scholarly journals An emergent group mind across a swarm of robots: Collective cognition and distributed sensing via a shared wireless neural network

2018 ◽  
Vol 37 (9) ◽  
pp. 1017-1061 ◽  
Author(s):  
Michael Otte
Keyword(s):  
Neural Network ◽  
Collective Behavior ◽  
Sensor Data ◽  
Collective Decision Making ◽  
Environmental Feature ◽  
Distributed Sensor ◽  
Neural Connections ◽  
Group Mind ◽  
Robotic Swarm ◽  
Feature Pattern

We pose the “trained-at-runtime heterogeneous swarm response problem,” in which a swarm of robots must do the following three things: (1) Learn to differentiate between multiple classes of environmental feature patterns (where the feature patterns are distributively sensed across all robots in the swarm). (2) Perform the particular collective behavior that is the appropriate response to the feature pattern that the swarm recognizes in the environment at runtime (where a collective behavior is defined by a mapping of robot actions to robots). (3) The data required for both (1) and (2) is uploaded to the swarm after it has been deployed, i.e., also at runtime (the data required for (1) is the specific environmental feature patterns that the swarm should learn to differentiate between, and the data required for (2) is the mapping from feature classes to swarm behaviors). To solve this problem, we propose a new form of emergent distributed neural network that we call an “artificial group mind.” The group mind transforms a robotic swarm into a single meta-computer that can be programmed at runtime. In particular, the swarm-spanning artificial neural network emerges as each robot maintains a slice of neurons and forms wireless neural connections between its neurons and those on nearby robots. The nearby robots are discovered at runtime. Experiments on real swarms containing up to 316 robots demonstrate that the group mind enables collective decision-making based on distributed sensor data, and solves the trained-at-runtime heterogeneous swarm response problem. The group mind is a new tool that can be used to create more complex emergent swarm behaviors. The group mind also enables swarm behaviors to be a function of global patterns observed across the environment—where the patterns are orders of magnitude larger than the robots themselves.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

scholarly journals Research on Distributed 5G Signal Coverage Detection Algorithm Based on PSO-BP-Kriging

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4390
Author(s):  
Tingli Xiang ◽  
Hongjun Wang
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Mobile Communication ◽  
Detection Algorithm ◽  
Base Station ◽  
Distributed Sensor ◽  
Mobile Communication Network ◽  
The Neural Network ◽  
Signal Coverage ◽  
5G Mobile Communication

In order to overcome the limitations of traditional road test methods in 5G mobile communication network signal coverage detection, a signal coverage detection algorithm based on distributed sensor network for 5G mobile communication network is proposed. First, the received signal strength of the communication base station is collected and pre-processed by randomly deploying distributed sensor nodes. Then, the neural network objective function is modified by using the variogram function, and the initial weight coefficient of the neural network is optimized by using the improved particle swarm optimization algorithm. Next, the trained network model is used to interpolate the perceptual blind zone. Finally, the sensor node sampling data and the interpolation estimation result are combined to generate an effective coverage of the 5G mobile communication network signal. Simulation results indicate that the proposed algorithm can detect the real situation of 5G mobile communication network signal coverage better than other algorithms, and has certain feasibility and application prospects.

scholarly journals Social Touch Gesture Recognition Using Convolutional Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Saad Albawi ◽  
Oguz Bayat ◽  
Saad Al-Azawi ◽  
Osman N. Ucan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gesture Recognition ◽  
Classification Accuracy ◽  
Recognition System ◽  
Human Robot Interaction ◽  
Sensor Data ◽  
Social Touch ◽  
Minimum Number ◽  
Original Frame

Recently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implement a social touch recognition system for raw input samples (sensor data) only. The touch gesture recognition is performed using a dataset previously measured with numerous subjects that perform varying social gestures. This dataset is dubbed as the corpus of social touch, where touch was performed on a mannequin arm. A leave-one-subject-out cross-validation method is used to evaluate system performance. The proposed method can recognize gestures in nearly real time after acquiring a minimum number of frames (the average range of frame length was from 0.2% to 4.19% from the original frame lengths) with a classification accuracy of 63.7%. The achieved classification accuracy is competitive in terms of the performance of existing algorithms. Furthermore, the proposed system outperforms other classification algorithms in terms of classification ratio and touch recognition time without data preprocessing for the same dataset.

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