Hsueh-Yi Lin

Adjacent Lane Detection and Lateral Vehicle Distance Measurement Using Vision-Based Neuro-Fuzzy Approaches

The aim of this article attempts to propose an advanced design of driver assistance system which can provide thedriver advisable information about the adjacent lanes and approaching lateral vehicles. The experimental vehiclehas a camera mounted at the left side rear view mirror which captures the images of adjacent lane. The detectionof lane lines is implemented with methods based on image processing techniques. The candidates for lateralvehicle are explored with lane-based transformation, and each one is verified with the characteristics of its length,width, time duration, and height. Finally, the distances of lateral vehicles are estimated with the well-trainedrecurrent functional neuro-fuzzy network. The system is tested with nine video sequences captured when thevehicle is driving on Taiwan’s highway, and the experimental results show it works well for different road conditionsand for multiple vehicles.

A Fuzzy Cerebellar Model Articulation Controller Using a Strategy-Adaptation-Based Bacterial Foraging Optimization Algorithm for Classification Applications

This paper proposes a fuzzy cerebellar model articulation controller using a strategy-adaptation-based bacterial foraging optimization (SABFO) algorithm to solve classification problems. A strategic approach to the chemotaxis step in the SABFO algorithm was adopted: in this approach, each virtual bacterium swims on different run-lengths, and bacterial diversity is increased. The simulation results indicated that the performance of the proposed method was more favorable than that of other methods.

Optimization of printed circuit board component placement using an efficient hybrid genetic algorithm

The hardware restrictions of surface mount placement machines, such as height, pick and place restrictions, and simultaneous pickup are often in printed circuit board (PCB)-related studies. This study proposes an efficient hybrid genetic algorithm (HGA) for solving the nozzle assignment problem and the component pick and place sequence problem. First, the proposed method obtains the sequence of the automatic nozzle changer (ANC) with the maximum number of simultaneous pickups and the minimum number of picks as the solution of the nozzle setup problem. Then, the proposed method uses the nearest neighbor search (NNS), 2-optimization, and a genetic algorithm (GA) with the known ANC sequences to obtain the PCB assembly time with the optimal component pick and place sequence. Experiments are conducted on the PCB of the EVEST EM-780 surface mount placement machine. Results show that the proposed HGA gives the lowest total number of picks, the shortest total head movement distance, and the minimum total PCB assembly time compared to those of other methods.

Smart Robot Wall-Following Control Using a Sonar Behavior-based Fuzzy Controller in Unknown Environments

Abstract This paper addresses a sonar behavior-based fuzzy controller (BFC) for mobile robot wall-following control. The wall-following task is usually used to explore an unknown environment. The proposed BFC consists of three sub-fuzzy controllers, including Straight-based Fuzzy Controller, Left-based Fuzzy Controller, and Right-based Fuzzy Controller. The proposed wall-following controller has three characteristics: the mobile robot keeps a distance from the wall, the mobile robot has a high moving velocity, and the mobile robot has a good robustness ability of disturbance. The proposed BFC will be used to control the real mobile robot. The Pioneer 3-DX mobile robot has sonar sensors in front and sides, and it is used in this study. The inputs of BFC are sonar sensors data and the outputs of BFC are robot’s left/right wheel speed. Experimental results show that the proposed BFC successfully performs the mobile robot wall-following task in a real unknown environment.

Using a hybrid of fuzzy theory and neural network filter for single image dehazing

When photographs are being taken in an outdoor environment, the medium in air will cause light attenuation and further reduce image quality, and this impact is especially obvious in a hazy environment. Reduction of image quality results in the loss of information, which renders an image recognition system unable to identify objects in the image. In order to eliminate the hazy effect on images and improve the visual quality, this paper presents an efficient method combining the fuzzy inference system and the neural network filter to solve image dehazing. During dehazing, the fuzzy inference system is adopted to estimate the variations in light attenuation, and the erosion of morphological operation and the neural network filter are used to eliminate the halation and achieve optimization in transmission map refinement. Finally, the brightest 1% of the atmospheric light is utilized to calculate the color vector of atmospheric light to eliminate color cast. Experimental results indicate that the proposed method is superior to other dehazing methods.

A Study of Digital Image Enlargement and Enhancement

Most image enlargement techniques suffer the problem of zigzagged edges and jagged images following enlargement. Humans are sensitive to the edges of objects; if the edges in the image are sharp, the visual is considered to be high quality. To solve this problem, this paper presents a new and effective method for image enlargement and enhancement based on adaptive inverse hyperbolic tangent (AIHT) algorithm. Conventional image enlargement and enhancement methods enlarge the image using interpolation, and subsequently enhance the image without considering image features. However, this study presents the method based on Adaptive Inverse Hyperbolic Tangent algorithm to enhance images according to image features before enlarging the image. Experimental results indicate that the proposed algorithm is capable of adaptively enhancing the image and extruding object details, thereby improving enlargements by smoothing the edge of the objects in the image.