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The levitation control system is a key part of maglev train. The measurement of levitation gap plays an important role in the suspension system. Traditional gap sensors have some disadvantages such as high power supply voltage, temperature influence and measurement distance limitation. In this paper, a method is designed on visual measurement of levitation gap. The maglev ball system is taken as the research object. We first obtain the original high-definition images of the ROI(region of interest) in the levitation gap. Then, the pixel area in the ROI of levitation gap is obtained by calculating the number of pixel points whose results are “1” in the binarization image. Finally, the pixel area is calibrated with the actual gap distance and the actual gap distance is calculated. This method calculates the actual gap according to the image pixel area in the ROI of levitation gap. It has high measuring accuracy and strong anti-interference ability, which could effectively remove the asymmetric interference in the ROI of levitation gap, locating and alarming the interfered fault position.

With the application of information technology in petroleum industry, petroleum companies have accumulated a lot of well drilling data. However, due to various reasons, these data may have many problems, such as data missing, data error. In this study, based on the actual drilling data of a petroleum company, a data governance platform for oil well drilling operation is designed and implemented. 3 main governance function: basic data governance, design data governance and well history data governance are realized to govern 17 data tables. The governance result indicates that the missing data and abnormal value are common in the existing drilling data.

The ultrasonic wireless power transmission (referred to as USWPT) technology is proposed to supply power for the suspension gap sensor, which maintains the structural integrity and sealing of the sensor metal shell, and solves the problems of high protection requirements, frequent maintenance and poor electromagnetic shielding effect caused by cable holes. In this paper, the complex working environment of maglev suspension gap sensor is analyzed, and the feasibility of USWPT technology for wireless power supply of sensor is explored. The validity of the equivalent circuit and impedance matching circuit of the ultrasonic transducer and the load characteristics of the overall system are analyzed by MATLAB simulation. In the end, the experimental platform of USWPT is designed and built to realize the low-power wireless power supply of the metal-isolated electrical equipment.

During the gap measurement of maglev system, aiming at the problem that gap sensor is easily affected by temperature, flatness of detection surface, inconvenient installation and other factors, a method of maglev gap measurement based on machine vision is proposed. First, according to the scale space theory, find the relative coordinates of the floating gap in the original image. Then use Canny operator to detect the edge characteristics of the electromagnet and the maglev ball, and sample the edge points of the electromagnet and the maglev ball according to the relative coordinates of the maglev gap. Finally, calculate the floating gap based on the pixel distance between the edge points. Experiments show that under the embedded ARM platform, the speed and accuracy of gap measurement meet the requirements of maglev control.

Aiming at the shortcomings of the traditional gap sensor, the measurement accuracy is greatly affected by environmental factors such as temperature, and a levitation gap measurement method based on image processing is designed. The high-speed industrial camera monitors the levitation gap in real time and transmits the image to the PC. The image is processed by the PC to obtain the value of the levitation gap, which can complete the function of the traditional gap sensor. The experimental results show that the method has high precision and fast speed, which can meet the stability control requirements of the levitation ball system.

Snow cover detection (SCD) from single cloud-interrupted remote sensing image is an important task in many applications like avalanche forecast, real-time hazard monitoring and loss assessment. Aimed at development of an automatic procedure for SCD, a two-stage approach is proposed in this study using single remote sensing image. Due to the interruption of cloud in the detection process, the two-stage approach includes crude snow detection and accurate snow detection. In the first stage, weighted fuzzy C-means (WFCM) is introduced for crude snow detection to extract snow and cloud. In the second stage, gradient optimization and morphological closed operation (GOMCO) are combined for accurate snow detection to remove the cloud. By iteratively using GOMCO, the result of snow is obtained. The experiment shows that the interference of the others can be effectively removed and the snow area is obtained accurately by using the proposed method.

The course of tourism electronic commerce should follow the idea of higher vocational education, give prominence to cultivating occupation ability. Through the teaching experience of "Travel E-commerce" curriculum and the analysis of the demand ability of information technology talents of tourism enterprises, this thesis explored and studied the curriculum system, experimental teaching, teaching methods etc.

While the data mining in education field gained more and more popularity in recent years, there have many research endeavors to find association rules in students' academic situation. The current methods normally apply traditional association rules mining technique to identify those rules. However, traditional association rules mining technique can not identify difference between different types of students' academic situation. To solve this problems, we applied a novel contrast target rules mining method in this paper. Real world data set from Computer Science department of a university of China, the empirical results show the difference characteristics of different types of students in their academic situation.

In major colleges and universities, in order to mobilize students enthusiasm for studying and participating in extracurricular activities, all colleges make an evaluation on students comprehensive quality and set different rewards regulations for the various level. The main way is to provide financial incentives, they distribute scholarship for students of meeting requirements. The Decision Tree algorithm is frequently used all the time, however, because of the tree node selected is based on attribute's mutual information, which will lead to some crucial attribute lost their decisive role. Therefore, in this paper, we focused on predicting whether students obtain scholarship on the comprehensive quality of students with Naive Bayes algorithm.

In this paper, it introduces a proposed hybrid control system which can simultaneously optimize deadtime and reverse inductor current with both the proposed Switched Capacitor Delay Deadtime Controller (SCD-DTC) and Unbalanced Input Pair Zero Current Detector (UIP-ZCD). Furthermore, the total silicon chip area of the hybrid control system occupies an area of 1.44mm2. The estimated power efficiency is 95.8% which has taken into account of losses due to wire bonding, package leads, PCB traces and other parasitic effects. The V IN_BUCK is 2.8–3.3V and being regulated to a V OUT value of 1.8V while driving 5–30mA of load current. The proposed SCD-DTC implemented in the buck converter minimizes the duration of body diode conduction to be < 0.1ns.

Traffic control is an effective and efficient method for the problem of traffic congestion. For complex urban traffic networks, it is necessary to design a high-level controller to regulate the traffic demands. Under the parallel control framework for complex traffic networks, the authors design a demand-balance Model Predictive Control (MPC) controller based on the Macroscopic Fundamental Diagrams (MFD)-based multi-subnetwork model, which can optimize the network traffic mobility and the network traffic throughput by regulating the input traffic flows of the subnetworks. The transferring traffic flows among subnetworks are indirectly controlled by the demand-balance MPC controller, and a global optimality can be achieved for the entire traffic network. The simulation results show the effectiveness of the proposed controller in improving the network traffic throughput.

Reducing travel time of emergency vehicles (EVs) has a potential in significant savings of life and property. Integrating modern intelligent transportation system (ITS) with EV signal preemption seems to be a solution. But existing EV signal preemption systems often break the current signal coordination and impact a lot on the normal traffic streams. In this paper the authors propose an emergency vehicle signal coordination (EVSC) approach, which is intended to provide "green wave" for EVs. Traffic simulations are conducted along an emergency corridor with eight (8) intersections in Qingdao, China. Multiple traffic measurements are compared between simulation outputs with and without EVSC operation. The result indicates that the proposed approach can reduce EV travel time by 26.9% without too much negative impact on the normal traffic streams.

Urban traffic congestion has already become an urgent problem. Artificial societies, Computational experiments, and Parallel execution (ACP) method is applied to urban traffic problems. In ACP framework, optimization for urban road networks achieves remarkable effect. Optimization for urban road networks is a problem of nonlinear and non-convex programming with typical large-scale continual and integer variables. Due to the complicated urban traffic system, this paper focuses on the ACP-based Computational experiments modeling. It hopes to find an optimization model that is further accord with the practical situation. To this end, the authors use a mixed integer nonlinear programming problem (MINLP) and an genetic algorithm (GA) for urban road networks optimization. The systemic simulation experiments show that the approach is more effective in improving traffic status and increasing traffic safety.

In this paper, a P2P pollution method named integrated pollution is proposed. It integrates the characteristic of fake-block-attack and index pollution to solve the limitation of each. The mathematic model of integrated pollution is built to discuss the factors which influence the effect of integrated pollution. The simulation of this model shows that the effect of integrated pollution depends on the amount of attackers, the amount of invalid peer information and the maximum connection of downloading peers. The influence of these 3 factors to the effect of integrated pollution is analyzed.

Current freeway traffic flow prediction techniques pay attention to time series prediction or introduce the upstream adjacent road segments in the short-term prediction model. In this paper, all of the road segments on the freeway are considered as candidates of the independent variables fed into the prediction model. A spatio-temporal multivariate adaptive regression splines (MARS) approach is proposed for the road network analysis and to predict the short-term traffic volume at the observation stations on the freeway. The actual traffic data are collected from a series of observation stations along a freeway in Portland every 15 minutes. In the first phase, the macroscopic dependency relationships of the stations on the freeway are investigated via MARS method. Subsequently the stations most related to the object station are selected and fed into the MARS prediction model to generate the short-term volume. The experiments are carried out on the actual traffic data and the results indicate that the proposed spatio-temporal MARS model can generate superior prediction accuracy in contrast with the historical data based MARS model, the parametric ARIMA, and the nonparametric PPR methods.

Keyword spotting is the task of identifying the occurrences of certain desired keywords in an arbitrary speech signal. Keyword spotting has many applications. One of them is emergency voice alarm systems, like command control and smart monitoring ATM etc. This paper presents a sensitive keyword spotting system applied to voice alarm occasions. By sensitive keyword retrieval, emergency warning systems can automatically monitor and follow up the situation, find the crisis occurs, and then give the alarm. Automatically retrieving and tracking for the particular occasion can not only save a lot of manpower and material resources, but is also more efficient. So the study of this subject has great reality and application value.

Video detection is one of the primary collection means of traffic states in Parallel traffic Management Systems (PtMS). In order to accurately and automatically obtain road areas in highway surveillance videos, this paper presents an automatic detection algorithm based on the frequency-domain information of video images. This algorithm uses the frequency-domain feature that is produced by the vehicles passing through road areas in videos, to realize automatic segmentation and recognition of the road areas. The experiment comparing with the traditional vehicle-tracking-based method, which uses the information in the time-space domain, illustrates the advantages of the proposed algorithm.

At present, the problems of urban traffic management and control have not yet been well solved, due to the great complexity of urban transportation systems. In this context, artificial transportation systems (ATS) theory was presented several years ago, and has been verified to be a promising approach to better management and control of complex transportation systems. On the basis of summarizing the research works on ATS to date, this paper systematically introduces the concept, theory frame and methods of ATS; meanwhile, the corresponding literatures are also reviewed. Then, as a major application approach of ATS, the parallel system theory is briefly introduced, and the major case studies of applying ATS are also reviewed. Finally, we analyze the current state-of-the-art of ATS, and point out the main research directions.

Accurate short-term traffic flow prediction plays a fundamental role in intelligent transportation systems (ITS), e.g. advanced traffic management systems (ATMS). To generate accurate short-term traffic volume, nonparametric models have gained credit from quantities of researchers. On the basis of the common thought that future traffic states can be predicted according to the similar states in the historical traffic data, this paper presents a novel nonparametric-model-based method to predict the short-term traffic volume. The applied nonparametric model is the classification and regression trees (CART) model. In the application, the CART model first classifies the historical traffic states into plentiful categories. Afterwards, the linear regression model is built corresponding to each traffic state pattern. Finally, the model predicts the short-term traffic state through clustering the current state vector into the most congenial historical pattern and regression model. In the experiments, the proposed method is tested by using the 15 minutes average traffic volumes on freeways and is compared with the classic nonparametric methods k-nearest neighbors (k-NN) model, and the parametric method Kalman filter model. The results indicate that the CART-based prediction method outperforms the k-NN and Kalman filter methods in both the mean absolute percentage error and the mean absolute scaled error.

A spatial short-term traffic flow prediction method based on the macroscopic urban traffic network (UTN) model is described and compared to the traditional time series forecasting methods. This paper presents a general macroscopic UTN model by adopting the transfer mechanism of vehicles between road links to represent the future distribution of vehicles in the whole network. Based on the model, we predict the short-term traffic flux without using any historical traffic data, which is completely different from previous approaches. Furthermore, to verify the effectivity of the UTN-based prediction model, we compare it to four classic models including two parametric and two nonparametric methods with the data produced by CORSIM, a commonly used microscopic traffic simulation software. Finally, the comparative results illustrate that the proposed method can reach the level of classic methods and predict the short-term traffic flow timely and accurately both for the steady or suddenly changed traffic states.

In the research field of Intelligent Transportation Systems (ITS), traffic flow prediction is a key technology for traffic guidance and advanced control strategy. Accuracy and immediacy are the main requirements for prediction methods. This paper presents a short-term prediction algorithm of traffic flow rate based on the macroscopic urban road network model. Classified into different typical elements, a traffic road network can be expressed as a matrix. Taking crosses and their links as basic research objects, the proposed prediction method can only use a few real traffic parameters obtained from loop detectors to realize accurate short-term prediction of traffic flow rate. This method can also be adaptable to different kinds of road network. In case study, the real traffic system is simulated with the microscopic traffic simulation platform (CORSIM). In the given simulation environment of road network, the experiment results illustrate that the proposed prediction algorithm can accurately predict flow rate in short term.

This paper addresses an issue of short-term traffic flow prediction in urban traffic networks with traffic signals in intersections. An effective spatial prediction approach is proposed based on a macroscopic urban traffic network model. In contrast with other time series based or spatio-temporal correlation methods, this research focuses on the substantial mechanism of vehicles transmission on road segments and the spatial model of the entire urban network. Furthermore, this approach employs a simple speed-density model based on the macroscopic fundamental diagram (MFD) to obtain a more accurate vehicle travel time on the link. Finally, the microscopic traffic simulation software, CORSIM, is adopted to simulate the real urban traffic, and the proposed method is used to predict the traffic flows generated by CORSIM. The simulation results illustrate that our approach performs effective prediction timely in the rush hours, as well as the suddenly changed traffic states.

Nowadays, probe vehicles equipped with Global Position System (GPS) are an effective way of collecting real-time traffic information. This paper first briefly introduces the Curve-Fitting Estimation Model (CFEM), which is one of the typical methods using GPS data to estimate the traffic flow state. After that, it is detailedly analyzed how many probe vehicles the CFEM requires in order to ensure enough estimated accuracy. Furthermore, a sample size algorithm is developed to calculate the minimum sample size of the CFEM. In the algorithm, the road type, the length of road section, and sample frequency are taken into account. Finally, the proposed algorithm of sample size analysis are tested by the experiments using the data collected from the road network of the whole center region of Shanghai.

In order to evaluate sub-health state, synchronous acquisition of pulse images and ECG signals multi-information was conducted by using self-designed synchronous acquisition system. Stable pulse waves were obtained through the grid area change of each frame pulse images. After the three dimensional pulse image was reconstructed for every frame image, pulse length and pulse width were calculated. The pulse and ECG features such as power spectrum peak, gravity frequency, and approximate entropy were extracted. Linear Discriminant Analysis (LDA) algorithm was used for sub-health state classification. The satisfactory recognition result was obtained by using all ECG and pulse features, which provide a new research approach for evaluating sub-health state.

Based on the requirement of integrating manufacture and modern industry maintenance mode, to solve the yes or no rigid determinant of maintenance decision which is adopted by Reliability-Centered Maintenance (RCM), as well as they brought judgment difficulty, misjudgement and difficult to synthesis etc. The decision process and method of reliability-centered maintenance are reconstructed. The decision method of Probability Reliability-Centered Maintenance (PRCM) is put forward. Thereby Reliability-Centered Maintenance is pressed close to enterprise maintenance requirement. Finally, the validity of this model is demonstrated by example of certain petrifaction corporation.

The problem of real-time rectangle detection on high-resolution image arises in actual panel production. This paper proposes a robust real-time method for panel rectangle detection. In line extraction part, a new technique called \textquotedblleft Crossing Dilation\textquotedblright is proposed to improve the Progressive Probabilistic Hough Transform performance, to extract long line segments that are not perfectly straight in large sparse binary image. The rectangle is detected using the proposed rectangle fitting method. Experiments on a variety of real cases of large panels are executed, some of which are badly disturbed by the fake edges caused by the dust in the background region. The results show great real-time performance on all the cases, and the proposed method is robust to the disturbances and different panel sizes.

This paper presents a novel method for detection and recognition of glass defects in low resolution images. First, the defect region is located by the method of Canny edge detection, and thus the smallest connected region (rectangle) can be found. Then, the binary information of the core region can be obtained based on a specific filter. After noises are removed, a novel Binary Feature Histogram (BFH) is proposed to describe the characteristic of the glass defect. Finally, the AdaBoost method is adopted for classification. The classifiers are designed based on BFH. Experiments with 800 bubble images and 240 non-bubble images prove that the proposed method is effective and efficient for recognition of glass defects, such as bubbles and inclusions.

A data processing software package is developed for passive seismic monitoring of hydraulic fracturing. The original seismic data is acquired by a surface array which has many stations work independently. The package converts original data files into SEG-Y format files, and files of all stations are synchronized and combined according to GPS time. Many methods are utilized to perform signal analyzing and filtering for SEG-Y files, such as frequency analysis, time-frequency analysis, digital filter design and digital filtering, wavelet analysis and wavelet denoising. Seismic emission tomography, which is suitable for surface passive seismic monitoring is used for source localization. The calculated hypocenters can be displayed in 3D and 2D mode, by which users can determine the direction, length and other parameters of injection induced fractures. The package is designed with Delphi and Matlab in hybrid. Complicated scientific algorithms are realized by Matlab toolboxes, interface design and system integration is performed by Delphi. Therefore the package has powerful processing abilities and friendly interface.

This paper builds a traffic state estimation system based on fusion of the information of the Geographic Information Systems for Transportation (GIS-T) and Global Positioning System (GPS). In this system, a new method is presented to construct the GIS-T digital map more accurately and efficiently. By this means, the system can perform better in estimation of traffic state with the information provided by the GPS probe vehicles. In addition, a fast affine algorithm is applied in the system to transform the GPS coordinates to the GIS-T coordinates. Finally, the experiments with the real GPS traffic data of Shanghai verify that the proposed method can improve the performance of the system in traffic state estimation.

This paper presents a real-time algorithm for detecting and tracking bicyclists or pedestrians using a laser device. By processing the sequence of the range images, the algorithm outputs trajectory and speed of each object during the period when he is in the detection region. The whole algorithm consists of two parts, which are the object detection and the object tracking. In the former, the multi-level thresholding method is combined with the Iterative Selforganizing Data Analysis Techniques Algorithm (ISODATA) to implement object segmentation. In the latter, Kalman Filter is applied to recognize and track moving objects. Experimental results demonstrated this algorithm is effective in object recognition and tracking, as well as robust in the applications.

Based on the requirement of integration manufacturing and modern production manner, requirement analysis is discussed. That is one of the core of ROM—Requirement-Oriented Maintenance which is a new theory and method of maintenance management. Four-source approaches of maintenance requirement are analyzed. Distinguish business maintenance requirement from technology management and policy environment is brought forward. Several methods to distinguish maintenance requirement are pointed out. Four basic levels of business maintenance of functionality, security, economy and environment are discussed. On this basis the influence factors of deciding enterprise maintenance management object are analyzed. Finally, administrative levels stair structure is put forward and three dimension orientation model or maintenance management object is studied.

P2P pollution technology has been used to control the unauthorized distribution of the copyrighted files in recent years. In this paper, two 2 kinds of P2P pollution, the index poisoning and the fake-block-attack, has been discussed. The experiment result of applying them to BitTorrent shows that the effect of controlling file distribution is not satisfactory under some conditions. In order to improve the effect and the applicability of the pollution, an approach which combines the characteristics of the index poisoning and the fake-blockattack is proposed. The evaluation of applying this approach on BitTorrent shows that the robustness of the BitTorrent swarm is damaged and most peers can not complete the downloading even though the attack is not distributed to every downloading peer evenly. The effect of this approach is better than the effect of the index poisoning and the fake-block-attack.

This paper suggests a thin cloud removing approach of color remote sensing image based on support vector machine in HSI color model. The intensity component is decomposed in multi-scale by using strong edge capture ability of support vector machine, and the coefficients in different scales are obtained. Then abundant high frequency information is obtained by combining with directional filter bank. Reconstructed image is obtained through enhancing coefficients of high frequency and suppressing coefficients of low frequency. The saturation component is processed by exponential expand method, while the hue component is invariant. Experiments show that thin cloud can be removed efficiently by using the method introduced in this paper.

In recent year, some popular P2P file-sharing systems have been identified to be polluted by the copyright Industry to control the unauthorized distribution of content. As one of the most common P2P pollution methods, the index poisoning controls the P2P file-sharing by inserting massive numbers of bogus records into the index of the P2P file-sharing system. In this paper, the index poisoning in the structured P2P file-sharing system is introduced firstly. A term “poison multiplication” is defined to describe the level of the poisoning and the probability model that a peer receives valid peer information on the condition of the index poisoning in BitTorrent is proposed. We run index poisoning in BitTorrent and evaluate the effect of it. The result of the evaluation shows that with the poison multiplication increases, the connection time and the randomness of the connection time increases. To our knowledge, this is the first study to employ BitTorrent as the platform to evaluate the effectiveness of the index poisoning.

This paper is about a vision-based system that automatically monitors intermodal freight trains for the quality of how the loads (containers) are placed along the train. An accurate and robust algorithm to segment the foreground of containers in videos of the moving train is indispensable for this purpose. Given a video of a moving train consisting of containers of different types, this paper presents a method exploiting the information in both frequency and spatial domains to segment these containers. This method can accurately segment all types of containers under a variety of background conditions, e.g illumination variations and moving clouds, in the train videos shot by a fixed camera. The accuracy and robustness of the proposed method are substantiated through a large number of experiments on real data of train videos.

Digital control has been widely used in power electronics and its sampling rate is important to the control precision. Generally, faster the sampling rate is, closer the digital control is to that of its analog/original antetype. However, in most power electronics system, the sampling rate is commonly once or twice of the PWM switching frequency, which is vital for switching losses. This coupled relationship may result in conflict between high digitalized resolution and high efficiency, especially for high power applications. Multi-sampling technology is to sample the signal many times in one PWM period. Therefore, it doesn't need to increase the switching speed proportionally while fastening the sampling rate. This paper adopts the multi-sampling in an Active Power Filter (APF) application. A logic method to deal with the problem during implementation due to the nature of digital controller is first proposed. Principle and implementation of this technology is discussed in detail. Analysis and experiment results show that the high compensation performance and low switching loss can be both achieved using the multisampling method.

Human matching between different fields of view is a difficult problem in intelligent video surveillance; whereas fusing multiple features has become a strong tool to solve it. In order to guide the fusion scheme, it is necessary to evaluate the matching performance of these features. In this paper, four typical features are chosen for the evaluation. They are the Color Histogram, UV Chromaticity, Major Color Spectrum Histogram, and Scale-Invariant Features (SIFT). Quantities of video data are collected to test their general accuracy, robustness, and real-time applicability. The robustness is measured under the conditions of illumination changes, Gaussian and salt noises, foreground errors, resolution changes, and camera angle differences. The experimental results show that the four features bear distinctive performances under the different conditions, which will provide important references for the feature fusion methods.

Human matching is fundamental in human tracking over non-overlapping cameras. Fusing multiple features is an efficient way to increase the ratio of matching. In this paper, we present an algorithm of iterative widening fusion (IWF) to fuse the multiple features, including color histogram, UV chromaticity, major color spectrum histogram and scale-invariant features (SIFT). Also, the Bayesian framework, as a classical fusion method, is compared with the IWF algorithm. The experimental results indicated that the IWF algorithm obtained the matching accuracy better than Bayesian framework in most cases.

In this paper, a GPS/GIS integrated system for urban traffic flow analysis is proposed. Urban GIS-T data are used to construct the GIS map of urban area when the system begins its work. Afterwards, real-time GPS data of probe vehicles are periodically collected to implement the location amendment. And then, location-amended GPS data are dynamically fitted with the adaptive traffic flow model. By using fitted traffic flow models, states of traffic flow are ultimately estimated along rolling time periods. Compared with conventional works, the proposed system successfully preserves the continuity of vehicle travels without additional data sources. Experiments based on GIS-T data of Shanghai and GPS signals of taxis in Shanghai indicate that the new system is both efficient and robust.

Shunt active power filter (APF) is suitable to compensate current-type harmonics generated by nonlinear load, but high compensation precision is difficult to be obtained due to very high slew rate of harmonic current, output impendence of voltage source converter (VSC) and control loop phase lag. LCL-filter reserving sufficient attenuation ration for switching ripple with small LC parameters is suitable to be used as output filter to get high slew rate of compensation current. However, LCL-filter, as a three order one, is difficult to be stable. This paper proposes a simple control method with one beat delay introduced to feedback current; meanwhile, repetitive control algorithm, as outer control loop, is adopted to eliminate steady-state error of the whole control system. Based on this double-loop control scheme, very high quality steady-state grid current waveform and fast dynamic response can be obtained. Simulation results validate the feasibility of the method proposed by this paper.

On the basis of previous researches, an improved evidential fusion approach is presented to integrate heterogeneous multi-sensor data in Urban Advanced Traveler Information Systems. The method inherits the advantages of the previous model in terms of the real-time processing feature; meanwhile, its performance is improved by adding a mechanism to evaluate the dynamic reliability of sensors. In this paper, the whole evidence reliability algorithms, which contain both the static one and the dynamic one, are provided primarily. After that, the frame and computational procedures of the new approach are given. Finally, a simulation test and a real-world data experiment are discussed to explain the advantage of the proposed method in comparison with the previous model. The real-world data of loop detectors and GPS probe vehicles were collected from an urban arterial road section in Shanghai.

Summary form only given. Complex plasmas are known to exist in a variety of natural and laboratory environments. Within an experimentally produced complex plasma such as that found in a GEC rf reference cell in a laboratory, it has been shown that dust particles often form two-, three-, or even longer particle chains, vertically aligned within the sheath. Of these, the two-particle chain remains the simplest structure. The interparticle (interlayer) interaction between upper and lower particles is of great interest and not yet completely understood. This paper discusses this interaction, providing data produced by perturbing such particle chains employing a modulated DC bias on the lower (powered) electrode. Using upper and lower particles having different masses (resulting in different charges), the resonant frequencies of each will be examined and analyzed.

Summary form only given. Over the past decade, dust particulate contamination has increasingly become an area of concern within the fusion research community. In a burning plasma machine design like the International Thermonuclear Experimental Reactor (ITER), dust contamination presents problems for diagnostic integration and may contribute to tritium safety issues. Additionally due to ITER design, such dust contamination problems are projected to become of even greater concern due to dust / wall interactions and possible instabilities created within the plasma by such particulates. Since the dynamics of such dust can in general be explained employing a combination of the ion drag, Coulomb force, and ion pre-sheath drifts, recent research in complex (dusty) plasma physics often offers unique insights for this research area. This paper discusses the possibility of how experimental observations of the dust and plasma parameters within a GEC rf Reference Cell might be employed to diagnose conditions within fusion reactors, hopefully providing insight into possible mechanisms for dust detection and removal.

Wakefield oscillations created by the ion wakefield existing below a dust particle within the plasma sheath generated above a powered lower electrode in a GEC rf reference cell carry information about the plasma sheath, the dust particle charge and the speed of the streaming ions. An experimental method to investigate such wakefield oscillations is discussed.

Summary form only given. Dusty plasmas have generated a large amount of interest since the discovery of ordered structure (crystal) formation in experimentally generated complex plasmas in 1994. Dust particles within the plasma sheath generated in these complex plasmas can form vertical chains due to the streaming ion wakefield. For the simplest of these configurations (a two-particle chain), the particle closest to the lower electrode will always remain in the shadow of the particle farthest from the lower electrode. This results in the two particles feeling differing ion drag forces: the top particle is acted on by an ion drag force directed from the plasma to the lower electrode, while the bottom particle is instead acted upon by the resulting 'wakefield' produced by the interaction of the upper particle with the ion drag force. This dynamic situation currently provides the best known experimental environment for examining the physics behind the ion drag force and should lead to a better understanding of its interaction with the plasma sheath. An experimental method for investigating the interaction between such pair-particle chains based on modulating the bias on the lower electrode employing a DC bias modulation technique is presented.

Summary form only given. Instabilities within plasmas have long been of interest to physicists working in the plasma field. In recent experiments both in microgravity and on Earth, it has been shown that under appropriate conditions such condensed states can form adjacent to regions containing liquid like dust phases. Under these circumstances, the complex plasma will often spontaneously form a stable, local, dust-free region having sharp boundaries called a void. Voids are typically small, relatively robust (stable) and can form in a variety of manners. For example, in the laboratory when dust grains are grown in situ, formation of the void is often preceded by the sudden onset of a filamentary mode while in microgravity, they often appear without any observable initial instability. In all cases, void size appears to be dependent upon RF power. Various mechanisms have been proposed to explain the underlying physics behind the formation of filamentary or void modes. The majority of these treat the dust system as either a one or two dimensional fluid with the ion drag, thermophoretic force, ionization instabilities or some other attractive potential between the dust grains proposed as a possible formation mechanism for the void. Recently within CASPER, an entirely new mechanism for creating instabilities has been discovered. This method allows the experimental generation of void-like structures within the sheath region above the lower electrode of a RF plasma discharge. This method will be discussed briefly along with recent experimental results. Theoretical results modeling the above will also be presented

A novel power and bit allocation algorithm for MIMO OFDM system with changeable BER is proposed. This algorithm is based on the waterfilling theorem and calculates the approximate upper bound of the BER in each subchannel, and then the bit and power can be allocated according to the variable BER. This approach maximizes the frequency efficiency under the average power constraint. The theoretic analysis and simulation results show that the frequency efficiency is greater than the algorithm with the fixed BER.