Search Results (13)

In marine engineering, the study of underwater explosion effects on naval and offshore structures has gained significant attention due to its critical impact on structural integrity and safety. In practical applications, a crucial aspect is determining the precise point at which an underwater explosive charge has detonated. This information is vital for assessing damage, implementing defensive and security strategies, and ensuring the structural integrity of marine structures. This paper presents a novel approach that combines coupled numerical simulations performed using the MSC Dytran suite with machine learning techniques to reconstruct the trigger point of underwater explosions based on onboard sensor data and leverage seabed wave reflection information. A Multi-Layer Neural Network (MLNN) was devised to identify the position of the denotation point of the charge using a classification task based on a user-defined two-dimensional grid of potential triggering locations. The MLNN underwent training, validation, and testing phases using simulation data from different underwater blast-loading scenarios for metallic target plates. Different positions of the charge, seabed typologies, and distances between the structure and the seabed are considered. The ability to accurately identify a detonation point using measurable data from onboard systems enhances the knowledge of ship and offshore structures’ response strategies and the overall safety of naval operations. Full article

Feed-forward neural networks (NNs) are widely used for the machine learning of properties of materials and molecules from descriptors of their composition and structure (materials informatics) as well as in other physics and chemistry applications. Often, multilayer (so-called “deep”) NNs are used. Considering that universal approximator properties hold for single-hidden-layer NNs, we compare here the performance of single-hidden-layer NNs (SLNN) with that of multilayer NNs (MLNN), including those previously reported in different applications. We consider three representative cases: the prediction of the band gaps of two-dimensional materials, prediction of the reorganization energies of oligomers, and prediction of the formation energies of polyaromatic hydrocarbons. In all cases, results as good as or better than those obtained with an MLNN could be obtained with an SLNN, and with a much smaller number of neurons. As SLNNs offer a number of advantages (including ease of construction and use, more favorable scaling of the number of nonlinear parameters, and ease of the modulation of properties of the NN model by the choice of the neuron activation function), we hope that this work will entice researchers to have a closer look at when an MLNN is genuinely needed and when an SLNN could be sufficient. Full article

This study considers the numerical design and practical implementation of a new multi-layer neural network observer-based control design technique for unmanned aerial vehicles systems. Initially, an adaptive multi-layer neural network-based Luenberger observer is designed for state estimation by employing a modified back-propagation algorithm. The proposed observer’s adaptive nature aids in mitigating the impact of noise, disturbance, and parameter variations, which are usually not considered by conventional observers. Based on the observed states, a nonlinear dynamic inversion-based fast terminal sliding mode controller is designed to attain the desired attitude and position tracking control. This is done by employing a two-loop control structure. Numerical simulations are conducted to demonstrate the effectiveness of the proposed scheme in the presence of disturbance, parameter uncertainty, and noise. The numerical results are compared with current approaches, demonstrating the superiority of the proposed method. In order to assess the practical effectiveness of the proposed method, hardware-in-loop simulations are conducted by utilizing a Pixhawk 6X flight controller that interfaces with the mission planner software. Finally, experiments are conducted on a real F450 quadrotor in a secured laboratory environment, demonstrating stability and good tracking performance of the proposed MLNN observer-based SMC control scheme. Full article

To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple-output (MIMO) receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, the square root of the maximum eigenvalue times the minimum eigenvalue (SR-MME) and the geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by detectors, we need to further confirm their number. Therefore, we perform feature extraction on the the eigenvalue sequence of a sample covariance matrix to construct a feature vector and innovatively propose a multi-layer neural network (ML-NN). Additionally, the support vector machine (SVM) and naive Bayesian classifier (NBC) are also designed. The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70% with the massive MIMO receive array. Finally, we analyze the classical signal classification methods, Akaike (AIC) and minimum description length (MDL). It is concluded that the two methods are not suitable for scenarios with massive MIMO arrays, and they also have much worse performance than machine learning-based classifiers. Full article

This paper defines a smart home use case to automatically adjust home temperature and/or hot water. The main objective is to reduce the energy consumption of cooling, heating and hot water systems in smart homes. To this end, the residents set a temperature (i.e., X degree Celsius) for home and/or hot water. When the residents leave homes (e.g., for work), they turn off the cooling or heating devices. A few minutes before arriving at their residences, the cooling or heating devices start working automatically to adjust the home or water temperature according to the residents’ preference (i.e., X degree Celsius). This can help reduce the energy consumption of these devices. To estimate the arrival time of the residents (i.e., drivers), this paper uses a machine learning-based street traffic prediction system. Unlike many related works that use machine learning for tracking and predicting residents’ behaviors inside their homes, this paper focuses on predicting resident behavior outside their home (i.e., arrival time as a context) to reduce the energy consumption of smart homes. One main objective of this paper is to find the most appropriate machine learning and neural network-based (MLNN) algorithm that can be integrated into the street traffic prediction system. To evaluate the performance of several MLNN algorithms, we utilize an Uber’s dataset for the city of San Francisco and complete the missing values by applying an imputation algorithm. The prediction system can also be used as a route recommender to offer the quickest route for drivers. Full article

Post Operation Clean Out (POCO) is the process to remove hazardous materials and decommission nuclear facilities at the end of a nuclear plant’s lifetime. The introduction of Internet of Things (IoT) technologies in the environment, especially radio frequency identification (RFID), would improve efficiency and safety by intelligently monitoring POCO activities. In this paper, we present a passive material identification and crack sensing method developed for the integration of sensing and communication using commercial off-the-shelf (COTS) RFID tags, which is a long-term solution to material property monitoring under insulation for harsh environmental conditions. To validate the effectiveness of material identification and crack monitoring, machine learning techniques have been applied, and the feasibility of the study has been outlined. The result shows that the material identification can be achieved with traditional features and obtain improved accuracy with three-layer multi-layer neural networks (MLNN). In crack characterization, the tree algorithm based on traditional features achieves a reasonable accuracy, while three-layer MLNN is the best solution, which supports the efficiency of traditional feature extraction methods in specific applications. Full article

In recent years, the phenomenon of urban warming has become increasingly serious, and with the number of urban residents increasing, the risk of heatstroke in extreme weather has become higher than ever. In order to mitigate urban warming and adapt to it, many researchers have been paying increasing attention to outdoor thermal comfort. The mean radiant temperature (MRT) is one of the most important variables affecting human thermal comfort in outdoor urban spaces. The purpose of this paper is to predict the distribution of MRT around buildings based on a commonly used multilayer neural network (MLNN) that is optimized by genetic algorithms (GA) and backpropagation (BP) algorithms. Weather data from 2014 to 2018 together with the related indexes of the grid were selected as the input parameters for neural network training, and the distribution of the MRT around buildings in 2019 was predicted. This study obtained very high prediction accuracy, which can be combined with sensitivity analysis methods to analyze the important input parameters affecting the MRT on hot summer days (the days with the highest air temperature over 30 °C). This has significant implications for the optimization strategies for future building and urban designers to improve the thermal conditions around buildings. Full article

Finding an available parking place has been considered a challenge for drivers in large-size smart cities. In a smart parking application, Artificial Intelligence of Things (AIoT) can help drivers to save searching time and automotive fuel by predicting short-term parking place availability. However, performance of various Machine Learning and Neural Network-based (MLNN) algorithms for predicting parking segment availability can be different. To find the most suitable MLNN algorithm for the above mentioned application, this paper evaluates performance of a set of well-known MLNN algorithms as well as different combinations of them (i.e., known as Ensemble Learning or Voting Classifier) based on a real parking datasets. The datasets contain around five millions records of the measured parking availability in San Francisco. For evaluation, in addition to the cross validation scores, we consider resource requirements, simplicity and execution time (i.e., including both training and testing times) of algorithms. Results show that while some ensemble learning algorithms provide the best performance in aspect of validation score, they consume a noticeable amount of computing and time resources. On the other hand, a simple Decision Tree (DT) algorithm provides a much faster execution time than ensemble learning algorithms, while its performance is still acceptable (e.g., DT’s accuracy is less than 1% lower than the best ensemble algorithm). We finally propose and simulate a recommendation system using the DT algorithm. We have found that around 77% of drivers can not find a free spot in their selected destinations (i.e., street or segment) and estimated that the recommendation system, by introducing alternative closest vacant locations to destinations, can save, in total, 3500 min drivers searching time for 1000 parking spot requests. It can also help to reduce the traffic and save a noticeable amount of automotive fuel. Full article

Real-time control of wastewater treatment plants (WWTPs) can have significant environmental and cost advantages. However, its application to small and decentralised WWTPs, which typically have highly varying influent characteristics, remains limited to date due to cost, reliability and technical restrictions. In this study, a methodology was developed using numerical models that can improve sustainability, in real time, by enhancing wastewater treatment whilst also optimising operational and energy efficiency. The methodology leverages neural network and regression modelling to determine a suitable soft sensor for the prediction of ammonium-nitrogen trends. This study is based on a case-study decentralised WWTP employing sequencing batch reactor (SBR) treatment and uses pH and oxidation-reduction potential sensors as proxies for ammonium-nitrogen sensors. In the proposed method, data were pre-processed into 15 input variables and analysed using multi-layer neural network (MLNN) and regression models, creating 176 soft sensors. Each soft sensor was then analysed and ranked to determine the most suitable soft sensor for the WWTP. It was determined that the most suitable soft sensor for this WWTP would achieve a 67% cycle-time saving and 51% electricity saving for each treatment cycle while meeting the criteria set for ammonium discharges. This proposed soft sensor selection methodology can be applied, in full or in part, to existing or new WWTPs, potentially increasing the adoption of real-time control technologies, thus enhancing their overall effluent quality and energy performance. Full article

This study explores investigation of applicability of impact factors to estimate solar irradiance by four machine learning algorithms using climatic elements as comparative analysis: linear regression, support vector machines (SVM), a multi-layer neural network (MLNN), and a long short-term memory (LSTM) neural network. The methods show how actual climate factors impact on solar irradiation, and the possibility of estimating one year local solar irradiance using machine learning methodologies with four different algorithms. This study conducted readily accessible local weather data including temperature, wind velocity and direction, air pressure, the amount of total cloud cover, the amount of middle and low-layer cloud cover, and humidity. The results show that the artificial neural network (ANN) models provided more close information on solar irradiance than the conventional techniques (linear regression and SVM). Between the two ANN models, the LSTM model achieved better performance, improving accuracy by 31.7% compared to the MLNN model. Impact factor analysis also revealed that temperature and the amount of total cloud cover are the dominant factors affecting solar irradiance, and the amount of middle and low-layer cloud cover is also an important factor. The results from this work demonstrate that ANN models, especially ones based on LSTM, can provide accurate information of local solar irradiance using weather data without installing and maintaining on-site solar irradiance sensors. Full article

In order to create an irrigation scheduling plan for use in large-area citrus orchards, an environmental information collection system of citrus orchards was established based on the Internet of Things (IoT). With the environmental information data, deep bidirectional long short-term memory (Bid-LSTM) networks are proposed to improve soil moisture (SM) and soil electrical conductivity (SEC) predictions, providing a meaningful reference for the irrigation and fertilization of citrus orchards. The IoT system contains SM, SEC, air temperature and humidity, wind speed, and precipitation sensors, while the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) were calculated to evaluate the performance of the models. The performance of the deep Bid-LSTM model was compared with a multi-layer neural network (MLNN). The results for the performance criteria reveal that the proposed deep Bid-LSTM networks perform better than the MLNN model, according to many of the evaluation indicators of this study. Full article

The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt changes in the quality of its waters caused a great social alarm. Understanding and modeling the level of a eutrophication indicator, such as chlorophyll-a (Chl-a), benefits the management of this complex system. In this study, we investigate the potential machine learning (ML) methods to predict the level of Chl-a. Particularly, Multilayer Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are evaluated using as a target dataset information of up to nine different water quality parameters. The most relevant input combinations were extracted using wrapper feature selection methods which simplified the structure of the model, resulting in a more accurate and efficient procedure. Although the performance in the validation phase showed that SVR models obtained better results than MLNNs, experimental results indicated that both ML algorithms provide satisfactory results in the prediction of Chl-a concentration, reaching up to 0.7 R²_CV (cross-validated coefficient of determination) for the best-fit models. Full article

This paper presents an approach for impedance-based sensor monitoring of dressing tool condition in grinding by using the electromechanical impedance (EMI) technique. This method was introduced in Part 1 of this work and the purpose of this paper (Part 2) is to achieve an optimal selection of the excitation frequency band based on multi-layer neural networks (MLNN) and k-nearest neighbor classifier (k-NN). The proposed approach was validated on the basis of dressing tool condition information obtained from the monitoring of experimental dressing tests with two industrial stationary single-point dressing tools. Moreover, representative damage indices for diverse damage cases, obtained from impedance signatures at different frequency bands, were taken into account for MLNN data processing. The intelligent system was able to select the most damage-sensitive features based on optimal frequency band. The best models showed a general overall error lower than 2%, thus robustly contributing to the efficient automation of grinding and dressing operations. The promising results of this study foster the EMI-based sensor monitoring approach to fault diagnosis in dressing operations and its effective implementation for industrial grinding process automation. Full article