A Deep Residual Network for Geometric Decontouring

Grayscale images are intensively used to construct or represent geometric details infield of computer graphics. In practice, displacement mapping technique often allows an 8-bit grayscale image input to manipulate the position of vertices. Human eyes are insensitive to the change of intensity between consecutive gray levels, so a grayscale image only provides 256 levels of luminances. However, when the luminances are converted into geometric elements, certain artifacts such as false contours become obvious. In this paper, we formulate the geometric decontouring as a constrained optimization problem from a geometric perspective. Instead of directly solving this optimization problem, we propose a data-driven method to learn a residual mapping function. We design a Geometric DeContouring Network (GDCNet) to eliminate the false contours effectively. To this end, we adopt a ResNet-based network structure and a normal-based loss function. Extensive experimental results demonstrate that accurate reconstructions can be achieved effectively. Our method can be used as a relief compressed representation and enhance the traditional displacement mapping technique to augment 3D models with high-quality geometric details using grayscale images efficiently.     

基于精化的可信执行环境内存隔离机制验证

可信执行环境(trusted execution environment, TEE)基于硬件隔离机制,为安全敏感应用提供隔离的执行环境,保护敏感数据的安全性.内存隔离机制是TEE的关键机制之一,用于对安全内存和非安全内存进行隔离,并对安全内存实施访问控制,如果其安全性不能保证,可能造成存储在安全内存中的敏感数据泄露.为验证TEE内存隔离机制的安全性,针对基于ARM TrustZone技术构建的TEE,提出一种基于精化的可信执行环境内存隔离机制安全性验证方法.建立抽象模型和具体模型,并定义两种模型之间的精化关系,在证明精化关系成立和抽象模型满足信息流安全性的前提下,验证具体模型的信息流安全性.具体模型建模了TEE内存隔离机制的关键硬件和软件,包括TrustZone地址空间控制器、MMU和TrustZone monitor等,在定理证明器Isabelle/HOL中,验证了该模型满足无干扰、无泄露、无影响等信息流安全属性.     

基于 MTF 和变分的全色与多光谱图像融合模型

Pan-sharpening将高分辨率图像全色(Panchromatic, Pan)波段的空间细节注入多光谱(Multispectral, MS)波段, 以生成同时具有高光谱和高空间分辨率的多光谱图像. 为改善融合效果, 需要考虑多光谱和全色波段的调制传输函数(Modulation transfer function, MTF). 本文提出了一个新的基于MTF和变分的Pan-sharpening模型. 该模型的能量泛函包括两项, 第1项为细节注入项, 基于高通滤波器从Pan波段中提取细节信息并注入融合图像;第2项为光谱保真项, 基于MTF设计多孔小波的低通滤波器以保持MS波段的多光谱信息. 在QuickBird、IKONOS和GeoEye数据集上的融合结果表明, 该模型可以生成同时具有高空间和高光谱质量的融合图像, 融合效果优于AWLP、IHS_BT、HPM-CC-PSF、NAWL、快速变分等算法.     

Project success prediction using an evolutionary support vector machine inference model

Accomplishing construction projects successfully requires continuous monitoring and control by construction managers of factors critical to project success. This research proposed using an Evolutionary Support Vector Machine Inference Model (ESIM) to predict project success dynamically. ESIM is a hybrid that integrates a support vector machine (SVM) with a fast messy genetic algorithm (fmGA). SVM is concerned primarily with learning and curve fitting, while fmGA deals primarily with optimization. Furthermore, the model integrates the process of Continuous Assessment of Project Performance (CAPP) to select factors that influence project success. Training and test patterns were collected from a CAPP database of 46 construction projects. These projects represent real data collected by Russell from 16 company members of the Construction Industry Institute (CII). Results show that ESIM is able to predict project success at a significant level of accuracy.     

Exploring managers' intention to use business intelligence: the role of motivations

In recent years, the amount of information has been growing quickly within organisations. Thus, several organisations have implemented analytics and business intelligence (BI) for Big Data solutions. To effectively foster the acceptance and usage of BI, this study examines how the motivational factors of both expectancy theory and social exchange theory (SET) affect managers' willingness to utilise BI. A survey of 271 managers supports the proposed model. The results show that intangible rewards of expectancy theory significantly influence the intention to read information (IRI) through the desire to make good decisions. Benefit factors (organisational rewards, reputation, and reciprocity) of SET are significantly associated with the desire to exchange reports (DER). The IRI significantly directly and indirectly influences the intention to create reports through the DER. Given the empirical findings, this study provides theoretical and managerial insights for organisations.     

Estimate at Completion for construction projects using Evolutionary Support Vector Machine Inference Model

Construction projects are influenced by a range of factors that impact upon final project cost. Estimate at Completion (EAC) is an important approach used to estimate final project cost, which takes into consideration probable project performance and risks. EAC helps project managers identify potential but still unknown problems and adopt response strategies. This study constructed an evolutionary EAC model to generate project cost estimates that proved significantly more reliable than estimates achievable using currently prevailing formulae. The developed learning model fused two artificial intelligence approaches, namely the fast messy genetic algorithm (fmGA) and Support Vector Machine (SVM), to create an Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM was then applied to estimate final project costs for historical cases. Finally, using the EAC estimate, project cost influence indices, and project cost diagrams, the discrepancy between estimate and practical values was examined to determine potential problems in order to help project managers better control project costs. The learning results were validated in real applications that showed good performance for training models. Providing project managers reliable EAC trend estimates is helpful for their effective control of project costs and taking appropriate peremptory measures to handle potential problems.     

基于SystemView的WCDMA-FDD系统仿真设计研究

选用美国ELANIX公司1999年出品的SystemView系统仿真工具,依据系统原理,对WCDMA-FDD下行链路中信号的扩频、调制、传输及解调与合成进行了仿真分析。其中Rake接收部分的设计相对独立,分信号捕捉、延迟与合成3个步骤完成对多径信号的搜索与合成,以实现宽带系统下独特的信号接收,为系统的硬件实现奠定了良好的基础。     

A new bottleneck detecting approach to productivity improvement of knowledgeable manufacturing system

Manufacturing systems are usually restricted by one or more bottlenecks. Identification of bottleneck is a key factor to improving the throughput of a production system. However, locating the bottleneck is no easy task. This paper proposes a novel method of bottleneck detecting for knowledgeable manufacturing system (KMS). Presented a net-like model of the knowledgeable manufacturing system for bottleneck detection, which adapts well to the bottleneck analysis in flexible product lines. Based on the model, the concept of entire production net is defined and an approach to identifying bottlenecks in entire production nets is developed and proven effective theoretically. A self-learning method is introduced for storing the knowledge of bottlenecks and their conditions in the knowledge base to detect which cells are required to upgrade their capacity. Validity of the approach is verified by the numerical experiment.     

基于行为自动机的构件可替换性分析与验证

在交互协议层面讨论构件的可替换性,采用非确定性有限状态自动机(nondeterministic finite automata,简称NFA)来建模构件的交互行为,在保证交互兼容性的前提下,提出了按构件环境的透明度和构件交互的变化度两维划分的可替换性模型,给出了4类可替换性的形式化定义及其之间的关系,并基于NFA理论给出了相关的验证算法。另外,该模型以构件的替换行为而不是其全部行为作为构件替换的参照,从而使替换时有更多的候选构件可供使用,提高了构件复用的几率。