Research of Fire Detection Based on Process Characteristic and LM Arithmetic

Preparation and artificial neural networks analysis of ultrafine β-Sialon powders by microwave-assisted carbothermal reduction nitridation of sol–gel derived powder precursors

Objectives: Evaluating daily functioning of people with mental illnesses with questionaires is time-consuming. Additionally, patients' familiarity with the context of function tests compromises test accuracy. Applying a computer-assisted support assessment system in this study, we intended to predict the daily functioning of the mentally ill objectively and conveniently. Methods: We collected 54 patients attending a psychiatric daycare ward at a medical center in the Taipei city. A five-fold cross-validation scheme was applied to minimize possible bias and to provide reliable estimates. We used discriminant analysis (DA) and a back-propagation neural network (BPN), to predict patients' daily functioning, according to gender, educational background, diagnosis, and age, on Chu's daily function scale. Results: Both models achieved high average overall accuracy of more than 70%. The BPN model had a high overall classification accuracy of 92.55%, 16.55% better than that of the DA model. Additionally, the discriminant function showed that young males not diagnosed with schizophrenia had better daily function. Conclusion: This study was found that the BPN as a computer-assisted assessment support system predicted daily functioning more effectively than DA. To predict daily functioning relatively more precisely, we suggest that future research need to expand the sample population and to use additional variables, such as patients' personality, family support, and living status.

Traditional methods of multivariate statistical analyses, used to recognize anomalous hydrocarbon signatures in petroleum exploration, have at least four shortcomings: (1) it is difficult to isolate anomalies where the data are not normally distributed; (2) it is difficult to separate distinct anomaly populations corresponding to distinct formation mechanisms while separating anomalies from background; (3) it is not fitting to present illustrations of multivariate anomalies on contour maps; and (4) it is not suitable for preparation for multivariate pattern recognition. These present serious obstacles to the application of exploration geochemistry in hydrocarbon exploration. This study demonstrates that the Back Propagation Artificial Neural Network (BP-ANN) with logic multiplication cluster analysis (a new cluster analysis proposed in this paper) overcomes the difficulties exhibited by traditional multivariate methods. The logic multiplication cluster analysis was designed to produce a training set for the BP-ANN. This approach was established on the basis of geochemical characteristics and origin of the various populations in geochemical data, such as background, micro-seepage anomalies and seepage anomalies. The topology of the BP-ANN was optimized using the outputs of the BP-ANN and the correct rate. In order to illustrate the multivariate anomalies recognized using BP-ANN on contour maps, we designed the expression functions for BP-ANN application in this field. With traditional methods of anomaly recognition, acid-extractable hydrocarbons in soils have not proven to be efficient indicators for hydrocarbon potential in East Anan Sag, Inner Mongolia. However, the BP-ANN has indicated that these indicators are efficient and that areas of East Anan Sag have potential reserves of petroleum.

To establish a practical and effective model in predicting the premature birth, using the Back-Propagation Artificial Neural Network (BPANN). This was a prospective cohort study. Data was gathered from pregnant women selected by cluster sampling method from 2010 to 2012 in Liuyang city, Hunan province and was randomly divided into training sample (to establish the prediction models), validation sample (to select the optimal network) and testing sample (to evaluate the prediction models) by ratio of 2:1:1. BPANN and logistic regression analysis were used to establish models while ROC was applied to evaluate the 'prediction models'. Among the 6 270 pregnant women, 265 premature births were seen, with the premature incidence as 4.22%. The 7 variables which entered into the forecasting model would include abnormal uterine or uterine deformity, parity, number of pregnancies, gestational hypertension, placenta previa, premature rupture of membrane and regular prenatal examination. Sensitivity, specificity, agreement rate and area under the ROC curve of BPANN were 67.65% , 84.87%, 84.12% and 0.795, respectively. However, the sensitivity, specificity, agreement rate and area under the ROC curve of logistic regression were 64.71%, 85.60%, 84.69% and 0.783, respectively. The newly established BPANN model was practical and reliable, which proved that this model was slightly better than the logistic regression in the prediction of premature birth.

Temporary storage for collection of municipal solid waste is designed with various parameters, such as size of bin, collection frequency, average filling rate, number of bins, volume of material needed for bin production, bin density, and catchment area. The environmental impact of temporary storage is evaluated with emissions generated due to the production of net material used for bin production. An artificial neural network-back propagation method is proposed for modeling the temporary storage. In this proposed artificial neural network-back propagation network, the number of bins and global warming potential (GWP) based on the quantity of generated wastes are predicted. Thus, a set of different structures of artificial neural network-back propagation are investigated and then the best model is developed for temporary storage of municipal solid waste. The artificial neural network-back propagation model is trained using various parameters, such as number of neurons in the hidden layers, training tolerance, momentum, and learning rate. This model is helpful for the decision makers in choosing the environmentally sound options in the design of temporary storage for municipal solid waste management. Artificial neural network-back propagation can be successfully used to predict number of bins, GWP from waste generation rate of the city.

Artificial neural networks (ANN) consider classification as one of the most dynamic research and application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by back propagation algorithm [1]. A neural network represent a mathematical models of information processing, benefited from the human biological systems (i.e. a brain or nerve cell), where the neutral network can be trained and learned the same as a human brain does. The learning will be done by changing the weight during the training process and by using certain formula. One of the most known neural networks is the Back Propagation Network. This net has been used in variety of application areas. One of the, the classification of certain objects by known only a portion of information of the object to be classified. In this paper we shall use the back propagation network to classify the human blood groups, also we shall use the same program to be same analysis to find the best number of neurons in hidden layer that gives lower number of iteration.

The European Datum 1950 (ED50) of the Turkish national geodetic network (TNGN) and the World Geodetic System 1984 (WGS84) of the Turkish national fundamental GPS network (TNFGN) are in use as geodetic reference frames in Turkey. According to the use of two reference systems, it is necessary to transform the three-dimensional (3D) coordinate data from ED50 to WGS84 or vice versa. The seven-parameter similarity transformation method is frequently used for 3D coordinate transformation in geodesy. In this study, a back propagation artificial neural network (BPANN) that has been more widely applied in engineering among all other neural network models is evaluated as an alternative 3D coordinate transformation method. BPANN is compared with a popular seven-parameter similarity transformation (Molodensky-Badekas) method over a test area, in terms of root mean square error (RMSE). The results indicated that the employment of BPANN transformed 3D coordinates (X, Y, Z) more accurate than Molodensky-Badekas method and can be useful for 3D coordinate transformation between ED50 and WGS84. Key words: 3D coordinate transformation, back propagation artificial neural network, seven-parameter similarity transformation, BPANN, Molodensky-Badekas.

In this study, a back propagation artificial neural network model utilizing characteristics of urban signalized intersections for occurrence prediction of intersection - related traffic crashes, along with its application for crash reduction, are proposed. The 1,593 traffic crashes reported at 62 signalized intersections in Taichung city, Taiwan during year 2000 - 2001 were analyzed. These intersections were decomposed into 636 approaching direction combinations (ADCs). Characteristics, as well as the number of crashes, corresponding to each ADC were investigated. A back propagation artificial neural network (ANN) model was then generated. It was found that the ANN model exhibited great prediction ability with 0.992 correlation coefficient, and 3.38E- 06 mean square error. With the ANN model, a proposed decision-making scheme for intersection rehabilitation was suggested. Case study indicated that the suggested scheme could serve well as a referable tool.

Based on the nonlinear mapping function of neural network and by the adoption of Back Propagation (BP) artificial neural network with three-layer structure, the nonlinear mapping between the processing state of Numerical Control (NC) machine tools and the related parameters is realized. The processing state monitoring of NC machine tools is achieved by the neural network training. In addition, the development cycle of the program is reduced and its reliability is improved by using MATLAB Script node of LABVIEW software to call the neural network toolkit of MATLAB. Through the milling experiment of the finger milling-cutter, the capability of BP artificial neural network to accomplish the inspection of tool state and prediction of wear extent is verified.KeywordsBP Neural NetworkNC Machine ToolsProcessing Condition MonitoringTool Wear

Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7 % of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations.

There has been a need for geodetic network densification since the early days of traditional surveying. In order to densify geodetic networks in a way that will produce the most effective reference frame improvements, the crustal velocity field must be modelled. Artificial Neural Networks (ANNs) are widely used as function approximators in diverse fields of geoinformatics including velocity field determination. Deciding the number of hidden neurons required for the implementation of an arbitrary function is one of the major problems of ANN that still deserves further exploration. Generally, the number of hidden neurons is decided on the basis of experience. This paper attempts to quantify the significance of pruning away hidden neurons in ANN architecture for velocity field determination. An initial back propagation artificial neural network (BPANN) with 30 hidden neurons is educated by training data and resultant BPANN is applied on test and validation data. The number of hidden neurons is subsequently decreased, in pairs from 30 to 2, to achieve the best predicting model. These pruned BPANNs are retrained and applied on the test and validation data. Some existing methods for selecting the number of hidden neurons are also used. The results are evaluated in terms of the root mean square error (RMSE) over a study area for optimizing the number of hidden neurons in estimating densification point velocity by BPANN.

Variants of feedforward backpropagation (BP) artificial neural network (ANNs) such as pseudo Mac Laurin power series order 1 (McL1BP), order 2 (McL2BP), order 3 (McL3BP) and order 4 (McL4BP) models are used to fill in mean annual streamflows. The baseline of modeling process is the standard feedforward backpropagation ANN (StandBP). The sigmoid function is used as activation function. Performance comparisons of data infilling models (ANNs) are conducted using the Root Mean Square Error of Predictions (RMSEp) and graphical plots. To test the performance of the data infilling techniques, selected streamflow gauges (i.e. the Diepkloof (control) gauge on the Wonderboomspruit River and the Molteno (target) gauge on Stormbergspruit River) of the Orange drainage river systems of South Africa have been used. The results demonstrated that relatively higher order ANN models; i.e. McL3BP, McL4BP can still outperform the rest of techniques for 7 % (except StandBP), 20 % and 30 % missing data proportions at Molteno gauge. At the same time they have been outperformed by the rest of ANN models at 13 %. Generally higher and low order Pseudo MacLaurin ANNs are acceptable to fill in missing mean annual flows at Molteno gauge. The accuracy of estimated mean annual values at Molteno is substantially negatively affected beyond 20 % gap size for all ANNs. Hence gap size beyond 20 % yields to relatively higher value of RMSEp for both high and low MacLaurin feedforward ANN techniques. A linear relationship could describe accuracy of estimated values and gap size at Molteno. Further work should include other data regimes as well as other South African catchment areas. Other activation functions should also be tested.

<p>Predicting river water quality in the Special Region of Yogyakarta (DIY) is crucial. In this research, we modeled a river water quality prediction system using the artificial neural network (ANN) backpropagation method. Backpropagation is one of the developments of the multilayer perceptron (MLP) network, which can reduce the level of prediction error by adjusting the weights based on the difference in output and the desired target. Water quality parameters included biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), dissolved oxygen (DO), total phosphate, fecal coliforms, and total coliforms. The research object was the upstream, downstream, and middle parts of the Oya River. The data source was secondary data from the DIY Environment and Forestry Service. Data were in the form of time series data for 2013–2023. Descriptive data results showed that the water quality of the Oya River in 2020–2023 was better than in previous years. However, increasing community and industrial activities can reduce water quality. This was concluded based on the prediction results of the ANN backpropagation method with a hidden layer number of 4. The prediction results for period 3 in 2023 and period 1 in 2024 are that 1) the concentrations of BOD, fecal coli, and total coli will increase and exceed quality standards, 2) COD and TSS concentrations will increase but will still be below quality standards, 3) DO and total phosphate concentrations will remain constant and still on the threshold of quality standards. The possibility of several water quality parameters increasing above the quality standards remains, so the potential for contamination of the Oya River is still high. Therefore, early prevention of river water pollution is necessary.</p>

Monitoring maize growth conditions by training a BP neural network with remotely sensed vegetation temperature condition index and leaf area index

Flavonoids, the most diverse class of plant secondary metabolites, exhibit high affinity toward the purified cytosolic NBD2(C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationships (QSARs) models were developed using back-propagation artificial neural networks (BPANN) and multiple linear regression (MLR). Molecular descriptors were calculated using PaDELDescriptor, and the number of descriptors was then reduced using a genetic algorithm (GA) and stepwise regression. The MLR (R(2)=0.855, q(2)=0.8138, Rext(2)=0.6916), 14-3-1 BPANN (R(2)=0.8514, q(2)=0.7695, Rext (2)=0.8142), 14-4-1 BPANN(R(2)=0.9199, q(2)=0.7733, Rext(2) =0.8731), and 14-5-1 BPANN (R(2)=0.8660, q(2)=0.7432, Rext(2)=0.8292) models all showed good robustness. While BPANN models exceeded significantly MLR in predictable performance for their flexible characters, could be used to predict the affinity of flavonoids for P-gp and applied in further drug screening.