Implementation of Neural Network Backpropagation Using Audio Feature Extraction For Classification Of Gamelan Notes

Online assessment of pear firmness by acoustic vibration analysis

ObjectiveConsidering the complex and nonlinear relationship between characteristic parameters of tumors (location and morphology) and lung dose‐volume parameters of patients with esophageal cancer (EPC) undergoing intensity‐modulated radiation therapy (IMRT), the model identification method of artificial neural network (ANN) was adopted to build the model to predict lung dose‐volume parameters under different characteristic parameters of tumors. The goal is to enhance the efficiency and quality of radiotherapy planning and reduce the risk of radiation‐induced lung injury (RILI) by providing pre‐planning dose predictions.Methods1). In the previous research work, a retrospective analysis was done on 103 cases of patients with EPC who were treated by radiotherapy, in which a linear regression model was employed and had proved the association between tumor morphology [including tumor relative length (L) and “tumor axial cross‐sectional area” (S)] and lung dose‐volume parameters (including V5, V10, V20 and V30 of lungs). 2). Building on earlier research, this study uses curve regression analysis to further explore the effect of tumor relative position (P) on lung dose‐volume parameters of patients with EPC undergoing IMRT. 3). A backpropagation (BP) neural network model was constructed with P, L, and S as inputs and lung dose‐volume parameters as outputs to establish their quantitative relationship. 4). Genetic algorithm (GA) was combined with BP neural network to optimize the initial weights and thresholds of BP neural network and avoid the model from falling into local optima, so as to improve the performance of BP neural network model.Results1). Quadratic regression models were applied to analyze the relationship of P with V5 and V10 of the lung, yielding adjusted R2 values of 0.177 and 0.081, respectively, with statistically significant regression coefficients (p < 0.01). The results suggest that as P increases, V5 and V10 initially increase, then decrease. Linear regression models were applied to analyze the relationship of P with V20 and V30 of the lung, yielding adjusted R2 values of 0.06 and 0.072, respectively, with significant regression coefficients (p < 0.01). The results show that both V20 and V30 decrease as P increases. 2). The finding of this study indicates that the modeling method of BP neural network can realize the prediction of lung dose‐volume parameters under different characteristic parameters of tumors. The GA‐BP model showed slight improvements in prediction accuracy (PA) and error index (EI) compared to the BP model alone. For both models, prediction accuracy is highest for V5, followed by V10 and V20, with V30 being the least accurate. Notably, both the BP and GA‐BP neural networks achieve outstanding recognition accuracy for V5, with EI and PA values of 0.121 and 88.61% for BP, and 0.113 and 89.00% for GA‐BP, respectively. 3). The re‐optimization of four test cases with under‐predicted whole‐lung V5 and V10 values resulted in substantial improvements over the original plans. Conclusion: The ANN model developed in this study can effectively predict lung dose‐volume parameters for patients with EPC undergoing IMRT. It provides pre‐planning guidance that enhances the efficiency and quality of radiotherapy planning and reducing the risk of RILI.

This paper presents a neural network method to locate common fault exactly in a distribution power system (DPS) with renewable sources. The back propagation (BP) neural network method is applied to identify patterns of voltage and current measured from distribution branches. The input matrix of BP network consists of the voltage and current values, which can identify the accurate fault position. The fault location of a common short-circuit fault is analyzed thoroughly in an active distribution network (ADN) with the renewable power sources. Simulation results prove the feasibility and usefulness of the fault location method based on the BP neural network, wherein the fault location accuracy can reach 0.09%.

Feature extraction is important to obtain information in digital images, where feature extraction results are used in the classification process. The success of a study to classify digital images is highly dependent on the selection of the feature extraction method used, from several studies providing a combination of feature extraction solutions to produce a more accurate classification. Classifying the types of marine fish is done by identifying fish based on special characteristics, and it can be through a description of the shape, fish body pattern, color, or other characteristics. This study aimed to classify coral reef fish species based on the characteristics contained in fish images using Backpropagation Neural Network (BPNN) method. Data used in this research was collected directly from Bunaken National Marine Park (BNMP) in Indonesia. The first stage was to extract shape features using the Geometric Invariant Moment (GIM) method, texture features using Gray Level Co-occurrence Matrix (GLCM) method, and color feature extraction using Hue Saturation Value (HSV) method. The third value of feature extraction was used as input for the next stage, namely the classification process using the BPNN method. The test results using 5-fold cross-validation found that the lowest test accuracy was 85%, the highest was 100%, and the average was 96%. This means that the intelligent model derived from the combination of the three feature extraction methods implemented in the BPNN training algorithm is very good for classifying coral reef fish.

Sanza, often translated as "thumb piano", is one of the important traditional musical instruments in Africa. It is neither a percussion nor a wind instrument, and because of this ambiguity it has been a problem to the Western ethnomusicologists who are interested in the classification of musical instruments. The authors describe the forms, decorations, raw materials and playing techniques of various sanzas, based on the materials stored at the National Museum of Zaire, and attempt to classify them, mainly according to the type of resonance box. Also pointed out in this paper is the importance of the study of musical instruments for better understanding of traditional society.

Regression prediction of hydrogen enriched compressed natural gas (HCNG) engine performance based on improved particle swarm optimization back propagation neural network method (IMPSO-BPNN)

Sirompak is a traditional Minangkabau art originating from Taeh Baruah, Limapuluh Kota, West Sumatra. In the past, Sirompak was known as a magical ritual ceremony carried out by a sirompak handler whose aim was to avenge the heart of a woman who had insulted a man. The basirompak performance is a presentation of vocal music called dendang Minangkabau presentation of singing vocal music with the production of a wind instrument called saluang sirompak. This musical instrument is included in the classification of aerophone musical instruments (the main sound is produced by the vibration of blowing air). Traditional musical instruments accompany melodic vocals (dendang/mantras) by a singer (singer). The composition work "manyampai" consists of two parts, using a traditional reinterpretation approach, in the first part of this work using the 'emergent' technique, in the first part the composer provides several repetitive vocal compositions, using the instruments saluang Sirompak, kerinding, cymbals, didgeredo, gong, rain stick. The second part of this work emphasizes the production of several screaming vocals, screams, laughing vocals, animal sounds and crying. This second part also uses the instruments cymbals, gongs, Sirompak saluang

Time-dependent structural systems (TDSSs) in engineering involve high dimensionality, nonlinearity, and complex uncertainties, complicating the reliability analysis compared to time-independent assessments. To address these challenges, this paper proposes an extremum-based back propagation neural network (BPNN) method for TDSS reliability analysis. The method adopts a double-loop structure. Specifically, the inner loop finds the minimum of the time-dependent performance function for a given realization of the random variables. This transformation converts the time-dependent problem into an equivalent time-invariant one. Then, the outer loop constructs a BPNN surrogate model to map the relationship between the random variables and the performance function minima. To improve computational efficiency, an adaptive sample selection strategy is integrated into the training process. This technique selects samples near the failure boundary to iteratively update the BPNN, ensuring high accuracy with a small training set. Once the stopping criterion is satisfied, the failure probability is estimated using Monte Carlo simulation (MCS). The trained BPNN model is used to rapidly predict the extremum for the large-scale sample pool. The proposed method is verified through three practical engineering cases: a four-bar mechanism, an aero-engine turbine disc, and a cantilever tube. Results show that the method remains accurate and efficient. The successful applications confirm the rationality and engineering applicability of the proposed model.

Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960–2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001–2010) and the predicting period (2011–2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans.

PurposeThe purpose of this paper is to analyze and estimate the stray current corrosion hazard of a buried metallic pipeline using a predictive model for stray current density.Design/methodology/approachA predictive model for stray current density of the buried metallic pipeline was built, using a back propagation (BP) neural network method and experimental data. The accuracy of the model was tested using test samples. The single sensitivity analysis predictive method was used to establish the relationship between stray current density with the soil resistivity. The effects of buried depth and the pipe‐to‐ground voltage offset were researched using this network model.FindingsThe feasibility of the BP neural network to forecast stray current effects from the buried metallic pipeline was confirmed.Originality/valueThe paper provides a new method to analyze and estimate the stray current corrosion hazard of buried metallic pipelines.

The acquisition of sea surface wind information is of great value for marine activities, such as navigation, fishing and sea operations. In this paper, the ERS-2 SAR data, covering the Yangtze River estuary and its adjacent waters at around 10:30 local time on March 15, 2007, was applied to retrieve wind fields. We improved the sea surface wind direction inversion algorithm based on two dimensions Fast Fourier Transform (2DFFT) in the spectral domain. These sea surface wind directions were then applied as input parameters to derive wind speeds employing Back Propagation Neural Network (BPNN) method. To present and testify the applicability of these algorithms, the wind fields derived from SAR were validated with QuikSCAT SeaWinds-1 measurements. The results show that the SAR inversion results are reliable, and within the range of error tolerance. Specifically, the sea surface wind direction has a root mean square (RMS) error of 9.21°, maximum error of 24.2°. The inversion accuracy is higher in the open sea area. Whereas, it is not really good in the area affected by land, ship, and other artifacts. The wind velocity has a RMS error of 1.74 m/s, maximum error of 3.8 m/s. The effectiveness and reliability of the improved wind direction inversion algorithm and BPNN method for wind speed are demonstrated.

The exploration of internet finance by using neural network

This paper examines the use of Mel-frequency Cepstral Coefficients in the classification of musical instruments. 2004 piano, violin and flute samples are analysed to get their coefficients. These coefficients are reduced using principal component analysis and used to train a multi-layered perceptron. The network is trained on the first 3, 4 and 5 principal components calculated from the envelope of the changes in the coefficients. This trained network is then used to classify novel input samples. By training and testing the network on a different number of coefficients, the optimum number of coefficients to include for identifying a musical instrument is determined. We conclude that using 4 principal components from the first 15 coefficients gives the most accurate classification results.

The grade of textile yarns is an important index in evaluating the yarn’s market value. This paper uses the backpropagation neural network (BNN) and Karhunen-Loeve (K-L) expansion method to construct a new and highly accurate grading system. Outcomes show that a highly accurate and neutral grading system can be obtained if the BNN learning sample is comprehensive or by adopting the BNN with a relearning technique (self-healing). Considering the possibility of reducing the dimension of BNN input vectors without losing the accuracy, this paper preprocesses the BNN grading system using the K-L expansion. Experiments demonstrate that the K-L expansion provides a way to reduce the input dimensions, and that a single principle axis value of the BNN with the K-L expansion grading system is able to grade textile yarns. In addition, the experiment demonstrates that as the input dimensions are reduced to four in a self-healing neural network with the K-L expansion, the grading system provides the high accuracy and robustness.

Vertical distributions of temperature and humidity are two essential factors for understanding the atmospheric structure, extreme weather events, and regional and global climate. The ground-based microwave radiometer (MWR), which acts as a passive sensor and operates continuously under all weather conditions, has an irreplaceable role in measuring the vertical information of the temperature and water content in the atmosphere. In this letter, we proposed a four-layer back-propagation neural network (BPNN) method to retrieve temperature and relative humidity (RH) profiles from the bright temperature measured by the MWR. In contrast to the traditional BPNN, this method has greater advantages in dealing with the problems of overfitting, gradient disappearance, and gradient explosion in vertical atmospheric retrieval. By adding dropout layers, it can also help to describe the nonlinear relationships for RH profiles. Results showed that the performance of the four-layer BPNN method was better than the quadratic regression (QR, provided by MWR manufacturer) method under both cloud and cloud-free conditions. Compared with measurements of radiosonde data, root-mean-square error of temperature and RH, BPNN achieves 1.88 K and 19.30% under cloud conditions and 2.03 K and 15.10% under cloud-free conditions, respectively, whereas the corresponding values by using the QR method were only 3.07 K and 24.28% under cloud conditions and 4.14 K and 18.96% under cloud-free conditions, respectively. Temperature and RH profiles retrieval with high precision have increased the efficiency of the MWR observations and provided a data foundation for further atmospheric climate research.