热管真空灌装封装系统研制及参数优化

热管真空灌装封装技术是制作热管的关键技术。工业上运用广泛的方法是"沸腾排气法",但该方法存在质量控制精度差和真空度不足等缺陷。常见的热管封装技术为冷焊技术,但封装过程中容易导致真空破坏。针对这些不足,研制了一种热管真空灌装封装系统:利用真空泵使热管内部实现高真空,同时利用液压装置对热管实现封装;对系统灌装封装流程进行优化设计,基于MFC编制了远程控制程序;通过实验,对液压压力进行优化,得到了最佳运行参数。该系统克服了沸腾排气法和现有冷焊技术的不足,达到较好的生产效果。     

Sub‐Micron Anisotropic InP‐based III–V Semiconductor Material Deep Etching for On‐Chip Laser Photonics Devices

Two InP‐based III–V semiconductor etching recipes are presented for fabrication of on‐chip laser photonic devices. Using inductively coupled plasma system with a methane free gas chemistry of chlorine and nitrogen at a high substrate temperature of 250 °C, high aspect ratio, anisotropic InP‐based nano‐structures are etched. Scanning electron microscopy images show vertical sidewall profile of 90° ± 3°, with aspect ratio as high as 10. Atomic Force microscopy measures a smooth sidewall roughness root‐mean‐square of 2.60 nm over a 3 × 3 μm scan area. The smallest feature size etched in this work is a nano‐ring with inner diameter of 240 nm. The etching recipe and critical factors such as chamber pressure and the carrier plate effect are discussed. The second recipe is of low temperature (?10 °C) using Cl₂ and BCl₃ chemistry. This recipe is useful for etching large areas of III–V to reveal the underlying substrate. The availability of these two recipes has created a flexible III–V etching platform for fabrication of on‐chip laser photonic devices. As an application example, anisotropic InP‐based waveguides of 3 μm width are fabricated using the Cl₂ and N₂ etch recipe and waveguide loss of 4.5 dB mm^?1 is obtained.

     

Laser‐Assisted Large‐Scale Fabrication of All‐Solid‐State Asymmetrical Micro‐Supercapacitor Array

The micro‐supercapacitors are of great value for portable, flexible, and integrated electronic equipments. Here, the large‐scale and integrated asymmetrical micro‐supercapacitor (AMSC) array is fabricated in virtue of the laser direct writing and electrodeposition technology. The AMSC shows the ideal flexibility, high areal specific capacitance (21.8 mF cm^?2), and good rate capability. Moreover, its energy density reaches 12.16 µW h cm^?2, outperforming most micro‐supercapacitors reported previously. Meanwhile, large‐scale series‐connected AMSCs are integrated on the flexible substrates (e.g., indium tin oxide‐polyethylene terephthalate film), which can power a veriety of the commercial electronics. The combination of AMSCs array, solar cell, and electronic device proves the feasibility for practical application in the portable, flexible, and integrated electronic equipments.     

基于粒子群优化算法的船舶动力定位云模型控制器设计

云模型控制理论是智能控制学科的新兴领域,因此如何扩展云模型的应用范围并使其走向工程化和实用化成为其研究重点;针对船舶运动模型具有不确定性和外部扰动随机性等特点,尝试将云模型应用于船舶动力定位的控制过程中;由于云模型控制器存在参数难以整定的问题,提出了基于粒子群算法的优化设计方法;针对标准粒子群优化算法容易出现早熟收敛的问题,引入自适应粒子群优化算法;仿真研究表明云模型控制及粒子群优化的可行性和有效性。     

基于混沌人工蜂群算法的无线传感器网络覆盖优化

针对无线传感器网络随机播撒的节点严重冗余并且导致网络寿命短、覆盖效率不高等缺陷,提出了一种混沌人工蜂群算法的无线传感器网络覆盖优化算法;将节点的利用率和覆盖率作为优化目标函数,建立与之对应的数学模型,之后用混沌人工蜂群算法改善人工蜂群算法陷入局部最优、收敛慢等问题,提高算法收敛速度和精度,对节点覆盖模型进行求解,得出网络最优覆盖方案;通过实验仿真,提出的算法提高了无线传感器网络的覆盖率,覆盖率可达93.48%以上,减少了网络节点冗余,提高了网络寿命,降低了网络成本。     

一种基于分层的人类动作识别和定位算法研究

人类动作识别在视频自动分析、视频检索等领域获得广泛应用,是目前的研究热点。然而现有的动作识别方法重点关注视频的非静态部分而忽略大部分静态部分,从而影响了动作识别和定位的效果。本文提出一种新的分层空间-时间分段表示法,以分层方式实现部位和整个身体的多分辨率表示,可用于运动识别和定位。该算法分为3个步骤。第一步,首先对每个视频帧进行分层分段,以得到一组分段树,每颗树是身体分段树的候选。第二步,利用视频的轮廓、接合对象结构、全局前景色等信息对候选分段树进行修剪。第三步,在时域上对剩余分段层的每个分段进行前向和后向跟踪。我们以难度较大的UCF-Sports和HighFive数据集为实验对象,对本文方法进行性能评估,实验结果表明,本文方法的性能要优于当前最新运动检测算法性能,运动定位性能与当前最新算法相当。     

基于K-近邻算法的物料含水率测量标准装置设计

目的 针对烘箱法耗时长且不适用于生产现场的问题,围绕短时干燥法设计物料含水率测量标准装置。方法 装置主要包括移动底盘、箱体、水分分析仪、可充电电源、配电板、工业触屏电脑以及上位机软件。建立基于K-近邻（K-Nearest Neighbor）算法的物料含水率预测模型,并通过实验获得预测结果与烘箱法测量结果之间的修正关系。以抽取自卷烟厂的烟草样品为例,对装置进行测量实验。结果 当设置测量时间为30min时,含水率测量结果的扩展不确定度低于0.5%。结论 该装置可代替烘箱法对物料含水率进行监控,也可以用于在线水分仪的原位校准。     

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization performance in terms of cancerous stages and grades associated with each type of gastric cancer. This paper proposed an optimized AI-based approach to diagnose and assess the risk factor of gastric cancer based on its type, stage, and grade in the endoscopic images for smart healthcare applications. The proposed method is categorized into five phases such as image pre-processing, Four-Dimensional (4D) image conversion, image segmentation, K-Nearest Neighbour (K-NN) classification, and multi-grading and staging of image intensities. Moreover, the performance of the proposed method has experimented on two different datasets consisting of color and black and white endoscopic images. The simulation results verified that the proposed approach is capable of perceiving gastric cancer with 88.09% sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.     

离心式振动抛光装置的设计

设计了一种适用于大长径比异形截面直通道孔腔抛光的振动抛光装置,分析了振动系统的幅频特性,了解了影响抛光装置振幅的主要参数。