Ergonomic assessment of office worker postures using 3D automated joint angle assessment

Sedentary activity and static postures are associated with work-related musculoskeletal disorders (WMSDs) and worker discomfort. Ergonomic evaluation for office workers is commonly performed by experts using tools such as the Rapid Upper Limb Assessment (RULA), but there is limited evidence suggesting sustained compliance with expert’s recommendations. Assessing postural shifts across a day and identifying poor postures would benefit from automation by means of real-time, continuous feedback. Automated postural assessment methods exist; however, they are usually based on ideal conditions that may restrict users’ postures, clothing, and hair styles, or may require unobstructed views of the participants. Using a Microsoft Kinect camera and open-source computer vision algorithms, we propose an automated ergonomic assessment algorithm to monitor office worker postures, the 3D Automated Joint Angle Assessment, 3D-AJA. The validity of the 3D-AJA was tested by comparing algorithm-calculated joint angles to the angles obtained from manual goniometry and the Kinect Software Development Kit (SDK) for 20 participants in an office space. The results of the assessment show that the 3D-AJA has mean absolute errors ranging from 5.6° ± 5.1° to 8.5° ± 8.1° for shoulder flexion, shoulder abduction, and elbow flexion relative to joint angle measurements from goniometry. Additionally, the 3D-AJA showed relatively good performance on the classification of RULA score A using a Random Forest model (micro averages F1-score = 0.759, G-mean = 0.811), even at high levels of occlusion on the subjects’ lower limbs. The results of the study provide a basis for the development of a full-body ergonomic assessment for office workers, which can support personalized behavior change and help office workers to adjust their postures, thus reducing their risks of WMSDs.     

An investigation of the reliability of Rapid Upper Limb Assessment (RULA) as a method of assessment of children's computing posture

Rapid Upper Limb Assessment (RULA) is a quick observation method of posture analysis. RULA has been used to assess children's computer-related posture, but the reliability of RULA on a paediatric population has not been established. The purpose of this study was to investigate the inter-rater and intra-rater reliability of the use of RULA with children. Video recordings of 24 school children were independently viewed by six trained raters who assessed their postures using RULA, on two separate occasions. RULA demonstrated higher intra-rater reliability than inter-rater reliability although both were moderate to good. RULA was more reliable when used for assessing the older children (8-12 years) than with the younger children (4-7 years). RULA may prove useful as part of an ergonomic assessment, but its level of reliability warrants caution for its sole use when assessing children, and in particular, younger children.     

Analysis of vehicle requirements in a general automated guided vehicle system based transportation system

This paper considers a new model of automated guided vehicle system arising from modern transportation systems. The objective is to minimize the vehicle number in order to guarantee the stability of the system. An approximately analytical method is proposed and a numerical example based on simulation is also presented to illustrate the method.     

Student perception and usage of an automated programming assessment tool

Automated assessment systems are gaining popularity within computer programming courses. In this paper we perform an empirical evaluation of Mooshak, an online judge that verifies program correctness, in order to determine its usefulness in classroom settings. In particular, we provide a detailed study on how students use the tool, analyze their opinions and critiques about it, and measure other features like its capability to reduce dropout rates. The experience was carried out within a course on algorithm design and analysis where we collected information through several questionnaires and data generated by the tool during the course. Among the main findings we highlight: (1) the usage of the tool was adequate in relation to students’ own testing; (2) its feedback needs to be richer in order to improve its acceptance among students; and (3) there was no statistical evidence to claim Mooshak reduced the dropout rate.     

基于计算机视觉的行人检测技术的发展

智能车辆作为智能交通系统的关键技术,是许多高新技术综合集成的载体。智能车辆的一个非常重要的研究课题就是在城市道路交通环境下如何避免行人被车辆碰撞。总结了基于计算机视觉的行人检测的现有的主要技术,针对摄像机在交通视频监控系统中的静止情况,以及在智能车辆上的运动情况下的行人检测算法及其性能进行了评述和比较,并分析了当前行人检测技术的研究现状,指出了存在的问题和研究前景。     

Productivity through computer management of a fully automated transportation system

The Morgantown Personal Rapid Transit System is a fully automated, collection and distribution, origin to destination, non-stop transportation system. The system consists of a feet of 73 electrically powered, rubber tired, passenger carrying vehicles, operating on a dedicated guideway network at 15 second headways. The system provides a safe, comfortable non-polluting, reliable means of public transportation between the Central Business District of Morgantown, West Virginia, and the three separated campuses of West Virginia University.

This paper will address the unique application of computers in operating a public transit system which is considered the first of its kind anywhere in the world.     

Ergonomic assessment and workstation design of shipping crane cabin in steel industry

Ergonomics plays vital role to improve health and productivity at workplace and in last two decades it find importance to redesign workplace. All Indian industries had taken initiative to redesign their workplace to overcome various musculoskeletal disorders (MSD) and work related injuries. In this context, the project has been carried out in an integrated steel plant located in central India where most of the crane operator was continuously suffering from muscular pain in different body parts. Risk of MSD was identified by detailed questionnaire from 27 crane operator. It was revealed that almost all crane operators were continuously suffering from some kind of MSD. Based on the anthropometric data of 50 percentile Indian male, ergonomic assessment, redesign and evaluation of crane cabin was carried out in CATIA-V5 software. To check the compatibility of the design, rapid upper limb assessment (RULA) for both existing as well as modified crane cabin was performed. This study shows that intervention of ergonomics in workplace reduces the mismatch between man and machine and makes workplace comfortable for work.     

Study of factors impacting remote diagnosis performance on a PLC based automated system

In this paper, we present systematically experimental and analytical evaluations on design of remote fault diagnosis systems for a programmable logic controller (PLC) based automated system. In order to investigate the factors of remote architecture, operator's skill level, and fault's effect on diagnosis performance, comprehensive experimental evaluations, statistical analysis and survey were conducted. The experiment compared three levels of remote architectures, two levels of operators’ skill levels on four typical faults in an automated system. Performance evaluation including detection time, amount of information search, number of diagnostic tests, number of asked questions, number of system runs, and performance score, were extracted from the experiment record. Two-stage statistical analysis including (1) analysis of variance (ANOVA) and (2) least significant difference (LSD) paired comparison was conducted on the performance evaluation data. From the statistical analysis results and expert survey, we concluded that: (1) the architecture sophistication eased the diagnosis on the faults that are related to the measurement signals, and (2) the diagnosis performance also increased with the sophistication of the architecture, but (3) operator's skill level did not significantly affect the diagnosis performance. The survey results on troubleshooters’ opinions and preferences about the diagnosis were also summarized, which can be applied for improvement on design of remote diagnosis system. The proposed evaluation approach is systematic; it can be applied on design and evaluation of diagnostics systems on other PLC based automated systems such as heating, ventilation, and air conditioning (HVAC), robotics assembly.     

基于计算机视觉及深度学习的无人机手势控制系统

传统的无人机人机交互需要专门的设备和专业的训练,便捷新颖的交互方式往往更令人青睐。利用普通相机,对基于计算机视觉以及深度学习的无人机手势控制系统进行了研究。该系统首先利用快速跟踪算法在视频序列中提取出操作者所在区域,大大减少后续视频处理压力的同时去除了复杂背景以及相机漂移的影响。其次,根据动作的时间信息,用不同颜色编码光流特征,叠加在一张图片上,将视频转换为同时包含时间特征以及空间特征的彩色纹理图。最后,利用卷积神经网络对彩色纹理图进行学习及分类,根据分类结果生成控制无人机的指令。该系统每0.4 s对1.6 s内的动作进行一次判定,利用卷积神经网络对图片的分类实现实时性的人机交互,系统在60 m范围内的识别准确率在93%以上,在室内和室外环境下,操作者可以通过模仿指令动作方便地控制无人机。     

一种入侵者视野下的复杂网络安全评估方案

针对现有复杂网络系统中存在的各种脆弱性评估问题,如脆弱性评估量化不精确、评估误差较大等。这里以邻接矩阵知识为理论基础,结合复杂网络系统的特定环境,设计出一种脆弱性定量评估方法。该方法一方面从单一专业入侵者的层面展开细致深入的探讨,另一方面还引入入侵者选择能力和其选定入侵网络节点的能力大小因素,除此之外,还设计了网络子站的信息结构,对这一个方法的有效性进行验证。验证结果显示该方法对网络系统脆弱性的研究有很大的优势。     

基于机器视觉的珍珠光洁度在线自动分级

为代替耗时的传统人工分选,设计了一种基于机器视觉的珍珠光洁度在线自动分级系统。首先,该系统在珍珠自由落体时对珍珠进行全方位成像;然后对图像进行实时分析,计算珍珠的有效特征;最后,根据多图的有效特征确定珍珠的光洁度等级。实践证明,该系统不但提高了珍珠光洁度的分选效率,而且节省了企业的生产成本。     

A survey on GANs for computer vision: Recent research,analysis and taxonomy

In the last few years, there have been several revolutions in the field of deep learning, mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not only provide an unique architecture when defining their models, but also generate incredible results which have had a direct impact on society. Due to the significant improvements and new areas of research that GANs have brought, the community is constantly coming up with new researches that make it almost impossible to keep up with the times. Our survey aims to provide a general overview of GANs, showing the latest architectures, optimizations of the loss functions, validation metrics and application areas of the most widely recognized variants. The efficiency of the different variants of the model architecture will be evaluated, as well as showing the best application area; as a vital part of the process, the different metrics for evaluating the performance of GANs and the frequently used loss functions will be analyzed. The final objective of this survey is to provide a summary of the evolution and performance of the GANs which are having better results to guide future researchers in the field.     

基于可靠度的计算机系统优化问题的研究

分析了计算机系统硬件优化配置问题,给出了该问题的优化模型,并利用模拟退火算法和遗传退火算法对求解该问题进行了具体设计。通过实例仿真比较,遗传退火算法能得到比文献中算法和模拟退火算法更好的结果,表明算法是非常有效的。     

Development and validation of a cumulative exposure shoulder risk assessment tool based on fatigue failure theory

Abstract

Objective

To present a new risk assessment tool for shoulder intensive occupational tasks based on fatigue failure theory.     

基于粗糙集的网络信息系统安全评估模型研究

从分析网络信息系统安全评估标准指标出发．提出基于安全树的信息系统安全最佳节点基本属性模型，并对相关评估指标进行优化；在此基础上，提出一种基于粗糙集的混合启发式约筒算法．进行指标属性约筒和权重集构建，进而建立融合的信息系统安全评估数学模型．最后通过实例说明该安全评估模型建立方法的有效性及实用性．     

基于计算机视觉的自动阅卷系统的设计

基于计算机视觉技术设计了一个自动阅卷系统,为解决试卷填涂信息识别问题和自动判卷问题,提出了基于感知哈希技术的试卷填涂信息识别算法和基于图像比较近似度结果的判卷算法。通过系统实现与测试,系统拥有较快的处理速度、较高的准确性和易用性。     

DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and Swin-Transformer prediction head-enabled YOLOv5 with attention mechanism

Objective.Computer vision-based up-to-date accurate damage classification and localization are of decisive importance for infrastructure monitoring, safety, and the serviceability of civil infrastructure. Current state-of-the-art deep learning (DL)-based damage detection models, however, often lack superior feature extraction capability in complex and noisy environments, limiting the development of accurate and reliable object distinction.Method.To this end, we present DenseSPH-YOLOv5, a real-time DL-based high-performance damage detection model where DenseNet blocks have been integrated with the backbone to improve in preserving and reusing critical feature information. Additionally, convolutional block attention modules (CBAM) have been implemented to improve attention performance mechanisms for strong and discriminating deep spatial feature extraction that results in superior detection under various challenging environments. Moreover, an additional feature fusion layers and a Swin-Transformer Prediction Head (SPH) have been added leveraging advanced self-attention mechanism for more efficient detection of multiscale object sizes and simultaneously reducing the computational complexity.Results.Evaluating the model performance in large-scale Road Damage Dataset (RDD-2018), at a detection rate of 62.4 FPS, DenseSPH-YOLOv5 obtains a mean average precision (mAP) value of 85.25%, F1-score of 81.18%, and precision (P) value of 89.51% outperforming current state-of-the-art models.Significance.The present research provides an effective and efficient damage localization model addressing the shortcoming of existing DL-based damage detection models by providing highly accurate localized bounding box prediction. Current work constitutes a step towards an accurate and robust automated damage detection system in real-time in-field applications.     

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research.     

基于计算机视觉的智能小车路径规划

针对危险或者不适合人类活动的场合,构建了基于计算机视觉的智能小车路径规划系统,系统主要实现了图像处理、单目视觉测距、虚拟地图生成、全局路径规划和小车避障等功能。率先提出基于地面颜色和纹理的单目视觉测距算法,并结合小车性能和实际运行环境对蚁群算法进行优化,经过大量实验证实,此设计较其他算法既能满足设计需求,又能大幅提高运行时间。     

Towards automated progress assessment of workpackage components in construction projects using computer vision

Traditional methods of collecting data on the progress of construction projects often involve human judgement, high costs, and are too infrequent to provide managers with timely and accurate control data. This study proposes a system that aims to address some of these shortcomings through automation. Our first contribution is a framework for the automatic generation of work packages. We describe the conceptual model and develop a prototype implementation, based on the results of a survey designed to capture the current practice of formulating work packages as performed in the industry. Our second contribution is a system that employs computer vision (CV) techniques to report on the progress of these work packages automatically. This is achieved by analysing sequences of digital images acquired regularly by on-site cameras. The result of this analysis, when compared with the expected state of construction, provides progress feedback on the work packages, allowing a project to be monitored more effectively.