Experts' Knowledge Fusion in Model-Based Diagnosis Based on Bayes Networks

In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.

Feedback Scheduling of Model-based Networked Control Systems with Flexible Workload

In this paper,a novel control structure called feedback scheduling of model-based networked control systems is proposed to cope with a flexible network load and resource constraints.The state update time is adjusted according to the real-time network congestion situation.State observer is used under the situation where the state of the controlled plant could not be acquired.The stability criterion of the proposed structure is proved with time-varying state update time.On the basis of the stability of the novel system structure,the compromise between the control performance and the network utilization is realized by using feedback scheduler. Examples are provided to show the advantage of the proposed control structure.

APPLICATION OF ARCHITECTURE- BASED NEURAL NETWORKS IN MODELING AND PARAMETER OPTIMIZATION OF HYDRAULIC BUMPER

The dynamic working process of 52SFZ-140-207B type of hydraulic bumper isanalyzed. The modeling method using architecture-based neural networks is introduced. Using thismodeling method, the dynamic model of the hydraulic bumper is established; Based on this model thestructural parameters of the hydraulic bumper are optimized with Genetic algorithm. The result showsthat the performance of the dynamic model is close to that of the hydraulic bumper, and the dynamicperformance of the hydraulic bumper is improved through parameter optimization.

On the model-based networked control for singularly perturbed systems

In this paper, the control of a two-time-scale plant, where the sensor is connected to a linear controller/ actuator via a network is addressed. The slow and fast systems of singularly perturbed systems are used to produce an estimate of the plant state behavior between transmission times, by which one can reduce the usage of the network. The approximate solutions of the whole systems are derived and it is shown that the whole systems via the network control are generally asymptotically stable as long as their slow and fast systems are both stable. These results are also extended to the case of network delay.

Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network

On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.

Model-based predictive controller design for a class of nonlinear networked systems with communication delays and data loss

This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation re- suits demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

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

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

An Energy-Based Stochastic Model for Wireless Sensor Networks

We propose an energy-based stochastic model of wireless sensor networks (WSNs) where each sensor node is randomly and alternatively in an active and a sleep mode. We first investigate the sensor model and derive the formula of the steady-state probability when there are a number of data packets in different sensor modes. We then determine important sensor’s performance measures in terms of energy consumptions, average data delay and throughput. The novelty of this paper is in its development of a stochastic model in WSN with active/sleep feature and the explicit results obtained for above mentioned energy consumption and performance characteristics. These results are expected to be useful as the fundamental results in the theoretical analysis and design of various hybrid WSNs with power mode consideration.

An elasto-plastic constitutive model of moderate sandy clay based on BC-RBFNN

Application research of neural networks to geotechnical engineering has become a hotspot nowadays.General model may not reach the predicting precision in practical application due to different characteristics in different fields.In allusion to this,an elasto-plastic constitutive model based on clustering radial basis function neural network(BC-RBFNN) was proposed for moderate sandy clay according to its properties.Firstly,knowledge base was established on triaxial compression testing data;then the model was trained,learned and emulated using knowledge base;finally,predicting results of the BC-RBFNN model were compared and analyzed with those of other intelligent model.The results show that the BC-RBFNN model can alter the training and learning velocity and improve the predicting precision,which provides possibility for engineering practice on demanding high precision.

Modeling Reading and Replying Activities in a BBS Social Network

This paper is devoted to analyze and model user reading and replying activities in a bulletin board system （BBS） social network. By analyzing the data set from a famous Chinese BBS social network, we show how some user activities distribute, and reveal several important features that might characterize user dynamics. We propose a method to model user activities in the BBS social network. The model could reproduce power-law and non-power-law distributions of user activities at the same time. Our results show that user reading and replying activities could be simulated through simple agent-based models. Specifically, manners of how the BBS server interacts with Internet users in the Web 2.0 application, how users organize their reading lists, and how user behavioral trait distributes are the important factors in the formation of activity patterns.

Attribute-Based Re-Encryption Scheme in the Standard Model

In this paper, we propose a new attribute-based proxy re-encryption scheme, where a semi-trusted proxy, with some additional information, can transform a ciphertext under a set of attributes into a new ciphertext under another set of attributes on the same message, but not vice versa, furthermore, its security was proved in the standard model based on decisional bilinear Diffie-Hellman assumption. This scheme can be used to realize fine-grained selectively sharing of encrypted data, but the general proxy rencryption scheme severely can not do it, so the proposed schemecan be thought as an improvement of general traditional proxy re-encryption scheme.

Improved Secure Trust-based Location-Aided Routing Model for MANETs

In view of the security weakness in resisting the active attacks by malicious nodes in mobile ad hoc networks,the trust metric is introduced to defend those attacks by loading a trust model on the previously proposed Distance-Based LAR.The improved Secure Trust-based Location-Aided Routing algorithm utilizes direct trust and recommendation trust to prevent malicious nodes with low trust values from joining the forwarding.Simulation results reveal that ST-LAR can resist attacks by malicious nodes effectively;furthermore,it also achieves better performance than DBLAR in terms of average end-to-end delay,packet delivery success ratio and throughput.

A generalized model of TiOx-based memristive devices and its application for image processing

Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density,versatility, and CMOS compatibility. For memristive devices, a general compact model is highly favorable for the realization of its circuits and applications. In this paper, we propose a novel memristive model of TiOx-based devices, which considers the negative differential resistance（NDR） behavior. This model is physics-oriented and passes Linn＇s criteria. It not only exhibits sufficient accuracy（IV characteristics within 1.5% RMS）, lower latency（below half the VTEAM model）,and preferable generality compared to previous models, but also yields more precise predictions of long-term potentiation/depression（LTP/LTD）. Finally, novel methods based on memristive models are proposed for gray sketching and edge detection applications. These methods avoid complex nonlinear functions required by their original counterparts. When the proposed model is utilized in these methods, they achieve increased contrast ratio and accuracy（for gray sketching and edge detection, respectively） compared to the Simmons model. Our results suggest a memristor-based network is a promising candidate to tackle the existing inefficiencies in traditional image processing methods.

Physical Network Approach Applied to Wind Turbine Modeling with Simscape Language

Model-Based Design is an efficient and cost-effective way to develop controls, signal processing, image processing, communications, mechatronics, and other embedded systems. Rather than re-lying on physical prototypes and textual specifications, Model-Based Design uses a system model as an executable specification throughout development. It supports system- and component-level design and simulation, automatic code generation, and continuous test and verification. This paper is focused firstly on the so-called model-based design and aims at presenting an up-to-date state of the art in this important field. Secondly, it develops a model based design for wind energy systems. Mathematical formulations and numerical implementations for different components of wind energy systems are highlighted with Simscape language. Finally, results are derived from simulations.

Development of a Model Driven ACSI Conformance Testing for IEC 61850-Based IED

Globally, traditional power systems are rapidly transforming towards the adoption of smart grid platforms. Substations which are at the center of the electric power transformation from the power plant are changing to IEC 61850 based digital substations. Therefore, within substation, there is a growing demand for the IEC 61850 based Intelligent Electronic Devices (IEDs). The operation of multiple manufacturers of IEDs in a single digital substation network increases the need for IEC 61850 communications specification conformance diagnosis to ensure interoperability for efficient data exchange between IEDs. The IEC 61850-10 presents test items for diagnosing communication specification conformance. There are many test tools available in the market today to test the compliance of the IEC 61850 communications specifications to the IED. In this paper, we propose a model-based diagnostic method for IED communication conformance testing. The proposed model-based software therefore uses the “drag and drop” technique to select the various IEC 61850 communication services (objects) required to design the test case in a user friendly Graphical User Interface (GUI). This makes the service conformance testing more flexible for test engineers and system integrators especially in situations that require test case modifications. Also, the proposed software tool makes it easy to understand the various IEC 61850 services using the friendly GUI.

Autonomous Kernel Based Models for Short-Term Load Forecasting

The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the setting of the hyperparameters involved in their training. Techniques based on meta-heuristics have been employed to determine appropriate values for those hyperparameters. However, because of the high noneonvexity of this estimation problem, which makes the search for a good solution very hard, an approach based on Bayesian inference, called relevance vector machine, has been proposed more recently. The present paper aims at investigating the suitability of this new approach to the short-term load forecasting problem.

Ensemble Based Learning with Accurate Motion Contrast Detection

Recent developments in computer vision applications have enabled detection of significant visual objects in video streams.Studies quoted in literature have detected objects from video streams using Spatiotemporal Particle Swarm Optimization(SPSOM)and Incremental Deep Convolution Neural Networks(IDCNN)for detecting multiple objects.However,the study considered opticalflows resulting in assessing motion contrasts.Existing methods have issue with accuracy and error rates in motion contrast detection.Hence,the overall object detection performance is reduced significantly.Thus,consideration of object motions in videos efficiently is a critical issue to be solved.To overcome the above mentioned problems,this research work proposes a method involving ensemble approaches to and detect objects efficiently from video streams.This work uses a system modeled on swarm optimization and ensemble learning called Spatiotemporal Glowworm Swarm Optimization Model(SGSOM)for detecting multiple significant objects.A steady quality in motion contrasts is maintained in this work by using Chebyshev distance matrix.The proposed system achieves global optimization in its multiple object detection by exploiting spatial/temporal cues and local constraints.Its experimental results show that the proposed system scores 4.8%in Mean Absolute Error(MAE)while achieving 86%in accuracy,81.5%in precision,85%in recall and 81.6%in F-measure and thus proving its utility in detecting multiple objects.

热液成矿系统构造控矿理论

构造对成矿的控制是热液成矿系统的典型特征之一,系统剖析多重尺度控矿构造的几何学、运动学、动力学、流变学和热力学对认识矿床成因和预测找矿至关重要;而如何实现控矿构造格架、渗透性结构、成矿流体通道和矿化变形网络由静态到多尺度时-空四维动态的转变,查明流体通道和矿床增量生长过程与控制因素,揭示热液成矿系统的构造-流体耦合成矿机制和定位规律是亟待解决的关键科学难题。为此,我们在对已有相关成果系统梳理的基础上,提出了科学构建热液成矿系统构造控矿理论的基本要点与对应方法及应用范畴:(1)流体而非构造是构造控矿理论的中心,热液系统的流体流动与成矿作用受控于断裂带格架及其渗透性结构,其中渗透率是将流体流动与流体压力变化联系起来理解控矿构造的核心;(2)不同控矿构造组合的关键控制是构造差应力和流体压力的大小,而矿化类型的变化可能是由于构造应力场引起的容矿构造方位的不同和赋矿围岩之间的强度差异所致;(3)流体通道的生长始于超压流体储库上游围岩中孤立的微裂隙沿流体压力梯度最大的方向、随裂隙发育且相互连结而形成新的长裂隙,并最终连通形成断裂网络内的流体通道,矿床的增量生长发生在高流体通量的短爆发期,断层反复滑动驱动其内流体压力、流速和应力快速变化,当由此诱发的流体通道生长破坏了流体系统的动态平衡时,随之而来的流体快速降压就成为金属沉淀成矿的关键驱动因素;(4)以热液裂隙-脉系统野外地质观测和构造-蚀变-矿化网络三维填图为基础,通过宏观与微观各级控矿构造相结合、地质历史与构造应力分析相结合、局部与区域点-线-面相结合、浅部与深部相结合、时间与空间相结合、定性和定量相结合,对各种控矿因素开展多学科、多尺度、多层次、全方位综合研究,是应遵循的基本原则;(5)通过构造-蚀变-矿化网络填图,将蚀变-矿化体与控矿构造的类型、形态、规模、产状和间距等几何学特征联系起来,利用热液裂隙-脉系统和断裂网络拓扑学及矿体三维几何结构分析等定量方法查明控矿构造格架和渗透性结构并揭示矿化变形网络的连通性与成矿潜力;(6)合理构建地质模型,选取合适的热力学参数和动力学边界条件,利用HCh和COMSOL等方法,定量模拟成矿过程中的流体流动、热-质传递、应力变形和化学反应等的时-空变化,是揭示构造-流体耦合成矿机理和定位规律、预测矿化中心和确定找矿目标的有效途径。进而提出了构造控矿理论的研究流程:聚焦构造-流体耦合成矿机制和定位规律这一关键科学问题,选择热液裂隙-脉系统和构造-蚀变-矿化网络为重点研究对象;通过几何学描述、运动学判断、流变学分析、动力学解析和热力学综合,厘定控矿构造格架,定位矿化中心,示踪成矿流体通道和多种矿化样式的增量生长过程及其关键控制,揭示渗透性结构的时-空演变规律及构造再活化与成矿定位的成因关联,建立构造-流体耦合成矿模式,服务新一轮战略找矿突破。以胶东焦家金矿田为例,开展控矿构造理论研究和成矿预测应用实践,证实了其科学性和有效性。

油气储层勘探建模技术新进展及未来展望

油气储层建模利用地质统计学等方法,综合测井、地质、地震等多学科信息,是油气田开发研究的利器,油藏地质模型可以将油藏各种地质特征在三维空间的变化及分布定量表征出来,是油气藏的类型、几何形态、规模、油藏内部结构、储层参数及流体分布的高度概括,储层地质模型是油藏地质模型的核心,可以对储层的沉积特征、非均质性、物性及流体等特征进行综合表征。但在勘探阶段,面对大尺度沉积体系和稀疏井网条件下的储层展布规律表征的建模难点为:①地质知识的量化表达问题,包括地质专家的经验认识如何数字化表征。②稀疏井网条件下无法直接用钻井资料对地质体的发育规模、展布方向和结构特征准确定量描述及构建地质模式,大尺度空间中复杂沉积体系无法用简单数学函数表征。③传统地质统计学等方法在勘探模型构建中如何实现地震、测井、地质、油藏等多维度数据的融合问题。因此,基于确定性建模和传统地质统计学等随机建模的储层建模理论和技术遇到极大挑战。笔者在系统剖析传统储层建模技术流程和方法的基础上,通过构建涵盖地质、测井、地震、分析化验等信息的多学科地学大数据知识库,开展多维数据凝聚层次聚类的沉积相模式库表征和基于生成式网络的智能建模,提出了多学科协同的油气储层勘探建模技术对策及技术体系,实现了构造、沉积及储层之间匹配关系的定量表征。该技术体系在东营凹陷北部陡坡带、洼陷带勘探部署中开展系统应用,构建融合古地貌、古物源、搬运通道、测井及地震属性等多信息的岩相、物性及油气运聚的地质模型,基于模型新范式指导部署井位,支撑了陆相断陷盆地复杂砂砾岩体、页岩油等勘探实践。笔者通过深度剖析东营凹陷北部陡坡带勘探建模实践难点及精度问题,进一步探讨了未来油气储层勘探建模技术发展趋势和应用前景。