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Closed loop detection can alleviate the error accumulation during the operation of Visual Simultaneous Localization and Mapping (VSLAM) system, which is of great significance to the accuracy and robustness of the robot. Triplet loss based metric learning has been proposed for closed loop detection in this paper. Firstly, a triplet selection strategy has been constructed. The Softplus function is applied to triplet loss so that the loss in the negative axis will be soft margin. The adaptive margin has been proposed to maintain the mapping distribution in metric space. Metric learning converts keyframes into feature vectors, evaluating the similarity of keyframes by calculating the Euclidean distance between feature vectors, which is utilized to determine whether a closed loop is formed. Secondly, detection strategy of candidate keyframes for loop detection is introduced according to Euclidean distance. Finally, triplet loss based metric learning is applied to closed loop detection for VSLAM system. VSLAM datasets have been applied to evaluate the precision and recall of metric learning model, and then the established VSLAM system has been implemented in real-time application. The experimental results illustrate the feasibility and effectiveness of proposed method, which can be further applied to practical VSLAM systems. • A triplet selection strategy has been constructed to speed up the convergence. • The Softplus is applied so that the loss in the negative axis will be soft margin. • The margin constructed for different classes has been proposed. • Metric learning has been proposed for closed loop detection. [ABSTRACT FROM AUTHOR]

Abstract: An improved particle swarm optimizer using the notion of chaos and species is proposed for solving a template matching problem which is formulated as a multimodal optimization problem. Template matching is one of the image comparison techniques. This technique is widely applied to determine the existence, location and alignment of a component within a captured image in the printed circuit board (PCB) industry where 100% quality assurance is always required. In this research, an efficient auto detection method using a multiple templates matching technique for PCB components detection is described. The new approach using chaotic species based particle swarm optimization (SPSO) is applied to the multi-template matching (MTM) process. To test its performance, the proposed Chaotic SPSO based MTM algorithm is compared with other approaches by using real captured PCB images. The Chaotic SPSO based MTM method is proven to be superior to other methods in both efficiency and effectiveness. [Copyright &y& Elsevier]

Abstract: This paper describes an improved genetic algorithm (GA) using the notion of species in order to solve an embroidery inspection problem. This inspection problem is actually a multiple template matching problem which can be formulated as a multimodal optimization problem. In many cases, the run time of the multiple template matching problem is dominated by repeating the similarity calculations and moving the templates over the source image. To cope with this problem, the proposed species based genetic algorithm (SbGA) is capable to determine its neighborhood best values for solving multimodal optimization problems. The SbGA has been statistically tested and compared with other genetic algorithms on a number of benchmark functions. After proving its effectiveness, it is integrated with multi-template matching method, namely SbGA–MTM method to solve the embroidery inspection problem. Furthermore, the notion of bounded partial correlation (BPC) is also adopted as an acceleration strategy, which enhances the overall efficiency. Experimental results indicate that the SbGA–MTM method is proven to solve the inspection problem efficiently and effectively. With the proposed method, the embroidered patterns can be identified and checked automatically. [Copyright &y& Elsevier]

Abstract: Failure mode and effects analysis (FMEA) is a methodology to evaluate a system, design, process or service for possible ways in which failures (problems, errors, etc.) can occur. The two most important issues of FMEA are the acquirement of FMEA team members’ diversity opinions and the determination of risk priorities of the failure modes that have been identified. First, the FMEA team often demonstrates different opinions and knowledge from one team member to another and produces different types of assessment information because of its cross-functional and multidisciplinary nature. These different types of information are very hard to incorporate into the FMEA by the traditional model and fuzzy logic approach. Second, the traditional FMEA determines the risk priorities of failure modes using the risk priority numbers (RPNs) by multiplying the scores of the risk factors like the occurrence (O), severity (S) and detection (D) of each failure mode. The method has been criticized to have several shortcomings. In this paper, we present an FMEA using the fuzzy evidential reasoning (FER) approach and grey theory to solve the two problems and improve the effectiveness of the traditional FMEA. As is illustrated by the numerical example, the proposed FMEA can well capture FMEA team members’ diversity opinions and prioritize failure modes under different types of uncertainties. [ABSTRACT FROM AUTHOR]