Power allocation in opportunistic cooperative relaying systems

To enhance the performance of opportunistic cooperative relaying systems, this paper proposes power allocation algorithms aimed at minimizing the outage probability. The optimization model of the system is constructed and proved to be a convex optimization problem. Then the optimal power allocation algorithm and its operation steps are presented. Furthermore, a simple and effective near-optimal power allocation strategy is developed, which only depends on the average channel gains of the relays and thus incurs little overhead. Simulation and numerical results show that significant performance gains can be achieved by the two proposed power allocation algorithms. __________ Translated from Acta Electronica Sinica, 2008, 36(10): 1944–1948 [译自: 电子学报]     

New Algorithm for Fault Superimposed Quantities Based on Superimposed Network

A new algorithm for fault superimposed quantity (FSIQ) is presented and analyzed. The network equations are built up by combining fault superimposed networks (FSIN) with the boundary conditions of FSIQ at the fault point and are solved with the Newton iterative method. The algorithm has clear physical meaning and does not require an intermediate procedure to derive FSIQ. The algorithm is implemented by computer programming, and the results of calculations show that the algorithm is fast and accurate. The method can be used not only to calculate FSIQ in the complex power systems with simple or multiple faults, but also to analyze and evaluate the performance of the protective relays and automatic devices based on FSIQ. Translated from Journal of Tsinghua University (Science and Technology), 2004, 44(1): 89–92 (in Chinese).     

ASAR: An ant-based service-aware routing algorithm for multimedia sensor networks

Aimed at three basic services (event-driven, data query and stream query), the paper presents a QoS routing model for multimedia sensor networks. Moreover, based on the traditional ant-based algorithm, we propose an ant-based service-aware routing (ASAR) algorithm. The ASAR chooses suitable paths to meet diverse QoS requirements from different kinds of services, thus maximizing network utilization and improving network performance. Finally, extensive simulation is conducted to verify the effectiveness of our solution and we give a detailed discussion on the effects of different system parameters. Compared to the typical routing algorithm in sensor networks and the traditional ant-based algorithm, our ASAR algorithm has better convergence and significantly provides better QoS for multiple types of services in the multimedia sensor networks. __________ Translated from Acta Electronica Sinica, 2007, 35(4): 705–711 [译自: 电子学报]     

Research on food-chain algorithm and its parameters

Based on the characteristics of colony emergence of artificial organisms, their dynamic interaction with the environment, and the food-chain crucial to the life system, the rules of local activities of artificial organisms at different levels are defined. The article proposes an artificial life-based algorithm, which is referred to as the food-chain algorithm. This algorithm optimizes computation by simulating the evolution of natural ecosystems and the information processing mechanism of natural organisms. The definition, idea and flow of the algorithm are introduced, and relevant rules on metabolic energy and change in the surroundings where artificial-life individuals live are depicted. Furthermore, key parameters of the algorithm are systematically analyzed. Test results show that the algorithm has quasi-life traits that include being autonomous, evolutionary, and self-adaptive. These traits are highly fit for optimization problems of life-like systems such as the location-allocation problem of a distribution network system. __________ Translated from Journal of Northeastern University (Natural Science), 2007, 28(7): 993–997 [译自: 东北大学学报(自然科学版)]     

Pattern synthesis of antennas based on a modified particle swarm optimization algorithm

In order to overcome the drawbacks of standard particle swarm optimization (PSO) algorithm, such as prematurity and easily trapping in local optimum, a modified PSO algorithm is proposed, in which special techniques, as global best perturbation and inertia weight jump threshold are adopted. The convergence speed and accuracy of the algorithm are improved. The test by some benchmark problems shows that the proposed algorithm achieves relatively higher performance. Thereafter, the applications of the modified PSO in the radiation pattern synthesis of antenna arrays are presented. __________ Translated from Chinese Journal of Radio Science, 2006, 21(6): 873–878 [译自: 电波科学学报]     

Optimal planning of high voltage distribution substations

Aimed at solving the problem of optimal planning for high voltage distribution substations, an efficient method is put forward. The method divides the problem into two sub-problems: source locating and combinational optimization. The algorithm of allocating and locating alternatively (ALA) is widely used to deal with the source locating problem, but it is dependent on the initial location to a large degree. Thus, some modifications were made to the ALA algorithm, which could greatly improve the quality of solutions. In addition, considering the non-convex and non-concave nature of the sub-problem of combinational optimization, the branch-and-bound technique was adopted to obtain or approximate a global optimal solution. To improve the efficiency of the branch-and-bound technique, some heuristic principles were proposed to cut those branches that may generate a global optimization solution with low probability. Examples show that the proposed algorithm meets the requirement of engineering and it is an effective approach to rapidly solve the problem of optimal planning for high voltage distribution substations. Translated from Journal of Tianjin University, 2006, 39(8): 889–894 [译自: 天津大学学报]     

A cross-layer design and ant-colony optimization based load-balancing routing protocol for ad-hoc networks

In order to periodically reassess the status of the alternate path route (APR) set and to improve the efficiency of alternate path construction existing in most current alternate path routing protocols, we present a cross-layer design and ant-colony optimization based load-balancing routing protocol for ad-hoc networks (CALRA) in this paper. In CALRA, the APR set maintained in nodes is aged and reassessed by the inherent mechanism of pheromone evaporation of ant-colony optimization algorithm, and load balance of network is achieved by ant-colony optimization combining with cross-layer synthetic optimization. The efficiency of APR set construction is improved by bidirectional and hop-by-hop routing update during routing discovery and routing maintenance process. Moreover, ants in CALRA deposit simulated pheromones as a function of multiple parameters corresponding to the information collected by each layer of each node visited, such as the distance from their source node, the congestion degree of the visited nodes, the current pheromones the nodes possess, the velocity of the nodes, and so on, and provide the information to the visiting nodes to update their pheromone tables by endowing the different parameters corresponding to different information and different weight values, which provides a new method to improve the congestion problem, the shortcut problem, the convergence rate and the heavy overheads commonly existed in existing ant-based routing protocols for ad-hoc networks. The performance of the algorithm is measured by the packet delivery rate, good-put ratio (routing overhead), and end-to-end delay. Simulation results show that CALRA performs well in decreasing the route overheads, balancing traffic load, as well as increasing the packet delivery rate, etc. Translated from Journal of ACTA Electronica Sinica, 2006, 34(7): 1199–1208 [译自: 电子学报]     

Optimal coordination of directional over-current relays using Teaching Learning-Based Optimization (TLBO) algorithm

Relay Coordination in a big distribution system with multiple meshes and bidirectional power feed becomes very Complex for protection engineers. Manual and graph theory based approaches were applied successfully in small power system. In a big distribution system linear and non-linear programming based optimizing techniques are applied for relay coordination. Presently, artificial intelligence techniques are applied for optimal co-ordination of directional overcurrent relays. This paper discusses the application of Teaching Learning Based Optimization (TLBO) algorithm for optimal coordination of DOCR relays in a looped power system. Combination of primary and backup relay is chosen by using Far vector of LINKNET structure, to avoid mis-coordination of relays. Coordination of DOCR is tested for IEEE 3, 4 and 6 bus systems using the TLBO. Also, the objective function is modified to optimize the operating time between backup and primary relays. The results are compared with the optimized values of Time dial setting and Plug setting values obtained from modified differential evolution algorithm. The proposed algorithm TLBO gives optimal coordination margin between 0.3 and 0.8 s and no miscoordination between primary and backup pairs. Results are also verified using Digsilient power factory simulation software.     

Parameter estimation for MIMO system based on MUSIC and ML methods

The frequency offset and channel gain estimation problem for multiple-input multiple-output (MIMO) systems in the case of flat-fading channels is addressed. Based on the multiple signal classification (MUSIC) and the maximum likelihood (ML) methods, a new joint estimation algorithm of frequency offsets and channel gains is proposed. The new algorithm has three steps. A subset of frequency offsets is first estimated with the MUSIC algorithm. All frequency offsets in the subset are then identified with the ML method. Finally, channel gains are calculated with the ML estimator. The algorithm is a one-dimensional search scheme and therefore greatly decreases the complexity of joint ML estimation, which is essentially a multi-dimensional search scheme. __________ Translated from Journal of Electronics and Information Technology, 2008, 30(7): 1552–1556 [译自: 电子与信息学报]     

The analysis of the convergence of ant colony optimization algorithm

The ant colony optimization algorithm has been widely studied and many important results have been obtained. Though this algorithm has been applied to many fields, the analysis about its convergence is much less, which will influence the improvement of this algorithm. Therefore, the convergence of this algorithm applied to the traveling salesman problem (TSP) was analyzed in detail. The conclusion that this algorithm will definitely converge to the optimal solution under the condition of 0<q ₀<1 was proved true. In addition, the influence on its convergence caused by the properties of the closed path, heuristic functions, the pheromone and q ₀ was analyzed. Based on the above-mentioned, some conclusions about how to improve the speed of its convergence are obtained. Translated from Control and Decision, 2006, 21(7): 763–766 [译自: 控制与决策]     

Diversity analysis of space-time-frequency coded broadband MIMO-OFDM system with correlation across space time and frequency

For the frequency selective and time variant multiple-input multiple-output (MIMO) channel model taking into account transmitting and receiving antenna correlation, the diversity of space-time-frequency coded broadband orthogonal frequency division multiplexing (MIMO-OFDM) system is analyzed. Based on the average pairwise error probability (PEP), the design criterion of space-time-frequency code (STFC) is expanded. For a given STFC, it is found that the achievable diversity order is related to the transmitter and the receiver correlation matrix as well as the time correlation and frequency correlation matrix. The maximum available diversity of STFC over the correlation channel is Lrank(P)rank(Q)rank(R _T). The space-time code and space-frequency code are special cases in our approach. Simulation results validate the findings. __________ Translated from Journal of Southeast University (Natural Science Edition), 2007, 37(4): 549–553 [译自: 东南大学学报(自然科学版)]     

Particle Swarm Optimization with Adaptive Mutation

A new adaptive mutation particle swarm optimizer, which is based on the variance of the population’s fitness, is presented in this paper. During the running time, the mutation probability for the current best particle is determined by two factors: the variance of the population’s fitness and the current optimal solution. The ability of particle swarm optimization (PSO) algorithm to break away from the local optimum is greatly improved by the mutation. The experimental results show that the new algorithm not only has great advantage of convergence property over genetic algorithm and PSO, but can also avoid the premature convergence problem effectively. Translated from “Particle Swarm Optimization with Adaptive Mutation” published in Acta Electronica Sinica, 2004, 32(3): 416–420 (in Chinese)     

CARMA-model-based j-step-ahead prediction for MIMO systems

The single-input single-output (SISO) j-step-ahead predictor for generalized predictive control (GPC) controllers was traditionally derived using the polynomial approach through the Diophantine equations. An equivalent version of the predictor in a state-space form is available in the literature. In this paper, a z-domain analysis of the multiple input multiple output (MIMO) extension of the state-space predictor is carried out, and then an MIMO j-step-ahead predictor in polynomial form based on the controlled autoregressive moving average model is derived. The predictor enables us to simplify the GPC algorithm design for multivariable systems. In the SISO case the predictor is just the traditional GPC predictor, therefore this paper gives rigorous proof of the equivalence between the traditional GPC predictor and the state-space predictor. Translated from Control and Decision, 2006, 21 (9): 1050–1053 [译自: 控制与决策]     

Multiple object detection and tracking with pseudo-particle filter

To tackle the divergence of the classical particle filter method for multiple object tracking in image sequences, a new particle filter, called pseudoparticle filter (PPF), is proposed. The PPF invokes subset particles of generic particle filters to form a continuous estimate of the posterior density function of the objects. After sampling-importance resampling (SIR), the subset particles converge to the observations. It is proved that, using an appropriate kernel function of the mean shift algorithm, we can get the subset particles of the observations and the fixed points of clustering results as the state of the objects. A multiple object data association and state estimation technique is proposed to resolve the subset particles correspondence ambiguities that arise when multiple objects are present. Experimental results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking. __________ Translated from Journal of Xidian University, 2008, 35(2): 248–253 [译自: 西安电子科技大学学报(自然科学版)]     

Research on run-time overhead of RM and improvement

The rate-monotonic (RM) algorithm is a classic fixed priority real-time scheduling algorithm. However, in most embedded real-time systems where the workload is composed of many tasks of high frequency and short execution time, the overheads from preemptions of the real-time operating system will lead to a low resource utilization rate if the RM algorithm is directly used. By studying the preemption relationship of the tasks scheduled by RM algorithm, a model of preemption overheads is established with task attributes, based on which the run-time preemption overheads of RM algorithm are reduced by optimizing the start time of the tasks in embedded real-time systems. Finally, the experimental results show the validity of the proposed strategy. __________ Translated from Journal on Communications, 2008, 29(2): 79–86 [译自: 通信学报]     

Novel P-Cycle protection strategy for asymmetric traffic in mesh optical networks

A novel prefigured-cycle (P-Cycle) algorithm was proposed to improve the protection success ratio for asymmetric traffic in mesh optical networks. The proposed algorithm was simulated in a Pan-European COST239 network environment under an asymmetric dynamic traffic model. The experimental results show that the proposed algorithm has lower capacity redundancy and higher utilization rate of configured resources than traditional P-Cycle methods. Network performance can be improved when implementing this algorithm under single-link failure circumstances. The system behavior is analyzed under different traffic volumes and the configuration strategy is also investigated for the heavyload and light-load networks respectively. __________ Translated from Journal of Beijing University of Posts and Telecommunications, 2008, 31(1): (1–4) [译自: 北京邮电大学学报]     

Application of BP neural networks in non-linearity correction of optical tweezers

The back-propagation (BP) neural network is proposed to correct nonlinearity and optimize the force measurement and calibration of an optical tweezer system. Considering the low convergence rate of the BP algorithm, the Levenberg-Marquardt (LM) algorithm is used to improve the BP network. The proposed method is experimentally studied for force calibration in a typical optical tweezer system using hydromechanics. The result shows that with the nonlinear correction using BP networks, the range of force measurement of an optical tweezer system is enlarged by 30% and the precision is also improved compared with the polynomial fitting method. It is demonstrated that nonlinear correction by the neural network method effectively improves the performance of optical tweezers without adding or changing the measuring system. __________ Translated from Optics and Precision Engineering, 2008, 16(1): 6–10 [译自: 光学精密工程]     

Selection of Embedding Dimension and Delay Time in Phase Space Reconstruction

A new algorithm is proposed for computing the embedding dimension and delay time in phase space reconstruction. It makes use of the zero of the nonbias multiple autocorrelation function of the chaotic time series to determine the time delay, which efficiently depresses the computing error caused by tracing arbitrarily the slop variation of average displacement (AD) in AD algorithm. Thereafter, by means of the iterative algorithm of multiple autocorrelation and Γ test, the near-optimum parameters of embedding dimension and delay time are estimated. This algorithm is provided with a sound theoretic basis, and its computing complexity is relatively lower and not strongly dependent on the data length. The simulated experimental results indicate that the relative error of the correlation dimension of standard chaotic time series is decreased from 4.4% when using conventional algorithm to 1.06% when using this algorithm. The accuracy of invariants in phase space reconstruction is greatly improved. Translated from Journal of Xi’an Jiaotong University, 2004, 38(4): 335–338 (in Chinese)     

Application layer multicast routing solution based on genetic algorithms

Application layer multicast routing is a multiobjective optimization problem. Three routing constraints, tree’s cost, tree’s balance and network layer load distribution are analyzed in this paper. The three fitness functions are used to evaluate a multicast tree on the three indexes respectively and one general fitness function is generated. A novel approach based on genetic algorithms is proposed. Numerical simulations show that, compared with geometrical routing rules, the proposed algorithm improve all three indexes, especially on cost and network layer load distribution indexes. __________ Translated from Control and Decision, 2006, 21(4): 381–384 [译自:控制与决策]     

Cooperated Bayesian algorithm for distributed scheduling problem

This paper presents a new distributed Bayesian optimization algorithm (BOA) to overcome the efficiency problem when solving NP scheduling problems. The proposed approach integrates BOA into the co-evolutionary schema, which builds up a concurrent computing environment. A new search strategy is also introduced for local optimization process. It integrates the reinforcement learning (RL) mechanism into the BOA search processes, and then uses the mixed probability information from BOA (post-probability) and RL (pre-probability) to enhance the cooperation between different local controllers, which improves the optimization ability of the algorithm. The experiment shows that the new algorithm does better in both optimization (2.2 %) and convergence (11.7 %), compared with classic BOA. __________ Translated from Journal of Tsinghua University (Science and Technology), 2005, 45(10): 1328–1331 (in Chinese)