International Journal of Artificial Intelligence & Robotics (IJAIR)

Prototype of pH and Water Temperature Control System in Discus Fish Farming Using IoT-based Sugeno Fuzzy

Abdullah Ahmad al-Badawi — 2024-05-31

Challenges in cultivating discus fish often arise from abrupt pH and temperature fluctuations attributed to manual and sluggish intervention. An IoT-based prototype for automatic pH and water temperature regulation was developed to address this. The study aimed to evaluate the efficacy of the prototype in controlling pH levels and water temperature and to explore the application of IoT-based fuzzy logic in discus fish cultivation. Test data from the implemented tools and sensors revealed an error comparison value of 0.0132% and an accuracy level of 99.986% for pH measurement. In comparison, temperature sensing yielded an error value of 0% with 100% accuracy. The IoT-based fuzzy Sugeno system demonstrated regular and effective operation in regulating pH and water temperature in discus fish cultivation, showcasing superiority over manual handling systems in mitigating sudden environmental changes.

Design of Predictive Control System for Lane Change in Autonomous Vehicle

Shervind Maharani Mega Permatasari — 2024-05-31

The automobile business is introducing a lot of autonomous vehicles in the modern day. Lane changes are one of the most complicated urban scenarios in which autonomous vehicles are used. Self-driving automobiles must thus interact with human-driven vehicles in a certain way. In this work, we concentrate on the autonomous vehicle's lane-changing control system for obstacle avoidance. This study employs a predictive control system as its methodology. The vehicle's next movements can be predicted by this control system. The vehicle's position, which is adjusted by the steering angle, is the controllable variable. The vehicle's position, which is adjusted by the steering angle, is the controllable variable. It is clear from the numerical simulation results that the predictive control system executes control actions on lane changes correctly, avoiding collisions with the running vehicle obstacles. RMSE (Root-Mean Square Error) is a performance metric that is derived from the difference between the vehicle's lateral position and the reference trajectory value. The RMSE of the planned predictive control is 0.9681.

Design of Pedal Bicycle Prototype using the PID Controller as an Alternative Energy Generator

Reyhan Rizanty — 2024-06-06

In recent years, electricity consumption in Indonesia rose to 1.109 kWh, as the Ministry of Energy and Mineral Resources reported. An alternate method for generating electrical energy is harvesting the energy produced via exercising on a stationary bike. By employing Arduino Mega 2560pro-powered torque control using the PID (Proportional – Integral – Derivative) technique, we can effectively save the generator's power in the battery and modify the paddle load to match the user's desired settings. The design incorporates a repurposed bicycle that has been rebuilt, along with the addition of a transmission gear, a controller box housing a control circuit, a relay, and an inverter. Additionally, it is equipped with a display and buttons. This system can generate a paddle load ranging from 1 to 17 in normal mode and 1 to 10 in PID mode. The system has a maximum current output of 3.2A and a battery capacity of 24VDC. This DC voltage is then transformed into a 220 VA AC voltage suitable for residential electrical use using an inverter. The PID controller will regulate the current flowing into the battery, ensuring it remains steady even with a consistent wood load. PID control can reach a set point at the settling time, 7 s, with an overshoot and a steady-state error of 0%. Every motor achieved the Pulse Width Modulation (PWM) value set to the ideal current. As the RPM increases, the PWM decreases until it reaches the preset set point with a constant current value.

Advancements in Edge Detection Techniques for Image Enhancement: A Comprehensive Review

Hewa Majeed Zangana — 2024-06-10

Edge detection is a fundamental algorithm in image processing and computer vision, widely applied in various domains such as medical imaging and autonomous driving. This comprehensive literature review critically evaluates the latest edge detection methods, encompassing classical approaches (Sobel, Canny, and Prewitt) and advanced techniques based on deep learning, fuzzy logic, and optimization algorithms. The review summarises the significant contributions and advancements in the field by synthesizing insights from numerous research papers. It also examines the combination of edge detection with current image processing methods and discusses its impact on real-life applications. The review highlights the strengths and limitations of existing edge detection strategies and proposes future avenues for investigation. Various research shows that classical edge detection methods like Sobel, Canny, and Prewitt still play a significant role in the field. However, advanced methods utilizing deep learning, fuzzy logic, and optimization algorithms have shown promising results in enhancing edge detection accuracy. Combining edge detection with current image processing methods has demonstrated improved clarity and interpretation of images in real-life applications, including medical imaging and machine learning systems. Despite the progress made, there are still limitations and challenges in existing edge detection strategies that require further investigation. Future research should address these shortcomings and explore new edge detection algorithm development avenues. By understanding the current state of the art and its implications, researchers and practitioners can make informed decisions and contribute to advancing edge detection in image processing and analysis. Overall, this review serves as a valuable guide for researchers and practitioners working in the field, providing a thorough understanding of the state-of-the-art edge detection techniques, their implications for image processing, and their potential for further development.