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This paper aims to develop a method to extract the weld seam from the welding image.

The initial step is to set the window for the region of the weld seam. Filter and edge‐operator are then applied to acquire edges of images. Based on the prior knowledge about characteristics of the weld seam, a series of routines is proposed to recognize the seam edges and calculate the seam representation.

The proposed method can be used to extract seams of different deviations from noise‐polluted images efficiently. Besides, the method is low time‐consuming and quick enough for real time processing.

Weld seam extraction is the key problem in passive vision based seam tracking technology. The proposed method can extract the weld seam even when the image is noisy, and it is quick enough to be applied in seam tracking technology. The method is expected to improve seam tracking results.

A useful method is developed for weld seam extraction from the noise‐polluted image based on prior knowledge of weld seam. The method is robust and quick enough for real time processing.

This paper aims to propose a robotic automation system for processing special-shaped thin-walled workpieces, which includes a measurement part and a processing part.

In the measurement part, to efficiently and accurately realize the three-dimensional camera hand-eye calibration based on a large amount of measurement data, this paper improves the traditional probabilistic method. To solve the problem of time-consuming in the extraction of point cloud features, this paper proposes a point cloud feature extraction method based on seed points. In the processing part, the authors design a new type of chamfering tool. During the process, the robot adopts admittance control to perform compensation according to the feedback of four sensors mounted on the tool.

Experiments show that the proposed system can make the tool smoothly fit the chamfered edge during processing and the machined chamfer meets the processing requirements of 0.5 × 0.5 to 0.9 × 0.9 mm².

The proposed design and approach can be applied on many types of special-shaped thin-walled parts. This will give a new solution for the automation integration problem in aerospace manufacturing.

A novel robotic automation system for processing special-shaped thin-walled workpieces is proposed and a new type of chamfering tool is designed. Furthermore, a more accurate probabilistic hand-eye calibration method and a more efficient point cloud extraction method are proposed, which are suitable for this system when comparing with the traditional methods.

The purpose of this paper is to propose a novel method of coaxial optical precision alignment based on surface roughness and reflectiveness matching.

The micro-assembly experiment system set-up was constructed according to the principle of the coaxial optical alignment. The coaxial optical alignment error is theoretically analyzed and calculated. When the prism orthogonal alignment mechanism produces the error of 0.001°, the theoretical deviation was less than 0.87 μm and the actual experimental micro-assembly platform assembly accuracy exceeded 3 μm. A peg-in-hole precise assembly of punching pin micro-assembly experiment was done in order to validate feasibility of this method.

The results indicate that coaxial optical precision alignment could be used for the assembly of complex micro-heterogeneous system which is integrated by similar devices, such as 3D complex micro-structures, silicon micro-electro-mechanical system (MEMS) devices and non-silicon MEMS devices with flat structure.

The paper provides certain methodological guidelines for MEMS for high precision automatic assembly of complex 3D micro-structures.

The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant. But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015. Thus, it is high time to explore a different approach to tackle this issue, keeping in view the characteristics and needs of the two fields. Processing at the Edge can boost applications with real-time deadlines while complementing security.

This review paper contributes towards three cardinal directions of research in the field of DL for IoT. The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices, forming the realm of the things for IoT. The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques. An appropriate combination of these techniques, including regularization, quantization, and pruning, can aid in building an effective compression pipeline for establishing DL models for IoT use-cases. The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.

DL models are growing deeper with every passing year. Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm. It is realized that a vertically partitioned compressed deep model can handle the trade-off between size, accuracy, communication overhead, bandwidth utilization, and latency but at the expense of an additionally considerable memory footprint. To reduce the memory budget, we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks. However, the critical point between accuracy and size for such models needs further investigation.

To the best of our knowledge, no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum. Besides covering techniques and frameworks that have tried to bring inference to the Edge, the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT. The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.

This paper seeks to describe the use of edge‐detection as an approach to support the process of analyzing visual scenes.

Edges‐detection is considered as a KM technique task. Several edge‐detection algorithms were discussed but Canny method was chosen in conjunction with JAVA to find the edges of an image.

The paper provides an overview of knowledge need by the visual scene analyst. It reviews the process of extracting knowledge from image data. Finally, the paper looks at the management aspects of image‐edge detection.

Experiments herein have proved the method robustness by considering edge‐detection as a knowledge management technique for visual scene analysis by deriving knowledge from semantic information based on object boundaries shadow.

The purpose of this paper is to describe work aimed to control the Al alloy welding penetration through the passive vision for welding robot.

First a passive vision system was established. The system can capture the Al alloy welding image. Based on the analysis of the characteristic of the welding image, the composite edge detectors were developed to recognize the shape of the weld seam and the weld pool. To realize the automatic control of the Al alloy‐weld process, the relation between the welding parameter and the quality of the weld appearance was established through the random welding experiment. The wire feed was chosen with PID controller adjusting the wire feed rate according to the weld gap variation.

This paper finds that the passive vision system can be captured the clear weld seam and weld pool image simultaneously. the method of composite edge detectors can be effectively and accurately recognize the weld seam edges. The wire feed rate controller ensured the welding robot to adjust the wire feed rate according to the gap variation.

This system has been applied to the industrial welding robot production.

The weld seam and weld pool image can be simultaneously captured by the passive vision system. The composite edge detectors have been developed for the passive vision method. The controller has been set up for Al alloy welding process based on the neural network.

The purpose of this paper is to explore the challenges faced by the automatic recognition systems over the conventional systems by implementing a novel approach for detecting and recognizing the vehicle license plates in order to increase the security of the vehicles. This will also increase the societal discipline among vehicle users.

From a methodological point of view, the proposed system works in three phases which includes the pre-processing of the input image from the database, applying segmentation to the processed image, and finally extracting and recognizing the image of the license plate.

The proposed paper provides an analysis that demonstrates the correctness of the algorithm to correctly capture the license plate using performance metrics such as detection rate and false positive rate. The obtained results demonstrate that the proposed algorithm detects vehicle license plates and provides detection rate of 93.34 percent with false positive rate of 6.65 percent.

The proposed license plate detection system eliminates the need of manually used systems for managing the traffic by installing the toll-booths on freeways and bridges. The design implemented in this paper attempts to capture the license plate by using three phase detection process that helps to increase the level of security and contribute in making a sustainable city.

This paper presents a distinctive approach to detect the license plate of the vehicles using the various image processing techniques such as dilation, grey-scale conversion, edge processing, etc. and finding the region of interest of the segmented image to capture the license plate of the vehicles.

The objective of the work is to analyze the impact of the Internet of Things (IoT) concepts and associated technologies in the framework of organizations and the management of their processes and how event orientation, as well as the structure of said business processes, can play an important role in this new organizational model. The main contribution of this work is to present a conceptualization of the research, identify approaches and challenges that require further study, and as a result, a proposal for future research.

The methodology comprises a qualitative analysis using secondary data. The approach relies on searches of scientific papers conducted in well-known databases, identifying research work around the IoT and Industry 4.0 applied to business process management. Based on the identified papers, the authors selected the most relevant and the latest publications, and categorized their contributions and findings based on open and selective coding. In total, the analysis is based on 95 papers that were selected and analyzed in depth.

The results of this research allow analyzing and ordering the existing contributions around Industry 4.0 and its impact on current organizations. The proposed conceptualization was derived from the analysis of the state of the research and identifies four categories: (1) improvements caused by Industry 4.0 and its impact on inter-organizational relations, (2) new architectural models and infrastructure of remote resources, their movement from the cloud to the edge and its effect on business processes, (3) context-aware concepts brought to business process management (BPM) linked to unstructured business processes and (4) complex event processing as a possible means for business processes sensitive to IoT signals.

The construction of current software ecosystems is strongly affected by the variety of information sources that feed them, as well as their volume. In addition, business processes represent organizations internally and are challenged to transcend the limits of companies due to the mentioned changes in software ecosystems. Industry 4.0 in conjunction with BPM re-defines the business process management paradigm and leads them to acquire the dynamism and sensitivity to the context that they usually did not have, as well as force them to move toward distributed platforms.

This paper assesses the state of the art in Industry 4.0 and business process management. The area can be defined as the intersection of two bigger areas highly relevant for organizations; on the one hand, the management and execution of business processes; and on the other hand, new conceptual, technological and methodological challenges to information systems that have to become more sensitive to event processing and also have to consume a large volume of data permanently and ubiquitously.

Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.

Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.

Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.

Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.

The use of lasers to selectively solder joints in electronic assemblies has a number of advantages over methods which involve heating of the whole assembly. However, the localised energy delivery means that the heating and melting behaviour of the solder is particularly dependent on external and process influences. This paper aims to propose a new approach to monitoring and feedback control of the melting process through image acquisition and processing.

In order to evaluate the proposed feedback strategy, a series of experiments have been performed using a semiconductor diode laser controlled by a PC, which also performs image acquisition and processing operations. Two main processing techniques, based on edge detection and Fourier analysis, have been evaluated.

It has been shown that the proposed technique is capable of controlling laser pulse duration to correct for variations in joint geometry, material parameters and laser energy delivery and results in more consistent solder joint formation than is achieved using fixed pulse durations.

The results demonstrate that image processing is a viable technique for the control of laser soldering processes. This could significantly increase the range of applications of laser soldering techniques where determination and control of pulse parameters have been one of the major challenges.

Although some work has been conducted on the use of pyrometer feedback to control laser processing, it appears that no work has been published on the use of image processing. While the processing algorithms themselves are not novel, this is a new application of these algorithms.