Tr en İnsansı robotların tasarım sürecinde, klasik yöntemler ile yürüme hareket denklemlerinin çıkarılması ve bu denklemlerin neticesinde yürüme uzuvlarını tahrik edecek büyüklüğün elde edilmesi oldukça zahmetli ve zaman isteyen bir süreçtir. İnsanın yürüyebilmesi için ayak uzvunu oluşturan her bir parçanın bir kas grubunun tahrik edilmesi gerekmektedir. Bu kas grupları yerine robotlarda actuatorler, servo motorlar, mekanizmalar gibi tahrik elemanı kullanılmakta ve bu tahrik elemanın seçimi ve şiddeti oldukça önem taşımaktadır. Bu makale de 2R manipülatör mekanizmasının ile insansı bir robotun bacak uzvu olarak kullanılmış ve yürüme yörüngesini veren denklemler oluşturularak insana en yakın yürüme modelini “Working Model 2D” adlı simülasyon programında elde edilmesi amaçlanmaktadır. For a person to walk, each piece forming a foot should be driven by a muscle group. Instead of these muscle groups, robots use actuators such as actuators, servo motors, and mechanisms. For this reason, the selection and the severity of this driving element are very important. In this thesis study, a humanoid robot is used as a limb with 2R manipulator mechanism and it is aimed to obtain the closest walking model to the human being in the simulation program called "Working Model 2D" by creating equations giving walking orbit. 

This study introduces a climate chamber developed to evaluate the performance of photovoltaic (PV) and solar air heater (SAH) panels based on 12 months of climate data specific to the province of Antalya. In the test environment, the temperature can be controlled between −5 and +50 °C, relative humidity between 10% and 90%, irradiance between 0 and 1500 W/m², and wind speed between 0 and 25 m/s. Experimental data revealed that PV panels achieved the lowest electricity production of 19.21 W in December and the highest of 73.47 W in June, while SAH panels reached an outlet temperature of 31.12 °C in July. As solar radiation increased, panel efficiency rose proportionally; however, an increase in relative humidity negatively impacted efficiency. The panel surface temperature increased from 16.86 °C in January to 39.33 °C in July. The original aspect of this study is the proposal and adaptation of chaos-integrated optimization-based explainable artificial intelligence (XAI) methods instead of classical regression-based models. These models have enabled the development of transparent, understandable, and interpretable rules based on environmental parameters, such as temperature, relative humidity, radiation, and airspeed, that affect panel performance. The methods used in this study make significant contributions to sustainable energy. In particular, the climate control test chamber developed to increase and optimize the efficiency of solar panels enables the investigation of the effects of environmental parameters on panel performance under realistic conditions, thereby facilitating the more effective use of renewable energy sources. Additionally, the use of chaos-integrated optimization-based explainable artificial intelligence (XAI) methods provides reliable, transparent, and understandable decision support models for the design and management of energy systems. This method promotes the adoption of renewable energy technologies, reduces dependence on fossil fuels, lowers carbon emissions, and supports long-term environmental sustainability.

Food drying is a widely used preservation technique; however, achieving high energy efficiency while maintaining product quality remains a significant challenge. This study aims to analyze comprehensive experimental data obtained during the hot-air drying process of the Paşa pear (regional pear) and the system’s autonomous control structure using an explainable artificial intelligence (XAI)-based method. The intelligent drying system, operating for approximately 17.5 h under two temperatures (50 °C and 65 °C) and two air speeds (0.63 m/s and 1.03 m/s), continuously adjusted the temperature and air speed using a PLC-based control mechanism; it ensured stable control throughout the process by monitoring parameters such as product weight, moisture, inlet–outlet temperatures, and air speed in real time. Experimental results showed that drying performance varied significantly with operating conditions, with product mass decreasing from 450 g to 103 g. The innovative aspect of the study is that it obtained quantitative, interpretable rules without discretization by applying the oscillatory chaotic sunflower optimization algorithm (OCSFO) to multidimensional control and process data for the first time. Thanks to its chaotic search mechanism, OCSFO accurately analyzed complex drying dynamics and created rules that achieved over 90% success for high, medium, and low performance classes. The obtained explainable rules clearly demonstrate that drying temperature and air velocity are the dominant determining parameters for drying efficiency, while energy consumption and cabin temperature distribution play a supporting role in distinguishing between efficiency classes. These rules clearly demonstrate how changes in controlled temperature and air velocity, combined with product weight and heat transfer, affect drying performance. Thus, the study offers a robust framework that identifies critical factors affecting drying performance through a transparent artificial intelligence approach that leverages both the autonomous control system and XAI-based rule mining.