Information Technology and Intelligent Transportation Systems

Higher driving volatility, e.g., hard accelerations or hard braking, can imply unsafe outcomes, more energy use, and higher emissions. This presentation will demonstrate how large-scale data, increasingly available from sensors, can be transformed into useful knowledge. This is done by creating a framework for combining data from multiple sources and comparing counties/regions in terms of driving volatility of resident drivers. The unique database was created from four sources that include large-scale travel surveys, historical traffic counts from California and Georgia Department of Transportation, socio-demographic information from Census, and geographic information from Google Earth. The database provides a rich resource to test hypothesis and model driving decisions at the micro-level, i.e., second-by-second. The database has 117,022 trips made by 4,560 drivers residing in 78 counties of 4 major US metropolitan areas across two states. They represent significant variations in land use types and populations; all trips were recorded by in-vehicle GPS devices giving 90,759,197 second-by-second speed records. The data integration helps explore links between driving behaviors and various factors structured in hierarchies, i.e., the data are structured at the levels of trips, drivers, counties, and regions. Appropriate hierarchical models are estimated to study correlates of driving performance and to compare traffic performance across regions. The implications of our analysis for intelligent transportation systems will be discussed.

Connected and Autonomous Vehicle (CAV)-enabled traffic system has demonstrated great potential to mitigate congestion, reduce travel delay, and enhance safety performance. According to the U.S. Based on seamless Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure communication as well as autonomous driving technologies, traffic management and control will be revolutionized. The existing studies indicate that traffic lights will be eliminated and 75 % of vehicles will be autonomous vehicles by 2040. National Highway Traffic Safety Administration (NHTSA) plans to mandate inter-vehicle communication technologies on every single vehicle by 2016. However, one should note that the current research regarding CAV system management and control is still in its early stage. The presented study concentrates on the VISSIM-based simulation platform development to enable an innovative autonomous intersection control mechanism and optimize CAV operations at intersections without signal lights. Simulation-based investigation on traffic system operations provides a cost-effective, risk-free means of exploring optimal management strategies, identifying potential problems, and evaluating various alternatives. In the study, a VISSIM-based simulation platform is developed for simulating individual-CAV-conflict-based traffic control optimization at intersections. A novel external module will be developed via VISSIM Component Object Model (COM) interfaces. A new CAV-based control algorithm entitled a Discrete Forward-Rolling Optimal Control (DFROC) model, is developed and implemented through the VISSIM COM server. This external module can provide sufficient flexibility to satisfy any specific demands from particular researchers and practitioners for CAV control operations. Research efforts will be made to calibrate driving behavior parameters in the simulation model using drivers’ characteristic data to further strengthen the simulation creditability. Furthermore, a method for statistically analyzing simulation outputs and examining simulation reliability is developed. The methodology developed is applicable for quantitatively evaluating the impacts of various CAV control strategies on urban arterials.

The authors examined changes that are likely to affect transportation behaviors in the future, developed a “fuzzy cognitive map” (FCM) of the relationships, and used the FCM model to investigate the effects of those relationships. This new FCM method enables modeling the potential consequences of new technologies and services using a variant of the fuzzy cognitive map (FCM) approach, which enables problems involving imprecise and uncertain information to be modeled. Significant modifications to the standard FCM approach were made to address deficiencies found in applying the standard approach. The new approach retains some basic FCM characteristics, but it deviates substantially in a number of ways as well. It has been found that this produces well-behaved models that can be explained in common-sense terms, be easily configured, run many scenarios quickly, and used to analyze scenarios of disruptive change. The results of the study show that FCM models offer a promising method for transportation planners to enhance their ability to reason about system effects when quantitative information is limited and uncertain. More specifically, the results provide some initial guidance on the potential impacts of disruptive changes on future travel, which may help in targeting limited research funds on the most consequential potential changes.

Connected Vehicle (CV) systems are envisioned to enhance a wide range of safety, mobility and environmental aspects to highway traffic. A critical research need lies in clarifying the cause-and-effect mechanism between the CV information and driver behaviors and subsequent adaptive resiliency of improvement in operation, safety, and emissions reductions. A novel approach is hence created via developing the simulation-based tool, Synthetic Adaptive V2X Effect (SAVE) Estimator, to explore the interactions between the CV system and transportation performance. This presentation will introduce preliminary results from the speaker’s on-going research on identifying factors possibly affecting travel behavior and rationale of their aggregated impact on mobility, safety, and vehicle emission in support of simulated outcomes of synthesized scenarios at a freeway and ramp conjunction facility in the Cincinnati are, Ohio (USA). The framework and associated modeling methodology for the development of the SAVE Estimator will be also introduced alongside the discussion of the case study.

Autonomous vehicles (AV) and connected vehicles (CV) are being designed with numerous sensors, including cameras, radar and Lidar, to enable features like adaptive cruise control, blind spot monitoring, collision avoidance and navigation. As AVs and CVs enter the vehicle fleet, practitioners are going to have the opportunity to monitor operations for freeways, intersections and urban environments to a degree that has not been possible or practical previously. But, the very availability of data will threaten practitioners with information overload. To properly use this newly abundant data, new algorithms and systems designs will be needed to automate data collection and processing into formats that practitioners can use.

Air travel in modern passenger aircraft has become extremely safe. This is largely due to the engine reliability, on-board computing system reliability and excellent flight crew training. Flight crews are highly trained to operate in the technical and human environments of the cockpit. Despite all these measures, accidents do happen. This talk presents the development of intelligent flight data monitoring system for improving the safety of aviation operations. The progress made in the development of an in-flight agent to monitor pilot situation awareness is also presented.

Connected Vehicle Safety Pilot Model Deployment presented a unique opportunity to demonstrate DSRC-based vehicle safety applications in real-world driving scenarios. A diverse team of industry, public agencies and academic institutions is involved in the planning and delivery of the project. The presentation illustrates the collaborative effort from the Safety Pilot and forthcoming deployment projects. The discussion also contributes to the exploration of future research opportunities to leverage the skills and experience from past projects and the infrastructure support in Ann Arbor.

Since the advent of automated vehicle technologies, the current trend of practice in this fast-evolving field has started to move from basic research and development in a lab environment to field trials and pilot testing. While a number of studies on V2V communications and vehicle control systems have been reported for the purpose of enhancing traffic safety, this research focuses on the efficiency benefit of the technologies in traffic operations when implemented even in a small number of vehicles in the traffic stream. Specifically, this presentation discusses the effects of automated vehicles in a traffic flow mixed with regular (human operated) vehicles on platoon formation and gap acceptance to increase roadway capacity and reduce delay. The theoretical basis for such improvements is reviewed first, followed by case studies involving intersection dilemma reduction, side street gap selection, and bottleneck management at a work zone. The resultant benefits are quantified under different rates of market penetration of the automated vehicles, which are distributed randomly in the traffic stream. Preliminary findings are summarized, including the pros and cons of the implementation.

As connected vehicle research moves into deployment, state, local and transit agencies, metropolitan planning organizations (MPOs) and the private sector will start experiencing the effects of vehicles, after-market devices, mobile devices, and infrastructure with dedicated, short-range wireless communications (DSRC) and other wireless connectivity at their cores. Along with other states and regions, the Oregon Department of Transportation (ODOT) can benefit from preliminary scoping, evaluation, and assessment of the impact of connected vehicles and infrastructure and a wide range of potential cooperative system applications. With this in mind, ODOT is aiming to determine whether or not to pursue the next phases of federal connected vehicle application funding. To assist ODOT in this assessment, a survey was distributed within the agency to gauge perception of connected and automated vehicle technology. However, many had concerns with cyber security and system failure having catastrophic consequences. Likewise, many voiced concerns about ODOT’s preparedness for connected or automated vehicles. ODOT can use these findings to help prepare for a better future of connected and automated vehicles.

Automate driving is enhancing the driving performance and reducing the crash risks. The presentation illustrates a new method for the management of the traffic flow on highways, based on the constant time to collision criterion. This criterion is addressing the car fallowing issue and it offers a speed adapted planner for the distance gap between cars. This method is able also to support a highway traffic flow management. The interface’s decision block is implemented by a fuzzy interpolative controller that is estimating the collision risk, taking into account the traffic intensity.

Income from parking and rental car facilities, for most of commercial airports in the U.S., are significant components in their revenue. Airports design the parking capacity and calculate the parking fees according to passenger throughput and mode split forecast, as well as the leasing rate of the rental car facilities. Nevertheless, with the emerging AV transportation modes, the fundamentals could change. In the future, if financially more economical, passengers may send their AVs back to their house instead of parking at the airports. Rental car industry could follow completely new business model and may not need to lease space on airport property. In addition, the flexibility of driverless may encourage more car sharing and real-time ridesharing. Given the information of mode split and parking information of one hub airport in the U.S., this study applies statistical and simulation methods to estimate potential parking needs and provide insights for future airport landside terminal planning and design.

Urban intersections are one of the key bottlenecks that cause recurring congestions. Traditional signalized control is effective but capacity-restrained. Availability of autonomous and connected vehicle technologies provides the possibility to improve intersection capacity. Powered by autonomous and connected vehicle technologies, a next-generation intersection control strategy ACUTA was developed by employing a reservation-based centralized control strategy. ACUTA converts the conflicts between traffic movements into conflicts between individual vehicles, hence enhancing intersection capacity. Comparison between ACUTA and optimized signal control revealed that ACUTA increased the intersection capacity by 33 %, resulting substantially lower delays. Particularly, comparison of the v/c ratios indicated that ACUTA could process 163 more vehicles per hour per lane without being oversaturated when compared to optimized signal control. Sustainability-wise, as ACUTA minimizes vehicle stops at intersections, it reduces emission and energy consumption as well. Sustainability effects compared with signalized intersection control include: (1) ACUTA reduces CO and PM 2.5 emissions by about 5 % under low to moderate volume conditions and by about 3 % under high volume condition; and (2) energy consumption is reduced by about 4 % under low to moderate volume conditions and by about 12 % under high volume condition. All these enhancements validate the potential benefits of implementing the next-generation intersection control.

We study the numerical solutions for modified Helmholz equation. Based on the potential theory, the problem can be converted into a boundary integral equation. Mechanical quadrature method (MQM) is presented for solving the equation, which possesses high accuracy order $$O(h_{max}^3)$$O(hmax3) and low computing complexities. Moreover, the multivariate asymptotic error expansion of MQM accompanied with $$O(h_i^3)$$O(hi3) for all mesh widths $$h_i$$hi is got. Hence, once discrete equations with coarse meshes are solved in parallel, the higher accuracy order of numerical approximations can be at least $$O(h_{max}^5)$$O(hmax5) by splitting extrapolation algorithm (SEA). The numerical examples support our theoretical analysis.

This paper studies the problem of generating obstacle avoidance trajectories through complex 3-D environments on-board for a group of non homonymic Unmanned Aerial Vehicles (UAVs). First, the collision-free multi-vehicle cooperative trajectory planning problem is mathematically formulated as a decentralized receding horizon optimal control problem (DRH-OCP). Next, a real-time trajectory planning framework based on a decentralized planning scheme which only uses local information, and an inverse dynamics direct method which has high computational efficiency and good convergence properties, is designed to solve the DRH-OCP. Finally, the simulation results demonstrate that the proposed planning strategies successfully generates the trajectories that satisfy the given mission objectives and requirements.

This paper introduces the measurement method on detection range of reversing assisting system, including reversing radar system and reversing vision system, which using laser distance measurementor and plannimeter. Fit the farthest positions that radar can detect smoothly in combination with the work principle of ultrasonic radar to realize visualization of detection range of reversing radar. A test bench was designed and established to simulate different installation size and location of the reversing assisting system of different types of vehicles. The deviation of test method was verified which can be controlled in a reasonable scope.

As to improve the attitude solving accuracy of strapdown inertial navigation system (SINS) boarded on the spacecraft for high dynamic application, a novel optimized coning error compensation algorithm is developed. Based on the rotation vector concept, a general scheme of N measurement samples coning error compensation by utilizing the last P measurement samples is proposed. Then, an optimized compensation algorithm is given in fixed frequency environment. And the key coefficients of this algorithm can be acquired by a simple matrix calculus rather than a complicated derivation. Finally, the algorithm is verified and tested by physical experiment. The results show that the coning error compensation accuracy does can be improved.

Laser Rapid Prototyping (LRP) can make the material accumulating by layer. And during the process, it has been known that shrinking stress may cause curl distortion. We have attempted to improve the processing speed and reduce the frequency of ON/OFF the laser. In this paper, we propose a new trajectory scan algorithm based on square lattice for Laser Rapid Prototyping (LRP). First, side-length of the square lattice is determined by the metal type and the material properties, and a set of regular square lattices is arranged based on the slices. Then, following the principles of not repeating and fewer breakpoints in solving Traveling Salesman Problem (TSP), the scanning sequence for each area is calculated. Therefore, a certain scan strategy can be determined, followed by the output of trajectory scan. We present the specific steps of the algorithm. The experiment proves the feasibility and superiority of the algorithm.

The differential evolution (DE) algorithm is known to be fairly robust among various global optimization algorithms. However, the application of this algorithm to an extensive set of test functions shows DE fails at least partially on 19 % of the test functions, and completely on 6.9 % of the test functions. The opposition-based DE in the literature, while aimed to improve the efficiency of DE, has a robustness even worse than that of the classic DE. In this paper we describe a new variant of DE called “passive oppositional differential evolution” (PODE) which utilizes opposition in such a way that a set of opposite vectors, while not part of the population, are used in mutation to gain diversity. Numerical experiments show that compared to DE and ODE, it has a much better robustness and a similar speed of convergence.

Two classes of skew cyclic codes over rings are studied in this paper. According to their features, we present proper automorphisms. Combining with the given automorphisms, we construct skew cyclic codes and discuss the properties of the codes.

The C-logit model proposed by Cascetta et al. in 1996 aims at resolving the route overlapping problem in logit-based stochastic traffic assignment, being lack of economic meanings. In this paper, we formulate the C-logit system optimum as a mathematical program through maximizing the system’s economic benefit. Numerical studies are conducted for illustrating C-logit system optimum and user equilibrium.

The identity recognition technology as an important aspect in the field of artificial intelligence. Especially with the development of intelligent robot has the function of identification, the application fields of further widening. This paper presents an identification algorithm of finger vein quality assessment based on the support vector machine. Through the analysis of some existing features, and analyses the three characteristic parameters of great influence on the finger vein image quality (image contrast, image gradient covariance feature values, and the effective area). Vein image by establishing a model of support vector machine to the known training quality, and then, classify the test image random sampling. The experimental results show that this algorithm can well distinguish the vein image high and low quality for enhance the performance of the finger vein identification system.

Internet $$+$$+ will become normal. Every social or commercial stage has a normal and development trends. The normal stage before “Internet $$+$$+” is introduced is in the big background that thousands of enterprises need to transform and upgrade. In the next period of time, Internet $$+$$+ will greatly develop in macro aspects. The widespread use of Internet $$+$$+ technology, Internet $$+$$+ service, and O2O has gradually become the mainstream of information society. As for the government, the implementation of “Internet $$+$$+” will become the main battlefield of realizing the Thirteen five planning. Wireless sensor network based on ZigBee technology has won an extensive popularity among engineers for the characteristics of low power consumption, self organized network, large network capacity and high security. In particular, the CC2430/31 wireless sensor network solution provided by Chipcon Corporation brings more conveniences to developers. In this paper, well design a wireless sensor network system based-on ZigBee technology that can give timely and accurate fore-warnings of the road congestion situations based on the information and analysis of the current existing congestion situation. Well also introduce the technology of wireless sensor networks and ZigBee, and describe the specific contents including network topology, hardware design, node design and software design, etc.

Since short cycles and trapping sets are culprits of performance and error floor of low-density parity-check (LDPC) codes, it is necessary to obtain information about the number and the distribution of all cycles in Tanner graphs. However, the established algorithms could not efficiently search all cycles on account of restrictions from both the structure and the girth of Tanner graphs. The proposed algorithm solve the above problems with message-passing schedule, counting and enumerating the cycles simultaneously. With information derived from the proposed algorithm, performance will be enhanced and error floor will be lower, which is meaningful for both adjustment and design of LDPC codes. Furthermore, the proposed algorithm can be applied on general bipartite graphs.

The vague set theory can overcome the shortcomings of fuzzy set by describing the membership from two sides of both TRUE and FALSE, rather than only by a single membership value, with the further generalization of fuzzy set theory. It is superior in mathematical analysis of system with uncertainty, since vague sets can provide more information than fuzzy sets. And it is more powerful in the describing and processing of uncertain, inaccurate, even conflicting information. In this paper, focused on the second-order uncertainty (SOU) issues in a weak knowledge environment which comes from generalized or ubiquitous sensors, a novel method of fuzzy decision fusion was presented based on vague set. The new method is more efficient and powerful to fulfill decision fusion with uncertain and inaccurate information.

Vehicle positioning system is an important component of traffic dispatch and intelligent control of modern tram. The selection of vehicle positioning scheme and wireless communication scheme decided the performance of the system. According to the characteristics and the requirements of modern tram vehicle positioning, the paper proposed the positioning method of modern tram vehicle by utilizing combined positioning method with satellite positioning and inertial navigation, and the communication scheme between various terminal is realized by Terrestrial Trunked Radio (TETRA) wireless communication system of rail transit, The proposed vehicle positioning scheme can save the cost of the whole project, ensure the precision of the system. Moreover, the vehicle positioning system have the merits of continuity, reliability and maintainability.

In the operation management and real-time monitoring of the highway, we always want to know the current position of all vehicles in real time, so that we can find the congestion and accident section timely and make effective treatment. But in reality, only a small part of the highway vehicles equipped with a global positioning system, and can access to the location information in real-time, for the most of the vehicles, we can only access the position point when they are in and out the highway by the toll data, and cannot get access to their specific routing when they are on the highway. Especially when the vehicle is moving to the shunt point, during the current state we cannot know exactly which direction the vehicle will choose next, which leads to the result that we cannot estimate the correct position of vehicles. In order to accurately identify the direction of vehicles in the shunt point, this paper proposes a framework that based on the highway toll data, the A* algorithm and logistic regression were used to predict the direction choose of vehicles on the shunt point of highway. The framework takes the historical toll data as a sample to train the feature weight in logistic regression model, and takes the actual direction of vehicles on the shunt point as a test set to evaluate the effectiveness of the method proposed by this paper.

This paper reviews two problems occur in the online real-time detection for cold rolled medium plates-low transmission efficiency and poor performance. The detecting process involves sending collected images to the industrial personal computer (IPC) of a camera where a rapid contour detection scheme of medium plates based on wavelet transform is presented. The overall architecture, contour detection principle and new two graded data transmission structure which are different from traditional ones will be analyzed. In the first place, the wavelet scalar quantization algorithm is used for pre-processing and compressing the picture captured by the image acquisition module. Subsequently, the processed data will be sent to the IPC where it is then uncompressed. Results show that the compressed image size equals 0.09 % of the original one; hence the new method is of capability to improve the overall system effectiveness significantly without affecting the accuracy.

In order to eliminate negative effects of transient power demands, configurations and power source characters of series hybrid electric vehicles were analyzed. The power dividing strategy was established based on wavelet transform, and then it was tested in RT-LAB model to compare with the result of constant temperature control strategy. As it was shown in the simulation, the power dividing strategy had an advantage in power following. Meanwhile, power supply quality could be promoted and battery life could be extended.

This study explores many details of the drivers response to dynamic travel information with variable message signs (VMS) which is the one of the most common advanced traveler information systems (ATIS) deployed in many areas all over the world. A stated preference (SP) survey was conducted in some parts of China to collect various drivers behavior information with VMS. Based on the findings from the surveys, seventeen potential affecting factors for driver compliance with VMS are identified and applied to further study. A binary logistic regression model is adopted to evaluate the significance of these seventeen factors. Gender, age, whether full-time worker, delay ratio of the current route, knowledge of an alternate route, length ratio of an alternate route, and crowded level on an alternate route are proved to be significant variables affecting driver compliance under dynamic travel information with VMS. Classification and regression trees (CART) is adopted to develop the driver compliance model. The CART model reveals the hierarchical structure of driver compliance and produces many interesting findings. The developed model is evaluated with collected data from SP survey and shows a reasonable performance. The CART model explains the behavior data most clearly and maintains the highest prediction rate.

This study explores many details of the drivers response to dynamic travel information with variable message signs (VMS) which is the one of the most common advanced traveler information systems (ATIS) deployed in many areas all over the world. A stated preference (SP) survey was conducted to collect various drivers route choice behavior with VMS. Based on the surveys, seventeen potential affecting factors such as city kind, region, gender, age, marital status, degree, job, whether full-time worker, monthly income, crowded level on the current route, vehicle queue length of the current route, delay ratio of the current route, knowledge of an alternate route, length ratio of an alternate route, crowded level on an alternate route, anticipated travel time saving ratio and quality of dynamic travel information were identified and applied to further study. A binary probit model was adopted to evaluate the significance of these seventeen factors. Gender, age, whether full-time worker, delay ratio of the current route, knowledge of an alternate route, length ratio of an alternate route, and crowded level on an alternate route were proved to be significant variables. Then a model for estimating drivers route choice results was build based on the significant variables. The verification results showed that the model estimating precision could reached 76 %.

Incomplete LU (ILU) methods are widely used in petroleum reservoir simulation and many other applications. However high complexity often makes them the hotspot in the whole simulation due to high complexity when problem size is large. ILU’s inherent serial nature also makes them difficult to take full advantage of computing power of multi-core and many-core devices. In this paper, a greedy graph coloring method is applied to the ILU(k) factorization and triangular solution phases. This method increases degree of parallelism and improves load balance. A block-wise storage format is employed in our ILU implementation in order to take advantage of hierarchical memory structures. Moreover, a dual intensive parallel model is proposed to further improve the performance of ILU(k) on GPUs. We test the performance of the proposed parallel ILU(k) with a set of Jacobian systems arising from petroleum reservoir simulation. Numerical results suggest that the proposed parallel ILU(k) method is effective and robust on multi-core and many-core architectures. On an Intel Xeon E5 multi-core CPU, the speedup compared with the serial execution time is $$5.6\times $$5.6× and $$5.4\times $$5.4× for factorization and triangular solution, respectively; on an Nvidia K40c GPU card, the speedup can reach $$8.6\times $$8.6× and $$12.7\times $$12.7× for factorization and triangular solution, respectively.

In this paper, we use mid-level features to solve the problems of indoor scene image classification. The mid-level patches should satisfy two conditions: (1) representative, they should occur frequently enough in the visual world; (2) discriminative, they need to be different enough from the rest of the visual world. In this paper, we propose a method to select the initial patches. It can eliminate a large number of patches which are mismatch the conditions, and there is no need manual processing. For initial patches we adopt unsupervised cluster algorithm on HOG space. Then, using the purity-discriminative evaluation criteria, the top r clusters were selected to represent each scene. The experimental results on MIT Indoor 67 scene image classification datasets indicate that our method can achieve very promising performance.

Recent years most object detection method tends to use the sliding window fashion, which need to search the whole images entirely, causing the extreme waste of search resources. In this paper we propose an object detection method based on improved Exemplar SVMs (IESVM). Our method mainly includes two steps: (1) Coarse object detection: we use a generic object measure to find a region that may contain objects. In this step we do not care about the category of the objects. (2) Precise object detection: we extract the regions created in last step, in where we use Exemplar SVMs to finish the mission of detection. It is proved that the our method can reduce the search space and improve the accuracy of detection. We evaluate our IESVM method on the PASCAL VOC 2007 dataset and find that our method achieves good results in the PASCAL object detection challenges.

In this paper, we present a kind of quadrature rules for evaluating hyper-singular integrals $$ \int _{a}^{b}g(x)q^{\alpha }(x,t)dx$$∫abg(x)qα(x,t)dx and $$\int _{a}^{b}g(x)q^{\alpha }(x,t)\ln |x-t| dx$$∫abg(x)qα(x,t)ln|x-t|dx, where $$q(x,t)=|x-t|$$q(x,t)=|x-t| (or $$x-t$$x-t), $$t\in (a,b)$$t∈(a,b) and $$\alpha \le -1$$α≤-1(or $$\alpha <-1$$α<-1). Since the derivatives of density function g(x) in the quadrature formulas can be eliminated by means of the extrapolation method, the formulas can be easily applied to solving corresponding hyper-singular boundary integral equations. The reliability and efficiency of the proposed formulas in this paper are demonstrated by some numerical examples.

The characteristic of micro DC-Grid power supply is very valuable to remote mountainous highway tunnel. It can operate not only on parallel mode, but also on isolated mode. Power electronic bidirectional three-level inverter is a key link in the system. A novel three-level inverter SHEPWM algorithm is studied in the paper according to the internal relation between SVPWM and SHEPWM. The switch angle of the algorithm can be achieved by synthesis of the space voltage vector. The method has same Harmonic elimination effect, and can completed the task of AC load in micro DC-Grid system, to achieve stable operation of new energy highway tunnels of DC power supply system.

For fault diagnosis problems where the historical data is from a number of monitors, conventional likelihood estimation approaches for Bayesian diagnosis are typically independent or lumped approach. However, for most chemical processes the monitor outputs are often not independent, but exhibit correlations to some extent; as for the lumped approach, it is infeasible due to the curse of dimensionality and the limited size of historical dataset. Also there is another limitation to the accuracy of the diagnosis that the continuous monitor readings are commonly discretized to discrete values, therefore information of the continuous data cannot be fully utilized. In this paper principal component analysis (PCA) approach is proposed to transform the evidence into independent pieces, and kernel density estimation is used to improve the diagnosis performance. The application to the Tennessee Eastman Challenge process using the benchmark data demonstrates the effectiveness of the proposed approach.

As the accuracy of standard Harris algorithm is not high in the application of behavior recognition, this paper proposed a behavior recognition model based on scale adaptive local spatio-temporal characteristics Harris algorithm, which firstly uses local spatio-temporal characteristics function to achieve fast convolution in order to reduce complexity of the algorithm, and then uses scale adaptive local spatio-temporal characteristics function to replace Gaussian function as the filter of the algorithm, and finally calculates the adaptive matrix and the response value of the angle points in order to improve the accuracy of behavior recognition. Simulation results show that the accuracy of the Harris Algorithm based on scale adaptive local spatio-temporal characteristics, proposed in this paper, is higher than the standard Harris algorithm on behavior recognition.

The calorific value of coal is important in both the direct use and conversion into other fuel forms of coals. Accurate calorific value predicting is essential in ensuring the economic, efficient, and safe operation of thermal power plants. Least squares support vector machine (LSSVM) is a variation of the classical SVM, which has minimal computational complexity and fast calculation. This paper presents Least squares support vector regression (LSSVR) to predict the calorific value of coal in Shanxi Coal Mining Region. The LSSVR model takes full advantage of the calorific value information. It derives excellent prediction accuracy, including the relative errors of less than 3.4 % and relatively high determination coefficients. Experimental results conform the engineering application, and show LSSVR as a promising method for accurate prediction of coal quality.

The shortest path problem is one of the most critical issues of intelligent transportation systems. Ant colony algorithm is an effective intelligent optimization method to solve the shortest path problem. However, there are some drawbacks when basic ant colony algorithm is used in solving the shortest path problem. So in this paper the basic ant colony algorithm is improved, so that the improved ant colony algorithm is suitable for solving the shortest path problem. In order to improve the efficiency of the algorithm, the search scope is limited, and the transfer rule and the ant colony algorithm pheromone update rule are improved. Simulation results show that the improved ant colony algorithm can efficiently obtain the shortest path.

Surface defect detection algorithm based on local neighborhood is proposed to improve the accuracy and real-time of surface defect detection in automation industrial production. A local neighborhood window slides over the entire inspection image, and the coefficient of variation is used as a homogeneity measure. A defect-free region will generate a smaller value of Variation Coefficient than that of a defective region. A simple threshold can thus be used to extract and segment the defective regions. The integral image is introduced to increase the computational efficiency. The proposed algorithm is used to detecting only one single discrimination feature. It could avoid complicated Spectral decomposition and sample learning. Experimental results from material surface detection in the industry, has shown the feasibility and effectiveness.

Ballistic missile tracking belongs to the domain of group tracking, and arouses new difficulty for the research of target tracking technique, due to numerous group members. This paper emphasized on the research of group tracking applied in ballistic missile tracking, and concluded the ballistic missile tracking technique from the development process of group tracking, tracking techniques based on data association and non-data association, etc., and analyzed the relative techniques of random finite set (RSF) applied in group tracking, and deduced the recursive formulation and defined the algorithm performance assessment index, and finally pointed out the development trend of ballistic missile tracking technique.

Based on the surface curvature feature, the identification scheme is put forward for curvature of the free-form surface and the complex free surface, which is divided into large curvature and small curvature surface, and the graphic example is given to illustrate scheme effectiveness; Aimed at the trajectory optimization problem of spray gun of spraying-robot on complex free surface, the spray gun trajectory optimization model of different curvature surface is put forward to complete the trajectory optimization on the free surface. Combined with the least square approximation and topology element meshing techniques, the trajectory optimization problem of the intersecting area of two patches of different curvature will be turned into the trajectory optimization problem of the intersecting area of a similar natural quadric patch and an approximate plane patch. Then the trajectory optimization evaluation function model is set up, which verifies the proposed scheme effectiveness by simulation results.

In this paper, we explore the characteristics of vehicle speed time series which described the processes that a driver finishing a specific driving task with different driving operations. Three types of vehicle driving behavior like driving towards an intersection for turn-left, driving for turn-right, and driving for go-straight are designed as a set of real vehicle driving experiments to be carried out on an urban road. Similar to the expected, the collected speed time series of all driving behavior types tend to be non-linear and non-stationary. Therefore, empirical mode decomposition (EMD) is introduced to analyze the characteristic values of speed time series intrinsic mode functions (IMF) and residues. After decomposing, there are 4 levels of IMF with a residue exist existed in the speed time series of turn-left driving behavior, as well as 3 levels in turn-right and 5 levels in go-straight. All the first level IMF of three types of vehicle driving behavior have relatively high frequencies which could be regarded as systematic errors of vehicle speed sensors. As the decomposition continued, subsequent IMF frequencies become lower but average amplitudes have different change trends which could help identifying the driving behavior types. All residue curves are firstly monotone increasing and then monotone decreasing, but the occurrence time of residue maximums are inconsistent. Through this research, we can distinguish the driving behavior type between turn-left, turn-right and go-straight with those vehicle driving behavior time series characteristic values and their changing trend. If all those judgment and statistics of characteristic values be implemented by vehicular industrial control computes, it would improve driving behavior recognition or prediction performances of an advanced driving assistance embedded on vehicle.

Most of carrier frequency offset (CFO) estimation methods for multi-band orthogonal frequency-division multiplexing (MB-OFDM) based ultra-wideband (UWB) systems focus on improving the robustness of CFO estimation. However, as the CFO between the transmitter and the receiver in MB-OFDM UWB systems is small, the accuracy of CFO estimation is also important. In this paper, a CFO estimator targeting at low complexity and high accuracy is proposed. A structure with dual auto-correlation (AC) is designed to improve the accuracy of CFO estimation. To maintain high robustness, a modified AC block, in which the samples that have been greatly affected by noise are removed, is proposed. The evaluation results show that the proposed CFO estimator has low implementation complexity, high accuracy and high robustness.

This paper analyzes the existing disadvantages of the rubber tyred vehicle scheduling system of mine, and design a video assistant monitoring system which helps the scheduling center to know the real-time status of the blind area and identify the vehicle license plate according to the video processing technology. Based on the license plate information from the control system database to call out the vehicle information. Provide decision making basis for scheduling.

This article derives an improved proportional navigation guidance law by using the extended gain scheduling law, which can be used in the nonlinear six-degree of freedom (6DOF) model of an airship directly, in which the airship equations of motion based on the Lagrangian approach. Nonlinear simulations for different conditions are performed, including the effect of the wind, and results are discussed. The simulation experiments indicate that, compared with the previous one, the modified guidance law which is used in specific waypoints tracking has better robust and tracking accuracy.

It is necessary to purify the convertor gas when it is recycled in gas tank. The electrostatic precipitator is widely used for convertor gas dedusting because of its high efficiency and low pressure loss. However, the collection efficiency of electrostatic precipitator is affected by the gas flow distribution uniformity of the inlet and outlet. Based on the computational fluid dynamic (CFD) method, numerical calculation for gas flow distribution is carried out by using the k-$$\epsilon $$ϵ two equation turbulence model and SIMPLE (Semi-Implicit Method for Pressure Linked Equations) algorithm. The results show that the optimal opening ratio of gas flow distribution plate is less than 30 $$\%$$%. The position of inlet gas flow distribution plate has great influence on the gas flow uniformity. Compared with the gas flow field difference between columnar electrostatic precipitator and rectangular one, the columnar electrostatic precipitator has the advantages of high collection efficiency and structure strength. The gas flow distribution uniformity in columnar electrostatic precipitator is also superior to the rectangular one.

This paper studies the optical characteristics of SiN antireflection coating (ARC) on the Si substrate and mainly focuses on the effects of different wavelength and angle of incident light on the ARC. In this paper the concept of antireflection window (ARW) is proposed and is used to analyze the situation of whole incident angles. It is found that the 3D surface of reflectivity shows a groove which results from the fast change of the light with 400–900 nm wavelength. Additionally, the angle of the incident light with $$=$$= 616 nm is smaller than 650 for the ARW.

On account of the space vector control system for PMSM (Permanent Magnet Synchronous Motor), adopt MBD (Model-Based Design) techniques, the simulation model of space vector control for PMSM was built with MATLAB/Simulink Embedded Coder toolbox, and the performance of the speed control system is simulated. After verifying the correctness of the simulation model, the fixed-point code model was built for the control system. And used the fixed-point code model to generate C code automatically, and the generated C code was debugged in the designed hardware test platform debugging. The experimental result shows that the space vector control system for PMSM implemented by MBD techniques has good dynamic response performance.

To reveal the impact of uncertain traffic information on traffic flow, a new car-following model with consideration of uncertainty in perceiving velocity is proposed. The linear stability criterion of the new model is derived through linear stability theory and it shows that the stable region for a small perceived velocity of the preceding vehicle is larger than that for a large perceived velocity of the preceding vehicle. Numerical simulation is in good agreement with the analytical results, which reveals that the uncertain traffic information would influence the stability of traffic flow importantly.

In this paper, we study the leader-following attitude consensus problem for multi-rigid-body systems under jointly connected topologies coupled with time-varying communication delays. By associating an auxiliary vector for each follower, the control algorithm is designed, and the stability of the whole system is proved by using Lyapunov Krasovskii function and contradiction method. In terms of linear matrix inequalities, we derive sufficient conditions that guarantee all followers asymptotically converge to the leader.

Urban road flooding often causes road capacity reduction, traffic congestion and inconvenience in citizens daily travel. This paper proposes an urgent traffic dispersion and assignment model for the case of urban road flooding, with the aim of maximizing the level of service in the road network by controlling the road capacity. First, based on prospect theory and taking the historical travel time average as a reference, the model adopts the BPR (Bureau of Public Roads) function to represent the cost-flow relationship to calculate the prospect value of each route. Second, the travelers route choice behavior is described in logit model with the prospect value as the utility. Third, based on the route choice results, the traffic flow on the congested road sections is dispersed by controlling the road capacity, so the traffic flow to the flooded roads can be adaptively assigned to other roads. Finally, a direct iterative method and genetic algorithm are used to solve the proposed model. The former attempts to implement the traffic assignment based on the travelers route choice behavior, and the latter is used to find the satisfying solution through selection, crossover and mutation. The proposed model is applied to a given road network with an assumption of some road capacity reduction due to road flooding. The results show that when the proposed model is applied, the saturation ratio (or level of service) of the roads in the entire network is more uniform and the distribution of the saturation ratio of main roads is reduced, so the traffic flow in the whole network can remain smooth and the level of service can remain high.

Because highway is fully sealed road, there is no immediately available alternative road for vehicular evacuation in the event of emergency. The impact on traffic flow and road capacity is far greater than ordinary roads, which will produce more serious than the average delay road loss. In this study, the VISSIM software was used to simulate the “3 off 1” situation on highway. The effective highway capacity under unexpected traffic incident is simulated at different mainline traffic amount, different road configuration and different speed limits in this article.

Compared to traditional logistics, the emergency logistics show the characteristics of sudden, uncertainty, time urgency. The regular commercial maps used in the traditional logistics system (such as Baidu map, Google map), cannot satisfy the need that the emergency logistics will plan the best route autonomouslyin order to ensure the rescue time under the condition of disaster. Openstreetmap (OSM) is an open sourcemap which the greatest strength is the open data and the free secondary development. In this paper, we designed three parts on the Android terminal including the view layer, the logic control layer and data interaction layer based on the OSM data and the REST service interface which the ArcGIS Server provides; The system has achieved the offline maps reading and parsing function and real-time locationfunction. So it will combine the open source OSM with emergency logistics demand and provide a reference for the emergency logistics to plan the bestroute.

UAV formation flight has many incomparable advantages with single UAV. In this paper, the “lead aircraft C wing aircraft” formation mode was used. The aerodynamic coupling effect of close formation, which was created by the leads vortex, was analyzed. Then the close formation equivalent aerodynamic model was established, which consists of two flying wing layout UAVs. The sliding mode variable structure control theory was introduced to design the UAV close formation controller. The controller was divided into longitudinal channel, lateral channel and vertical channel. These three channels were designed individually, thereby simplify the design of controller, reduce the parameters adjustment difficulty and explore a new method for close formation keeping research. The simulation results show that the formations geometry is maintained in the face of lead aircraft maneuver, and the feasibility and effectiveness of established UAV close formation flight controller was verified.

This paper research on how to adopt visual technique such as bar code, RFID, sensor etc. To set up an aircraft equipments warehouse, including storage bar code management system, storage RFID management system, warehouses audio-frequency and video-frequency supervision and drilling system, warehouses temperature and humidity monitoring and controlling visual system, warehouses fire fight visual system are proposed.

Vehicle-to-vehicle distance calculation has a great significance to driving assistance and estimation of traffic condition. In this paper, we present an on-board video-based method about calculating distance gap. The method is mainly divided into three major stages. At first stage, an Adaboost cascade classifier using Haar-like features of sample pictures is used to detect preceding vehicles. At second stage, a fusion algorithm combining Maximally Stable Extremal Regions (MSER) for far vehicles with vertical texture method for close vehicles is applied to locate license plate. At the third stage, distance gap is calculated according to the pixel height of plate and the proportion of plate pixel height. Experimental results in this paper showed excellent performance of the method in calculating distance gap.

By studying the evolution of the Internet behavior, this brief paper introduces a weighted evolution network model based on the Internet Autonomous System (AS) layer. This model considers four kinds of Internet evolutionary behavior: on one hand the AS nodes birth and the links growth based on a preferential attachment rule and on the other hand the AS nodes recession and the links death based on an anti-preferential attachment rule. Through a theoretical analysis with the rate equation method and a simulation experiments on MATLAB environment, the node’s degree and strength distribution both follows a power-law distribution. The power-law coefficient is proportional to the ratio of the preferential attachment tendency and anti-preferential attachment tendency. The experimental results were basically consistent with the theoretical analysis value, confirming the correctness of the model.

In this paper we address the question: How should developing cities green their urban transport systems? As a new developing paradigm in the urban transport field, green transport has been advocated and fostered for over 20 years. It is regarded as an effective model to face a series of problems emerging in urban area and support sustainable developments. It desires much further development in the future. However even today most successful practices are found in developed cities and developing cities have performed it poorly. Although many technical solutions were testified useful in developed cities, they may be not so suitable for developing cities for their technology and capital intensive nature. We review non-technical solutions for developing cities, emphasizing on those that contribute to changing the composition of travel modes, i.e. increasing the share of walking, cycling and transit modes. Methodologies working toward a travel style in which walking, cycling and transit modes occupy dominant proportion are studied underlying four strategies: changing land use, giving priority to public transport, promoting walking and cycling and restricting car owning and using. We highlight their effects and limitations and assess their adaptabilities in developing cities. And in the end we summarize some matters need attention for applying the methodologies reviewed.

The present link division methods based on GPS floating vehicles to collect traffic information largely ignored the difference of different directional traffic operating conditions on the intersections, which leads to lower traffic information quality and cannot effectively meet the data demand for dynamic traffic management system. A link division method that is able to distinguish traffic flow directions and to count traffic data is designed. In addition, the corresponding simplified method of road network and the network connected relation expression method are studied, which improves information quality for dynamic traffic management system from the aspects of road network spatial data structure.

According to the complexity and uncertainty characteristics of the explosive road transportation accident, this paper proposed a improved fuzzy synthetic evaluation based on information entropy, established a risk evaluation index system for explosives in road transportation, taken thermal sensitivity, mechanical sensitivity, electrostatic sensitivity, shock sensitivity of explosives as the criterion level. Information entropy method is utilized to determine the index weight coefficient of influence factors, which overcome effectively the subjective influence of index weight caused by experts assigning artificially. Finally the synthetic weight coefficient is obtained by combining information entropy and subjective weighting method of traditional fuzzy evaluation, provided a new effective method for the risk evaluation of explosives in road transportation. The empirical analysis is shown that the transportation risk was relatively large and in line with the actual situation, which shown that the evaluation model is practical and feasible.

In order to evaluate the influence of cryptographic protocols on the property of communication networks, the method of simulation was adopted. Firstly, a 9-tuples abstract model of cryptographic protocols based on Finite State Machine (FSM) was given, and the process for building FSM models was provided. Secondly, by analyzing the FSM simulation theory of OMNeT++ platform, a dynamic behavior algorithm of simulation models was designed. Thirdly, the validity of the modeling and simulation strategies were tested by taking Internet Key Exchange (IKEv2) protocol as an example and designing a simulation scene of Denial of Service (DoS) attack, and then the usability of cryptographic protocols was analyzed. The simulation results show that, the modeling and simulation strategies are valid, and the average time delay of point to point is approximately increased by 12 % when using IKEv2 protocol in communication networks.

In order to analyze urban link travel time reliability on odd or even limit period, firstly, the reliability evaluation index of travel time is established: the 90 % stroke time and the probability of reaching the level of specific service. Then odd or even limit and daily morning peak travel time probability distribution are compared. And we draws a conclusion that for the odd or even limit period, lognormal distribution can better reflect the link travel time probability distribution, while working and rest days are more in line with normal distribution. Finally, travel time reliability is analyzed. And travel time reliability of odd and even limit period is greatly improved compared with daily. The study has guiding significance for odd and even limit period.

The selection of spread spectrum sequence in CDMA system directly affects the performance of the whole system, which is one of the key technologies of CDMA system. The generation model of m sequence and m sequence is constructed, and the CDMA communication simulation system is built by using MATLAB/Simulink platform. Then the spread spectrum sequence is introduced into the CDMA system to run and analyze the performance of the two sequences in the CDMA system with the 6 order and 7 order. The results show that the m sub-sequence is rich in resources, and the bit error rate performance of the multi user CDMA communication system is better than that of the m sequence.

While driving in typical traffic scenes with drastic drift or sudden jump of GPS positions, the localization methods based on wrong initial positions could not select the properly overlapping data from the pre-built map to match with current data, rendering the localizations as not feasible. In this paper, we first propose to estimate an initial position by matching in the infrared reflectivity maps. The maps consists of a highly precise prior map built with offline SLAM technique and a smooth current map built with the integral over velocities. Considering the attributes of the low-texture maps, we adopt the stable, rich line segments to match. A affinity graph to measure the pairwise consistency of the candidate line matches is constructed using the local appearance, pairwise geometric attribute and is efficiently solved with a spectral technique. The initial global position is obtained by converting the structure between current position and matched lines. Experiment on the campus with GPS error of dozens of meters shows that our algorithm can provide an robust initial value with meter-level accuracy.

The method that detects the tumor’s position indirectly has played an important role in dynamic radiotherapy for lung cancer because of its non-invasion property. The key problem of this approach, which has not been solved well, is to create a robust correlation model between the respiration and the tumor motion. Aiming at this problem and considering the fact that the lung tumor has similar motion law with the lung organ, we selected the patient’s breathing and the fluoroscopic images of lung organ as research objects to investigate the correlation between the respiration and the lung organ motion. The optical flow algorithm is utilized to track the motion of the lung organ in the fluoroscopic images sequence automatically, and the waveform of the lung organ motion is compared with the respiratory signal. Experimental results show that there is a phase discrepancy between these two kinds of waveforms. After phase aligning, a linear relationship can be found between the respiratory signal and the lung organ motion. By using this model, the lung organ motion can be measured with a maximum error of 25 % by means of respiration.

The power allocation optimization algorithm can improve the efficiency of resource utilization, which is of great significance to the power restrained satellite communication systems. This paper taking both performance analysis of the end-to-end link and influence of repeater gain on power allocation into consideration, and a repeater gain settings based resource allocation model is built. On the basis of analyzing the above model, an algorithm of joint optimization of repeater gain setting and power allocation is proposed. After dual decomposition algorithm searching the optimal solution of power allocation with certain repeater gain, the proposed algorithm searches the optimal repeater gain by applying the steepest descent method. Simulation results show that global optimization solution of system capacity is obtained with detailed results of power allocation and repeater gain.

With the development of intelligent transportation system, vehicle networks (VNs) have brought new security challenges due to their mobile and infrastructure-less nature. The existing security solutions lack the comprehensive cybersecurity model for vehicle networks. This paper proposes a combined attack and defense petri net (ADPN) model to describe cybersecurity states and processes of vehicle network. To evaluate security assessment and optimize defense strategies, an attack and defense decision making method for vehicle network (VN-ADDM) is proposed. The method utilizes stochastic games net to analyze and quantify security assessment properties, calculate attack and defense cost, and implement mixed strategy Nash equilibrium. The rationality of the proposed cybersecurity decision making method is well demonstrated by extensive analysis in a detailed case study.

Storing and managing the large scale moving objects data is one of the research hotspots and difficulties in data mining, trajectory analyzing, location-based services and many other applications. To solve these problems, firstly we design the trajectory point representing model, the trajectory representing model, the moving object data storage model and their relationships based on object-oriented ideology; then we construct a moving objects database using in PostGIS according to the presented models; finally we test the effectiveness of the moving object database with real data. The experimental results show that using the method presented in this paper to store large scale moving objects data can reduce the storage space obviously, meanwhile, it can increase the spatial and temporal querying efficiency effectively.

In this paper, we firstly introduce two BPX-like preconditioners $$B_J^1$$BJ1 and $$B_J^3$$BJ3, and present an equivalent but more robust BPX-like preconditioner $$B_J^2$$BJ2 for the solution of the linear finite element discretization of Poisson problem. Secondly, we implement these preconditioners and their preconditioned conjugate gradient (PCG) solvers $$B_l^p$$Blp-CG($$l=1,2,3$$l=1,2,3) under Compute Unified Device Architecture (CUDA), where we exploit the hierarchical and the overall storage structure, take advantage of the multicolored Gauss–Seidel smoother. Finally, comparisons are made among these PCG solvers and the state-of-the-art SA-AMG preconditioned CG solver (SA-CG) in CUSP library. Numerical results demonstrate that the iteration numbers of $$B_2^p$$B2p-CG holds the weakest dependence on the grid size, while $$B_3^p$$B3p-CG is the most efficient solver. Furthermore, $$B_3^p$$B3p-CG possesses considerable advantages over SA-CG in computational capability and efficiency. In particular, $$B_3^p$$B3p-CG runs 3.67 times faster than SA-CG when solving a problem with about one-million unknowns.

A method to estimate the time of vehicles traveling in roads is presented using Bluetooth sensors. The unique Bluetooth device address of Bluetooth-enabled device inside vehicle can be monitored by some spatially separated Bluetooth sensors. Correspondingly, the travel time can be calculated by the collecting data of these sensors. The reliability and accuracy of our proposed method are demonstrated using field testing data that were collected in an 1863 m long segment of Jiangnan Road in Hangzhou. The experimental results show the consistent detection with the manual recording reference data when the distance between a pair of sensors is bigger than the value of two times of the one by dividing the identification radius of sensor by the system error.

Since taxis are rather hard to find during rush hours, an intelligent system for calling taxi is proposed. Its main functions include calling taxi, picking up passengers, managing the user information and dispatching the centralized taxi. This paper introduces the Baidu map API for intelligent route planning, avoids going the congested road and shows the traffic route to users vividly. Changing the status of the order timely can ensure the users, the drivers and the administrators get the order’s changes in the shortest time. Another feature of this paper is the road information mining by using taxi GPS data of the Beijing. It can dig out where the passengers often appear in a certain period of time. Therefore, it can reach the optimization of resource allocation.

Energy efficiency is paid more and more attention in railway systems for reducing the cost of operation companies and emissions to the environment. We both consider the optimizations on timetable and driving strategy are two important and closely dependent parts of energy-efficient operations. It not only regulates the fleet size and the trip time at infestations, but also determines the control sequences of traction and braking force during the trip. In this paper, we analysis and establish the dynamic model of train and build single train optimization Model between two stations and three stations, then propose an iterative search algorithm to get the optimal speed, which can get better energy-saving performance. The simulation results based on a Subway Line in China illustrate that the iterative search algorithm can provide good performance with energy saving.

In order to define the factors of commercial vehicles risky driving behaviors and develop driver assistant system about commercial vehicles, this article began to screen risky driving behaviors and its factors for the questionnaire in terms of actual accident cases. We determine the sample size by statistical methods, and then we carry on the actual investigation. Based on the survey data, from perspectives of frequency and cause of accident probability, we researched the correlation between risky driving behaviors and their factors by ordinal polytomous logistic regression model. Researches show that driver characteristics have a significant impact to the judgment on the frequency and cause of accident probability of risky driving behaviors. This article stated the importance of risky driving behavior research in commercial vehicles operation from the perspective of human biology.

Wisdom laboratory based on the internet of things is designed. Cloud computing is the core of this system. Campus laboratory information should be intelligent collection and transmission, intelligent processing and control, intelligent display and push. For the three aspects of “safety, energy saving and high efficiency”, paper gives a lot of analysis. Give the flow chart of the subsystem. This system should effectively solve the problem of experimental course.

For the receiver of Global Navigation Satellite System, quadrature digital down converter of low intermediate frequency signal can reduce the data rate and improve the real-time processing ability of baseband. But the phase quantization and amplitude quantization of existing methods may introduce new errors which could have a serious impact on the receiver’s ultimate accuracy. According to the result of extensive research in the processing method for quadrature digital down converter, optimization the structure of polyphase filter, Integrated planning for the receiver with the unified optimization of RF chip and baseband circuit. Design a quadrature digital down converter structure which shared with capture mode and tracking mode, and does not introduce gain error and phase error. Simulation and application test results showed that compared with classic quadrature digital down converters, the design put forward in this pater can avoid data and phase truncation with a simple structure and faster processing speed. Under the same image frequency rejection ratio, its circuit is smaller. Practice has proved that the proposed FPGA structure is very suitable for the BeiDou Navigation Satellite System.

Capacitated location-routing problem (CLRP), is one of the new research areas in distribution management. This topic combines two problems: locating the facilities and vehicle routing. The goal of CLRP is to open a set of depots, allocating the costumers to depots and then to design the vehicle tours in order to minimize the overall cost. The limitations of time windows has many applications in the real world, however it has not been noticed enough in the CLRP problem. This article considers the capacitated location-routing problem with hard time windows (CLRPHTW). In this paper, first a mixed-integer linear programming model for CLRPHTW problem is presented and then in order to solve this problem a meta-heuristic method based on variable neighborhood search algorithm is proposed. To assess the performance of the proposed method, this framework is examined with a set of examples. The computational tests demonstrate the efficiency of the proposed method.