Accurate Index Research Articles

Rapid distance estimation of odor sources by electronic nose with multi-sensor fusion based on spiking neural network

The inference of odor source locations holds great potential value in chemical leak detection, explosive searches and flammable substance detection. Sensor response and derivative changes are common indicators used to estimate source distance. However, most studies often rely on single-feature methods using a single sensor. In this study, a novel distance indicator based on a spiking neural network (SNN) that integrates multiple sensors is proposed. SNN was introduced for effective multi-sensor data fusion, which can provide accurate and rapid source distance estimation. The indicator's accuracy and quick detection capabilities were tested using a publicly available wind tunnel dataset and compared with single-sensor indicators. Additionally, an olfactory robot platform was constructed to test the method's real-time performance. Results demonstrate that this method effectively utilizes sensor arrays to estimate source distance, with an average root mean square error (RMSE) of less than 0.1 m across different wind speeds, outperforming single-sensor-based indicators. Additionally, the model exhibits good distance estimation performance within a relatively short detection time, with an average RMSE of 0.118 m. Practical testing on a mobile robot platform confirms the applicability of this distance indicator for real-time distance monitoring. In conclusion, the proposed distance indicator enables effective distance estimation for odor sources, providing a promising method for odor source localization and olfactory navigation.

Classification performance analysis of a louver classifier with a tilted sidewall and its application to the separation of unburned carbon from biomass combustion ash

Separating unburned carbon (UC) from biomass combustion ash is necessary to achieve high-quality ash as recyclable raw material and improve energy efficiency by reburning the UC in the furnace. In this study, a novel louver classifier geometry with tilted sidewall was proposed to separate the UC from biomass ash and its classification performance was analyzed via experiments and simulations. The louver classifier with a tilted sidewall provided a smaller 50% cut size and classification accuracy index indicating that the separation performance is improved. The CFD simulations revealed that tilted sidewall in the louver increased gas flow downward velocity in the classification region, increasing the inertia of the particles, and causing more particles to leave gas flow and deposit into the collection box as coarse ash. The UC of classified combustion ash increased with a decrease in the median mass diameter, indicating that the classification process for combustion ash produced coarse ash with a lower UC. The tilted sidewall in the modified louver classifier increased the reduction of unburned carbon (RUC) in ash up to 83.8%. The RUC increased with a cut size of the classified ash which was achieved when a high inlet flow velocity and high blow-up ratio are applied.

Measurement of the effective refractive index of silicon-on-insulator waveguide using Mach–Zehnder interferometers

We propose and demonstrate an accurate method of measuring the effective refractive index of silicon-on-insulator waveguides. By conducting the combined analysis to the troughs’ wavelength in spectra of Mach–Zehnder interferometers on chip. The wavelength-dependent and temperature-dependent effective refractive index of the fabricated waveguides are measured experimentally, and obtained the thermo-optic coefficient of silicon-on-insulator waveguides is about 2×10−4 /℃ in the 1550 nm communication band. The maximum measurement error for effective and group refractive index respectively are 1.5×10−5 and 1.5×10−3 obtained by numerical simulation. And an improved method for taking value of the free spectral range was discussed to obtain a more accurate group refractive index. It proves a fast and lost-cost measurement way to evaluate key optical parameters of waveguide, which can indicate the quality of fabrication process and optimize photonic components.

Mechanisms of speed-accuracy trade-off in tennis topspin forehand of college players

ABSTRACT This study aimed to elucidate the mechanisms underlying the speed-accuracy relationship in a tennis topspin single-handed forehand groundstroke. Groundstrokes at three different speeds by nine college players were captured, with the variability of the ball landing position evaluated as indices of accuracy. Variabilities of ball launch variables (speed, launch angle, spin, etc.) and conversion ratios from these variabilities to the variability of the ball landing position were quantified. These variabilities and their conversion ratios could be influenced by different efforts exerted to generate ball speeds and different ball trajectories required to hit a target at each speed, respectively. The speed-accuracy trade-off was observed only in the hitting direction. While the variability of the spin axis increased, it had minimal influence on the ball landing position. Conversely, the conversion ratio in the hitting direction of the velocity elevation angle significantly increased, while its variability remained unchanged. Consequently, the geometrical requirements of ball trajectories can be responsible for the speed-accuracy trade-off. Therefore, even skilled players capable of maintaining consistent ball launch variables regardless of the shot speed should deliberately choose a moderately slower ball speed to avoid an inevitable increase in the variability of the ball landing position due to geometrical requirements.

Accurate and efficient stock market index prediction: an integrated approach based on VMD-SNNs

The stock market index typically mirrors the financial market's performance. Hence, accurate prediction of stock market index trends is essential for investors aiming to mitigate financial risk and enhance future investment returns. Traditional statistical approaches often struggle with the non-linear nature of stock market index data, leading to potential inaccuracies in long-term predictions. To address this issue, we introduce the TCN-LSTM-SNN (TLSNN) model, a hybrid framework that integrates Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) for robust feature extraction, within a highly efficient Spiking Neural Network (SNN) architecture. Additionally, we employ the Subtraction-Average-Based Optimizer (SABO) to refine the Variational Mode Decomposition (VMD) technique, thereby separating the periodic and trend components of stock indices, reducing noise interference, and establishing a decomposition ensemble framework to bolster the model's resilience. The experimental results show that the VMD-TLSNN hybrid model suggested in this study surpasses other individual benchmark models and their hybrid models in prediction accuracy. Additionally, it demonstrates notably lower energy consumption compared to other hybrid models.

Optimizing agricultural data security: harnessing IoT and AI with Latency Aware Accuracy Index (LAAI).

The integration of Internet of Things (IoT) and artificial intelligence (AI) technologies into modern agriculture has profound implications on data collection, management, and decision-making processes. However, ensuring the security of agricultural data has consistently posed a significant challenge. This study presents a novel evaluation metric titled Latency Aware Accuracy Index (LAAI) for the purpose of optimizing data security in the agricultural sector. The LAAI uses the combined capacities of the IoT and AI in addition to the latency aspect. The use of IoT tools for data collection and AI algorithms for analysis makes farming operation more productive. The LAAI metric is a more holistic way to determine data accuracy while considering latency limitations. This ensures that farmers and other end-users are fed trustworthy information in a timely manner. This unified measure not only makes the data more secure but gives farmers the information that helps them to make smart decisions and, thus, drives healthier farming and food security.

Multi-method piecewise low-frequency identification

Inter-area frequency modes of electromechanical oscillations are decisive factors that influence the performance of electrical power systems. However, the accurate estimation of these frequencies remains a challenge due to the non-linear behavior of the response system following a disturbance and the strong coupling among the frequencies. This work describes a curve fitting and electromechanical frequency estimation algorithm, called Multi-method Piecewise Identification (MPI), based on a combination of the Matrix Pencil Method (MPM), the Prony method, and Subspace Identification (SSI), choosing the best approximation of an interval basis according to the model accuracy index (MAI) and the mean square error (MSE). The advantages and limitations of MPI were analyzed by computational analyses that were carried out with Python-based code that estimates the low frequencies for several ringdown signals, including oscillating signals from Australian and Brazilian systems that arise after large disturbances. To ensure robustness to random and noisy conditions, the method was tested with a synthetic signal with Gaussian white noise and time-variant frequencies, and compared to the methods mentioned above, the Hilbert–Huang Transform (HHT), and Eigensystem Realization Algorithm (ERA). The MPI has shown robust results and is capable of obtaining a better fitting for the signal than the other methods.

Theoretical analysis and technical application of mechanical intelligent manufacturing based on system digital-driven technology

With the accelerating process of global industrialization, the automated fabrication industry, as an important part of the industry, is also developing rapidly. Due to the low efficiency and high resource consumption of traditional manufacturing, it is more important to improve the production efficiency of conventional manufacturing and realize the resource-saving development mode. Due to the characteristics of system digital-driven technology, it can promote the intelligent development of the manufacturing industry. Therefore, the deep integration of system digital-driven technology and manufacturing is conducive to the intelligent promotion of manufacturing. Comparing the difference between the traditional manufacturing industry and the intelligent manufacturing industry, the application potential of system digitalization technology in the manufacturing field is analyzed. Based on the analysis of the theoretical basis of the system digital-driven technology and the application of the system digital technology in the main fields, the importance of the technology for the improvement of the accuracy index in the manufacturing process is emphatically analyzed. The maximum increase can be 5.3%, and the minimum increase can be 3.3%. It can be seen that the deep integration of digital-driven technology and manufacturing can not only improve the intelligent level of manufacturing, realize the intensive development of the industry, but also realize the data sharing of the entire industrial chain.

Diagnostic value of antibody testing in comparison with lung scan and PCR in patients suspected of having COVID-19.

SARS-CoV-2 is a newly discovered viral infection. It's still unclear how antibodies react in infected individuals, and there is not enough evidence to support the clinical use of antibody examination. This study evaluates the diagnostic value of serologic tests for diagnosing COVID-19. 32 patients for whom serologic testing was performed within 7 to 21 days from symptom onset and whether they were diagnosed with COVID-19 by both PCR and lung HRCT as gold standard tests at the same time, were included in the study. Serologic tests (IgM / IgG) compared to PCR and lung HRCT scan to diagnose COVID-19, were 89.3% specific and 59.6% sensitive. Positive predictive value (PPV) was 95% and negative predictive value (NPV) was 37%. The diagnostic accuracy index of the serologic test was 0.745 (CI 0.651-0.838) (p-value <0.001). Serologic testing can be a complementary alternative for SARA-CoV-2 nucleic acid RT-PCR, although it cannot replace it completely. IgG/IgM combo test kits and RT-PCR together can give more insight into the diagnosis of SARS-CoV-2.

The Role of Field Measurements of Fine Dead Fuel Moisture Content in the Canadian Fire Weather Index System—A Study Case in the Central Region of Portugal

The Canadian Fire Weather Index System (CFWIS), empirically developed for forests in Canada, estimates the fuel moisture content (mf) at different depths and loads through meteorological parameters. While it is often suggested that adapting an existing fire danger rating system like CFWIS for a new environment requires developing new relationships or modifying existing ones, it is worth considering if such adaptations are always necessary. Based on a dataset of field measurements for surface litter (Pinus pinaster) carried out in the central region of Portugal (2014–2023), we propose a correction of mf based on the Fine Fuel Moisture Code (FFMC) of the CFWIS. This moisture correction was used to determine the Initial Spread Index (ISI) directly and, subsequently, the Fire Weather Index (FWI). Fire records from the study region were used to analyze the performance of the corrected indices. We found that the moisture correction led to higher values and potentially more accurate indices under dry conditions but did not provide a significant improvement in predicting the number of fires and burned areas compared to the original indices. The results suggest that, in relation to fire activity, the CFWIS is sufficiently robust to variations in the fuel moisture content in the study region.

Optimizing Kernel Density Estimation Bandwidth for Road Traffic Accident Hazard Identification: A Case Study of the City of London

Road traffic accidents pose significant challenges to sustainable urban safety and intelligent transportation management. The effective hazard identification of crash hotspots is crucial in implementing targeted safety measures. A severity-weighted system was adopted to quantify crash hazard levels. Using 1059 valid crash records of the City of London, the spatial correlations of crash points were first examined via average nearest neighbor analysis. Then, the optimal KDE bandwidth was determined via ArcGIS’s automatic extraction method, multi-distance spatial cluster analysis, and incremental spatial autocorrelation (ISA) analysis. The predictive accuracy index (PAI) was used to evaluate the accuracy of KDE results at various bandwidths. The results revealed a clustered spatial distribution of crash points. The optimized KDE bandwidth obtained via ISA analysis was 134 m, and the yielded PAI was 4.381, indicating better predictive accuracies and balanced hotspot distributions and reflecting both local concentrations and the overall continuity of crash hazard hotspots. Applying this bandwidth to the validation data allowed the successful identification of most high-risk areas and potential crash hazard hotspots attributed to traffic environmental factors; this method exhibits reliability, accuracy, and robustness over medium to long time scales. This workflow can serve as an analytical template for assisting planners in improving the identification accuracy of hazard hotspots, thereby reducing crash occurrences, actively promoting sustainable traffic safety development, and providing valuable insights for targeted crash prevention and intelligent traffic safety management in urban areas.

A convolutional neural network model for accurate short-term leaf area index prediction

A convolutional neural network model for accurate short-term leaf area index prediction

Adopting artificial intelligence algorithms for remote fetal heart rate monitoring and classification using wearable fetal phonocardiography

Fetal phonocardiography (FPCG) is a non-invasive Fetal Heart Rate (FHR) monitoring technique that can detect vibrations and murmurs in heart sounds. However, acquiring fetal heart sounds from a wearable FPCG device is challenging due to noise and artefacts. This research contributes a resilient solution to overcome the conventional issues by adopting Artificial Intelligence (AI) with FPCG for automated FHR monitoring in an end-to-end manner, named (AI-FHR). Four sequential methodologies were used to ensure reliable and accurate FHR monitoring. The proposed method removes low-frequency noises and high-frequency noises by using Chebyshev II high-pass filters and Enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ECEEMDAN) in combination with Phase Shifted Maximal Overlap Discrete Wavelet Transform (PS-MODWT) filters, respectively. The denoised signals are segmented to reduce complexity, and the segmentation is performed using multi-agent deep Q-learning (MA-DQL). The segmented signal is provided to reduce the redundancies in cardiac cycles using the Artificial Hummingbird Optimization (AHBO) algorithm. The segmented and non-redundant signals are converted into 3D spectrograms using a machine learning algorithm called variational auto-encoder-general adversarial networks (VAE-GAN). The feature extraction and classification are carried out by adopting a hybrid of the bidirectional gated recurrent unit (BiGRU) and the multi-boosted capsule network (MBCapsNet). The proposed method was implemented and simulated using MATLAB R2020a and validated by adopting effective validation metrics. The results demonstrate that the proposed method performed better than the current method with accuracy (81.34%), sensitivity (72%), F1-score (83%), Energy (0.808 J), and complexity index (13.34). Like other optimization methods, AHO needs precise parameter adjustment in order to function well. Its performance may be greatly impacted by the selection of parameters, including population size, exploration rate, and learning rate.

Comparison of enhanced neural network and response surface models in predicting bio-dissolution of aluminum and vanadium from bauxite residue by isolated Aspergillus niger strains

Background: Recovery of Al and V from red mud, a hazardous residue of the Bayer process, using bioleaching is a nature-based waste management solution that simultaneously improves environment protection and metallurgical recoveries.Methods: For the first time, a novel hybrid multilayer Perceptron (MLP) network enhanced by an Imperialist Competitive Algorithm (ICA), and a response surface developed based on a factorial experiment design methodology (RSM) were employed and compared for, prediction optimization and optimization prediction of Al and V bioleaching by strains of A. Niger microorganisms isolated from pistachio shells and grape skins. The controlling variables were fungi source (A), adoption strategy (B), solid activation (C), solid percent (D), and bioleaching time. Considering the stochastic nature of ICA, a multi-criteria-ranking system based on accuracy and error indices was developed to select the best MLP. Probability value at 95% confidence interval, lack-of-fit, analysis of variance, R2, Adj. R2, and predicted R2 were the judges for the determination of the developed model's statistical significance.Significant Findings: Based on ANOVA, between A, B, C, and D, the effectiveness orders of A > D > C>AC>AD on Al, and A>AD>C>AC>D on V bioleaching were obtained. The superiority of the developed hybrid ICA-MLP model over the developed RSM model in predicting Al and V dissolution was determined by NSE, RSME, MAE, and MEDAE. Parameters optimization and consequently evaluating optimum condition repeatability through three repeating experiments resulted in maximum dissolution recoveries of RSM: 96.5% for Al and 91.2% for V, and ICA-MLP: 97.1% for Al and 90.3% for V. Considering the lower relative errors of repeating validation tests, it can be concluded that, although both models provide reasonable results, but the ICA-MLP methodology is more reliable (relative error <1.8%).

Rapid identification model of mine water inrush source using random forest optimized by multi-strategy improved sparrow search algorithm

Mine water inrush accident is one of the most threatening disasters in coal mine production process. In order to improve the identification accuracy of mine water inrush source, a fast identification method of mine water inrush source based on improved sparrow search (SSA) algorithm coupled with Random Forest algorithm was proposed. Firstly, taking Zhaogezhuang Mine as the research object, six factors were selected as the discriminant index and three principal components were extracted by kernel principal component analysis. Secondly, four strategies are employed to enhance the SSA for achieving the ISSA, while multiple benchmark functions are utilized to validate its performance. The extracted principal components serve as input, and the categories of water inrush sources act as output. Subsequently, the prediction results of Random Forest (RF) algorithm after optimizing hyperparameters through Improve SSA are compared with those obtained from other models. The research findings demonstrate that optimizing the RF model using Improve SSA yields superior predictive performance compared to alternative models. Finally, this model is applied to identify water inrush sources in a mine located in Shandong province. The discrimination results exhibit higher accuracy, precision, recall, and F1 index than other models, thereby confirming the reliability and stability of this approach. The results demonstrate that the kernel principal component analysis-based rapid identification model for mine water outburst source, combined with an improved sparrow search algorithm to optimize Random Forest, exhibits excellent robustness and accuracy. This model effectively fulfills the requirements of identifying mine water outbursts and provides a reliable guarantee for ensuring mining safety production.

Land use mapping from sentinel satellite imagery using remote sensing and GIS techniques

Abstract The need to classify sentinel-2 satellite images to extract land use /land cover (LULC) are essential to analysis the processes of environment problems and to improve living conditions. Hence, this research aims to create LULC maps from sentinel-2 satellite images through Maximum likelihood (ML) algorithm using remote sensing and GIS. The selected study area for this research is Baghdad city because of it has a unique political stability and due to rapid urbanization, that lead to rise additional request for natural resources and affected on LULC in Baghdad city. After preprocessing and processing of satellite images, thematic maps were created and classified into five main classes based on visual interpretation and visit the field of the study area that containing: urban, vegetation, soil, asphalt roads, and water bodies. The results showed that classification accuracy assessment of ML algorithm is acceptable because of overall accuracy and Kappa index equal (86%, 0.82) respectively.

Defining an exposure index along the Schleswig-Holstein Baltic Sea coast

A wind exposure index (EI), which indicates the main physical driver of a coastal system, was developed along the Schleswig-Holstein Baltic Sea (SH) coast – Germany, to demonstrate the highly dynamic coastal stretches (i.e., potential erosion hotspots). The approach used three steps to define more accurate EIs. Initially, a representative wind year (RWY), which has similar physical characteristics as in the long-term data, was defined by analysing measured wind data from 2000 to 2019 at four stations distributed in the entire area of interest. The RWY was identified by a statistical comparison of wind speeds in 5 classes and 36 directional sectors between summer to summer yearly wind and the overall data. The selected RWY spanned from 01.09.2016 to 31.08.2017 and showed a reasonable agreement with the overall data (Skill = 0.77 and rmsd = 0.56 m/s). Next, high spatiotemporal nearshore hydrodynamics over the RWY were predicted using a model nesting approach of two domains in Delft3D. The predicted nearshore hydrodynamics indicated fair agreements with the measured data (R2: 0.87–0.90 for water levels and 0.75–0.86 for wave heights). Finally, the predicted water level and wave height time series in the nearshore area (∼ 5 m MSL depth) were used for the analysis of the EI adopting a 2-step procedure capturing short- and long-term correlations as well as seasonal long-range dependencies of the time series. This approach allows to model the clustering behaviour of extreme values of both parameters and provides reasonable EIs along the SH coast. The exposed areas display high EIs (e.g., 1 at the east of Fehmarn), while sheltered areas and bays have low values (e.g., 0 at Eckernförde Bay). The higher the EI the stronger the coastal dynamics and thus strong erosion can be expected. Interestingly, the EI varies considerably even along the exposed coastal stretches with long fetches, which indicates the sensitivity of the EI to the local morphology, which determines the nearshore hydrodynamics. Therefore, a definition of the EI based on nearshore hydrodynamics provides an accurate index of local physical drivers of a coastal system. The developed approach can be adopted to any coast, and provides useful information on the potential erosion areas for the coastal managers.

Research on time series prediction of hybrid intelligent systems based on deep learning

Power forecasting plays a crucial role in the operation of smart grid system, which is indispensable for making the operation plan of power system, improving economic efficiency and ensuring the quality of power supply. In order to enhance the accuracy of power load forecasting, a hybrid intelligent power load forecasting system is proposed in this paper. The system first preprocesses the raw data using Savitzky-Golay smoothing technology to eliminate noise and improve data quality. Then, a long and short term memory network with attention mechanism is used to enhance the generalization ability of the model. In addition, in order to further improve the prediction performance, an improved genetic algorithm is integrated to optimize the model parameters. Finally, a data set is used to verify the proposed prediction method. In terms of short-term forecasting ability of experiment of the testing data set, compared with LSTM model, the proposed method shows superior performance in root mean square error and mean absolute error indicators, with root mean square error reduced by 18.7 % and mean absolute error reduced by 26.2 %. In terms of long-term prediction ability of experiment of the testing data set, compared with GBRT model, the proposed method reduces root mean square error and mean absolute error by 24.8 % and 30.7 %, respectively. The experimental results show that compared with the existing benchmark algorithm, the proposed method is significantly improved in two key indexes of prediction accuracy, which proves its effectiveness and superiority in power load prediction.

MGB-Unet: An Improved Multiscale Unet with Bottleneck Transformer for Myositis Segmentation from Ultrasound Images.

Myositis is the inflammation of the muscles that can arise from various sources with diverse symptoms and require different treatments. For treatment to achieve optimal results, it is essential to obtain an accurate diagnosis promptly. This paper presents a new supervised segmentation architecture that can efficiently perform precise segmentation and classification of myositis from ultrasound images with few computational resources. The architecture of our model includes a unique encoder-decoder structure that integrates the Bottleneck Transformer (BOT) with a newly developed Residual block named Multi-Conv Ghost switchable bottleneck Residual Block (MCG_RB). This block effectively captures and analyzes ultrasound image input inside the encoder segment at several resolutions. Moreover, the BOT module is a transformer-style attention module designed to bridge the feature gap between the encoding and decoding stages. Furthermore, multi-level features are retrieved using the MCG-RB module, which combines multi-convolution with ghost switchable residual connections of convolutions for both the encoding and decoding stages. The suggested method attains state-of-the-art performance on a benchmark set of myositis ultrasound images across all parameters, including accuracy, precision, recall, dice coefficient, and Jaccard index. Despite its limited training data, the suggested approach demonstrates remarkable generalizability by yielding exceptional results. The proposed model showed a substantial enhancement in accuracy when compared to segmentation state-of-the-art methods such as Unet++, DeepLabV3, and the Duck-Net. The dice coefficient and Jaccard index obtained improvements of up to 3%, 6%, and 7%, respectively, surpassing the other methods.

A new fault location method for high‐voltage transmission lines based on ICEEMDAN‐MSA‐ConvGRU model

AbstractGiven the complex form of distribution line faults, the accuracy of fault location using traditional artificial intelligence networks needs to be further improved. Here, a combined fault location method is proposed for a 110 kV distribution line based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), mantis search algorithm (MSA), and convolutional gate recurrent unit (ConvGRU). Firstly, the study used the ICEEMDAN algorithm to decompose the signals and discard the high‐frequency signals with low correlation so as to achieve the purpose of noise cancellation. Then, the study used the root mean square error (RMSE) of the ConvGRU model training as the adaptation value, optimized the internal parameters of the model using the MSA algorithm, and obtained a combined fault locating model. By using the proposed model, the effects of the fault form and transition impedance changes on the location accuracy were analysed, and the location accuracy was compared with other artificial intelligence methods. The location accuracy index showed that the proposed model had a better convergence speed of training error than the traditional model. Also, the RMSE of the localization results was reduced by 50%, with a higher fault location accuracy.