A systematic approach to evaluating domain-specific modeling language environments for multi-agent systems

Multi-agent systems (MASs) include multiple interacting agents within an environment to provide a solution for complex systems that cannot be easily solved with individual agents or monolithic systems. However, the development of MASs is not trivial due to the various agent properties such as autonomy, responsiveness, and proactiveness, and the need for realization of the many different agent interactions. To support the development of MASs various domain-specific modeling languages (DSMLs) have been introduced that provide a declarative approach for modeling and supporting the generation of agent-based systems. To be effective, the proposed DSMLs need to meet the various stakeholder concerns and the related quality criteria for the corresponding MASs. Unfortunately, very often the evaluation of the DSML is completely missing or has been carried out in idiosyncratic approach. If the DSMLs are not well defined, then implicitly this will have an impact on the quality of the MASs. In this paper, we present an evaluation framework and systematic approach for assessing existing or newly defined DSMLs for MASs. The evaluation is specific for MAS DSMLs and targets both the language and the corresponding tools. To illustrate the evaluation approach, we first present SEA_ML, which is a model-driven MAS DSML for supporting the modeling and generation of agent-based systems. The evaluation of SEA_ML is based on a multi-case study research approach and provides both qualitative evaluation and quantitative analysis. We report on the lessons learned considering the adoption of the evaluation approach as well as the SEA_ML for supporting the generation of agent-based systems.     

A new approach to detecting and designing living structure of urban environments

Sustainable urban design or planning is not a LEGO-like assembly of prefabricated elements, but an embryo-like growth with persistent differentiation and adaptation towards a coherent whole. The coherent whole has a striking character – called living structure – that consists of far more small substructures than large ones. To detect the living structure, natural streets or axial lines have been previously adopted to be topologically represent an urban environment as a coherent whole. This paper develops a new approach to detecting the underlying living structure of urban environments. The approach takes an urban environment as a whole and recursively decomposes it into meaningful subwholes at different levels of hierarchy (or scale) ranging from the largest to the smallest. We compared the new approach to natural street and axial line approaches and demonstrated, through four case studies, that the new approach is better and more powerful. Based on the study, we further discuss how the new approach can be used not only for understanding but also – probably more importanly – for effectively designing or planning an urban environment to be living or more living.     

A multi-model ensemble approach for quantifying sub-pixel land cover fractions in the urban environments

Land cover information is essential for sustainable management of the environment in urban areas. Satellite images have increasingly been used to extract such information, yet the accuracy has been challenged by the spectral and spatial heterogeneity of urban land covers. This paper presents a framework to develop a more skilful and reliable model for estimating land cover fractions using a multi-model ensemble technique, named Bayesian Model Averaging (BMA). The BMA is a statistical technique that combines the estimates of different models using Bayesian probability theory. In the BMA, each individual estimate is assigned a weight that is optimised in such a way that the likelihood of an individual estimate given the observation is maximised. In this study, three methods, viz. Multi-layer Perceptron (MLP), Pre-screened and Normalised Multiple Endmember Spectral Mixture Analysis (PNMESMA) and Support Vector Regression (SVR) have been used to develop an Ensemble Model (EM). We used a cluster-based approach for applying the BMA to utilise the diverse advantages in individual models. First, the image pixels were separated into three clusters by applying Normalised Difference Vegetation Index (NDVI) thresholds. Second, an ensemble of models for each cluster was derived using the BMA, and these ensembles were finally combined to derive the final output. The EM was tested in a heterogeneous urban area, viz. South Delhi, India, using two multi-spectral images, including Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Spaceborne Thermal Emission Reflectance Radiometer (ASTER). The modelled land cover fractions were compared with the reference land cover fractions derived from a high-resolution (approximately 1 m) panchromatic image of the OrbitView-3 satellite. The accuracy assessment revealed that the EM estimates more accurate and reliable land cover fractions than the individual models on both the images. The performance of the EM in terms of Root Mean Square Error (RMSE), bias and kappa coefficient (k) is generally superior to that of the best of the individual models. These findings can help improve the accuracy of land cover fractions in heterogeneous landscapes by combining the outputs of various diverse models.     

基于分层策略的三维非刚性模型配准算法

在三维非刚性模型分析中,通常需要对不同姿态下的模型进行配准。针对传统配准算法存在复杂度高、计算量大、精确度低等问题,提出一种新的基于分层策略的三维非刚性模型配准算法。首先,定义热核签名函数为模型的标量域,使用同源聚类算法提取模型的特征点和特征区域,进而提出三维几何模型的树形表示方法:它的根节点为三维几何模型,内部节点为模型的特征区域,叶节点为包含在相应区域的特征点。然后,根据三维几何模型的树形表示提出模型的分层配准算法。在SHREC 2010模型配准数据集上对比分析了分层配准算法、推广的多维尺度变换算法（GMDS）和博弈论方法在等距变换、孔洞、小孔洞、尺度变换、局部尺度变换、重采样、噪声、散粒噪声以及拓扑变换等情况下的性能。实验结果表明,在以上三维几何模型数据受干扰的情况下,分层配准算法的准确性明显优于GMDS方法和博弈论方法,同时具有较低的计算复杂度。     

A combined approach for estimating vegetation cover in urban/suburban environments from remotely sensed data

The spatio-temporal distribution of vegetation is an important component of the urban/suburban environment. Therefore, correct estimation of vegetation cover in urban/suburban areas is fundamental in land use studies. In this study, the potential of extracting fractional vegetation cover (FVC) from remotely sensed data and ground measurements is explored. Based on the assumption that pixel has a mosaic structure, sub-pixel models for FVC estimation are first introduced. Then a combined approach of using different sub-pixel models for FVC estimation based on land cover classification is proposed. The experimental result, derived from a case study in Haidian district, Beijing, indicates that the accuracy of FVC estimation using the proposed method can be up to 80.7%. The results suggest that this method may be generally useful for FVC estimation in urban and suburban areas.     

A contribution to vision-based autonomous helicopter flight in urban environments

A navigation strategy that exploits the optic flow and inertial information to continuously avoid collisions with both lateral and frontal obstacles has been used to control a simulated helicopter flying autonomously in a textured urban environment. Experimental results demonstrate that the corresponding controller generates cautious behavior, whereby the helicopter tends to stay in the middle of narrow corridors, while its forward velocity is automatically reduced when the obstacle density increases. When confronted with a frontal obstacle, the controller is also able to generate a tight U-turn that ensures the UAV’s survival. The paper provides comparisons with related work, and discusses the applicability of the approach to real platforms.     

Semantic visualization of 3D urban environments

The purpose of this work is the semantic visualization of complex 3D city models containing numerous dynamic entities, as well as performing interactive semantic walkthroughs and flights without predefined paths. This is achieved by using a 3D multilayer scene graph that integrates geometric and semantic information as well as by the performance of efficient geometric and what we call semantic view culling. The proposed semantic-geometric scene graph is a 3D structure composed of several layers which is suitable for visualizing geometric data with semantic meaning while the user is navigating inside the 3D city model. BqR-Tree is the data structure specially developed for the geometric layer for the purpose of speeding up rendering time in urban scenes. It is an improved R-Tree data structure based on a quadtree spatial partitioning which improves the rendering speed of the usual R-trees when view culling is implemented in urban scenes. The BqR-Tree is defined by considering the city block as the basic and logical unit. The advantage of the block as opposed to the traditional unit, the building, is that it is easily identified regardless of the data source format, and allows inclusion of mobile and semantic elements in a natural way. The usefulness of the 3D scene graph has been tested with low structured data, which makes its application appropriate to almost all city data containing not only static but dynamic elements as well.     

A systematic approach to data security

The problem of selecting internal controls, or that subset of those controls we call security measures, in a data processing environment yields rather readily to an orderly, systematic approach. Such an approach requires recognition that a control should not be implemented if it costs more than tolerating the problem. Further, no control should be implemented which is more costly or less effective or displaces less potential loss than does some other control. The basic concept and outline of the systematic approach is described and references to supplemental papers for guidance in specific areas is provided through the bibliography.     

A systematic approach to host interface design for high-speednetworks

Optical fiber has made it possible to build networks with link speeds of over a gigabit per second; however, these networks are pushing end-systems to their limits. For high-speed networks (100 Mbits per second and up), network throughput is typically limited by software overhead on the sending and receiving hosts. Minimizing this overhead improves application-level latency and throughput and reduces the number of cycles that applications lose to communication overhead. Several factors influence communication overhead: communication protocols, the application programming interface (API). and the network interface hardware architecture. The author describes how these factors influence communication performance and under what conditions hardware support on the network adapter can reduce overhead. He first describes the organization of a typical network interface and discusses performance considerations for interfaces to high-speed networks. He then discusses software optimizations that apply to simple network adapters and show how more powerful adapters can improve performance on high-speed networks     

A systematic approach to adaptive observer synthesis for nonlinearsystems

Geometric techniques of controller design for nonlinear systems have enjoyed great success. A serious shortcoming, however, has been the need for access to full-state feedback. This paper addresses the issue of state estimation from limited sensor measurements in the presence of parameter uncertainty. An adaptive nonlinear observer is suggested for Lipschitz nonlinear systems, and the stability of this observer is shown to be related to finding solutions to a quadratic inequality involving two variables. A coordinate transformation is used to reformulate this inequality as a linear matrix inequality. A systematic algorithm is presented, which checks for feasibility of a solution to the quadratic inequality and yields an observer whenever the solution is feasible. The state estimation errors then are guaranteed to converge to zero asymptotically. The convergence of the parameters, however, is determined by a persistence-of-excitation-type constraint     

A systematic approach to language constructs for concurrent programs

Several styles and notations for representing concurrent programs are shortly explained and related to each other. It is basically demonstrated how the different language concepts found in concurrent programming conceptually evolve from classical styles of functional and applicative programming.     

A canonical form-based approach to affine registration of DTI

Due to the orientation feature of diffusion tensor images (DTI), tensors need to be reoriented during an affine registration. There exists two active reorientation schemes: finite strain (FS) and preserving principal direction (PPD). However, FS scheme limits its application on rigid deformation and PPD scheme suffers from computation load caused by the iteration. In order to overcome these shortcomings, we propose a canonical form-based affine registration of DTI, named as CFARD. We transform voxel sets into canonical forms where an affine registration is simplified as a rigid registration, while still preserves the effects of non-rigid components. This transforming thus extends the application of FS scheme to affine deformation. Furthermore, to reduce computation load, the quaternion technique is skillfully employed to seek a closed-form solution of the optimal rotation where no iteration is required. Extensive experiments are conducted on synthetic and real DTI data from the human brain. In contrast to four existing algorithms, the proposed CFARD improves the consistency between tensor orientation and the anatomical structures after deformation, and performs a better balance between accuracy and computational complexity.

     

A two-phase approach to interactivity enhancement for large-scale distributed virtual environments

Distributed virtual environments (DVEs) are distributed systems that allow multiple geographically distributed clients (users) to interact simultaneously in a computer-generated, shared virtual world. Applications of DVEs can be seen in many areas nowadays, such as online games, military simulations, collaborative designs, etc. To support large-scale DVEs with real-time interactions among thousands or even more distributed clients, a geographically distributed server architecture (GDSA) is generally needed, and the virtual world can be partitioned into many distinct zones to distribute the load among the servers. Due to the geographic distributions of clients and servers in such architectures, it is essential to efficiently assign the participating clients to servers to enhance users’ experience in interacting within the DVE. This problem is termed the client assignment problem (CAP) in this paper. We propose a two-phase approach, consisting of an initial assignment phase and a refined assignment phase to address the CAP. Both phases are shown to be NP-hard. Several heuristic assignment algorithms are then devised and evaluated via extensive simulations with realistic settings. We find that, even under heterogeneous environments like the Internet where accurate input data for the assignment algorithms are usually impractical to obtain, the proposed algorithms are still beneficial to the performances of DVE.     

参数自适应的图像亚像素级配准方法

目前基于区域的图像配准方法不能同时满足宽范围运动参数和高准确度的配准要求。基于图像变换的频域和空间域特性,提出一种运动参数自适应的图像配准方法,设计了旋转参数、平移参数的估计步骤和融合方法。基于仿真实验对参数自适应方法与Vandewalle方法、Keren改进方法的效果进行了比较分析,采用误差的标准差和均方误差两项指标评价配准算法的参数自适应性和配准准确度,参数自适应方法的两项评价指标均低于另两种方法,表明其在宽范围运动参数估计方面有自适应能力和高配准精度。     

A CNN-based 3D patch registration approach for integrating sequential models in support of progress monitoring

Significant advancements in three-dimensional (3D) imaging technologies have enabled the ability to effectively monitor and manage the progress of works in construction. Traditionally, 3D point clouds have been used in conjunction with building information models to visualize the progress of works. The discrepancies between ‘as-planned’ and ‘actual’ models are unable to be automatically identified using the existing approaches due the absence of an effective registration algorithm. To ensure the registration accuracy of multi-scanned point clouds, an automated method based on a data-driven Convolutional Neural Network (CNN) deep learning algorithm is proposed. In this instance, 3D Point cloud patches are aligned with spatial datasets that are scanned from different locations using range cameras. The registration results are used to automatically detect spatial changes when compared with different point clouds. The quantified changes are utilized to determine the percentage of work that has been completed at fixed intervals. The developed registration approach is tested and validated using a series of experiments. It is demonstrated that discrepancies between ‘as-planned’ and ‘actual’ models can be identified with a higher level of accuracy, which can enable the baseline for monitoring construction to be undertaken in real-time.     

A systematic approach to business process reengineering

In this study, a business process is defined as a set of various tasks which are closely inter-related and is assumed to have its own objectives to achieve. A set of processes constitute a business system. Many business process reengineering plans turned out to be unsuccessful because they are based on the existing process which assumed tasks along the process independent or based on the assumption that a process is independent of other processes. This approach generated redundant tasks or led to conflict between business processes. Under this approach, it very difficult to predict the result of the plan due to localized analysis of the process. To avoid aforementioned problems, a systematic, analytic and iterative approach to build up a master plan for business process reengineering is proposed.     

A compensation-based approach for view maintenance in distributed environments

Data integration over multiple heterogeneous data sources has become increasingly important for modern applications. The integrated data is usually stored as materialized views to allow better access, performance, and high availability. In loosely coupled environments, such as the data grid, the data sources are autonomous. Hence, tie source updates can be concurrent and cause erroneous results during view maintenance. State-of-the-art maintenance strategies apply compensating queries to correct such errors, making the restricting assumption that all source schemata remain static over time. However, in such dynamic environments, the data sources may change not only their data but also their schema. Consequently, either the maintenance queres or the compensating queries may fail. In this paper, we propose a novel framework called DyDa that overcomes these limitations and handles both source data updates and schema changes. We identify three types of maintenance anomalies, caused by either source data updates, data-preserving schema changes, or non-data-preserving schema changes. We propose a compensation algorithm to solve the first two types of anomalies. We show that the third type of anomaly is caused by the violation of dependencies between maintenance processes. Then, we propose dependency detection and correction algorithms to identify and resolve the violations. Put together, DyDa extends prior maintenance solutions to solve all types of view maintenance anomalies. The experimental results show that DyDa imposes a minimal overhead on data update processing while allowing for the extended functionality to handle concurrent schema changes.