网络模化在高层建筑火灾通风中的应用

根据建筑火灾防灭火的需要， 针对火灾条件下烟气流动及室内温度分布问题， 将矿井网络模化成熟的思想和方法推广到高层建筑火灾模化， 提出了高层建筑火灾网络模化数学模型及网络简化方法， 并进行实例编程计算， 计算结果表明， 提出的模型及方法应用于高层建筑是可行的。

Gated Neural Network-Based Unsteady Aerodynamic Modeling for Large Angles of Attack

Modeling of unsteady aerodynamic loads at high angles of attack using a small amount of experimental or simulation data to construct predictive models for unknown states can greatly improve the efficiency of aircraft unsteady aerodynamic design and flight dynamics analysis.In this paper,aiming at the problems of poor generalization of traditional aerodynamic models and intelligent models,an intelligent aerodynamic modeling method based on gated neural units is proposed.The time memory characteristics of the gated neural unit is fully utilized,thus the nonlinear flow field characterization ability of the learning and training process is enhanced,and the generalization ability of the whole prediction model is improved.The prediction and verification of the model are carried out under the maneuvering flight condition of NACA0015 airfoil.The results show that the model has good adaptability.In the interpolation prediction,the maximum prediction error of the lift and drag coefficients and the moment coefficient does not exceed 10%,which can basically represent the variation characteristics of the entire flow field.In the construction of extrapolation models,the training model based on the strong nonlinear data has good accuracy for weak nonlinear prediction.Furthermore,the error is larger,even exceeding 20%,which indicates that the extrapolation and generalization capabilities need to be further optimized by integrating physical models.Compared with the conventional state space equation model,the proposed method can improve the extrapolation accuracy and efficiency by 78%and 60%,respectively,which demonstrates the applied potential of this method in aerodynamic modeling.

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

火山岩气藏裂缝地质建模在徐深气田的应用

徐深气田xsX区块属构造岩性气藏,储层复杂、非均质性强。以xsX区块为研究对象,在岩芯、成像测井、地震等数据的基础上,基于离散化裂缝网格建模技术(DFN)建立xsX区块双重介质地质模型,利用测井、试井资料检验了离散化网络模型准确性,验证结果显示模型符合气藏的地质和生产特征。通过分析裂缝孔隙度、裂缝渗透率等参数研究了xsX区块的储层裂缝分布特征,发现无论是平面上还是垂向上都存在非常强非均质性。该模型为区块数值模拟的开展奠定基础,为气藏调整挖潜提供技术支撑。

Combining the genetic algorithms with artificial neural networks for optimization of board allocating

This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.

A New Hybrid Method for Constrained Global Optimization

By combining properly the simulated annealing algorithm and the nonlinear programming neural network, a new hybrid method for comtrained global optimization is proposed in this paper. To maintain the applicability of the simulated annealing algorithm used in the hybrid method as general as possible, the nonlinear programming neural network is employed at each iteration to find only a feasible solution to the original constrained problem rather than a local optimal solution. Such a feasible solution is obtained by solving an auxiliary optimization problem with a new objective function. The computational results for two numerical examples indicate that the proposed hybrid method for constrained global optimization is not only highly reliable but also much more effcient than the simulated annealing algorithm using the penalty function method to deal with the constraints.

TWO-DIMENSIONAL STOCHASTIC AIRFOIL OPTIMIZATION DESIGN METHOD BASED ON NEURAL NETWORKS

To avoid the aerodynamic performance loss of airfoil at non-design state which often appears in single point design optimization, and to improve the adaptability to the uncertain factors in actual flight environment, a two-dimensional stochastic airfoil optimization design method based on neural networks is presented. To provide highly efficient and credible analysis, four BP neural networks are built as surrogate models to predict the airfoil aerodynamic coefficients and geometry parameter. These networks are combined with the probability density function obeying normal distribution and the genetic algorithm, thus forming an optimization design method. Using the method, for GA（W）-2 airfoil, a stochastic optimization is implemented in a two-dimensional flight area about Mach number and angle of attack. Compared with original airfoil and single point optimization design airfoil, results show that the two-dimensional stochastic method can improve the performance in a specific flight area, and increase the airfoil adaptability to the stochastic changes of multiple flight parameters.

Integrated logistics facilities network design for 3PLS under uncertainty

According to the operational characteristics of the logistics networks for the third party logistics supplier （3PLS）, the forward and reverse logistics networks together for 3PLS under the uncertain environment are designed. First, a fuzzy model is proposed by taking multiple customers, multiple commodities, capacitated facility location and integrated logistics facility layout into account. In the model, the fuzzy customer demands and transportation rates are illustrated by triangular fuzzy numbers. Secondly, the fuzzy model is converted into a crisp model by applying fuzzy chance constrained theory and possibility theory, and one hybrid genetic algorithm is designed for the crisp model. Finally, two different examples are designed to illustrate that the model and solution discussed are valid.

Artificial neural network modeling of gold dissolution in cyanide media

The effects of cyanidation conditions on gold dissolution were studied by artificial neural network （ANN） modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid percentage, P50 of particle, NaCN content in cyanide media, temperature of solution and pH value were used. For selecting the best model, the outputs of models were compared with measured data. A fourth-layer ANN is found to be optimum with architecture of twenty, fifteen, ten and five neurons in the first, second, third and fourth hidden layers, respectively, and one neuron in output layer. The results of artificial neural network show that the square correlation coefficients （R2） of training, testing and validating data achieve 0.999 1, 0.996 4 and 0.9981, respectively. Sensitivity analysis shows that the highest and lowest effects on the gold dissolution rise from time and pH, respectively It is verified that the predicted values of ANN coincide well with the experimental results.

Allocation optimization of bicycle-sharing stations at scenic spots

Bicycle-sharing system is considered as a green option to provide a better connection between scenic spots and nearby metro/bus stations. Allocating and optimizing the layout of bicycle-sharing system inside the scenic spot and around its influencing area are focused on. It is found that the terrain, land use, nearby transport network and scenery point distribution have significant impact on the allocation of bicycle-sharing system. While the candidate bicycle-sharing stations installed at the inner scenic points, entrances/exits and metro stations are fixed, the ones installed at bus-stations and other passenger concentration buildings are adjustable. Aiming at minimizing the total cycling distance and overlapping rate, an optimization model is proposed and solved based on the idea of cluster concept and greedy heuristic. A revealed preference/stated preference （RP/SP） combined survey was conducted at Xuanwu Lake in Nanjing, China, to get an insight into the touring trip characteristics and bicycle-sharing tendency. The results reveal that 39.81% visitors accept a cycling distance of 1-3 km and 62.50% respondents think that the bicycle-sharing system should charge an appropriate fee. The sttrvey indicates that there is high possibility to carry out a bicycle-sharing system at Xuanwu Lake. Optimizing the allocation problem cluster by cluster rather than using an exhaustive search method significantly reduces the computing amount from O（2^43） to O（43 2）. The 500 m-radius-coverage rate for the alternative optimized by 500 m-radius-cluster and 800 m-radius-cluster is 89.2% and 68.5%, respectively. The final layout scheme will provide decision makers engineering guidelines and theoretical support.

Short-term traffic flow prediction with PSR-XGBoostconsidering chaotic characteristics

To improve the level of active traffic management,a short-term traffic flow prediction model is proposed by combining phase space reconstruction(PSR)and extreme gradient boosting(XGBoost)algorithms.Firstly,the traditional data preprocessing method is improved.The new method uses hierarchical clustering to determine the traffic flow state and fills in missing and abnormal data according to different traffic flow states.Secondly,one-dimensional data are mapped into a multidimensional data matrix through PSR,and the time series complex network is used to verify the data reconstruction effect.Finally,the multidimensional data matrix is inputted into the XGBoost model to predict future traffic flow parameters.The experimental results show that the mean square error,average absolute error,and average absolute percentage error of the prediction results of the PSR-XGBoost model are 5.399%,1.632%,and 6.278%,respectively,and the required running time is 17.35 s.Compared with mathematical-statistical models and other machine learning models,the PSR-XGBoost model has clear advantages in multiple predictive indicators,proving its feasibility and superiority in short-term traffic flow prediction.

Modeling and linearizing broad-band power amplifier based on real and complex-valued hybrid time-delay neural network

A new real and complex-valued hybrid time-delay neural network(TDNN)is proposed for modeling and linearizing the broad-band power amplifier(BPA).The neural network includes the generalized memory effect of input signals,complex-valued input signals and the fractional order of a complex-valued input signal module,and,thus,the modeling accuracy is improved significantly.A comparative study of the normalized mean square error(NMSE)of the real and complex-valued hybrid TDNN for different spread constants,memory depths,node numbers,and order numbers is studied so as to establish an optimal TDNN as an effective baseband model,suitable for modeling strong nonlinearity of the BPA.A 51-dBm BPA with a 25-MHz bandwidth mixed test signal is used to verify the effectiveness of the proposed model.Compared with the memory polynomial(MP)model and the real-valued TDNN,the real and complex-valued hybrid TDNN is highly effective,leading to an improvement of 5 dB in the NMSE.In addition,the real and complex-valued hybrid TDNN has an improvement of 0.6 dB over the generalized MP model in the NMSE.Also,it has better numerical stability.Moreover,the proposed TDNN presents a significant improvement over the real-valued TDNN and the MP models in suppressing out-of-band spectral regrowth.

Transition Features from Simplicity-Universality to Complexity-Diversification Under UHNTF

A large unified hybrid network model with a variable speed growth （LUHNM-VSG） is proposed as third model of the unified hybrid network theoretical framework （UHNTF）. A hybrid growth ratio vg of deterministic linking number to random linking number and variable speed growth index a are introduced in it. The main effects of vg and a on topological transition features of the LUHNM-VSC are revealed. For comparison with the other models, we construct a type of the network complexity pyramid with seven levels, in which from the bottom level-1 to the top level-7 of the pyramid simplicity-universality is increasing but complexity-diversity is decreasing. The transition relations between them depend on matching of four hybrid ratios （dr, fd, gr, vg）. Thus the most of network models can be investigated in the unification way via four hybrid ratios （dr, fd, gr, vg）. The LUHNM-VSG as the level-1 of the pyramid is much better and closer to description of real-world networks as well as has potential application.

Prediction of Process Trends Based on Neural Networks

In order to catch more process details in chemical processes, adynamic model for prediction of process trends is proposed bymodifying traditional time-series ANN (artificial neural networks)model with impulse response identification means. The applicationresults of the model is briefly discussed.

Optimization of Fermentation Media for Enhancing Nitrite-oxidizing Activity by Artificial Neural Network Coupling Genetic Algorithm

Two artificial intelligence techniques, artificial neural network and genetic algorithm, were applied to optimize the fermentation medium for improving the nitrite oxidization rate of nitrite oxidizing bacteria. Experiments were conducted with the composition of medium components obtained by genetic algorithm, and the experimental data were used to build a BP (back propagation) neural network model. The concentrations of six medium components were used as input vectors, and the nitrite oxidization rate was used as output vector of the model. The BP neural network model was used as the objective function of genetic algorithm to find the optimum medium composition for the maximum nitrite oxidization rate. The maximum nitrite oxidization rate was 0.952 g 2 NO-2-N·(g MLSS)-1·d-1 , obtained at the genetic algorithm optimized concentration of medium components (g·L-1 ): NaCl 0.58, MgSO 4 ·7H 2 O 0.14, FeSO 4 ·7H 2 O 0.141, KH 2 PO 4 0.8485, NaNO 2 2.52, and NaHCO 3 3.613. Validation experiments suggest that the experimental results are consistent with the best result predicted by the model. A scale-up experiment shows that the nitrite degraded completely after 34 h when cultured in the optimum medium, which is 10 h less than that cultured in the initial medium.

Soft measurement model of ring's dimensions for vertical hot ring rolling process using neural networks optimized by genetic algorithm

Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ring's position is asymmetrical.All of these cause that the ring's dimensions cannot be measured directly.Through analyzing the relationships among the dimensions of ring blanks,the positions of rolls and the ring's inner and outer diameter,the soft measurement model of ring's dimensions is established based on the radial basis function neural network(RBFNN).A mass of data samples are obtained from VHRR finite element(FE) simulations to train and test the soft measurement NN model,and the model's structure parameters are deduced and optimized by genetic algorithm(GA).Finally,the soft measurement system of ring's dimensions is established and validated by the VHRR experiments.The ring's dimensions were measured artificially and calculated by the soft measurement NN model.The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data.In addition,the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model.The research results suggest that the soft measurement NN model has high precision and flexibility.The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process.

An analytical method to calculate station evacuation capacity

The major objective of this work was to calculate evacuation capacity and solve the optimal routing problem in a given station topology from a network optimization perspective where station facilities were modelled as open finite queueing networks with a multi-objective set of performance measures. The optimal routing problem was determined so that the number of evacuation passengers was maximized while the service level was higher than a certain criterion. An analytical technique for modelling open finite queueing networks, called the iteration generalized expansion method(IGEM), was utilized to calculate the desired outputs. A differential evolution algorithm was presented for determining the optimal routes. As demonstrated, the design methodology which combines the optimization and analytical queueing network models provides a very effective procedure for simultaneously determining the service level and the maximum number of evacuation passengers in the best evacuation routes.

LMI-based approach for global asymptotic stability analysis of continuous BAM neural networks

Studies on the stability of the equilibrium points of continuous bidirectional associative memory (BAM) neural network have yielded many useful results. A novel neural network model called standard neural network model (SNNM) is ad- vanced. By using state affine transformation, the BAM neural networks were converted to SNNMs. Some sufficient conditions for the global asymptotic stability of continuous BAM neural networks were derived from studies on the SNNMs’ stability. These conditions were formulated as easily verifiable linear matrix inequalities (LMIs), whose conservativeness is relatively low. The approach proposed extends the known stability results, and can also be applied to other forms of recurrent neural networks (RNNs).

Mobile Internet Applications：Impact on Future Wireless Communications System

Based on the analysis of application status in real network,the trace model of some typical mobile Internet applications data is given and their impact on 2G/3G network is discussed in this paper.Furthermore,in order to support the mobile Internet application efficiently in future,the issues including the impact on the Long Term Evolution (LTE-A) system and some potential solutions for performance optimization are studied.Based on the trace data model of IM traffic,the performacne evaluaiton of LTE-A system shows that some specific configuration machanisms can play an important role in improving network system efficiency in the case of IM traffic.

Combined mass and heat exchange network synthesis based on stage-wise superstructure model

Integrating multiple systems into one has become an important trend in Process Systems Engineering research field since there is strong demand from the modern industries. In this study, a stage-wise superstructurebased method is proposed to synthesize a combined mass and heat exchange network(CM&HEN) which has two parts as the mass exchange network(MEN) and heat exchange network(HEN) involved. To express the possible heat exchange requirements resulted from mass exchange operations, a so called "indistinct HEN superstructure(IHS)", which can contain the all potential matches between streams, is constructed at first. Then, a non-linear programming(NLP) mathematical model is established for the simultaneous synthesis and optimization of networks. Therein, the interaction between mass exchange and heat exchange is modeling formulated.The NLP model has later been examined using an example from literature, and the effectiveness of the proposed method has been demonstrated with the results.