The Job Shop Scheduling problem is critical in the manufacturing industry. At present, the decisi... more The Job Shop Scheduling problem is critical in the manufacturing industry. At present, the decision tree reasoning technique and data mining are often used in multi-objective optimization research to solve flexible job shop scheduling issues. Unfortunately, when job shop scheduling problems involve complex logic, it becomes difficult to implement data-driven automatic scheduling without human intervention. Based on the analysis of mass data and specialized knowledge in the scheduling domain, an ontology-based scheduling knowledge model and a method of knowledge representation can be established. Considering the relationship between data mining and knowledge, this paper illustrates the acquisition process of scheduling rules. These scheduling rules were applied to improve the initialization process of the artificial fish algorithm. Then, a scheduling experiment was designed, the results of which show that the efficiency and accuracy of the algorithm has been improved. The desired uncertain information analysis, decision-making support for production planning and scheduling on the shop floor are provided and an adaptive scheduling algorithm for complex manufacturing systems is established by building a knowledge-based system.
2020 XI National Conference with International Participation (ELECTRONICA), 2020
With the introduction of a new wave of IT in production and the deep convergence between informat... more With the introduction of a new wave of IT in production and the deep convergence between information technology and industry, industrial production is developing towards intellectualization and networking. Because the current production system cannot fully mine the value of technical data and the existence of information islands in the production process, this paper introduces an industrial Internet of things (IoT) platform based on 5G, OPC-UA, and other communication technologies. This paper first analyzes the basic requirements of the industrial Internet of things, introduces the architecture and detailed information flow of this platform, and finally shows the application of this platform in the preventive maintenance field. On the one hand, this paper shows the implementation of the industrial Internet of things platform; on the other hand, it provides a reference for industrial data processing.
Users may download and print one copy of any publication from the public portal for the purpose... more Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
2021 XXX International Scientific Conference Electronics (ET), 2021
CNC machine tools play a pivotal role in the manufacturing field. Due to the complexity and diver... more CNC machine tools play a pivotal role in the manufacturing field. Due to the complexity and diversity of faults, experienced engineers who can quickly locate the cause of faults are rather scarce. This paper takes expert knowledge in the field of fault diagnosis as the research object and proposes an ontology-based knowledge expression structure to improve fault retrieval efficiency and fault diagnosis accuracy; then applies the SimRank algorithm to calculate the similarity between fault phenomena and fault causes in the case base to guide the work of maintenance engineers. Finally, this paper introduces the diagnosis process of machine tool fault diagnosis expert system with Siemens 808d CNC system as a case study.
Forecasting energy demand of residential buildings plays an important role in the operation of sm... more Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-distance data in energy forecasting relative to the standard regression model. However, there are still two problems to be solved for energy forecasting, including the encoding of categorical characteristics and adaptive extraction of the most relevant characteristics for the use in predictions. To address the problems, we proposed a sequential forecasting model for medium-and long-term energy demand forecasting based on an embedding mechanism and a twostage attention-based long-term memory neural network. An empirical study was conducted on three years of daily electricity consumption data from the residential buildings of the Pudong district of Shanghai to evaluate the model. The results show that the model can effectively extract the key features that are highly correlated with energy consumption dynamics by employing long-term dependencies in time series. In addition, the hybrid model outperforms others in terms of long-term forecasting capability. This paper also discusses future research directions and the possibilities for applying deep learning techniques in the energy sector.
Forecasting energy demand of residential buildings plays an important role in the operation of sm... more Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-distance data in energy forecasting relative to the standard regression model. However, there are still two problems to be solved for energy forecasting, including the encoding of categorical characteristics and adaptive extraction of the most relevant characteristics for the use in predictions. To address the problems, we proposed a sequential forecasting model for medium-and long-term energy demand forecasting based on an embedding mechanism and a twostage attention-based long-term memory neural network. An empirical study was conducted on three years of daily electricity consumption data from the residential buildings of the Pudong district of Shanghai to evaluate the model. The results show that the model can effectively extract the key features that are highly correlated with energy consumption dynamics by employing long-term dependencies in time series. In addition, the hybrid model outperforms others in terms of long-term forecasting capability. This paper also discusses future research directions and the possibilities for applying deep learning techniques in the energy sector.
The Job Shop Scheduling problem is critical in the manufacturing industry. At present, the decisi... more The Job Shop Scheduling problem is critical in the manufacturing industry. At present, the decision tree reasoning technique and data mining are often used in multi-objective optimization research to solve flexible job shop scheduling issues. Unfortunately, when job shop scheduling problems involve complex logic, it becomes difficult to implement data-driven automatic scheduling without human intervention. Based on the analysis of mass data and specialized knowledge in the scheduling domain, an ontology-based scheduling knowledge model and a method of knowledge representation can be established. Considering the relationship between data mining and knowledge, this paper illustrates the acquisition process of scheduling rules. These scheduling rules were applied to improve the initialization process of the artificial fish algorithm. Then, a scheduling experiment was designed, the results of which show that the efficiency and accuracy of the algorithm has been improved. The desired uncertain information analysis, decision-making support for production planning and scheduling on the shop floor are provided and an adaptive scheduling algorithm for complex manufacturing systems is established by building a knowledge-based system.
2020 XI National Conference with International Participation (ELECTRONICA), 2020
With the introduction of a new wave of IT in production and the deep convergence between informat... more With the introduction of a new wave of IT in production and the deep convergence between information technology and industry, industrial production is developing towards intellectualization and networking. Because the current production system cannot fully mine the value of technical data and the existence of information islands in the production process, this paper introduces an industrial Internet of things (IoT) platform based on 5G, OPC-UA, and other communication technologies. This paper first analyzes the basic requirements of the industrial Internet of things, introduces the architecture and detailed information flow of this platform, and finally shows the application of this platform in the preventive maintenance field. On the one hand, this paper shows the implementation of the industrial Internet of things platform; on the other hand, it provides a reference for industrial data processing.
Users may download and print one copy of any publication from the public portal for the purpose... more Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
2021 XXX International Scientific Conference Electronics (ET), 2021
CNC machine tools play a pivotal role in the manufacturing field. Due to the complexity and diver... more CNC machine tools play a pivotal role in the manufacturing field. Due to the complexity and diversity of faults, experienced engineers who can quickly locate the cause of faults are rather scarce. This paper takes expert knowledge in the field of fault diagnosis as the research object and proposes an ontology-based knowledge expression structure to improve fault retrieval efficiency and fault diagnosis accuracy; then applies the SimRank algorithm to calculate the similarity between fault phenomena and fault causes in the case base to guide the work of maintenance engineers. Finally, this paper introduces the diagnosis process of machine tool fault diagnosis expert system with Siemens 808d CNC system as a case study.
Forecasting energy demand of residential buildings plays an important role in the operation of sm... more Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-distance data in energy forecasting relative to the standard regression model. However, there are still two problems to be solved for energy forecasting, including the encoding of categorical characteristics and adaptive extraction of the most relevant characteristics for the use in predictions. To address the problems, we proposed a sequential forecasting model for medium-and long-term energy demand forecasting based on an embedding mechanism and a twostage attention-based long-term memory neural network. An empirical study was conducted on three years of daily electricity consumption data from the residential buildings of the Pudong district of Shanghai to evaluate the model. The results show that the model can effectively extract the key features that are highly correlated with energy consumption dynamics by employing long-term dependencies in time series. In addition, the hybrid model outperforms others in terms of long-term forecasting capability. This paper also discusses future research directions and the possibilities for applying deep learning techniques in the energy sector.
Forecasting energy demand of residential buildings plays an important role in the operation of sm... more Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-distance data in energy forecasting relative to the standard regression model. However, there are still two problems to be solved for energy forecasting, including the encoding of categorical characteristics and adaptive extraction of the most relevant characteristics for the use in predictions. To address the problems, we proposed a sequential forecasting model for medium-and long-term energy demand forecasting based on an embedding mechanism and a twostage attention-based long-term memory neural network. An empirical study was conducted on three years of daily electricity consumption data from the residential buildings of the Pudong district of Shanghai to evaluate the model. The results show that the model can effectively extract the key features that are highly correlated with energy consumption dynamics by employing long-term dependencies in time series. In addition, the hybrid model outperforms others in terms of long-term forecasting capability. This paper also discusses future research directions and the possibilities for applying deep learning techniques in the energy sector.
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