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All Journal International Journal of Robotics and Control Systems Automotive Experiences
Ibrahim, Mohd Zamri Bin
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Mechanical Engineering

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Investigation of the Vehicle Driving Trajectory During Turning at Intersectional Roads Using Deep Learning Model Yong, Ericsson; Peeie, Mohamad Heerwan Bin; Zulkifli, Abdullah Bin; Ishak, Muhammad Izhar; Ibrahim, Mohd Zamri Bin; Zakaria, Muhammad Aizzat Bin; Mohd Razelan, Intan Suhana Binti; Ab. Nasir, Ahmad Fakhri Bin; Mohd Jawi, Zulhaidi
Automotive Experiences Vol 7 No 1 (2024)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.10649

Abstract

Two-thirds of vehicle accidents in Malaysia occurred at the straight type of roads, followed by intersection-type roads. Despite the deployment of traffic lights on the road, accidents still occur which are caused by illegal maneuvers, speeding or misjudgment of other’s actions. Hence, motivated by the lack of previous research regarding causes of accidents on intersectional roads, this study aims to observe the pattern of the vehicles’ speed and turning angle during the right turn after the traffic stop at the intersection road. To obtain these parameters, video samples of vehicles at two types of intersections were obtained and analyzed via YOLOV7 and DeepSORT. The two road intersections researched are four-legged intersection and three-legged intersection. 153 and 35 vehicle samples were collected from these types of road intersections, respectively. It was observed that 78 and 75 vehicles exit towards the nearest and furthest lanes at four-leg controlled crossings on divided roads. While, at a single-lane to a dual carriageway road intersection, 26 and 9 vehicles exit towards the nearest and furthest lanes, respectively. From the research, 16.52 - 17.53 km/h and 67.57°-73.33° are the most optimal turning speeds and angles respectively for vehicles at four-leg controlled crossings. Whereas 14.48 - 15.51 km/h and 144.77° - 154.403° are the most optimal turning speeds and angles respectively for vehicles at a single-lane to a dual carriageway road intersection.
Impact of Smart Greenhouse Using IoT for Enhanced Quality of Plant Growth Ali, Munawar; Gunawan, Anak Agung Ngurah; Prasetya, Dwi Arman; Ibrahim, Mohd Zamri Bin; Diyasa, I Gede Susrama Mas
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1277

Abstract

Greenhouses play a crucial role in manipulating environmental conditions for optimal plant growth. While existing greenhouses enhance control over environmental factors, manual controls such as watering and humidity regulation often lead to suboptimal production and increased costs. This study proposes the development of a smart greenhouse with an automatic control system using fuzzy logic, specifically fuzzy Sugeno, to regulate watering and lighting based on soil moisture, temperature, and light intensity. The system's architecture involves sensor inputs, microcontroller processing, and the activation of actuators, such as UV lights and water pumps. Fuzzy logic is applied to interpret soil moisture and temperature inputs and determine optimal irrigation durations. The system's functionality is tested and validated through functional testing, Blynk application testing, and fuzzy Sugeno testing. Results indicate the successful implementation of the proposed smart greenhouse system. Functional testing demonstrates accurate sensor readings, including temperature and soil moisture. The Blynk application enables real-time monitoring and control of environmental conditions. Fuzzy Sugeno testing validates the irrigation control system, with an average error rate of 1.3%, affirming the system's alignment with desired specifications. Plant testing in different conditions showcases the effectiveness of the smart greenhouse in supporting plant growth and development.
Co-Authors A. A. Ngurah Gunawan Ab. Nasir, Ahmad Fakhri Bin Ali, Munawar Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Ishak, Muhammad Izhar Mohd Jawi, Zulhaidi Mohd Razelan, Intan Suhana Binti Peeie, Mohamad Heerwan Bin Yong, Ericsson Zakaria, Muhammad Aizzat Bin Zulkifli, Abdullah Bin