Aligning Operational Decisions to Enterprise Objectives Through a Dynamic Enterprise Architecture Approach

As the rate of change in both business models and business complexity increases, enterprise architecture can be positioned to supply decision support for executives. The authors propose a dynamic enterprise architecture framework that supports business executive needs for rapid response and contextualized numerical decision support. The classic approaches to business decision making are both over simplified and insufficient to account for the dynamic complexities of reality. Recent failures of historically sound businesses demonstrate that a more robust mathematical approach is required to establish and maintain the alignment between operational decisions and enterprise objectives. We begin with an enterprise architecture (EA) framework that is robust enough to capture the elements of the business within the structure of a meta model that describes how the elements will be stored and tested for completeness and coherence. We add to that the analytical tools needed to innovate and improve the business. Finally, dynamic causal and agent layers are added to account for the qualitative and evolutionary elements that are normally missing or over simplified in most decision systems. This results in a dynamic model of an enterprise that can be simulated and analyzed to answer key business questions and provide decision support. We present a case study and demonstrate how the models are used within the decision framework to support executive decision makers.

Instruction Generation for Assembly Operations Performed by Humans

This paper presents the design of an instruction generation system that can be used to automatically generate instructions for complex assembly operations performed by humans on factory shop floors. Multimodal information—text, graphical annotations, and 3D animations—is used to create easy-to-follow instructions. This thereby reduces learning time and eliminates the possibility of assembly errors. An automated motion planning subsystem computes a collision-free path for each part from its initial posture in a crowded scene onto its final posture in the current subassembly. Visualization of this computed motion results in generation of 3D animations. The system also consists of an automated part identification module that enables the human to identify, and pick, the correct part from a set of similar looking parts. The system’s ability to automatically translate assembly plans into instructions enables a significant reduction in the time taken to generate instructions and update them in response to design changes.

Towards Automated Evaluation of Vehicle Dynamics in System-Level Designs

We describe the use of the Cyber-Physical Modeling Language (CyPhyML) to support trade studies and integration activities in system-level vehicle designs. CyPhyML captures parameterized component behavior using acausal models (i.e. hybrid bond graphs and Modelica) to enable automatic composition and synthesis of simulation models for significant vehicle subsystems. Generated simulations allow us to compare performance between different design alternatives. System behavior and evaluation are specified independently from specifications for design-space alternatives. Test bench models in CyPhyML are given in terms of generic assemblies over the entire design space, so performance can be evaluated for any selected design instance once automated design space exploration is complete. Generated Simulink models are also integrated into a mobility model for interactive 3-D simulation.

Kansei Engineering and Web Site Design

Capturing users’ needs is critical in web site design. However, a lot of attention has been paid to enhance the functionality and usability, whereas much less consideration has been given to satisfy the emotional needs of users, which is also important to a successful design. This paper explores a methodology based on Kansei Engineering, which was significant used in product and industrial design but not quite been adopted in the IT field, in order to discover implicit emotional needs of users toward web site and transform them into design details. Survey, interview techniques and statistical methods were performed in this paper. A prototype web site was developed based on the Kansei study results integrated with technical expertise and practical considerations. The results showed that the Kansei Engineering methodology, in this paper, played a significant role in web site design in terms of satisfying the emotional needs of users.

Fitting Weighted Total Least-Squares Planes and Parallel Planes to Support Tolerancing Standards

We present elegant algorithms for fitting a plane, two parallel planes (corresponding to a slot or a slab) or many parallel planes in a total (orthogonal) least-squares sense to coordinate data that is weighted. Each of these problems is reduced to a simple 3×3 matrix eigenvalue/eigenvector problem or an equivalent singular value decomposition problem, which can be solved using reliable and readily available commercial software. These methods were numerically verified by comparing them with brute-force minimization searches. We demonstrate the need for such weighted total least-squares fitting in coordinate metrology to support new and emerging tolerancing standards, for instance, ISO 14405-1:2010. The widespread practice of unweighted fitting works well enough when point sampling is controlled and can be made uniform (e.g., using a discrete point contact Coordinate Measuring Machine). However, we demonstrate that nonuniformly sampled points (arising from many new measurement technologies) coupled with unweighted least-squares fitting can lead to erroneous results. When needed, the algorithms presented also solve the unweighted cases simply by assigning the value one to each weight. We additionally prove convergence from the discrete to continuous cases of least-squares fitting as the point sampling becomes dense.

Human Carrying Simulation With Symmetric and Asymmetric Loads

Human carrying is simulated in this work by using a skeletal digital human model with 55 degrees of freedom (DOFs). Predictive dynamics approach is used to predict the carrying motion with symmetric and asymmetric loads. In this process, the model predicts joints dynamics using optimization schemes and task-based physical constraints. The results indicated that the model can realistically match human motion and ground reaction forces data during symmetric and asymmetric load carrying task. With such prediction capability the model could be used for biomedical and ergonomic studies.

Foundation for Model Integration: Semantic Backplane

One of the primary goals of the Adaptive Vehicle Make (AVM) program of DARPA is the construction of a model-based design flow and tool chain, META, that will provide significant productivity increase in the development of complex cyber-physical systems. In model-based design, modeling languages and their underlying semantics play fundamental role in achieving compositionality. A significant challenge in the META design flow is the heterogeneity of the design space. This challenge is compounded by the need for rapidly evolving the design flow and the suite of modeling languages supporting it. Heterogeneity of models and modeling languages is addressed by the development of a model integration language – CyPhy – supporting constructs needed for modeling the interactions among different modeling domains. CyPhy targets simplicity: only those abstractions are imported from the individual modeling domains to CyPhy that are required for expressing relationships across sub-domains. This “semantic interface” between CyPhy and the modeling domains is formally defined, evolved as needed and verified for essential properties (such as well-formedness and invariance). Due to the need for rapid evolvability, defining semantics for CyPhy is not a “one-shot” activity; updates, revisions and extensions are ongoing and their correctness has significant implications on the overall consistency of the META tool chain. The focus of this paper is the methods and tools used for this purpose: the META Semantic Backplane. The Semantic Backplane is based on a mathematical framework provided by term algebra and logics, incorporates a tool suite for specifying, validating and using formal structural and behavioral semantics of modeling languages, and includes a library of metamodels and specifications of model transformations.

Mask Image Planning for Deformation Control in Projection-Based Stereolithography Process

In this research, we investigate the shrinkage related deformation control for a mask-image-projection-based Stereolithography process (MIP-SL). Based on a Digital Micromirror Device (DMD), MIP-SL uses an area-processing approach by dynamically projecting mask images onto a resin surface to selectively cure liquid resin into layers of an object. Consequently, the related additive manufacturing process can be much faster with a lower cost than the laser-based Stereolithography Apparatus (SLA) process. However, current commercially available MIP-SL systems are based on Acrylate resins, which have bigger shrinkages than epoxy resins that are widely used in the SLA process. Consequently controlling size accuracy and shape deformation in the MIP-SL process is challenging. To address the problem, we evaluate different image exposing strategies for projection mask images. A mask image planning method and related algorithms have been developed for the MIP-SL process. The planned mask images have been tested by using a commercial MIP-SL machine. The experimental results illustrate that our method can effectively reduce the deformation by as much as 32%. A discussion on the test results and future research directions are also presented.

Motion-Aware Adaptive Dead Reckoning Algorithm for Distributed Virtual Reality Systems

Dead reckoning algorithms are employed in distributed virtual reality systems (DVR systems) for predicting objects states at any given moment of time that makes it possible to minimize bandwidth requirements while maintaining required data consistency. However, existing implementations often do not take into account information on the object motion dynamics and, in general, apply static prediction models. In this paper a novel motion-aware adaptive dead reckoning algorithm is introduced based on dynamical prediction model selection depending on the object motion pattern. The results show that considerable reduction in update messages can be achieved without sacrificing prediction accuracy. In addition, it becomes possible to dynamically adjust the size of update messages according to the motion pattern and, thus, provide more flexible use of network bandwidth.