin was undertaken. Specimens were tested until their fatigue life expended reached 100% of the component fatigue life. Then, scanning electron microscopy was used to quantify the size and location of fatigue cracks within the high stress regions of simulated fastener holes. Distributions for crack size, nearest neighbor distances, and spatial location were characterized statistically in order to estimate residual life and to provide input for life cycle management. Insights into crack initiation and growth are also provided.

An algorithm, which determines the range of a preceding vehicle by a single image, had been proposed. It uses a “Range-Window Algorithm”. Here in order to realize higher robustness and stability, the pattern matching is incorporated into the algorithm. A single camera system using this algorithm has an advantage over the high cost of stereo cameras, millimeter wave radar and non-robust mechanical scanning in some laser radars. And it also provides lateral position of the vehicle. The algorithm uses several portions of a captured image, namely windows. Each window is corresponding to a predetermined range and has the fixed physical width and height. In each window, the size and position of objects in the image are estimated through the ratio between the widths of the objects and the window, and a score is given to each object. The object having the highest score is determined as the best object. The range of the window corresponding to the best object becomes an estimated range.

Maximum Brake Torque (MBT) timing for an internal combustion engine is the minimum advance of spark timing for best torque. Traditionally, MBT timing is an open loop feedforward control whose values are experimentally determined by conducting spark sweeps at different speed, load points and at different environmental operating conditions. Almost every calibration point needs a spark sweep to see if the engine can be operated at the MBT timing condition. If not, a certain degree of safety margin is needed to avoid pre-ignition or knock during engine operation. Open-loop spark mapping usually requires a tremendous amount of effort and time to achieve a satisfactory calibration. This paper shows that MBT timing can be achieved by regulating a composite feedback measure derived from the in-cylinder ionization signal referenced to a top dead center crank angle position. A PI (proportional and integral) controller is used to illustrate closed-loop control of MBT timing.

This paper describes the applicability of optical imaging techniques to the analysis of the scratch resistance of automotive interior plastic materials. The evaluation of so-called “finger testing” has traditionally relied upon human vision for detection of the initial scratch position. Commonly performed under uniform and defined illumination conditions, the relative contrast difference signified by whitening on a surface as determined by unaided human vision is a highly variable subjective perception; thus individual inspectors may determine the “whitening” point differently. This paper compares test data obtained from both visual and instrumental evaluation methods and discusses the advantages of optical imaging techniques for surface defect analysis.