Artificial Neural Network In Order Research Articles

Constructing and Testing AI International Legal Education Coupling-Enabling Model

In this paper, we aim to assess the coupling capability of artificial intelligence in international legal education, delving into crucial aspects of its implementation and effectiveness. This paper constructs a coupling empowerment model of AI international legal education by using artificial intelligence technology. It also discusses the application of Pearson product–moment correlation coefficient in correlation analysis, the implementation of AI knowledge mapping in the help of intelligent parents, and the application of BP neural algorithm in artificial neural networks in order to establish a cognitive student model. This teaching mode can provide personalized learning experience and intelligent teaching support and allow accurate assessment of students’ learning level and cognitive ability. The results show that the employment rate of students is increased from 75% to 100%, and the evaluation of practicability is maintained at 10 points. It proves that AI technology provides an innovative approach to international law education, which is expected to promote the efficient use of educational resources and improve students’ performance and employment rate.

Estimated volume of eucalyptus plantations through ALOS satellite images

Estimates of wood volume, either in even or uneven-aged forests, is necessary for planning forest management, studies concerning carbon sequestration and natural resources conservation, as well as energy planning in regions where wood is the primary fuel for power generation. Such purposes mainly involve analysis of large areas, which leads to challenges that can be solved through remote sensing. Using multispectral and microwave data, respectively, from the AVNIR-2 and PALSAR sensors, both on board in the ALOS satellite, the wood volume of a commercial eucalyptus plantation located in the state of Minas Gerais, Brazil was estimated using artificial neural. Neural networks emerged as a powerful machine learning technique, capable of modeling intrinsically nonlinear relationships that are present in the data. Thus, the obtained estimates for wood volume in the eucalyptus plantations showed a correlation coefficient with the corresponding observed volumes (forest inventory) of 0.99, a square root of the quadratic error (RQEM) of 0.3% and errors with amplitude between -1 to 1%. The efficiency of artificial neural networks for estimating wood volume in homogeneous plantations has been proven. The multilayer perceptron neural network was efficient in modeling the complex relationships that exist between multispectral, microwave and wood volume data, producing highly accurate estimates. Only a small amount of plots was sufficient to train the artificial neural networks in order to estimate volume. In addition, the fast convergence obtained by the BFGS algorithm allowed a comprehensive data analysis.

Short-Term Load Forecasting using Artificial Neural Networks techniques: A case study for Republic of North Macedonia

Modernization and liberalization of power system in North Macedonia offers an opportunity to supervise and regulate the power consumption and power grid. This paper proposes models for short-term load forecasting using artificial neural network in order to balance the demand and load requirements and to determine electricity price. Neural network approach has the advantage of learning directly from the historical data. This method uses multiple data points. Results from the research show that the quality of the short-term prediction depends on the size of the data set and the data transformation.

Predictive and Prescriptive Analytics in Identifying Opportunities for Improving Sustainable Manufacturing

Environmental issues and sustainability performance are more and more significant in today’s business world. A growing number of manufacturing companies are searching for changes to improve their sustainability in the areas of products and manufacturing processes. These changes should be introduced in the design process and affect the whole product life cycle. This paper is concerned with developing a method based on predictive and prescriptive analytics to identify opportunities for increasing sustainable manufacturing through changes incorporated at the product design stage. Predictive analytics uses parametric models obtained from regression analysis and artificial neural networks in order to predict sustainability performance. In turn, prescriptive analytics refers to the identification of opportunities for improving sustainability performance in manufacturing, and it is based on a constraint programming implemented within a constraint satisfaction problem (CSP). The specification of sustainability performance in terms of a CSP provides a pertinent framework for identifying all admissible solutions (if there are any) of the considered problem. The identified opportunities for improving sustainability performance are dedicated to specialists in product development, and aim to reduce both resources used in manufacturing and negative effects on the environment. The applicability of the proposed method is illustrated through reducing the number of defective products in manufacturing.

An FPGA-based memristor emulator for artificial neural network

Emulating memristor via FPGA offers flexibility for quick prototyping and design space exploration because memristor manufacture and integration are currently insufficient for large-scale application in neural networks. Although important, the creation of fast and lightweight FPGA memristor emulators is still very difficult. In this work, a reconfigurable memristor emulator that can be implemented in digital circuits is proposed. With less than 1% hardware utilization and a 258 MHz operating frequency, the suggested solution has been successfully synthesized and confirmed on a Xilinx ZYNQ-7000 FPGA. Additionally, this work builds a memristor crossbar array for extracting digital picture information and then performs the digital recognition task using a quantitative artificial neural network in order to assess the synaptic function of the suggested memristor model. The experimental results show that the recognition accuracy of MNIST dataset images by the memristor network circuit reaches 91.89%.

Increasing the metrological characteristics of anechoic chambers due to a posteriori analysis based on artificial neural networks

This article considers the possibility of improving the metrological characteristics of an anechoic chamber due to a posteriori processing of measurement results based on a generative adversarial model of an artificial neural network in order to reduce the influence on the distribution of the electromagnetic field in the measuring zone of waves reflected from the outer boundaries of the chamber and the equipment located in it. The training of the neural network was carried out on a data set obtained as part of a computational experiment and including the distribution of the electromagnetic field in the anechoic region for the model of an anechoic chamber and free space for given source layouts. The distributions of the real and imaginary parts of the electric component of the electromagnetic field were encoded with colour images. On the example of two-dimensional models of anechoic chambers, the practical feasibility of the proposed approach to a posteriori processing of measurement results is shown. Methods for estimating the accuracy of a posteriori processing of measurement results based on the metrics used to assess the quality of graphic images and calculating the errors in the amplitudes of the electric component of the electromagnetic field are given. The possibility of implementing the proposed method of a posteriori analysis in the framework of natural microwave measurements in anechoic chambers is assessed.

Air quality forecasting using artificial intelligence techniques

Air pollution is among the most challenging issues modern societies are currently facing with direct impacts on society, economy, politics, as well as on the health of the population and the environment (European Environmental Agency, 2020). This study aims to introduce a state-of-the-art air quality forecasting model using a Deep Long-Short Memory Network integrating data from various sources, from in-situ measurements coming from official ground-based stations to Earth Observation from the Copernicus Atmosphere Monitoring Service (CAMS). The overall aim for the development of this model is the production of air quality forecasts with higher spatial and temporal resolution than those offered from Copernicus, capturing air pollution dynamics at a city level, something that will significantly affect decision-making allowing the adoption and implementation of better measures by policy makers to minimise the effects of the degraded air quality. The air quality forecasting model presented in this study uses a novel method based on artificial neural networks in order to produce forecasts of future air quality conditions with high spatial and temporal resolution, by leveraging data from a wide range of sources. Even though it was developed in the context of addressing the challenging issue of air pollution, the model presented in this study could also be used, with a few modifications, to produce forecasts of thermal comfort and the probability of forest fires. Keywords: air pollution, artificial intelligence, artificial neural networks, forecast

Development of condition-based maintenance strategy for fault diagnosis for ship engine systems

Shipping effectively covers a large part of cargo transportation in the world in an economical and reliable way. Thus, energy-efficient operation on ships is an essential subject to ensure both increasing of the efficiency level of the global transportation and economic savings. In this subject, an effective maintenance strategy followed in the ship's engine room is one of the powerful solutions. In this way, system reliability and operational safety increase while operational expenses decrease. A condition-based strategy is an up-to-date approach for maintenance. Within this method, decisions could be made about the system based on past information. In this study, some parameters of the large-sized container ship are collected for the development of the condition-based maintenance strategy. The dataset is analysed by the artificial neural network in order to the constitution of the engine performance model. Finally, the usability and effectiveness of the developed maintenance strategy are demonstrated with three scenarios. As a result of the exemplified scenarios, the improved maintenance strategy ensures that the fault diagnosis could be made effectively, depending on the instant condition of the examined engine and its past information. The proposed methodology could also adapt to any system or engine for any kind of ship.

Artificial Intelligence-Based Tools for Process Optimization: Case Study—Bromocresol Green Decolorization with Active Carbon

This study highlights the benefits of optimizing the decolorization of bromocresol green (a colorant/pH indicator widely used in the industry, whose degradation produces toxic byproducts) by adsorption on active carbon. A set of experiments were planned and performed based on the design of experiments methodology for the following parameters: the colorant concentration (0.009-0.045 g/L), the amount of adsorbent (0.5-3 g/L), and the contact time (60-240 min). Modeling and optimization strategies were employed to determine the working conditions leading to efficiency maximization. Using the response surface methodology, the optimum values of the primary process parameters were established. In addition, a modified bacterial foraging optimization algorithm was applied as an alternative optimizer in combination with artificial neural networks in order to determine multiple combinations of parameters that can lead to maximum process efficiency. Different solutions were obtained with the considered strategies, and the maximum efficiency obtained was >99%. The study emphasizes that adsorption on active carbon is an effective method for bromocresol green decolorization in wastewater that can be further improved using advanced optimization methods.

Application of radioactive particle tracking and an artificial neural network to calculating the flow rate in a two-phase (oil–water) stratified flow regime

A multiphase flow is defined as the transport of two or more fluids with different properties flowing together inside a pipeline. After offshore oil production, it is necessary to control the amount of transported fluids based on flow rate measurements. Therefore, in this study, we developed a simulation method for predicting the volume fraction and calculating the superficial velocity for a two-phase flow based on radioactive particle tracking, which involves using a sealed radiation source inside the pipeline in order to obtain volume fraction measurements. The test section for the multiphase flow comprised oil and saltwater under a stratified flow regime, with a polyvinyl chloride pipe, four NaI(Tl) detectors, and a137Cs radioactive particle that emitted gamma-rays at 662 keV. Simulations were conducted using the MCNP6 code, which is a mathematical code based on the Monte Carlo method. Volume fraction predictions were obtained using a multilayer perceptron neural network with a backpropagation algorithm. The novel feature of this method is the combination of radioactive particle tracking with an artificial neural network in order to predict volume fractions in multiphase flows. The results showed that 91.65% of the predicted patterns were within 5% of the relative error. In addition, the time delay was determined using the cross-correlation function to obtain the superficial velocity in three different volume fractions, which allowed each phase flow rate to be calculated in these cases.

A Hybrid Finite Element-Machine Learning Backward Training Approach to Analyze the Optimal Machining Conditions.

As machining processes are complex in nature due to the involvement of large plastic strains occurring at higher strain rates, and simultaneous thermal softening of material, it is necessary for manufacturers to have some manner of determining whether the inputs will achieve the desired outputs within the limitations of available resources. However, finite element simulations—the most common means to analyze and understand the machining of high-performance materials under various cutting conditions and environments—require high amounts of processing power and time in order to output reliable and accurate results which can lead to delays in the initiation of manufacture. The objective of this study is to reduce the time required prior to fabrication to determine how available inputs will affect the desired outputs and machining parameters. This study proposes a hybrid predictive methodology where finite element simulation data and machine learning are combined by feeding the time series output data generated by Finite Element Modeling to an Artificial Neural Network in order to acquire reliable predictions of optimal and/or expected machining inputs (depending on the application of the proposed approach) using what we describe as a backwards training model. The trained network was then fed a test dataset from the simulations, and the results acquired show a high degree of accuracy with regards to cutting force and depth of cut, whereas the predicted/expected feed rate was wildly inaccurate. This is believed to be due to either a limited dataset or the much stronger effect that cutting speed and depth of cut have on power, cutting forces, etc., as opposed to the feed rate. It shows great promise for further research to be performed for implementation in manufacturing facilities for the generation of optimal inputs or the real-time monitoring of input conditions to ensure machining conditions do not vary beyond the norm during the machining process.

Prediction of Cotton Yarn’s Characteristics by Image Processing and ANN

Machine learning and computer vision were employed in quality assessment in the textile field for more objectivity and less expense. The estimation of yarn various parameters is of great importance for the producers and customers in order to achieve optimal quality. This research utilized image processing and artificial neural networks in order to evaluate yarn tenacity, elongation%, coefficient of mass variation%, and yarn imperfections for ring-spun and compact cotton yarns. Cotton yarn samples were collected from two mills: ring spinning and compact spinning mills. The images were taken and image analysis was employed then feature vectors were defined as the inputs of the backpropagation neural networks. Two systems were built; each one contained three modules for the estimation of the different yarn’s properties. Using the multilayer network structure proved to improve the performance of the networks leading to better parameters’ modeling. Yarn properties estimation for different yarn types was achieved using a moderately priced method.

ANN approaches to determine the dielectric strength improvement of MgO based low density polyethylene nanocomposite

This paper presents the modification occurred to the dielectric strength feature of low density polyethylene compounded with nano magnesia (LDPE/MgO). MgO nanoparticles were prepared using sol–gel technique, MgO filler surface was functionalized to improve the interfacial bonding. Specimen’s groups of composites with different filler concentrations were fabricated by mix blend method. Samples exposed to various salinity media by immersion, dielectric strength test was applied on each set according to relevant ASTM standard with identical testing technique. The results were statistically processed then compared to the pristine material. Tests results utilized to learn Artificial Neural Network in order to acquire the value of dielectric strength of compounds having similar composition but containing different doping amounts or influenced with various salinity level media. The dielectric strength is enhanced by the addition of MgO nanofiller. From the investigation of the obtained results, it is concluded that additives of 1.4% filler concentration by weight is the optimum MgO content for LDPE/MgO nanofiller material. We think that this paper may promote a good researching methodology that gather both empirical work and numerical tools in this field.

Artificial Neural Network Prediction of the Optimal Setup Parameters of a Seven Degrees of Freedom Mathematical Model of a Race Car: IndyCar Case Study

The aim of this paper is the development of a 7-DOF (Degrees Of Freedom) mathematical model of an IndyCar and the implementation of an Artificial Neural Network in order to predict the optimal setup parameters of the car, reducing time and costs for race teams. The mathematical model is created by using MATLABTM and Simulink software starting from a telemetry acquisition at the Houston circuit and is based on Vertical Vehicle Dynamic equations. The optimal setup parameters have been predicted through an Artificial Neural Network (ANN) by using the NFTOOL Toolbox of MATLABTM software. ANN is implemented in a Quarter Car model, firstly, in order to train the network and predict the parameters able to reduce tire deflection and suspension travel in the time domain and the resonance peaks amplitude in the frequency domain. Then, it is implemented in the 7-DOF model in order to predict the best setup parameters able to reduce body movements and the weight transfers of the car.

Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles

This publication presents a multi-faceted analysis of the fuel consumption of motor vehicles and the way human impacts the environment, with a particular emphasis on the passenger cars. The adopted research methodology is based on the use of artificial neural networks in order to create a predictive model on the basis of which fuel consumption of motor vehicles can be determined. A database containing 1750 records, being a set of information on vehicles manufactured in last decade, was used in the process of training the artificial neural networks. The MLP (Multi-Layer Perceptron) 22-10-3 network has been selected from the created neural networks, which was further subjected to an analysis. In order to determine if the predicted values match the real values, the linear Pearson correlation coefficient r and coefficient of determination R2 were used. For the MLP 22-10-3 neural network, the calculated coefficient r was within range 0.93–0.95, while the coefficient of determination R2 assumed a satisfactory value of more than 0.98. Furthermore, a sensitivity analysis of the predictive model was performed, determining the influence of each input variable on prediction accuracy. Then, a neural network with a reduced number of neurons in the input layer (MLP-20-10-3) was built, retaining a quantity of the hidden and output neurons and the activation functions of the individual layers. The MLP 20-10-3 neural network uses similar values of the r and R2 coefficients as the MLP 22-10-3 neural network. For the evaluation of both neural networks, the measures of the ex post prediction errors were used. Depending on the predicted variable, the MAPE errors for the validation sets reached satisfactory values in the range of 5–8% for MLP 22-10-3 and 6–10% for MLP 20-10-3 neural network, respectively. The prediction tool described is intended for the design of passenger cars equipped with internal combustion engines.

Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

The necessity of an abundance of training data commonly hinders the broad use of machine learning in the plastics processing industry. Induced network-based transfer learning is used to reduce the necessary amount of injection molding process data for the training of an artificial neural network in order to conduct a data-driven machine parameter optimization for injection molding processes. As base learners, source models for the injection molding process of 59 different parts are fitted to process data. A different process for another part is chosen as the target process on which transfer learning is applied. The models learn the relationship between 6 machine setting parameters and the part weight as quality parameter. The considered machine parameters are the injection flow rate, holding pressure time, holding pressure, cooling time, melt temperature, and cavity wall temperature. For the right source domain, only 4 sample points of the new process need to be generated to train a model of the injection molding process with a degree of determination R2 of 0.9 or and higher. Significant differences in the transferability of the source models can be seen between different part geometries: The source models of injection molding processes for similar parts to the part of the target process achieve the best results. The transfer learning technique has the potential to raise the relevance of AI methods for process optimization in the plastics processing industry significantly.

Coupling artificial neural networks with the artificial bee colony algorithm for global calibration of hydrological models

Hydrological models are widely used tools in water resources management. Their successful application requires an efficient calibration of the model parameters. Nowadays, there are very powerful global search methods applied to this end, but they have the disadvantage of presenting a high computational cost, because the numerical model to be calibrated needs to be evaluated a large number of times with different parameter sets. In this context, surrogate models can reduce significantly the run time of hydrological models, easing the total computational burden of global search methods. In the present work, we propose and explore the combination of a swarm intelligence-based optimization method, the artificial bee colony algorithm, with a surrogate model based on artificial neural networks in order to calibrate hydrological models. The proposed approach (ABC-ANN) is applied to the calibration and validation of a nine-parameter continuous hydrological model in two basins located in the northwest of Spain. Several aspects of the algorithm are evaluated, including its capability to calibrate the model parameters and its efficiency in terms of CPU time compared to a standard implementation of the ABC algorithm. Results show that the ABC-ANN algorithm is able to identify the location of suitable parameter sets with an accuracy rate within 89 and 99 %, and a reduction in CPU time of more than three orders of magnitude when compared to a sequential implementation. In addition, the frequency distribution of the parameter sets identified gives valuable information about the sensitivity of model output to the input parameters.

Application of architecture using AI in the training of a set of pixels of the image at aid decision-making diagnostic cancer

Medical Medicine specialists are using images more and more as support in decision making in the identification of pathologies with greater complexity and severity in the specific case. Predictive and diagnostic models in images with neoplasia (collagen V) associated with exposure to asbestos fibers were studied. In this article we intend to initially assess machine learning on the basis containing 100 images classified by specialists with characteristics of normality and abnormality to aid diagnosis. Therefore, the objective is to analyze and use the concepts of learning by the technique of artificial intelligence with neural networks that culminated in significant advances of 81% of correct answers in the image diagnostics and to propose the application of the Paraconsistent Standard Analyser Unit of Artificial Neural Networks in order to categorize the degree of abnormality (normal, almost normal, almost abnormal, abnormal) with the use of the extreme and non-extreme states of Paraconsistent Logic and thus support specialists in decision making in the diagnosis of cancer.

Retinal blood vessels segmentation using classical edge detection filters and the neural network

Abstract Retinal blood vessels analysis is of interest for medical screening, especially in the diagnosis of diabetic retinopathy. In this paper, we propose a new method for the segmentation of blood vessels in retinal photographs. This method is based on classical edge detection filters and artificial neural networks. Firstly, edge detection filters are applied to extract the features vector. The resulting features are used to train an artificial neural network in order to recognize each pixel as belonging to blood vessels or not. The obtained algorithm is evaluated with the publicly available DRIVE, CHASE and STARE datasets, containing retinal images frequently used for this goal. The performance of the proposed system is calculated in terms of detection accuracy, sensitivity, specificity, and the area under the ROC curve. Our model is compared to other vessel segmentation models with encouraging results obtained. The proposed algorithm is a suitable tool for automated retinal image analysis.

Gradient-Based Algorithm for Tracking the Activity of Neural Network Weights Changing

The research conducted in this paper is in the field of machine learning. The main object of the research is the learning process of an artificial neural network in order to increase its efficiency. The algorithm based on the analysis of retrospective learning data. The dynamics of changes in the values of the weights of an artificial neural network during training is an important indicator of training efficiency. The algorithm proposed in this work is based on changing the weight gradients values. Changing of the gradients weights makes it possible to understand how actively the network weights change during training. This knowledge helps to diagnose the training process and makes an adjusting the training parameters. The results of the algorithm can be used to train an artificial neural network. The network will help to determine the set of measures (actions) needed to optimize the learning process by the algorithm results.