Direct 2D-to-3D transformation of pen drawings

Pen drawing is a method that allows simple, inexpensive, and intuitive two-dimensional (2D) fabrication. To integrate such advantages of pen drawing in fabricating 3D objects, we developed a 3D fabrication technology that can directly transform pen-drawn 2D precursors into 3D geometries. 2D-to-3D transformation of pen drawings is facilitated by surface tension-driven capillary peeling and floating of dried ink film when the drawing is dipped into an aqueous monomer solution. Selective control of the floating and anchoring parts of a 2D precursor allowed the 2D drawing to transform into the designed 3D structure. The transformed 3D geometry can then be fixed by structural reinforcement using surface-initiated polymerization. By transforming simple pen-drawn 2D structures into complex 3D structures, our approach enables freestyle rapid prototyping via pen drawing, as well as mass production of 3D objects via roll-to-roll processing

Natural facial expression recognition using differential-AAM and manifold learning

This paper proposes a novel natural facial expression recognition method that recognizes a sequence of dynamic facial expression images using the differential active appearance model (AAM) and manifold learning as follows. First, the differential-AAM features (DAFs) are computed by the difference of the AAM parameters between an input face image and a reference (neutral expression) face image. Second, manifold learning embeds the DAFs on the smooth and continuous feature space. Third, the input facial expression is recognized through two steps: (1) computing the distances between the input image sequence and gallery image sequences using directed Hausdorff distance (DHD) and (2) selecting the expression by a majority voting of k-nearest neighbors (k-NN) sequences in the gallery. The DAFs are robust and efficient for the facial expression analysis due to the elimination of the inter-person, camera, and illumination variations. Since the DAFs treat the neutral expression image as the reference image, the neutral expression image must be found effectively. This is done via the differential facial expression probability density model (DFEPDM) using the kernel density approximation of the positively directional DAFs changing from neutral to angry (happy, surprised) and negatively directional DAFs changing from angry (happy, surprised) to neutral. Then, a face image is considered to be the neutral expression if it has the maximum DFEPDM in the input sequences. Experimental results show that (1) the DAFs improve the facial expression recognition performance over conventional AAM features by 20% and (2) the sequence-based k-NN classifier provides a 95% facial expression recognition performance on the facial expression database (FED06). (C) 2008 Elsevier Ltd. All rights reserved.X1172sciescopu

Metaheuristic Identification for an Analytic Dynamic Model of a Delta Robot with Experimental Verification

The dynamic-parameter identification process for developing a suitable precise mathematical model for the implementation and operation of parallel-link robots has received attention. In this study, an efficient and reliable system-identification method for a delta robot is proposed. The parallel-link robot’s dynamic behavior was mathematically modeled according to the principle of virtual work. The dynamic equations of motion are extended to the system of equations that explicitly characterizes the inertial and centripetal/Coriolis forces, and the frictional effects on the robot’s dynamic behavior. Next, the dynamic-parameter identification technique is presented to directly estimate a set of uncertain parameters that are included in the extended dynamic model. In addition, the development of the dynamic model with a generalized inertia matrix for determining the impact of the inertia-coupling characteristic on the robot’s dynamic behaviors is examined. Experimental results indicate that the proposed parameter-estimation technique is an extremely useful tool that can achieve the high-quality identification of an analytic dynamic model for a parallel-link robot

Automatic Dual Crane Cooperative Path Planning Based on Multiple RRT Algorithm for Narrow Path Finding Scenario

Dual crane lifting, wherein two cranes collaborate to lift a single workpiece, serves as an essential solution in scenarios in which employing a single, sufficiently large crane is impractical due to cost constraints, ground conditions, and spatial limitations. Due to the complexity of double crane lifting operations, the implementation of automated path generation minimizes the risk of human error and removes the potential for accidents by simulating and validating the generated crane path. We propose a novel multiple rapidly-exploring random trees (RRT) based algorithm designed specifically for dual crane systems to produce lifting paths, particularly in challenging ‘narrow path finding’ scenarios. The multiple RRT method is an efficient way to find paths in environments with high complexity and low connectivity through a strategy that allows new trees to be generated and grown whenever a newly generated node that cannot be connected to an existing tree occurs. The proposed path planning algorithm not only adapts the multiple RRT method to the dual crane systems but also incorporates ideas to enhance the optimality of generated paths while reducing computational time. The effectiveness of this algorithm has been validated through a case studies covering various scenario

Metaheuristic Identification for an Analytic Dynamic Model of a Delta Robot with Experimental Verification

The dynamic-parameter identification process for developing a suitable precise mathematical model for the implementation and operation of parallel-link robots has received attention. In this study, an efficient and reliable system-identification method for a delta robot is proposed. The parallel-link robot’s dynamic behavior was mathematically modeled according to the principle of virtual work. The dynamic equations of motion are extended to the system of equations that explicitly characterizes the inertial and centripetal/Coriolis forces, and the frictional effects on the robot’s dynamic behavior. Next, the dynamic-parameter identification technique is presented to directly estimate a set of uncertain parameters that are included in the extended dynamic model. In addition, the development of the dynamic model with a generalized inertia matrix for determining the impact of the inertia-coupling characteristic on the robot’s dynamic behaviors is examined. Experimental results indicate that the proposed parameter-estimation technique is an extremely useful tool that can achieve the high-quality identification of an analytic dynamic model for a parallel-link robot

Comparative Corporate Governance Trends in Asia

Poor corporate governance was identified as one of the root causes of the recent Asian financial crisis. The absence of effective disciplines on corporate managers, coupled with complicated and opaque relationships between corporations, their owners and their finance providers, affected severely investors’ confidence in the region’s corporate sectors. Economies that took early steps to improve corporate governance have been recovering from the crisis at a more rapid pace than those who have not addressed this issue. The Asian crisis showed that good corporate governance is important not only for individual corporations to raise capital but also for an economy to achieve sustainable growth. This publication includes papers submitted to the "Conference on Corporate Governance in Asia: A Comparative Perspective" held in Seoul in March 1999. These papers describe vividly the corporate governance practices in the region and the recent changes largely prompted by the crisis. Also included are papers on corporate governance in major OECD countries, which serve as a good source of comparative information on this issue. This review is part of the OECD's ongoing co-operation with non-Member economies around the world.TRU

Forecasting Taxi Demands with Fully Convolutional Networks and Temporal Guided Embedding

Learning complex spatiotemporal patterns is a key to predict future taxi demand volumes. We propose temporal guided networks (TGNet), which is an efficient model architecture with fully convolutional networks and temporal guided em- bedding, to capture spatiotemporal patterns. Existing approaches use complex architectures, historical demands (day/week/month ago) to capture the recurring patterns, and external data sources such as meteorological, traffic flow, or tex- ture data. However, TGNet only uses fully convolutional networks and temporal guided embedding without those external data sources. In this study, only pick-up and drop-off volumes of NYC-taxi dataset are used to utilize the full potential of the hidden patterns in the historical data points. We show that TGNet provides notable performance gains on a real-world benchmark, NYC-taxi dataset, over previous state-of-the-art models. Finally we explain how to extend our architecture to incorporate external data sources.1