中断驱动系统模型检验

针对一类中断驱动系统提出了一种建模和模型检验的方法.该系统通常由中断处理程序和操作系统调度的任务组成,前者由中断源触发后处理中断事件,后者则负责处理系统的日常任务以及某些中断处理事件的后续处理.因为这类系统是实时控制系统,对中断事件的处理需要在规定时间内响应并完成,否则可能造成严重的系统失效.为了帮助系统设计人员在系统设计过程中应用模型检验技术来提高系统的正确性,首先确定了此类系统中与时序性质相关的系统要素(包括系统调度任务、中断源、中断处理程序)和相关参数,并要求设计人员在设计阶段明确指出这些要素的参数.然后,提出了将这些要素和参数自动转化为形式化模型的方法:使用时间自动机对中断事件进行建模,使用中断向量表和CPU处理栈对中断处理过程进行建模.对于得到的形式化模型,给出了针对中断处理超时错误的检测方法,并在此基础上给出了针对共享资源的完整性、子程序原子性的检验方法.     

Monitoring unit root and multiple structural changes: An information criterion approach

An information criterion-based model selection method is proposed for monitoring unit root and multiple structural changes. In this method, a battery of possible models is considered by changing the integration order (I(0) or I(1)) and the combinations of change points. Next, the best model is selected from among alternative models via a modified Bayesian information criterion (BIC). Accordingly, on the basis of the selected model, the process that generates the observed time series is determined. The BIC is modified in order to adjust the frequency count of incorrectly selecting stationary models via the conventional BIC. The simulation results of monitoring unit root and structural change suggest that the proposed method outperforms the conventional hypothesis testing method in terms of detection accuracy and detection speed. Furthermore, the empirical results suggest that the proposed method exhibits better performances with regard to detection stability and forecastability.     

一种新型实时数据模型——钟面模型

动态的实时数据总是包含对应的时间信息,现有实时数据库技术没有充分考虑时间信息的建模问题;另一方面,大型实时数据库中数据存取效率的瓶颈在于磁盘的机械运动.提出了一种新型实时数据模型--钟面模型.建立了钟面模型与实时数据的时间属性之间的逻辑对应关系,分析了钟面模型与磁盘存储介质的圆柱状结构之间的逻辑对应关系,以及根据数据在逻辑钟面的偏移位置,确定数据在物理磁盘上的偏移位置的计算方法.初步研究结果表明基于钟面模型的实时数据模型及其对应的数据存取技术,可以减少磁盘的机械运动,缩短定位操作所消耗的时间,显著提高数据处理效率.     

Gaussian-Hermite moment-based depth estimation from single still image for stereo vision

Depth information of objects plays a significant role in image-based rendering. Traditional depth estimation techniques use different visual cues including the disparity, motion, geometry, and defocus of objects. This paper presents a novel approach of focus cue-based depth estimation for still images using the Gaussian-Hermite moments (GHMs) of local neighboring pixels. The GHMs are chosen due to their superior reconstruction ability and invariance properties to intensity and geometric distortions of objects as compared to other moments. Since depths of local neighboring pixels are significantly correlated, the Laplacian matting is employed to obtain final depth map from the moment-based focus map. Experiments are conducted on images of indoor and outdoor scenes having objects with varying natures of resolution, edge, occlusion, and blur contents. Experimental results reveal that the depth estimated from GHMs can provide anaglyph images with stereo quality better than that provided by existing methods using traditional visual cues.     

COMPSPEN:对形状性质与数据约束进行融合推理的分离逻辑求解器

分离逻辑是经典霍尔逻辑的针对操作指针和动态数据结构的扩展,已经广泛用于对基础软件(比如操作系统内核等)的分析与验证.分离逻辑约束自动求解是提升对操作指针和动态数据结构的程序的验证的自动化程度的重要手段.针对动态数据结构的验证一般同时涉及形状性质(比如单链表、双链表、树等)和数据性质(比如有序性、数据不变性等).主要介绍能对动态数据结构的形状性质与数据约束进行融合推理的分离逻辑求解器COMPSPEN.首先介绍COMPSPEN的理论基础,包括能够同时描述线性动态数据结构的形状性质和数据约束的分离逻辑子集SLID_data、SLID_data的可满足性和蕴涵问题的判定算法.然后,介绍COMPSPEN工具的基本框架.最后,使用COMPSPEN工具进行了实例研究.收集整理了600个测试用例,在这600个测试用例上将COMPSPEN与已有的主流分离逻辑求解器Asterix、S2S、Songbird、SPEN进行了比较.实验结果表明COMPSPEN是唯一能够求解含有集合数据约束的分离逻辑求解器,而且总体来讲,能对线性数据结构上的同时含有形状性质和线性算术数据约...     

Improved Bounded Model Checking for the Universal Fragment of CTL

SAT-based bounded model checking (BMC) has been introduced as a complementary technique to BDD-based symbolic model checking in recent years, and a lot of successful work has been done in this direction. The approach was first introduced by A. Biere et al. in checking linear temporal logic (LTL) formulae and then also adapted to check formulae of the universal fragment of computation tree logic (ACTL) by W. Penczek et al. As the efficiency of model checking is still an important issue, we present an impr...     

Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

High dimensionality in real-world multi-reservoir systems greatly hinders the application and popularity of evolutionary algorithms, especially for systems with heterogeneous units. An efficient hierarchical optimization framework is presented for search space reduction, determining the best water distributions, not only between cascade reservoirs, but also among different types of hydropower units. The framework is applied to the Three Gorges Project (TGP) system and the results demonstrate that the difficulties of multi-reservoir optimization caused by high dimensionality can be effectively solved by the proposed hierarchical method. For the day studied, power output could be increased by 6.79 GWh using an optimal decision with the same amount of water actually used; while the same amount of power could be generated with 2.59 × 10⁷ m³ less water compared to the historical policy. The methodology proposed is general in that it can be used for other reservoir systems and other types of heterogeneous unit generators.     

Optimal-correlation-based reconstruction for distributed compressed video sensing

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient joint reconstruction by leveraging more realistic signal models is still an open challenge. In this paper, we present a novel optimal-correlation-based reconstruction method for compressively sampled videos from multiple measurement vectors. In our method, the sparsity is mainly exploited through inter-signal correlations rather than the traditional frequency transform, wherein the optimization is not only over the signal space to satisfy data consistency but also over all possible linear correlation models to achieve minimum-l₁-norm correlation noise. Additionally, a two-phase Bregman iterative based algorithm is outlined for solving the optimization problem. Simulation results show that our proposal can achieve an improved reconstruction performance in comparison to the conventional approaches, and especially, offer a 0.7–9.9 dB gain in the average PSNR for DCVS.     

A middleware framework for application-aware and user-specific energy optimization in smart mobile devices

Mobile battery-operated devices are becoming an essential instrument for business, communication, and social interaction. In addition to the demand for an acceptable level of performance and a comprehensive set of features, users often desire extended battery lifetime. In fact, limited battery lifetime is one of the biggest obstacles facing the current utility and future growth of increasingly sophisticated “smart” mobile devices. This paper proposes a novel application-aware and user-interaction aware energy optimization middleware framework (AURA) for pervasive mobile devices. AURA optimizes CPU and screen backlight energy consumption while maintaining a minimum acceptable level of performance. The proposed framework employs a novel Bayesian application classifier and management strategies based on Markov Decision Processes and Q-Learning to achieve energy savings. Real-world user evaluation studies on Google Android based HTC Dream and Google Nexus One smartphones running the AURA framework demonstrate promising results, with up to 29% energy savings compared to the baseline device manager, and up to 5×savings over prior work on CPU and backlight energy co-optimization.     

Open-set face recognition across look-alike faces in real-world scenarios

The open-set problem is among the problems that have significantly changed the performance of face recognition algorithms in real-world scenarios. Open-set operates under the supposition that not all the probes have a pair in the gallery. Most face recognition systems in real-world scenarios focus on handling pose, expression and illumination problems on face recognition. In addition to these challenges, when the number of subjects is increased for face recognition, these problems are intensified by look-alike faces for which there are two subjects with lower intra-class variations. In such challenges, the inter-class similarity is higher than the intra-class variation for these two subjects. In fact, these look-alike faces can be created as intrinsic, situation-based and also by facial plastic surgery. This work introduces three real-world open-set face recognition methods across facial plastic surgery changes and a look-alike face by 3D face reconstruction and sparse representation. Since some real-world databases for face recognition do not have multiple images per person in the gallery, with just one image per subject in the gallery, this paper proposes a novel idea to overcome this challenge by 3D modeling from gallery images and synthesizing them for generating several images. Accordingly, a 3D model is initially reconstructed from frontal face images in a real-world gallery. Then, each 3D reconstructed face in the gallery is synthesized to several possible views and a sparse dictionary is generated based on the synthesized face image for each person. Also, a likeness dictionary is defined and its optimization problem is solved by the proposed method. Finally, the face recognition is performed for open-set face recognition using three proposed representation classifications. Promising results are achieved for face recognition across plastic surgery and look-alike faces on three databases including the plastic surgery face, look-alike face and LFW databases compared to several state-of-the-art methods. Also, several real-world and open-set scenarios are performed to evaluate the proposed method on these databases in real-world scenarios.