Localizing structural damage based on auto-regressive with exogenous input model parameters and residuals using a support vector machine based learning approach

Machine learning algorithms operating in an unsupervised fashion has emerged as promising tools for detecting structural damage in an automated fashion.Its essence relies on selecting appropriate features to train the model using the reference data set collected from the healthy structure and employing the trained model to identify outlier conditions representing the damaged state.In this paper,the coefficients and the residuals of the autoregressive model with exogenous input created using only the measured output signals are extracted as damage features.These features obtained at the baseline state for each sensor cluster are then utilized to train the one class support vector machine,an unsupervised classifier generating a decision function using only patterns belonging to this baseline state.Structural damage,once detected by the trained machine,a damage index based on comparison of the residuals between the trained class and the outlier state is implemented for localizing damage.The two-step damage assessment framework is first implemented on an eight degree-of-freedom numerical model with the effects of measurement noise integrated.Subsequently,vibration data collected from a one-story one-bay reinforced concrete frame inflicted with progressive levels of damage have been utilized to verify the accuracy and robustness of the proposed methodology.

Global qualitative analysis of new Monod type chemostat model with delayed growth response and pulsed input in polluted environment

In this paper, we consider a new Monod type chemostat model with time delay and impulsive input concentration of the nutrient in a polluted environment. Using the discrete dynamical system determined by the stroboscopic map, we obtain a ＂microorganism-extinction＂ periodic solution. Further, we establish the sufficient conditions for the global attractivity of the microorganism-extinction periodic solution. Using new computational techniques for impulsive and delayed differential equation, we prove that the system is permanent under appropriate conditions. Our results show that time delay is ＂profitless＂.

Construction of Two-Region Input-output Model——A Case Study of Henan Province

On the basis of input-output table of Henan Province and China in 2007, this paper advances a simple method of constructing two-region input-output model using MRIO model, to research the economic link between the industries of Henan Province and the industries of other regions. I summarize the characteristics of this method based on this as follows: when researching inter-regional economic link, the multi-region or two-region input-output model has prominent superiority, and we can conduct preliminary estimation on the multi-region input-output model using location quotient approach.

Multimodel-based flight control system reconfiguration control in the presence of input constraints

In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model switching.The μ-modification is introduced in the model reference architecture to construct the adaptive controller.The proof of stability is based on the candidate Lyapunov function,while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals.Simulation results illustrate the efficiency of the proposed method.

The Influences of Energy Price Variation on the Prices of Other Industries: A Study Based on Input-Output Price Model

Energy has laid material foundation for human society during its development. Meanwhile, any change of price in the energy industry may influence social production and people’s life at all levels via an input-output mechanism under which the change related to energy is surely transmitted to other industries. The price change thus incurred in all industries may adversely affect the realization of macroeconomic objective-maintaining prices at a stable level. It is, therefore, needed to conduct an empirical research related to the impact of price change in energy industry on that in other industries. According to the data coming from “China’s 2015 Input-Output Extension Table (42 Departments)” and four hypothetical basis, this article focuses on four energy sectors and analyzes how deeply the price change of them, by use of input-output model, affects that of other industrial products under five conditions where each of their price rises by 10% individually or simultaneously, and why such an influence occurs. The results show that the price rising of the energies in question leads to an upward growth in the prices of other industrial products, especially when their prices go up simultaneously. Besides, the price increase in the four energy sectors doesn’t influence other industries in an accumulation form but actually leads to a rollback in some of other industries. It is recommended to adopt diversified pricing strategies for different energy products, thus maximizing the value of each specific energy, and meanwhile achieving the goals of energy consumption reduction and price equilibrium.

Environmental input-output model and its analysis with a focus on the solid waste management sectors

An environmental input output model (EIOM) was introduced to the regional solid waste management sectors, which can reflect the direct and indirect relations between the environment and the regional economy development. Some details about how to use the EIOM was discussed. The EIOM was applied to the Changsha City in China. The example results indicate that much useful information related to the environment and the regional economy development can be gained from the solution of the EIOM. Thus, the EIOM can be used as a useful tool for the sustainable development planning including the solid waste management sectors.

T-Y TUBE MODEL OF HUMAN ASCENDINGAORTIC INPUT IMPEDANCE

This paper proposed a T- Y tube model to simulate foe input impedance of arterial system. It improves and extends the asymmetric T-tube model which was firstproposed by O' Rourke[1] and developed laier by Liu et al.[2]. Based on foe asymmetricT-tube model. a T-Y tube model was proposed by adding branching tubes whichrepresem the iliac arteries.All the tubes are considered to be uniform,viscoelasticlongitudinally tethered cylindrical tubes.The upper tube terminates with a windkesselmodel, while the terminal arterioles of the lowr tube are expressed as a resistance.After proper eraluation of the parameters.the impedance of the arterial system iscalculated under normal physiological and hypertensive condition.The model canpredict impedance in good agreement with the experimentally obtained data no matterin normal physiological condition or in pathological condition In comparison with theasymmeric T-tube model,T- Y tube model is closer to anatomy structure of the human arlerial system and at the sametime much simpler than the extremely complex multiplebranching tube model Therefore it will be a valuable model in studying the influencesof various parameters on aorta impedance and ventricular-vascular coupling.

New model reference adaptive control with input constraints

A new scheme of adaptive control is proposed for a class of linear time-invariant（ LTI） dynamical systems,especially in aerospace,with matched parametric uncertainties and input constraints. Based on a typical and conventional direct model reference adaptive control scheme,various modifications have been employed to achieve the goal. ＂C omposite model reference adaptive control＂of higher performance is seam-lessly combined with ＂positive μ-mod＂,which consequently results in a smooth tracking trajectory despite of the input constraints. In addition,bounded-gain forgetting is utilized to facilitate faster convergence of parameter estimates. The stability of the closed-loop systemcan be guaranteed by using Lyapunov theory.The merits and effectiveness of the proposed method are illustrated by a numerical example of the longitudinal dynamical systems of a fixed-wing airplane.

Regional Agricultural Input-Output Model and Countermeasure for Production and Income Increase of Farmers in Southern Xinjiang,China

Agricultural input and output status in southern Xinjiang,China is introduced,such as lack of agricultural input,low level of agricultural modernization,excessive fertilizer use,serious damage of environment,shortage of water resources,tremendous pressure on ecological balance,insignificant economic and social benefits of agricultural production in southern Xinjiang,agriculture remaining a weak industry,agricultural economy as the economic subject of southern Xinjiang,and backward economic development of southern Xinjiang.Taking the Aksu area as an example,according to the input and output data in the years 2002-2007,input-output model about regional agriculture of the southern Xinjiang is established by principal component analysis.DPS software is used in the process of solving the model.Then,Eviews software is adopted to revise and test the model in order to analyze and evaluate the economic significance of the results obtained,and to make additional explanations of the relevant model.Since the agricultural economic output is seriously restricted in southern Xinjiang at present,the following countermeasures are put forward,such as adjusting the structure of agricultural land,improving the utilization ratio of land,increasing agricultural input,realizing agricultural modernization,rationally utilizing water resources,maintaining eco-environmental balance,enhancing the awareness of agricultural insurance,minimizing the risk and loss,taking the road of industrialization of characteristic agricultural products,and realizing the transfer of surplus labor force.

Nonlinear Systems Identification via an Input-Output Model Based on a Feedforward Neural Network

This paper develops a feedforward neural network based input output model for a general unknown nonlinear dynamic system identification when only the inputs and outputs are accessible observations. In the developed model, the size of the input space is directly related to the system order. By monitoring the identification error characteristic curve, we are able to determine the system order and subsequently an appropriate network structure for systems identification. Simulation results are promising and show that generic nonlinear systems can be identified, different cases of the same system can also be discriminated by our model.

Application of Linear Model Predictive Control and Input-Output Linearization to Constrained Control of 3D Cable Robots

Cable robots are structurally the same as parallel robots but with the basic difference that cables can only pull the platform and cannot push it. This feature makes control of cable robots a lot more challenging compared to parallel robots. This paper introduces a controller for cable robots under force constraint. The controller is based on input-output linearization and linear model predictive control. Performance of input-output linearizing (IOL) controllers suffers due to constraints on input and output variables. This problem is successfully tackled by augmenting IOL controllers with linear model predictive controller (LMPC). The effecttiveness of the proposed method is illustrated by numerical simulation.

GREY DISPLACEMENT INPUT－OUTPUT MODELS OF MINERAL PROCESSING－METALLURGY SYSTEM

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A Generalization of the Input-Output Pollution Control Model and Product Selection

The input-output pollution control model given in [1] is generalized. The generalization makes it easier for the model to handle many problem instances. A linear program is used to solve the new model. An example is given to show that the new model can handle classes of problems that the original model cannot handle.

APPLICATION OF FRF ESTIMATOR BASED ON ERRORS-IN-VARIABLES MODEL IN MULTI-INPUT MULTI-OUTPUT VIBRATION CONTROL SYSTEM

The FRF estimator based on the errors-in-variables （EV） model of multi-input multi-output （MIMO） system is presented to reduce the bias error of FRF HI estimator. The FRF HI estimator is influenced by the noises in the inputs of the system and generates an under-estimation of the true FRF. The FRF estimator based on the EV model takes into account the errors in both the inputs and outputs of the system and would lead to more accurate FRF estimation. The FRF estimator based on the EV model is applied to the waveform replication on the 6-DOF （degree-of-freedom） hydraulic vibration table. The result shows that it is favorable to improve the control precision of the MIMO vibration control system.

基于Input-Output模型的新疆水资源合理配置研究

根据2007和2012年新疆统计年鉴和投入产出表等相关资料,利用Input-output模型和水资源结构偏差系数对国民经济各产业的用水效率、用水效益指数等进行了分析。研究表明:2012年新疆各产业水资源利用效率和效益显著提高,但农业用水消耗量远高于其产出效益,第三产业和生态环境产业的水资源消耗呈现递减趋势。此外,工业和建筑业的水资源配置较为合理。最后,为促进新疆水资源优化配置提出了相应的对策建议。

基于Input Estimation的VSIMM机动目标跟踪

IMM算法的跟踪性能很大程度上取决于模型集的选择。提出了基于Input Estimation的自适应改变模型集的变结构多模型算法IE_VSIMM。对IMM算法输出的状态估计及其误差协方差进行准Kalman滤波,由Input Estimation算法得到的加速度增量估计,可检测目标机动和生成新的模型集。修正过程则由IMM算法在新模型集上对状态估计及其误差协方差进行更正。仿真结果表明IE_VSIMM算法的跟踪性能更好。

A Nonlinear Observer Approach of SOC Estimation Based on Hysteresis Model for Lithium-ion Battery

In this paper, a state of charge U+0028 SOC U+0029 estimation approach for lithium-ion battery based on equivalent circuit model and the input-to-state stability U+0028 ISS U+0029 theory has been proposed. According to the electrochemical performance of lithiumion battery, the equivalent circuit model with two RC networks is established, which includes hysteresis characteristic in inner electrochemical response process. The nonlinear relation between open circuit voltage U+0028 OCV U+0029 and SOC is obtained from a rapid test. Exponential fitting method is used to identify the parameters of the model. A novel state observer based on ISS theory is designed for lithium-ion battery SOC estimation. The designed observer is tested on AMESim and Simulink co-simulation. The simulation results show that the proposed method has a high SOC estimation accuracy with an error of about 2 percent. © 2017 Chinese Association of Automation.

Constrained predictive control based on T-S fuzzy model for nonlinear systems

A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.

Iterative learning control of SOFC based on ARX identification model

This paper presents an application of iterative learning control (ILC) technique to the voltage control of solid oxide fuel cell (SOFC) stack. To meet the demands of the control system design, an autoregressive model with exogenous input (ARX) is established. Firstly, by regulating the variation of the hydrogen flow rate proportional to that of the current, the fuel utilization of the SOFC is kept within its admissible range. Then, based on the ARX model, three kinds of ILC controllers, i.e. P-, PI- and PD-type are designed to keep the voltage at a desired level. Simulation results demonstrate the potential of the ARX model applied to the control of the SOFC, and prove the excellence of the ILC controllers for the voltage control of the SOFC.

Seismic attenuation relationship with homogeneous and heterogeneous prediction-error variance models

Peak ground acceleration(PGA) estimation is an important task in earthquake engineering practice.One of the most well-known models is the Boore-Joyner-Fumal formula,which estimates the PGA using the moment magnitude,the site-to-fault distance and the site foundation properties.In the present study,the complexity for this formula and the homogeneity assumption for the prediction-error variance are investigated and an effi ciency-robustness balanced formula is proposed.For this purpose,a reduced-order Monte Carlo simulation algorithm for Bayesian model class selection is presented to obtain the most suitable predictive formula and prediction-error model for the seismic attenuation relationship.In this approach,each model class(a predictive formula with a prediction-error model) is evaluated according to its plausibility given the data.The one with the highest plausibility is robust since it possesses the optimal balance between the data fi tting capability and the sensitivity to noise.A database of strong ground motion records in the Tangshan region of China is obtained from the China Earthquake Data Center for the analysis.The optimal predictive formula is proposed based on this database.It is shown that the proposed formula with heterogeneous prediction-error variance is much simpler than the attenuation model suggested by Boore,Joyner and Fumal(1993).