Search Results - (( java implementation path algorithm ) OR ( using welding learning algorithm ))
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1
Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer
Published 2017“…The development tools used in developing this project is NetBeans by using Java for the implementation of the coding. The methodology that used for developing this system is the Dijkstra’s algorithm. …”
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2
Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization
Published 2019“…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
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3
Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment
Published 2021“…In this study, a fast iterative method known as Rotated Successive Over-Relaxation (RSOR) is introduced. The algorithm is implemented in a self-developed 2D Java tool, UAV Planner. …”
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4
Applications of machine learning to friction stir welding process optimization
Published 2020“…Machine learning (ML) is a branch of artificial intelligent which involve the study and development of algorithm for computer to learn from data. …”
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5
Smart appointment organizer for mobile application / Mohd Syafiq Adam
Published 2009“…The main component of this prototype is the use of Dijkstra algorithm to compute the shortest path from source of appointment to the 6 points of destinations within UiTM Shah Alam. …”
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6
Spot welding quality check using artificial intelligence / Jagadisa Rajarathnam
Published 2018“…Spot welding is widely used in the automotive industry as the preferred method to weld the body parts. …”
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7
Quality prediction and classifcation of resistance spot weld using artifcial neural network with open‑sourced, self‑executable andGUI‑based application tool Q‑Check
Published 2023“…A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg–Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. …”
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8
Quality prediction and classifcation of resistance spot weld using artifcial neural network with open‑sourced, self‑executable andGUI‑based application tool Q‑Check
Published 2023“…A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg–Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. …”
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9
Quality prediction and classifcation of resistance spot weld using artifcial neural network with open‑sourced, self‑executable andGUI‑based application tool Q‑Check
Published 2023“…A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg–Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. …”
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10
Quality prediction and classifcation of resistance spot weld using artifcial neural networkbwith open‑sourced, self‑executable andGUI‑based application tool Q‑Check
Published 2023“…A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg–Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. …”
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11
Quality prediction and classifcation of resistance spot weld using artifcial neural network with open‑sourced, self‑executable andGUI‑based application tool Q‑Check
Published 2023“…A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg–Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. …”
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12
Quality prediction and classifcation of resistance spot weld using artifcial neural network with open‑sourced, self‑executable andGUI‑based application tool Q‑Check
Published 2023“…A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg–Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. …”
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13
Quality prediction and classifcation of resistance spot weld using artifcial neural network with open‑sourced, self‑executable andGUI‑based application tool Q‑Check
Published 2023“…A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg–Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. …”
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14
A technical perspective on integrating artificial intelligence to solid‑state welding
Published 2024“…This study investigates thoroughly how AI-based predictions have impacted SSW by looking at methods like Artificial Neural Networks (ANN), Fuzzy Logic (FL), Machine Learning (ML), Meta-Heuristic Algorithms, and Hybrid Methods (HM) as applied to Friction Stir Welding (FSW), Ultrasonic Welding (UW), and Diffusion Bonding (DB). …”
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15
Quality prediction and classifcation of resistance spot weld using artifcial neural network with open‑sourced, self‑executable andGUI‑based application tool Q‑Check
Published 2023“…A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg–Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. …”
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16
Quality prediction and classifcation of resistance spot weld using artifcial neural network with open‑sourced, self‑executable andGUI‑based application tool Q‑Check
Published 2023“…A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg–Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. …”
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17
A technical perspective on integrating artificial intelligence to solid‑state welding
Published 2024“…This study investigates thoroughly how AI-based predictions have impacted SSW by looking at methods like Artificial Neural Networks (ANN), Fuzzy Logic (FL), Machine Learning (ML), Meta-Heuristic Algorithms, and Hybrid Methods (HM) as applied to Friction Stir Welding (FSW), Ultrasonic Welding (UW), and Diffusion Bonding (DB). …”
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18
Evolutionary cost-cognizant regression test case prioritization for object-oriented programs
Published 2019“…Therefore, this study proposed a cost-cognizant TCP approach for object-oriented software that uses path-based integration testing to identify the possible execution path extracted from the Java System Dependence Graph (JSDG) model of the source code using forward slicing technique. …”
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