Robotic disassembly sequence planning using enhanced discrete bees algorithm in remanufacturing

Increasing attention is being paid to remanufacturing due to environmental protection and resource saving. Disassembly, as an essential step of remanufacturing, is always manually finished which is time-consuming while robotic disassembly can improve disassembly efficiency. Before the execution of disassembly, generating optimal disassembly sequence plays a vital role in improving disassembly efficiency. In this paper, to minimise the total disassembly time, an enhanced discrete Bees algorithm (EDBA) is proposed to solve robotic disassembly sequence planning (RDSP) problem. Firstly, the modified feasible solution generation (MFSG) method is used to build the disassembly model. After that, the evaluation criterions for RDSP are proposed to describe the total disassembly time of a disassembly sequence. Then, with the help of mutation operator, EDBA is proposed to determine the optimal disassembly sequence of RDSP. Finally, case studies based on two gear pumps are used to verify the effectiveness of the proposed method. The performance of EDBA is analysed under different parameters and compared with existing optimisation algorithms used in disassembly sequence planning (DSP). The result shows the proposed method is more suitable for robotic disassembly than the traditional method and EDBA generates better quality of solutions compared with the other optimisation algorithms.     

Dense range map reconstruction from a versatile robotic sensor system with an active trinocular vision and a passive binocular vision.

One major research issue associated with 3D perception by robotic systems is the creation of efficient sensor systems that can generate dense range maps reliably. A visual sensor system for robotic applications is developed that is inherently equipped with two types of sensor, an active trinocular vision and a passive stereo vision. Unlike in conventional active vision systems that use a large number of images with variations of projected patterns for dense range map acquisition or from conventional passive vision systems that work well on specific environments with sufficient feature information, a cooperative bidirectional sensor fusion method for this visual sensor system enables us to acquire a reliable dense range map using active and passive information simultaneously. The fusion algorithms are composed of two parts, one in which the passive stereo vision helps active vision and the other in which the active trinocular vision helps the passive one. The first part matches the laser patterns in stereo laser images with the help of intensity images; the second part utilizes an information fusion technique using the dynamic programming method in which image regions between laser patterns are matched pixel-by-pixel with help of the fusion results obtained in the first part. To determine how the proposed sensor system and fusion algorithms can work in real applications, the sensor system is implemented on a robotic system, and the proposed algorithms are applied. A series of experimental tests is performed for a variety of configurations of robot and environments. The performance of the sensor system is discussed in detail.     

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.     

Image tracking of motorcycles and vehicles on urban roads and its application to traffic monitoring and enforcement

Abstract

Image tracking has increasingly gained attention for use in vision‐based traffic monitoring and surveillance applications. For many cities in Asia countries, it is desirable to detect multiple motorcycles as well as cars for urban traffic monitoring and enforcement. In this paper, a novel contour initialization and tracking algorithm is presented to track multiple motorcycles and vehicles at any position on the roadway. This method has the capability to detect moving vehicles of various sizes and to generate their initial contours for image tracking. The proposed method is not constrained by lane boundaries or vehicle size. To track vehicles on roadways, dynamic models are designed to predict the horizontal and vertical positions of vehicle contours. A Kalman filter is designed to update the prediction based on real‐time image measurement. Practical experimental studies using video clips are presented to evaluate the performance of the proposed method. Traffic parameters such as traffic flow, vehicle speeds and traffic density are obtained with satisfactory accuracy.     

激光位移传感器的计算机辅助设计和实验验证

根据被测物体的表面反射模型，采用光线追踪法对所设计的激光位移传感器进行了计算机仿真，利用复合形法进行结构参数优化。在结构优化基础上，使用单片球面透镜获得了较高的线性关系。实验表明，此方法可以缩小传感器的结构尺寸、提高精度、降低成本。     

General approach to high-efficiency simulation of affinity capillary electrophoresis

The differential equation describing electrophoretic migration can be evaluated with various finite difference schemes (FDSs). However, the accuracy and efficiency can be dramatically different depending on the FDS chosen and the way the algorithm is implemented in a computer simulation program. The monotonic transport scheme is used as the algorithm for the hyperbolic part of the differential equation, and the first-order fully explicit scheme is used for the parabolic part of the equation. The combination of these algorithms minimizes the errors and maintains high efficiency. A circular arrangement of the cells in the computer's memory is used in the implementation of the algorithms, and the use of concentration thresholds to enable and disable cells along the capillary makes the new algorithm highly efficient. Either thermodynamic or kinetic constants can be used in this program to simulate binding interactions between two species for equilibrium and nonequilibrium affinity CE. Simulation results with various parameters are presented. The simulated peak with proper parameters for an equilibrium affinity CE experiment has shape and position similar to that of the experimental peak. The simulated electropherograms for a nonequilibrium affinity CE experiment also show characteristics of the experimental electropherograms.     

A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

An efficient process monitoring system is important for achieving sustainable manufacturing. The control charting technique is one of the most effective techniques to monitor process quality. In certain processes where the process mean and variance are not independent of one another, the coefficient of variation (CV), which measures the ratio of the standard deviation to the mean, should be monitored. In line with industrial settings, where at least two or more variables are monitored simultaneously in most processes, this paper proposes a variable parameter (VP) chart to monitor the multivariate CV (MCV). Formulae and algorithms to optimize the various performance measures are discussed. The proposed VP MCV chart is designed based on a Markov chain approach. The performance comparison shows that the proposed VP MCV chart prevails over the existing MCV charts, in terms of the average time to signal (ATS), standard deviation of the time of signal (SDTS), and expected average time to signal (EATS) criteria. An example is presented to illustrate the implementation of the proposed VP MCV chart.     

Measuring height of high-voltage transmission poles using unmanned aerial vehicle (UAV) imagery

Aerial imagery is important in remote sensing applications. Unmanned aerial vehicle (UAV) has a wide range of applications in remote sensing and presents a substantial cost-effective solution when monitoring objects on the earth’s surface. Moreover, object detection and classification are important aspects of global information system, especially for remote sensing applications and power line monitoring, which are essential for the proper distribution of electricity to consumers. Manual inspection consumes much time and involves risk, especially in remote areas that host dangerous wildlife; hence, UAV-based approaches are more feasible for such monitoring. The authors propose an UAV approach that utilises a digital surface model and incorporates a stereo matching algorithm based on UAV stereo images. The proposed algorithm was based on a graph-cut (GC) algorithm that measured the disparity map. Results were compared with well-known algorithms; including, for example, global and local stereo matching algorithms. The proposed solution introduces and integrates ordering constraints along with a submodular energy minimisation function to/with the GC algorithm to enhance performance. The authors measured sensitivity and recall for all parameters against ground truth data for differently cropped images of 16 power poles. Results showed that the proposed model performed more accurately compared to extant methods.     

Design and comparison of some Shewhart‐type schemes for simultaneous monitoring of Weibull parameters

Weibull distribution is one of the most important probability models used in modeling time between events, system reliability, and particle sizes, among others. Therefore, efficiently and consequently monitoring certain changes in Weibull process is considered as an important research topic. Various statistical process monitoring schemes have been developed for monitoring different process parameters, including some for Weibull parameters. Most of these schemes are, however, designed to monitor and control a single process parameter, although there are two important model parameters for Weibull distribution. Recently, several researchers studied various schemes for jointly monitoring the mean and variance of a normally distributed process using a single plotting statistic. Nevertheless, there is still dearth of researches in joint monitoring of non‐normal process parameters. In this context, we develop some control schemes for simultaneously monitoring the scale and shape parameters of processes that follow the Weibull distribution. Implementation procedures are developed, and performance properties of various proposed schemes are investigated. We also offer an illustrative example along with a summary and recommendations.     

基于工业焊接小车的视觉伺服控制算法研究

针对基于视觉传感器的焊接小车，提出了提高跟踪精度和鲁棒性的视觉伺服控制方法。首先，建立了图像特征的数学模型，并设计了错误数据的检测算法，其次，对于折线焊缝跟踪过程中存在的位置延迟问题，采用了一种自适应视觉伺服控制结构。这种自适应结构在线检测焊缝方向，并能够在焊缝方向发生变化时对控制参数进行相应的调整。针对该控制结构，设计了基于Kalman滤波器的焊缝方向变化检测算法、参考像素坐标调整算法以及预测与实时调整相结合的控制方法。焊缝自动跟踪实验表明所提方法具有较强的鲁棒性和较高的精度。     

An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features by recording a small number of training images from vehicle frontal view images. The proposed system employs extensive data-augmentation techniques for effective training while avoiding the problem of data shortage. In order to capture rich and discriminative information of vehicles, the convolutional neural network is fine-tuned for the classification of vehicle types using the augmented data. The network extracts the feature maps from the entire dataset and generates a label for each object (vehicle) in an image, which can help in vehicle-type detection and classification. Experimental results on a public dataset and our own dataset demonstrated that the proposed method is quite effective in detection and classification of different types of vehicles. The experimental results show that the proposed model achieves 96.04% accuracy on vehicle type classification.     

Knowledge base design for flexible assembly robots

The paper presents a knowledge based model for selecting robotic systems for mechanical assembly. The knowledge based system tool was used to select a robot from available robotic systems using cost and performance criteria along with the parameters for the assembly cell. An efficient computer program was written to store information on available robotic systems in a knowledge base. The information is accessible in an interactive mode using the knowledge of the assembly task. A validation study showed that robots could be easily and conveniently chosen for assembly using assembly variables such as demand and layout specifications. In an evaluative test of the knowledge base model, 90% of the subjects chose the same robot for a test assembly problem.     

Computer-Vision Based Object Detection and Recognition for Service Robot in Indoor Environment

The near future has been envisioned as a collaboration of humans with mobile robots to help in the day-to-day tasks. In this paper, we present a viable approach for a real-time computer vision based object detection and recognition for efficient indoor navigation of a mobile robot. The mobile robotic systems are utilized mainly for home assistance, emergency services and surveillance, in which critical action needs to be taken within a fraction of second or real-time. The object detection and recognition is enhanced with utilization of the proposed algorithm based on the modification of You Look Only Once (YOLO) algorithm, with lesser computational requirements and relatively smaller weight size of the network structure. The proposed computer-vision based algorithm has been compared with the other conventional object detection/recognition algorithms, in terms of mean Average Precision (mAP) score, mean inference time, weight size and false positive percentage. The presented framework also makes use of the result of efficient object detection/recognition, to aid the mobile robot navigate in an indoor environment with the utilization of the results produced by the proposed algorithm. The presented framework can be further utilized for a wide variety of applications involving indoor navigation robots for different services.     

基于北斗通信的河道水质监测系统设计

针对当前水污染监测费时费力的问题,为了实现实时在线检测水质信息,提出了一种基于北斗通信的浮标监测系统,该系统包括浮标体数据采集系统和岸基接收站系统。以新型设计的浮标为载体的数据采集系统,采用STM32芯片,通过RS485协议读取传感器数据后发送并保存至SD卡中;同时通过北斗模块将数据发送至岸基,岸基上位机可远程浏览浮标状态数据、水质数据以及位置信息。整个系统采用太阳能供电方式,以此完成从数据采集、数据保存到数据传输、数据处理。经测试,整个系统运行状况良好,可实现水质自动化监测,达到预期设计要求。     

基于无人机与计算机视觉的中国古建筑木结构裂缝监测系统设计

中国古建筑木结构中裂缝繁多,裂缝成因与发展规律复杂,易引起木结构构件脆断,从而严重威胁中国古建筑木结构健康情况。该文基于无人机与计算机视觉技术设计了一套适用于中国古建筑木结构裂缝的监测系统,该监测系统包含无人机系统、相机系统和图像处理系统。在无人机系统中,该文设计了一款适合于中国古建筑木结构裂缝监测的无人机,并分析其悬停拍照的可行性。在相机系统中,进行了相机畸变矫正、像素解析度标定,并提出了一种改进的SIFT+RANSAC方法以提高裂缝图像拼接精度。在图像处理系统中,选择了适用于中国古建筑木结构裂缝图像的预处理方式,并将Hessian矩阵与自适应阈值分割法融合,有效地提取了中国古建筑木结构裂缝特征,进而通过计算机视觉测量方法准确识别构件和裂缝的尺寸。最后,基于中国古建筑木结构亭子模型验证了所提出中国古建筑木结构裂缝监测系统的可行性。     

Behavioral Feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles

Undoubtedly, uncooperative or malicious nodes threaten the safety of Internet of Vehicles (IoV) by destroying routing or data. To this end, some researchers have designed some node detection mechanisms and trust calculating algorithms based on some different feature parameters of IoV such as communication, data, energy, etc., to detect and evaluate vehicle nodes. However, it is difficult to effectively assess the trust level of a vehicle node only by message forwarding, data consistency, and energy sufficiency. In order to resolve these problems, a novel mechanism and a new trust calculating model is proposed in this paper. First, the four tuple method is adopted, to qualitatively describing various types of nodes of IoV; Second, analyzing the behavioral features and correlation of various nodes based on route forwarding rate, data forwarding rate and physical location; third, designing double layer detection feature parameters with the ability to detect uncooperative nodes and malicious nodes; fourth, establishing a node correlative detection model with a double layer structure by combining the network layer and the perception layer. Accordingly, we conducted simulation experiments to verify the accuracy and time of this detection method under different speed-rate topological conditions of IoV. The results show that comparing with methods which only considers energy or communication parameters, the method proposed in this paper has obvious advantages in the detection of uncooperative and malicious nodes of IoV; especially, with the double detection feature parameters and node correlative detection model combined, detection accuracy is effectively improved, and the calculation time of node detection is largely reduced.     

Adaptive Runtime Monitoring of Service Level Agreement Violations in Cloud Computing

The cloud service level agreement (SLA) manage the relationship between service providers and consumers in cloud computing. SLA is an integral and critical part of modern era IT vendors and communication contracts. Due to low cost and flexibility more and more consumers delegate their tasks to cloud providers, the SLA emerges as a key aspect between the consumers and providers. Continuous monitoring of Quality of Service (QoS) attributes is required to implement SLAs because of the complex nature of cloud communication. Many other factors, such as user reliability, satisfaction, and penalty on violations are also taken into account. Currently, there is no such policy of cloud SLA monitoring to minimize SLA violations. In this work, we have proposed a cloud SLA monitoring policy by dividing a monitoring session into two parts, for critical and non-critical parameters. The critical and non-critical parameters will be decided on the interest of the consumer during SLA negotiation. This will help to shape a new comprehensive SLA based Proactive Resource Allocation Approach (RPAA) which will monitor SLA at runtime, analyze the SLA parameters and try to find the possibility of SLA violations. We also have implemented an adaptive system for allocating cloud IT resources based on SLA violations and detection. We have defined two main components of SLA-PRAA i.e., (a) Handler and (b) Accounting and Billing Manager. We have also described the function of both components through algorithms. The experimental results validate the performance of our proposed method in comparison with state-of-the-art cloud SLA policies.     

Optimisation of a set of or principal components control charts using genetic algorithms

When a multivariate process is to be monitored, there are the options of employing a set of univariate control charts or a single multivariate chart. This paper shows how to effectively design a multivariate control scheme consisting of two or three X charts, using genetic algorithms to optimise the charts parameters. The procedure is implemented using software tools, which we designed. A complete performance comparison of the scheme with the Hotelling's T ² control chart can be made in order to help the user in choosing the most adequate option for the process under consideration. Also, if the user prefers to employ charts based on principal components rather than on the original variables, the software can be used in the same way to optimise a set of two or three control charts based on these components, and to compare their performance with the performance of the T ² chart on the principal components.     

Automatic Fault Isolation by Cultural Algorithms With Differential Influence

An evolutionary algorithm with a cultural mechanism of evolution influence for effectiveness and efficiency higher than classical genetic algorithms is proposed for industrial fault isolation. Moreover, the evolution influence is based on a differential concept in order to move toward better zones of the solution space by sensing the fitness gradient. The proposed cultural algorithm is designed in order to be portable and easily configurable in different diagnostic applications. On-field results of an industrial application to motor-vehicle fleet remote monitoring and automatic fault isolation of vehicle wear, operating danger, and fraud in a company that transports dangerous goods are shown.