Sealing in aseptic packages, one of the healthiest and cheapest technologies to protect food from parasites in the liquid food industry, requires a detailed and careful control process. Since the controls are made manually and visually by expert machine operators, the human factor can lead to the failure to detect defects, resulting in high cost and food safety risks. Therefore, this study aims to perform a leak test in aseptic package seals by a system that makes decisions using independent deep learning methods. The proposed Faster R-CNN and the Updated Faster R-CNN deep learning models were subjected to training and testing with a total of 400 images taken from a real production environment, resulting in a correct classification rate of 99.25%. As a result, it can be said that the study is the second study that performs a computer-aided quality control process with promising results, having distinctive features such as being the first study that conducts analysis using the deep learning method.
Teknoloji çağında yaşıyor olmamız her alanda olduğu gibi ulaşım planlanması ve çözümlerinde de kendini sürekli yenileyen bilimsel gelişmeleri kullanmamızı kaçınılmaz hale getirmektedir. Artan insan nüfusu ve şehirlere olan göç sonrasında mevcut ulaşım alt yapıları artık yetersiz gelmektedir. Büyükşehirlerde yönetimler çözüm üretmeye çalışıyor olsa da ön görülemeyen hızlı göç ve kent insanlarının ihtiyaçlarını karşılayacak yerleşim planlamaları yapılmaması şehir hayatını karmaşaya dönüştürmektedir. Şehir planlaması yapılırken üniversiteler de bu plan içerisinde düşünülerek yapılmalıdır. Ülkemizde, üniversitelerin büyük şehirlerde kurulması ve zamanla gelişmesi bu şehirlere olan nüfus göçünü de beraberinde getirmektedir. Birçok üniversite yerleşkesi dağınık plana sahip olduğu için artık bulunduğu alana sığamaz hale gelmiş ve yeni yerleşke alanları oluşturmaya başlamıştır. Yerleşke alanlarını planlarken, kıymetli olan bu alanların verimli kullanılması gerekmektedir. Yerleşke içi ulaşım ve yerleşkelerin kent merkezleri ile olan ulaşımlarını akıllı ulaşım sistemleri ve teknolojilerini kullanmak hem zaman verimliliği oluşturacak hem de kıymetli alanların daha iyi şekilde kullanılmasını sağlayacaktır. Bu çalışmada yerleşkelerin bilimsel ve teknolojik gelişime katkısı bağlamında, bünyesinde ve şehirle arasında uygulanan ve uygulanabilecek akıllı ulaşım teknolojileri anlatılarak ya da önerilerek gelecekte ele alınacak yerleşke ulaşım planlamalarındaki akıllı ulaşım sistemlerinin ve teknolojilerinin uygulanmalarına kaynak oluşturmak amaçlanmıştır.
Otomatik depolama ve boşaltma sistemleri (ODBS) firmalardaki ürünlerin depo içerisinde depolanması ve boşaltılması için kullanılan sistemlerdir. ODBS'lerde farklı depo yerleşimlerinde çeşitli otomatik depolama ve boşaltma araçları kullanılmaktadır. Bu sistemlerde yükleme ve ürün toplama işlemi genellikle kullanılan koridor robotları (KR) ve taşıyıcı robotları (TR) ile gerçekleştirilmektedir. Ürünlerin depo içerisindeki raflara yerleştirilmesinde ve toplanmasında operatör müdahalesi minimum seviyededir. Bu çalışmada ODBS'lerin tipleri, uygulanan değişik depo kurulumları ve çözümleri, ürünlerin toplanması ve yüklenmesi esnasında kullanılan optimizasyon çalışmaları hakkında yapılmış yayınlardan bir derleme yapılmıştır. AbstractAutomated storage and retrieval systems (ASRSs) are used for loading and retrieving products in the companies' warehouses. A variety of automated storage and retrieval vehicles are used in different storage layouts in ASRSs. The loading and retrieving process is usually performed with aisle robots (ARs) and shuttle robots (SRs) at these systems. While the placement and retrieving of products on/from the shelves, the operator intervention is minimized. In this paper, a literature review has been given about ASRSs' types, applied various storage installations and solutions and optimization studies used while loading and retrieving products.
Being one of the most basic needs of human life, vehicles are one of the basic building blocks of the transportation sector. Since automobiles are highly preferred, they cause intensity in daily traffic and the need for human control increases accordingly. Approximately 88% of traffic accidents occur due to driver-related errors and approximately 1.1% of the accidents are mortal. Although there are products and studies aimed to prevent human defects technologically, such as semi-autonomous, autonomous driving systems, and driving safety components, studies to improve people's driving abilities are rare. In this study, first of all, the conditions regarding proper and correct vehicle drive in traffic are examined. Then, the sensor and sensor systems that can control the conditions of frequently used cars are investigated. Fuzzy logic decision making model of the sensors and subsystems used in vehicles were designed and simulated in order to develop a car driver control system (CDCS) used to provide a safety control the vehicle in traffic. As a result of the study, the conceptual structure of a system that can solve decision making problem with fuzzy logic in controlling the car driver and a complex fuzzy logic model are presented. It is aimed to decrease the human defects in traffic, to teach driver to drive vehicle correctly, rapidly and economic.
Cervical cancer is one of the most successful types of treatment when diagnosed early. In this study, it is aimed to find and classify the disease with data mining methods on the digitized data set obtained as a result of the pap-smear test. Two-stage architecture has been proposed for the diagnosis of cervical cancer. In the first stage of the study, missing data were extracted from the used dataset, and in the second stage, a new dataset was obtained by using the Synthetic Minority Oversampling Technique (SMOTE) algorithm to balance the target classes in the dataset. By applying the majority voting (MV) method to the dataset used in the study, the structure with 4 target variables was reduced to a single target variable. On two data sets, Artificial Neural Network (ANN), Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN) algorithms from data mining methods were used for the diagnosis of cervical cancer. The results obtained from the original dataset and the dataset produced with Smote were compared. ANN is the best method evaluated according to classification success and F-score, and the major voted target variable in the balanced data group produced with the Smote algorithm gave the most successful result. The experimental results showed that the use of MV and SMOTE algorithms together increased the classification success from 93% to 99%.
In this study, a flywheel inverted pendulum was modeled as simulation. The model controlled by fuzzy logic and PID controller for comparison. Fuzzy logic controllers were designed using triangular and Gaussian membership functions and various methods that are "and", "implication" and "aggregation". All gains from fuzzy logic controllers and PID were tuned by the trial-and-error method. The best performance was obtained by fuzzy logic controller that uses a triangular membership function and "prob/probor" functions. The results were evaluated in terms of three phenomena. In terms of Settling Time and Maximum Overshoot, Fuzzy Triangle MF with 0.15 s and 0 degrees, respectively, and PID and Fuzzy Triangle MF models with 0 degrees in terms of Steady-State error achieved the best success. In addition, the robustness of the control system was tested by applying two different types of disturbance inputs, random and impulse. The results show that fuzzy logic is a good alternative for balance control of a flywheel inverted pendulum, but PID has an acceptable performance.
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