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 A Practical and Adaptive Approach to Predicting Indoor CO2

2021 ◽  
Vol 11 (22) ◽  
pp. 10771
Author(s):  
Giacomo Segala ◽  
Roberto Doriguzzi-Corin ◽  
Claudio Peroni ◽  
Tommaso Gazzini ◽  
Domenico Siracusa
Keyword(s):  
Data Collection ◽  
Prediction Accuracy ◽  
Network Models ◽  
Window Size ◽  
Environmental Data ◽  
Neural Network Models ◽  
Using Data ◽  
Carbon Dioxide Co2 ◽  
Long Term Performance ◽  
Term Performance

COVID-19 has underlined the importance of monitoring indoor air quality (IAQ) to guarantee safe conditions in enclosed environments. Due to its strict correlation with human presence, carbon dioxide (CO2) represents one of the pollutants that most affects environmental health. Therefore, forecasting future indoor CO2 plays a central role in taking preventive measures to keep CO2 level as low as possible. Unlike other research that aims to maximize the prediction accuracy, typically using data collected over many days, in this work we propose a practical approach for predicting indoor CO2 using a limited window of recent environmental data (i.e., temperature; humidity; CO2 of, e.g., a room, office or shop) for training neural network models, without the need for any kind of model pre-training. After just a week of data collection, the error of predictions was around 15 parts per million (ppm), which should enable the system to regulate heating, ventilation and air conditioning (HVAC) systems accurately. After a month of data we reduced the error to about 10 ppm, thereby achieving a high prediction accuracy in a short time from the beginning of the data collection. Once the desired mobile window size is reached, the model can be continuously updated by sliding the window over time, in order to guarantee long-term performance.

scholarly journals Detection of Fake Doctored Images used to Circulate Fake News

2020 ◽  
Vol 9 (7) ◽  
pp. 1031-1036
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Neural Network Models ◽  
The Public ◽  
Learning Techniques ◽  
The World ◽  
Classification Of Images

Over the few years the world has seen a surge in fake news and some people are even calling it an epidemic. Misleading false articles are sold as news items over social media, whatsapp etc where no proper barrier is set to check the authenticity of posts. And not only articles but news items also contain images which are doctored to mislead the public or cause sabotage. Hence a proper barrier to check for authenticity of images related to news items is absolutely necessary. And hence classification of images(related to news items) on the basis of authenticity is imminent. This paper discusses the possibilities of identifying fake images using machine learning techniques. This is an introduction into fake news detection using the latest evolving neural network models

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.

Neuronet-Based Approach To Modeling the Durability of Aggregate in Concrete Pavement Construction

1997 ◽  
Vol 1582 (1) ◽  
pp. 29-33 ◽  
Author(s):  
Yacoub M. Najjar ◽  
Imad A. Basheer ◽  
Richard L. McReynolds
Keyword(s):  
Physical Properties ◽  
Network Models ◽  
Concrete Pavements ◽  
Department Of Transportation ◽  
Freezing And Thawing ◽  
Neural Network Models ◽  
Durability Test ◽  
Wide Scatter ◽  
Pavement Construction ◽  
Concrete Elements

The durability of aggregate used in concrete pavements construction is commonly assessed by subjecting small concrete beams containing the aggregate to cyclic freezing and thawing. The durability of aggregate and concrete specimens is quantified by measuring the durability factor (DF) and percent expansion (EXP). A typical durability test may last 3 to 5 months and involve high costs. It was assumed that the durability of aggregate used as a constituent in concrete elements may be related to some easily measured physical properties of the aggregate. A data base obtained from records of the Kansas Department of Transportation contained a total of 750 durability tests. The observed wide scatter in the experimental data when DF or EXP is related to one physical parameter suggested the use of artificial neural networks to model durability. Neural network models were developed to predict durability of aggregate from five basic physical properties of the aggregate. The models were found to classify the aggregates with regard to their durability with a relatively high accuracy. In addition, the models were used to assess the reliability of prediction. To illustrate the use of the models, numerical examples are presented.

scholarly journals General-Purpose Computation with Neural Networks: A Survey of Complexity Theoretic Results

2003 ◽  
Vol 15 (12) ◽  
pp. 2727-2778 ◽  
Author(s):  
Jiří Šíma ◽  
Pekka Orponen
Keyword(s):  
Neural Networks ◽  
Relevant Literature ◽  
Network Models ◽  
Network Size ◽  
General Purpose ◽  
Neural Network Models ◽  
Time Model ◽  
Continuous State ◽  
Computational Aspects ◽  
Winner Take All

We survey and summarize the literature on the computational aspects of neural network models by presenting a detailed taxonomy of the various models according to their complexity theoretic characteristics. The criteria of classification include the architecture of the network (feedforward versus recurrent), time model (discrete versus continuous), state type (binary versus analog), weight constraints (symmetric versus asymmetric), network size (finite nets versus infinite families), and computation type (deterministic versus probabilistic), among others. The underlying results concerning the computational power and complexity issues of perceptron, radial basis function, winner-take-all, and spiking neural networks are briefly surveyed, with pointers to the relevant literature. In our survey, we focus mainly on the digital computation whose inputs and outputs are binary in nature, although their values are quite often encoded as analog neuron states. We omit the important learning issues.

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 An Assessment of Machine Learning Integrated Autonomous Waste Detection and Sorting of Municipal Solid Waste

2021 ◽  
Vol 20 (4) ◽  
Author(s):  
Sonam Chaturvedi ◽  
Bikarama Prasad Yadav ◽  
Nihal Anwar Siddiqui
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Network Models ◽  
Machine Learning Algorithms ◽  
Health It ◽  
Neural Network Models ◽  
The Public ◽  
Air Jet

Municipal solid waste deposition in metropolitan areas has become a major concern that, if not addressed, can lead to environmental degradation and possibly endanger human health. It is important to adopt a smart waste management system in place to cope with a range of waste materials. This research aims to develop a smart modelling method that could accurately predict and forecast the production of municipal solid waste. An integrated convolution neural network and air-jet system-based framework developed for pre-processing and data integration were developed. The results showed that machine learning algorithms could be used to detect different types of waste with high accuracy. The best performers were obtained from neural network models, which captured 72% of the information variation. The method proposed in this study demonstrates the feasibility of developing tools to assist urban waste through the supply, pre-processing, integration, and modelling of data accessible to the public from a variety of sources.

Networking nature: Building cybercabinets of digital curiosities

2014 ◽  
Vol 28 (1-2) ◽  
pp. 63-76
Author(s):  
R Sargent
Keyword(s):  
Natural History ◽  
Relevant Literature ◽  
Heuristic Evaluation ◽  
Evaluation Tool ◽  
User Survey ◽  
Digital Resources ◽  
The Public ◽  
Online Access ◽  
User Friendly ◽  
Project Personnel

Abstract In the age of the Internet, engaging the public online is critical to building audiences and broadening support for natural history. While collections managers have been providing online access to collections through sophisticated database search interfaces, less progress has been made to present these resources in a user-friendly framework. Some museums are thinking in terms of networked online knowledge and radically shifting the way they broker their digital content. This research examines ways natural history can be effectively presented online to the public by reviewing relevant literature, analyzing six model sites with a heuristic evaluation tool and a user survey, and exploring three case studies through project personnel interviews. Findings summarize important strategies for cultivating creative online access to natural history digital resources and culminate in offering guidelines for building these “cybercabinets” of digital natural history specimens.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper
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