云杉八齿小蠹Ips typographus L.虚拟模型构建方法研究

本文以云杉八齿小蠹Ips typographus Linnaeus为例,经标本选取,观察虫体整体形态,绘制整体结构草图,电子显微镜观察局部、得到局部数字图像,分别建立虫体每一部分的高精度细节。把模型每部分拼装在一起组成整体模型,构建出了云杉八齿小蠹三维虚拟昆虫数字化模型。     

Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Abstract

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.     

Modeling the effect of deep traps on the capacitance–voltage characteristics of p-type Si-doped GaAs Schottky diodes grown on high index GaAs substrates

Numerical simulation, using SILVACO-TCAD, is carried out to explain experimentally observed effects of different types of deep levels on the capacitance–voltage characteristics of p-type Si-doped GaAs Schottky diodes grown on high index GaAs substrates. Two diodes were grown on (311)A and (211)A oriented GaAs substrates using Molecular Beam Epitaxy (MBE). Although, deep levels were observed in both structures, the measured capacitance–voltage characteristics show a negative differential capacitance (NDC) for the (311)A diodes, while the (211)A devices display a usual behaviour. The NDC is related to the nature and spatial distribution of the deep levels, which are characterized by the Deep Level Transient Spectroscopy (DLTS) technique. In the (311)A structure only majority deep levels (hole traps) were observed while both majority and minority deep levels were present in the (211)A diodes. The simulation, which calculates the capacitance–voltage characteristics in the absence and presence of different types of deep levels, agrees well with the experimentally observed behaviour.     

Reality-based interaction affecting mental workload in virtual reality mental arithmetic training

ABSTRACT

The concept of digital game-based learning (DGBL) evolves rapidly together with technological enhancements of virtual reality (VR) and smart phones. However, the mental workload (MWL) that VR-training applications demand and motivational qualities originating from user experience (UX) should be identified in order to create effective and enjoyable training/learning challenges that fit with individual users’ capabilities. This study examined the effects of reality-based interaction (RBI) and VR on measures of student motivation and MWL, in a mental arithmetic game for secondary school pupils. In a randomised controlled trial with sixty school children, a mental arithmetic game was tested with three different interaction and two different presentation methods – VR RBI, VR head-mounted-display tapping and tablet flick-gesture. Results found a significant effect of RBI on MWL but no differences in enjoyment of training were found between VR-experience and tablet training-experience. In fact, adding the gaming-context to the mental arithmetic task created an enjoyable, motivating experience regardless of presentation or interaction-style.     

Electrochemical growth and characterization of iron doped cadmium sulfide thin films

Cadmium Sulfide and Ferrous doped Cadmium Sulfide thin films have been prepared on different substrates using an electrodeposition technique. Linear sweep voltammetric analysis has been carried out to determine deposition potential of the prepared films. X-ray diffraction analysis showed that the prepared films possess polycrystalline nature with hexagonal structure. Surface morphology and film composition have been analyzed using Scanning electron microscopy and Energy dispersive analysis by X-rays. Optical absorption analysis showed that the prepared films are found to exhibit Band gap value in the range between 2.3, 2.8 eV for Cadmium Sulfide and Ferrous doped Cadmium Sulfide.     

Marginal cost and congestion in the Italian electricity market: An indirect estimation approach

In this paper we construct an indirect measure of the supply marginal cost function for the main generators from the observed bid data in the Italian electricity market in the period 2004–2007. We compute the residual demand function for each generator, taking explicitly into account the issue of transmission line congestion. This procedure allows recovering correct zonal Lerner index and the implied measure of the marginal cost function. We find evidence of a stable U-shaped marginal cost function for three main Italian generators, but a flat function for ENEL, the former national monopolist. The policy relevance of our approach lies in the possibility to offer some empirical knowledge of the marginal cost function of each generator to the regulator to design appropriate policy measures geared to the promotion of competitive market conditions. We propose a new market surveillance mechanism, which is based on the principle of sanctioning excessive deviations from the estimated measure of the marginal cost function presented in this work.     

An ideal host-guest system to accomplish high-performance greenish yellow and hybrid white organic light-emitting diodes

Greenish yellow organic light-emitting diodes (GYOLEDs) have steadily attracted researcher's attention since they are important to our life. However, their performance significantly lags behind compared with the three primary colors based OLEDs. Herein, for the first time, an ideal host-guest system has been demonstrated to accomplish high-performance phosphorescent GYOLEDs, where the guest concentration is as low as 2%. The GYOLED exhibits a forward-viewing power efficiency of 57.0 lm/W at 1000 cd/m², which is the highest among GYOLEDs. Besides, extremely low efficiency roll-off and voltages are achieved. The origin of the high performance is unveiled and it is found that the combined mechanisms of host-guest energy transfer and direct exciton formation on the guest are effective to furnish the greenish yellow emission. Then, by dint of this ideal host-guest system, a simplified but high-performance hybrid white OLED (WOLED) has been developed. The WOLED can exhibit an ultrahigh color rendering index (CRI) of 92, a maximum total efficiency of 27.5 lm/W and a low turn-on voltage of 2.5 V (1 cd/m²), unlocking a novel avenue to simultaneously achieve simplified structure, ultrahigh CRI (>90), high efficiency and low voltage.     

相关条件下基于N-P准则的分布式检测理论研究

本文针对并行网络、二元局部判决的情况,研究了在相关条件下基于N-P准则的分布式检测融合算法,给出了在联合概率密度已知和未知两种情况下的最优融合规则的理论推导及相应的解决方案,并在实验仿真的基础上,得到了部分有益的结论。     

一种软件实现虚拟景深的方法

影视后期制作或图像合成中，动态实时改变显示画面的景深能进一步增强画面的真实感，但由于实时性要求较高，软件实现在处理速度方面容易产生瓶颈。为此，采用预处理的办法，分别对前景和背景进行低通滤波，得到前、背景各自的模拟帧序列，通过调节前景和背景帧序列的位置，模拟确定其各自的变焦程度，然后将前景和背景动态合成具有虚拟景深效果的画面。     

Development of a recurrent Sigma-Pi neural network rainfall forecasting system in Hong Kong

At the moment, weather forecasting is still an art — the experience and intuition of forecasters play a significant role in determining the quality of forecasting. This paper describes the development of a new approach to rainfall forecasting using neural networks. It deals with the extraction of information from radar images and an evaluation of past rain gauge records to provide shortterm rainfall forecasting. All of the meteorological data were provided by the Royal Observatory of Hong Kong (ROHK). Preprocessing procedures were essential for this neural network rainfall forecasting. The forecast of the rainfall was performed every half an hour so that a storm warning signal can be delivered to the public in advance. The network architecture is based on a recurrent Sigma-Pi network. The results are very promising, and this neural-based rainfall forecasting system is capable of providing a rain storm warning signal to the Hong Kong public one hour ahead.