感性工学在产品配色设计中的应用研究

在感性工学的研究领域中,类神经网络被认为是颇具潜力的分析工具。为了降低产品配色的意象偏差,基于配色理论设计问卷,配合类神经遗传模拟消费者的意象评价,提供符合产品形态的配色建议,并在此基础上提出了计算机辅助配色设计系统的框架和实现方法。

FLIGHT CONFLICT DETECTION AND RESOLUTION

The air traffic control (ATC) systems are facing more and more serious congestive because of the increasing of air traffic flow in China. One of the most available ways to solve the problem is 'free flight' that the pilots may choose the air route and flight speed suitable for them. But this will lead to the difficulties for the controllers. This paper presents how ATC genetic algorithms can be used to detect and to solve air traffic control conflicts in free flight. And it also shows that this algorithm perfectly suits for solving flight conflicts resolution because of its short computing time.

Research on a non-linear chaotic prediction model for urban traffic flow

In order to solve serious urban transport problems, according to the proved chaotic characteristic of traffic flow, a non linear chaotic model to analyze the time series of traffic flow is proposed. This model reconstructs the time series of traffic flow in the phase space firstly, and the correlative information in the traffic flow is extracted richly, on the basis of it, a predicted equation for the reconstructed information is established by using chaotic theory, and for the purpose of obtaining the optimal predicted results, recognition and optimization to the model parameters are done by using genetic algorithm. Practical prediction research of urban traffic flow shows that this model has famous predicted precision, and it can provide exact reference for urban traffic programming and control.

Fuzzy traffic signal control with DNA evolutionary algorithm

In order to optimize the signal control system, this paper proposes a method to design an optimized fuzzy logic controller （FLC） with the DNA evolutionary algorithm. Inspired by the DNA molecular operation characteristics, the DNA evolutionary algorithm modifies the corresponding genetic operators. Compared with the traditional genetic algorithm （GA）, the DNA evolutionary algorithm can overcome weak local search capability and premature convergence. The parameters of membership functions are optimized by adopting the quaternary encoding method and performing corresponding DNA genetic operators. The relevant optimized parameters are combined with the FLC for single intersection traffic signal control. Simulation experiments shows the better performance of the FLC with the DNA evolutionary algorithm optimization. The experimental results demonstrate the efficiency of the nrotmsed method.

Fault Diagnosis Approach of Local Ventilation System in Coal Mines Based on Multidisciplinary Technology

In order to reduce the probability of fault occurrence of local ventilation system in coal mine and prevent gas from exceeding the standard limit, an approach incorporating the reliability analysis, rough set theory, genetic algorithm (GA), and intelligent decision support system (IDSS) was used to establish and develop a fault diagnosis system of local ventilation in coal mine. Fault tree model was established and its reliability analysis was performed. The algorithms and software of key fault symptom and fault diagnosis rule acquiring were also analyzed and developed. Finally, a prototype system was developed and demonstrated by a mine instance. The research results indicate that the proposed approach in this paper can accurately and quickly find the fault reason in a local ventilation system of coal mines and can reduce difficulty of the fault diagnosis of the local ventilation system, which is significant to decrease gas exploding accidents in coal mines.

Genetic Algorithm-Based Redundancy Optimization Method for Smart Grid Communication Network

This paper proposes a redundancy optimization method for smart grid Advanced Metering Infrastructure(AMI) to realize economy and reliability targets.AMI is a crucial part of the smart grid to measure,collect,and analyze data about energy usage and power quality from customer premises.From the communication perspective,the AMI consists of smart meters,Home Area Network(HAN) gateways and data concentrators;in particular,the redundancy optimization problem focus on deciding which data concentrator needs redundancy.In order to solve the problem,we first develop a quantitative analysis model for the network economic loss caused by the data concentrator failures.Then,we establish a complete redundancy optimization model,which comprehensively consider the factors of reliability and economy.Finally,an advanced redundancy deployment method based on genetic algorithm(GA) is developed to solve the proposed problem.The simulation results testify that the proposed redundancy optimization method is capable to build a reliable and economic smart grid communication network.

Adaptive genetic algorithm for path planning of loosely coordinated multi-robot manipulators

Adaptive genetic algorithm A SA GA, a novel algorithm, which can dynamically modify the parameters of Genetic Algorithms in terms of simulated annealing mechanism, is proposed for path planning of loosely coordinated multi robot manipulators. Over the task space of a multi robot, a strategy of decoupled planning is also applied to the evolutionary process, which enables a multi robot to avoid falling into deadlock and calculating of composite C space. Finally, two representative tests are given to validate A SA GA and the strategy of decoupled planning.

Neural network and genetic algorithm based global path planning in a static environment

Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environmental information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective.

THE NOVEL COMMUNICATION ALGORITHM AND THE THROUGHPUT ANALYSIS FOR WIRELESS SENSOR NETWORKS

Aiming at the significance of the energy controls of wireless sensor networks, an economical energy consumption algorithm for wireless communicating in Wireless Sensor Networks (WSN) is presented. Based on the algorithm, the maximal system throughput of WSN is analyzed, and the upper bound of throughput of WSN is proposed and proved. Some numerical simulations are conducted and analyzed. The conclusions include that the transmitting radius of sensor node and the parameters of the energy cost function have significant influence upon the throughput, but the monitoring region radius has little influence. For the same transmitting distance, the more the hopping of information trans- mitting, the better the throughput of WSN. On the other hand, for the energy optimization of the whole WSN, the trade-off problem between the throughput capacity and the relay nodes is proposed, and the specific expression of relay hops that minimized the energy consumptions and the maximal throughput of WSN under the specific situation is derived.

Multi-path planning algorithm based on fitness sharing and species evolution

A new algorithm is proposed for underwater vehicles multi-path planning. This algorithm is based on fitness sharing genetic algorithm, clustering and evolution of multiple populations, which can keep the diversity of the solution path, and decrease the operating time because of the independent evolution of each subpopulation. The multi-path planning algorithm is demonstrated by a number of two-dimensional path planning problems. The results show that the multi-path planning algorithm has the following characteristics: high searching capability, rapid convergence and high reliability.

Simulation of Flux Distribution in Central Metabolism of Saccharo-myces cerevisiae by Hybridized Genetic Algorithm

A scheme of investigating the intracellular metabolic fluxes in central metabolism of Saccharomyces cerevisiae based on isotope model and tracer experiment was developed. The metabolic model applied in this study includes the Embden-Meyerhof-Parnas pathway,the pentose phosphate pathway,the tricarboxylic acid cycle,CO2 anaplerotic reactions,ethanol and acetate formation,and pathways involved in amino acid synthesis. The approach of hybridized genetic algorithm combined with the sequential simplex technique was used to optimize a quadratic error function without the requirement of the information on the partial derivatives. The impact of some key pa-rameters on the algorithm was studied. This approach was proved to be rapid and numerically stable in the analysis of the central metabolism of S.cerevisiae.

A Joint Source and Channel Video Coding Algorithm for H.223 Based Wireless Channel

Robust video streaming through high error prone wireless channel has attracted much attention. In this paper the authors introduce an effective algorithm by joining the Unequal Error Protection ability of the channel multiplexing protocol H.223 Annex D, and the new H.263++ Annex V Data Partition together. Based on the optimal trade off of these two technologies, the Joint Source and Channel Coding algorithm can result in stronger error resilience. The simulation results have shown its superiority against separate coding mode and some Unequal Error Protection mode under recommended wireless channel error patterns.

Bus coordination holding control for transit hubs under APTS

To increase the passenger transferring efficiency, the bus coordination holding control for transit hubs, which is as an important dynamic dispatching method for improving the service level of transit hubs, was studied in the framework of bus coordination dispatching mode. Firstly, the bus coordination holding control flow was studied based on Advanced Public Transportation Systems (APTS) environment. Then a control model was presented to optimize the bus vehicle holding time, and a genetic algorithm was designed as the solving method. In the end, an example was given to illustrate the effectiveness of the control strategy and the algorithm.

On Multi-Frequency Channel Interference Alignment of 3 Channels and 4 Users

This paper explores the multi-frequency independent channel interference alignment(MFC-IA) system of 3 channels and4 users,and single data stream transmit,i.e.(3×3,1)~4 system.We derive the analytic solution for(3×3,1)~4 MFC-IA system.Based on the analytic solution,an optimization problem is proposed aim at the optimal IA solution.Then based on such a math model,we propose a simulated annealing(SA) algorithm to search optimal IA solution.The simulation results show that the simulated annealing IA algorithm has a better sum rate performance than iterative maximize signal to interference plus noise ratio(Max-SINR) algorithm.This result can be extended to single data stream multi-antenna IA system with 3 antennas and4 users.

Hybrid genetic algorithm for the optimization of mine ventilation network

Used genetic algorithm (GA) to optimize the network of ventilation in order toavoid artificial convergence and speed up the convergence rate to introduce the Powellalgorithm. The Powell algorithm had been integrated into GA. Powell had the effectivecapacity of solving the local optimal solution. Powell and the cross as a method ofchoice, a variation of the parallel operator, can be a better solution to the prematureconvergence of the GA problem. The two methods will be improved to make it an effective combination of hybrid GA called hybrid genetic algorithm (HGA) for the introductionof mine ventilation network optimization and to be used to solve the problem of regulating mine optimization.

Multi-variable Optimization of HVAC System Using a Genetic Algorithm

Geothermal is a fast-growing alternative heat source for HVAC systems, however, the initial cost of using a ground source HVAC system is higher compared to an air source system. Studies about system design and operation are necessary to reduce the initial cost and ensure that the ground source heat pump system has high efficiency, resulting in a lower total life-time cost. In this study, a multi-variable evolutionary computation algorithm is proposed for generating optimal parameters for a geothermal source HVAC system. The system was modeled and simulated using MATLAB. The design parameters were calculated by minimizing the energy consumption, Based on an experimental building, a case study was presented. Using this model, the optimal set points were calculated and used as a designed system. Energy consumption of this system was reduced by about 10% compared to the system operated with a fixed supply cold water temperature （7 ℃）.

Numerical Optimization of Cooling Process of the Gas Turbine Blade

The paper presents a scheme of optimization of the cooling process of the gas turbine blade. As an optimization criterion has been taken into account on the outer surface temperature of the blade. Inverse problem is solved for stationary heat conduction in which beside the optimization criterion of the heat transfer coefficient on the outer surface of the blade the temperature distribution is known, and the values sought are the heat transfer coefficients and surface temperature of the cooling channels. This problem was solved by the boundary element method using SVD algorithm and Tikhonov regularization. The temperature and heat transfer coefficient of cooling channels obtained from the inverse problem was oscillating in nature. This solution is nonphysical, so the heat transfer coefficients on the surface of cooling channels were averaged. Then the problem was solved simply with averaged coefficients of heat transfer on the surface of the cooling channels and the known distribution on the outer surface of blade. The temperature distribution obtained from the solution of direct problem with averaged values of heat transfer coefficient was compared with the criterion of optimization.The calculation results obtained using the SVD algorithm gave the temperature distribution on the external wall of the blade closer to the criterion of optimization.

Virtual speed sensors based algorithm for expressway traffic state estimation

The accurate estimation of expressway traffic state can provide decision-making for both travelers and traffic managers. The speed is one of the most representative parameter of the traffic state. So the expressway speed spatial distribution can be taken as the expressway traffic state equivalent. In this paper, an algorithm based on virtual speed sensors (VSS) is presented to estimate the expressway traffic state (the speed spatial distribution). To gain the spatial distribution of expressway traffic state, virtual speed sensors are defined between adjacent traffic flow sensors. Then, the speed data extracted from traffic flow sensors in time series are mapped to space series to design virtual speed sensors. Then the speed of virtual speed sensors can be calculated with the weight matrix which is related with the speed of virtual speed sensors and the speed data extracted from traffic flow sensors and the speed data extracted from traffic flow sensors in time series. Finally, the expressway traffic state (the speed spatial distribution) can be gained. The acquisition of average travel speed of the expressway is taken for application of this traffic state estimation algorithm. One typical expressway in Beijing is adopted for the experiment analysis. The results prove that this traffic state estimation approach based on VSS is feasible and can achieve a high accuracy.