Traffic characterization in a communications channel for monitoring and control in real-time systems

The response time for remote monitoring and control in real-time systems is a sensitive issue in device interconnection elements. Therefore, it is necessary to analyze the traffic of the communication system in pre-established time windows. In this paper, a methodology based on computational intelligence is proposed for identifying the availability of a data channel and the variables or characteristics that affect the performance and data transfer, which is made up of four stages: a) integration of a communication system with an acquisition module and a final control structure; b) communication channel characterization by means of traffic variables; and c) relevance analysis from the characterization space using SFFS (sequential forward oating selection); d) Channel congestion classification as Low or High using a classifier based on Naive Bayes algorithm. The experimental setup emulates a real process using an on/off remote control of a DC motor on an Ethernet network. The communication time between the client and server was integrated with the operation and control times, to study the whole response time. This proposed approach allows support decisions about channel availability, to establish predictions about the length of the time window when the availability conditions are unknown

An Efficient Hardware-Oriented Dropout Algorithm

This paper proposes a hardware-oriented dropout algorithm, which is efficient for field programmable gate array (FPGA) implementation. In deep neural networks (DNNs), overfitting occurs when networks are overtrained and adapt too well to training data. Consequently, they fail in predicting unseen data used as test data. Dropout is a common technique that is often applied in DNNs to overcome this problem. In general, implementing such training algorithms of DNNs in embedded systems is difficult due to power and memory constraints. Training DNNs is power-, time-, and memory- intensive; however, embedded systems require low power consumption and real-time processing. An FPGA is suitable for embedded systems for its parallel processing characteristic and low operating power; however, due to its limited memory and different architecture, it is difficult to apply general neural network algorithms. Therefore, we propose a hardware-oriented dropout algorithm that can effectively utilize the characteristics of an FPGA with less memory required. Software program verification demonstrates that the performance of the proposed method is identical to that of conventional dropout, and hardware synthesis demonstrates that it results in significant resource reduction

Automated Fiber Placement: A Review of History, Current Technologies, and Future Paths Forward

Automated fiber placement (AFP) is a composite manufacturing technique used to fabricate complex advanced air vehicle structures that are lightweight with superior qualities. The AFP process is intricate and complex with various phases of design, process planning, manufacturing, and inspection. An understanding of each of these phases is necessary to achieve the highest possible manufacturing quality. This literature review aims to summarize the entire AFP process from the design of the structure through inspection of the manufactured part to generate an overall understanding of the lifecycle of AFP manufacturing. The review culminates with highlighting the challenges and future directions for AFP with the goal of achieving a closed loop AFP process

Machine Learning Methods for Rapid Inspection of Automated Fiber Placement Manufactured Composite Structures

The advanced manufacturing capabilities provided through the automated fiber placement (AFP) system has allowed for faster layup time and more consistent production across a number of different geometries. This contributes to the modern production of large composite structures and the widespread adaptation of composites in industry in general and aerospace in particular. However, the automation introduced in this process increases the difficulty of quality assurance efforts and inspection. The AFP process can induce a number of manufacturing defects including wrinkles, twists, gaps, and overlaps. The manual identification of these defects is often laborious and requires a measure of expert knowledge. A software package for the assistance of the inspection process has been used in conjunction with automated inspection hardware for the automated inspection, identification, and characterization of AFP manufacturing defects. Image analysis algorithms were developed and demonstrated on a number of defect types. Defects are identified in scan images and exact size and shape characteristics are extracted for export

Throughput optimizations for FPGA-based deep neural network inference

Deep neural networks are an extremely successful and widely used technique for various pattern recognition and machine learning tasks. Due to power and resource constraints, these computationally intensive networks are difficult to implement in embedded systems. Yet, the number of applications that can benefit from the mentioned possibilities is rapidly rising. In this paper, we propose novel architectures for the inference of previously learned and arbitrary deep neural networks on FPGA-based SoCs that are able to overcome these limitations. Our key contributions include the reuse of previously transferred weight matrices across multiple input samples, which we refer to as batch processing, and the usage of compressed weight matrices, also known as pruning. An extensive evaluation of these optimizations is presented. Both techniques allow a significant mitigation of data transfers and speed-up the network inference by one order of magnitude. At the same time, we surpass the data throughput of fully-featured x86-based systems while only using a fraction of their energy consumption

Hybrid Theory-Machine Learning Methods for the Prediction of AFP Layup Quality

The advanced manufacturing capabilities provided through the automated fiber placement (AFP) system has allowed for faster layup time and more consistent production across a number of different geometries. This contributes to the modern production of large composite structures and the widespread adaptation of composites in industry in general and aerospace in particular. However, the automation introduced in this process increases the difficulty of quality assurance efforts. Industry available tools for predicting layup quality are either limited in scope, or have extremely high computational overhead. With the advent of automated inspection systems, direct capture of semantic inspection data, and therefore complete quality data, becomes available. It is therefore the aim of this document to explore and develop a technique to combine semantic inspection data and incomplete but fast physical modeling tool into a comprehensive hybridized model for predicting and optimizing AFP layup quality. To accomplish this, a novel parameterization of Gaussian Process Regression is developed such that nominal behavior is dictated through theory and analytic models, with latent variables being accounted for in the stochastic aspect of the model. Coupled with a unique clustering approach for data representation, it is the aim of this model to improve on the current state of the art in quality prediction as well as provide a direct path to process parameter optimization