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Search Results: 1 - 10 of 78396 matches for " Wenping CHEN "
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Resistant Mechanism and Treatment Strategy of Tyrosine Kinase Inhibitors
Jianbing QIAO,Wenping CHEN
Chinese Journal of Lung Cancer , 2011, DOI: 10.3779/j.issn.1009-3419.2011.10.07
A Novel Particle Swarm Optimization for Optimal Scheduling of Hydrothermal System  [PDF]
Wenping Chang
Energy and Power Engineering (EPE) , 2010, DOI: 10.4236/epe.2010.24033
Abstract: A fuzzy adaptive particle swarm optimization (FAPSO) is presented to determine the optimal operation of hydrothermal power system. In order to solve the shortcoming premature and easily local optimum of the standard particle swarm optimization (PSO), the fuzzy adaptive criterion is applied for inertia weight based on the evolution speed factor and square deviation of fitness for the swarm, in each iteration process, the inertia weight is dynamically changed using the fuzzy rules to adapt to nonlinear optimization process. The performance of FAPSO is demonstrated on hydrothermal system comprising 1 thermal unit and 4 hydro plants, the comparison is drawn in PSO, FAPSO and genetic algorithms (GA) in terms of the solution quality and computational efficiency. The experiment showed that the proposed approach has higher quality solutions and strong ability in global search.
Discovery of WTTS candidates in high-galactic latitude translucent molecular clouds
Jinzeng Li,Jingyao Hu,Wenping Chen
Chinese Science Bulletin , 1999, DOI: 10.1007/BF03182697
Abstract: The results of the survey of low-mass star formation in high-galactic latitude molecular clouds, especially those of the translucent category, based on the ROSAT All-Sky Survey are presented. Six new WTTS candidates have been discovered at high-galactic latitude, among which, two are found to be possibly associated with translucent molecular clouds MBM 19 and MBM 51 for projecting onto the vicinity or inner region of corresponding clouds. Further study on the 2 Li-rich X-ray active sources is needed to resolve the possibility of star formation in translucent molecular clouds.
Current Status and Prospects of Maintenance Therapy in Advanced Stage Non-small Cell Lung Cancer
Lin QUAN,Wenping CHEN,Yongqian SHU
Chinese Journal of Lung Cancer , 2010,
The layer impact of DNA translocation through graphene nanopores
Wenping Lv,Maodu Chen,Renan Wu
Physics , 2012, DOI: 10.1039/C2SM26476E
Abstract: Graphene nanopore based sensor devices are exhibiting the great potential for the detection of DNA. To understand the fundamental aspects of DNA translocating through a graphene nanopore, in this work, molecular dynamics (MD) simulations and potential of mean force (PMF) calculations were carried out to investigate the layer impact of small graphene nanopore (2 nm-3 nm) to DNA translocation. It was observed that the ionic conductance was sensitive to graphene layer of open-nanopores, the probability for DNA translocation through graphene nanopore was related with the thickness of graphene nanopores. MD simulations showed that DNA translocation time was most sensitive to the thickness of graphene nanopore for a 2.4 nm aperture, and the observed free energy barrier of PMFs and the profile change revealed the increased retardation of DNA translocation through bilayer graphene nanopore as compared to monolayer graphene nanopore.
Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm
Wenping Zou,Yunlong Zhu,Hanning Chen,Beiwei Zhang
Discrete Dynamics in Nature and Society , 2011, DOI: 10.1155/2011/569784
Abstract: Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee, and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems. 1. Introduction In the real world, many optimization problems have to deal with the simultaneous optimization of two or more objectives. In some cases, however, these objectives are in contradiction with each other. While in single-objective optimization the optimal solution is usually clearly defined, this does not hold for multiobjective optimization problems. Instead of a single optimum, there is rather a set of alternative trade-offs, generally known as Pareto optimal solutions. These solutions are optimal in the wider sense that no other solutions in the search space are superior to them when all objectives are considered. In the 1950s, in the area of operational research, a variety of methods have been developed for the solution of multiobjective optimization problems (MOP). Some of the most representative classical methods are linear programming, the weighted sum method, and the goal programming method [1]. Over the past two decades, a number of multiobjective evolutionary algorithms (MOEAs) have been proposed [2, 3]. A few of these algorithms include the nondominated sorting genetic algorithm II (NSGA-II) [4], the strength Pareto evolutionary algorithm 2 (SPEA2) [5], and the multiobjective particle swarm optimization (MOPSO) which is proposed by Coello and Lechuga [6]. MOEA’s success is due to their ability to find a set of representative Pareto optimal solutions in a single run. Artificial bee colony (ABC) algorithm is a new swarm intelligent algorithm that was first introduced by Karaboga in Erciyes University of Turkey in 2005 [7], and the performance of ABC is analyzed in 2007 [8]. The ABC algorithm imitates the behaviors of real bees in
A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm
Wenping Zou,Yunlong Zhu,Hanning Chen,Xin Sui
Discrete Dynamics in Nature and Society , 2010, DOI: 10.1155/2010/459796
Abstract: Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed. 1. Introduction Swarm Intelligence (SI) is an innovative artificial intelligence technique for solving complex optimization problems. In recently years, many SI algorithms have been proposed, such as Ant Colony Optimization (ACO) [1], Particle Swarm Algorithm (PSO) [2], and Bacterial Foraging Optimization (BFO) [3]. Artificial Bee Colony (ABC) algorithm is a new swarm intelligent algorithm that was first introduced by Karaboga in Erciyes University of Turkey in 2005 [4], and the performance of ABC is analyzed in 2007 [5]. The ABC algorithm imitates the behaviors of real bees in finding food sources and sharing the information with other bees. Since ABC algorithm is simple in concept, easy to implement, and has fewer control parameters, it has been widely used in many fields. Until now, ABC has been applied successfully to some engineering problems, such as constrained optimization problems [6], neural networks [7], and clustering [8]. However, like other stochastic optimization algorithms, such as PSO and Genetic Algorithm (GA), as the dimensionality of the search space increases, ABC algorithm possesses a poor convergence behavior. Cooperative search is one of the solutions to this problem, which has been extensively studied in the past decade. Potter proposed cooperative coevolutionary genetic algorithm (CCGA) [9], Van den Bergh and Engelbrecht proposed cooperative particle swarm optimizer, called CPSO [10], and Chen et al. proposed cooperative bacterial foraging optimization [11]. This paper applies Potter’s cooperative search technique to the
Target Q-Coverage Problem with Bounded Service Delay in Directional Sensor Networks
Deying Li,Hui Liu,Xianling Lu,Wenping Chen,Hongwei Du
International Journal of Distributed Sensor Networks , 2012, DOI: 10.1155/2012/386093
Abstract: Maximizing network lifetime is an important objective for the target-coverage problem. With practicable manufacture and cost reduction, directional sensor has been widely used in wireless sensor networks to save energy. In this paper, we address the target Q-coverage (TQC) problem to prolong the network lifetime with bounded service delay constraint in directional sensor networks. We propose a protocol to find a collection of coverage sets that satisfy the coverage quality requirement and the bounded service delay constraint, where the target in each coverage set may not be served continuously but can be served with tolerant service delay. By steering some sensors’ directional antennas, our protocol could deal with the changes of network topology or monitoring tasks. Simulation results show that the performance of our protocol is close to the upper bound of the optimal solution.
Fast-Converging Distance Vector Routing Mechanism for IP Networks
Bin Wang,Jianhui Zhang,Yunfei Guo,Wenping Chen
Journal of Networks , 2010, DOI: 10.4304/jnw.5.9.1068-1075
Abstract: In order to solve the problem of routing protocols using the Distributed Bellman-Ford (DBF) algorithm converge very slowly to the correct routes when link costs increase, and in the case when a set of link failures results in a network unavailable. We propose a new fast converge distance vector routing paradigm FC-DVRP where the goal is to reduce times of routes triggered convergence, in the meantime insure network availability. To this end, we present a suppression-failure strategy and multiple feasible backups per destination for every node. The analysis result show that FC-DVRP provide better network stability and availability, and the simulation experiments show that FC-DVRP can improve survivability of the network effectively.
Canonical Quantization of Crystal Dislocation and Electron-Dislocation Scattering in an Isotropic Medium
Mingda Li,Wenping Cui,M. S. Dresselhaus,Gang Chen
Physics , 2015,
Abstract: Crystal dislocations govern the plastic mechanical properties of materials but also affect the electrical and optical properties. However, a fundamental and quantitative quantum-mechanical theory of dislocation remains undiscovered for decades. Here by introducing a new quasiparticle "dislon", we present an exact Hamiltonian-based theory for both edge and screw dislocations in an isotropic medium, where the effective Hamiltonian of a single dislocation line can be written in a harmonic-oscillator-like form, with a closed-form quantized 1D phonon-like excitation. Moreover a closed-form, position-dependent electron-dislocation coupling strength is obtained, from which we compute the electron self-energy and relaxation time which can be reduced to well-known classical results. This framework opens up vast possibilities to study the effect of dislocations on other materials' non-mechanical properties consistently.
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