scholarly journals Deriving equations from sensor data using dimensional function synthesis

2021 ◽  
Vol 64 (7) ◽  
pp. 91-99
Author(s):  
Vasileios Tsoutsouras ◽  
Sam Willis ◽  
Phillip Stanley-Marbell
Keyword(s):  
Data Streams ◽  
Laboratory Experiments ◽  
Network Models ◽  
Sensor Data ◽  
Neural Network Models ◽  
Physical Systems ◽  
Computing Platform ◽  
Comparable Accuracy ◽  
Two Phases ◽  
The Relationship

We present a new method for deriving functions that model the relationship between multiple signals in a physical system. The method, which we call dimensional function synthesis , applies to data streams where the dimensions of the signals (e.g., length, mass, etc.) are known. The method comprises two phases: a compile-time synthesis phase and a subsequent calibration using sensor data. We implement dimensional function synthesis and use the implementation to demonstrate efficiently summarizing multimodal sensor data for two physical systems using 90 laboratory experiments and 10,000 synthetic idealized measurements. The results show that our technique can generate models in less than 300 ms on average across all the physical systems we evaluated. This is a marked improvement when compared to an average of 16 s for training neural networks of comparable accuracy on the same computing platform. When calibrated with sensor data, our models outperform traditional regression and neural network models in inference accuracy in all the cases we evaluated. In addition, our models perform better in training latency (up to 1096X improvement) and required arithmetic operations in inference (up to 34X improvement). These significant gains are largely the result of exploiting information on the physics of signals that has hitherto been ignored.

scholarly journals Mobile Health App for Adolescents: Motion Sensor Data and Deep Learning Technique to Examine the Relationship between Obesity and Walking Patterns

2022 ◽  
Vol 12 (2) ◽  
pp. 850
Author(s):  
Sungchul Lee ◽  
Eunmin Hwang ◽  
Yanghee Kim ◽  
Fatih Demir ◽  
Hyunhwa Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Body ◽  
Network Models ◽  
Sensor Data ◽  
Motion Sensors ◽  
Neural Network Models ◽  
Prevalence Of Obesity ◽  
The Relationship ◽  
Potential Use

With the prevalence of obesity in adolescents, and its long-term influence on their overall health, there is a large body of research exploring better ways to reduce the rate of obesity. A traditional way of maintaining an adequate body mass index (BMI), calculated by measuring the weight and height of an individual, is no longer enough, and we are in need of a better health care tool. Therefore, the current research proposes an easier method that offers instant and real-time feedback to the users from the data collected from the motion sensors of a smartphone. The study utilized the mHealth application to identify participants presenting the walking movements of the high BMI group. Using the feedforward deep learning models and convolutional neural network models, the study was able to distinguish the walking movements between nonobese and obese groups, at a rate of 90.5%. The research highlights the potential use of smartphones and suggests the mHealth application as a way to monitor individual health.

A Multi-Scale Modeling Method Based on Big Data

2015 ◽  
Vol 734 ◽  
pp. 447-450 ◽  
Author(s):  
Ji Wei Liu
Keyword(s):  
Big Data ◽  
Network Models ◽  
Modeling Method ◽  
Scale Model ◽  
Neural Network Models ◽  
Multi Scale ◽  
Scale Modeling ◽  
Input And Output ◽  
Multi Scale Modeling ◽  
The Relationship

A multi-scale modeling method based on big data was proposed to establish neural network models for complex plant. Wavelet transform was used to decompose input and output parameters into different scales. The relationship between these parameters were researched in every scale. Then models in each scale were established and added together to form a multi-scale model. A model of coal mill current in power plant was established using the multi-scale modeling method based on big data. The result shows that, the method is effective.

Prediction of Characteristics of Wave Breaking in Shallow Water Using Neural Network Techniques

2017 ◽  
Author(s):  
Nicholas Kouvaras ◽  
Manhar R. Dhanak
Keyword(s):  
Neural Network ◽  
Wave Height ◽  
Predictive Models ◽  
Wave Breaking ◽  
Laboratory Experiments ◽  
Network Models ◽  
Classification Model ◽  
Neural Network Models ◽  
Wave Setup ◽  
Fringing Reef

The characteristics of wave breaking over a fringing reef are considered using a set of laboratory experiments and the results are used to develop associated predictive models. Various methods are typically used to estimate the characteristics of nearshore wave breaking, mostly based on empirical, analytical and numerical techniques. Deo et al. (2003) used an artificial neural network approach to predict the breaking wave height and breaking depth for waves transforming over a range of simply sloped bottoms. The approach is based on using available representative data to train appropriate neural network models. The Deo et al. (2003) approach is extended here to predict other characteristics of wave breaking, including the type of wave breaking, and the position of breaking over a fringing reef, as well as the associated wave setup, and the rate of dissipation of wave energy, based on observations from a series of laboratory experiments involving monochromatic waves impacting on an idealized reef. Yao et al. (2013) showed that for such geometry, the critical parameter is the ratio of deep-water wave height to the depth of the shallow reef flat downstream of the position of wave breaking, H1/hs, rather than the slope of the reef. H1/hs, and the wave frequency parameter, fH1/g, are provided as inputs to the neural network models of the feed-forward type that are developed to predict the above characteristics of wave breaking. The models are trained using the experimental data. The breaker type classification model has a success rate of over 95%, implying that the neural networks method outperforms previously used criteria for classifying breaker types. The numeric prediction model for the dimensionless position of wave breaking also performs well, with a high degree of correlation between the predicted and actual positions of wave breaking. The performance is higher when only the plunging breaker instances are considered, but lower when only the spilling breaker instances are considered. The corresponding neural network models for wave setup within the surf zone and the difference in energy flux between the incident and broken wave have success rates of approximately 89% and 94% respectively. The method may be extended to provide predictive models for consideration of a range of natural coastal conditions, random waves, and various bottom profiles and complex geometry, based on training and testing of the models using representative field and laboratory observational data, in support of accurate prediction of near-shore wave phenomena.

scholarly journals Research of Vehicle Rear-End Collision Model considering Multiple Factors

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Qiang Luo ◽  
Xiaodong Zang ◽  
Jie Yuan ◽  
Xinqiang Chen ◽  
Junheng Yang ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Traffic Accident ◽  
Model Performance ◽  
Network Models ◽  
Neural Network Models ◽  
Collision Model ◽  
Car Following ◽  
Research Findings ◽  
Two Parameters ◽  
The Relationship

The accuracy of the rear-end collision models is crucial for the early warning of potential traffic accident identification, and thus analyzes of the main factors influencing the rear-end collision relevant models is an active topic in the field. The previous studies have tried to determine the single factor influence on the rear-end collision model performance. Less attention was paid to exploit mutual influences on the model performance. To bridge the gap, we proposed an improved vehicle rear-end collision model by integrating varied factors which influence two parameters (i.e., response time and road adhesion coefficient). The two parameters were solved with the integrated weighting and neural network models, respectively. After that we analyzed the relationship between varied factors and the minimum car-following distance. The research findings support both the theoretical and practical guidance for transportation regulations to release more reasonable minimum headway distance to enhance the roadway traffic safety.

How Optimal Stimuli for Sensory Neurons Are Constrained by Network Architecture

2008 ◽  
Vol 20 (3) ◽  
pp. 668-708 ◽  
Author(s):  
Christopher DiMattina ◽  
Kechen Zhang
Keyword(s):  
Firing Rate ◽  
Network Architecture ◽  
Single Neuron ◽  
Network Models ◽  
Synaptic Connections ◽  
Trial And Error ◽  
Neural Network Models ◽  
Stimulus Response ◽  
The Relationship ◽  
Topological Boundary

Identifying the optimal stimuli for a sensory neuron is often a difficult process involving trial and error. By analyzing the relationship between stimuli and responses in feedforward and stable recurrent neural network models, we find that the stimulus yielding the maximum firing rate response always lies on the topological boundary of the collection of all allowable stimuli, provided that individual neurons have increasing input-output relations or gain functions and that the synaptic connections are convergent between layers with nondegenerate weight matrices. This result suggests that in neurophysiological experiments under these conditions, only stimuli on the boundary need to be tested in order to maximize the response, thereby potentially reducing the number of trials needed for finding the most effective stimuli. Even when the gain functions allow firing rate cutoff or saturation, a peak still cannot exist in the stimulus-response relation in the sense that moving away from the optimum stimulus always reduces the response. We further demonstrate that the condition for nondegenerate synaptic connections also implies that proper stimuli can independently perturb the activities of all neurons in the same layer. One example of this type of manipulation is changing the activity of a single neuron in a given processing layer while keeping that of all others constant. Such stimulus perturbations might help experimentally isolate the interactions of selected neurons within a network.

scholarly journals Human Activity Recognition for People with Knee Osteoarthritis—A Proof-of-Concept

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3381
Author(s):  
Jay-Shian Tan ◽  
Behrouz Khabbaz Beheshti ◽  
Tara Binnie ◽  
Paul Davey ◽  
J.P. Caneiro ◽  
...  
Keyword(s):  
Knee Osteoarthritis ◽  
Activity Recognition ◽  
Human Activity ◽  
Network Models ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Measurement Unit ◽  
Proof Of Concept ◽  
Neural Network Models ◽  
Direction Of Movement

Clinicians lack objective means for monitoring if their knee osteoarthritis patients are improving outside of the clinic (e.g., at home). Previous human activity recognition (HAR) models using wearable sensor data have only used data from healthy people and such models are typically imprecise for people who have medical conditions affecting movement. HAR models designed for people with knee osteoarthritis have classified rehabilitation exercises but not the clinically relevant activities of transitioning from a chair, negotiating stairs and walking, which are commonly monitored for improvement during therapy for this condition. Therefore, it is unknown if a HAR model trained on data from people who have knee osteoarthritis can be accurate in classifying these three clinically relevant activities. Therefore, we collected inertial measurement unit (IMU) data from 18 participants with knee osteoarthritis and trained convolutional neural network models to identify chair, stairs and walking activities, and phases. The model accuracy was 85% at the first level of classification (activity), 89–97% at the second (direction of movement) and 60–67% at the third level (phase). This study is the first proof-of-concept that an accurate HAR system can be developed using IMU data from people with knee osteoarthritis to classify activities and phases of activities.

Improving neural network models of physical systems through dimensional analysis

2003 ◽  
Vol 2 (4) ◽  
pp. 283-296 ◽  
Author(s):  
David J. Gunaratnam ◽  
Taivas Degroff ◽  
John S. Gero
Keyword(s):  
Neural Network ◽  
Dimensional Analysis ◽  
Network Models ◽  
Neural Network Models ◽  
Physical Systems

scholarly journals An Overview of Corrosion Risk of Metal Culverts in Arkansas

2019 ◽  
Vol 271 ◽  
pp. 02004
Author(s):  
Mdariful Hasan ◽  
Zahid Hossain
Keyword(s):  
Laboratory Experiments ◽  
Relevant Literature ◽  
Network Models ◽  
Environmental Data ◽  
Department Of Transportation ◽  
Neural Network Models ◽  
The Public ◽  
Corrosion Risk ◽  
User Friendly ◽  
Highway System

Metal culverts or pipes used in cross-drains along or across the Arkansas highway system are susceptible to corrode over time. Catastrophic incidents such as a complete washout of metal culverts along with roadway can be prevented if proper metals can be selected during the construction project. At present, the Arkansas Department of Transportation (ArDOT) does not have enough information about corrosion effects on metal culverts. The main objective of this study is to develop a user-friendly corrosion map for Arkansas by analyzing soil properties, water properties, and environmental data collected from the public domain as well as those gathered from laboratory experiments. Experimental data will be used to develop mathematical models to predict the resistivity and corrosive nature of soils. In this paper, relevant literature has been reviewed to narrow down the specific gaps in the available data and limitations in using methods to analyze the risk, challenges in developing regression and neural network models and risk mapping. Findings of this study have helped the research team to design the experimental plan and appropriate metrics need to be considered for developing the predictive models for this study.

scholarly journals Residual Life Prediction for Induction Furnace by Sequential Encoder with s-Convolutional LSTM

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1121
Author(s):  
Yulim Choi ◽  
Hyeonho Kwun ◽  
Dohee Kim ◽  
Eunju Lee ◽  
Hyerim Bae
Keyword(s):  
Life Prediction ◽  
Induction Furnace ◽  
Short Term Memory ◽  
Residual Life ◽  
Network Models ◽  
Sensor Data ◽  
Short Term ◽  
Neural Network Models ◽  
Long Short Term Memory ◽  
Convolutional Lstm

Induction furnaces are widely used for melting scrapped steel in small foundries and their use has recently become more frequent. The maintenance of induction furnaces is usually based on empirical decisions of the operator and an explosion can occur through operator error. To prevent an explosion, previous studies have utilized statistical models but have been unable to generalize the problem and have achieved a low accuracy. Herein, we propose a data-driven method for induction furnaces by proposing a novel 2D matrix called a sequential feature matrix(s-encoder) and multi-channel convolutional long short-term memory (s-ConLSTM). First, the sensor data and operation data are converted into sequential feature matrices. Then, N-sequential feature matrices are imported into the convolutional LSTM model to predict the residual life of the induction furnace wall. Based on our experimental results, our method outperforms general neural network models and enhances the safe use of induction furnaces.

scholarly journals Attention bias toward threat is associated with exaggerated fear expression and impaired extinction in PTSD

2011 ◽  
Vol 42 (3) ◽  
pp. 533-543 ◽  
Author(s):  
N. Fani ◽  
E. B. Tone ◽  
J. Phifer ◽  
S. D. Norrholm ◽  
B. Bradley ◽  
...  
Keyword(s):  
Neural Network ◽  
Critical Role ◽  
Network Models ◽  
Fear Learning ◽  
Future Research ◽  
Post Traumatic Stress ◽  
Neural Network Models ◽  
Post Traumatic ◽  
Fear Potentiated Startle ◽  
The Relationship

BackgroundPost-traumatic stress disorder (PTSD) develops in a minority of traumatized individuals. Attention biases to threat and abnormalities in fear learning and extinction are processes likely to play a critical role in the creation and/or maintenance of PTSD symptomatology. However, the relationship between these processes has not been established, particularly in highly traumatized populations; understanding their interaction can help inform neural network models and treatments for PTSD.MethodAttention biases were measured using a dot probe task modified for use with our population; task stimuli included photographs of angry facial expressions, which are emotionally salient threat signals. A fear-potentiated startle paradigm was employed to measure atypical physiological response during acquisition and extinction phases of fear learning. These measures were administered to a sample of 64 minority (largely African American), highly traumatized individuals with and without PTSD.ResultsParticipants with PTSD demonstrated attention biases toward threat; this attentional style was associated with exaggerated startle response during fear learning and early and middle phases of extinction, even after accounting for the effects of trauma exposure.ConclusionsOur findings indicate that an attentional bias toward threat is associated with abnormalities in ‘fear load’ in PTSD, providing seminal evidence for an interaction between these two processes. Future research combining these behavioral and psychophysiological techniques with neuroimaging will be useful toward addressing how one process may modulate the other and understanding whether these phenomena are manifestations of dysfunction within a shared neural network. Ultimately, this may serve to inform PTSD treatments specifically designed to correct these atypical processes.

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