In this study, an interface design was carried out in order to provide convenience to the user in the control and monitoring of the Robot Operating System (ROS) based autonomous mobile robot (AMR). Qt Designer and Python were used in the interface design. Thanks to the designed interface, autonomous and manual control of AMR was provided. Using the gmapping algorithm, the environment in the virtual world was mapped and transformed into a picture in .png format and visualized in the interface. The location information from the ROS was transferred to the said picture and the instant tracking of the AMR was done via the interface. It was shown which algorithm is used locally and globally at that moment. While in autonomous mode, the vehicle was provided to move to the previously recorded point. The total distance and time spent by the AMR while moving between two points were also calculated by the interface. The location (x, y, z) and orientations (x, y, z, w) of the previously recorded station were monitored from the stop list. On the other hand, the position (x, y, z) and orientation (x, y, z, w) information of the AMR could be followed as real time via the interface. In this way, when the AMR reaches the goal station, the time elapsed between two points, the transportation distance, settlement information such as the location and orientation of the vehicle can be tracked and be compared via the interface.

The studies such as navigating the AMR between stations, docking to the station, and assigning autonomous tasks to other stations are costly in terms of time and energy consumption. This situation creates the need for an interface where the entire work area can be observed and AMRs can be controlled from a single center in the installation of the system in the field. In this study, an interface that can be used in AMR control and monitoring was designed. With this interface; It is thought to prevent costly situations such as determining the stations, calculating the time spent in the transportation of products between stations, determining the movement route. The interface developed in this context was used in an application where ROS-based path planning algorithms were compared. A total of six different stations was identified. With three different local planners: DWA, TEB and Trajectory planner, AMR was given the task of acting autonomously to each station. Thanks to the developed interface, the distance and time required to reach each station were calculated by performing autonomous movement to the desired points. In this way, a comparison of ROS-based path planning algorithms was made. It was calculated that the DWA was 10.55% more successful than the TEB and 2.33% more successful than the Trajectory in terms of distance covered. Additionally, when examined in terms of arrival time, it was calculated that the DWA was 24.64% more successful than the TEB and 2.39% more successful than the Trajectory.

Force measurement systems are used in machine tools to determine the forces generated during machining. Optionally available force measurement systems have a considerable cost. In this study, a model capable of measuring force in 3 axes with 2 N measurement accuracy has been developed. The measurement system developed in the study gives effective results in the range of 0-1 ton defined by a Labview based algorithm. The system calibrated according to ASTM E74-13 has produced successful results in metal forming operations. At the end of the study, suggestions were made to improve the efficiency of the system.

This work ensures the usability of wearable and non-contact sensor technologies in the industrial field. In this study, it is aimed that operators can easily control industrial robots in real time without the need for a specialized programming environment. For this purpose, a new human robot interface, in which industrial robots can be controlled by hand movements, has been created. In this interface, Leap Motion Controller(LMC), which uses an IR Camera for the control of robot arm positioning and motion trajectories, and MYO armband, which detects wrist and hand movements with EMG sensors, is used for the control of Robot end effector (tool) actions. Robot movements are controlled by industrial communication after hand movements, classification and data processing stages with high accuracy. Various robotic processes such as holding, transporting and painting will be carried out easily and flexibly without the need for programming.

Classification of signals that are received from the human body and control systems is one of the most important subjects of the machine learning application. In this study, classification algorithms were used to classify electromyography and depth sensor data. First, electromyography and joint angle data were obtained from software developed in Python environment. Five different types of movements have been identified for classification and thousand different samples have been collected as training for each of these movements. Support Vector Machine, Random Forest, and K-Nearest Neighbour algorithms were used for classification. To measure success algorithms, results have been compared for achieving criteria. The results show which of three different algorithms was the most successful on two different sensors. While Random Forest provides the best results for non-contact sensor, K- Nearest Neighbour produces the best results for contact sensors. This paper evaluated the classification success of two different sensors. The results will be utilized in online classification to control a graphical user interface.

The Internet of Things (IoT) is becoming increasingly popular around the world. Efficiency in terms of time and cost is ensured in all sectors, thanks to the internet of things technology. A novel IoT system design was created in this study to add Internet of Things technology to a new perspective. All clients can connect to the server using this IoT platform, regardless of their hardware or software capabilities, even if they are not capable of industrial communication. The platform brings flexibility and ease of use to IoTapplications. Thanks to the platform's architecture, any device, whether inputs or outputs, can be easily added to an IoT network while it is running using the developed modules. The server software was created using the LabVIEW visual programming language and Raspberry Pi and MSP340F5529 embedded systems are used to develop hardware modules. To provide IoT data security, the TCP/IP protocol is used for data communication, and all data is encrypted using the AES (Advanced Encryption Standard) algorithm with a 128-bit key. A dynamic 1-N server-client IoT system has been implemented and tested with various analog, digital, and smart sensors, as well as smart devices. When the results are evaluated, it is found that industrial applications can be developed with the platform. Particularly in the case of transformation of industrial automation applications to IoT containing non-smart sensor and actuators, the developed platform can be preferred. In this case, the platform will bring opportunities to engineers in terms of cost and ease of development phase.

İnsan aktivite tespiti son zamanlarda popülerliği artan bir makine öğrenmesi problemidir. Hareketi tespit etmek için ivmeölçer, jiroskop v.b sensörler veya kamera yardımıyla görüntü işleme yapılarak tahminler yapılabilmektedir. Bireylerden sensörler vasıtasıyla alınan veriler ön işlemden geçerek sınıflandırma algoritmaları ile sınıflandırılarak kişilerin hangi hareketi yaptıkları tespit edilmeye çalışılmaktadır. Bu çalışma kapsamında mobil cihaz için yapılan android yazılım ile cihazın ivmeölçer sensörü kullanılarak nesnelerin interneti tabanlı insan hareketlerinin tespiti gerçekleştirilmiştir. İlk önce tespiti yapılacak hareketler için veri toplanmıştır ve ön işlemden geçirilmiştir. Daha sonra oluşan veri setinden özellik çıkarımı yapılmıştır. Elde edilen veri üzerine Destek Vektör Makinaları, Rastgele Orman ve K En Yakın Komşuluk algoritmaları uygulanarak yapılan hareketler sınıflandırılmıştır. Sınıflandırma başarıları tespit edilmiş olup en başarılı sınıflandırma algoritması nesnelerin interneti tabanlı uygulama ile gerçek zamanlı sınıflandırma işlemi için kullanılmıştır.

Insan aktivite tespiti son zamanlarda populerligi artan bir makine ogrenmesi problemidir. Hareketi tespit etmek icin ivmeolcer, jiroskop v.b sensorler veya kamera yardimiyla goruntu isleme yapilarak tahminler yapilabilmektedir. Bireylerden sensorler vasitasiyla alinan veriler on islemden gecerek siniflandirma algoritmalari ile siniflandirilarak kisilerin hangi hareketi yaptiklari tespit edilmeye calisilmaktadir. Bu calisma kapsaminda mobil cihaz icin yapilan android yazilim ile cihazin ivmeolcer sensoru kullanilarak nesnelerin interneti tabanli insan hareketlerinin tespiti gerceklestirilmistir. Ilk once tespiti yapilacak hareketler icin veri toplanmistir ve on islemden gecirilmistir. Daha sonra olusan veri setinden ozellik cikarimi yapilmistir. Elde edilen veri uzerine Destek Vektor Makinalari, Rastgele Orman ve K En Yakin Komsuluk algoritmalari uygulanarak yapilan hareketler siniflandirilmistir. Siniflandirma basarilari tespit edilmis olup en basarili siniflandirma algoritmasi nesnelerin interneti tabanli uygulama ile gercek zamanli siniflandirma islemi icin kullanilmistir.

This work deals with the likelihood of merging a 3D sensor into a robotic manipulator, with an objective to automatically detect, track and grasp an object, placing it in another location. To enhance the flexibility and easy functionality of the robot, MATLAB, a versatile and powerful programming language is used to control the robot. For this work, a common industrial task in many factories of pick and place is implemented. A robotic system consisting of an ABB IRB120 robot equipped with a gripper and a 3D Kinect for Windows camera sensor is used. The three-dimensional data acquisition, image processing and some different parameters of the camera are investigated. The information in the image acquired from the camera is used to determine the robot’s working space and to recognize workpieces. This information is then used to calculate the position of the objects. Using this information, an automatic path to grasp an object was designed and developed to compute the possible trajectory to an object in real time. To be able to detect the workpieces, object recognition techniques are applied using available algorithms in MATLAB’s Computer Vision Toolbox and Image Acquisition Toolbox. These give information about the position of the object of interest and its orientation. The information is therefore sent to the robot to create a path through a server-to-client connection over a computer network in real time.

With the recent advances in technology, sensors that can detect signals in the human body are being developed. Various systems can be controlled by processing the signals received from the sensors. In the study, the control of the industrial robot arm was analyzed by using the MYO Armband device produced by Thalmic Labs, EMG (Electromyography) and IMU (Inertial Measurement Unit) signals. EMG signals are preprocessed first and the size is reduced with the PCA (Principle Component Analysis) algorithm. Then, Random Forest algorithm is used to classify EMG signals. Three different movements are determined from the classification result and the industrial robot arm is controlled with these movements. IRB120 industrial robot arm belonging to ABB robot company was used in the study. With the developed software, EMG and IMU signals are transformed into motion and position information, allowing real-time control of the robot arm.

Gelişen teknoloji ile giyilebilir cihazlar üzerine geliştirme çalışmaları devam etmekte ve ticari ürünler piyasaya çıkmaya devam etmektedir. Bu çalışmaların önemli bir kısmı insan vücudundaki hareketleri algılayabilen giyilebilir sensörler üzerine odaklanmaktadır. Bu çalışmada Thalmic Labs tarafından üretilen Myo Armband ürünü ile insan kol hareketlerinin algılanması ve endüstriyel robot kolunu kontrol etmesi ile bir İnsan Robot Arayüzü geliştirilmiştir. Myo Armband bileklik şeklinde olup üzerinde bulunan EMG (Elektromiyografi) ve jiroskop sensörleri ile kolun hareketinin algılanmasında yardımcı olmaktadır. Myo Armband ile bilgisayar sistemi arasında kablosuz bağlantı kurularak ham EMG ve jiroskop verilerinin bilgisayara gönderilmesi mümkündür. Pratik çalışma için ABB firması tarafından üretilen IRB120 endüstriyel robotu kullanılmıştır. IRB120 endüstriyel robotu kendi kontrolörü dışında kontrol edilebilmektedir. Geliştirilen yazılım ile EMG ve jiroskop verilerinden elde edilen veriler hareket ve konum bilgilerine dönüştürülerek ethernet üzerinden gerçek zamanlı gönderilmesi ile endüstriyel robotun insan hareketlerini takip etmesi sağlanmıştır.

In this paper some experiences are described, from requirements gathering to design and implementation, in the European project “Intelligent Serious Games for Social and Cognitive Competence”. The main goal of the project is to develop serious games for social and cognitive competence of children with learning difficulties. The aim of these games is to teach youth with mild disabilities about social skills, basic skills, key cognitive competence skills and work skills. Using these interactive mobile games and 3D simulations helps the social integration and personal development of these children and youth. The project uses serious games and 3D simulations so that teaching and learning becomes interesting, playful, attractive and efficient.

This study is based on measuring the Electrocardiogram (ECG) signals from the human body in real-time with the help of the software called NI LabVIEW. Not only the raw ECG signals, the digital filtered version of the ECG signals can also be displayed in real-time by processing the signals using the digital filtering tools of the program. The ECG itself provides various diagnostic information and NI LabVIEW biomedical toolkit offers many tools that helps to process the signals and perform feature extraction. Thus, this software was preferred for the ECG data acquisition. In this project, heart rate of a patient is calculated by detecting R-R intervals on the ECG tracing using the method called Teager Energy. In order to test the system, several experiments have been conducted with 12 subjects (6 non-smokers + 6 smokers). Their ECG signals were taken in relaxed and after running conditions. The experimental results were recorded for the graphical and statistical analysis. According to the results, the effect of smoking to the heart rate was discussed.

Pollutant exhaust emissions, particularly NOx, produced by diesel engines must be reduced to limit values defined by the environmental regulations as the emissions have many harmful influences on the environment. Recently, the application of the Miller cycle into the internal combustion engines has been proposed to abate NOx emissions. In the present study, the Miller cycle with late intake valve closing (LIVC) version is applied into a single cylinder, four-stroke, direct injection, naturally aspirated diesel engine. Three different cam shafts have been manufactured to provide 5, 10 and 15 crank angle (CA) retarding compared to original camshaft. The optimum retarding angle has been determined as 5 CA in terms of NOx reduction. The attained results have been compared with conventional diesel engine which has standard CA (0 crank angle retarding) in point of the performance and NO, HC, CO emissions. In order to provide a model validation for engine torque, brake power, brake efficiency, specific fuel consumption (SFC) and NO, the Miller cycle diesel engine is modeled by using two-zone combustion model for 5 CA retarding at full load conditions. The simulation results have been verified with experimental data with non-considerable errors. In the experimental results, NO emissions decreased by 30% with 2.5% power loss and a remarkable change is not seen in the HC, CO emissions. The results show that the method could be easily applied into the diesel engine in order to minimize NO emissions. (C) 2014 Elsevier Ltd. All rights reserved.

The application of the Miller cycle into the internal combustion engines is proposed to decrease NOx emissions, in the recent years. Another NOx control technique is the steam injection method (SIM). In this study, the application of these methods together into a single cylinder, direct injection diesel engine is experimentally and theoretically performed. Two different Miller cycles, which provide 5 and 10 crank angle (CA) retarding compared to standard condition, are applied with two different camshafts. SIM is applied at three different injection rates which are 10%, 20% and 30% of the fuel mass. The results obtained are compared with standard conditions in terms of the performance and emissions. The simulation results are verified with experimental data with non-notable errors. In the experimental results, NO and CO2 emissions decreased up to 48% and 2.2%; HC and CO emissions increased by 46% and 34% with the penalty by 6.4% and 9.2% for the effective power and efficiency. The optimum condition has been defined as 10 CA retarding and 30% steam injection rate (C62-S30) in terms of the maximum NO reduction. The results demonstrate that the combination can be applied into the diesel engines to minimize NO and CO2 emissions.

Steam injection technique newly proposed with EGR (exhaust gas recirculation) is applied into a direct injection diesel engine to decrease NOx emissions for the objective of reaching the new emission regulations. Experimental and combustion model as a theoretical methodology has been done. Steam injected diesel engine with EGR has been performed using a combustion model for 20% steam (S20) and 10% EGR (E10) ratios of fuel mass injected per cycle at full-load conditions. The results have been compared with S20 in terms of performance and NO, CO, CO2, and HC emissions. In the experimental results, NO emissions decreased up to 48.3% at the condition of S20 + E10 when compared to S20 with the increase of 3.5% in SFC (specific fuel consumption). As a result, when the small deterioration in the SFC is tolerated, the presented study could be used as an essential tool by the real-engine designers. (C) 2014 Elsevier Ltd. All rights reserved.

Accurate tests and performance analysis of engines are required to minimize measurement errors and so the use of the advanced test equipment is imperative. In other words, the reliable test results depend on the measurement of many parameters and recording the experimental data accurately which is depended on engine test unit. This study aims to design the control system of an internal combustion engine test unit. In the study, the performance parameters of an available internal combustion engine have been transferred to computer in real time. A data acquisition (DAQ) card has been used to transfer the experimental data to the computer. Also, a user interface has been developed for performing the necessary procedures by using LabVIEW. The dynamometer load, the fuel consumption, and the desired speed can easily be adjusted precisely by using DAQ card and the user interface during the engine test. Load, fuel consumption, and temperature values (the engine inlet-outlet, exhaust inlet-outlet, oil, and environment) can be seen on the interface and also these values can be recorded to the computer. It is expected that developed system will contribute both to the education of students and to the researchers' studies and so it will eliminate a major lack.

Determining the structural behavior using neural network (NN)-based approach is becoming common since it is found as a quicker and an easier method. The objective of this study was to investigate whether NN-based model can be used to determine the interaction diagram of confined and unconfined reinforced concrete (RC) columns. In the application of NN model, the radial basis function network (RBFN) was preferred after various tests. RBFN model was developed, trained and tested in MATLAB-based program. The training and validation data sets were obtained from a commercial software package, Xtract, in which cross-sectional capacity of RC sections can be calculated for both confined and unconfined case. The validity of the proposed RBFN model was verified by comparing the results obtained from RBFN models and those obtained from Xtract software package. It was demonstrated that RBFN-based model has high accuracy to determine interaction diagram of confined and unconfined RC columns with rectangular cross section. As a result of this study, it became clear that using RBFN model is more suitable for confined sections. The proposed model can determine interaction diagram for both confined and unconfined sections in the safety range required by the codes.

In this study, the effects of steam injection at different injection rates on the evaluations of performance parameters and emissions of a gasoline engine have been investigated. Electronically controlled steam injection method has been used to inject the steam into the engine. The optimum steam ratio has been determined as 20% of fuel mass (S20) in terms of performance and emission parameters. Steam injected gasoline engine has been modeled by using zero-dimensional two-zone combustion model for optimum steam ratio at full load condition. The obtained results have been compared with conventional gasoline engine in terms of performance and NO, CO, CO2, HC emissions. The results of theoretical combustion model agree with experimental data quite well. In the experimental results, it is seen that the engine torque and the effective power increase up to 4.65% at 3200 rpm, specific fuel consumption reduces up to 6.44% at 2000 rpm. There is 40% average reduction in NO emissions at 2800 rpm and it is 31.5% in HC emissions at 2000 rpm. (C) 2013 Elsevier Ltd. All rights reserved.