Search

Cloud computing has emerged as a promising paradigm for the Internet of Things (IoT) and Cyber-Physical-Social Systems (CPSS). However, the problem of how to ensure the security of data transmission and data storage in CPSS is a key issue to address. We need to protect the confidentiality and privacy of users’ data and users’ identity during the transmission and storage process in CPSS. In order to avoid users’ personal information leakage from IoT devices during the process of data processing and transmitting, we propose a certificateless encryption scheme, and conduct a security analysis under the assumption of Computational Diffie-Hellman(CDH) Problem. Furthermore, based on the proposed cryptography mechanism, we achieve a novel anonymous communication protocol to protect the identity privacy of communicating units in CPSS. In the new protocol, an anonymous communication link establishment method and an anonymous communication packet encapsulation format are proposed. The Diffie-Hellman key exchange algorithm is used to construct the anonymous keys distribution method in the new link establishment method. And in the new onion routing packet encapsulation format, the session data are firstly separated from the authentication data to decrease the number of cryptography operations. That is, by using the new onion routing packet we greatly reduces the encryption operations and promotes the forwarding efficiency of anonymous messages, implementing the privacy, security and efficiency in anonymous communication in cyber-physical-social systems.

In order to provide help to the research and development of security defense system for pressurizer in nuclear power plant, this paper studies the potential attack mode in pressurizer distributed control system (DCS) and proposes a covert attack approach based on least square support vector machine (LSSVM) algorithm. This method uses the LSSVM algorithm to identify the system to obtain a high-precision estimation model of the attacked area of pressurizer, and use this estimation model to realize the covert attack. The simulation results show that the attack method not only causes some damage to the pressure control system of pressurizer, but also has high concealment performance.

The steam power system consumes many energies, which lays a foundation for the long cycle operation of petrochemical enterprises. However, most optimized operation scheduling schemes only considered the savings in energy quantity, disregarding the difference in energy quality. In this paper, exergy analysis method in thermodynamics is used to evaluate the quality of various energy sources, and exergy objective function is established. Moreover, combined with economic objective function, a mixed-integer nonlinear multi-objective model of SPS is established. Finally, the e constraint method is used to obtain the Pareto front of multi-objective problems, and the fuzzy decision is used to determine the optimal strategy of the system. By introducing the multi-objective optimization of SPS model considering the economic and exergy objective, an optimal scheduling scheme weighing economic and exergy objectives is obtained. Compared with single objective optimization results, the results indicate the feasibility and effectiveness of the model and the solution method.

Coin-tap detection technology is widely used in the field of composite material detection. It measures the health of the material by the pulse width of the coin-tap response signal. During the process of coin-tap detection, various parameters of the internal defects of the material will affect the pulse width of the coin-tap response signal, but the specific relationship between the defect parameters and the pulse width is not yet clear. In this paper, the gray correlation analysis method is used to comprehensively consider the influence of factors such as the size of the defect, the depth of the defect location, and the thickness of the material. The degree of grey relation between each defect parameter and the pulse width of the coin-tap response signal is obtained. The analysis results show that the material thickness has the most obvious influence on the pulse width. In practical applications, the judging criteria should be distinguished according to the thickness of the material, which provides a new direction for the standardized development of tapping detection technology.

A certain type of pump is a product with long life, few failure data and high reliability, as a result, the best way to evaluate the reliability is to use the method based on performance degradation data. The structure of the pump is complex and there are multi-performance parameters. Furthermore, there are correlations among the parameters. If the traditional performance reliability evaluation methods are used to evaluate the reliability, the errors may be so large that it cannot be acceptable. Therefore, the multivariate linear regression method is used to analyze multi-performance parameters and establish regression equation to standardize the performance degradation data. Then, the Wiener process is used to fit and replace the standardized data to realize the performance reliability evaluation of the equipment.

For long-wait-short-run equipment, it is in the standby state with very short operating time during its lifetime. The performance degradation of this equipment depends on not only the accumulative operating time, but also the accumulative non-operating time. In this paper, the reliability model of the long-wait-short-run equipment is established based on Gamma process in which both the accumulative operating time and the accumulative non-operating time are treated as independent variables. The method to evaluate the reliability is also proposed. Finally, a numerical experiment is given and the result shows that the proposed method is proper to evaluate the reliability of the long-wait-short-run equipment.

A fast algorithm of the parallel computing load equilibrium MMET model has been designed: which made by exchanging every grid of the maximum executing time and the minimum executing time of the computational nodes, and we choose the minus least. Then the two grids which will be exchanged were found. Compared with the old MMET model, the computational time of this method are largely reduced. The efficiency of the fast algorithm improved significantly.

The influence factors of pH and contact time were used to evaluated the biosorption ability of the heavy metal cadmium (CdII) onto the lyophilized Zygosaccharomyces rouxii CICC1379 biomass from aqueous solutions. And the biosorption equilibrium models of biosorption and biosorption kinetics modeling were also studied. The initial pH of the aqueous solution with Cd(II) had strongly influences on the biosorption and the optimum initial pH was 5∼7. The biosorption process of Cd(II) onto the yeast was rapid and the biosorption value reached its maximum within approximately 30 min. Langmuir isotherm model shows a better fit with the equilibrium data than the Freundlich isotherm model does. The analysis on the biosorption kinetics data at 30 °C indicated the rate of biosorption of Cd(II) fitted well with the pseudo-second order kinetics. The results of this study indicated the possible application of Cd(II) removal from the water onto the lyophilized yeast biomass.

A novel, bio-based sorbent for As(III), α-Fe2O3-impregnated chitosan beads (FICB), has been successfully synthesized and tested. Batch wise adsorption experiments were performed to evaluate the adsorption conditions, reusability as well as the effect of ionic strength and coexistence of other ions. The results indicated that the maximum adsorption capacity of FICB for As(III) was 4.46 mg/g at pH 5.0 and 20 °C with equilibrium adsorption time 8 h. The ionic strength and coexistence of other ions had no significantly effect on the adsorption of FICB. It was also revealed that FICB could be reused for at least 6 times with only 7% regeneration loss. This suggests that FICB is a very promising adsorbent for the removal of As(III) from aqueous solution.

Aiming at a class of linear time-invariant fractional-order system (LTI-FOS) with commensurate order- which is similar to traditional second order system, the range of damping ratio is derived for the closed-loop stability of this special kind of FOS based on sufficient and necessary conditions. The stable parameters region is wider than that of traditional second order system. The existing fractional Nyquist stability criterion and logarithmic Frequency stability criterion can not be directly used while fractional order integral unit exists in open-loop transfer function. A method of drawing auxiliary arc on the Nyquist diagram and auxiliary line on the Bode diagram is given. Then the stability and relative stability analysis for this special kind of FOS is evaluated using this method. Finally a conclusion under the condition of the same angular frequency and damping ratio is drawn: the bigger of the fractional order, the smaller of relative stability.

Derivative order introduced by non-integer differentiation and integration concept constitutes an additional degree of freedom allowing a more accurate modeling of several physical phenomena. At the same time its complexity coming from the character of history dependence and universe mutuality makes the identification process more difficult. According to the dynamic characteristics of some typical thermal processes such as drum water level, main steam temperature and bed temperature of a 450 t/h circulating fluidized bed boiler, a kind of fractional transfer function is designed. Particle Swarm Optimization (PSO) is used to estimate the parameters include the order and the coefficients. The fitness function is the Integral of Squared Errors (ISE) between the output of actual system and the model identified. The data used for the calculations are step response data collected from a power plant. Simulation results show that the proposed scheme offers a higher degree of accuracy, compared with integral models which are obtained with the same method.

Coordinated Control System(CCS) in thermal power plant is a system with big inertia, large time delay and slow parameter variance as well as the property of fast parameter variance while the unit load changes. Conventional PID controller and Direct Energy Balance(DEB) Strategy which is tuned at typical operating point can hardly work well at different unit load. By using the resource of Automatic Generating Control(AGC), this paper proposes a novel predictive control of CCS based on Adaptive Neuro Fuzzy Inference System(ANFIS). Aiming at a 500MW thermal unit, multiple linear models are obtained at several typical operating points. Corresponding multivariable predictive controllers are designed for the linear model group, which are used to train an ANFIS controller, thus ANFIS based multi-model preview predictive control is realized for CCS. Computer simulation results show that the new control strategy is very effective for CCS. It makes that the load has good tracking property, main steam pressure gets better dynamic and static performance, control input value is very smooth also. Very strong robust performance is obtained.

IMC-PID(Internal Model Control-Proportional plus Integral plus Derivative) cascade control strategy is presented based on multiple models control theory, which keeps the merit of cascade control system as well as multiple models control. Fixed parameters PID control is used in inner loop in order to eliminate disturbances, then the inner loop and main inertia plant are regarded as the generalized object to be controlled by IMC method. FOPDT (First Order Plus Dead Time) model is used to describe the internal models for IMC, this avoids the problem of realization of a high order controller. The method of scheduling among the local models is according to power load that is the primary factor. Smoothiing switch is realized by using fuzzy weighting function. The simulation results demonstrate that the strategy proposed is well suited to nonlinear multi-model and time-various systems such as superheated steam temperature plant.

The particle swarm optimization algorithm is an extremely effective method in evolutionary computation. But it also has some disadvantages such as finite sampling space and being easy to run into prematurity. In this paper, a new particle swarm optimization algorithm based on quantum individual is proposed (QPSO,). On basis of QPSO, a novel method of nonlinear system identification is proposed with constructing radial basis function neural network. The simulation results of a nonlinear system reveal the effectiveness of this method. A special program is compiled to identify the object model of the thermal process, and the dynamic process between primary air feed rate and bed temperature is identified. The results show that the approach is easy to be used for identification and has a certain practical value.

The target to solve multiobjective optimization problems (MOOP) is to find as many Pareto-optimal solutions as possible. A new algorithm aimed to solve MOOP was proposed in this paper — multi-agent quantum evolutionary algorithm (MAQEA) on the basis of quantum mechanics theory, the study and competition ability of multi-agent system and organic evolutionary strategy. In the multi-agent system, the agents learn from and compete with others in neighborhood under the quantum evolution mechanism. From the minimization results of two duality functions, the proposed algorithm can find evenly distributed Pareto-optimal solutions effectively. Furthermore, this algorithm was applied to the PID controller parameter tuning. The simulation result showed that this algorithm can obtain different optimal controller parameters with respective targets.

There have been a large number of experimental studies concerning the ionoshperic perturbations before earthquakes. These perturbations include variations in electron and ion densities, anomalous electromagnetic emissions at various frequencies and so on. The real physical process of seismo- ionospheric couping is not well known up to now, though many research results have been published [1]. It is an effective way to monitor the ionospheric variations related to seismic activity by satellite observations, and this is the main scientific objective of the French micro-satellite DEMETER, which was launched on June 29, 2004. Its orbit is polar and circular with an altitude of 710 km (changed to 665 km later). The electromagnetic emission in a frequency range from DC to 3.3 MHz can be detected by the scientific payload IMSC (search-coil magnetometer instrument) and ICE (electric field instrument). The electron density and ion density variations are studied by the ISL (Langmuir probe instrument) and IAP (plasma analysis instrument). There are two operation modes: the survey mode which record low bit rate data all around the Earth at invariant latitudes less than ~ 65deg and burst mode which record high bit rate data above seismic regions. Data and plots are available on the web server according to the user's authorization. All data and plots are organized by half-orbits. Some ionospheric variations during earthquakes using DEMETER data have been published in the past few years.

Because the accuracy of model could be significantly improved by combining multiple models, a support vector machine (SVM) fusion modeling approach was proposed to build the soft sensor model. The model is built based on the time series data and the sub-model is built based on least square SVM algorithm in every sub-space. In order to minimize the severe correlation among sub-models, and improve the accuracy and robustness of the model, the sub-models are combined by SVM algorithm. In view of the problems that current power plant boiler ash carbon measurement methods are time-lag and low accuracy, we build a model using the method. The procedure of simulation and theoretical analysis indicate that the proposed method is effective.

In view of the features of superheated steam temperature system in the power plant, with the models of controlled object under different circumstances, a multiple models modeling method based on mixed logical dynamical (MLD) model is given, and the GPC for the MLD is put forward, its stability is also discussed. The control algorithm is simple and more adaptable for the change of circumstance, also can avoid the disturbance of controllers' switch while using the common method. Considering the main steam temperature plant of a supercritical once-through 600MW boiler, the simulation is done to validate the control strategy. The result show the method has a strong robustness and better tracking performance, the satisfying control of the total operating conditions is also realized.

System identification adopting an open loop step response curve is a feasible way to obtain the mathematic model of the control object. Due to the satisfying performance in global optimization, evolution computing (EC) methods such as genetic algorithm have been applied to the open loop step response curve analysis and achieved effective results. In this paper, particle swarm optimization (PSO) algorithm which is considered as a new relative addition to the EC methods is introduced to solve the system identification problem for thermal process control objects. Typical forms of transfer functions for the thermal process are adopted, utilizing PSO algorithm to estimate the parameters, for the convenient application of which, a set of software is also developed. With these softwares, some characters of the experimental data are specified by the user. And then the initial values for the model parameters will be deduced from these characters. Around these initial values, a smaller search space will be determined, within which the PSO algorithm will search the optima for the model parameters. Thus the search efficiency can be improved remarkably. The software has been applied in some power plants, the results of which prove the effectiveness of the method.

Dynamic matrix control builds predictive model with step response which is easy to obtain in engineering practice. As model mismatch, disturbance and noise inevitably involve the modeling error, this impacts the control performance. Considers to this kind of situation, an algorithm of dynamic matrix control based on support vector regressions error compensation is proposed. Namely, build error model on-line using support vector regressions and do one-step prediction to compensate the predictive model. Thus realize the compensation for model error and strength the robustness of the algorithm. The simulation results show that the algorithm has better control ability, and it has better anti-inference ability than conventional dynamic matrix control.

AlGaN/GaN double heterostructure high electron mobility transistors (DH-HEMT's) with a 2.0 /spl mu/m gate length and a 4 /spl mu/m channel length, exhibiting good temperature characteristics, have been demonstrated. The maximum drain current I/sub ds/ and extrinsic transconductance G/sub m/ are 1300 mA/mm 235 mS/mm, 85.0 mA/mm 174 mS/mm, and 475 mA/mm, 95 mS/mm, respectively at T = -194/spl deg/C, 20/spl deg/C and 400/spl deg/C. The temperature coefficient of I/sub ds/ and G/sub m/ are -1.4 mA//spl deg/C and 0.24 mS//spl deg/C respectively.

Component-based development (CBD) techniques have been widely used to enhance the productivity and reduce the cost for software systems development. However, applying CBD techniques to embedded software development faces additional challenges. For embedded systems, it is crucial to consider the quality of service (QoS) attributes, such as timeliness, memory limitations, output precision, battery constraints. Frequently, multiple components implementing the same functionality with different QoS properties can be used to compose a system. Also, software components may have parameters that can be configured to satisfy different QoS requirements. Composition analysis, which is used to determine the most suitable component selections and parameter settings to best satisfy the system QoS requirement, is very important in embedded software development process. In this paper, we present a model and the methodologies to facilitate composition analysis. We define QoS requirements as constraints and objectives. Composition analysis is performed based on the QoS properties and requirements to find solutions (component selections and parameter settings) that can optimize the QoS objectives while satisfying the QoS constraints. We use a multiobjective concept to model the composition analysis problem and use an evolutionary algorithm to determine the Pareto-optimal solutions efficiently.

Bed temperature of circulating fluidized bed (CFB) boiler is measured by multiple sensors equally fixed around the furnace wall. Big inertia of the combustion process and the large time delay of bed temperature measure are unfavorable for thermal process control. Based on the redundant distributed measuring property of bed temperature, a new sensor signal synthesis method was proposed. The new method is based on information fusion technique, which uses multi-sensor resource to decrease the measure uncertainty, eliminate the failure sensor/sensors, so the measure result is more credible. That is very important for optimal control of combustion system. Grey modeling theory and fuzzy control strategy are combined to control the combustion process. Numerical simulation results of bed temperature measure and grey fuzzy control for a CFB boiler show that the proposed new strategy is feasible and effective.

In view of control performance is not ideal while traditional Proportional-Integral-Derivative (PID) controller being used to high order and large inertia system, a novel control method called Virtual Cascade PID (VC-PID) controller is proposed in this paper, which is based on a Virtual Model (VM) and a Virtual Controller (VC). On the basic theory of traditional PID controller, cascade PID control, Smith predictor control and Internal Model Control (IMC), virtual model and virtual controller becomes inner-loop, a filter is introduced in outer-loop in order to improve the closed-loop performance, negative effect due to large time constant plant is overcome effectively. From this ideal, a novel cascade correction method is deduced, i.e. virtual model and virtual controller together equals to cascade correction unit, and so correction unit has physical meaning. Simulation results of several kinds of models mismatch and signal disturbance show that the new method is much better than traditional PID controller in control performance and robustness.

Aiming at the large time-delay and fast time-varying characteristics of steam temperature system of Circulating Fluidized Bed Boiler (CFBB), steam temperature properties effected by spray-water and feed-water are studied in different steam load. Multi-model forward-feedback adaptive dynamic matrix predictive control strategy is proposed. The new method has ability of overcoming some shortcomings while single model used, and eliminate the coupling property from feed-water varying. Simulation results show that the proposed predictive control method has better control quality, stronger robustness and good adaptive capacity.

There are many applications that can be modeled as evolutions of continuous state spaces, such as real-time embedded systems, etc. For such applications, the divide-and-conquer method is usually used to achieve high quality software development. Instead of decomposing the state space based on phases, objects, etc., we decompose the state space based on aspects. Typically, there are four kinds of aspects, namely, constraint aspects, goal aspects, fairness aspects, and performance aspects. In our previous work (Bastini et al., 2001), we have investigated the decomposition of a system into constraint and goal aspects. In this paper, we further discuss the separation of fairness and performance aspects from constraint aspects and goal aspects, and the decomposition of fairness and performance aspects. All the decomposed aspects are guaranteed to be independently developable end-user assessable logical (IDEAL) aspects, meaning that each aspect can be designed and implemented independently, and can be tested or verified by the end user independently. Also, the system level properties (safety, stability, and reliability) can be inferred from the decomposed aspects mathematically to a high degree of confidence. A vehicle control system is used to illustrate the handling of the fairness and performance aspects in continuous process-control systems.

In the view of the large time-delay and fast time-varying characteristics of steam temperature system of circulating fluidized bed boiler (CFBB), steam temperature properties effected by spray-water and feed-water are studied in different steam load. Multi-model forward-feedback adaptive generalized predictive control (GPC) is proposed. The new method has ability of overcoming some shortcomings while single model used, and eliminate the coupling property from feed-water varying. Simulation results show that the new method has better control quality, stronger robustness and adaptive capacity.

Boiler-turbine unit is a complex multivariable system with strong time-variation, uncertainty and coupling. The improvement of its control quality is a key problem for automation improvement in thermal engineering. Adaptive Neuro-Fuzzy Inference System (ANFIS) is the neural network realization for Takagi and Sugeno fuzzy inference system. ANFIS can not only approach any linear and nonlinear function with any precision, but also quicken convergence speed, decrease errors, and lessen training data that are needed. The paper put forward to use ANFIS as a controller to control boiler-turbine unit. Under several typical operating points, the Linear Quadratic Regulators (LQR) are designed and generate the training data for the ANFIS. Therefore, LQR control is realized based on ANFIS, on the coordinated control system of a thermal power unit, multi-model LQR control is approached. The simulation results show that the control system has better control performance and stronger robustness.

In the separating process with air-solid fluidized bed (ASFB), the density and height are the key factors of affecting the effect of dry cleaning of coal with ASFB. It required higher demands of the measure and control precision on the density and height of the dry cleaning of coal with ASFB and the demand of the controlling method. The ANFIS is used as an identifier to model on the separating process with the air-solid fluidized bed. Then, ANFIS is used as a controller to control the separating process with ASFB. Therefore, we achieve the on-line self-adaptive control on the separating process of ASFB. Finally, the simulation results and analysis are presented. Simulation results show that ANFIS has better control quality, stronger robustness and adaptive capacity.