Yazar "Dikmen, Ismail Can" seçeneğine göre listele

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    Control method simulation and application for autonomous vehicles
    (Ieee, 2018) Arikan, Abdullah; Kayaduman, Abdulsamed; Polat, Suat; Simsek, Yasin; Dikmen, Ismail Can; Bakir, Hincal Gokhan; Karadag, Teoman
    Autonomous vehicles can be operated without the need for human intervention by perceiving their physical environment, thanks to their automatic control systems. At present, extensive studies on autonomous land, sea, and air vehicles are ongoing all around the world. This project is focused on autonomous land vehicles. In the simplest examples, the speed and position of the vehicle can be controlled with the position sensor in the front wheel section and the speed sensors in the rear wheel section. Autonomy is achieved by generating signals for the control system by interpreting the data from various sensors on the vehicle with the aid of an algorithm. Different control methods can be applied for such vehicles. Within the scope of this project, the control of the vehicle will be provided by PID (Proportional-Integral-Derivative) control method, in order to constitute a basis for autonomous vehicle technology,
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    Design and implementation of ultrasonic sonar system for autonomous vehicles
    (Ieee, 2018) Seven, Sibel; Dikmen, Ismail Can; Karadag, Teoman; Bakir, H. Gokhan; Abbasov, Teymuraz
    Autonomous vehicles are vehicles that contain a variety of sensors that sense the environment and that can be actuated without driver intervention thanks to these sensors. Researches on autonomous vehicle technology continues at full speed. In this study, data from ultrasonic sensor and the temperature sensor are processed in the micro controller and displayed on the computer screen. For this, the microcontroller controls both the servo motor, calculates the sound velocity in the environment, and encodes the signal to be transmitted from the ultrasonic sensor. In this case, more accurate results are obtained than using fixed sound speed. In addition, the encoding of the signal sent from the ultrasonic transmitter is also prevented from being affected by noise or other sensors in the environment. This study was designed and conducted for the model vehicle to be used for autonomous vehicle studies in the laboratories of our university.
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    Development and application of sensor network for autonomous vehicles
    (Ieee, 2018) Kayaduman, Abdulsamed; Arikan, Abdullah; Dikmen, Ismail Can; Bakir, Hincal Gokhan; Karadag, Teoman; Abbasov, Teymuraz
    Autonomous vehicles are self-driving vehicles by interpreting data gathered from the environment through sensors. A set of sensors on the vehicle collects the raw data from the environment in which the vehicle is interacting. The software algorithms interpret the data from the sensors and generate commands for the vehicle to follow the path, change direction and maneuver. In this project, the data from the camera module and various sensors were processed and interpreted by means of software libraries on a network composed of micro controllers. Camera modules and image processing algorithms are used to detect strips along the roadside. The resulting images and the data from the other sensors generate the necessary commands for the vehicle's movement with a pivotal software formed on the embedded system. By this means the model autonomous vehicle gain self-motion capability by perceiving its surroundings.
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    Electrical Method for Battery Chemical Composition Determination
    (Ieee-Inst Electrical Electronics Engineers Inc, 2022) Dikmen, Ismail Can; Karadag, Teoman
    Storage of electrical energy is one of the most important technical problems in terms of today's technology. The increasing number of high-capacity high-power applications, especially electric vehicles and grid scale energy storage, points to the fact that we will be faced with a large number of batteries that will need to be recycled and separated in the near future. Additionally multi-chemistry battery management systems that enables the collective use of superior features of different batteries with different chemical composition. Here, battery chemical composition determination emerges as a technical problem. In this study, an alternative method to the currently used methods for categorizing batteries according to their chemistry is discussed. As the foundation, batteries with four different chemical composition including Lithium Nickel Cobalt Aluminium Oxide, Lithium Iron Phosphate, Nickel Metal Hydride, and Lithium Titanate Oxide aged with a battery testing hardware. Fifth, is Lithium Sulphur battery which is simulated. Brand new and aged batteries are used in experimental setup that is consist of a programmable electronic DC load and a software developed to run the algorithm on it. According to the algorithm, batteries are connected to two different loads one by one and voltage-current data are stored. Collected data are pre-processed by framing them and framed data are processed with a separation function. Eventually, the determination problem is converted to a classification problem. In order to solve this, artificial neural network and classification tree algorithms are applied. Because the artificial neural network algorithm is applied in previous studies and the high computational cost of it is presented; classification tree algorithm is concluded to be more applicable especially on low-power microcontroller applications. Consequently, 100% accuracy for battery chemical composition determination is achieved and results are presented comparatively.