Binary Decision Diagrams (BDDs) can be graphically manipulated to reduce the number of nodes and hence the area. In this context, ordering of BDDs play a major role. Most of the algorithms for input variable ordering ...Binary Decision Diagrams (BDDs) can be graphically manipulated to reduce the number of nodes and hence the area. In this context, ordering of BDDs play a major role. Most of the algorithms for input variable ordering of OBDD focus primarily on area minimization. However, suitable input variable ordering helps in minimizing the power consumption also. In this particular work, we have proposed two algorithms namely, a genetic algorithm based technique and a branch and bound algorithm to find an optimal input variable order. Of course, the node reordering is taken care of by the standard BDD package buddy-2.4. Moreover, we have evaluated the performances of the proposed algorithms by running an exhaustive search program. Experi-mental results show a substantial saving in area and power. We have also compared our techniques with other state-of-art techniques of variable ordering for OBDDs and found to give superior results.展开更多
Based on the principle of “pre-disaster prevention outweighs rescue during disasters”, this study targets areas threatened by natural disasters, and develops an automatic algorithm based on the Prim algorithm to ser...Based on the principle of “pre-disaster prevention outweighs rescue during disasters”, this study targets areas threatened by natural disasters, and develops an automatic algorithm based on the Prim algorithm to serve as an automatic identification system. In the face of natural disasters that disable key facilities in the region and prevent settlements from contacting the outside world or outsiders from sending rescuers to the settlements, the proposed system helps to identify whether these regions will become isolated areas and conduct disaster mitigation and relief resource allocation before any natural disaster in order to reduce potential disaster losses. An automatic identification system, based on the threshold of channel blocking due to broken roads and bridges, determines through the decision tree model and relevant patterns whether such regions will become isolated areas by identifying areas based on the results of model analysis. The proposed system’s identification results are verified by actual case histories and comparisons;the results can be used to correctly identify isolated areas. Finally, Microsoft Visual Studio C # and Google Map are employed to apply the results and to produce an information mode for the determination and decision support of isolated areas affected by natural disasters.展开更多
Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective funct...Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.展开更多
文摘Binary Decision Diagrams (BDDs) can be graphically manipulated to reduce the number of nodes and hence the area. In this context, ordering of BDDs play a major role. Most of the algorithms for input variable ordering of OBDD focus primarily on area minimization. However, suitable input variable ordering helps in minimizing the power consumption also. In this particular work, we have proposed two algorithms namely, a genetic algorithm based technique and a branch and bound algorithm to find an optimal input variable order. Of course, the node reordering is taken care of by the standard BDD package buddy-2.4. Moreover, we have evaluated the performances of the proposed algorithms by running an exhaustive search program. Experi-mental results show a substantial saving in area and power. We have also compared our techniques with other state-of-art techniques of variable ordering for OBDDs and found to give superior results.
文摘Based on the principle of “pre-disaster prevention outweighs rescue during disasters”, this study targets areas threatened by natural disasters, and develops an automatic algorithm based on the Prim algorithm to serve as an automatic identification system. In the face of natural disasters that disable key facilities in the region and prevent settlements from contacting the outside world or outsiders from sending rescuers to the settlements, the proposed system helps to identify whether these regions will become isolated areas and conduct disaster mitigation and relief resource allocation before any natural disaster in order to reduce potential disaster losses. An automatic identification system, based on the threshold of channel blocking due to broken roads and bridges, determines through the decision tree model and relevant patterns whether such regions will become isolated areas by identifying areas based on the results of model analysis. The proposed system’s identification results are verified by actual case histories and comparisons;the results can be used to correctly identify isolated areas. Finally, Microsoft Visual Studio C # and Google Map are employed to apply the results and to produce an information mode for the determination and decision support of isolated areas affected by natural disasters.
基金supported by the the Youth Science and Technology Innovation Fund (Science)(Nos.NS2014070, NS2014070)
文摘Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.
