MOTION DETECTION AND DIRECTION DETECTION IN LOCAL NEURAL NETS

1989 ◽  
Vol 01 (02) ◽  
pp. 187-192 ◽  
Author(s):  
H.-U. Bauer ◽  
T. Geisel
Keyword(s):  
Motion Detection ◽  
Pulse Velocity ◽  
Neural Nets ◽  
Global Network ◽  
Training Set ◽  
Backpropagation Algorithm ◽  
Short Term ◽  
Memory Time ◽  
Direction Detection ◽  
Order Of Magnitude

We present a model for motion and direction detection of moving pulses whose performance is independent of pulse velocity, size and shape. The input signal activates one row of instantaneous nodes and one row of time integrating input nodes acting as short-term memories. Motion detection is achieved locally by subnetworks which are trained with a synthetic training set using the backpropagation algorithm. The global network is constructed from these subnetworks, one for each position. We test its performance with different pulse shapes and sizes and find the response to be invariant in a window of pulse velocities an order of magnitude wide. The window can be shifted by adjusting the memory time of the input nodes.

scholarly journals Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks

2011 ◽  
Vol 6 (1) ◽  
pp. 55-58 ◽  
Author(s):  
C. Gallego ◽  
A. Costa ◽  
A. Cuerva
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Regime Switching ◽  
Neural Nets ◽  
Ann Model ◽  
Short Term ◽  
Explanatory Variables ◽  
Ramp Events ◽  
Artificial Neural ◽  
Short Term Forecasting

Abstract. Ramp events are large rapid variations within wind power time series. Ramp forecasting can benefit from specific strategies so as to particularly take into account these shifts in the wind power output dynamic. In the short-term context (characterized by prediction horizons from minutes to a few days), a Regime-Switching (RS) model based on Artificial Neural Nets (ANN) is proposed. The objective is to identify three regimes in the wind power time series: Ramp-up, Ramp-down and No-ramp regime. An on-line regime assessment methodology is also proposed, based on a local gradient criterion. The RS-ANN model is compared to a single-ANN model (without regime discrimination), concluding that the regime-switching strategy leads to significant improvements for one-hour ahead forecasts, mainly due to the improvements obtained during ramp-up events. Including other explanatory variables (NWP outputs, local measurements) during the regime assessment could eventually improve forecasts for further horizons.

scholarly journals The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yinping Gao ◽  
Daofang Chang ◽  
Ting Fang ◽  
Yiqun Fan
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Arima Model ◽  
Training Set ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Box Plot

The effective forecast of container volumes can provide decision support for port scheduling and operating. In this work, by deep learning the historical dataset, the long short-term memory (LSTM) recurrent neural network (RNN) is used to predict daily volumes of containers which will enter the storage yard. The raw dataset of daily container volumes in a certain port is chosen as the training set and preprocessed with box plot. Then the LSTM model is established with Python and Tensorflow framework. The comparison between LSTM and other prediction methods like ARIMA model and BP neural network is also provided in this study, and the prediction gap of LSTM is lower than other methods. It is promising that the proposed LSTM is helpful to predict the daily volumes of containers.

Uptake of free amino acids by bacteria-free larvae of the sand dollar Dendraster excentricus

1984 ◽  
Vol 247 (4) ◽  
pp. R733-R739 ◽  
Author(s):  
J. P. Davis ◽  
G. C. Stephens
Keyword(s):  
Amino Acids ◽  
High Performance ◽  
Short Term ◽  
Entry Rate ◽  
Significant Difference ◽  
Net Uptake ◽  
Order Of Magnitude ◽  
Aseptic Conditions ◽  
Dendraster Excentricus ◽  
Production Of Ammonia

Larvae of Dendraster excentricus were produced by collecting gametes and carrying out fertilization under aseptic conditions. Since gametes are free of bacteria in the gonad, bacteria-free (axenic) suspensions of larvae result. Net rates of entry of 14 amino acids and the rate of production of ammonia were simultaneously determined by high-performance liquid chromatography. The net rates of uptake of neutral amino acids were an order of magnitude greater than rates for basic and acidic amino acids. Influx of 14C-labeled leucine, arginine, and glutamate accurately reflects the net entry rate of these substrates. Uptake of amino acids by axenic suspensions of larvae was compared with uptake by suspensions prepared without aseptic precautions. There was no significant difference in net uptake of the 14 amino acids or in the pattern of oxidation and assimilation of [14C]leucine during short-term experiments of 4-h duration or less.

Neuromodulation of Reactive Sensorimotor Mappings as a Short-Term Memory Mechanism in Delayed Response Tasks

2002 ◽  
Vol 10 (3-4) ◽  
pp. 185-199 ◽  
Author(s):  
Tom Ziemke ◽  
Mikael Thieme
Keyword(s):  
Short Term Memory ◽  
Neural Nets ◽  
Memory Representation ◽  
Delayed Response ◽  
Short Term ◽  
Term Memory ◽  
Memory Mechanism ◽  
Control Networks ◽  
Behavioral Dynamics ◽  
Sensorimotor Mechanisms

This article addresses the relation between memory, representation, and adaptive behavior. More specifically, it demonstrates and discusses the use of synaptic plasticity, realized through neuromodulation of sensorimotor mappings, as a short-term memory mechanism in delayed response tasks. A number of experiments with extended sequential cascaded networks, that is, higher-order recurrent neural nets, controlling simple robotic agents in six different delayed response tasks are presented. The focus of the analysis is on how short-term memory is realized in such control networks through the dynamic modulation of sensorimotor mappings (rather than through feedback of neuronal activation, as in conventional recurrent nets), and how these internal dynamics interact with environmental/behavioral dynamics. In particular, it is demonstrated in the analysis of the last experimental scenario how this type of network can make very selective use of feedback/memory, while as far as possible limiting itself to the use of reactive sensorimotor mechanisms and occasional switches between them.

Neural Networks as Classification Mechanisms of Complex Human Activities

2020 ◽  
Vol 29 (05) ◽  
pp. 2050011
Author(s):  
Anargyros Angeleas ◽  
Nikolaos Bourbakis
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Neural Nets ◽  
Quality Of Data ◽  
Short Term ◽  
Video Frames ◽  
Data Engineering ◽  
Formal Framework

Within this paper, we present two neural nets for view-independent complex human activity recognition (HAR) from video frames. For our study here, we reduce the number of frames produced by a video sequence given that we can identify activities from a sparsely sampled sequence of body poses, and, at the same time, we are able to reduce the processing complexity and response while hardly affecting the accuracy, precision, and recall. To do so, we use a formal framework to ensure the quality of data collection and data preprocessing. We utilize neural networks for the classification of single and complex body activities. More specifically, we consider the sequence of body poses as a time-series problem given that they can provide state-of-the-art results on challenging recognition tasks with little data engineering. Deep Learning in the form of Convolutional Neural Network (CNN), Long Short-Term Neural Network (LSTM), and a one-dimensional Convolutional Neural Network Long Short-Term Memory model (CNN-LSTM) are used as benchmarks to classify the activity.

scholarly journals Short-Term and Long-Term Surface Soil Moisture Memory Time Scales Are Spatially Anticorrelated at Global Scales

2019 ◽  
Vol 20 (6) ◽  
pp. 1165-1182 ◽  
Author(s):  
Kaighin A. McColl ◽  
Qing He ◽  
Hui Lu ◽  
Dara Entekhabi
Keyword(s):  
Soil Moisture ◽  
Time Scales ◽  
Time Scale ◽  
Surface Soil ◽  
Satellite Observations ◽  
Surface Soil Moisture ◽  
Short Term ◽  
Term Memory ◽  
Memory Time

Abstract Land–atmosphere feedbacks occurring on daily to weekly time scales can magnify the intensity and duration of extreme weather events, such as droughts, heat waves, and convective storms. For such feedbacks to occur, the coupled land–atmosphere system must exhibit sufficient memory of soil moisture anomalies associated with the extreme event. The soil moisture autocorrelation e-folding time scale has been used previously to estimate soil moisture memory. However, the theoretical basis for this metric (i.e., that the land water budget is reasonably approximated by a red noise process) does not apply at finer spatial and temporal resolutions relevant to modern satellite observations and models. In this study, two memory time scale metrics are introduced that are relevant to modern satellite observations and models: the “long-term memory” τL and the “short-term memory” τS. Short- and long-term surface soil moisture (SSM) memory time scales are spatially anticorrelated at global scales in both a model and satellite observations, suggesting hot spots of land–atmosphere coupling will be located in different regions, depending on the time scale of the feedback. Furthermore, the spatial anticorrelation between τS and τL demonstrates the importance of characterizing these memory time scales separately, rather than mixing them as in previous studies.

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