Full Eulerian deformable solid-fluid interaction scheme based on building-cube method for large-scale parallel computing

We propose a full Eulerian incompressible solid-fluid interaction scheme capable of achieving high parallel efficiency and easily generating meshes for complex solid geometries. While good scalability of a full Eulerian solid-fluid interaction formulation has been reported by Sugiyama et al, their analysis was carried out using uniform Cartesian mesh and an artificial compressibility method. Typically, it is more challenging to achieve good scalability for hierarchical Cartesian meshes and a fully incompressible formulation. In addition, the conventional full Eulerian methods require a large computational cost to resolve complex solid geometries due to the usage of uniform Cartesian meshes. In an attempt to overcome the aforementioned issues, we employ the building-cube method, where the computational domain is divided into cubic regions called cubes. Each cube is divided at equal intervals, the same number of cubes is assigned to each core, and the spatial loop processing is executed for each cube. The numerical method is verified by computing five numerical examples. In the weak scaling test, the parallel efficiency at 32768 cores with 32 cores as a reference is 93.6%. In the strong scaling test, the parallel efficiency at 32768 cores with 128 cores as a reference is 70.2%.     

Epos – an instrument for the assessment of the ethical position in software development

Digital technology becomes more powerful, intelligent, pervasive and ubiquitous. Ethical aspects of this development have not yet drawn the appropriate attention of researchers and engineers. This paper presents an instrument that aims at measuring the individual ethical position with regard to the design and development of computer software. The development of the Epos tool was based on two data collections. The data of the first survey (n₁ = 147 participants) were used to select items and to determine the factorial structure of the questionnaire. Results show that the Epos instrument reliably assesses peoples’ ethical opinion with respect to five central components: (1) regulation, (2) data privacy, (3) domain specific knowledge, (4) societal responsibility and (5) company responsibility. In the second survey, we determined the stability of the instruments factor structure by assessing a sample of n₂?=?196 participants. A confirmatory factor analysis (CFA) supported the initial factor structure. Next steps and further implications are discussed regarding the final version of the questionnaire.     

Visual detection and tracking with UAVs,following a mobile object

ABSTRACT

Conception and development of an Unmanned Aerial Vehicle (UAV) capable of detecting, tracking and following a moving object with unknown dynamics is presented in this work, considering a human face as a case of study. Object detection is accomplished by a Haar cascade classifier. Once an object is detected, it is tracked with the help of a Kalman Filter (KF), and an estimation of the relative position with respect to the target is obtained. A linear controller is used to validate the proposed vision scheme and for regulating the aerial robot's position in order to keep a constant distance with respect to the mobile target, employing as well the extra available information from the embedded sensors. The proposed system was extensively tested in real-time experiments, through different conditions, using a commercial quadcopter connected via wireless to a ground station running under the Robot Operative System (ROS). The proposed overall strategy shows a good performance even under disadvantageous conditions as outdoor flight, being robust against illumination changes, image noise and the presence of other people in the scene.     

卷积神经网络用于关节角度识别与姿势评估

为了快速准确地输出各种工作姿势风险评估结果，提出采用Kinect v2与卷积神经网络识别人体各关节角度，并输出标准姿势风险的评估得分。首先使用亚像素角点提取的棋盘标定算法标定Kinect两个摄像头，其次使用改进后的双边滤波对深度图像去噪，使用卷积神经网络识别人体关节二维位置，结合深度信息获取实际三维坐标并计算人体关节角度，最后输出姿势风险评估得分。通过两种实验分别验证了提出的Kinect角度识别与姿势评估的准确性，表明该方法关节角度识别与姿势风险评估的准确率均较高，是一种低成本、高可靠性的姿势评价方法，具有一定的科学意义和工程应用价值。     

Learning a Deep Predictive Coding Network for a Semi-Supervised 3D-Hand Pose Estimation

In this paper we present a CNN based approach for a real time 3D-hand pose estimation from the depth sequence. Prior discriminative approaches have achieved remarkable success but are facing two main challenges: Firstly, the methods are fully supervised hence require large numbers of annotated training data to extract the dynamic information from a hand representation. Secondly, unreliable hand detectors based on strong assumptions or a weak detector which often fail in several situations like complex environment and multiple hands. In contrast to these methods, this paper presents an approach that can be considered as semi-supervised by performing predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without supervision. The hand is modelled using a novel latent tree dependency model (LDTM) which transforms internal joint location to an explicit representation. Then the modeled hand topology is integrated with the pose estimator using data dependent method to jointly learn latent variables of the posterior pose appearance and the pose configuration respectively. Finally, an unsupervised error term which is a part of the recurrent architecture ensures smooth estimations of the final pose. Experiments on three challenging public datasets, ICVL, MSRA, and NYU demonstrate the significant performance of the proposed method which is comparable or better than state-of-the-art approaches.      

CFD modeling of thermal contact resistance between solid contacting surfaces

Heat transfer has considerable applications in different industries such as designing of heat exchanger, nuclear reactor cooling, control system for spacecraft, and designing of microelectronics cooling. As the surfaces of two metals contact each other, this issue becomes so crucial. Thermal contact resistance (TCR) is one of the key physical parameters in heat transfer of mentioned surfaces. Measuring the experimental value of TCR in laboratory is highly expensive and difficult. As an alternative, numerical modeling methods could be engaged. In this study, inverse problem method solution is utilized as a proper method for estimation of TCR value. In this order, three different configurations (flat-flat, flat-cylinder, and cylinder-cylinder) were utilized in two steady and unsteady state conditions to predict the value of TCR. A comparison between the measured values and obtained values from the simulation show the errors for flat-flat, flat-cylinder, and cylinder-cylinder configuration after 10 min from starting the experiment are 4.6074%, 0.1662%, and 0.5622%, respectively. And in steady-state condition, the corresponding errors are 6.06e-3%, 1.506%, and 0.846%, respectively. In conclusion, the final results establish the fact that the inverse problem method solution can predict TCR values between contacting surfaces.     

Selective hydrogenation of acetylene over Pd/CeO2

Five hundred ppm Pd/CeO₂ catalyst was prepared and evaluated in selective hydrogenation of acetylene in large excess of ethylene since ceria has been recently found to be a reasonable stand-alone catalyst for this reaction. Pd/CeO₂ catalyst could be activated in situ by the feed gas during reactions and the catalyst without reduction showed much better ethylene selectivity than the reduced one in the high temperature range due to the formation of oxygen vacancies by reduction. Excellent ethylene selectivity of ~100% was obtained in the whole reaction temperature range of 50°C–200°C for samples calcined at temperatures of 600°C and 800°C. This could be ascribed to the formation of Pd_xCe_1−xO_2−y or Pd-O-Ce surface species based on the X-ray diffraction and X-ray photoelectron spectroscopy results, indicating the strong interaction between palladium and ceria.     

Catalytic combustion of methane over a highly active and stable NiO/CeO2 catalyst

In the last decades, many reports dealing with technology for the catalytic combustion of methane (CH₄) have been published. Recently, attention has increasingly focused on the synthesis and catalytic activity of nickel oxides. In this paper, a NiO/CeO₂ catalyst with high catalytic performance in methane combustion was synthesized via a facile impregnation method, and its catalytic activity, stability, and water-resistance during CH₄ combustion were investigated. X-ray diffraction, low-temperature N₂ adsorption, thermogravimetric analysis, Fourier transform infrared spectroscopy, hydrogen temperature programmed reduction, methane temperature programmed surface reaction, Raman spectroscopy, electron paramagnetic resonance, and transmission electron microscope characterization of the catalyst were conducted to determine the origin of its high catalytic activity and stability in detail. The incorporation of NiO was found to enhance the concentration of oxygen vacancies, as well as the activity and amount of surface oxygen. As a result, the mobility of bulk oxygen in CeO₂ was increased. The presence of CeO₂ prevented the aggregation of NiO, enhanced reduction by NiO, and provided more oxygen species for the combustion of CH₄. The results of a kinetics study indicated that the reaction order was about 1.07 for CH₄ and about 0.10 for O₂ over the NiO/CeO₂ catalyst.