التصنيف التلقائي للهياكل الجيولوجية باستخدام الرؤية الحاسوبية ونموذج التعلم العميق

دراسة الهياكل الجيولوجية المكشوفة على سطح الأرض ذات أهمية كبيرة بشكل عام وخصوصا في التصميم الهندسي والبناء. في هذا البحث ، استخدمنا 2206 صورة مع 12 ملصق للتعرف على الهياكل الجيولوجية بناءً على نموذج Inception-v3. تم اعتماد الصور ذات التدرج الرمادي واللون في النموذج. كما تم بناء نموذج الشبكة العصبية التلافيفية (CNN) وتم تطبيق خوارزمية أقرب جار (KNN) والشبكة العصبية الاصطناعية (ANN) وتعزيز التدرج الشديد (XGBoost) في تصنيف الهياكل الجيولوجية بناءً على الميزات المستخرجة من مكتبة رؤية الكمبيوتر مفتوحة المصدر (OpenCV). أخيرًا ، تمت مقارنة أداء الطرق الخمس وأظهرت النتائج أن أداء KNN و ANN و XGBoost كان ضعيفًا وبدقة أقل من 40.0٪. أما CNN فعد عانت من فرط التدريب Overfitting. كان للنموذج الذي تم تدريبه باستخدام التعلم بالنقل تأثير كبير على مجموعة بيانات صغيرة من صور التركيب الجيولوجي. وأفضل نموذجين وصلوا إلى دقة 83.3٪ و 90.0٪ على التوالي. هذا يدل على أن النسيج هو السمة الرئيسية في هذا البحث. يمكن أن يستخرج التعلم القائم على نموذج التعلم العميق ميزات بيانات البنية الجيولوجية الصغيرة بشكل فعال ، وهو قوي في تصنيف صور الهيكل الجيولوجي.

Fuzzy logic type-2 microcontroller design to control automatic washing machine using Raspberry PI

As we enter the age of artificial intelligence, the need for intelligent home appliances has become very important for what this smart equipment can provide in the provision of electrical energy and water resources that are treasures should human preservation, in addition to the contribution of this equipment to protect the environment from pollution, where we face the challenges next: High prices of electrical equipment.  The number of hours of electricity supply in many areas is low because of the current conditions in our country. - Water shortage. - The rise in prices of materials used in daily life in general and household detergents in particular - Great waste of electricity. - Pollution of the environment and groundwater with detergents used in the laundry process. Moreover, the unjust economic blockade imposed on our country is pushing us to work to produce low-cost national housing equipment that competes with foreign products in order to alleviate the material burden on the citizens and promote the national economy. In order to accomplish this smart washing machine, we have written a code for f type-2 fuzzy microcontroller, using the Python programming language. This controller has received four entries, which are: The first income (clothing color), obtained by taking a picture of the clothes that we need to wash by a camera with a resolution of 8 megapixels, analyzed using OpenCV library, and the second income (clothing type), determined by the local binary pattern algorithm, which is common digital image processing algorithm that widely used to identify shapes that follow specific pattern and structure, the third income (degree of dirt), and was identified by taking a picture of the clothes after soaking them with water for two minutes. The image was then analyzed by the OpenCV library and the fourth (washing weight) that getting From the Load Cell, which measures the physical weights. The readings were converted to digital values via the HX711 digital analogue converter and then sent to Arduino UNO to determine the weight. The weight values were eventually sent to the Raspberry PI for use in the controller. The system generates three exits: washing time (the length of time the laundry was washed), the temperature required for washing, and the amount of detergent required. After selecting all the previous values, we transferred to control Wattar washing machine model 402, where the water valve was controlled to allow the water to pass into the powder box and from it to the washing basin. The water heater was controlled, which heated the water to the temperature determined by the Fuzzy algorithm, The temperature was monitored by the DS18B20 temperature sensor, which gives a signal to the Raspberry PI at the arrival of the temperature to the required value, and the washing machine engine is controlled for a third of the time specified in the Fuzzy algorithm and we controlled the pump Water to empty basin Washing from water, the process repeated for three consecutive times, we control using a software interface designed using TKinter library  We have been able to design a smart Fuzzy logic type-2 controller with the following advantages: o save electricity consumption o Provide quantity of detergents o Shortenwashingtime  We have been able to control the following physical components within the washing machine: o Control the water pump o control Water valve o controlMotor o controlTemperaturesensor o controlLCDscreen  We have built a smart washing machine with the following characteristics: o Have the ability to recognize the condition of clothes o Identify the type of clothing o Identify the color of clothes o Dothewashingwithoutusingapredefinedprogram.  The controller we designed gives good results to calculate the following: o Washingtime o Quantity of detergents o Temperature All diagrams appear in the case of the incremental gradient with an increased degree of dirt and as values correspond to each type of clothing. Keywords: smart washing machine, saving electricity, saving detergent, shortening washing time, color and clothing distinction, artificial intelligence, fuzzy logic type-2, Raspberry PI, control, Python programming language, HX711.