Comparing Statistical Feature and Artificial Neural Networks for Control Chart Pattern Recognition: A Case Study

Control chart has been widely used for monitoring production process, especially in evaluating the quality performance of a product. An uncontrolled process is usually known by recognizing its chart pattern, and then performing some actions to overcome the problems. In high speed production process, real-time data is recorded and plotted almost automatically, and the control chart pattern needs to be recognized immediately for detecting any unusual process behavior. Neural networks for automatic control chart recognition have been studied in detecting its pattern. In the field of computer science, the performance of its automatic and fast recognition ability can be a substitution for a conventional method by human. Some researchers even have developed newer algorithm to increase the recognition process of this neural networks control chart. However, artificial approaches have some difficulties in implementation, especially due to its sophisticated programming algorithm. Another competing method, based on statistical feature also has been considered in recognition process. Control chart is related to applied statistical method, so it is not unreasonable if statistical properties are developed for its pattern recognition. Correlation coefficient, one of classic statistical features, can be applied in control chart recognition. It is a simpler approach than the artificial one. In this paper, the comparison between these two methods starts by evaluating the behavior of control chart time series point, and measured for its closeness to some training data that are generated by simulation and followed some unusual control chart pattern. For both methods, the performance is evaluated by comparing their ability in detecting the pattern of generated control chart points. As a sophisticated method, neural networks give better recognition ability. The statistical features method simply calculate the correlation coefficient, even with small differences in recognizing the generated pattern compared to neural networks, but provides easy interpretation to justify the unusual control chart pattern. Both methods are then applied in a case study and performances are then measured

A Sensor Fusion Horse Gait Classification by a Spiking Neural Network on SpiNNaker

The study and monitoring of the behavior of wildlife has always been a subject of great interest. Although many systems can track animal positions using GPS systems, the behavior classification is not a common task. For this work, a multi-sensory wearable device has been designed and implemented to be used in the Doñana National Park in order to control and monitor wild and semiwild life animals. The data obtained with these sensors is processed using a Spiking Neural Network (SNN), with Address-Event-Representation (AER) coding, and it is classified between some fixed activity behaviors. This works presents the full infrastructure deployed in Doñana to collect the data, the wearable device, the SNN implementation in SpiNNaker and the classification results.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130

Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results

This paper focuses on current control in a permanentmagnet synchronous motor (PMSM). The paper has two main objectives: The first objective is to develop a neural-network (NN) vector controller to overcome the decoupling inaccuracy problem associated with conventional PI-based vector-control methods. The NN is developed using the full dynamic equation of a PMSM, and trained to implement optimal control based on approximate dynamic programming. The second objective is to evaluate the robust and adaptive performance of the NN controller against that of the conventional standard vector controller under motor parameter variation and dynamic control conditions by (a) simulating the behavior of a PMSM typically used in realistic electric vehicle applications and (b) building an experimental system for hardware validation as well as combined hardware and simulation evaluation. The results demonstrate that the NN controller outperforms conventional vector controllers in both simulation and hardware implementation

How exerting control over outcomes affects the neural coding of tasks and outcomes

Humans make choices every day, which are often intended to lead to desirable outcomes. While we often have some degree of control over the outcomes of our actions, in many cases this control remains limited. Here, we investigate the effect of control over outcomes on the neural correlates of outcome valuation and implementation of behavior, as desired outcomes can only be reached if choices are implemented as intended. In a value-based decision-making task, reward outcomes were either contingent on trial-by-trial choices between two different tasks, or were unrelated to these choices. Using fMRI, multivariate pattern analysis, and model-based neuroscience methods, we identified reward representations in a large network including the striatum, dorso-medial prefrontal cortex (dmPFC) and parietal cortex. These representations were amplified when rewards were contingent on subjects’ choices. We further assessed the implementation of chosen tasks by identifying brain regions encoding tasks during a preparation or maintenance phase, and found them to be encoded in the dmPFC and parietal cortex. Importantly, outcome contingency did not affect neural coding of chosen tasks. This suggests that controlling choice outcomes selectively affects the neural coding of these outcomes, but has no effect on the means to reach them. Overall, our findings highlight the role of the dmPFC and parietal cortex in processing of value-related and task-related information, linking motivational and control-related processes in the brain. These findings inform current debates on the neural basis of motivational and cognitive control, as well as their interaction

Social Influence on Self -Control

As Duckworth and Kern (2011) note, currently over 1% of the abstracts in PsycInfo are indexed by self-control or one its synonyms. As part of this widespread interest, cognitive and neural scientists are debating the psychological mechanisms of self-control (Ainslie, 1975; Metcalfe & Mischel, 1999; Muraven & Baumeister, 2000), and the implementation of these mechanisms in the brain (Figner, et al., 2010; Hare, Camerer, & Rangel, 2009; Hare, Malmaud, & Rangel, 2011; Kable & Glimcher, 2007, 2010; McClure, Ericson, Laibson, Loewenstein, & Cohen, 2007; McClure, Laibson, Loewenstein, & Cohen, 2004). These efforts, however, currently proceed without much agreement on a theoretical or operational definition regarding what constitutes self-control (Duckworth & Kern, 2011). Definitions have been offered, of course, but one gets the sense that many investigators are content defining self-control in much the same manner that American courts define pornography - I know it when I see it (Jacobellis vs Ohio, 1964). Just as our intuitions regarding physics can be mistaken, so too can our intuitions regarding psychology (Stanovich, 1985). This essay argues that an over-reliance on intuitive psychics is hindering efforts to identify the cognitive and neural processes involved in self-control. Specifically, current theories tend to underemphasize or ignore completely a factor of critical importance – the social world. Yet, self-control is a concept that only emerges at the level of the person in society: it is the social world that defines what is and is not a self-control problem. This realization has important implications for people interested in cognitive and neural mechanisms: it suggests that self-control is unlikely to be a single process; that the computation of social norms is an understudied process that is likely critical for self-controlled behavior; and that interventions that target the social context to increase the influence of norms may prove the strongest way to increase self-controlled behavior

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

Application of Biological Learning Theories to Mobile Robot Avoidance and Approach Behaviors

We present a neural network that learns to control approach and avoidance behaviors in a mobile robot using the mechanisms of classical and operant conditioning. Learning, which requires no supervision, takes place as the robot moves around an environment cluttered with obstacles and light sources. The neural network requires no knowledge of the geometry of the robot or of the quality, number or configuration of the robot's sensors. In this article we provide a detailed presentation of the model, and show our results with the Khepera and Pioneer 1 mobile robots.Office of Naval Research (N00014-96-1-0772, N00014-95-1-0409

Hardware design of LIF with Latency neuron model with memristive STDP synapses

In this paper, the hardware implementation of a neuromorphic system is presented. This system is composed of a Leaky Integrate-and-Fire with Latency (LIFL) neuron and a Spike-Timing Dependent Plasticity (STDP) synapse. LIFL neuron model allows to encode more information than the common Integrate-and-Fire models, typically considered for neuromorphic implementations. In our system LIFL neuron is implemented using CMOS circuits while memristor is used for the implementation of the STDP synapse. A description of the entire circuit is provided. Finally, the capabilities of the proposed architecture have been evaluated by simulating a motif composed of three neurons and two synapses. The simulation results confirm the validity of the proposed system and its suitability for the design of more complex spiking neural network