Level Set Based Simulations of Two-Phase Oil–Water Flows in Pipes

We simulate the axisymmetric pipeline transportation of oil and water numerically under the assumption that the densities of the two fluids are different and that the viscosity of the oil core is very large. We develop the appropriate equations for core-annular flows using the level set methodology. Our method consists of a finite difference scheme for solving the model equations, and a level set approach for capturing the interface between two liquids (oil and water). A variable density projection method combined with a TVD Runge–Kutta scheme is used to advance the computed solution in time. The simulations succeed in predicting the spatially periodic waves called bamboo waves, which have been observed in the experiments of [Bai et al. (1992) J. Fluid Mech. 240, 97–142.] on up-flow in vertical core flow. In contrast to the stable case, our simulations succeed in cases where the oil breaks up in the water, and then merging occurs. Comparisons are made with other numerical methods and with both theoretical and experimental results.     

Time-of-flight sensor and color camera calibration for multi-view acquisition

This paper presents a multi-view acquisition system using multi-modal sensors, composed of time-of-flight (ToF) range sensors and color cameras. Our system captures the multiple pairs of color images and depth maps at multiple viewing directions. In order to ensure the acceptable accuracy of measurements, we compensate errors in sensor measurement and calibrate multi-modal devices. Upon manifold experiments and extensive analysis, we identify the major sources of systematic error in sensor measurement and construct an error model for compensation. As a result, we provide a practical solution for the real-time error compensation of depth measurement. Moreover, we implement the calibration scheme for multi-modal devices, unifying the spatial coordinate for multi-modal sensors. The main contribution of this work is to present the thorough analysis of systematic error in sensor measurement and therefore provide a reliable methodology for robust error compensation. The proposed system offers a real-time multi-modal sensor calibration method and thereby is applicable for the 3D reconstruction of dynamic scenes.     

A novel iterative shape from focus algorithm based on combinatorial optimization

Shape from focus (SFF) is a technique to estimate the depth and 3D shape of an object from a sequence of images obtained at different focus settings. In this paper, the SFF is presented as a combinatorial optimization problem. The proposed algorithm tries to find the combination of pixel frames which produces maximum focus measure computed over pixels lying on those frames. To reduce the high computational complexity, a local search method is proposed. After the estimate of the initial depth map solution of an object, the neighborhood is defined, and an intermediate image volume is generated from the neighborhood. The updated depth map solution is found from the intermediate image volume. This update process of the depth map solution continues until the amount of improvement is negligible. The results of the proposed SFF algorithm have shown significant improvements in both the accuracy of the depth map estimation and the computational complexity, with respect to the existing SFF methods.     

Effective pedestrian detection using deformable part model based on human model

Recently, pedestrian detection systems have become an important technology in the development of the advanced driver assistance system (ADAS) for the autonomous car. The histogram of oriented gradients (HOG) is currently the most basic algorithm for detecting pedestrians, but it treats the entire body of the pedestrian as one single feature. In other words, if the entire body of the pedestrian is not visible, the detection rate under HOG decreases markedly. To solve this problem, we propose a detection system using a deformable part model (DPM) that divides the pedestrian data into two parts using a latent support vector machine (SVM)-based machine-learning technique. Experimental results show that our approach achieves better performance in a detection system than the existing method. In practice, there are many occlusions in the environment in front of the vehicle. For example, the surrounding transport facilities, such as a car or another obstacle, can occlude a pedestrian. These occlusions can increase the false detection rate and cause difficulties during the detection process. Our proposed method uses a different approach and can easily be applied in real-world scenarios, regardless of occlusions.

     

A new vertical‐alignment LC mode with high‐transmittance and fast‐response‐time features

Abstract— A novel pixel design for vertical‐alignment LCDs with superior transmittance has been developed. The new liquid‐crystal mode, refered to as the hole‐induced vertical‐alignment mode (Hi‐VA), uses a via hole of an organic layer on a TFT substrate to achieve multi‐domain alignment. Compared to the conventional design, the Hi‐VA mode has a transmittance of up to 135% with a contrast ratio of 2000:1. Moreover, the new structure is free from ITO patterning or protrusion on the color‐filter side, which makes the fabrication process simple and low cost.     

Persistent UAV Service: An Improved Scheduling Formulation and Prototypes of System Components

The flight duration of unmanned aerial vehicles (UAVs) is limited by their battery or fuel capacity. As a consequence, the duration of missions that can be pursued by UAVs without supporting logistics is restricted. However, a system of UAVs that is supported by automated logistics structures, such as fuel service stations and orchestration algorithms, may pursue missions of conceivably indefinite duration. This may be accomplished by handing off the mission tasks to fully fueled replacement UAVs when the current fleet grows weary. The drained UAVs then seek replenishment from nearby logistics support facilities. To support the vision of a persistent fleet of UAVs pursuing missions across a field of operations, we develop an improved mixed integer linear programming (MILP) model that can serve to support the system’s efforts to orchestrate the operations of numerous UAVs, missions and logistics facilities. Further, we look toward the future implementation of such a persistent fleet outdoors and develop prototype components required for such a system. In particular, we develop and demonstrate the concerted operation of a scheduling model, UAV onboard vision-based guidance system and replenishment stations.     

An exploratory study of radio frequency identification (RFID) adoption in the healthcare industry

This study examines the radio frequency identification (RFID) adoption decision process and proposes a model predicting the likelihood of adopting RFID within organizations in the healthcare industry. A considerable number of studies have been conducted regarding organizational information technology (IT) adoption, but the nature of the organizational IT adoption process is still not well understood. Especially, although there are a number of variables and categories that have been found empirically to be related to adoption behavior, there is little in the way of evidence to suggest the origin or motivation behind the adoption. Thus, this study investigates the underlying motivations and driving forces behind the adoption of RFID using the theory of technology-push and need-pull. In this study, an organizational RFID adoption model is proposed and empirically tested by a survey using a sample of 126 senior executives in U.S. hospitals. The model posits that three categories of factors, technology push, need pull, and presence of champions, determine the likelihood of adopting RFID within organizations. This study also found that the relationships between those three categories and the likelihood of adopting RFID are strengthened or weakened by organizational readiness.     

Design and development of a maintenance and virtual training system for ancient Chinese architecture

Ancient Chinese architecture is an important aspect of traditional Chinese culture and has been studied by many scholars around the world via historical documents, photographs, and three-dimensional models. In this paper, a building information model (BIM) and virtual reality (VR) and video analysing technology are used to develop a maintenance and virtual training system for ancient architecture. A digital ancient architecture model that includes a three-dimensional model and attributes is established, and the model can be visualized using a VR video processing system. Based on this system, we propose a method of fire detection in the maintenance system to ensure the safety of ancient buildings. After performing lightweight processing of the three-dimensional model, the Forge platform, which can achieve high-speed browsing via Web browsers, is used to perform the virtual construction, dismantling and other functions. By providing an immersive experience, users will develop a deeper understanding of ancient architectural structures and construction processes, which will accelerate research on ancient architecture.     

An Empirical Study Evaluating Social Networking Continuance and Success

Social networking site (SNS) use decisions have led to major economic and social transformations worldwide. While many organizations seek to use SNSs from a strategic perspective to reach their customer, it is important to understand what makes SNSs successful in order to use them for competitive purposes. The current research evaluates the influence of the social capital theory on SNS success measures. A model was developed and empirically tested using two data samples to ensure valid and reliable results for success of SNSs. The results display the importance of social capital in SNS success followed by practitioner and academic implications.