This paper presents a Fuzzy Preference Function-based Robust Multidisciplinary Design Optimization(FPF-RMDO) methodology. This method is an effective approach to multidisciplinary systems, which can be used to designe...This paper presents a Fuzzy Preference Function-based Robust Multidisciplinary Design Optimization(FPF-RMDO) methodology. This method is an effective approach to multidisciplinary systems, which can be used to designer experiences during the design optimization process by fuzzy preference functions. In this study, two optimizations are done for Predator MQ-1 Unmanned Aerial Vehicle(UAV):(A) deterministic optimization and(B) robust optimization. In both problems, minimization of takeoff weight and drag is considered as objective functions, which have been optimized using Non-dominated Sorting Genetic Algorithm(NSGA). In the robust design optimization, cruise altitude and velocity are considered as uncertainties that are modeled by the Monte Carlo Simulation(MCS) method. Aerodynamics, stability and control, mass properties, performance, and center of gravity are used for multidisciplinary analysis. Robust design optimization results show 46% and 42% robustness improvement for takeoff weight and cruise drag relative to optimal design respectively.展开更多
This paper discusses the definition and properties of multivalued symmetric functions, points out that a multivalued symmetric function can be decomposed according to the value of the function j. The subfunction Lj co...This paper discusses the definition and properties of multivalued symmetric functions, points out that a multivalued symmetric function can be decomposed according to the value of the function j. The subfunction Lj corresponding to j must be a symmetric function, and it may be expressed as the sum of products form of degenerated multivalued fundamental symmetric functions. Based on this consideration, the circuit realization for the multivalued symmetric functions based on full adders is proposed.展开更多
The paper consists in the use of some logical functions decomposition algorithms with application in the implementation of classical circuits like SSI, MSI and PLD. The decomposition methods use the Boolean matrix cal...The paper consists in the use of some logical functions decomposition algorithms with application in the implementation of classical circuits like SSI, MSI and PLD. The decomposition methods use the Boolean matrix calculation. It is calculated the implementation costs emphasizing the most economical solutions. One important aspect of serial decomposition is the task of selecting “best candidate” variables for the G function. Decomposition is essentially a process of substituting two or more input variables with a lesser number of new variables. This substitutes results in the reduction of the number of rows in the truth table. Hence, we look for variables which are most likely to reduce the number of rows in the truth table as a result of decomposition. Let us consider an input variable purposely avoiding all inter-relationships among the input variables. The only available parameter to evaluate its activity is the number of “l”s or “O”s that it has in the truth table. If the variable has only “1” s or “0” s, it is the “best candidate” for decomposition, as it is practically redundant.展开更多
In this paper, the authors continue the researches described in [1], that consists in a comparative study of two methods to eliminate the static hazard from logical functions, by using the form of Product of Sums (POS...In this paper, the authors continue the researches described in [1], that consists in a comparative study of two methods to eliminate the static hazard from logical functions, by using the form of Product of Sums (POS), static hazard “0”. In the first method, it used the consensus theorem to determine the cover term that is equal with the product of the two residual implicants, and in the second method it resolved a Boolean equation system. The authors observed that in the second method the digital hazard can be earlier detected. If the Boolean equation system is incompatible (doesn’t have solutions), the considered logical function doesn’t have the static 1 hazard regarding the coupled variable. Using the logical computations, this method permits to determine the needed transitions to eliminate the digital hazard.展开更多
文摘This paper presents a Fuzzy Preference Function-based Robust Multidisciplinary Design Optimization(FPF-RMDO) methodology. This method is an effective approach to multidisciplinary systems, which can be used to designer experiences during the design optimization process by fuzzy preference functions. In this study, two optimizations are done for Predator MQ-1 Unmanned Aerial Vehicle(UAV):(A) deterministic optimization and(B) robust optimization. In both problems, minimization of takeoff weight and drag is considered as objective functions, which have been optimized using Non-dominated Sorting Genetic Algorithm(NSGA). In the robust design optimization, cruise altitude and velocity are considered as uncertainties that are modeled by the Monte Carlo Simulation(MCS) method. Aerodynamics, stability and control, mass properties, performance, and center of gravity are used for multidisciplinary analysis. Robust design optimization results show 46% and 42% robustness improvement for takeoff weight and cruise drag relative to optimal design respectively.
文摘This paper discusses the definition and properties of multivalued symmetric functions, points out that a multivalued symmetric function can be decomposed according to the value of the function j. The subfunction Lj corresponding to j must be a symmetric function, and it may be expressed as the sum of products form of degenerated multivalued fundamental symmetric functions. Based on this consideration, the circuit realization for the multivalued symmetric functions based on full adders is proposed.
文摘The paper consists in the use of some logical functions decomposition algorithms with application in the implementation of classical circuits like SSI, MSI and PLD. The decomposition methods use the Boolean matrix calculation. It is calculated the implementation costs emphasizing the most economical solutions. One important aspect of serial decomposition is the task of selecting “best candidate” variables for the G function. Decomposition is essentially a process of substituting two or more input variables with a lesser number of new variables. This substitutes results in the reduction of the number of rows in the truth table. Hence, we look for variables which are most likely to reduce the number of rows in the truth table as a result of decomposition. Let us consider an input variable purposely avoiding all inter-relationships among the input variables. The only available parameter to evaluate its activity is the number of “l”s or “O”s that it has in the truth table. If the variable has only “1” s or “0” s, it is the “best candidate” for decomposition, as it is practically redundant.
文摘In this paper, the authors continue the researches described in [1], that consists in a comparative study of two methods to eliminate the static hazard from logical functions, by using the form of Product of Sums (POS), static hazard “0”. In the first method, it used the consensus theorem to determine the cover term that is equal with the product of the two residual implicants, and in the second method it resolved a Boolean equation system. The authors observed that in the second method the digital hazard can be earlier detected. If the Boolean equation system is incompatible (doesn’t have solutions), the considered logical function doesn’t have the static 1 hazard regarding the coupled variable. Using the logical computations, this method permits to determine the needed transitions to eliminate the digital hazard.
