电容式油量测量系统的姿态误差分析

针对目前飞机上电容式油量传感器测量燃油时产生的姿态误差问题进行了研究,以飞机上的规则油箱为例,从理论上详细地分析了电容式油量测量系统的姿态误差,并绘制出其误差曲线,给出了减小姿态误差的方法,最后提出了在姿态误差最小的准则下确定传感器的最佳安装位置;文中提出的减小姿态误差的方法,不仅有利于提高燃油测量系统的可靠性和安全性,而且可以改善燃油测量系统的维修性,使燃油量测量技术跃上了一个新的台阶。     

融合多尺度特征的复杂手势姿态估计网络

目的 基于单幅RGB图像的手势姿态估计受手势复杂性、手指特征局部自相似性及遮挡问题的影响,导致手势姿态估计准确率低。为此,提出一种面向单目视觉手势姿态估计的多尺度特征融合网络。方法 1）采用ResNet50(50-layer residual network)模块从RGB图像提取不同分辨率特征图,通过通道变换模块显式地学习特征通道间的依赖关系,增强重要的特征通道信息,弱化次要的特征通道信息。2）在全局回归模块中,通过设计节点间的连接方式融合不同分辨率特征图,以便充分利用图像的细节与整体信息。采用局部优化模块继续提取更深层的特征信息,获得手部关节点的高斯热图,以此修正遮挡等原因造成部分关节点回归不准确的问题。3）计算经通道变换模块处理后的最小特征图,通过全局池化和多层感知机处理该特征图以获得手势类别和右手相对于左手的深度。4）综合以上结果获得最终的手势姿态。结果 采用InterHand2.6M和RHD(rendered handpose dataset)数据集训练多尺度特征融合网络,评估指标中根节点的平均误差和关节点的平均误差,均低于同类方法,且在一些复杂和遮挡的场景下鲁棒性更高。在In...     

A two stream convolutional neural network with bi-directional GRU model to classify dynamic hand gesture

Dynamic hand gesture recognition is still an interesting topic for the computer vision community. A set of feature vectors can represent any hand gesture. A Recurrent Neural Network (RNN) can recognize these feature vectors as a hand gesture that analyzes the temporal and contextual information of the gesture sequence. Thus, we proposed a hybrid deep learning framework to recognize dynamic hand gestures. In the Hybrid model GoogleNet is pipelined with a Bidirectional GRU unit to recognize the dynamic hand gesture. Dynamic hand gestures consist of many frames, and features of each frame need to be extracted to get the temporal and dynamic information of the performed gesture. As RNN takes input as a sequence of feature vectors, we extract features from videos using pretrained GoogleNet. As Gated Recurrent Unit is one of the variants of RNN to classify the sequential data, we created a feature vector that corresponds to each video and passed it to the bidirectional GRU (BGRU) network to classify the gestures. We evaluate our model on four publicly available hand gesture datasets. The proposed method performs well and is comparable with the existing methods. For instance, we achieved 98.6% accuracy on Northwestern University Hand Gesture(NWUHG), 99.6% on SKIG, 99.4% on Cambridge Hand Gesture (CHG) datasets respectively. We performed our experiments on DHG14/28 dataset and achieved an accuracy of 97.8% with 14-gesture classes and 92.1% on 28-gesture classes. DHG14/28 dataset contains skeleton and depth data, and our proposed model used depth data and achieved comparable accuracy.     

Human‐Finger Electronics Based on Opposing Humidity‐Resistance Responses in Carbon Nanofilms

Carbon nanomaterials have excellent humidity sensing properties. Here, it is demonstrated that multiwalled carbon‐nanotube (MWCNT)‐ and reduced‐graphene‐oxide (rGO)‐based conductive films have opposite humidity/electrical resistance responses: MWCNTs increase their electrical resistance (positive response) and rGOs decrease their electrical resistance (negative response). The authors propose a new phenomenology that describes a “net”‐like model for MWCNT films and a “scale”‐like model for rGO films to explain these behaviors based on contributions from junction resistances (at interparticle junctions) and intrinsic resistances (of the particles). This phenomenology is accordingly validated via a series of experiments, which complement more classical models based on proton conductivity. To explore the practical applications of the converse humidity/resistance responses, a humidity‐insensitive MWCNT/rGO hybrid conductive films is developed, which has the potential to greatly improve the stability of carbon‐based electrical device to humidity. The authors further investigate the application of such films to human‐finger electronics by fabricating transparent flexible devices consisting of a polyethylene terephthalate substrate equipped with an MWCNT/rGO pattern for gesture recognition, and MWCNT/rGO/MWCNT or rGO/MWCNT/rGO patterns for 3D noncontact sensing, which will be complementary to existing 3D touch technology.     

Microsphere‐Assisted Robust Epidermal Strain Gauge for Static and Dynamic Gesture Recognition

A novel and robust epidermal strain gauge by using 3D microsphere arrays to immobilize, connect, and protect a multiwalled carbon nanotubes (MWNTs) pathway is presented. During the solvent deposition process, MWNTs sedimentate, self‐assemble, and wrap onto surface of polystyrene (PS) microspheres to construct conductive networks, which further obtain excellent stretchability of 100% by combining with commercially used elastomer. Benefiting from its 3D conductive pathway defined by microspheres, immobilized MWNT (I‐MWNT) network can be directly used in practical occasions without further packaging and is proved by tape tests to be capable of defend mechanical damage effectively from external environment. By parameter optimization, the strain sensor with 3 µm PS spheres obtains stable resistive responses for more than 1000 times, and maintains its gauge factor (GF) of 1.35. This thin‐film conductive membrane built by this effective construction method can be easily attached onto fingers of both robot and human, and is demonstrated in sensitive epidermal strain sensing and recognizing different hand gestures effectively, in static and dynamic modes, respectively.     

非规则油箱油量的测量方法

针对采用电容传感器测量油箱中油量的传统测量方法的弊端,提出了非规则油箱的数学模型建立方法及其油量的计算方法,从理论上详细地分析了利用线性电容传感器测量非规则油箱时产生姿态误差的原理,给出了姿态误差的修正方法,最后提出了在姿态误差最小的准则下确定传感器的最佳安装位置;该研究对提高非规则油箱的油量测量精度具有很大的实用价值.     

Never blame the umpire – a review of Situation Awareness models and methods for examining the performance of officials in sport

As sport becomes more complex, there is potential for ergonomics concepts to help enhance the performance of sports officials. The concept of Situation Awareness (SA) appears pertinent given the requirement for officials to understand what is going on in order to make decisions. Although numerous models exist, none have been applied to examine officials, and only several recent examples have been applied to sport. This paper examines SA models and methods to identify if any have applicability to officials in sport (OiS). Evaluation of the models and methods identified potential applications of individual, team and systems models of SA. The paper further demonstrates that the Distributed Situation Awareness model is suitable for studying officials in fastball sports. It is concluded that the study of SA represents a key area of multidisciplinary research for both ergonomics and sports science in the context of OiS.

Practitioner Summary: Despite obvious synergies, applications of cognitive ergonomics concepts in sport are sparse. This is especially so for Officials in Sport (OiS). This article presents an evaluation of Situation Awareness models and methods, providing practitioners with guidance on which are the most suitable for OiS system design and evaluation.     

Exploring educational immersive videogames: an empirical study with a 3D multimodal interaction prototype

ABSTRACT

Gestural interaction devices emerged and originated various studies on multimodal human–computer interaction to improve user experience (UX). However, there is a knowledge gap regarding the use of these devices to enhance learning. We present an exploratory study which analysed the UX with a multimodal immersive videogame prototype, based on a Portuguese historical/cultural episode. Evaluation tests took place in high school environments and public videogaming events. Two users would be present simultaneously in the same virtual reality (VR) environment: one as the helmsman aboard Vasco da Gama’s fifteenth-century Portuguese ship and the other as the mythical Adamastor stone giant at the Cape of Good Hope. The helmsman player wore a VR headset to explore the environment, whereas the giant player used body motion to control the giant, and observed results on a screen, with no headset. This allowed a preliminary characterisation of UX, identifying challenges and potential use of these devices in multi-user virtual learning contexts. We also discuss the combined use of such devices, towards future development of similar systems, and its implications on learning improvement through multimodal human–computer interaction.     

Automatic Schaeffer's gestures recognition system

Schaeffer's sign language consists of a reduced set of gestures designed to help children with autism or cognitive learning disabilities to develop adequate communication skills. Our automatic recognition system for Schaeffer's gesture language uses the information provided by an RGB‐D camera to capture body motion and recognize gestures using dynamic time warping combined with k‐nearest neighbors methods. The learning process is reinforced by the interaction with the proposed system that accelerates learning itself thus helping both children and educators. To demonstrate the validity of the system, a set of qualitative experiments with children were carried out. As a result, a system which is able to recognize a subset of 11 gestures of Schaeffer's sign language online was achieved.