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Enhancing Maritime SATCOM Security: Orbit Integrates NetShroud+ Cyber Solution into OceanTRx MIL Systems -Today at 11:06 am

Enhancing Maritime SATCOM Security: Orbit Integrates NetShroud+ Cyber Solution into OceanTRx MIL Systems The integrated terminals will operate.

Tel avivKu kaDany eshcharOrbit communication systems ltdInvestment fundOrbit communications systemsArtificial neural networks annsTel aviv stock exchangeLondon septemberCommunications systemsArtificial neural networksArtificial intelligenceWhite box cryptographyBlacklist filteringZero trust micro segmentationOrbit communications

Orbit launches the advanced NetShroud+ solution – SatNews

Orbit launches the advanced NetShroud+ solution – SatNews
satnews.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from satnews.com Daily Mail and Mail on Sunday newspapers.

Artificial neural networks annsOrbit communications systems incOrbit communications systemsWhite box cryptographyBlack list filteringZero trust micro segmentationArtificial neural networksArtificial intelligence

Slope stability prediction using the Artificial Neural Network (ANN) by Al Muhalab Al-Dughaishi, Shivakumar Karekal et al

Slope failure is a significant risk in both civil and mining operations. This failure phenomenon is more likely to occur during the high rainfall season, areas with a high probability of seismic activity and in cold countries due to freezing-thawing. Further, a poor understanding of hydrogeology and geotechnical factors can contribute to erroneous engineering designs. Several Limit Equilibrium Methods (LEMs) and numerical modelling tools have been developed over the years. However, the highlighted success of the Artificial Neural Networks (ANNs) in other disciplines/sectors has motivated researchers to implement ANNs to forecast the Factor Of Safety (FOS). This paper aims to develop ANNs to predict the value of the FOS for slopes formed by (i) uniform one soil/rock material and (ii) formed by two soil/rock materials. Each of these slopes contains three sub-models with 6, 7 and 8 input material parameters. Thousands of FOS values were generated for each sub-model using LEMs by randomly

Artificial neural networks annsLimit equilibrium methodsArtificial neural networksFactor of safetyMean square error

Slope stability prediction using the Artificial Neural Network (ANN) by Al Muhalab Al-Dughaishi, Shivakumar Karekal et al

Slope failure is a significant risk in both civil and mining operations. This failure phenomenon is more likely to occur during the high rainfall season, areas with a high probability of seismic activity and in cold countries due to freezing-thawing. Further, a poor understanding of hydrogeology and geotechnical factors can contribute to erroneous engineering designs. Several Limit Equilibrium Methods (LEMs) and numerical modelling tools have been developed over the years. However, the highlighted success of the Artificial Neural Networks (ANNs) in other disciplines/sectors has motivated researchers to implement ANNs to forecast the Factor Of Safety (FOS). This paper aims to develop ANNs to predict the value of the FOS for slopes formed by (i) uniform one soil/rock material and (ii) formed by two soil/rock materials. Each of these slopes contains three sub-models with 6, 7 and 8 input material parameters. Thousands of FOS values were generated for each sub-model using LEMs by rando

Artificial neural networks annsLimit equilibrium methodsArtificial neural networksFactor of safetyMean square error

Pumps-as-Turbines (PaTs) performance prediction improvement using evo by Akbar Telikani, Mosé Rossi et al

Energy production from clean sources is mandatory to reduce pollutant emissions. Among different options for hidden hydropower potential exploitation, Pump-as-Turbine (PaT) represents a viable solution in pico- and micro-hydropower applications for its flexibility and low-cost. Pumps are widely available in the global market in terms of both sizes and spare parts. To date, there are several PaTs’ performance prediction models in the literature, but very few of them use optimization algorithms and only for specific and limited prediction goals. The present work proposes evolutionary Artificial Neural Networks (ANNs) based on JADE, which is a typology of differential evolution algorithm, to forecast Best Efficiency Point (BEP) and performance curves of a PaT starting from the pump operational data. In this model, JADE is employed as optimizer of basic ANNs to upgrade parameter values of the learning rate, weights, and biases. The accuracy of the proposed model is evaluated through expe

Artificial neural networks annsArtificial neural networksBest efficiency pointAdaptive differential evolutionArtificial neural networkEvolutionary algorithmsIdden hydropowerPerformance forecastUmps as turbines