This paper presents a microservices-driven digital twin (DT) model for precision farming in controlled indoor environments, addressing the critical need for scalable, modular, and real-time agricultural systems. As th...
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ISBN:
(数字)9798350364750
ISBN:
(纸本)9798350364767
This paper presents a microservices-driven digital twin (DT) model for precision farming in controlled indoor environments, addressing the critical need for scalable, modular, and real-time agricultural systems. As the global demand for food rises, integrating Industry 4.0, IoT, edge, and cloud computing technologies becomes essential to improve farming process. The proposed model enhances scalability, modularity, and real-time decision making through decentralized data processing. A master-agent containerized computing architecture was employed to reduce latency and optimize environmental control. Moreover, the integration of a Kalman filter improved the accuracy of key environmental parameter adjustments. This research explored how this model can be scaled for larger farming operations and addresses challenges in system interoperability and implementation. Future work will expand this research to diverse agricultural settings, real-world validation, and ensuring data privacy and security. This study provides a framework for advancing precision farming through digital twin technology.
Several coffee producers rely on manual inspection for quality control, which is prone to inconsistencies and time consumption. Recent studies have proposed Comparison performance on State-of-The-Art (SOTA). Deep lear...
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ISBN:
(数字)9789887581598
ISBN:
(纸本)9798331540845
Several coffee producers rely on manual inspection for quality control, which is prone to inconsistencies and time consumption. Recent studies have proposed Comparison performance on State-of-The-Art (SOTA). Deep learning models were investigated as part of the proposed intelligent computer vision for faster and more reliable coffee bean grading. Performance Deep learning models, i.e., MobileNetV3Small, ResNet50, EfficientNetV2L, and DenseNet201, were evaluated to grade the quality of coffee beans that followed a grading procedure based on Indonesian National Standard (SNI) 01-2907-2008. A specific tray was designed to position coffee beans evenly without overlap for sample preparation, which consist of about 500–600 coffee beans. The process of capturing images is conducted systematically, focusing on each type of coffee bean defect individually. For comparison of SOTA models, five classes of coffee bean defects, With 1053 beans per class, the total dataset comprises 5265 beans. This dataset was divided into 10% for testing and 90% for training. Within the training data, 12% was reserved for validation. The result from this paper shows that MobileNetV3Small achieves the highest accuracy (99.43%) with minimal memory and parameters. Its efficient design allows for robust feature extraction, crucial for distinguishing subtle defect features. While larger models like ResNet50 and EfficientNetV2L also perform well, they exhibit higher memory and parameters.
Urban traffic congestion presents critical challenges, resulting in delays, environmental pollution, and significant economic costs. While traditional traffic control methods, such as fixed-time and adaptive systems, ...
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ISBN:
(数字)9798350364750
ISBN:
(纸本)9798350364767
Urban traffic congestion presents critical challenges, resulting in delays, environmental pollution, and significant economic costs. While traditional traffic control methods, such as fixed-time and adaptive systems, have been widely implemented, they often fail in unpredictable conditions, particularly when confronted with noisy or inaccurate data. This research proposes a robust reinforcement learning (RL) solution for traffic signal control using the RADIAL-DQN (Robust Adversarial Loss DQN) algorithm. Integrating adversarial training enhances the system's robustness to real-world uncertainties. Evaluations conducted using a SUMO traffic simulation show that the robust agent produced an increase of only 60.71% in queue length under a projected gradient descent (PGD) adversarial attack, outperforming standard RL agents, which experienced an 89% increase. These results demonstrate the potential of robust RL to improve both the reliability and scalability of urban traffic control systems.
For acute stroke patients, time to recognize the stroke symptom onset is crucial for the lifesaving treatment. The automatic detection of stroke signs has been increasingly developed for practical use. The better time...
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Falls are one of the most dangerous problems for the elderly. A reliable fall detection system can aid in reducing the harmful repercussions of an unintentional fall. The focus of this paper is on a dataset that inclu...
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Robotic manipulators are multi-input multi-output (MIMO) systems with nonlinear points affected by numerous uncertainties and disturbances. PID controllers are widely used in industry for kinematic and dynamic control...
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ISBN:
(数字)9781665462808
ISBN:
(纸本)9781665462815
Robotic manipulators are multi-input multi-output (MIMO) systems with nonlinear points affected by numerous uncertainties and disturbances. PID controllers are widely used in industry for kinematic and dynamic control. However, when applied to MIMO systems, they are not easy to tune and require performance improvements. In this work, a PID controller is proposed with a fuzzy precompensator (FP-PID), both tuned by the bioinspired particle swarm optimization (PSO) algorithm to a two-degree of freedom (2-DOF) robotic manipulator representing a human leg. To validate the system, two real datasets of human gait were used: normal walking and stair climbing to estimate the error trajectory of the manipulator. The statistical analysis of the PSO algorithm with 16 experiments was satisfactory, and the addition of the fuzzy precompensator to the conventional PID resulted in a reduction of the mean square error of one of the manipulator links by up to 73 percent.
In this research, the Human Following Robot (HFR) prototype has been designed and implemented using YOLO v3-Tiny and Tensor Flow Lite on Raspberry Pi hardware named Rewang. The HFR Rewang is designed to assist aircraf...
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ISBN:
(数字)9798331507930
ISBN:
(纸本)9798331507947
In this research, the Human Following Robot (HFR) prototype has been designed and implemented using YOLO v3-Tiny and Tensor Flow Lite on Raspberry Pi hardware named Rewang. The HFR Rewang is designed to assist aircraft technicians by bringing maintenance tool kits to aircraft shelters. Rewang's movement is based on a camera as a vision sensor following the movement of human entities. Image input is processed by YOLO v3-Tiny through the determination of the bounding box, the calculation of Euclidean Distance, and the determination of the pixel value of the camera as the basis of Rewang's movement which is regulated by an L298N driver motor that controls two Direct Current (DC) motors for the left and right wheels. The test results show that Rewang can move at an average speed of 0.18 m/s at 2 m with a travel time of 11 seconds with a light intensity of more than 250 lux. The minimum and maximum distance of object detection is as far as 120 cm and 400 cm, with an object detection accuracy of 95.8%. Rewang allows the HFR to have a larger carrying capacity to bring more complete aircraft maintenance tools and is prospective for other aerospace and general applications.
Electric vehicles (EV s) boast zero tailpipe emissions, directly improving air quality and lowering greenhouse gas emissions. Despite the promise of EVs for sustainable transportation, their limited range is a key cha...
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ISBN:
(数字)9798350364750
ISBN:
(纸本)9798350364767
Electric vehicles (EV s) boast zero tailpipe emissions, directly improving air quality and lowering greenhouse gas emissions. Despite the promise of EVs for sustainable transportation, their limited range is a key challenge. Regenerative braking is a promising approach for enhancing the driving range. This research proposes a study of Interval Type-2 Fuzzy Logic (IT2FL) control to improve regenerative braking efficiency in an electric trike. Employing IT2FL control for regenerative braking recovers 35.84% of energy, translating to a 91.053 km increase in driving range. The IT2FL method achieved a range of 50 km/kWh, representing a significant 41.7% improvement. Furthermore, utilizing the Nie-Tan Method reducer within the IT2FL framework could extend the range by an additional 2.3 km, resulting in a 3 % increase in energy recovery. These findings demonstrate the effectiveness of IT2FL control in enhancing regenerative braking efficiency for the electric trike.
A not stable mechanical movement transmission between systems produces equilibrium losses, such as a rotor of motors that are coupled in rotating machines. This can be studied as a disturbance "vibration" ei...
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This study aims to present the results of applying a computational tool developed to perform a technical-economic feasibility analysis of a large-scale Linear Fresnel Reflector (LFR) solar power plant. The viability a...
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