An efficient face segmentation algorithm based on binary partition tree

This paper presents an efficient face segmentation algorithm based on binary partition tree. Skin-like regions are first obtained by integrating the results of pixel classification and watershed segmentation. Facial features are extracted by the techniques of valley detection and entropic thresholding, and are used to refine the skin-like regions. In order to segment the facial regions from the skin-like regions, a novel region merging algorithm is proposed by considering the impact of the common border ratio between adjacent regions, and the binary partition tree is used to represent the whole region merging process. Then the facial likeness of each node in the binary partition tree is evaluated using a set of fuzzy membership functions devised for a number of facial primitives of geometrical, elliptical and facial features. Finally, an efficient algorithm of node selecting in the binary partition tree is proposed for the final face segmentation, which can exactly segment the faces without any underlying assumption. The performance of the proposed face segmentation algorithm is demonstrated by experimental results carried out on a variety of images in different scenarios.     

Desktop inkjet printer as a tool to print conducting polymers

In order to exploit mechanical flexibility of organic-based electronic devices, conducting polymer anodes, such as polyaniline or poly(3,4-ethylenedioxy)-thiophene-poly(styrene sulfonate) (PEDOT-PSS), have been extensively studied. Along with the use of solution based processing techniques, conducting polymers can simplify the device fabrication procedure and yield themselves easily to printing techniques. In this paper, we present the results of utilizing desktop inkjet printer as a tool for direct printing and patterning of conducting polymer. Design of printable patterns and adjustment of printing parameters can be performed using any software such as Power Point. PEDOT-PSS suspension can be loaded into an inkjet cartridge and deposited on a given substrate in any designed pattern. The gray-scale color scheme can be employed to control the layer thickness and sheet resistivity of the inkjet printed layers. These layers are then used as anodes in organic light-emitting devices (OLEDs).     

基于电涡流传感器的硬币辨伪系统的设计

根据壹元真、假硬币不同的物理特性和电涡流传感器的性能特点,针对壹元硬币,提出一种基于电涡流传感器的简易辨伪系统。其主要通过硬币的材质和厚度辨认真假,当不同性质的硬币通过时,传感器的电感不同,则被测信号的频率不同。实验结果表明,该系统可以实现真、假硬币的快速鉴别、真硬币的个数显示和假币报警等功能,且电路简单。     

基于Java EE多层架构的进销存管理系统

针对传统的进销存管理系统的开发方式所带来的一些问题,提出使用基于S2SH框架的N层体系结构来开发系统,介绍Struts2、Hibernate和Spring三个框架,分析系统功能,阐述系统实现所使用的一些关键技术,并使用基于S2SH框架的N层体系结构对系统进行总体架构设计,有效提高了系统的可维护性、可扩展性和可复用性。     

New Paradigm of Data-Driven Smart Customisation through Digital Twin

Big data is one of the most important resources for the promotion of smart customisation. With access to data from multiple sources, manufacturers can provide on-demand and customised products. However, existing research of smart customisation has focused on data generated from the physical world, not virtual models. As physical data is constrained by what has already occurred, it is limited in the identification of new areas to improve customer satisfaction. A new technology called digital twin aims to achieve this integration of physical and virtual entities. Incorporation of digital twin into the paradigm of existing data-driven smart customisation will make the process more responsive, adaptable and predictive. This paper presents a new framework of data-driven smart customisation augmented by digital twin. The new framework aims to facilitate improved collaboration of all stakeholders in the customisation process. A case study of the elevator industry illustrates the efficacy of the proposed framework.     

Distributed patching for mobile wireless sensor networks

Mobile wireless sensor networks (MWSNs), the continuous movement of sensor nodes, may cause complete disconnection of the network or at best a part of it. The design of such networks should guarantee that all sensor nodes at all times have a path to the sink node(s). Prolonging the lifetimes of the MWSNs is a crucial design issue but should not be at the expense of other essential functions such as connectivity. In this paper, we propose an energy efficient distributed framework for connectivity maintenance of MWSNs. In the proposed framework, sensor nodes of the MWSNs schedule and control their radio frequency (RF) modules based on dynamic coordinated reconstruction mechanism, some senor nodes may lose their connectivity with the network due to mobility, depletion of energy and/or incurred faults. Two protocols are developed to patch up the disconnection of the MWSNs. The first protocol reestablishes the network connection consuming far less energy than state-of-the-art alternatives. However, it is capable of reestablishing the connection when no more than 20% of the MWSN backbone nodes lose connection concurrently. The second protocol extends this limit to operate when up to 35% of the MWSN backbone nodes lose connection simultaneously.     

SPI总线扩展PLC输出点数的设计与实现

针对PLC主模块可用输出点数少的缺点,提出了采用SPI总线和移位寄存器HC595相结合扩展PLC输出点数,并设计了输出扩展电路;根据SPI总线及HC595的时序特点,确定了SPI的时钟模式,解决了时序的配合问题,并确定了输出点数的逻辑顺序;UDTY端接一下拉电阻,保证其处于高电平,SPI2CLK信号取反后输入到HC595,保证其采样到稳定的SPI输出数据,滤波电容,提高了电路抗干扰性能;编程实现对扩展输出点数的控制。实际应用表明,该设计方案经济、可靠。     

Design of an Ultra-Compact and Highly-Sensitive Temperature Sensor Using Photonic Crystal Based Single Micro-Ring Resonator and Cascaded Micro-Ring Resonator

Silicon - In the present report, a photonic crystal based micro-ring resonator (MRR) structure is proposed which is very compact in size and has very fast response and is employed for temperature...     

An informationally complete Wigner function for the Tavis–Cummings model

Journal of Computational Electronics - Here we consider an informationally complete Wigner function approach to look at multiple atoms (qubits) coupled to a field mode. We consider the...     

基于多模型融合的CNN-LSTM-XGBoost短期电力负荷预测方法

短期电力负荷的精准预测可以有效指导机组组合调度、经济调度与电力市场运营。针对输入数据特征量受限时负荷预测的低精度问题,提出一种基于多模型融合的CNN-LSTM-XGBoost短期电力负荷预测方法。通过建立融合局部特征预提取模块的LSTM（long short term memory）网络结构,并将其与XGBoost（eXtreme boosting system）预测模型并行结合,之后结合MAPE-RW（mean absolute percentage error-reciprocal weight）算法进行模型融合初始权重设置,对最佳权重进行搜索,构建最佳融合模型。通过运用电力负荷数据对所提方法进行预测实验,结果表明CNN-LSTM- XGBoost模型的MAPE（mean absolute percentage error）与RMSE（root mean square error）分别为0.377%与148.419 MW,相比于单一网络模型与融合模型结构实现了误差指标的显著降低,验证了基于多模型融合的CNN-LSTM-XGBoost短期电力负荷预测方法具有较快的模型训练速度、较高的预测准确度与较低的预测误差。