In this paper, the feasibility and objectives coordination of real-time optimization (RTO) are systemically investigated under soft constraints. The reason for requiring soft constraints adjustment and objective relax...In this paper, the feasibility and objectives coordination of real-time optimization (RTO) are systemically investigated under soft constraints. The reason for requiring soft constraints adjustment and objective relaxation simultaneously is that the result is not satisfactory when the feasible region is apart from the desired working point or the optimization problem is infeasible. The mixed logic method is introduced to describe the priority of the constraints and objectives, thereby the soft constraints adjustment and objectives coordination are solved together in RTO. A case study on the Shell heavy oil fractionators benchmark problem illustrating the method is finally presented.展开更多
The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations.Internet of Things(IoT)based Modern fish farming systems can significantly optimi...The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations.Internet of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production.This objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding rate.This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming.The proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption problems.Fish farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted parameters.Fuzzy logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy consumption.To evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried out.The results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.展开更多
In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movemen...In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movements is presented. This method has an adaptive signal timing ability, and can make adjustments to signal timing in response to observed changes.The 'urgency degree' term, which can describe the different user's demand for green time is used in decision-making by which strategy of signal timing can be determined. Using a fuzzy logic controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. In this paper, a method based on fuzzy-neuro can be used to predict traffic parameters used in fuzzy logic controller. The feasibility of using a multi-objective genetic algorithm ( MOGA) to find a group of optimizing sets of parameters for fuzzy logic controller depending on different objects is also demonstrated. Simulation results show that the proposed methed is effecfive to adjust the signal timing in response to changing traffic conditions on a real-time basis, and the controller can produce lower vehicle delays and percentage of stopped vehicles than a traffic-actuated controller.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60474051) the Key Technology and Development Program of Shanghai Science and Technology Department (No. 04DZ11008) partly by the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20020248028).
文摘In this paper, the feasibility and objectives coordination of real-time optimization (RTO) are systemically investigated under soft constraints. The reason for requiring soft constraints adjustment and objective relaxation simultaneously is that the result is not satisfactory when the feasible region is apart from the desired working point or the optimization problem is infeasible. The mixed logic method is introduced to describe the priority of the constraints and objectives, thereby the soft constraints adjustment and objectives coordination are solved together in RTO. A case study on the Shell heavy oil fractionators benchmark problem illustrating the method is finally presented.
基金funded by the Ministry of Science,ICT CMC,202327(2019M3F2A1073387)this work was supported by the Institute for Information&communications Technology Promotion(IITP)(NO.2022-0-00980,Cooperative Intelligence Framework of Scene Perception for Autonomous IoT Device).
文摘The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations.Internet of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production.This objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding rate.This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming.The proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption problems.Fish farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted parameters.Fuzzy logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy consumption.To evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried out.The results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.
基金This project was supported by China Postdoctoral Science Foundation: "Research on Traffic Signal Control Method for Urban Intersection Based on Intelligent Techniques, 2001" .
文摘In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movements is presented. This method has an adaptive signal timing ability, and can make adjustments to signal timing in response to observed changes.The 'urgency degree' term, which can describe the different user's demand for green time is used in decision-making by which strategy of signal timing can be determined. Using a fuzzy logic controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. In this paper, a method based on fuzzy-neuro can be used to predict traffic parameters used in fuzzy logic controller. The feasibility of using a multi-objective genetic algorithm ( MOGA) to find a group of optimizing sets of parameters for fuzzy logic controller depending on different objects is also demonstrated. Simulation results show that the proposed methed is effecfive to adjust the signal timing in response to changing traffic conditions on a real-time basis, and the controller can produce lower vehicle delays and percentage of stopped vehicles than a traffic-actuated controller.
