the protection system on a three-phase induction motor is designed so that the motor can be used for a longer period of time and avoid damage. Disturbances such as overloads and high temperatures that occur can interf...
the protection system on a three-phase induction motor is designed so that the motor can be used for a longer period of time and avoid damage. Disturbances such as overloads and high temperatures that occur can interfere withthe performance of the induction motor and shorten its use time. therefore, a protection system for the induction motor is needed. the method used in this paper is the design of a simulation of an overload and high temperature protection system on a three-phase induction motor with a Zelio smart relay using Zeliosoft 2.0 software by making a Ladder diagram as well as the simulation. To protect the three-phase induction motor, additional components are used, namely the thermal Overload Relay (TOR) for overload protection and the Temperature Controller for high temperatures. the maximum limit of current to be protected by TOR is 1.6 A, and the maximum temperature limit to be protected by the Temperature Controller is 50 °C. Both components are integrated by the Zelio smart relay. In the process stage of the program itself, the TOR and Temperature Controller will send a trip signal to the Zelio smart relay when there is more load interference and a high temperature. the Zelio smart relay will then secure the motor from interference by stopping the power to the motor.
Despite enduring criticisms spanning several decades, jump statements such as goto, break, continue, and return remain prevalent in imperative programming languages, including but not limited to C++, Java, and Python....
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ISBN:
(纸本)9798400708688
Despite enduring criticisms spanning several decades, jump statements such as goto, break, continue, and return remain prevalent in imperative programming languages, including but not limited to C++, Java, and Python. the academic community has yet to reach a consensus regarding whether the refactoring of source code in these languages to eliminate such statements can indeed enhance code readability. Nevertheless, it is evident that automated program analysis would derive substantial benefits from this refactoring, given that structured code analysis is more straightforward than analyzing code that exhibits capricious alterations in its control flow. While algorithms tailored for this refactoring process have been proposed for certain imperative languages, we introduce a congruent algorithm, specifically designed for a dataflow programming language. It’s important to note that although dataflow languages lack jump statements, they might incorporate jump-objects (in object-oriented contexts) or jump-functions (within functional paradigms). Our algorithm has been instantiated as a command-line tool tailored for refactoring EO, an object-oriented dataflow language. Preliminary tests with several EO programs have validated the tool’s efficacy. Leveraging φ -calculas, we provide a formal proof underscoring the validity of every transformation encompassed within our algorithm.
the Faculty of Science, Sriracha Campus is in the Eastern Economic Corridor (EEC) to recognize the importance of the development of Industry4.0, therefore it has been developed a control and data analysis program base...
the Faculty of Science, Sriracha Campus is in the Eastern Economic Corridor (EEC) to recognize the importance of the development of Industry4.0, therefore it has been developed a control and data analysis program based on a controlled automatic sorting simulation system to disseminate the technology of PLC and data analysis to develop into a smart factory. By developing personnel to have knowledge and developing the capabilities of machines for Industry4.0. automated machines for smart factories, such as production systems, can be adjusted according to the situation by responding in real time. Moreover, by creating intelligent machines, trackers and forecasters can analyze production data by receiving information from machines to prevent errors from occurring. Being able to develop a machine status monitoring program can help extend the working life of machines. therefore, developing a training set to program and study data analysis in factory machinery. It is important to develop technological knowledge and understanding in developing smart factory systems such as automatic control. Machine maintenance through data analysis from various sensors using production data to analyze production rates or detecting work controls through sensors that have complex functions from various research developments through studies on this simulation set in the future.
Clustering is an effective strategy to minimize the energy consumption of nodes in energy-constrained wireless sensor networks. In clustered wireless sensor networks, the network is divided into multiple clusters, and...
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Although Large Language Models (LLMs) have established pre-dominance in automated code generation, they are not devoid of shortcomings. the pertinent issues primarily relate to the absence of execution guarantees for ...
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ISBN:
(数字)9798400705014
ISBN:
(纸本)9798350352177
Although Large Language Models (LLMs) have established pre-dominance in automated code generation, they are not devoid of shortcomings. the pertinent issues primarily relate to the absence of execution guarantees for generated code, a lack of explainabil-ity, and suboptimal support for essential but niche programming languages. State-of-the-art LLMs such as GPT-4 and LLaMa2 fail to produce valid programs for Industrial Control Systems (ICS) op-erated by Programmable logic Controllers (PLCs). We propose LLM4PLC, a user-guided iterative pipeline leveraging user feed-back and external verification tools - including grammar checkers, compilers and SMV verifiers - to guide the LLM's generation. We further enhance the generation potential of LLM by employing Prompt Engineering and model fine-tuning through the creation and usage of LoRAs. We validate this system using a FischerTech-nik Manufacturing TestBed (MFTB), illustrating how LLMs can evolve from generating structurally-flawed code to producing verifiably correct programs for industrial applications. We run a complete test suite on GPT-3.5, GPT-4, Code Llama-7B, a fine-tuned Code Llama-7B model, Code Llama-34B, and a fine-tuned Code Llama-34B model. the proposed pipeline improved the generation success rate from 47% to 72%, and the Survey-of-Experts code quality from 2.25/10 to 7.75/10. To promote open research, we share the complete experi-mental setup, the LLM Fine-Tuning Weights, and the video demonstrations of the different programs on our dedicated webpage
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https://***/***/llm4plc/home.
CS1 programming courses often exhibit low academic performance. One way to address this issue is by implementing early interventions for students. In higher education, various theories have contributed to the topic of...
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Smart environments enabled by the Internet of things aim at improving our daily lives by automatically tuning ambient parameters and by achieving energy savings through self-managing cyber-physical systems. Commercial...
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ISBN:
(纸本)9781665412520
Smart environments enabled by the Internet of things aim at improving our daily lives by automatically tuning ambient parameters and by achieving energy savings through self-managing cyber-physical systems. Commercial solutions, however, only permit setting simple target goals on those parameters and do not mediate between conflicting goals among different users and/or system administrators, nor across different IoT verticals. In this article, we propose a declarative approach (and its open-source Prolog prototype) to represent smart environments, user-set goals and customisable mediation policies to reconcile contrasting goals across multiple IoT systems.
Coffee consumption in the Philippines has been continuously growing but the production of green coffee beans has been decreasing since 2015, resulting in demand and supply imbalances. Two of the identified root causes...
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ISBN:
(数字)9798350375886
ISBN:
(纸本)9798350375893
Coffee consumption in the Philippines has been continuously growing but the production of green coffee beans has been decreasing since 2015, resulting in demand and supply imbalances. Two of the identified root causes are low yield and poor quality of coffee beans. thus, this study aims to leverage image processing and Convolutional Neural Networks (CNN) to develop a deep learning model that can detect common defects (i.e., black, sour, broken, quaker, and foreign material) of Robusta coffee beans and classify them. Withautomated defect classification, farmers can use this data and look for trends to identify potential problems during production as coffee bean defects can be attributed to issues during planting and/or cultivation. With such data, farmers will be able to implement preventive measures early on which can eventually lead to higher overall yield and improved quality of coffee beans. Using Python, all programming, model training, and evaluation were developed through Jupyter Notebook. A modified AlexNet CNN architecture was used, and it was optimized through a series of hyperparameter tuning. Based on the results of a five-fold crossvalidation, the optimized AlexNet CNN model has an average classification accuracy of $\mathbf{92.67} \%$, F1-score of $\mathbf{92.68} \%$, and Cohen’s Kappa of $91.20 \%$.
Aside from the necessary learning skills of logical thinking, problem-solving, and creativity, undergraduate students in the programming discipline must also be self-motivated because these abilities enable them to ov...
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ISBN:
(纸本)9798400708732
Aside from the necessary learning skills of logical thinking, problem-solving, and creativity, undergraduate students in the programming discipline must also be self-motivated because these abilities enable them to overcome obstacles and achieve success. In the digital learning era, a pedagogical approach emphasizing inquiry and collaborative approaches can enhance students' programming skills anywhere and anytime. As a result, this research aims to investigate the effects of collaborative learning on academic achievement and motivation by managing ubiquitous learning in computer courses at three universities in northern thailand using a collaborative inquiry-based approach. the learning environment allowed students to practice independently using the instructor-provided content and exercises. then, as part of group learning, students gave advice and shared knowledge until they found the best solution. Following class, the instructor discussed the merits of each group's responses with all students. Students were then asked to repeat the questions to ensure they completely understood the logical reasoning and problem-solving process. According to the findings, ubiquitous learning management based on collaborative inquiry-based approaches could help them learn more effectively, with higher learning achievement and motivation. the study's findings inspire further research into creating a collaborative digital learning environment in computer education, particularly from the standpoint of ubiquitous learning.
We currently have access to a plethora of statistical analyses based on sampling limited parts of a population. Meta-analysis is the task of combining several statistical results to obtain a more precise and reliable ...
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ISBN:
(纸本)9780999241172
We currently have access to a plethora of statistical analyses based on sampling limited parts of a population. Meta-analysis is the task of combining several statistical results to obtain a more precise and reliable picture of the population. By the nature of sampling, all these results are uncertain, and difficult to combine with other knowledge. In this position paper, we propose a first approach for automatedreasoning in meta-analyses.
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