Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram

Spiking neural networks (SNNs) are receiving increased attention as a means to develop "biologically plausible" machine learning models. These networks mimic synaptic connections in the human brain and produce spike trains, which can be approximated by binary values, precluding high computational cost with floating-point arithmetic circuits. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. In this paper, the feasibility of using a convolutional spiking neural network (CSNN) as a classifier to detect anticipatory slow cortical potentials related to braking intention in human participants using an electroencephalogram (EEG) was studied. The EEG data was collected during an experiment wherein participants operated a remote controlled vehicle on a testbed designed to simulate an urban environment. Participants were alerted to an incoming braking event via an audio countdown to elicit anticipatory potentials that were then measured using an EEG. The CSNN's performance was compared to a standard convolutional neural network (CNN) and three graph neural networks (GNNs) via 10-fold cross-validation. The results showed that the CSNN outperformed the other neural networks.Comment: 14 pages, 6 figures, Scientific Reports submissio

Analysis of Geometric Accuracy and Thickness Reduction in Multistage Incremental Sheet Forming using Digital Image Correlation

Incremental Sheet Forming (ISF) is a freeform manufacturing method whereby a 3D geometry is created by progressively deforming a metal sheet with a single point tool following a defined trajectory. The thickness distribution of a formed part is a major consideration of the process and is believed to be improved by forming the geometry in multiple stages. This paper describes a series of experiments in which truncated cone geometries were formed using two multistage methods and compared to the same geometry formed using the traditional single stage method. The geometric accuracy and thickness distributions, including 3D thickness distribution plots, of each are examined using digital image correlation (DIC). The data collected indicate that multistage forming, compared to single stage forming, has a significant effect on the geometric accuracy of the processed sheets. Moreover, the results of the experiments conducted in this paper show that sheets processed with multistage forming do not have a uniform sheet thickness reduction, rather they have a parabolic-like thickness distribution in the processed region

Iterative Learning Control of Single Point Incremental Sheet Forming Process using Digital Image Correlation

Single Point Incremental Sheet Forming (SPIF) is a versatile forming process that has gained significant traction over the past few decades. Its increased formability, quick part adaption, and reduced set-up costs make it an economical choice for small batch and rapid prototype forming applications when compared to traditional stamping processes. However, a common problem with the SPIF process is its tendency to produce high geometric error due to the lack of supporting dies and molds. While geometric error has been a primary focus of recent research, it is still significantly larger for SPIF than traditional forming processes. In this paper, the convergence behavior and the ability to reduce geometric error using a simple Iterative Learning Control (ILC) algorithm is studied with two different forming methods. For both methods a tool path for the desired reference geometry is generated and a part is formed. A Digital Image Correlation (DIC) system takes a measurement and the geometric error along the tool path is calculated. The ILC algorithm then uses the geometric error to alter the tool path for the next forming iteration. The first method, the Single Sheet Forming (SSF) method, performs each iteration on the same sheet. The second method, the Multi Sheet Forming (MSF) method, performs each iteration on a newly replaced sheet. Multiple experiments proved the capability of each method at reducing geometric error. It was concluded that using the MSF method allows for negative corrections to the forming part and, therefore, leads to better final part accuracy. However, this method is less cost effective and more time consuming than using the standard SSF methodology. In addition, it was found that in order to effectively correct a part with an ILC algorithm, steps must be taken to increase the controllability of the part geometry

Comparison of Design-Centric and Data-Centric Methods for Distributed Attack Detection in Cyber-Physical Systems

Cyber-physical systems are vulnerable to a variety of cyber, physical and cyber-physical attacks. The security of cyber-physical systems can be enhanced beyond what can be achieved through firewalls and trusted components by building trust from observed and/or expected behaviors. These behaviors can be encoded as invariants. Information flows that do not satisfy the invariants are used to identify and isolate malfunctioning devices and cyber intrusions. However, the distributed architectures of cyber-physical systems often contain multiple access points that are physically and/or digitally linked. Thus, invariants may be difficult to determine and/or computationally prohibitive to check in real time. Researchers have employed various methods for determining the invariants by analyzing the designs of and/or data generated by cyber-physical systems such as water treatment plants and electric power grids. This chapter compares the effectiveness of detecting attacks on a water treatment plant using design-centric invariants versus data-centric rules, the latter generated using a variety of data mining methods. The methods are compared based on the maximization of true positives and minimization of false positives