BAW气流信息检测专用信号处理技术研究

研究了体声波(BAW)气流信息检测专用信号处理系统的3个关键技术。首先,改进了采用MOSFET驱动电路提供激励信号的方式,改善了抖动和拖尾现象;其次,设计了专用的匹配网络,实现了阻抗匹配、调谐和滤波;最后,设计和实现了3种测时算法,并通过实验比较了3种算法的优劣。

STUDY ON ARTIFICIAL NEURAL NETWORK FORECASTING METHOD OF WATER CONSUMPTION PER HOUR

An artificial neural network (ANN) short term forecasting model of consumption per hour was built based on seasonality,trend and randomness of a city period of time water consumption series.Different hidden layer nodes,same inputs and forecasting data were selected to train and forecast and then the relative errors were compared so as to confirm the NN structure.A model was set up and used to forecast concretely by Matlab.It is tested by examples and compared with the result of time series trigonometric function analytical method.The result indicates that the prediction errors of NN are small and the velocity of forecasting is fast.It can completely meet the actual needs of the control and run of the water supply system.

Real-time Threshold Routing Algorithm Based on Wireless Sensor Network

[Objective] This study was to design an intelligent greenhouse real-time monitoring system based on the core technology of Internet of Things in order to meet the needs of agricultural informatization and intellectualization. [Method] Based on the application characteristics of Wireless Sensor Network （WSN）, the intelligent greenhouse monitoring system was designed. And for the incompleteness strategy of load balancing in the Low-Energy Adaptive Clustering Hierarchy （LEACH）, a Real- time Threshold Routing Algorithm （RTRA） was proposed. [Result] The performance of network lifetime and network delay of RTRA were tested in MATLAB and found that, within the same testing environment, RTRA can save nodes energy consumption, prolong network lifetime, and had better real-time performance than LEACH. The al- gorithm satisfies the crops＇ requirements on real-time and energy efficiency in the greenhouse system. [Conclusion] For the good performance on real-time, the de- signed intelligent greenhouse real-time monitoring system laid the foundation for the research and development of agricultural informatization and intellectualization.

An Improved Fuzzy Predictive Control Algorithm and Its Application to an Industrial CSTR Process

A finite horizon predictive control algorithm, which applies a saturated feedback control law as its local control law, is presented for nonlinear systems with time-delay subject to input constraints. In the algorithm, N free control moves, a saturated local control law and the terminal weighting matrices are solved by a minimization problem based on linear matrix inequality （LMI） constraints online. Compared with the algorithm with a nonsaturated local law, the presented algorithm improves the performances of the closed-loop systems such as feasibility and optimality. This model predictive control （MPC） algorithm is applied to an industrial continuous stirred tank reactor （CSTR） with explicit input constraint. The simulation results demonstrate that the presented algorithm is effective.

FPGA-based High-precision Measurement Algorithm for the Ultrasonic Echo Time of Flight

Based on the evaluation of advantages and disadvantages of high-precision digital time interval measuring algorithms, and combined with the principle of the typical time-difference ultrasonic flow measurement, the requirements for the measurement of echo time of flight put forward by the ultrasonic flow measurement are analyzed. A new high-precision time interval measurement algorithm is presented, which combines the pulse counting method with the phase delay interpolation. The pulse counting method is used to ensure a large dynamic measuring range, and a double-edge triggering counter is designed to improve the accuracy and reduce the counting quantization error. The phase delay interpolation is used to reduce the quantization error of pulse counting for further improving the time measurement resolution. Test data show that the systexn for the measurement of the ultrasonic echo time of flight based on this algorithm and implemented on an Field Programmable Gate Army（FleA） needs a relatively short time for measurement, and has a measurement error of less than 105 ps.

Non-classical Algorithm for Time Series Prediction of the Range of Economic Phenomena With Regard to the Interaction of Financial Market Indicators

The aim of the article is to present non-clasical copyrighted algorithm for prediction of time series, presenting macroeconomic indicators and stock market indices. The algorithm is based on artificial neural networks and multi-resolution analysis （the algorithm is based on Daubechies wavelet）. However, the main feature of the algorithm, which gives a good quality of the forecasts, is all included in the series analysis division into, a few partial under-series and prediction dependence on a number of other economic series. The algorithm used for the prediction, is copyrighted algorithm, labeled M.H-D in this article. Application of the algorithm was performed on a series presenting WIG 20. The forecast of WIG 20 was conditional on trading the Dow Jones, DAX, Nikkei, Hang Seng, taking into account the sliding time window. As an example application of copyrighted model, the forecast of WIG 20 for a period of two years, one year, six month was appointed. An empirical example is described. It shows that the proposed model can predict index with the scale of two years, one year, a half year and other intervals. Precision of prediction is satisfactory. An average absolute percentage error of each forecast was： 0.0099%---for two-year forecasts WIG 20; 0.0552%--for the annual forecast WIG 20; and 0.1788%---for the six-month forecasts WIG 20.

Improved Real-time Implementation of Adaptive Gassian Mixture Model-based Object Detection Algorithm for Fixed-point DSP Processors

Foreground moving object detection is an important process in various computer vision applications such as intelligent visual surveillance, HCI, object-based video compression, etc. One of the most successful moving object detection algorithms is based on Adaptive Gaussian Mixture Model （AGMM）. Although ACMM-hased object detection shows very good performance with respect to object detection accuracy, AGMM is very complex model requiring lots of floatingpoint arithmetic so that it should pay for expensive computational cost. Thus, direct implementation of the AGMM-based object detection for embedded DSPs without floating-point arithmetic HW support cannot satisfy the real-time processing requirement. This paper presents a novel rcal-time implementation of adaptive Gaussian mixture model-based moving object detection algorithm for fixed-point DSPs. In the proposed implementation, in addition to changes of data types into fixed-point ones, magnification of the Gaussian distribution technique is introduced so that the integer and fixed-point arithmetic can be easily and consistently utilized instead of real nmnher and floatingpoint arithmetic in processing of AGMM algorithm. Experimental results shows that the proposed implementation have a high potential in real-time applications.

Application of Snapback Chronometry Method in Calculation of Regulation

Work study is an area of construction production rationalization in which with scientific, logical, holistic and system analysis methods of the process we gain optimum in way of work and time of work. Chronometry method is one of the work study methods which is appropriate for recording shorter cyclic processes and is based on statistical sampling theory. Determination of cyclic times and work performances of standard cyclic construction machines （SCCM） is one of key assumptions of dynamic planning of machine work on every construction site. Calculation methods of SCCM work performances arc one of basic research objects in the field of construction organization and technology. Study shows applied chronometry method in work of standard cyclic construction machine. Goal of this study is to accomplish regulation through measured time cycle and compare measured effects with effects obtained with standard methodology of calculating hydraulic excavator practical achievement for gaining regulation in order to determine main reasons which effect work performance on site.

A New Transient Impedance-Based Algorithm for Earth Fault Detection in Medium Voltage Networks

This paper presents a new earth-fault detection algorithm for unearthed （isolated） and compensated neutral medium voltage （MV） networks. The proposed algorithm is based on capacitance calculation from transient impedance and dominant transient frequency. The Discrete Fourier Transform （DFT） method is used to determine the dominant transient frequency. The values of voltage and current earth modes are calculated in the period of the dominant transient frequency, then the transient impedance can be determined, from which we can calculate the earth capacitance. The calculated capacitance gives an indication about if the feeder is faulted or not. The algorithm is less dependent on the fault resistance and the faulted feeder parameters; it mainly depends on the background network. The network is simulated by ATP/EMTP program. Several different fault conditions are covered in the simulation process, different fault inception angles, fault locations and fault resistances.

A robust system for real-time pedestrian detection and tracking

A real-time pedestrian detection and tracking system using a single video camera was developed to monitor pedestrians. This system contained six modules: video flow capture, pre-processing, movement detection, shadow removal, tracking, and object classification. The Gaussian mixture model was utilized to extract the moving object from an image sequence segmented by the mean-shift technique in the pre-processing module. Shadow removal was used to alleviate the negative impact of the shadow to the detected objects. A model-free method was adopted to identify pedestrians. The maximum and minimum integration methods were developed to integrate multiple cues into the mean-shift algorithm and the initial tracking iteration with the competent integrated probability distribution map for object tracking. A simple but effective algorithm was proposed to handle full occlusion cases. The system was tested using real traffic videos from different sites. The results of the test confirm that the system is reliable and has an overall accuracy of over 85%.

Detection of Selfish Behavior in Wireless Ad Hoc Networks Based on CUSUM Algorithm

The cumulative sum （CUSUM） algorithm is proposed to detect the selfish behavior of a node in a wireless ad hoc network. By tracing the statistics characteristic of the backoff time between successful transmissions, a wireless node can distinguish if there is a selfish behavior in the wireless network. The detection efficiency is validated using a Qualnet simulator. An IEEE 802.11 wireless ad hoc network with 20 senders and 20 receivers spreading out randomly in a given area is evaluated. The well-behaved senders use minimum contention window size of 32 and maximum con- tention window size of I 024, and the selfish nodes are assumed not to use the binary exponential strategy for which the contention window sizes are both fixed as 16. The transmission radius of all nodes is 250 m. Two scenarios are investigated： a single-hop network with nodes spreading out in 100 m^100 m, and all the nodes are in the range of each other; and a multi-hop network with nodes spreading out in 1 000 m~ 1 000 m. The node can monitor the backoff time from all the other nodes and run the detection algorithms over those samples. It is noted that the threshold can significantly affect the detection time and the detection accuracy. For a given threshold of 0.3 s, the false alarm rates and the missed alarm rates are less than 5%. The detection delay is less than 1.0 s. The simulation results show that the algorithm has short detection time and high detection accuracy.

Behavior Clustering for Anomaly Detection

We presented a novel framework for automatic behavior clustering and unsupervised anomaly detection in a large video set. The framework consisted of the following key components： 1 ） Drawing from natural language processing, we introduced a compact and effective behavior representation method as a stochastic sequence of spatiotemporal events, where we analyzed the global structural information of behaviors using their local action statistics. 2） The natural grouping of behavior patterns was discovered through a novel clustering algorithm. 3 ） A run-time accumulative anomaly measure was introduced to detect abnormal behavior, whereas normal behavior patterns were recognized when sufficient visual evidence had become available based on an online Likelihood Ratio Test （LRT） method. This ensured robust and reliable anomaly detection and normal behavior recognition at the shortest possible time. Experimental results demonstrated the effectiveness and robustness of our approach using noisy and sparse data sets collected from a real surveillance scenario.

A Precise Localization Method for a High Speed Mobile Robot Using iGS and Dual Compass

This paper proposes a precise localization algorithm for a quickly moving mobile robot.In order to localize a mobile robot with active beacon sensors,a relatively long time is needed,since the distance to the beacon is measured by transmitting time of the ultrasonic signal.The measurement time does not cause a high error rate when the mobile robot moves slowly.However,with an increase of the mobile robot’s speed,the localization error becomes too high to use for accurate mobile robot navigation.Therefore,in this research into high speed mobile robot operations,instead of using two active beacons for localization,an active beacon and dual compass are utilized to localize the mobile robot.This new approach resolves the high localization error caused by the speed of the mobile robot.The performance of the precise localization algorithm is verified by comparing it to the conventional method through real-world experiments.

Prediction of Temperature Daily Profile by Stochastic Update of Backpropagation through Time Algorithm

The authors will examine prediction of temperature daily profile using various modifications of BPTT （backpropagation through time algorithm） done by stochastic update in the artificial RCNN （recurrent neural networks）. The general introduction was provided by Salvetti and Wilamowski in 1994 in order to improve probability of convergence and speed of convergence. This update method has also one another quality, its implementation is simple for arbitrary network topology. In stochastic update scenario, constant number of weights/neurons is randomly selected and updated. This is in contrast to classical ordered update, where always all weights/neurons are updated. Stochastic update is suitable to replace classical ordered update without any penalty on implementation complexity and with good chance without penalty on quality of convergence. They have provided first experiments with stochastic modification on BP （backpropagation algorithm） used for artificial FFNN （feed-forward neural network） in detail described in the article ＂Stochastic Weight Update in the Backpropagation Algorithm on Feed-Forward Neural Networks＂ presented on the conference IJCNN （International Joint Conference of Neural Networks） 2010 in Barcelona. The BPTT on RCNN uses the history of previous steps stored inside of the NN that can be used for prediction. They will describe exact implementation on the RCNN, and present experiment results on temperature prediction with recurrent neural network topology. The dataset used for temperature prediction consists of the measured temperature from the year 2000 till the end of February 2011. Dataset is split into two groups： training dataset, which is provided to network in learning phase, and testing dataset, which is unknown part of dataset to NN and used to test the ability of NN to predict the temperature and the ability of NN to generalize the model hidden in the temperature profile. The results show promising properties of stochastic weight update with toy-task data, and the higher complexity of the temperature daily profile prediction.

AN IMPROVED GN ALGORITHM OF NETWORK COMMUNITY DETECTION METHOD

.GN algorithm has high classification accuracy on community detection, but its time complexity is too high. In large scale network, the algorithm is lack of practical values. This paper puts forward an improved GN algorithm. The algorithm firstly get the network center nodes set, then use the shortest paths between center nodes and other nodes to calculate the edge betweenness, and then use incremental module degree as the algorithm terminates standard. Experiments show that, the new algorithm not only ensures accuracy of network community division, but also greatly reduced the time complexity, and improves the efficiency of community division.

Modelling and Predictive Control of a Buck-Boost DC-DC Converter

A predictive current control algorithm for the Buck-Boost DC-DC converter is presented in this paper. The continuous time model of the system is properly introduced, then, by imposing a proper PWM modulation pattern, its discrete time model is achieved. This last one is successfully employed in determining the steady state locus of the Buck-Boost converter, both in CCM （continuous conduction mode） and DCM （discontinuous conduction mode）. A novel continuous time equivalent circuit of the converter is introduced too, with the aim of determining a ripple free representation of the state variables of the system, over both transient and steady state operation. Then, a predictive current control algorithm, suitable in both CCM and DCM, is developed and properly checked by means of computer simulations. The corresponding results have highlighted the effectiveness of the proposed modelling and of the predictive control algorithm, both in CCM and DCM.

A time-series modeling method based on the boosting gradient-descent theory

The forecasting of time-series data plays an important role in various domains. It is of significance in theory and application to improve prediction accuracy of the time-series data. With the progress in the study of time-series, time-series forecasting model becomes more complicated, and consequently great concern has been drawn to the techniques in designing the forecasting model. A modeling method which is easy to use by engineers and may generate good results is in urgent need. In this paper, a gradient-boost AR ensemble learning algorithm (AREL) is put forward. The effectiveness of AREL is assessed by theoretical analyses, and it is demonstrated that this method can build a strong predictive model by assembling a set of AR models. In order to avoid fitting exactly any single training example, an insensitive loss function is introduced in the AREL algorithm, and accordingly the influence of random noise is reduced. To further enhance the capability of AREL algorithm for non-stationary time-series, improve the robustness of algorithm, discourage overfitting, and reduce sensitivity of algorithm to parameter settings, a weighted kNN prediction method based on AREL algorithm is presented. The results of numerical testing on real data demonstrate that the proposed modeling method and prediction method are effective.

Real-Time Distributed Fiber Optic Sensor for Security Systems： Performance, Event Classification and Nuisance Mitigation

The success of any perimeter intrusion detection system depends on three important performance parameters： the probability of detection （POD）, the nuisance alarm rate （NAR）, and the false alarm rate （FAR）. The most fundamental parameter, POD, is normally related to a number of factors such as the event of interest, the sensitivity of the sensor, the installation quality of the system, and the reliability of the sensing equipment. The suppression of nuisance alarms without degrading sensitivity in fiber optic intrusion detection systems is key to maintaining acceptable performance. Signal processing algorithms that maintain the POD and eliminate nuisance alarms are crucial for achieving this. In this paper, a robust event classification system using supervised neural networks together with a level crossings （LCs） based feature extraction algorithm is presented for the detection and recognition of intrusion and non-intrusion events in a fence-based fiber-optic intrusion detection system. A level crossings algorithm is also used with a dynamic threshold to suppress torrential rain-induced nuisance alarms in a fence system. Results show that rain-induced nuisance alarms can be suppressed for rainfall rates in excess of 100mm/hr with the simultaneous detection of intrusion events. The use of a level crossing based detection and novel classification algorithm is also presented for a buried pipeline fiber optic intrusion detection system for the suppression of nuisance events and discrimination of intrusion events. The sensor employed for both types of systems is a distributed bidirectional fiber-optic Mach-Zehnder （MZ） interferometer.

TESTING k-EDGE-CONNECTIVITY OF DIGRAPHS

This paper presents an algorithm that tests whether a given degree-bounded digraph is k-edge-connected or E-far from k-edge-connectivity. This is the first testing algorithm for k-edge- connectivity of digraphs whose running time is independent of the number of vertices and edges. A digraph of n vertices with degree bound d is ε-far from k-edge-connectivity if at least εdn edges have to be added or deleted to make the digraph k-edge-connected, preserving the degree bound. Given a constant error parameter ε and a degree bound d, our algorithm always accepts all k-edge-connected digraphs and reiects all digraphs that is ε-far from k-edge-connectivity with orobabilitv at least 2/3.It runs in O（d（εd^-c）^k logεd^-1O）（c〉1 is a constant）time when input digraphs are restricted to be （k-1）-edge connected and runs in O（d（εd^-ck）^klogεd^-kO）（c〉1 is a constant）time for general digraphs.

Time synchronization and ranging under unknown positions and velocities

The process to achieve time synchronization and ranging for a network of mobile nodes is raising a concern among researchers, and hence a variety of joint time synchronization and ranging algorithms have been proposed in recent years. However, few of them handle the case of all-node motion under unknown positions and velocities. This study addresses the problem of determining ranging and time synchronization for a group of nodes moving within a local area. First, we examined several models of clock discrepancy and synchronous two-way ranging. Based upon these models, we present a solution for time synchronization with known positions and velocities. Next, we propose a functional model that jointly estimates the clock skew, clock offset, and time of flight in the absence of a priori knowledge for a pair of mobile nodes. Then, we extend this model to a network-wide time synchronization scheme by way of a global least square estimator. We also discuss the advantages and disadvantages of our model compared to the existing algorithms, and we provide some applicable scenarios as well. Finally, we show that the simulation results verify the validity of our analysis.