Stability of multiple fans in mine ventilation networks

In large mines,single fan is usually not enough to ventilate all the working areas.Single mine-fan approach cannot be directly applied to multiple-fan networks because the present of multiple pressures and air quantities associated with each fan in the network.Accordingly,each fan in a multiple-fan system has its own mine characteristic curve,or a subsystem curve.Under some consideration,the conventional concept of a mine characteristic curve of a single-fan system can be directly extended to that of a particular fan within a multiple-fan system.In this paper the mutual effect of the fans on each other and their effect on the stability of the ventilation network were investigated by Hardy Cross algorithm combined with a switching-parameters technique.To show the validity and reliability of this algorithm,the stability of the ventilation system of Abu-Tartur Mine(one of the largest underground mine in Egypt)has been studied.

Large Thinned Array Design Based on Multi-objective Cross Entropy Algorithm

To consider multi-objective optimization problem with the number of feed array elements and sidelobe level of large antenna array, multi-objective cross entropy(CE) algorithm is proposed by combining fuzzy c-mean clustering algorithm with traditional cross entropy algorithm, and specific program flow of the algorithm is given.Using the algorithm, large thinned array(200 elements) given sidelobe level(-10,-19 and-30 d B) problem is solved successfully. Compared with the traditional statistical algorithms, the optimization results of the algorithm validate that the number of feed array elements reduces by 51%, 11% and 6% respectively. In addition, compared with the particle swarm optimization(PSO) algorithm, the number of feed array elements from the algorithm is more similar, but the algorithm is more efficient.

Social Choice Meets Graph Drawing: How to Get Subexponential Time Algorithms for Ranking and Drawing Problems

We analyze a common feature of p-Kemeny AGGregation（p-KAGG） and p-One-Sided Crossing Minimization（p-OSCM） to provide new insights and findings of interest to both the graph drawing community and the social choice community. We obtain parameterized subexponential-time algorithms for p-KAGG—a problem in social choice theory—and for p-OSCM—a problem in graph drawing. These algorithms run in time O＊（2O（√k log k））,where k is the parameter, and significantly improve the previous best algorithms with running times O.1.403k/and O.1.4656k/, respectively. We also study natural ＂above-guarantee＂ versions of these problems and show them to be fixed parameter tractable. In fact, we show that the above-guarantee versions of these problems are equivalent to a weighted variant of p-directed feedback arc set. Our results for the above-guarantee version of p-KAGG reveal an interesting contrast. We show that when the number of ＂votes＂ in the input to p-KAGG is odd the above guarantee version can still be solved in time O＊（2O（√k log k））, while if it is even then the problem cannot have a subexponential time algorithm unless the exponential time hypothesis fails（equivalently, unless FPT D M[1]）.