Kablosuz sensör ağlarının IP tabanlı ağlarla birleştirilmesi

Kablosuz sensör ağları, çevreden veri toplamaya yarayan birçok sensör düğümünden oluşan kablosuz kişisel alan ağlarıdır. Kablosuz sensör ağı düğümleri arasındaki haberleşmede IEEE 802.15.4 ve ZigBee standartları kullanılmaktadır. Bu standartlar ile düşük güç tüketen, az maliyetli, güvenilir ve genişletilebilir ağlar oluşturulabilmektedir. Tezde kablosuz sensör ağı düğümleriyle IP-tabanlı ağlar arasında haberleşme yapılıp internete girilebilen her noktadan sensör ağı düğümlerine ulaşmak hedeflenmektedir. Bu hedef doğrultusunda kablosuz sensör ağı ve IP tabanlı ağlarda kullanılan standartlar hakkında literatür araştırması yapılmıştır. Literatür araştırması sonucunda iki ağ arasında haberleşme yapılabilmesi için veri paketleme, adresleme, servis keşfi ve güvenlik problemlerinin aşılması gerektiği sonucuna varılmıştır. Problemlerin tespitinden sonra bunları aşmak için hali hazırda kullanılan yöntemler incelenmiştir. Ağlar arası haberleşme tekniklerinin incelemesiyle mevcut standartları koruyan en verimli yöntemin sunucu tabanlı yöntem olduğu sonucuna varılmıştır. Literatür araştırması sonuçlarına dayanılarak çalışmada ağlar arası haberleşme için sunucu tabanlı yöntem tercih edilmiştir. Çalışmada oluşturulan sunucu tabanlı ağlar arası haberleşme sistemi sunucu, ağ geçidi ve kablosuz sensör ağından oluşmaktadır. Sistemde kullanılan sunucu internet tarafındaki kullanıcılara kablosuz sensör ağı kaynaklarını sunan birimdir. Çalışmada sunucu sistemi olarak bir mesajlaşma protokolü olan XMPP teknolojisi tercih edilmiştir. XMPP teknolojisinin tercih edilmesinin sebebi sunucu olarak klasik web sunucusu yerine, farklı teknolojilerin sunucu olarak kullanılabilirliğini bir uygulamayla örneklemektir. Sistemde ağlar arası tercüme işleminin gerçekleştirildiği birim ağ geçididir. Ağ geçidi hem internet hem de kablosuz sensör ağıyla haberleşebilen tek cihazdır. Ağ geçitleri, sistemde veri depolama, servis sağlama, paketleme, adresleme, servis keşfi ve güvenlik işlemlerini üstlenmektedir. Kablosuz sensör ağı ise sensör düğümlerinden oluşmaktadır ve bulunduğu ortam ile çeşitli şekillerde etkileşime girmektedir. Sonuçta kablosuz sensör ağı ile IP tabanlı ağların bütünleştirilmesi sunucu tabanlı bir sistem tasarlanarak gerçekleştirilmiştir. Tasarlanan sistem ile sunucu tabanlı yöntem kullanılarak ağlar arası haberleşmede ZigBee standardının avantajlarının korunması sağlanmıştır. Ayrıca sunucu tabanlı sistemlerde klasik web sunucusu yerine XMPP sunucusunun kullanılabilirliği tez kapsamında yapılan uygulamayla sınanmıştır.Wireless sensor networks are a kind of personal area network that consists of sensor nodes which collect data from environment. IEEE 802.15.4 and ZigBee standards are used in communication among the wireless sensor network nodes. Low power consuming, low cost, reliable and expandable networks can be created with these standards. The aim of this thesis is enlarging accessibility of wireless sensor networks by internetworking with IP-based networks. The mean of enlarging accessibility is to provide access to wireless sensor networks from where we can access internet connection. A literature study was made about standards which are used in IP based networks and wireless sensor networks to find out problems in internetworking. The literature study concluded that packaging, addressing, service discovery and security are major problems in internetworking. After the identification of problems an examination was made about to find best internetworking method which conserves standards and provides effective communication. Examination of existing internetworking techniques concluded that server based method is the most appropriate method about protecting existing standards and efficiency. Basing on literature studies, server based systems was preferred for the internetworking system which is used in thesis. System which is created in the application is consisted from server, gateway and wireless sensor network. The server which is an internet unit of the server based system serves wireless sensor networks resources to users who located in internet. XMPP which is an instant messaging protocol is used as server system in this thesis. The reason of preferring XMPP technology is sampling usage of different technology instead of classical web server. XMPP server which is located on internet side of the system provides instant messaging service. Gateway is translator unit of server-based system which is between sensor network and XMPP server. Gateway is only device can communicate both wireless sensor networks and IP-based networks. Data storage, service delivery, packing, addressing, service discovery and security operations are handled by gateways. Wireless sensor networks are consisted from sensor nodes which measuring environmental magnitudes and interact with the environment in various ways. As a result of the application in this thesis, integration of wireless sensor network with IP-based networks was realized by designing a server based system. ZigBee standard benefits were protected with server based internetworking application. In addition, the availability of using XMPP server instead of conventional web server in the server based systems has been tested

Özdeş olmayan bileşenlerden oluşan multikopter yapıları ve çoklu yapı uygulamaları: sürü modelleri tasarımı

Bu tez çalışması Pamukkale Üniversitesi tarafından 2015 FBE031 nolu proje ile desteklenmiştir.Bu tezde, özdeş olmayan multikopter tiplerini ve çoklu robot uygulamalarını çalıştırabilecek uçuş denetim, konumlandırma ve çoklu robot denetim sistemleri tasarlanması amaçlanmıştır. Amaç doğrultusunda öncelikle otonom olarak çalışabilecek uçan robot sistemi tasarımı sunulmuştur. Robotun uçuş denetiminde irtifa ve durum denetimi gerçekleştirilmiş olup uçuş için PID denetim kullanılmıştır. Tasarlanan sistem dört rotorlu ve altı rotorlu hava araçlarının uçuş denetimi için denenmiştir. Bu uygulamanın ardından, çoklu robot uygulamalarında önemli olan konumlandırma problemine görüntü işleme tabanlı çözümler üretilmiştir. Konumlandırma için görsel eşleme ile robotlar arası konumlandırma ve tek gözlü etkin poz takibi yöntemleri oluşturulmuştur. Bu yöntemlerden görsel eşleme tabanlı konumlandırmada robotlar aşağı yönde bakan kameralardan aldıkları eşsiz görüntüleri birbirleriyle paylaşmakta ve dağıtık olarak konum tahmini yapmaktadırlar. Tek gözlü etkin poz takibinde ise robotların çalışma esnasında ön tarafında bulunan kameradan aldıkları görüntüler ile her robot kendi konum değişimini hesaplamaktadır. Son olarak, robotların konum bilgileri ve bulanık mantık kullanılarak akın etme ve düzen alma için bir model önerilmiştir. Bu modelde robotlar düzen deseninin tanımlı olduğu bölgeye kadar Boids algoritmasından türetilen bulanık akın algoritmasını kullanarak ulaşmakta ve sonrasında geometrik deseni oluşturan belirli hedef noktalara gitme eylemini gerçekleştirmektedir. Tez sonucunda tasarlanan denetim sistemi ile farklı tipte hava araçlarının uçuş denetimi gerçekleştirilmiştir. Önerilen konumlandırma yöntemleri benzetimler ve gerçek görüntüler üzerinde çalıştırılarak uygulanabilirlikleri ortaya konmuştur. Birden çok robotun verilen görevi bireysel veya işbirlikli olarak yerine getirmesi incelenmiş ve geliştirilen bulanık akın algoritmasının sağladığı avantaj ve dezavantajlar ortaya konmuştur.In this thesis, it is aimed to design flight control, positioning and multirobot control systems which can operate non-identical multicopter types and multi-robot applications. For the purpose, firstly, a flying robot system design which can work autonomously was presented. PID control was used for the flight. The designed system has been tested for flight control of quadcopter and hexacopter. Subsequently, image processing based solutions have been produced for positioning problems which are important in multi-robot applications. Interrobot positioning with feature matching based model matching and monocular odometry methods were created. From these methods, in the feature matching based positioning, the robots share and match the unique images they receive from down facing cameras and distribute for the position estimation. In monocular odometry mode, each robot calculates its own position change with the images taken from the camera located on the front side of the robot during operation. Finally, a model for flocking and formation using robots' position information and fuzzy logic has been proposed. In this model, the robots arrive at the target area using the fuzzy flocking algorithm derived from the Boids algorithm until the region where the pattern is defined, and then perform a specific target point action that creates the geometric pattern. With the control system designed as a result of the thesis, flight control of different types of aircraft was realized. The proposed positioning methods are simulated and run on actual images and demonstrated their applicability. Multiple robots have been investigated individually or collaboratively to perform tasks and the advantages and disadvantages of the developed fuzzy flocking algorithm have been revealed

Production fault simulation and forecasting from time series data with machine learning in glove textile industry

Although textile production is heavily automation-based, it is viewed as a virgin area with regard to Industry 4.0. When the developments are integrated into the textile sector, efficiency is expected to increase. When data mining and machine learning studies are examined in textile sector, it is seen that there is a lack of data sharing related to production process in enterprises because of commercial concerns and confidentiality. In this study, a method is presented about how to simulate a production process and how to make regression from the time series data with machine learning. The simulation has been prepared for the annual production plan, and the corresponding faults based on the information received from textile glove enterprise and production data have been obtained. Data set has been applied to various machine learning methods within the scope of supervised learning to compare the learning performances. The errors that occur in the production process have been created using random parameters in the simulation. In order to verify the hypothesis that the errors may be forecast, various machine learning algorithms have been trained using data set in the form of time series. The variable showing the number of faulty products could be forecast very successfully. When forecasting the faulty product parameter, the random forest algorithm has demonstrated the highest success. As these error values have given high accuracy even in a simulation that works with uniformly distributed random parameters, highly accurate forecasts can be made in real-life applications as well. © The Author(s) 2019.The authors would like to thank U?ELSAN Textile Products Company and the owner ?mit ?zk?r for the information provided for the study. The author(s) received no financial support for the research, authorship, and/or publication of this article

Hierarchical fusion of machine learning algorithms in indoor positioning and localization

Wi-Fi-based indoor positioning offers significant opportunities for numerous applications. Examining the Wi-Fi positioning systems, it was observed that hundreds of variables were used even when variable reduction was applied. This reveals a structure that is difficult to repeat and is far from producing a common solution for real-life applications. It aims to create a common and standardized dataset for indoor positioning and localization and present a system that can perform estimations using this dataset. To that end, machine learning (ML) methods are compared and the results of successful methods with hierarchical inclusion are then investigated. Further, new features are generated according to the measurement point obtained from the dataset. Subsequently, learning models are selected according to the performance metrics for the estimation of location and position. These learning models are then fused hierarchically using deductive reasoning. Using the proposed method, estimation of location and position has proved to be more successful by using fewer variables than the current studies. This paper, thus, identifies a lack of applicability present in the research community and solves it using the proposed method. It suggests that the proposed method results in a significant improvement for the estimation of floor and longitude. © 2019 by the authors

Çoklu Robotlarda İşbirlikli Davranışların Karşılaştırılması ve Bulanık Mantık Yaklaşımı

Günümüzde pek çok karmaşık görev için donanım ve çalışma hızı açısından hantal ve maliyetli robotik sistemler kullanılmaktadır. Pahalı ve hantal bir robot yerine daha küçük ve basit robotlardan oluşan sistemlerle aynı karmaşık görevleri yerine getirme konusunda yapılan çalışmalar çoklu robotiği ortaya çıkarmıştır. Çoklu ajanlardan oluşan sürülerin hareketi için ilk çözüm modeli Reynolds tarafından hazırlanan Boids algoritmasıdır. Boids algoritması birleşme (cohesion), ayrılma (dispersion) ve hizalanma (align) kurallarının oluşturduğu vektörlerin her ajan için birleştirilip her ajana ayrı uygulanması olarak tanımlanmıştır. Diğer çalışmalardan farklı olarak bu çalışmada bulanık-işbirlikli bir algoritma tasarlanarak hem akın hem de düzen alma davranışlarının gerçekleştirilmesi amaçlanmıştır. Amaç doğrultusunda birden fazla robotun bireysel, işbirlikli ve bulanık-işbirlikli halde akın etmesi ve düzen alması incelenmiş ve türetilen bulanık-işbirlikli algoritmanın başarımı sınanmıştır. Çalışmada sırasıyla, benzetim ortamında kullanılan robot ve görev algoritmaları sunulmuş olup ardından benzetim sonuçları verilmiştir. Elde edilen bulgulara göre tasarlanan Bulanık akın algoritması, Boids algoritmasından daha kısa görev tamamlama süresi ve daha az haberleşme tekrarı sağladığı görülmüştür

Warehouse Drone: Indoor Positioning and Product Counter with Virtual Fiducial Markers

The use of robotic systems in logistics has increased the importance of precise positioning, especially in warehouses. The paper presents a system that uses virtual fiducial markers to accurately predict the position of a drone in a warehouse and count items on the rack. A warehouse scenario is created in the simulation environment to determine the success rate of positioning. A total of 27 racks are lined up in the warehouse and in the center of the space, and a 6 x 6 ArUco type fiducial marker is used on each rack. The position of the vehicle is predicted by supervised learning. The inputs are the virtual fiducial marker features from the drone. The output data are the cartesian position and yaw angle. All input and output data required for supervised learning in the simulation environment were collected along different random routes. An image processing algorithm was prepared by making use of fiducial markers to perform rack counting after the positioning process. Among the regression algorithms used, the AdaBoost algorithm showed the highest performance. The R-2 values obtained in the position prediction were 0.991 for the x-axis, 0.976 for the y-axis, 0.979 for the z-axis, and 0.816 for the gamma-angle rotation.Pamukkale University in Turkey [2020FEBE046]This study was carried out within the scope of the doctoral thesis named Positioning System Design for Independent Moving Aircraft. The study was supported by the project numbered 2020FEBE046. The authors thank Pamukkale University in Turkey

Warehouse Drone: Indoor Positioning and Product Counter with Virtual Fiducial Markers

The use of robotic systems in logistics has increased the importance of precise positioning, especially in warehouses. The paper presents a system that uses virtual fiducial markers to accurately predict the position of a drone in a warehouse and count items on the rack. A warehouse scenario is created in the simulation environment to determine the success rate of positioning. A total of 27 racks are lined up in the warehouse and in the center of the space, and a 6 × 6 ArUco type fiducial marker is used on each rack. The position of the vehicle is predicted by supervised learning. The inputs are the virtual fiducial marker features from the drone. The output data are the cartesian position and yaw angle. All input and output data required for supervised learning in the simulation environment were collected along different random routes. An image processing algorithm was prepared by making use of fiducial markers to perform rack counting after the positioning process. Among the regression algorithms used, the AdaBoost algorithm showed the highest performance. The R2 values obtained in the position prediction were 0.991 for the x-axis, 0.976 for the y-axis, 0.979 for the z-axis, and 0.816 for the γ-angle rotation

Failure load prediction of adhesively bonded pultruded composites using artificial neural network

Mechanical joining and adhesive bonding provide convenience for manufacturing of complex structures, which made of composite materials. Failure load is directly related with process parameters of mechanical joining or adhesive bonding. In this study, the effects of bonding angle, patching type (single side and double side) and patching structure on the failure load were investigated in the pultruded composite specimens. For this aim, the pultruded composite specimens, which bonded with five different bonding angles (45°, 51°, 59°, 68° and 90°) and five different bonding types as unpatched, single-side woven patch, single-side knitting patch, double-side woven patch and double-side knitting patch were exposed to tensile loads at room temperature. In the view of experimental results, the failure loads of bonded pultruded composite specimens were predicted by training six different artificial neural network algorithms. The only three best prediction results of Bayesian regularization, Levenberg-Marquardt and scaled conjugate gradient were given in the figures for better understanding. © SAGE Publications