13,095 research outputs found

    Experimental Methodology for Estimation of Local Heat Fluxes and Burning Rates in Steady Laminar Boundary Layer Diffusion Flames.

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    Modeling the realistic burning behavior of condensed-phase fuels has remained out of reach, in part because of an inability to resolve the complex interactions occurring at the interface between gas-phase flames and condensed-phase fuels. The current research provides a technique to explore the dynamic relationship between a combustible condensed fuel surface and gas-phase flames in laminar boundary layers. Experiments have previously been conducted in both forced and free convective environments over both solid and liquid fuels. A unique methodology, based on the Reynolds Analogy, was used to estimate local mass burning rates and flame heat fluxes for these laminar boundary layer diffusion flames utilizing local temperature gradients at the fuel surface. Local mass burning rates and convective and radiative heat feedback from the flames were measured in both the pyrolysis and plume regions by using temperature gradients mapped near the wall by a two-axis traverse system. These experiments are time-consuming and can be challenging to design as the condensed fuel surface burns steadily for only a limited period of time following ignition. The temperature profiles near the fuel surface need to be mapped during steady burning of a condensed fuel surface at a very high spatial resolution in order to capture reasonable estimates of local temperature gradients. Careful corrections for radiative heat losses from the thermocouples are also essential for accurate measurements. For these reasons, the whole experimental setup needs to be automated with a computer-controlled traverse mechanism, eliminating most errors due to positioning of a micro-thermocouple. An outline of steps to reproducibly capture near-wall temperature gradients and use them to assess local burning rates and heat fluxes is provided

    Research of Indonesian license plates recognition on moving vehicles

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    The recognition of the characters in the license plate has been widely studied, but research to recognize the character of the license plate on a moving car is still rarely studied. License plate recognition on a moving car has several difficulties, for example capturing still images on moving images with non-blurred results. In addition, there are also several problems such as environmental disturbances (low lighting levels and heavy rain). In this study, a novel framework for recognizing license plate numbers is proposed that can overcome these problems. The proposed method in this study: detects moving vehicles, judges the existence of moving vehicles, captures moving vehicle images, deblurring images, locates license plates, extracts vertical edges, removes unnecessary edge lines, segments license plate locations, Indonesian license plate cutting character segmenting, character recognition. Experiments were carried out under several conditions: suitable conditions, poor lighting conditions (dawn, evening, and night), and unfavourable weather conditions (heavy rain, moderate rain, and light rain). In the experiment to test the success of the license plate number recognition, it was seen that the proposed method succeeded in recognizing 98.1 % of the total images tested. In unfavorable conditions such as poor lighting or when there are many disturbances such as rain, there is a decrease in the success rate of license plate recognition. Still, the proposed method's experimental results were higher than the method without deblurring by 1.7 %. There is still unsuccessful in recognizing license plates from the whole experiment due to a lot of noise. The noise can occur due to unfavourable environmental conditions such as heavy rain

    Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection

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    في السنوات الأخيرة، كان هناك تطور مستمر في مجال تطبيق المركبات وعدد المركبات التي تتحرك على الطرق في جميع أنحاء البلاد. يعتبر تحديد رقم لوحة السيارة العربية بناءً على معالجة الصور مجالًا ديناميكيًا لهذا العمل ، وتم استخدام هذه التقنية لأغراض أمنية مثل تتبع السيارات المسروقة والتحكم في الوصول إلى المناطق المحظورة. يستخدم نظام تمييز اللوحات المرورية الكاميرا الرقمية لالتقاط صورة للسيارة متضمنة لوحة المرور وتعتبر كمدخل لنظام التعرف المقترح. يتكون النظام المقترح من ثلاث مراحل، تحديد لوحة ترخيص السيارة، تقسيم الاحرف والارقام الموجودة في الصورة الاساسية الى صور صغيرة تحتوي على (حرف– رقم) كلا على حدة ، والتعرف على الأحرف، يتم تحديد لوحة الرخصة  (LP) باستخدام خوارزمية كاني في الكشف على الحواف، وقد تم استخدام Connect Component Analysis (CCA) لتقسيم الحروف⸲ وأخيرًا يتم استخدام نموذج الشبكة العصبية الاصطناعية المتعددة الطبقات للتعرف على الرموز الموجودة في كل صورة، وبالتالي يتم عرض النتائج كنص على واجهة المستخدم الرسومية. وحقق النظام المقترح أداءً إجماليًا يبلغ 96 ٪ و 97.872 ٪  في تحديد لوحات المرور المتعددة الانماط والتعرف على الرموز العربية الموجودة في اللوحات على التوالي وفي ظل ظروف مختلفة.            In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to identify and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully detects LP and recognizes multi-style Arabic characters with rates of 96% and 97.872% respectively under different conditions

    Vision-based Detection of Mobile Device Use While Driving

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    The aim of this study was to explore the feasibility of an automatic vision-based solution to detect drivers using mobile devices while operating their vehicles. The proposed system comprises of modules for vehicle license plate localisation, driver’s face detection and mobile phone interaction. The system were then implemented and systematically evaluated using suitable image datasets. The strengths and weaknesses of individual modules were analysed and further recommendations made to improve the overall system’s performance

    Automatic Identification of Personal Automobiles Plates of Iran Using Genetic Algorithm

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    In this study, a new method for using LPR systems for Iranian plates number has been presented. Increasing the precision of the letter recognition process and reducing the amount of training are in fact the main advantages of the new hybrid model. The K-NN has been implemented as the first classification method, because it was simple, and it was resistant to the noisy data, and for large datasets it is also effective at zero cost. The confusion problem related to the similarity of letters in plate numbers has also been resolved by using the classification model of the multi-class genetic algorithm. The genetic algorithm improves K-NN performance in the recognition of similar letters. Vehicle license plate recognition (LPR) plays an important role in ITS and is mainly used in access control systems.The purpose of this research is to determine the Iranian plate automobiles that are specifically owned by the automobile. The confusion caused by the similarity between the letters of the alphabet and numeric characters is one of the problems of the Persian LPR systems at the diagnostic stage. In this regard, a method using the KNN-based advantages of genetic algorithm as a hybrid model is presented in this study to overcome the above problem. The genetic algorithm has been trained and tested only with the same letters, thus the cost of training for the genetic algorithm has significantly decreased. Comparison of the results obtained from the experiments carried out in this study with the results of a similar study shows that the combined KNN-genetic algorithm model significantly improved the detection rate of the letters for all tested cases from 94% to 97.03% . Keywords: Coding, plate recognition, genetics, Iran automobile, Genetic Algorithm DOI: 10.7176/CEIS/10-6-04 Publication date:July 31st 2019

    IMPROVED LICENSE PLATE LOCALIZATION ALGORITHM BASED ON MORPHOLOGICAL OPERATIONS

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    Automatic License Plate Recognition (ALPR) systems have become an important tool to track stolen cars, access control, and monitor traffic. ALPR system consists of locating the license plate in an image, followed by character detection and recognition. Since the license plate can exist anywhere within an image, localization is the most important part of ALPR and requires greater processing time. Most ALPR systems are computationally intensive and require a high-performance computer. The proposed algorithm differs significantly from those utilized in previous ALPR technologies by offering a fast algorithm, composed of structural elements which more precisely conducts morphological operations within an image, and can be implemented in portable devices with low computation capabilities. The proposed algorithm is able to accurately detect and differentiate license plates in complex images. This method was first tested through MATLAB with an on-line public database of Greek license plates which is a popular benchmark used in previous works. The proposed algorithm was 100% accurate in all clear images, and achieved 98.45% accuracy when using the entire database which included complex backgrounds and license plates obscured by shadow and dirt. Second, the efficiency of the algorithm was tested in devices with low computational processing power, by translating the code to Python, and was 300% faster than previous work

    Car license plate detection method for Malaysian plates-styles by using a web camera

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    Recently, license plate detection has been used in many applications especially in transportation systems. Many methods have been proposed in order to detect license plates, but most of them work under restricted conditions such as fixed illumination, stationary background, and high resolution images. License plate detection plays an important role in car license plate recognition systems because it affects the accuracy and processing time of the system. This work aims to build a Car License Plate Detection (CLPD) system at a lower cost of its hardware devices and with less complexity of algorithms' design, and then compare its performance with the local CAR Plate Extraction Technology (CARPET). As Malaysian plates have special design and they differ from other international plates, this work tries to compare two likely-design methods. The images are taken using a web camera for both the systems. One of the most important contributions in this paper is that the proposed CLPD method uses Vertical Edge Detection Algorithm (VEDA) to extract the vertical edges of plates. The proposed CLPD method can work to detect the region of car license plates. The method shows the total time of processing one 352x288 image is 47.7 ms, and it meets the requirement of real time processing. Under the experiment datasets, which were taken from real scenes, 579 out of 643 images were successfully detected. Meanwhile, the average accuracy of locating car license plate was 90%. In this work, a comparison between CARPET and the proposed CLPD method for the same tested images was done in terms of detection rate and efficiency. The results indicated that the detection rate was 92% and 84% for the CLPD method and CARPET, respectively. The results also showed that the CLPD method could work using dark images to detect license plates, whereas CARPET had failed to do so

    Inward electrostatic precipitation of interplanetary particles

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    An inward precipitator collects particles initially dispersed in a gas throughout either a cylindrical or spherical chamber onto a small central planchet. The instrument is effective for particle diameters greater than about 1 µm. One use is the collection of interplanetary dust particles which are stopped in a noble gas (xenon) by drag and ablation after perforating the wall of a thin-walled spacecraft-mounted chamber. First, the particles are positively charged for several seconds by the corona production of positive xenon ions from inward facing needles placed on the chamber wall. Then an electric field causes the particles to migrate toward the center of the instrument and onto the planchet. The collection time (of the order of hours for a 1 m radius spherical chamber) is greatly reduced by the use of optimally located screens which reapportion the electric field. Some of the electric field lines terminate on the wires of the screens so a fraction of the total number of particles in the chamber is lost. The operation of the instrument is demonstrated by experiments which show the migration of carbon soot particles with radius of approximately 1 µm in a 5-cm-diam cylindrical chamber with a single field enhancing screen toward a 3.2 mm central collection rod
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