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In public open space there are often many civilians engaged in normal activities. Violent attack against civilians may lead to a large number of casualties. There is an urgent demand for a tool which could systematically explore the evacuation process of crowd under violent attacks. This paper presents the general characteristics of public open space and build an agent-based model of evacuation under violent attack. All the participants are classified into two types of agent, the civilians and the attackers, and there is complex interaction among them. First, the activity of civilians and attacker are described with a framework of finite state machine. Then, focusing on the attacker using a sharp instrument, this paper analyzes his target search strategy, and quantitatively model the killing range of sharp instruments. For civilians, their exit choice behavior is formulated based on discrete choice model, and the civilian's movement is calculated with an expanded social force model. Finally, the simulation platform is developed based on Anylogic. The simulation results of a typical scenario show that the platform could be used to study evacuation dynamics of crowd during violent attacks.

This paper proposes a novel zero current detector (ZCD) for single-inductor double-output (SIDO) DC-DC converter which works in discontinuous conduction mode (DCM). The proposed ZCD circuit can accurately acquires inductor voltage and output voltage, and deal with these voltage signals to implement a high speed and high precision controller to avoid reverse current. The test chip was simulated by 110nm CMOS process with input voltage of 0.8V to 1.5V. Simulation results demonstrate a high efficiency of 88.5% at input voltage of 1.5V.

Unmanned Automatic Guided Vehicle (AGV) is a critical part of advanced manufacturing system. The navigation and localization technology are the key components of AGV design. In this paper, we propose a navigation method based on Kalman Filter and a magnetic nail localization. By the method, the state equation can be obtained by carrying out the kinematics analysis. Heading angle and position can be estimated by data from gyroscope and encoders using Kalman Filter (KF) algorithm, and they can be also adjusted once the vehicle pass through a magnetic nail. The magnetic nails localization method improves the navigation accuracy with low-cost.

Based on the research of dispersion relationship and field distribution of clinotron theoretically, the resonator used for 0.3THz clinotron is designed. The mode distribution on transverse section area of optimum structure is analyzed, which is meaningful for the further development of 0.3THz clinotron.

A novel read-while-write (RWW) algorithm for phase change memory (PCM) is proposed. It performs the verify operation for last cell, the write operation for current cell and the pre-read operation for next cell simultaneously. Therefore, it can reduce the timing cost by the data-comparison write (DCW) algorithm and the program & verify (P&V) algorithm. The performance analysis and simulation results show that the percent of saving timing by RWW is >22.6%.

Cognitive Radio requires improving spectrum utilization, and focusing on complexity of the sub carrier power allocation algorithm in OFDM system, A power allocation method which plots the entire channel into lots sub carriers segments is proposed. It gets the maximum communication throughput by auto-adapted modulation. After improved the method, the system performance is also improved. The result showed that method can improve the spectrum efficiency.

A SI n RS epidemic model is formulated by means of dividing classical infected population into n subgroups according to viral levels vary widely between infected individuals, we study the impact of variations in infectiousness, applying ordinary equation theory and nonlinear dynamics methods, we derive the reproductive number, the threshold condition of global stability of the infection-free equilibrium, discuss the main factors affecting the spread of disease and carry out numerical simulations.

In the process of re-entry of hypersonic flight vehicle, attitude control model is a complex object of fast time-varying parameters of a wide uncertain range. This paper design fractional order PID controller to increased the complexity of the controller designed using modern control methods. Fractional order PID (FOPID) controller inherits the advantages of the traditional PID controller and has stronger robustness and better control quality. In this paper, Fractional order PID controller is achieved by optimal Oustaloup digital algorithm and based on time-varying nonlinear model of hypersonic flight vehicle. Use D-decomposition to analyze the affect to Mach number and angle of attack stability region caused by the order of Fractional order PID. Simulation results show that Fractional order PID controller not only improves the control quality, but also is not sensitive to the changes of system parameters, the stability of hypersonic flight vehicle can be achieved within a wide range.

This paper introduces the C language-based expert system development platform-CLIPS, discusses the CLIPS expert system as a platform shearer 3LS fault diagnosis tool necessity and its concrete realization, and discusses fault diagnosis expert system of the system structure, tectonic theory, the difficulties and at the same time give the implementation program of system based on fault tree.

A digital designing way of laser drive circuit for continuously modulate the frequency, pulse width and amplitude of the output of semiconductor laser has been demonstrated. Complex programmable logic device (CPLD) was utilized to generate square waves with tunable frequency, the accurate delaying chip and logic circuit which are digital controlled were used to generate electrical pulses with adjustable pulse width. The pulses were amplified by the laser diode driver. The maximum output current of the driver can be up to 600 mA. When the signal was used to drive the semiconductor laser, the shape of the pulses generated by the laser is identical with the electrical pulses. The repetition rate and pulse width of the laser pulse can be continuously adjustable from 1 to 10 kHz and 3 to 20 ns, respectively. Furthermore, the time of rising and falling edges for the laser pulse were less than 2 ns.

The vibration and acoustic noise of PM machines by inverter are investigated in this paper. The main work includes calculation of 3-D magnetic field and sound field with the effect of higher time harmonics; the noise experiment effect of varying the switching frequency is explored and analyzed; The method for calculation 3-D sound field with the influence of switching frequencies is mentioned, and the calculating results agree with the measured one. The experiment of changing the switching frequency is completed, and the suitable switching frequencies for axial flux PM machines are determined.

Supply chains are typical complex systems, which can be studied from the perspective of Complex Networks. After introducing Complex Networks theory, the paper analyses the situation and problems about supply chain modeling based on complex networks. Finally, the directions of this field are also discussed.

Human capital value measurement has been a difficult problem for researchers, especially for the top management team members. The paper use empirical research top to build a measurement system of TMT human capital value, the measurement system has 10 value factors and 21 measures.

Pedestrian detection is a challenging research task of computer vision, which can be seen as a classification problem in the sliding window framework. Many supervised learning based methods require a large number of labeled data for training. However, training and testing data are not independent identically distributed in most cases, due to the complex background, and it is expensive to re-collect and label the data. This paper proposes a semi-supervised method for pedestrian classification, which is based on transfer learning and sparse coding and just requires a small quantity of labeled data. Firstly, we use sparse coding to learn a slightly higher-level, more succinct feature representation from the unlabeled data that randomly downloaded from the Internet. Then we apply this representation to the target classification problem by transfer learning. The quantitative experiment results demonstrate that this method can improve the performance of pedestrian classification and just needs only a few labeled data.

For the problem in the route discovery process of AOMDV route protocol: lacking congestion control leads to the inappropriate main path selected by source node in many cases, this paper proposes an improved protocol which is based on the traffic load and location information of the network. In the route discovery process, the relay node adopts a delay algorithm before broadcasting RREQ message. So the relatively idle node can be selected automatically to establish a new appropriate main path by adding congestion control. The simulation results show that the improved protocol reduces the rate of packet loss as well as end-to-end delay and increases the network's overall resource utilization rate.

HMIPv6(Hierarchical Mobile IPv6) adopts hierarchical architecture to manage the communication between mobile node(MN) and correspondent node(CN) by introducing a mobility anchor point(MAP) so that it may greatly reduce signaling cost during MN movement. However, it also brings about some new problems, one of which is the issue of the route. When a MN moves to some special location, there maybe exists a situation that it communicates with a CN through a long route. In this paper, we propose an evaluation mechanism based route optimization in HMIPv6, which introduces an evaluation function to compare the total overhead between the non-optimization route and optimization route. According to the value of the function, it is determined whether the route optimization is initiated. The simulation result shows that the scheme is feasible and efficient.

From the hydrodynamic feature of the near-shore waters, the actual flow mainly is induced by tidal, wind, salinity, and the run-off should also be included as a component in the estuary waters. Based on the theoretical velocity distribution of the tidal current and the wind-induced current and so on, a method to separate the observed current into the wind induced current by use of the least square method combination of enumeration is advanced. First of all, for verification of its value, this method is applied to the velocity data observed from the North Branch of Yangtze River estuary and laboratory test data. All the results are satisfactory. Then, it is applied to the South Branch of Yangtze River estuary. By use of this approach, not only is the surface velocity of wind induced current obtained, for estimating the wind velocity above the water surface, but also is the friction wind velocity determined. Finally, by combination of the logarithmic distribution of wind velocity, the distribution in low layers over the sea surface is predicted. The research results show that this method is simple in principle, feasible in process, and reasonable in result. So this method is a valid method to analyze and calculate the field data.

Knowledge is increasingly regarded as the critical resources of firms and has been advocated extensively in the management literature. Much recent attention has been focused on the importance of effective ldquoknowledge sharingrdquo and its relationship with firmspsila performance. A plethora of work has also found evidence to support the notion that absorptive capacity plays an important role in the firmpsilas ability to acquire knowledge and learn from various sources. This paper attempts to build an integrated framework, exploring the effects of knowledge sharing within firms on absorptive capacity and the relationship between these two constructs and innovation capability of firms. Based on the sample of 115 Chinese firms, this paper employs the structural equation modeling technique to empirically investigate the influence of knowledge sharing behavior on absorptive capacity and innovation capability, and the mediating effects of absorptive capacity. The managerial implications are inferred from the empirical results.

The establishment of business units influenced by city residents shopping journey is mainly formed on the basis of residents' journey flow for different commodities in accordance with the availability of the commodities and the traffic convenience. At the same time, businesses management must be aware of the effects of urban regional structure on their economic benefits and make correct decisions of the location of business units. Therefore, the process of urban regional economy development is a dynamical coordinated process between the buyers and the sellers within the region. The article takes Anyang City for example to illustrate how the distribution of all kinds of commercial centers is determined by the interaction between purchase behavior and businesses operation in small and medium sized cities.

As a result of geographical location and management model, large-scale clothing corporations have expressed their requirements for distributed data management and multi-hierarchy decision support. Based on large-scale clothing corporations, the paper proposes a model of distributed data warehouse for sales decision support system in clothing industry, and illustrates some key techniques in designing data warehouse. The paper also proposes a solution for data transmission from local data warehouse to main data warehouse using the technology of Web service, and gives an actualization of Web-oriented OLAP.

Naive Bayes algorithm in data mining is studied online. The information and knowledge gained can be used for training new tuple online, which gives rise to the algorithm that can deal with huge amounts of database quickly. The program of the algorithm is also proposed. The practice usage of the algorithm shows its merit and efficiency.

This paper proposes framework of rough set (RS) theory based internal model control (IMC) system for coagulation control. Decision rules are extracted from RS information system for modeling and controller design. Correlation analysis method is used for time delay estimation. Discretization method based on Boolean reasoning is used for real value attributes coding. Matrix computation methods are used for rule extraction and dynamic modification. The problems of inconsistence and incompleteness are analyzed. Comparing with neural network (NN) methodology, the properties of RS based modeling are discussed. Simulations show that RS theory is a valuable technique for IMC.

The cross-directional (CD) basis weight signal analysis and estimation for paper machines are investigated, and a new approach is proposed by using the wavelet multiresolution analysis theory. Orthogonal wavelet functions are used to represent CD basis weight variations at various resolutions in the discrete wavelet transform domain. By selecting a suitable threshold resolution, the controllable components can be separated from the original signal. Simulation results indicate that the dimension of the approximating signal can be significantly reduced. It is more suitable for CD modeling and control of basis weight variations on paper machines.

This paper proposes a novel approach for cross-directional (CD) modeling and control of a paper machine based on discrete wavelet transforms. CD basis weight variations and the spatial shape of the slice lip are approximated at various resolutions, respectively. A new response model describing the relationship between the settings of the slice-screws to the basis weight profile is established in the discrete wavelet transform domain. Simulation results show that the size of the optimization and control problem associated with large dimensions can be significantly reduced.

Discusses approximate modelling and adaptive control of discrete time linear time-varying systems (LTVS). A novel model, the representation of LTVS in the wavelet time-frequency space, is established based on discrete wavelet transforms. The joint time-frequency properties of wavelet analysis are appropriate for describing the nonstationary characteristic of LTVS. In this model parameter estimation is fulfilled by means of some algebraic calculations. Finally, an adaptive predictive control scheme is proposed based on this representation. Simulation results demonstrate its good behaviour.

Process monitoring and fault diagnosis using the principal component analysis (PCA) has been studied intensively and applied to industry processes. The emphasis of most PCA-based works has been mainly on procedures to perform monitoring and diagnosis for given a set of sensors, and little attention is paid to the actual location of sensors for efficient detection and identification of process faults. In this paper, graph-based techniques are used to optimize sensor locations to obtain the maximum fault resolution. Based on the optimized sensor network, an improved PCA is proposed by introducing two new statistics of PVR and CVR to replace the Q statistic in the conventional PCA. The improved PCA can efficiently detect weak changes, and give an insight into the root cause of process faults. Simulation results of a CSTR process show that the improved PCA with optimized sensor locations is superior to the conventional methods.

Proposes a multiscale approach for nonlinear dynamic system identification. Based on wavelet multiresolution analysis theory, model parameters are estimated step by step from coarse to finer scales. Some criteria and empirical formulae are discussed that explain how to select initial scale, approximating resolution and size of identification samples. Further, the fundamental frame and identification algorithms of the proposed method are described. The computing complexity and local maximum of neural network methods can be avoided. Finally, the convergence and precision of the identification algorithm are analyzed. The simulating case study on a nonlinear dynamic system shows the high efficiency and effectiveness of this methodology.

Principal component analysis (PCA) is an efficient method to extract relationships between correlated variables and thus has been widely applied to the multivariate statistical process monitoring. However, the validity of the PCA is highly depending on the quality of measured process data which is usually contaminated by noises and gross errors in practice. In this paper, an improved PCA is presented to minimize the influences of corrupted data. The original measured data is first processed using conventional PCA to partially eliminate noises by abandoning principal components with small eigenvalues. Then the retained principal components are decomposed and rectified online by boundary-corrected wavelets, combined with techniques of shift-invariant transform and median filtering. The monitoring results of a simulated binary distillation column show that the proposed method has superior performance and more robust to noises and gross errors than conventional methods.

Injection moulding is a kind of highly automatic machine. The hydraulic system is an important part for its proper operation. But because of the frequent faults, the productivity of injection moulding is significantly decreased. Traditionally, the expert system for fault diagnosis is only based on expert experiences. However, considering the complexity of faults of the hydraulic system, it sometimes leads to inaccurate results, even no results. We present a method based on the combination of an expert's experiences and the object's structure and function. By using shallow and deep knowledge synthetically, the framework of an efficacious system is proposed. We describe the generalized knowledge base and the new diagnosis strategies and reference mechanism. We also give some practical rules in LISP language. In most cases, especially unexpected ones, this method can lead to better results.