新高考模式下历史“速学法”的五个到位

在高考历史复习中,通过学生思考、讨论、归纳、提问,教师点拨、纠错方式进行系统复习,做到"五个到位",即答题技巧讲解、重点知识理解、历史阶段特征与重要专题归纳、常考热点分析、年度热点掌握到位,以便快速提高历史成绩。

Determination the Vigor of Rice Seed with Different Degrees of Aging with Ultraweak Chemiluminescence During Early Imbibition

用高灵敏度的单光子计数系统探测人工老化 (40℃ ,相对湿度 90 % ) 0、7、14和 2 1d的水稻种子吸胀初期的超弱化学发光强度变化 ,首次观测到水稻 (OryzasativaL .)种子吸胀初期超弱化学发光强度与其老化程度有关。加水激活水稻种子的超弱化学发光 ,其强度依赖于种子的老化程度。实验结果表明水稻种子的人工老化时间越短 ,则吸胀初期 (2 0～ 30min)超弱化学发光的强度越强 ,萌发率越高。水稻种子萌发率与超弱化学发光强度呈显著正相关。观测水稻种子吸胀初期超弱化学发光强度的变化可望成为检测种子老化程度的一种快速、简便、无损伤的新方法。

Monitoring Longitudinal Vehicular Velocity by Using Driving Wheels Information

In order to enhance the accuracy and overcome the limitation of representing the vehicular velocity with non driving wheel speed signals, which is commonly used in researching on automotive dynamic control systems at present, the dynamic and kinematics models of running vehicles and wheels are established. The concept that expresses vehicle velocity using only the driving wheel speed information with adjustable weight factors is described and an algorithm is proposed. A Matlab program with the algorithm embedded is made to simulate the vehicle’s accelerating under different road conditions, and it’s simulation results coincide well with the experimental results, which demonstrates the validity of the algorithm.

ANN Model and Learning Algorithm in Fault Diagnosis for FMS

The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm

A Heterogeneous Information Fusion Deep Reinforcement Learning for Intelligent Frequency Selection of HF Communication

The high-frequency(HF) communication is one of essential communication methods for military and emergency application. However, the selection of communication frequency channel is always a difficult problem as the crowded spectrum, the time-varying channels, and the malicious intelligent jamming. The existing frequency hopping, automatic link establishment and some new anti-jamming technologies can not completely solve the above problems. In this article, we adopt deep reinforcement learning to solve this intractable challenge. First, the combination of the spectrum state and the channel gain state is defined as the complex environmental state, and the Markov characteristic of defined state is analyzed and proved. Then, considering that the spectrum state and channel gain state are heterogeneous information, a new deep Q network(DQN) framework is designed, which contains multiple sub-networks to process different kinds of information. Finally, aiming to improve the learning speed and efficiency, the optimization targets of corresponding sub-networks are reasonably designed, and a heterogeneous information fusion deep reinforcement learning(HIF-DRL) algorithm is designed for the specific frequency selection. Simulation results show that the proposed algorithm performs well in channel prediction, jamming avoidance and frequency channel selection.

On the Acceleration Problem of q-Bernstein Polynomials

In this paper, we investigate not only the acceleration problem of the q-Bernstein polynomials Bn（f, q; x） to B∞ （f, q; x） but also the convergence of their iterated Boolean sum. Using the methods of exact estimate and theories of modulus of smoothness, we get the respective estimates of the convergence rate, which suggest that q-Bernstein polynomials have the similar answer with the classical Bernstein polynomials to these two problems.

Nonlinear Time-Varying Systems Identification Using Basis Sequence Expansions Combined with Neural Networks

A new method for identifying nonlinear time varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non linearity of the system, characterize time varying dynamics of the system by the time varying parametric vector of the network, then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black box modeling ability of neural networks, the presented method can identify nonlinear time varying systems with unknown structure. In order to improve the real time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results.

Global optimization by small-world optimization algorithm based on social relationship network

A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociology. Firstly, the solution space was organized into a small-world network model based on social relationship network. Secondly, a simple search strategy was adopted to navigate into this network in order to realize the optimization. In SWO, the two operators for searching the short-range contacts and long-range contacts in small-world network were corresponding to the exploitation and exploration, which have been revealed as the common features in many intelligent algorithms. The proposed algorithm was validated via popular benchmark functions and engineering problems. And also the impacts of parameters were studied. The simulation results indicate that because of the small-world theory, it is suitable for heuristic methods to search targets efficiently in this constructed small-world network model. It is not easy for each test mail to fall into a local trap by shifting into two mapping spaces in order to accelerate the convergence speed. Compared with some classical algorithms, SWO is inherited with optimal features and outstanding in convergence speed. Thus, the algorithm can be considered as a good alternative to solve global optimization problems.

Experimental and Numerical Procedures of a Sonar Platform with a Sound Absorption Wedge

Experiments involving a sonar platform with a sound absorption wedge were carried out for the purpose of obtaining the low frequency acoustic characteristics. Acoustic characteristics of a sonar platform model with a sound absorption wedge were measured, and the effects of different wedge laid areas on platform acoustic characteristic were tested. Vibration acceleration and self-noise caused by model vibration were measured in four conditions: 0%, 36%, 60%, and 100% of wedge laid area when the sonar platform was under a single frequency excitation force. An experiment was performed to validate a corresponding numerical calculation. The numerical vibration characteristics of platform area were calculated by the finite element method, and self-noise caused by the vibration in it was predicted by an experiential formula. The conclusions prove that the numerical calculation method can partially replace the experimental process for obtaining vibration and sound characteristics.

Fast Learning in Spiking Neural Networks by Learning Rate Adaptation

For accelerating the supervised learning by the SpikeProp algorithm with the temporal coding paradigm in spiking neural networks (SNNs), three learning rate adaptation methods (heuristic rule, delta-delta rule, and delta-bar-delta rule), which are used to speed up training in artificial neural networks, are used to develop the training algorithms for feedforward SNN. The performance of these algorithms is investigated by four experiments: classical XOR (exclusive or) problem, Iris dataset, fault diagnosis in the Tennessee Eastman process, and Poisson trains of discrete spikes. The results demonstrate that all the three learning rate adaptation methods are able to speed up convergence of SNN compared with the original SpikeProp algorithm. Furthermore, if the adaptive learning rate is used in combination with the momentum term, the two modifications will balance each other in a beneficial way to accomplish rapid and steady convergence. In the three learning rate adaptation methods, delta-bar-delta rule performs the best. The delta-bar-delta method with momentum has the fastest convergence rate, the greatest stability of training process, and the maximum accuracy of network learning. The proposed algorithms in this paper are simple and efficient, and consequently valuable for practical applications of SNN.

An Accelerated Iterative Method for Simultaneously Determining All Roots of Polynomial Equation

In this paper, an accelerated iteration method for simultaneously determining of a polynomial equation’s roots is proposed. The new method is an improvement of modified Newton method. At the same time, convergence properties and the order of convergence rate are discussed. At last, some numerical results are reported and listed.

Mathematical Algorithm for Calculating the Velocity Vectors of Fluid by CTA in Spherical Coordinates

This paper presents a mathematical algorithm that determines the fluid flow velocity vector （direction, intensity and orientation）, based on measured voltages on multi-channel hot-wire anemometer. As the voltage on Constant Temperature hot-wire Anemometer （CTA） is non-linear function of velocity and angle of the fluid, inverse function is also non-linear and has several mathematically correct solutions. In the Laboratory of Non-linear Mechanics at the Faculty of Mechanical Engineering in Ljubljana, the authors have decided to try developing multi-charmel hot-wire anemometer with constant temperature at which it is possible to select physically correct solutions from several mathematically correct solutions. The mathematical algorithm works correctly if the range of instrument operation is limited for the value of spherical angles ｜φ｜≤ 60°and ｜ψ｜＇1 ≤ 58°.

Fast reading： Significance and skills

As far as fast reading is concerned, most of the people, who engage in work relate to English, are supposed to have a good command of the skill. The skill is of great help in English examination, scientific research and practical work. In this paper, the significance and skills of fast reading are mentioned. Fast reading is composed of two types： One is scanning, the other is skimming. They play different roles in English speed-reading. For the readers, five practical skills of fast reading can be helpful, such as making use of the efficiency of eyes to the greatest extent, training the ability of direct response, etc.

Managing Complexity and Speed of Processes in the Organizations—A Philosophical and Practical Approach

The business challenges of today are managing complexity and the speed of changes with processes and systems since there are continuously new requirements from market, consumers, suppliers, employees, government etc. For example, when product and service innovations are launched on the market, the relevant processes, policies, people, systems, and technologies should have been prepared and set up in advance. Depending on the business sector, the implementation of new processes and systems as well as their continuous improvement can be easy to very complex and of course very costly. Responding to those challenges, there are several methodologies and approaches like Continuous Improvement Process, Lean Management and Kaizen etc. On the other hand, the application of these methodologies to other industries like service and technology companies is not always successful since their conditions and requirements are different than those of manufacturing organizations. Because of these reasons, the management needs to find out （new） ways of working and to implement new solutions in order to master those challenges and problems. This is not always easy and successful. There are different types of solution options that can be applied situational in every organization. Philosophy can help essentially by asking and answering critical questions in order to provide with solution options to reduce the complexity and to increase the speed of processes. There are several proven and new governance concepts, frameworks, and tools which follow their philosophies. This paper demonstrates how those challenges can be responded and managed as well as how a philosophical approach can help primarily to provide with appropriate solutions.

Effects of GPU Structuring on Accelerated Schemes of LBM and Classical CFD for Flow over a Flat Plate

Current trends on CFD （Computational Fluid Dynamics） along with high performance computing are moving towards using modem GPUs （Graphics Processing Units） in order to achieve substantial execution speedup. Consequently, it is definitely crucial to have some understanding about execution time of different computational methods and parallelization procedures. In this study, two GPU accelerated schemes namely LBM （Lattice Boltzmann Method） and SFV （Stream Function-Vorticity） formulation are investigated and compared for simulation of flow over a fiat plate. The comparisons are based on solvers runtime and speedup and GPU type of structuring. On study the authors found that although the runtime for SFV formulation is less than LBM, LBM has greater potential to obtain higher speedup by using modern GPUs like NVIDIA GTX 480. In addition, a performance sensitivity is done to investigate for different GPU type of structures as a considerable parameter. The difference between GPU runtime in optimum and regular conditions indicates that using one-dimensional structuring can improve the GPU performance about 50 percent.

Adsorption of Rhenium（Ⅶ） with Anion Exchange Resin D318

The adsorption and mechanism of Re(Ⅶ)on resin D318 were studied using chemical methods and IR spectrometry.At pH 5.2,the static and dynamic saturation adsorption capacities were 351.4 and 366.5 mg·g-1,respectively.The adsorption behavior obeyed the Freundlich empirical equation and the adsorption rate constant k298 was 6.37×10-4s-1.The desorption percentage was up to 99.7%when 2.0 mol·L-1KSCN was used for dynamic desorption.

Kinetic Behavior of Aggregation-Exchange Growth Process with Catalyzed-Birth

We propose an aggregation model of a two-species system to mimic the growth of cities＇ population and assets, in which irreversible coagulation reactions and exchange reactions occur between any two aggregates of the same species, and the monomer-birth reactions of one species occur by the catalysis Of the other species. In the case with population-catalyzed birth of assets, the rate kernel of an asset aggregate Bκ of size k grows to become an aggregate Bκ＋1 through a monomer-birth catalyzed by a population aggregate Aj of size j is J（κ,j） = Jkjλ. And in mutually catalyzed birth model, the birth rate kernels of population and assets are H（k,j）=Hkjη and J（k,j） = Jkjλ, respectively. The kinetics of the system is investigated based on the mean-field theory. In the model of population-catalyzed birth of aseets, the long-time asymptotic behavior of the assets aggregate size distribution obeys the conventional or modified scaling form. In mutually catalyzed birth system, the asymptotic behaviors of population and assets obey the conventional scaling form in the case of η=λ =0, and they obey the modified scaling form in the case of η=0, λ=1. In the case of η = λ= 1, the total mass of population aggregates and that of asset aggregates both grow much faster than those in population-catalyzed birth of assets model, and they approaches to infinite values in finite time.

InP-Based RTD/HEMT Monolithic Integration

Monolithic integration of resonant tunneling diodes (RTDs) and high electron mobility transistors (HEMTs) is an important development direction of ultra-high speed integrated circuit. A kind of top-RTD and bottom-HEMT material structure is epitaxied on InP substrate through molecular beam epitaxy. Based on wet chemical etching, metal lift-off and air bridge interconnection technology, RTD and HEMT are fabricated simultaneously. The peak-to-valley current ratio of RTD is 7.7 and the peak voltage is 0.33 V at room temperature. The pinch-off voltage is -0.5 V and the current gain cut-frequency is 30 GHz for a 1.0 μm gate length depletion mode HEMT. The two devices are conformable in current magnitude, which is suitable for the construction of various RTD/HEMT monolithic integration logic circuits.

Study on Dynamic Response of Fiber-Reinforced Plastic Float for Light Seaplane

The marine area of Japan, including territorial waters and the exclusive economic zone, is the sixth largest in the world at about 4,470,000 km2. Therefore, it is becoming necessary to establish appropriate means of transportation other than ships in order to utilize the area efficiently. In this respect, ultra-light seaplanes are attracting attention from the viewpoint of protecting the natural environment. Accordingly, JRPS （Japan Reinforced Plastics Society） is currently developing FRP （fiber-reinforced plastic） floats for such planes. In this study, we conducted simulations of seaplane behavior during alighting by using the smoothed particle hydrodynamics method, which is one of the functions in the PAM-CRASH solver, and we present the observed trend in the vertical acceleration of the floats as a first step toward deriving the impact force from analytical data.

Computational fluid dynamic simulations on liquid film behaviors at flooding in an inclined pipe

The complex liquid film behaviors at flooding in an inclined pipe were investigated with computational fluid dynamic(CFD) approaches. The liquid film behaviors included the dynamic wave characteristics before flooding and the transition of flow pattern when flooding happened. The influences of the surface tension and liquid viscosity were specially analyzed. Comparisons of the calculated velocity at the onset of flooding with the available experimental results showed a good agreement. The calculations verify that the fluctuation frequency and the liquid film thickness are almost unaffected by the superficial gas velocity until the flooding is triggered due to the Kelvin–Helmholtz instability. When flooding triggered at the superficial liquid velocity larger than0.15 m·s-1, the interfacial wave developed to slug flow, while it developed to entrainment flow when it was smaller than 0.08 m·s-1. The interfacial waves were more easily torn into tiny droplets with smaller surface tension, eventually evolving into the mist flow. When the liquid viscosity increases, the liquid film has a thicker holdup with more intensive fluctuations, and more likely developed to the slug flow.