Design and Development of Artificial Intelligence (Al)-Based Desicion Support System For Manufacturing Applications

In this report, the research on welding defect detection and classification using radiograph images is presented. The first part of the report describes work on collection of digital radiograph images while the second part covers work on image processing and analysis using the collected images. The radiograph images from the Fuji DynamIX DynaView Workstation are custom-exported with the help of the NDT specialist. The collection of interpreted images is diverted from radiograph images captured using the old X-ray tube {Tube A) to the new X-ray tube (Tube B). Tube B images are needed to evaluate the performance of the developed defect detection algorithm under different radiography conditions. However, the total number of requested images remains approximately the same so that no extra workload is imposed to the NDT specialist. In the image processing stage, a flaw map, as described in the previous report, is used. Six welding defect types, namely Porosity{POR), Drop Through{DT), and Lack of Fusion{LOF), Lack of Penetration{LOP), Linear Indication{LI) and Undercut{UC), have been investigated. DT is detected using the background subtraction technique along with some heuristic rules as described in the previous report. For other detects, a set of image features including shape and texture information is extracted to characterize the welding defect flaws at the regions of interest (ROl). For POR, a series of sub-regions are further segmented in order to better represent the characteristics of POR at different locations in the ROl. To perform classification of the welding defects, an artificial intelligence (AI) technique, i.e., the Fuzzy ARTMAP (FAM) neural network, is applied. A series of experiments has been conducted by using the sample images collected from Tubes A and B. The overall performance is around 73% for accuracy, sensitivity, and specificity for both CF6-80 Connector Weld and Cover Weld programs. The only exception is that the sensitivity rate of the Connector Weld program stands around 63%. Further work will focus on ascertaining the stability of the FAM network in defect classification, as well as on improving the overall performance of the defect detection algorithms developed in this project. Il

Application Of The Fuzzy Min-Max Neural Networks To Medical Diagnosis.

Abstract. In this paper, the Fuzzy Min-Max (FMM) neural network along with two modified FMM models are used for tackling medical diagnostic problems. The original FMM network establishes hyperboxes with fuzzy sets in its structure for classifying input patterns into different output categories

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

In this research, we propose an enhanced Grey Wolf Optimizer (GWO) for designing the evolving Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) networks for time series analysis. To overcome the probability of stagnation at local optima and a slow convergence rate of the classical GWO algorithm, the newly proposed variant incorporates four distinctive search mechanisms. They comprise a nonlinear exploration scheme for dynamic search territory adjustment, a chaotic leadership dispatching strategy among the dominant wolves, a rectified spiral local exploitation action, as well as probability distribution-based leader enhancement. The evolving CNN-LSTM models are subsequently devised using the proposed GWO variant, where the network topology and learning hyperparameters are optimized for time series prediction and classification tasks. Evaluated using a number of benchmark problems, the proposed GWO-optimized CNN-LSTM models produce statistically significant results over those from several classical search methods and advanced GWO and Particle Swarm Optimization variants. Comparing with the baseline methods, the CNN-LSTM networks devised by the proposed GWO variant offer better representational capacities to not only capture the vital feature interactions, but also encapsulate the sophisticated dependencies in complex temporal contexts for undertaking time-series tasks

Intelligent human action recognition using an ensemble model of evolving deep networks with swarm-based optimization.

Automatic interpretation of human actions from realistic videos attracts increasing research attention owing to its growing demand in real-world deployments such as biometrics, intelligent robotics, and surveillance. In this research, we propose an ensemble model of evolving deep networks comprising Convolutional Neural Networks (CNNs) and bidirectional Long Short-Term Memory (BLSTM) networks for human action recognition. A swarm intelligence (SI)-based algorithm is also proposed for identifying the optimal hyper-parameters of the deep networks. The SI algorithm plays a crucial role for determining the BLSTM network and learning configurations such as the learning and dropout rates and the number of hidden neurons, in order to establish effective deep features that accurately represent the temporal dynamics of human actions. The proposed SI algorithm incorporates hybrid crossover operators implemented by sine, cosine, and tanh functions for multiple elite offspring signal generation, as well as geometric search coefficients extracted from a three-dimensional super-ellipse surface. Moreover, it employs a versatile search process led by the yielded promising offspring solutions to overcome stagnation. Diverse CNN–BLSTM networks with distinctive hyper-parameter settings are devised. An ensemble model is subsequently constructed by aggregating a set of three optimized CNN–BLSTM​ networks based on the average prediction probabilities. Evaluated using several publicly available human action data sets, our evolving ensemble deep networks illustrate statistically significant superiority over those with default and optimal settings identified by other search methods. The proposed SI algorithm also shows great superiority over several other methods for solving diverse high-dimensional unimodal and multimodal optimization functions with artificial landscapes

Periodic event-triggered output regulation for linear multi-agent systems

This study considers the problem of periodic event-triggered (PET) cooperative output regulation for a class of linear multi-agent systems. The advantage of the PET output regulation is that the data transmission and triggered condition are only needed to be monitored at discrete sampling instants. It is assumed that only a small number of agents can have access to the system matrix and states of the leader. Meanwhile, the PET mechanism is considered not only in the communication between various agents, but also in the sensor-to-controller and controller-to-actuator transmission channels for each agent. The above problem set-up will bring some challenges to the controller design and stability analysis. Based on a novel PET distributed observer, a PET dynamic output feedback control method is developed for each follower. Compared with the existing works, our method can naturally exclude the Zeno behavior, and the inter-event time becomes multiples of the sampling period. Furthermore, for every follower, the minimum inter-event time can be determined \textit{a prior}, and computed directly without the knowledge of the leader information. An example is given to verify and illustrate the effectiveness of the new design scheme.Comment: 17 pages, 13 figures, submitted to Automatica. accepte

Development Of An Automated Inspection System For Welding Defect Detection.

In industrial radiograph inspection, it is an important step to extract welding region from the radiograph image to avoid processing of complex background

A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming, following and random behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the family of AFSA, encompassing the original ASFA and its improvements, continuous, binary, discrete, and hybrid models, as well as the associated applications. A comprehensive survey on the AFSA from its introduction to 2012 can be found in [1]. As such, we focus on a total of {\color{blue}123} articles published in high-quality journals since 2013. We also discuss possible AFSA enhancements and highlight future research directions for the family of AFSA-based models.Comment: 37 pages, 3 figure