分数跳-扩散下外汇期权定价

用保险精算方法,在股票价格服从分数跳-扩散过程,且无风险利率、波动率和期望收益率为时间的非随机函数,给出了外汇期权定价公式.     

CRT显示器的颜色转换模型

在分析CRT显示器色彩呈现原理的基础上,用色彩管理软件ProfileMaker 5.0以及Eye-One Pro分光光度仪对N700DF CRT显示器进行屏幕的校准和特性化;采用多项式回归算法建立了CIERGB到CIEXYZ颜色转换模型,并对转换模型所得到的X、Y、Z计算值与实际测量值进行了比较.在所抽取的12个色块中...     

Hierarchical control for cornering stability of dual-motor RWD vehicles with electronic differential system using PSO optimized SOSMC method

This paper proposes a novel control scheme with a three-layer hierarchical structure to improve the cornering stability of the dual-motor rear-wheel drive (RWD) vehicles with the electronic differential system (EDS). The proposed hierarchical structure for the control system includes the observing layer, control layer, and actuation layer. In the observing layer, the driver model is designed to obtain the nominal steering angle, and the state observer is designed to obtain the yaw angle which cannot be easily measured. Then, particle swarm optimization (PSO) and second order sliding mode control (SOSMC) are employed in the control layer. The SOSMC part is used to design the control law to eliminate the chattering problem in the sliding mode algorithm, and the PSO part is used to obtain the optimal weights in the sliding mode surface to meet the minimum sideslip angle error and yaw rate error. The actuation layer allocates the corrected yaw moment by distributing the driving force to each independent driving wheel. Finally, the numerical tests are carried out under the double line change (DLC) maneuver. The results show that the proposed control system can effectively improve the cornering stability of the dual-motor RWD vehicles and reduce their motor power consumption.     

A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism

Dam displacements can effectively reflect its operational status, and thus establishing a reliable displacement prediction model is important for dam health monitoring. The majority of the existing data-driven models, however, focus on static regression relationships, which cannot capture the long-term temporal dependencies and adaptively select the most relevant influencing factors to perform predictions. Moreover, the emerging modeling tools such as machine learning (ML) and deep learning (DL) are mostly black-box models, which makes their physical interpretation challenging and greatly limits their practical engineering applications. To address these issues, this paper proposes an interpretable mixed attention mechanism long short-term memory (MAM-LSTM) model based on an encoder-decoder architecture, which is formulated in two stages. In the encoder stage, a factor attention mechanism is developed to adaptively select the highly influential factors at each time step by referring to the previous hidden state. In the decoder stage, a temporal attention mechanism is introduced to properly extract the key time segments by identifying the relevant hidden states across all the time steps. For interpretation purpose, our emphasis is placed on the quantification and visualization of factor and temporal attention weights. Finally, the effectiveness of the proposed model is verified using monitoring data collected from a real-world dam, where its accuracy is compared to a classical statistical model, conventional ML models, and homogeneous DL models. The comparison demonstrates that the MAM-LSTM model outperforms the other models in most cases. Furthermore, the interpretation of global attention weights confirms the physical rationality of our attention-based model. This work addresses the research gap in interpretable artificial intelligence for dam displacement prediction and delivers a model with both high-accuracy and interpretability.     

土的剪切动态空间滑动面及其强度准则分析

提出了多种动态空间滑动面及其相应的强度准则,分析了不同强度准则应力空间描述的强度破坏面,探讨了在多种动态空间滑动面下土强度准则的变化可能性及实用性。结果表明:对于方次动态空间滑动面,随着方次越高,其动态空间滑动面越垂直于小主应力坐标轴,平行于大主应力和中主应力平面,空间面上的正应力越来越接近于小主应力,而剪应力越来越退化;对于开方次空间滑动面,随着开方次越高,其空间滑动面越接近于正八面体面,空间面上的正应力和剪应力越来越平均化;对于方次空间滑动面强度准则,随着方次越高,其对应强度准则破坏面的规律性越来越差,强度准则随参数的变化也越来越敏感,只有在特定参数条件下其空间面才具有较好的规律性;对于开方次空间滑动面,随着开方次越高,越来越均化了三个主应力的贡献,开方次的次数和土的结构性强弱相关。     

一种面向网络入侵检测系统的多媒体链表结构

为了解决网络入侵检测系统在流量较大的情况下出现很高的丢包率的问题,设计出一种多媒体链表结构.该链表结构在传统链表基础上,通过增设多媒体类型结点将链表分解.能够对网络流量中已识别的各类多媒体数据快速处理,并对其进行预先检测,使安全的多媒体数据包越过网络入侵检测系统的常规检测过程.通过实验,在网络入侵检测系统中使用多媒体链表后,丢包率降低18%以上,对多媒体数据包的检测率有不同程度的提高.实验结果表明:使用多媒体规则链表可有效降低网络入侵检测系统的丢包率.     

Creep behavior and failure mechanisms of CVI and PIP SiC/SiC composites at temperatures to 1650 °C in air

Creep properties of 2D woven CVI and PIP SiC/SiC composites with Sylramic™-iBN SiC fibers were measured at temperatures to 1650 °C in air and the data was compared with the literature. Batch-to-batch variations in the tensile and creep properties, and thermal treatment effects on creep, creep parameters, damage mechanisms, and failure modes for these composites were studied. Under the test conditions, the CVI SiC/SiC composites exhibited both matrix and fiber-dominated creep depending on stress, whereas the PIP SiC/SiC composites displayed only fiber-dominated creep. Creep durability in both composite systems is controlled by the most creep resistant phase as well as oxidation of the fibers via cracking matrix. Specimen-to- specimen variations in porosity and stress raisers caused significant differences in creep behavior and durability. The Larson-Miller parameter and Monkman-Grant relationship were used wherever applicable for analyzing and predicting creep durability.     

Spectral clustering with eigenvector selection

The task of discovering natural groupings of input patterns, or clustering, is an important aspect of machine learning and pattern analysis. In this paper, we study the widely used spectral clustering algorithm which clusters data using eigenvectors of a similarity/affinity matrix derived from a data set. In particular, we aim to solve two critical issues in spectral clustering: (1) how to automatically determine the number of clusters, and (2) how to perform effective clustering given noisy and sparse data. An analysis of the characteristics of eigenspace is carried out which shows that (a) not every eigenvectors of a data affinity matrix is informative and relevant for clustering; (b) eigenvector selection is critical because using uninformative/irrelevant eigenvectors could lead to poor clustering results; and (c) the corresponding eigenvalues cannot be used for relevant eigenvector selection given a realistic data set. Motivated by the analysis, a novel spectral clustering algorithm is proposed which differs from previous approaches in that only informative/relevant eigenvectors are employed for determining the number of clusters and performing clustering. The key element of the proposed algorithm is a simple but effective relevance learning method which measures the relevance of an eigenvector according to how well it can separate the data set into different clusters. Our algorithm was evaluated using synthetic data sets as well as real-world data sets generated from two challenging visual learning problems. The results demonstrated that our algorithm is able to estimate the cluster number correctly and reveal natural grouping of the input data/patterns even given sparse and noisy data.     

Image categorization: Graph edit distance+edge direction histogram

This paper presents a novel algorithm for computing graph edit distance (GED) in image categorization. This algorithm is purely structural, i.e., it needs only connectivity structure of the graph and does not draw on node or edge attributes. There are two major contributions: (1) Introducing edge direction histogram (EDH) to characterize shape features of images. It is shown that GED can be employed as distance of EDHs. This algorithm is completely independent on cost function which is difficult to be defined exactly. (2) Computing distance of EDHs with earth mover distance (EMD) which takes neighborhood bins into account so as to compute distance of EDHs correctly. A set of experiments demonstrate that the newly presented algorithm is available for classifying and clustering images and is immune to the planar rotation of images. Compared with GED from spectral seriation, our algorithm can capture the structure change of graphs better and consume 12.79% time used by the former one. The average classification rate is 5% and average clustering rate is 25% higher than the spectral seriation method.     

Hidden Markov model-based ensemble methods for offline handwritten text line recognition

This paper investigates various ensemble methods for offline handwritten text line recognition. To obtain ensembles of recognisers, we implement bagging, random feature subspace, and language model variation methods. For the combination, the word sequences returned by the individual ensemble members are first aligned. Then a confidence-based voting strategy determines the final word sequence. A number of confidence measures based on normalised likelihoods and alternative candidates are evaluated. Experiments show that the proposed ensemble methods can improve the recognition accuracy over an optimised single reference recogniser.