Artificial Neural Network-based System Research Articles

IoT-Enabled Single-Camera Speed Sensor for Smart City Tasks

This article presents an innovative IoT-enabled single-camera speed sensor designed for smart city applications. The research encompasses the development of both hardware and software components, focusing on a computer vision and artificial neural network-based system. A novel aspect of this study is the implementation of a distance-measuring algorithm that eliminates the need for expensive LIDAR sensors traditionally used in speed cameras. Instead, the system relies on convolutional neural networks (CNN) and computer vision algorithms to estimate vehicle speed accurately. Field testing was conducted in real conditions over several months, generating sufficient data to assess the device’s ability to function under various adverse conditions. The results demonstrate the system’s capability to perform vehicle speed detection consistently across diverse conditions, showcasing its potential as a scalable solution for urban traffic management. This makes the proposed system not only more affordable but also simpler to deploy and maintain, thereby enhancing its suitability for widespread use in smart city environments.

A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites

In this work, the fire behavior of a sol-gel in-situ hybrid Mg(OH)2-epoxy nanocomposite was investigated and an artificial neural network-based system built on a fully connected feed-forward artificial neural network was developed to predict its heat release capacity. The nanocomposite containing only ∼5 wt% loading of Mg(OH)2 promoted a remarkable decrease in heat release capacity (∼34%) measured by pyrolysis combustion flow calorimetry and in peak of heat release rate (∼37%), and heat release rate (∼29%), as assessed by cone calorimetry, as well as a significant decrease of total smoke release and smoke extinction area about 22 and 5%, respectively, indicating the suitability of Mg(OH)2 as an effective smoke suppressant. The proposed machine learning approach may be used as a promising alternative for a cost- and time-saving prediction of the fire performances of novel flame retardant polymer-based nanocomposites.

An artificial neural network-based system to estimate the thermal comfort of buildings with energy efficiency

Heating Ventilation Air Condition (HVAC) systems consume the majority of energy in a building; it is essential to optimize this energy while improving the thermal comfort of the occupants. The Predict Mean Vote (PMV) model is considered as one of the most efficient models to define the thermal comfort of a structure. In this context this paper proposes a prediction of PMV index using ANN algorithm to classify the real-time thermal comfort states of occupants, which may provide future energy savings by adopting time-varying setpoints where real-time changes in thermal comfort may require less energy. The performance of studied algorithm was tested using several evaluation parameters such as mean square error (MSE) and correlation coefficient (R2). The algorithm studied in this article showed promising results in terms of correlation coefficient R2 and MSE.

Material flow characteristics for biodiesel

The precise identification and characterization of small different oils are a necessary requirement for the completion of valuable screening. It is necessary to create an automated technique to identify the different types of oil. Many scholars have presented various methods in the past, but predicting with precision has always been a difficult issue. In this paper, an artificial neural network-based system for determining oil kinds is provided. Biodiesel is the one which can prepared naturally from either vegetable oils or animal fat. It is very difficult to identify the different oils, which are mixed together. In this paper, an automated testing technique is proposed based on the image processing techniques. Four different oil samples have been taken and they are classified using artificial neural network based algorithms. Results are promising to develop an automated tool. Wavelets are used in this work to decompose in to different frequency rang. Many types of wavelets are available for decomposing the images. Some types of wavelets suitable for medical images and some are for classification purpose. The wavelets are chosen based on the it’s characteristics like decomposition levels and vanishing moments. In this work wavelet transforms are used for decomposing purpose.The oils are classified based on the features using Artificial neural network.

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction.

Maternal health is an important aspect of women's health during pregnancy, childbirth, and the postpartum period. Specifically, during pregnancy, different health factors like age, blood disorders, heart rate, etc. can lead to pregnancy complications. Detecting such health factors can alleviate the risk of pregnancy-related complications. This study aims to develop an artificial neural network-based system for predicting maternal health risks using health data records. A novel deep neural network architecture, DT-BiLTCN is proposed that uses decision trees, a bidirectional long short-term memory network, and a temporal convolutional network. Experiments involve using a dataset of 1218 samples collected from maternal health care, hospitals, and community clinics using the IoT-based risk monitoring system. Class imbalance is resolved using the synthetic minority oversampling technique. DT-BiLTCN provides a feature set to obtain high accuracy results which in this case are provided by the support vector machine with a 98% accuracy. Maternal health exploratory data analysis reveals that the health conditions which are the strongest indications of health risk during pregnancy are diastolic and systolic blood pressure, heart rate, and age of pregnant women. Using the proposed model, timely prediction of health risks associated with pregnant women can be made thus mitigating the risk of health complications which helps to save lives.

Predicting the flammability of epoxy resins from their structure and small-scale test results using an artificial neural network model

Developing optimal flame retardant polymer compositions that meet all aspects of a given application is energy and cost-intensive. To reduce the number of measurements, we developed an artificial neural network-based system to predict the flammability of polymers from small-scale test data and structural properties. The system can predict ignition time, peak and total heat release, and mass residue after the burning of reference and flame retarded epoxy resins. Total heat release was predicted most accurately, followed by the peak heat release rate. We ranked the input parameters according to their impact on the output parameters using a sensitivity analysis. This ranking allowed us to establish a relationship between the input and output parameters taking into account their physical content.

Healthy and Faulty Experimental Performance of a Typical HVAC System under Italian Climatic Conditions: Artificial Neural Network-Based Model and Fault Impact Assessment

The heating, ventilation, and air conditioning (HVAC) system serving the test room of the SENS i-Lab of the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Aversa, south of Italy) has been experimentally investigated through a series of tests performed during both summer and winter under both normal and faulty scenarios. In particular, five distinct typical faults have been artificially implemented in the HVAC system and analyzed during transient and steady-state operation. An optimal artificial neural network-based system model has been created in the MATLAB platform and verified by contrasting the experimental data with the predictions of twenty-two different neural network architectures. The selected artificial neural network architecture has been coupled with a dynamic simulation model developed by using the TRaNsient SYStems (TRNSYS) software platform with the main aims of (i) making available an experimental dataset characterized by labeled normal and faulty data covering a wide range of operating and climatic conditions; (ii) providing an accurate simulation tool able to generate operation data for assisting further research in fault detection and diagnosis of HVAC units; and (iii) evaluating the impact of selected faults on occupant indoor thermo-hygrometric comfort, temporal trends of key operating system parameters, and electric energy consumptions.

Performance evaluation of an adopted model based on big-bang big-crunch and artificial neural network for cloud applications

High-performance computing is changing the way we compute. In the past decade, the cloud computing paradigm has changed the way we compute, communicate, and technology. Cover real-world problems. There are still many complex challenges in the cloud computing paradigm. Improving effective planning strategies is a complex problem in the service-oriented computing paradigm.In this article, our research focuses on improving task scheduler strategies to improve the performance of cloud applications. The proposed model is inspired by an artificial neural network-based system and astrology base scheduler Big-Bang Big-Crunch. The results show that the proposed strategy based on BBBC and neural network is superior to the method based on astrology (BigBang BigCrunch costaware), genetic cost and many other existing methods.The proposed BB-BC-ANN model is validated using standard workload file (San Diego Supercomputer Center (SDSC) Blue Horizon logs). The results show that the proposed BB-BC-ANN model performs better than some of the existing approaches using performance indicators like total completion time (ms), average start time (ms), average finish time(ms), scheduling time(ms), and total execution time(ms).

Artificial Neural Network-Based System for Location of Structural Faults on Anchor Rods Using Input Impedance Response

Structural faults on anchor rods due to corrosive processes are among the major natural causes worldwide for the collapse of guyed towers. As such towers are widely used on both power transmission lines and antenna systems, their collapse may cause unexpected interruptions of telecommunications and power delivery services. Despite being of utmost importance, underground corrosion monitoring is still a technological challenge, because it is a highly nonlinear problem, influenced by several soil characteristics and its complex iterations with the metallic structure of the rods. Some techniques have already been proposed to locate such faults; however, either they require previous knowledge about the medium or they need the interpretation of the acquired signal by a human specialist. These requirements make it difficult to use such solutions in automatic monitoring as well as to make their integration with Internet of Things (IoT) systems. In this article, a novel technique is proposed to automatically locate structural faults on anchor rods, based on a two-step machine learning (ML) solution. The system is fed by a vector composed of frequency-domain samples of the input impedance electromagnetic (EM) signals measured on the rods through a dedicated designed connector. Experimental results have demonstrated that the proposed combination of EM signal acquisition and ML data processing was able to perform the fault location on anchor rods accurately and with a total independence of any additional human analysis.

Thermoeconomic modeling and artificial neural network optimization of Afyon geothermal power plant

The Afyon Geothermal Power Plant is modeled using the Multi-Layer Feed-Forward Artificial Neural Network. The 100 × 8 data set obtained from the real Binary Geothermal Power Plant is divided into two parts: 80 × 8 training data and 20 × 8 test data. Geothermal Power Plant system modeling has been performed numerically on Matlab with three inputs and five outputs. There are ten neurons in the hidden layer in the Artificial Neural Network-based system, and the logarithmic sigmoid transfer function is used as the transfer function in each neuron. The neurons in the output layer have the purelin transfer function. As a result of the training process, the 3.06 × 10E-2 mean square error value was obtained from the ANN-based Binary Geothermal Power Plant system. The main point of the study is the optimization of the binary geothermal power plant. The genetic algorithm method with Artificial Neural Network-based is used for this purpose. The results obtained from the outputs of the Artificial Neural Network-based Binary Geothermal Power Plant system are presented. The plant’s geothermal water temperature and mass flow rates are 110 °C and 150 kg/s. Energy and exergy efficiencies of the plant are calculated as 10.4% and 29.7%. The optimized simple payback period and exergy cost of the electricity generated in the plant is calculated as 2.87 years and 0.0176 $/kWh, respectively.

An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

The prediction of future events based on available time series measurements is a relevant research area specifically for healthcare, such as prognostics and assessments of intervention applications. A measure of brain dynamics, electroencephalogram time series, are routinely analyzed to obtain information about current, as well as future, mental states, and to detect and diagnose diseases or environmental factors. Due to their chaotic nature, electroencephalogram time series require specialized techniques for effective prediction. The objective of this study was to introduce a hybrid system developed by artificial intelligence techniques to deal with electroencephalogram time series. Both artificial neural networks and the ant-lion optimizer, which is a recent intelligent optimization technique, were employed to comprehend the related system and perform some prediction applications over electroencephalogram time series. According to the obtained findings, the system can successfully predict the future states of target time series and it even outperforms some other hybrid artificial neural network-based systems and alternative time series prediction approaches from the literature.

FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced Resilient State-Awareness of Hybrid Energy Systems

Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifies system behavior even when one or more sensors are faulty. Furthermore, as intelligent cyber adversaries become more capable, incorrect values may be fed to the operators. To address these needs, this paper proposes a fuzzy-neural data fusion engine (FN-DFE) for resilient state-awareness of control systems. The designed FN-DFE is composed of a three-layered system consisting of: 1) traditional threshold based alarms; 2) anomalous behavior detector using self-organizing fuzzy logic system; and 3) artificial neural network-based system modeling and prediction. The improved control system state-awareness is achieved via fusing input data from multiple sources and combining them into robust anomaly indicators. In addition, the neural network-based signal predictions are used to augment the resiliency of the system and provide coherent state-awareness despite temporary unavailability of sensory data. The proposed system was integrated and tested with a model of the Idaho National Laboratory's hybrid energy system facility known as HYTEST. Experiment results demonstrate that the proposed FN-DFE provides timely plant performance monitoring and anomaly detection capabilities. It was shown that the system is capable of identifying intrusive behavior significantly earlier than conventional threshold-based alarm systems.

Artificial Neural Network-Based System for PET Volume Segmentation.

Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

Prediction of mechanical properties in spheroidal cast iron by neural networks

An artificial neural network-based system is proposed to predict mechanical properties in spheroidal cast iron. Several castings of various compositions and modules were produced, starting from different inoculation temperatures and with different cooling times. The mechanical properties were then evaluated by means of tension tests. Process parameters and mechanical properties were then used as a training set for an artificial neural network. Different neural structures were tested, from the simple perceptron up to the multilayer perceptron with two hidden layers, and evaluated by means of a validation set. The results have shown excellent predictive capability of the neural networks as regards maximum tensile strength, when the variation range of strength does not exceed 100 MPa.