Design and Implementation of Automatic Fault Identification System Based on Computer Vision

Chagas disease (CD) is a vector-borne zoonotic disease affecting large parts of the world. It is imposing a tremendous social burden on public health and ranks as one of the most severe threats to human health. CD is often transmitted to humans by the feces of insects called triatomine or kissing bugs. The diagnosis of CD can be performed at any stage of the disease and involves the analysis of clinical, epidemiological, and laboratory data. The CD has two different phases, acute phase and chronic phase. Since controlling and treating CD is easier in the early stages, detecting it in the acute phase plays an essential role in overcoming and controlling it. There are many clinical trials dedicated to this problem, but progress in applicational research (automatic identification) has been slower. Due to this shortcoming and the importance of this problem, this research is dedicated to present two automatic CD vector identification systems that classify several different vectors of kissing bugs with an acceptable and promising identification rate. Our proposed methods are composed of preprocessing, feature extraction, and classification phases. Principal component analysis (PCA) is utilized for feature extraction and Random Forrest (RF) and Support Vector Machine (SVM) are employed in the classification stages. A dataset consisting of more than two thousand kissing bug images is used as input of our methods. The accuracy for the first proposed approach, namely PCA-SVM, is 87.62% for 410 images of 12 Mexican and 75.26% for 1620 images of 39 Brazilian species. The second proposed approach, namely PCA-RF, has an accuracy of 100% for both Brazilian and Mexican species. We achieved perfect results with the PCA-RF method. Our results are promising and outperform the results of other available developed automatic identification systems for CD vectors.

Electronic Toll Collection (ETC) system is an important part of modern intelligent transport system, which plays an important role in intelligent car parks and congestion charging. Electronic Toll Collection (ETC) system includes automatic vehicle identification system and electronic billing system. The automatic vehicle identification system includes three parts: automatic vehicle identification (AVI), automatic vehicle model identification (AVC) and inspection system (ES).The application of ETC in intelligent car parks and congestion charging also includes automatic vehicle identification system (automatic vehicle identification, automatic vehicle model identification and inspection system) and electronic billing system. In intelligent car parks, ETC technology achieves fast and accurate automatic toll payment by automatically identifying the ETC tags carried by vehicles, which greatly improves the efficiency of car parks and reduces the queuing and waiting time due to manual toll collection. In terms of congestion charging, ETC technology provides an effective means to implement congestion charge collection. By setting up ETC toll lanes in core urban areas or busy road sections, the system can automatically record the information of the vehicles passing through and carry out toll settlement, thus realising dynamic toll collection according to the degree of congestion and the frequency of vehicle use. This congestion charging strategy aims to regulate urban traffic flow through economic means, alleviate traffic congestion and promote the sustainable development of urban transport. Although China's theoretical research on congestion charging, has been relatively mature, but affected by a variety of factors, no city has yet to start the implementation of congestion charging, the lack of experience in practice. Singapore will not stop the car charging system (ETC) technology successfully applied to urban congestion charging and intelligent car parks, ETC system in intelligent car parks and congestion charging application, help to promote the development of urban transport to a more intelligent, green direction. China can learn from the successful experience of foreign countries and apply it to its own intelligent car parks and congestion charging. This paper introduces the basic principle of ETC, discusses the research progress of ETC in terms of technical characteristics, and analyses the application scenarios of ETC in intelligent car parks and congestion charging. By examining the opportunities offered by Electronic Toll Collection (ETC) systems in smart car parks and congestion pricing, this study aims to delve into the transformative impacts of Electronic Toll Collection (ETC) systems on intelligent mobility and contribute to the sustainable development of intelligent mobility.

In the drilling exploitation of hot dry rock for geothermal energy, whirling is one of the main low-efficiency conditions that affect the efficiency of Polycrystalline Diamond Compact (PDC) bit in horizontal well drilling. Realizing automatic and real-time identification with whirling is of great significance to save non-productive time and ensure drilling safety and benefit. In this paper, we first constructed a real-time drilling mechanical specific energy (MSE) model combined with while-drilling testing to reflect the real-time drilling conditions. The MSE model is used to normalize multi-source parameters. Secondly, we constructed a Back Propagation (BP) artificial neural network (ANN), and then the normalization effect verification, optimization of network parameters, and identification effect verification of whirling were carried out. The final results show that the established MSE model has a favourable effect on data normalization, which could also reduce the complexity of the required network model, and shorten the training time by 20–30 s/step. The optimal algorithm is Trainscg, whose optimal number of hidden layer nodes is 5, and the optimal maximum number of iteration steps is 1000. The established ANN model can accurately identify whirling based on MSE, the accuracy is about 0.94, and the average relative error is 1.3%. The method established in this paper provides a reference for the automatic identification of various low-efficiency conditions based on MSE.

This is a combination of YOLO (you only look once) target recognition detection algorithm and AIS (Automatic Identification System) automatic ship identification system to assist vessels in inland navigation to carry out vessels and obstacle avoidance. This system proposes a ship-assisted collision avoidance system for ship collision accidents caused by a narrow inland waterway and complex navigation channel. The system can process the image and video information acquired by the high-definition camera mounted on the ship, and frame and mark the ships in the current sea area, and combine the AIS system to realize the visualization of the ship information and improve the global view. The experiment shows that the system can effectively identify and frame the ship and realize the visualization of AIS information, and the transformed image and video information can be used for decision-making by the ship's driver.

Aplicación para el estudio de implantación de un sistema no satelitario de identificación de buques

Automatic identification systems (AIS) play an increasing role in toda's industrial processes. Although there are numerous articles regarding the selection of single technologies, there is no concentration on automatic identification selection which covers single or multiple technologies. The purpose of this project was to develop an expert system to help in the selection of automatic identification systems. Presently, the expert system CASAIS (computer-aided selection of automatic identification systems) covers four of the most widely used technologies; bar coding, radio frequency, optical character recognition, and magnetic stripe. The expert system prompts users to provide important operation parameters for the intended automatic identification application. From these parameters, the rule base (using the Personal Consultant Plus environment) develops intermediate and final solutions. The intermediate result is a database of different vendors and their product's information. The final results show different potential solutions given the user's input.

In order to effectively reduce the risk of high-risk intrusion signals to wireless communication networks, an automatic anomaly identification of wireless communication signals based on nonlinear analysis is proposed. The fuzzy phase frequency feature of denoised abnormal signal is extracted and the wireless communication signal parameters are estimated by optimizing the fuzzy network abnormal signal feature extraction method of particle filter. The wireless communication signal is collected based on nonlinear analysis, the wireless communication signal network model is constructed, and the Back Propagation Neural Network—Dempster Shafer method is used to classify and fuse the results of feature extraction to realize the automatic identification of wireless communication signal anomalies. The experimental results show that the signal initial frequency estimation and signal slope estimation obtained by the research method have high accuracy, strong coverage performance, large total amount of automatic identification abnormal signals and high compatibility level.

This paper reports the design, implementation, and evaluation of a research work for developing an automatic person identification system using hand signatures biometric. The developed automatic person identification system mainly used toolboxes provided by MATLAB environment‥ In order to train and test the developed automatic person identification system, an in-house hand signatures database is created, which contains hand signatures of 100 persons (50 males and 50 females) each of which is repeated 30 times. Therefore, a total of 3000 hand signatures are collected. The collected hand signatures have gone through pre-processing steps such as producing a digitized version of the signatures using a scanner, converting input images type to a standard binary images type, cropping, normalizing images size, and reshaping in order to produce a ready-to-use hand signatures database for training and testing the automatic person identification system. Global features such as signature height, image area, pure width, and pure height are then selected to be used in the system, which reflect information about the structure of the hand signature image. For features training and classification, the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network (ANN) is used. This paper also investigates the effect of the persons' gender on the overall performance of the system. For performance optimization, the effect of modifying values of basic parameters in ANN such as the number of hidden neurons and the number of epochs are investigated in this work. The handwritten signature data collected from male persons outperformed those collected from the female persons, whereby the system obtained average recognition rates of 76.20% and74.20% for male and female persons, respectively. Overall, the handwritten signatures based system obtained an average recognition rate of 75.20% for all persons.

A real-time automatic personal identification system should meet the conflicting dual requirements of accuracy and response time. In addition, it also should be user-friendly. We introduce a medium-size realtime automatic personal identification system, F2ID, which integrates faces and fingerprints to make a personal identification. F2ID overcomes some of the limitations of face recognition systems and fingerprint verification systems and can achieve a desirable identification accuracy with a tolerable response time. We have tested our system on a limited set of face and fingerprint images collected in a laboratory environment. Experimental results show that that our system meets both the identification accuracy as well as the speed requirements.

The proposed system will be designed as for the automatic vehicle identification for the security purpose. The number plate of the vehicle is identified by image identification technology. This system can be implemented for the security checking in the area of the parliament and defence military area and also used in the toll plaza, parking fee collection etc. The images of the vehicles will be captured by cameras and those captured images will be processed by MAT LAB. Here the MaTLAB is used for the image identification and vehicle number extraction. The system contains embedded section for the automatic control of the gate and communication purpose. A LCD screen will be provided at the entry of the gate to gives the instruction to the driver and whether he is permitted to this area or not and the message will be passed to security cabin. The communication between MATLAB and microcontroller is done by using Zigbee. We can also extend to get the information of the vehicle owner, by incorporating the RTO database. The embedded section is programmed by using embedded c.

Recently, automatic vehicle identification system has great significance in practical life and with modern technology now widely available, the research on automatic vehicle recognition based on computer vision is becoming increasingly thorough and extensive. However, automatic vehicle identification also faces significant challenges, such as changes in lighting, weather conditions and distortion of captured images in natural road scenes. Since 2012, as a deep learning in image classification, the convolution neural network has become a major subject in many fields, so this study would focus on the vehicle identification in terms of the convolutional neural network. The Cars196 data set and an improved Convolutional Neural Network (CNN) network capable of multi-attribute recognition are applied to study the optimization network problem of the convolutional neutral network and to realize the multi-attribute classification and identification of automobile brands, models and colors. The data analysis shows that the improved CNN recognition network has a recognition accuracy in automatic vehicle recognition, but the overall recognition network is affected by many factors. According to the results of data analysis, in practice, alongside the improvement of traditional convolutional neural network, the improved new neural network structure should also be used for vehicle recognition, so as to improve the accuracy of vehicle identification to better serve the community.

A system for identifying moving objects such as vehicles, trucks, buses, containers, etc., in which a passive label having a number of dipoles in accordance with a code is attached to the object, is described. A synthetic aperture radar consisting of a Gunn diode mounted within a resonant cavity and connected to a horn is used to illuminate the label. The Gunn diode operates as a homodyne so that signals from the label are also detected by the diode as a number of overlapping chirp signals, which are sent to a computer for processing. The computer output is a pulse train with pulses corresponding to dipoles in the label. A number of tests have been made indicating that labels can be read regardless of range and vehicle speed. A number of horns may be strategically located in a city or along a highway, and these horns can be multiplexed into a common computer to provide an inexpensive all weather automatic vehicle identification system.

Cables are essential elements in bridge engineering. The cable force is usually determined by the vibration method using the cable frequency. This paper proposes a real-time online identification method of cable modal frequency based on bandpass filter and Hilbert transform. A recursive form of band-pass filtering (RBF) is employed so that the original signal can be filtered for the target frequency band to obtain a single modal signal of the target order of the cable. The Recursive Hilbert transform (RHT) is then used to obtain a real-time resolved signal for real-time frequency identification, allowing for real-time identification and tracking of the time-varying modal frequencies at the cable's target order. This method is more efficient than traditional time-frequency analysis methods. According to numerical simulations and actual measurement data. The method is noise-resistant and does not necessitate prior knowledge of the order. The method allows online identification and tracking of time-varying frequency of interest at a specific order. The procedure can be applied to the real-time identification of cable incident frequencies in an online monitoring system, and can also provide a basis for further real-time identification of time-varying cable forces.

Three-dimensional Feature Recognition-based Automated Patient Treatment Mismatch Verification System for Radiation Therapy

Surface quality assessment of magnetic tile before mounting is extremely significant. At present, this task is mainly accomplished by experienced workers in industry, which exposes the drawbacks of low efficiency and high cost. To overcome these issues, an intelligent system is developed to perform this task, which appears to be an efficient and reliable substitute for human workers. In this article, deep learning technique is embedded into our system for automatic defect identification. However, conventional convolutional neural network (CNN) is not suitable for this classification task, since the input is a sample rather than a single image. To overcome this problem, an end-to-end CNN architecture is proposed, termed fusion feature CNN (FFCNN). FFCNN consists of three modules: feature extraction module, feature fusion module, and decision-making module. The feature extraction module is designed to extract features from different images. The feature fusion module is to fuse the features extracted by feature extraction module. The decision-making module is to predict the label by the fused features. Furthermore, an attention mechanism is introduced to focus on more representative parts and suppress less important information. Experimental results demonstrated that the developed system is effective and efficient for magnetic tile surface defect detection.