In Multiple-Input Multiple-Out （MIMO） systems, the user selection algorithm plays an important role in the realization of multiplexing gain. In this paper, an improved Semi-orthogonal User Selection algorithm based ...In Multiple-Input Multiple-Out （MIMO） systems, the user selection algorithm plays an important role in the realization of multiplexing gain. In this paper, an improved Semi-orthogonal User Selection algorithm based on condition number is proposed. Besides, a new MIMO pre- coding scheme is designed. The proposed SUS- CN （SUS with condition number） algorithm outperforms the SUS algorithm for the selection of users with better matrix inversion property, thus a higher information rate for selected user pair is achieved. The designed MIMO precoding matrix brings benefits of the power equality at transmitted terminals, the limited dynamic range of the power over time, and a better power efficiency. The simulation results give the key insights into the im- pact of the different condition number value and users on the sum-rate capacity.展开更多
Pockets in proteins have been known to be very important for the life process. There have been several studies in the past to automatically extract the pockets from the structure information of known proteins. However...Pockets in proteins have been known to be very important for the life process. There have been several studies in the past to automatically extract the pockets from the structure information of known proteins. However, it is difficult to find a study comparing the precision of the extracted pockets from known pockets on the protein. In this paper, we propose an algorithm for extracting pockets from structure data of proteins and analyze the quality of the algorithm by comparing the extracted pockets with some known pockets. These results in this paper can be used to set the parameter values of the pocket extraction algorithm for getting better results.展开更多
Binary particle swarm optimization algorithm(BPSOA) has the excellent characters such as easy to implement and few set parameters.But it is tendentious to stick in the local optimal solutions and has slow convergence ...Binary particle swarm optimization algorithm(BPSOA) has the excellent characters such as easy to implement and few set parameters.But it is tendentious to stick in the local optimal solutions and has slow convergence rate when the problem is complex.Cultural algorithm(CA) can exploit knowledge extracted during the search to improve the performance of an evolutionary algorithm and show higher intelligence in treating complicated problems.So it is proposed that integrating binary particle swarm algorithm into cultural algorithm frame to develop a more efficient cultural binary particle swarm algorithm (CBPSOA) for fault feature selection.In CBPSOA,BPSOA is used as the population space of CA;the evolution of belief space adopts crossover,mutation and selection operations;the designs of acceptance function and influence function are improved according to the evolution character of BPSOA.The tests of optimizing functions show the proposed algorithm is valid and effective.Finally,CBPSOA is applied for fault feature selection.The simulations on Tennessee Eastman process (TEP) show the CBPSOA can perform better and more quickly converge than initial BPSOA.And with fault feature selection,more satisfied performance of fault diagnosis is obtained.展开更多
基金This paper was supported by the National Natural Science Foundation of China under Grant No.61390513 and 61201225,and National Science and Technology Major Project of China under Grant No.2013ZX03003004,the Natural Science Foundation of Shanghai under Grant No.12ZR1450800,and sponsored by Shanghai Pujiang Program under Grant No.13PJD030.It was also supported by the Fundamental Research Funds for the Central Universities under Grant No.20140767,the Program for Young Excellent Talents in Tongji University under Grant No.2013KJ007,and 'Chen Guang' project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant No.13CG18
文摘In Multiple-Input Multiple-Out （MIMO） systems, the user selection algorithm plays an important role in the realization of multiplexing gain. In this paper, an improved Semi-orthogonal User Selection algorithm based on condition number is proposed. Besides, a new MIMO pre- coding scheme is designed. The proposed SUS- CN （SUS with condition number） algorithm outperforms the SUS algorithm for the selection of users with better matrix inversion property, thus a higher information rate for selected user pair is achieved. The designed MIMO precoding matrix brings benefits of the power equality at transmitted terminals, the limited dynamic range of the power over time, and a better power efficiency. The simulation results give the key insights into the im- pact of the different condition number value and users on the sum-rate capacity.
基金Project supported by Creative Research Initiative from the Ministry of Science and Technology (MOST), Korea. BHAK Jonghwa is supported by Biogreen21 Fund and MOST Funds, Korea
文摘Pockets in proteins have been known to be very important for the life process. There have been several studies in the past to automatically extract the pockets from the structure information of known proteins. However, it is difficult to find a study comparing the precision of the extracted pockets from known pockets on the protein. In this paper, we propose an algorithm for extracting pockets from structure data of proteins and analyze the quality of the algorithm by comparing the extracted pockets with some known pockets. These results in this paper can be used to set the parameter values of the pocket extraction algorithm for getting better results.
基金National High Technology Research and Development Program of China(No.2007AA04Z171)
文摘Binary particle swarm optimization algorithm(BPSOA) has the excellent characters such as easy to implement and few set parameters.But it is tendentious to stick in the local optimal solutions and has slow convergence rate when the problem is complex.Cultural algorithm(CA) can exploit knowledge extracted during the search to improve the performance of an evolutionary algorithm and show higher intelligence in treating complicated problems.So it is proposed that integrating binary particle swarm algorithm into cultural algorithm frame to develop a more efficient cultural binary particle swarm algorithm (CBPSOA) for fault feature selection.In CBPSOA,BPSOA is used as the population space of CA;the evolution of belief space adopts crossover,mutation and selection operations;the designs of acceptance function and influence function are improved according to the evolution character of BPSOA.The tests of optimizing functions show the proposed algorithm is valid and effective.Finally,CBPSOA is applied for fault feature selection.The simulations on Tennessee Eastman process (TEP) show the CBPSOA can perform better and more quickly converge than initial BPSOA.And with fault feature selection,more satisfied performance of fault diagnosis is obtained.
