Modeling Real Objects for Kansei-based Shape Retrieval

A large number of 3D models are created on computers and available for networks. Some content-based retrieval technologies are indispensable to find out particular data from such anonymous datasets. Though several shape retrieval technologies have been developed, little attention has been given to the points on human＇s sense and impression （as known as Kansei） in the conventional techniques, In this paper, the authors propose a novel method of shape retrieval based on shape impression of human＇s Kansei. The key to the method is the Gaussian curvature distribution from 3D models as features for shape retrieval. Then it classifies the 3D models by extracted feature and measures similarity among models in storage.

Investment in deepwater oil and gas exploration projects:a multi-factor analysis with a real options model

Deepwater oil and gas projects embody high risks from geology and engineering aspects, which exert substantial influence on project valuation. But the uncer- tainties may be converted to additional value to the projects in the case of flexible management. Given the flexibility of project management, this paper extends the classical real options model to a multi-factor model which contains oil price, geology, and engineering uncertainties. It then gives an application example of the new model to evaluate deepwater oil and gas projects with a numerical analytical method. Compared with other methods and models, this multi-factor real options model contains more project information. It reflects the potential value deriving not only from oil price variation but also from geology and engi- neering uncertainties, which provides more accurate and reliable valuation information for decision makers.

On modeling approach for embedded real-time software simulation testing

Modeling technology has been introduced into software testing field. However, how to carry through the testing modeling effectively is still a difficulty. Based on combination of simulation modeling technology and embedded real-time software testing method, the process of simulation testing modeling is studied first. And then, the supporting environment of simulation testing modeling is put forward. Furthermore, an approach of embedded real-time software simulation testing modeling including modeling of cross-linked equipments of system under testing （SUT）, test case, testing scheduling, and testing system service is brought forward. Finally, the formalized description and execution system of testing models are given, with which we can realize real-time, closed loop, mad automated system testing for embedded real-time software.

BRAIN INJURY BIOMECHANICS IN REAL WORLD VEHICLE ACCIDENT USING MATHEMATICAL MODELS

This paper aims at investigating brain injury mechanisms and predicting head injuries in real world accidents. For this purpose, a 3D human head finite element model （HBM-head） was developed based on head-brain anatomy. The HBM head model was validated with two experimental tests. Then the head finite element（FE） model and a multi-body system （MBS） model were used to carry out reconstructions of real world vehicle-pedestrian accidents and brain injuries. The MBS models were used for calculating the head impact conditions in vehicle impacts. The HBM-head model was used for calculating the injury related physical parameters, such as intracranial pressure, stress, and strain. The calculated intracranial pressure and strain distribution were correlated with the injury outcomes observed from accidents. It is shown that this model can predict the intracranial biomechanical response and calculate the injury related physical parameters. The head FE model has good biofidelity and will be a valuable tool for the study of injury mechanisms and the tolerance level of the brain.

Experimental Model and Analytic Solution for Real-time Observation of Vehicle's Additional Steer Angle

The current research of real-time observation for vehicle roll steer angle and compliance steer angle（both of them comprehensively referred as the additional steer angle in this paper） mainly employs the linear vehicle dynamic model, in which only the lateral acceleration of vehicle body is considered. The observation accuracy resorting to this method cannot meet the requirements of vehicle real-time stability control, especially under extreme driving conditions. The paper explores the solution resorting to experimental method. Firstly, a multi-body dynamic model of a passenger car is built based on the ADAMS/Car software, whose dynamic accuracy is verified by the same vehicle＇s roadway test data of steady static circular test. Based on this simulation platform, several influencing factors of additional steer angle under different driving conditions are quantitatively analyzed. Then ε-SVR algorithm is employed to build the additional steer angle prediction model, whose input vectors mainly include the sensor information of standard electronic stability control system（ESC）. The method of typical slalom tests and FMVSS 126 tests are adopted to make simulation, train model and test model＇s generalization performance. The test result shows that the influence of lateral acceleration on additional steer angle is maximal （the magnitude up to 1°）, followed by the longitudinal acceleration-deceleration and the road wave amplitude （the magnitude up to 0.3°）. Moreover, both the prediction accuracy and the calculation real-time of the model can meet the control requirements of ESC This research expands the accurate observation methods of the additional steer angle under extreme driving conditions.

A Novel Approach to Study Regional Ionospheric Variations Using a Real-Time TEC Model

Since IGY (International Geophysical Year), through coordinated global observations, ionospheric research has been carried out by many countries. This effort primarily helped in the design and operation of HF radio wave communication systems. The Indian region covers a highly variable part of the equatorial electrojet and EIA (Equatorial Ionisation Anomaly) phenomena making its predictability difficult. With the advent of satellite communication and navigation, the need for accurate ionospheric TEC (Total Electron Content) models at global and regional scales has been stressed. The GAGAN (GPS Aided Geo Augmented Navigation) project jointly undertaken by the Indian Space Research Organisation (ISRO) and the Airport Authority of India (AAI) aims at effectively utilising the Global Navigational Satellite System (GNSS) to determine position coordinates accurately for aircraft precision landing applications. For this purpose the range errors are estimated by using a ground network of TEC stations spread over Indian region. The near simultaneous data collected from these dual frequency GPS stations can be used to generate the geo-referenced TEC values for various applications. The author has developed necessary algorithm and associated computer programmes for a real-time vertical TEC (VTEC) model based on TEC data collected from the GAGAN ground based network stations. The model has been tested and sample results presented here show that it adequately provides for the latitudinal resolution of 1° for the entire longitude span and also for two longitude blocks (73 - 83 & 83 - 93°E) separately. Cubic spline and bilinear interpolation techniques are used for filling up temporal and spatial data gaps. The model provides tabulated output of hourly average VTEC data with latitude for ready use, as well as graphical displays of VTEC maps and contours for monitoring purpose. The real-time model and its extensions are also being used for detailed scientific studies;examples of these show small day to day variability of VTEC without any change in solar activity and indication of the change in the shape of the VTEC diurnal curve with season. The present model will be used for further studies to derive the monthly average variation of the diurnal pattern and the relationship between VTEC peak amplitudes with changes in solar activity. The new information generated can be fed back to improve the real-time model so that eventually the dependence of such models on ground based network stations data can be minimised.

Application of PPC Model Based on RAGA in Real Estate Investment Decision-Making

According to the size of the projector function to evaluate the merits of the program, Projection Pursuit method is applied to real estate investment decision-making by using the real coding based on Accelerating Genetic Algorithm (RAGA) to optimize the Projection Pursuit Classification (PPC) process and a wide range of indicators value was projected linearly. The results are reasonable and verified with an example. At the same time, the subjective of the target weight can be avoided. It provides decision-makers with comprehensive information on all the indicators of new ideas and new

THE DLSTRIBOTED MCU MODEL FOR SYNCHRONOUS REAL-TIME TELETEACHING

Aiming at issues on multimedia communication in synchronous real-time teleteaching (SRT) systems over IP network, a 4-tuple structural mode of multimedia communication is proposed in the paper, and an SRT-oriented distributed MCU model is built according to the mode. Moreover, the mechanism of multicast communication across subnets is discussed. The distributed MCU model has been applied successfully in our interactive synchronous real-time telesteaching system RealClass and has shown good extendibility in operation.

Formal Modeling and Analysis of AADL Threads in Real Time Maude

This paper presents, without altering the AADL meta-model, a formal description of static and behavioral aspects of the AADL thread component. This active and concurrent applicative component of AADL poses many challenges to its formalization and analysis including instantaneous and/or delayed communications, concurrent tasks and time-dependent features, and the need to analyze correctness. This formalization, based on real-time object-oriented theories, allows not only a precise description of the semantics of threads composition with respect to their timing requirements but also makes possible the formal verification of behavioral properties.

基于Cesium for Unreal的自然资源远程协同调查方法

针对传统自然资源地籍调查依赖大量现场工作和后期数据处理导致的调查周期长、成本高等问题,提出了一种基于Cesium for Unreal的自然资源远程协同调查方法。该方法以倾斜摄影获取的实景三维模型为基础,叠加权属、土地利用等业务数据构建调查场景,并通过优化实景三维模型的渲染效果和性能,使自然资源信息以更加直观、真实的方式呈现。在此基础上,自然资源所在地相关政府部门可直接在系统中查看界址、权属等信息,并与实景进行对比核实,然后在线反馈和记录问题;调查单位根据反馈在系统中直接修改数据,完成后即可与政府部门进行线上确认和签字。肇庆某湿地公园的试点应用结果表明,该方法能显著提高自然资源地籍调查效率,压缩工期,具有较好的应用前景。

Model-based framework for multi-axial real-time hybrid simulation testing

Real-time hybrid simulation is an efficient and cost-effective dynamic testing technique for performance evaluation of structural systems subjected to earthquake loading with rate-dependent behavior. A loading assembly with multiple actuators is required to impose realistic boundary conditions on physical specimens. However, such a testing system is expected to exhibit significant dynamic coupling of the actuators and suffer from time lags that are associated with the dynamics of the servo-hydraulic system, as well as control-structure interaction （CSI）. One approach to reducing experimental errors considers a multi-input, multi-output （MIMO） controller design, yielding accurate reference tracking and noise rejection. In this paper, a framework for multi-axial real-time hybrid simulation （maRTHS） testing is presented. The methodology employs a real-time feedback-feedforward controller for multiple actuators commanded in Cartesian coordinates. Kinematic transformations between actuator space and Cartesian space are derived for all six-degrees-of- freedom of the moving platform. Then, a frequency domain identification technique is used to develop an accurate MIMO transfer function of the system. Further, a Cartesian-domain model-based feedforward-feedback controller is implemented for time lag compensation and to increase the robustness of the reference tracking for given model uncertainty. The framework is implemented using the 1/5th-scale Load and Boundary Condition Box （LBCB） located at the University of Illinois at Urbana- Champaign. To demonstrate the efficacy of the proposed methodology, a single-story frame subjected to earthquake loading is tested. One of the columns in the fraane is represented physically in the laboratory as a cantilevered steel column. For real- time execution, the numerical substructure, kinematic transformations, and controllers are implemented on a digital signal processor. Results show excellent performance of the maRTHS framework when six-degrees-of-freedom are controUed at the interface between substructures.

Three-Dimensional Nonlinear Dynamic Model and Macro Control of Real Estate

In this paper, according to economics of real estate and macro-control theory, combine with the characteristics of the real estate market, macro-control of the real estate market is studied. After giving the dynamic model of three-dimensional nonlinear differential equations based on the total number of houses on the real estate business, the government’s averages housing investment funds and the standard price, systematically established the stability conditions of equilibrium point for this model. What’s more, through the use of extreme value analysis model, government funds have been invested in real estate business building devotion principles and the construction base of the real estate businessmen has also been estimated successfully. This provides the corresponding theoretical basis for government macro control policy-making.

基于病菌孢子捕捉和real-time PCR技术的田间空气中小麦白粉病菌孢子动态监测及病情估计模型研究

利用Burkard定容式孢子捕捉器结合real-time PCR定量技术,分别对种植高抗、中感和高感白粉病小麦品种的田间空气中白粉病菌分生孢子浓度进行监测,结果表明,real-time PCR定量与传统的显微观察计数两种方法测得的孢子浓度呈显著正相关(P≤0.01),且两种病菌孢子计数方法在同一抗性品种上监测到的孢子浓度动态相近。此外,两种方法测得的孢子浓度与各气象因子的相关性分析结果一致,空气中的白粉病菌孢子浓度主要与空气相对湿度显著正相关。在此基础上,利用两种方法测定的田间空气中白粉病菌孢子浓度分别建立了基于累积孢子浓度的田间病情估计模型。分析发现,基于两种孢子浓度测定方法建立的病情估计模型间无显著性差异,表明real-time PCR定量技术测定的孢子浓度在构建白粉病病情估计模型上具有一定可行性。该结果为real-time PCR定量技术与病菌孢子捕捉技术相结合用于小麦白粉病的监测和预测提供理论依据。

An Effective Prediction Method for Supporting Decision Making in Real Estate Area Selection

Real estate has been a dominant industry in many countries. One problem for real estate companies is determining the most valuable area before starting a new project. Previous studies on this issue mainly focused on market needs and economic prospects, ignoring the impact of natural disasters. We observe that natural disasters are important for real estate area selection because they will introduce considerable losses to real estate enterprises. Following this observation, we first develop a self-defined new indicator named Average Loss Ratio to predict the losses caused by natural disasters in an area. Then, we adopt the existing ARIMA model to predict the Average Loss Ratio of an area. After that, we propose to integrate the TOPSIS model and the Grey Prediction Model to rank the recommendation levels for candidate areas, thereby assisting real estate companies in their decision-making process. We conduct experiments on real datasets to validate our proposal, and the results suggest the effectiveness of the proposed method.

MODEL-BASED DEVELOPMENT OF REAL-TIME SOFTWARE SYSTEM FOR ELECTRONIC UNIT PUMP SYSTEM

A real-time operating system （RTOS）, also named OS, is designed based on the hardware platform of MC68376, and is implemented in the electronic control system for unit pump in diesel engine. A parallel and time-based task division method is introduced and the multi-task software architecture is built in the software system for electronic unit pump （EUP） system. The V-model software development process is used to control algorithm of each task. The simulation results of the hardware-in-the-loop simulation system （HILSS） and the engine experimental results show that the OS is an efficient real-time kernel, and can meet the real-time demands of EUP system; The built multi-task software system is real-time, determinate and reliable. V-model development is a good development process of control algorithms for EUP system, the control precision of control system can be ensured, and the development cycle and cost are also decreased.

基于四分模型和ReAL模型的IAT联想加工分离

内隐联想测验(IAT)效应在多大程度上反映了联想加工,联想加工又在多大程度上与态度等心理结构相关是多年来研究争论的焦点,也是有效应用IAT的前提.四分模型和ReAL模型分别回应了上述两个问题,并为研究者重新认识IAT效应的内在机制提供了概念框架和分析技术.文章通过介绍和比较这两个模型,揭示了它们在IAT数据分析上的独特作用,在明确IAT价值的同时强调了多重加工机制对行为的解释和预测功能.

运用Real-time quantification PCR方法建立副溶血性弧菌在即食虾中的生长预测模型

运用Real-time quantification PCR(real-time qPCR)方法建立副溶血性弧菌在即食虾中生长预测模型。首先构建质粒标准品,梯度稀释后建立标准曲线,然后用Real-time qPCR方法检测虾中副溶血性弧菌的数量,最后建立37℃下即食虾中副溶血性弧菌生长预测模型,并与传统涂布计数方法进行比较。结果表明,Real-time qPCR方法和传统计数方法均可建立Gmopertz模型、Logistic模型和Richards模型,模型拟合的相关系数R2均在0.9以上。基于Real-timeqPCR方法省时省力、特异性好等优点,用Real-time qPCR方法建立微生物预测模型是未来预测微生物学领域的一种发展趋势。

基于多线程UI-Model的远程实时交互教学系统

针对远程实时交互系统中亟待解决的交互功能扩充与复用及通用文档共享的问题,提出了多线程UI-Model体系结构,利用用户界面委托层,对传统的UI-Model体系结构进行了改进,实现了远程实时交互教学系统中交互功能的扩充与交互模块的复用;应用基于WebBrowser控件实现了通用文档共享模块,并结合GDI+的应用实现了在文档共享中同步绘图交互的功能.利用多线程UI-Model体系结构设计和实现了一种易扩展、可复用的具有文字、视频、电子白板、文档共享等多种实时交互功能的远程实时交互教学系统.