个人化无线网络的利器——蓝牙技术及应用

系统的介绍了蓝牙技术协议及应用模式 ,全面详细的对蓝牙技术的应用 。

个人化网络社区空间视觉样式探析

现在,在视觉已经占有"霸权"的时代,对网络空间的视觉样式研究显得迫切和必要。笔者将以四大网络社区空间为研究对象,通过分析,得出其空间视觉样式;进而对这类网络空间视觉样式进行理论深入探析,以期挖掘其视觉样式背后的现实根源和理论依据,希望对正在不断出现的新的网络社区空间有一定的借鉴意义。

对机器人化热潮的策略思考

本文首先概括国际机器人学的发展大潮流,指出许多先进工业国家竞相在更高层面、更多领域和更大规模上开展智能机器人研究与应用;接着介绍我国新的机器人技术发展计划,一个全面开发与应用机器人的热潮正席卷全国;然后逐一探讨网络化、智能化与机器人化的关系,提出网络化进一步提高机器人产品的性能水平和应用领域,实现"网络化+机器人化"的机器人系统产品模式,并在此基础上,引入与应用各种人工智能技术,实现"网络化+机器人化+智能化"的机器人集成系统产品模式,从而极大地增强机器人系统的功能,减轻脑力劳动和提高自动化水平。

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.

利用文本挖掘实现Web智能服务

目前网络服务个人化成为人们关注的焦点 ,虽然各大型网站已推出个人化主页服务 ,但是仍存在需要改进的问题 ,首先是个人化网页的自动维护 ,其次是用户的需求信息存在不完全性 .本文通过一个具体应用——实现了 Web智能服务的技术文档检索系统 ,提出将文本挖掘与情报检索技术相结合解决上述问题 ,该系统能够根据用户兴趣自动生成及维护个人化网页 .

基于多Agent的E-Learning系统设计与实现

Agent具有很好的自治性、智能性、反应性及能动性等优点,用于E-Learning系统能有效地解决其缺乏智能性、交互性、测试和评价功能不强、适应性差等问题。基于多Agent技术提出了一个自主性的E-Learning系统,详细介绍了系统各部分功能,并进行了实验验证。结果表明,应用自主性E-Learning学习系统效率高、灵活、自主性强。

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.

Springback prediction for incremental sheet forming based on FEM-PSONN technology

In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.

论个人化音乐网络广播的发展现状

个人化音乐网络广播是伴随着互联网的产生而出现的,这一新型音乐广播形式改变了传统音乐广播一点对多点的传播形式。本文以现阶段我国个人化音乐网络广播的具体实践为依托,深入探讨其发展现状以及存在的问题。

OPTIMIZATION METHOD ON IMPELLER MERIDIONAL CONTOUR AND 3D BLADE

An optimization method for 3D blade and meridional contour of centrifugal or mixed-flow impeller based on the 3D viscous computational fluid dynamics （CFD） analysis is proposed. The blade is indirectly parameterized using the angular momentum and calculated by inverse design method. The design variables are separated into two categories： the meridional contour design vari- ables and the blade design variables. Firstly, only the blade is optimized using genetic algorithm with the meridional contour remained constant. The artificial neural network （ANN） techniques with the training sample data schemed according to design of experiment theory are adopted to construct the response relation between the blade design variables and the impeller performance. Then, based on the ANN approximated relation between the meridional contour design variables and impeller per- formance, the meridional contour is optimized. Fewer design variables and less calculation effort is required in this method that may be widely used in the optimization of three-dimension impellers. An optimized impeller in a mixed-flow pump, where the head and the efficiency are enhanced by 12.9% and 4.5% respectively, confirms the validity of this newly proposed method.

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).

Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution ＋ Neural Network ＋ Particle Swarm Optimization

The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD = 1.8%; AAE = 6.2 K).

Neural Network Based on Quantum Chemistry for Predicting Melting Point of Organic Compounds

The melting points of organic compounds were estimated using a combined method that includes a backpropagation neural network and quantitative structure property relationship （QSPR） parameters in quantum chemistry. Eleven descriptors that reflect the intermolecular forces and molecular symmetry were used as input variables. QSPR parameters were calculated using molecular modeling and PM3 semi-empirical molecular orbital theories. A total of 260 compounds were used to train the network, which was developed using MatLab. Then, the melting points of 73 other compounds were predicted and results were compared to experimental data from the literature. The study shows that the chosen artificial neural network and the quantitative structure property relationships method present an excellent alternative for the estimation of the melting point of an organic compound, with average absolute deviation of 5%.

A new artificial immune algorithm and its application for optimization problems

A new artificial immune algorithm （AIA） simulating the biological immune network system with selfadjustment function is proposed in this paper. AIA is based on the modified immune network model in which two methods of affinity measure evaluated are used, controlling the antibody diversity and the speed of convergence separately. The model proposed focuses on a systemic view of the immune system and takes into account cell-cell interactions denoted by antibody affinity. The antibody concentration defined in the immune network model is responsible directly for its activity in the immune system. The model introduces not only a term describing the network dynamics, but also proposes an independent term to simulate the dynamics of the antigen population. The antibodies＇ evolutionary processes are controlled in the algorithms by utilizing the basic properties of the immune network. Computational amount and effect is a pair of contradictions. In terms of this problem, the AIA regulating the parameters easily attains a compromise between them. At the same time, AIA can prevent premature convergence at the cost of a heavy computational amount （the iterative times）. Simulation illustrates that AIA is adapted to solve optimization problems, emphasizing muhimodal optimization.

Exponential synchronization of general chaotic delayed neural networks via hybrid feedback

This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model,which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator,and covers several well-known neural networks,such as Hopfield neural networks,cellular neural networks(CNNs),bidirectional associative memory(BAM)networks,recurrent multilayer perceptrons(RMLPs).By virtue of Lyapunov-Krasovskii stability theory and linear matrix inequality(LMI)technique,some exponential synchronization criteria are derived.Using the drive-response concept,hybrid feedback controllers are designed to synchronize two identical chaotic neural networks based on those synchronization criteria.Finally,detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.

Study on optimization control method based on artificial neural network

In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limi-tations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advan-tages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With op-timization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.

Ratio of Fe-Al compound at interface of steel-backed Al-graphite semi-solid bonding plate

The ratio of Fe-Al compound at the bonding interface of solid steel plate to Al-7graphite slurry was used to characterize the interracial structure of steel-Al-7graphite semi-solid bonding plate quantitatively. The relationship between the ratio of Fe-Al compound at interface and bonding parameters （such as preheat temperature of steel plate, solid fraction of Al-7graphite slurry and rolling speed） was established by artificial neural networks perfectly. The results show that when the bonding parameters are 516 ℃ for preheat temperature of steel plate, 32.5% for solid fraction of Al-7graphite slurry and 12 mm/s for rolling speed, the reasonable ratio of Fe-Al compound corresponding to the largest interfacial shear strength of bonding plate is obtained to be 70.1%. This reasonable ratio of Fe-Al compound is a quantitative criterion of interracial embrittlement, namely, when the ratio of Fe-Al compound at interface is larger than 70.1%, interfacial embrittlement will occur.