地震勘探解释软件远程共享的构建方法探讨

地震勘探解释软件价格昂贵,且软件许可证数量有限,严重影响了科研生产的正常开展。为此,提出了地震勘探解释软件远程共享的构建方法,使常规的地震解释软件Landmark及Jason等,可通过FLEX许可管理软件实现软件共享;其他软件可通过共享模式的组合达到软件共享目的;软件共享管理平台用于共享解释软件的综合管理,并利用网络专线实现远程软件共享。该软件远程共享的构建方法,对其他需要实现远程软件共享的单位或部门,具有一定的参考与借鉴价值。     

Pattern recognition and control by adaptive methods for an intelligent mobile vehicle

This paper describes adaptive methods for both pattern recognition and control in an experimental mobile vehicle (MV). An adaptive resonance theory (ART) neural network is used as the character recognizer. It can self-organize and self-stabilize in response to complex binary input vectors. New input patterns can be saved in such a fashion that the stored patterns are not forgotten or destroyed. By merging the advantages of the feed-forward neural network, adaptive algorithm, and fuzzy control, a neuro-fuzzy system also is proposed. This can deal with a large amount of training data in the neural network, from these data produce more reasonable fuzzy rules with the adaptive algorithm, and then control the object by fuzzy control. This is not a simple combination of the three methods, but a merger into one intelligent control system. Finally, the experimental results and some conclusions are given. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

Using intelligent methods to predict air-demand ratio in venturi weirs

Artificial intelligent methods are today extensively used in many areas. They are known as powerful tools to solve engineering problems with uncertainties. The purpose of this study was to develop a model, using artificial intelligent methods, for estimating air-demand ratio in venturi weirs. For this aim, Adaptive Network based Fuzzy Inference Systems (ANFIS) and Artificial Neural Network (ANNs) methods were used. The test results revealed that ANFIS model predicted the measured values at higher accuracy than ANNs model. Average correlation coefficients (R²) in ANFIS models were achieved equal to 0.9623 for β = 0.75 and 0.9666 for β = 0.50. Extremely good agreement between the predicted and measured values confirms that ANFIS model can be successfully used to predict air-demand ratio in venturi weirs.     

Automated design of a new integrated intelligent computing paradigm for constructing a constitutive model applicable to predicting rock fractures

Making a relation between strains and stresses is an important subject in the rock engineering field. Shear behaviors of rock fractures have been extensively investigated by different researchers. Literature mostly consists of constitutive models in the form of empirical functions that represent experimental data using mathematical regression techniques. As an alternative, this study aims to present a new integrated intelligent computing paradigm to form a constitutive model applicable to rock fractures. To this end, an RBFNN-GWO model is presented, which integrates the radial basis function neural network (RBFNN) with grey wolf optimization (GWO). In the proposed model, the hyperparameters and weights of RBFNN were tuned using the GWO algorithm. The efficiency of the designed RBFNN-GWO was examined comparing it with the RBFNN-GA model (a combination of RBFNN and the Genetic Algorithm). The proposed models were trained based on the results of a systematic set of 84 direct shear tests gathered from the literature. The finding of the current study demonstrated the efficiency of both the RBFNN-GA and RBFNN-GWO models in predicting the dilation angle, peak shear displacement, and stress as the rock fracture properties. Among the two models proposed in this study, the statistical results revealed the superiority of RBFNN-GWO over RBFNN-GA in terms of prediction accuracy.

     

具有噪声信息与状态模型不确定系统的IMM自适应滤波

卡尔曼滤波器广泛用于解决线性高斯系统的状态估计问题.然而,在实际应用中过程噪声和系统模型参数先验信息未知,且量测受到异常值干扰,给准确估计系统状态带来极大困难.针对具有噪声信息和状态模型不确定的动态系统,提出一种广义交互式多模型自适应滤波算法.该算法设计多个模型并行的方式对系统不确定进行处理,对于每个模型,建立Skew-T分布非对称重尾噪声表示模型,为了解决过程噪声与系统协方差相互耦合难以求解的问题,利用逆威沙特分布对系统预测协方差矩阵进行描述,并通过变分贝叶斯推理递归计算系统状态的后验分布.仿真结果和实验验证表明,在噪声信息和状态模型不确定条件下,所提出算法具有较高的估计精度.     

A well-balanced and adaptive variational model for the removal of mixed noise

Image denoising is one of the fundamental problems concerning image processing. Over the last decade mathematical models based on partial differential equations and variational techniques have led to superior results related to denoising problems. The additive noise models have been studied extensively, however, the reconstruction of images corrupted by nonadditive noise has not yet been thoroughly studied. In this paper, a novel variational method for the reconstruction of images corrupted by non-uniformly distributed noise is presented. The proposed model includes a balance between the data term and the regularization term in the energy functional, which takes into account the statistical control of the parameters and the position of the noisy points related to the edges presented in the image. The parameters are determined by the given initial noisy image. The obtained results have shown the effectiveness and robustness of the proposed model and in restoring images with multiplicative noise or mixed Gaussian noise, while preserving edges and small structures belonging to the image.     

云南省建设智慧水文的思考

水利部《智慧水利总体方案》是全国水利信息化建设的指导性文件,将带动地方智慧水利、水文建设。通过分析总体方案的架构和构建感知网及水文现代化建设技术装备的要求,解析智慧水利对云南智慧水文的定位,分析云南省水文信息化建设现状及存在问题,提出建设智慧水文的工作思路,为“十四五”水文信息化规划编制和今后的项目实施提供对策,以推动云南水文加速补齐信息化短板,有效支撑水利强监管。     

Using efference copy and a forward internal model for adaptive biped walking

To behave properly in an unknown environment, animals or robots must distinguish external from self-generated stimuli on their sensors. The biologically inspired concepts of efference copy and internal model have been successfully applied to a number of robot control problems. Here we present an application of this for our dynamic walking robot RunBot. We use efference copies of the motor commands with a simple forward internal model to predict the expected self-generated acceleration during walking. The difference to the actually measured acceleration is then used to stabilize the walking on terrains with changing slopes through its upper body component controller. As a consequence, the controller drives the upper body component (UBC) to lean forwards/backwards as soon as an error occurs resulting in dynamical stable walking. We have evaluated the performance of the system on four different track configurations. Furthermore we believe that the experimental studies pursued here will sharpen our understanding of how the efference copies influence dynamic locomotion control to the benefit of modern neural control strategies in robots.     

Affectively intelligent and adaptive car interfaces

In this article, we describe a new approach to enhance driving safety via multi-media technologies by recognizing and adapting to drivers’ emotions with multi-modal intelligent car interfaces. The primary objective of this research was to build an affectively intelligent and adaptive car interface that could facilitate a natural communication with its user (i.e., the driver). This objective was achieved by recognizing drivers’ affective states (i.e., emotions experienced by the drivers) and by responding to those emotions by adapting to the current situation via an affective user model created for each individual driver. A controlled experiment was designed and conducted in a virtual reality environment to collect physiological data signals (galvanic skin response, heart rate, and temperature) from participants who experienced driving-related emotions and states (neutrality, panic/fear, frustration/anger, and boredom/sleepiness). k-Nearest Neighbor (KNN), Marquardt-Backpropagation (MBP), and Resilient Backpropagation (RBP) Algorithms were implemented to analyze the collected data signals and to find unique physiological patterns of emotions. RBP was the best classifier of these three emotions with 82.6% accuracy, followed by MBP with 73.26% and by KNN with 65.33%. Adaptation of the interface was designed to provide multi-modal feedback to the users about their current affective state and to respond to users’ negative emotional states in order to decrease the possible negative impacts of those emotions. Bayesian Belief Networks formalization was employed to develop the user model to enable the intelligent system to appropriately adapt to the current context and situation by considering user-dependent factors, such as personality traits and preferences.     

基于遗传算法岗位能力考核智能组卷模型设计

针对目前组织对具体岗位的专业能力考核时,不能根据其专业特性组题出卷,考核试题过于笼统宽泛,组卷出题效率不高,缺乏模型化管理等问题,设计了一种新的考核组卷模型。该模型通过提出以三级知识点为结构的知识领域,并设计知识领域的管理模型,精细化岗位能力考核的组题范围;通过设计基于遗传算法理论的智能组卷算法,提高了出题组卷效率以及出题的科学性、合理性。最后给出了模型性能验证数据,并加以分析。     

Building a cluster of intelligent,adaptive web sites

Many attempts have been made to track web usage patterns and provide dynamic suggestions that might help users reach the information they seek more quickly. These mechanisms rely on mining the web log files for usage patterns. While this is easy for a single server, it would be much more difficult for multiple servers. We propose a mechanism that uses agent-based technologies and dynamic self-organizing maps to combine usage patterns in multiple web server logs and structures of multiple web sites to provide a more efficient and informative set of suggested links. This paper includes results of an ongoing project, which builds upon previously published results.     

Equipping the ACT-R cognitive architecture with a temporal ratio model of memory and using it in a new intelligent adaptive interface

ACT-R, as a useful and well-known cognitive architecture, is a theory for simulating and understanding human cognition. However, the standard version of this architecture uses a deprecated forgetting model. So, we equipped it with a temporal ratio model of memory that has been named as SIMPLE (Scale-Independent Memory, Perception, and Learning). On the other hand, one of the usages of cognitive architectures is to model the user in an Intelligent Adaptive Interface (IAI) implementation. Thus, our motivation for this effort is to use this equipped ACT-R in an IAI to deliver the right information at the right time to users based on their cognitive needs. So, to test our proposed equipped ACT-R, we designed and implemented a new IAI to control a swarm of Unmanned Aerial Vehicles (UAVs). This IAI uses the equipped ACT-R for user cognitive modeling, to deliver the right information to the users based on their forgetting model. Thus, our contributions are: equipping the ACT-R cognitive architecture with the SIMPLE memory model and using this equipped version of ACT-R for user modeling in a new IAI to control a group of UAVs. Simulation results, which have been obtained using different subjective and objective measures, show that we significantly improved situation awareness of the users using the IAI empowered by our equipped ACT-R.

     

Using consensus methods to construct adaptive interfaces in multimodal web-based systems

This paper presents a concept of adaptive development of user interfaces in multimodal web-based systems. Today, it is crucial for general access web-based systems that the user interface is properly designed and adjusted to user needs and capabilities. It is believed that adaptive interfaces could offer a possible solution to this problem. Here, we introduce the notion of the user profile for classification, the interface profile for describing the system interface, and the compound usability measure for evaluation of the interface. Consensus-based methods are applied for constructing the interface profiles appropriate to classes of users.     

基于智能交通信息的预期与适应性元胞传输模型

动态智能交通(ITS)信息在微观层面提升驾驶员的预测能力和适应能力,在宏观层面表现为车队群体的预期性和适应性.本文引入期望密度/速度函数以及预期性和适应性调节系数扩展了元胞传输模型,弥补了原始模型未考虑ITS信息影响的不足.线性稳定性分析和收敛性仿真结果表明预期性和适应性会减少弛豫带来的负效应,增大交通流的线性稳定域,不同的状态调节系数组合刻画了不同程度的预期性和适应性,改变了平衡曲线与收敛速度,智能交通信息将有益于辅助安全驾驶和减少道路拥堵.     

改进自适应对消算法在工业噪声处理中的应用

分析了工业环境噪声的特点,将自适应噪声对消算法应用到工业噪声的处理当中.在传统最小均方(LMS)算法及基于Lorentzian函数的变步长LMS算法的基础上进一步进行约束稳定性条件处理,提出了一种约束稳定性变步长LMS算法,并在Matlab平台上进行了仿真验证.结果表明:算法具有更快的收敛速度以及更小的稳态误差,并且能有效地降低梯度噪声对算法性能的影响.     

A model for radar images and its application to adaptive digital filtering of multiplicative noise

Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.     

Using a business process management system to model dynamic teaching methods

Enterprise Information Systems are enjoying an extensive trajectory in the optimization of organizations worldwide, of which predominantly the Business Process Management (BPM) systems stand out for their great flexibility. BPM models describe business workflows and are highly useful in detecting errors and bottlenecks and in identifying possible improvements. On the other hand, educational management software tools offer a large number of functionalities, but have yet to take advantage of these techniques. Our main objective is to perform an empirical analysis in this unexplored area to evaluate the advantages of applying BPM in the implementation of innovative and dynamic teaching activities.Using this methodology, we have designed RubricaSoft, a BPM system focused on providing dynamic educational processes. It automates multiple tasks, including peer evaluation, information integration and the management of deadlines. The results have been very promising from the point of view of the three axes upon which the evaluation has been carried out: satisfaction of students, improvement in academic results and increase in the productivity of teachers. In one of the processes, the time spent by the teacher has been reduced by 80% and student participation increased by 41%.     

Adaptive recursive deconvolution and adaptive noise cancellation

In this paper we apply a recursive deconvolution method to active noise cancellation (ANC) in a linear system: the observation of the output of a linear system of relative degree one, read at discrete time instants, is fed to a deconvolution algorithm which identifies the disturbance (with the delay of one step). This information is used in order to reduce the effect of the disturbance itself. Deconvolution being an ill posed problem, a regularization parameter is to be introduced. The choice of the value of the parameter is a delicate issue. We show that, when studying ANC, the discrepancy principle (applied recursively) is a feasible method for the choice of the parameter.