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Browsing by Subject "Fuzzy Logic"
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Item FAULT DIAGNOSIS OF ELECTRONIC FUEL CONTROL (EFC) VALVES VIA DYNAMIC PERFORMANCE TEST METHOD(ProQuest, 2009) Tugsal, Umut; Li, Lingxi; Chen, Yaobin; Anwar, SohelElectronic Fuel Control (EFC) valve regulates fuel flow to the injector fuel supply line in the Cummins Pressure Time (PT) fuel system. The EFC system controls the fuel flow by means of a variable orifice that is electrically actuated. The supplier of the EFC valves inspects all parts before they are sent out. Their inspection test results provide a characteristic curve which shows the relationship between pressure and current provided to the EFC valve. This curve documents the steady state characteristics of the valve but does not adequately capture its dynamic response. A dynamic test procedure is developed in order to evaluate the performance of the EFC valves. The test itself helps to understand the effects that proposed design changes will have on the stability of the overall engine system. A by product of this test is the ability to evaluate returned EFC valves that have experienced stability issues. The test determines whether an EFC valve is faulted or not before it goes out to prime time use. The characteristics of a good valve and bad valve can be observed after the dynamic test. In this thesis, a mathematical model has been combined with experimental research to investigate and understand the behavior of the characteristics of different types of EFC valves. The model takes into account the dynamics of the electrical and mechanical portions of the EFC valves. System Identification has been addressed to determine the transfer functions of the different types of EFC valves that were experimented. Methods have been used both in frequency domain as well as time domain. Also, based on the characteristic patterns exhibited by the EFC valves, fuzzy logic has been implemented for the use of pattern classification.Item Improving the Robustness of Object Detection Through a Multi-Camera–Based Fusion Algorithm Using Fuzzy Logic(Frontiers, 2021) Khan, Md Nazmuzzaman; Al Hasan, Mohammad; Anwar, Sohel; Mechanical and Energy Engineering, School of Engineering and TechnologyA single camera creates a bounding box (BB) for the detected object with certain accuracy through a convolutional neural network (CNN). However, a single RGB camera may not be able to capture the actual object within the BB even if the CNN detector accuracy is high for the object. In this research, we present a solution to this limitation through the usage of multiple cameras, projective transformation, and a fuzzy logic–based fusion. The proposed algorithm generates a “confidence score” for each frame to check the trustworthiness of the BB generated by the CNN detector. As a first step toward this solution, we created a two-camera setup to detect objects. Agricultural weed is used as objects to be detected. A CNN detector generates BB for each camera when weed is present. Then a projective transformation is used to project one camera’s image plane to another camera’s image plane. The intersect over union (IOU) overlap of the BB is computed when objects are detected correctly. Four different scenarios are generated based on how far the object is from the multi-camera setup, and IOU overlap is calculated for each scenario (ground truth). When objects are detected correctly and bounding boxes are at correct distance, the IOU overlap value should be close to the ground truth IOU overlap value. On the other hand, the IOU overlap value should differ if BBs are at incorrect positions. Mamdani fuzzy rules are generated using this reasoning, and three different confidence scores (“high,” “ok,” and “low”) are given to each frame based on accuracy and position of BBs. The proposed algorithm was then tested under different conditions to check its validity. The confidence score of the proposed fuzzy system for three different scenarios supports the hypothesis that the multi-camera–based fusion algorithm improved the overall robustness of the detection system.Item A Novel Fusion Technique for 2D LIDAR and Stereo Camera Data Using Fuzzy Logic for Improved Depth Perception(2021-08) Saksena, Harsh; Anwar, Sohel; Li, Lingxi; Tovar, AndresObstacle detection, avoidance and path finding for autonomous vehicles requires precise information of the vehicle’s system environment for faultless navigation and decision making. As such vision and depth perception sensors have become an integral part of autonomous vehicles in the current research and development of the autonomous industry. The advancements made in vision sensors such as radars, Light Detection And Ranging (LIDAR) sensors and compact high resolution cameras is encouraging, however individual sensors can be prone to error and misinformation due to environmental factors such as scene illumination, object reflectivity and object transparency. The application of sensor fusion in a system, by the utilization of multiple sensors perceiving similar or relatable information over a network, is implemented to provide a more robust and complete system information and minimize the overall perceived error of the system. 3D LIDAR and monocular camera are the most commonly utilized vision sensors for the implementation of sensor fusion. 3D LIDARs boast a high accuracy and resolution for depth capturing for any given environment and have a broad range of applications such as terrain mapping and 3D reconstruction. Despite 3D LIDAR being the superior sensor for depth, the high cost and sensitivity to its environment make it a poor choice for mid-range application such as autonomous rovers, RC cars and robots. 2D LIDARs are more affordable, easily available and have a wider range of applications than 3D LIDARs, making them the more obvious choice for budget projects. The primary objective of this thesis is to implement a smart and robust sensor fusion system using 2D LIDAR and a stereo depth camera to capture depth and color information of an environment. The depth points generated by the LIDAR are fused with the depth map generated by the stereo camera by a Fuzzy system that implements smart fusion and corrects any gaps in the depth information of the stereo camera. The use of Fuzzy system for sensor fusion of 2D LIDAR and stereo camera is a novel approach to the sensor fusion problem and the output of the fuzzy fusion provides higher depth confidence than the individual sensors provide. In this thesis, we will explore the multiple layers of sensor and data fusion that have been applied to the vision system, both on the camera and lidar data individually and in relation to each other. We will go into detail regarding the development and implementation of fuzzy logic based fusion approach, the fuzzification of input data and the method of selection of the fuzzy system for depth specific fusion for the given vision system and how fuzzy logic can be utilized to provide information which is vastly more reliable than the information provided by the camera and LIDAR separately