Average Relative Root Mean Square Research Articles

A flue gas velocimeter based on the hyperbolic chirp modulation method of acoustic source signal

Accurate measurement of flue gas velocity is of great significance for the safe production, energy saving, and emission reduction of coal-fired power plants. In this paper, a method of flue gas velocimetry based on a hyperbolic chirp-modulating acoustic source was proposed, and its superiority was verified by numerical simulations and field experiments. For an acoustic velocimeter, it is crucial to ensure the accuracy of the acoustic Time-of-Flight (TOF), which directly affects the flue gas velocity measurements. The improvement of the accuracy of estimation of the TOF directly determines the accuracy of the flue gas velocity measurement. Simulation on the different types of acoustic sources at different signal-to-noise ratios (SNRs) was conducted, and the results proved that the hyperbolic chirp signal is more accurate in the measurement of TOF. In the strong noise range of − 10 dB to − 20 dB, the average relative root mean square error (RRMSE) obtained using this hyperbolic chirp modulating acoustic source is 0.60 dB and 0.36 dB lower than obtained using the linear and logarithmic modulating modes, respectively. The average relative error (RE) is approximately 0.87 times that of the linear chirp. Power plant flue field experiments show that the measurement error using this hyperbolic modulating mode is about 0.52 times and 0.74 times that of using linear and logarithmic modulating modes, respectively. In harsh measurement environments with strong noise, the proposed acoustic source modulation scheme can significantly improve the measurement SNR and effectively enhance the accuracy and robustness of flue gas velocimetry.

Measurements of density and viscosity of carbon dioxide-loaded and -unloaded nano-fluids: Experimental, genetic programming and physical interpretation approaches

In the present study, the density and viscosity of the CO2-loaded and –unloaded base solution and nano-fluid were experimentally measured and investigated from an intermolecular point of view. Nano-fluids are composed of nano-particles such as Al2O3 (0.1 wt.%), Silica-2-methylimidazole, zinc salt (Si-ZIF-8) (0.02 wt.%), and super activated carbon (SAC) (0.1 wt.%) dispersed in aqueous and hybrid Methyl diethanolamine context (MDEA, 40 wt.%) +Sulfolane (SFL), 30 wt.%) +H2O) Experimental measurements were carried out at the low-temperature ranges 303.15–315.15 K, atmospheric pressure, and three different CO2 loadings. The results show that nanomaterials do not have a significant effect on the density and viscosity of the unloaded suspension; however, the density and viscosity of loaded suspensions and base solvent become more by increasing CO2 concentration. In the case of CO2-loaded fluids, the comparison of the results in the presence and absence of nanoparticles shows that the density of the solution is not much different in the two cases, but the viscosity of CO2-loaded in Si-ZIF-8, SAC, and γ-Al2O3 base nano-fluids in comparison with base solvent shows an increase of 35 % in high CO2 loading, ∼0.3 mol CO2 per mol MDEA. Density and viscosity experimental data were modeled using the Genetic Programming approach. The highest values of absolute average relative deviation (AARD) and root mean square error (RMSE) parameters obtained for modeling data are 3.04 and 0.317, respectively, and the lowest value of regression coefficient (R2) is 0.995, which indicates the appropriate fitting of the results.

Study on the Constitutive Modeling of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 Composites under Hot Compression Conditions.

The hot deformation behavior of titanium matrix composites plays a crucial role in determining the performance of the formed components. Therefore, it is significant to establish an accurate constitutive relationship between material deformation parameters and flow stress. In this study, hot compression experiments were conducted on a (2.5 vol%TiB + 2.5 vol%TiC)/TC4. The experiments were performed under temperatures ranging from 1013.15 to 1133.15 K and strain rates ranging from 0.001 to 0.1 s-1. Based on the stress-strain data obtained from the experiment, the constitutive models were established by using the Arrhenius model and the BP neural network algorithm, respectively. Considering the relationship between strain rate, hot working temperature, and flow stress, a comparative analysis was conducted to evaluate the prediction accuracy of two different constitutive models. The research results indicate that the flow stress of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 increases with decreasing temperature and increasing strain rate, and the stress-strain curve shows obvious work hardening and softening behaviors. Both the Arrhenius model and the BP neural network algorithm are effective in predicting the hot compression flow stress of (2.5 vol%TiB + 2.5 vol%TiC)/TC4, but the average relative error and root mean square error of the BP neural network algorithm are smaller and the correlation coefficient is higher, thus possessing higher accuracy and reliability.

Short-term PV power prediction based on VMD-CNN-IPSO-LSSVM hybrid model

Abstract This article discusses the significance and obstacles of short-term power prediction in photovoltaic systems and introduces a hybrid model for photovoltaic short-term power prediction technology based on variational mode decomposition (VMD), convolutional neural network (CNN), improved particle swarm optimization (IPSO) and least squares support vector machine (LSSVM). In the initial stage, the photovoltaic generation signal is decomposed into multiple Intrinsic mode functions (IMFs) using VMD to enhance the extraction of signal time–frequency characteristics. Subsequently, CNN is utilized for feature learning and extraction of each IMF, modeling the nonlinear and non-stationary features. Following this, the IPSO-LSSVM optimization algorithm is employed to establish and optimize multiple LSSVM models, predicting power fluctuations at different time scales. Finally, the predictions from each model are synthesized to obtain the final photovoltaic short-term power forecast. Through validation with actual photovoltaic generation data, this hybrid model demonstrates high accuracy and reliability in short-term power prediction, showing an average relative error and root mean square error reduction of 15.23 and 53.60%, respectively, compared to a certain comparative model. This proposed method based on VMD-CNN-IPSO-LSSVM hybrid model for photovoltaic power prediction holds promising prospects and practical value in the operation and scheduling of photovoltaic generation systems.

Study on the influence of dust accumulation on the optical performance of trough solar concentrator under different rainfall intensities based on alpine areas

Aiming at the typical characteristics of natural rainfall in alpine areas and the structure of the concentrator surface, based on the design scheme of the trough system test platform, the optical performance and cleaning characteristics of the mirror with rainfall intensity and concentrator tilt angle as variables are studied experimentally. The results show that small-scale rainfall has a critical tilt angle of positive and negative cleaning effect on transverse region1. Lower than this tilt angle causes pollution to the mirror surface. Rainfall above medium intensity has a positive cleaning effect on all mirror regions. The cleaning ability is the highest under the rainfall of 60° tilt angle of 38.9 mm. The bottom-up enhanced cleaning law in the transverse region is more significant under high-intensity rainfall, and the concentrator tilt angle is more sensitive to dust desorption than rainfall intensity. The average relative error and average root mean square error of the predicted reflectivity of the global mirror are 3.82% and 0.061 respectively, and the fitting accuracy is high. It provides theoretical guidance for the application of dust removal schemes and prediction of the optical performance of trough solar system in alpine areas.

Comparative studies of Markov chain-based driving cycles for light-duty vehicles

ABSTRACT With the massive ongoing efforts to mitigate climate change and reduce emission factors in the transportation sector, driving cycles (DCs) are becoming an essential tool for the testing and certification of distinct types of vehicles. Nevertheless, the open literature lacks careful comparative studies on the impact of different approaches in the development of Markov chain (MC)-based DCs, as well as DCs, developed specifically for the developing and highly congested urban areas in the Middle East and North Africa. Using a large dataset of 43 light-duty vehicles, driven over different areas in Greater Cairo, Egypt, this study aims to develop, compare, and benchmark 24 candidate MC-based DCs, against two clustering-based DCs, as well as four cycles used widely in the US and Europe. These 24 DCs differ in terms of the clustering algorithm (K-medoids and K-means), clustering parameters (different combinations of vehicle’s speed, acceleration, specific power, and percentage idling time), and definitions of microtrips (start-stop and fixed distance). The results show that the MC method outperforms random chaining of microtrips, with average relative root mean square errors (RRMSEs) of 15.8% and 23.6%, respectively. Clustering 350 m fixed distance-based microtrips using the vehicle’s speed, acceleration, and percentage idling time shows the least RRMSE of 8.207%. Defining microtrips based on fixed distance is also better than starts/stops for most vehicle types. The reference cycles (WLTP, NEDC, UDDS, and FTP-17) showed poor representativeness of the real-world data, with an average RRMSE of 76.8%. The same inferior performance of reference cycles, compared to the newly proposed ones, was also highlighted in the estimation of fuel consumption and emission factors. Hence, the proposed cycles and the reported comparative studies can be valuable tools for the assessment of emissions and fuel consumption in such developing metropolitan areas.

Research on the Prediction of Wax Deposition Thickness on Pipe Walls Based on the Optimal Weighted Combination Model

Wax deposition seriously affects the safe and economic operation of pipelines. Mastering the variation laws of wax deposition thickness is the premise of formulating reasonable pigging schemes. Although the GM (1,1) model (a kind of gray model) is an effective method for predicting wax deposition thickness on pipe walls, its prediction accuracy is easily affected by the smoothness of the original sequence. The improved GM (1,1) was established by introducing the idea of translation transformation, and an optimal weighted combination model based on the traditional gray model and a logarithmic function model was proposed. The differences in the predicted results of the established models were compared and analyzed through indoor wax deposition experimental data. The research results indicate that the optimal weighted combination model has the highest fitting accuracy, followed by the logarithmic function model and the improved GM (1,1), while the fitting accuracy of the traditional gray model is poor. When the number of modeling samples is five, the average relative error and root mean square error of the prediction results of the optimal weighted combination model are 1.313% and 0.021, respectively, which shows the highest prediction accuracy. When the number of modeling samples is six, the average relative error and root mean square error of the optimal weighted combination model are 2.143% and 0.031, respectively, and its prediction accuracy is still the highest. Overall, the optimal weighted combination model has the advantages of high accuracy and easy implementation, and has strong promotion and application value.

Composite property price index forecasting with neural networks

PurposeThe Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.Design/methodology/approachThe goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.FindingsThe authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.Originality/valueResults here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

Smart Approaches for Evaluating Photosynthetically Active Radiation at Various Stations Based on MSG Prime Satellite Imagery

Photosynthetically active radiation (PAR) is the 400–700 nm portion of the solar radiation spectrum that photoautotrophic organisms including plants, algae, and cyanobacteria use for photosynthesis. PAR is a key variable in global ecosystem and Earth system modeling, playing a prominent role in carbon and water cycling. Alongside air temperature, water availability, and atmospheric CO2 concentration, PAR controls photosynthesis and consequently biomass productivity in general. The management of agricultural and horticultural crops, forests, grasslands, and even grasses at sports venues is a non-exhaustive list of applications for which an accurate knowledge of the PAR resource is desirable. Modern agrivoltaic systems also require a good knowledge of PAR in conjunction with the variables needed to monitor the co-located photovoltaic system. In situ quality-controlled PAR sensors provide high-quality information for specific locations. However, due to associated installation and maintenance costs, such high-quality data are relatively scarce and generally extend over a restricted and sometimes non-continuous period. Numerous studies have already demonstrated the potential offered by surface radiation estimates based on satellite information as reliable alternatives to in situ measurements. The accuracy of these estimations is site-dependent and is related, for example, to the local climate, landscape, and viewing angle of the satellite. To assess the accuracy of PAR satellite models, we inter-compared 11 methods for estimating 30 min surface PAR based on satellite-derived estimations at 33 ground-based station locations over several climate regions in Europe, Africa, and South America. Averaged across stations, the results showed average relative biases (relative to the measurement mean) across methods of 1 to 20%, an average relative standard deviation of 25 to 30%, an average relative root mean square error of 25% to 35% and a correlation coefficient always above 0.95 for all methods. Improved performance was seen for all methods at relatively cloud-free sites, and quality degraded towards the edge of the Meteosat Second Generation viewing area. A good compromise between computational time, memory allocation, and performance was achieved for most locations using the Jacovides coefficient applied to the global horizontal irradiance from HelioClim-3 or the CAMS Radiation Service. In conclusion, satellite estimations can provide a reliable alternative estimation of ground-based PAR for most applications.

Track Unevenness Prediction Based on Static Track Inspection Data Matching

A track static misalignment prediction model based on track median deviation is created using an IGA-BP neural network in order to precisely predict the trend of ballastless track static misalignment. The historical static track median deviation detection data are matched using actual compensation edit distance (ERP) to finish the correspondence processing of the original data. The precisely matched data are used to train the model, forecast irregularities in the track median, and compare results with other traditional prediction techniques. The outcomes demonstrate that the IGA-BP neural network can more accurately predict the nonlinear time series data development trend. In comparison to other prediction models, the IGA-BP neural network model’s average relative error and root mean square error are 0.091 and 0.110, respectively. The prediction accuracy is raised by between 43% and 60%, demonstrating the IGA-BP neural network model’s efficacy in predicting static upsets on ballastless tracks and presenting a workable strategy for track predictive maintenance.

Office property price index forecasting using neural networks

PurposeThe Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.Design/methodology/approachThe authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.FindingsThe authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.Originality/valueThe results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

Solubility determination, model correlation, thermodynamic analysis, molecular dynamic simulation of Favipiravir in twelve pure solvents

The solubility of Favipiravir in twelve pure solvents, including methanol (MeOH), ethanol (EtOH), n-propanol (NPOH), isopropanol (IPOH), n-butanol (NBOH), acetone (DMK), acetonitrile (MeCN), dichloromethane (DCM), N, N-Dimethylformamide (DMF), methyl acetate (MtAC), ethyl acetate (EtAC), butyl acetate (NBAC) was measured by gravimetric method at a temperature range of 293.15 to 333.15 K at 0.1 MPa. The maximum and minimum of Favipiravir solubility were obtained in DMF at 333.15 K (0.02599 mol·mol−1) and MeOH at 293.15 K (0.00147 mol·mol−1), respectively. At 298.15 K, the order of solubility in different solvents is: DMF > NBAC > DMK > MtAC > EtAC > NBOH > DCM > EtOH > NPOH > IPOH > MeCN > MeOH. The Hansen solubility parameter (HSP) was used to analyze the miscibility of Favipiravir in selected solvents. In addition, the solvent effect was studied by analyzing solvent properties, which indicate that the solubility of Favipiravir in selected solvents is influenced by the synergistic effect of multiple factors. The Modified Apelblat model, λh model, NRTL model, and Wilson model were employed to correlate the solubility data, indicating the Apelblat model provides the highest regression accuracy corresponding to the lowest average value of average relative deviation (ARD, 0.017459) and root mean square deviation (RMSD, 0.00012). In order to explain the dissolution behavior of Favipiravir in different solvents, the intermolecular interactions were analyzed by using Hirshfeld surface (HS), Molecular electrostatic potential surface (MEPS), and solvation free energy. The thermodynamic parameters derived from van’t Hoff equation including ΔdisG0 (10.71–16.57 kJ·mol−1), ΔdisH0 (6.33–18.67 kJ·mol−1), and ΔdisS0 (−32.77–25.47 J·mol−1·K−1) indicate that the dissolution of Favipiravir is endothermic, entropy-driven or has no driving force, and non-spontaneous.

Neural network-based speech fuzzy enhancement algorithm for smart home interaction

With the rapid development of artificial intelligence and the continuous improvement of machine learning technology, speech recognition technology is also developing rapidly and the recognition accuracy is improving to meet the higher requirements of people for smart home devices, and combining smart home with voice recognition technology is an inevitable trend for future development. This study aims to propose a speech fuzzy enhancement algorithm based on neural network for smart home interactive speech recognition technology, so the study proposes a combination of fuzzy neural network algorithm (FNN) and stacked self-encoder (SAE) to form SAE-FNN algorithm, which has better non-linear characteristics and can better achieve feature learning, thus improving the performance of the whole system. The results show that with the SAE-FNN algorithm, the maximum relative error absolute value, average relative error and root mean square error are 0.355, 0.063 and 0.978, which are significantly higher than the other two individual algorithms, and the noise of the sound signal has little effect on the SAE-FNN algorithm. Therefore, it can be seen that the proposed SAE-FNN algorithm has excellent noise immunity performance. In summary, it can be seen that this neural network-based speech fuzzy enhancement algorithm for smart home interaction is extremely feasible.

Investigation on florasulam in aqueous blends including solubility, preferential solvation, thermodynamic properties and enthalpy–entropy compensation

The equilibrium solubility of florasulam in solvent mixtures (methanol/ethanol /n-propanol/propylene glycol (PG) + water) was determined experimentally by isothermal saturation method at 278.15 K to 323.15 K under 101.2 kPa. Both high temperature and low water content of solvent promoted the dissolution of florasulam. Under given conditions, the mole fraction of florasulam was greater in (n-propanol + water) than other three solvent mixtures. Besides, the solubility of florasulam was correlated with the Jouyban-Acree (J-A) model and van’t Hoff-Jouyban-Acree (V-J-A) model. The obtained average relative deviations (RAD) and root mean square deviations (RMSD) were no >4.04 % and 1.73 × 10-4. The calculated preferential solvation parameter δx1,3 was used to analyze the preferential dissolution of florasulam between water and alcohols from the molecular perspective. The result is that in the intervals 1 > xmethanol > 0.32, 1 > xethanol > 0.23 and 1 > xn-propanol > 0.19, the florasulam was preferentially dissolved by methanol/ethanol/n-propanol. In other concentration ranges, the florasulam was preferentially dissolved by water. The reason for the special existence of PG + water is that |δx1,3| < 1 × 10-2 and the dissolution of florasulam in it conformed to the uncertainty propagation rather than preferential solvation theory. The parameters (ΔsolHo, ΔsolGo, ΔsolSo, ΔtrGo, ΔtrHo and ΔtrSo) that could reflect the apparent and transfer thermodynamic properties were obtained. It was concluded that the solubility change of florasulam was dominated by entropy and the transfer of florasulam from pure water to mixtures was an exothermic process. Finally, the enthalpy–entropy compensation analysis further studied the mechanism of enthalpy and entropy in the dissolution process.

Solid-liquid equilibrium solubility, thermodynamic properties, and solvent effect of flavoxate hydrochloride in twelve pure solvents ranged from 278.15 K to 323.15 K

The mole fraction solubility data of flavoxate hydrochloride in twelve experimentally selected solvents were measured at atmospheric pressure by the laser monitoring method. The experimental temperature range was 278.15 to 328.15 K. The solubility of flavoxate hydrochloride showed a nonlinear increase with temperature increase. At a fixed experimental temperature, the order of solubility is: ethylene glycol > methanol > 2-methoxyethanol ≈ dimethyl sulfoxide > N-Methyl-2-pyrrolidone > water > 2-propoxyethanol > 2-ethoxyethanol > ethanol > n-butanol > n-propanol > acetonitrile. Also, solubility data were correlated by two semi-empirical models and three activity coefficient models. From the average relative deviation and root mean square deviation data of the five models, it can be seen that the Modified Apelblat model has the best correlation. Furthermore, in this work, the effect of solvent on the solubility of flavoxate hydrochloride was analyzed using the KAT-LSER model. Furthermore, the thermodynamic parameters (ΔdisG0, ΔdisH0, ΔdisS0) of the dissolution process of flavoxate hydrochloride were calculated by the Van’t Hoff model. The data indicate that the dissolution process of flavonoid hydrochloride in twelve experimentally selected solvents is endothermic and entropy-driven.

Research on Power Price Forecasting Based on PSO-XGBoost

With the reform of the power system, the prediction of power market pricing has become one of the key problems that needs to be solved in time. Power price prediction plays an important role in maximizing the profits of the participants in the power market and making full use of power energy. In order to improve the prediction accuracy of the power price, this paper proposes a power price prediction method based on PSO optimization of the XGBoost model, which optimizes eight main parameters of the XGBoost model through particle swarm optimization to improve the prediction accuracy of the XGBoost model. Using the electricity price data of Australia from January to December 2019, the proposed model is compared with the XGBoost model. The experimental results show that PSO can effectively improve the performance of the model. In addition, the prediction results of PSO-XGBoost are compared with those of SVM, LSTM, ARIMA, RW and XGBoost, and the average relative error and root mean square error of different power price prediction models are calculated. The experimental results show that the prediction accuracy of the PSO-XGBoost model is higher and more in line with the actual trend of power price change.

Compactness prediction of asphalt concrete using Ground-Penetrating Radar: A comparative study

The compactness is an important indicator of quality control during the construction of the asphalt concrete pavement. The construction quality of asphalt concrete can be continuously, quickly, and non-destructively tested by establishing the correlation between dielectric constant and compactness through ground-penetrating radar. In this study, five compactness prediction models were established based on the relationship between volume parameters and the dielectric constant of each component of asphalt concrete. 18 samples of Sup20 asphalt concrete (Superior Performing Asphalt Pavement with the nominal aggregate size of 19 mm) were used to verify the established compactness prediction models. The results showed that the average relative error, maximum error, and root mean square error of the CP_Rayleigh model (compactness prediction model of Rayleigh.) were minimum, and thus the CP_Rayleigh model was selected for the following-up research in this study. The shapes of scatterers (aggregates and pores) were not spherical, and a shape factor u of 0.033 was introduced to modify the CP_Rayleigh model. The prediction accuracy of compactness can be effectively improved by the modified CP_Rayleigh model. Combined with field engineering verification, it was shown that the modified CP_Rayleigh model can be used to predict the compactness of asphalt concrete in the field.

Mapping Forest Stock Volume Based on Growth Characteristics of Crown Using Multi-Temporal Landsat 8 OLI and ZY-3 Stereo Images in Planted Eucalyptus Forest

Labeled as a fast-growing tree species, eucalyptus has outstanding carbon sequestration capacity. Forest stock volume (FSV) is regarded as an important parameter for evaluating the quality of planted eucalyptus forests. However, it is an intractable problem to map FSV of planted eucalyptus forests using optical images because of growth characteristics of the crown and low saturation levels. To improve the accuracy of FSV in planted eucalyptus forests, time series Landsat 8 OLI (LC8) images and ZY-3 stereo images were acquired in the study area. Additionally, then, three composite images were proposed using acquired Landsat 8 OLI images based on the size and shape of eucalyptus crowns, and several spectra variables were extracted from these composite images. Furthermore, corrected canopy height model (CCHM) was also extracted from ZY-3 stereo images. Meanwhile, four models (random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and multiple linear regression (MLR)) were used to estimate the FSV with various variable sets using the importance of the alternative variables ranked by RF. The results show that the sensitivity between proposed spectral variables and FSV is significantly improved using proposed composed images based on the growth characteristics of the crown, especially for young eucalyptus forests. After adding CCHM and stand age to the optimal variable set, the average relative root mean square error (rRMSE) of estimated FSV decreased from 41.01% to 29.94% for single LC8 images and from 32.64% to 26.47% for proposed composite LC8 images, respectively. After using the variable set extracted from composite LC8 images, the number of samples with overestimated FSV was significantly decreased for the young forest. Furthermore, forest height plays an important role in improving the accuracy of mapping FSV, whether young or mature eucalyptus forest. It was also proved that composite images related to crown close and CCHM have great potential to delay the saturation phenomenon for mapping FSV in planted eucalyptus forest.

Retail Property Price Index Forecasting through Neural Networks

Rapid growth has been seen in the Chinese housing market during the past decade, and housing price forecasts have become more significant to investors and policy makers. In this study, we explore neural networks for retail property price index forecasts from ten major Chinese cities for July 2005–April 2021. Our goal is to build simple and accurate neural networks to contribute to purely technical forecasts of the Chinese retail property market. To facilitate the analysis, we examine different model settings based on algorithms, delays, hidden neurons, and data splitting ratios and construct a simple neural network with five delays and two hidden neurons. This leads to a rather stable performance of about 1.6% average relative root mean square error across the ten cities for the training, validation, and testing phases. Results here can be used on a stand-alone basis or combined with fundamental forecasts to form perspectives of retail property price trends and conduct policy analyses.

Acoustic Tomography Temperature Reconstruction Based on Virtual Observation and Residual Network

High-quality measurement of temperature distribution information is of great significance for industrial production. As a typical non-contact temperature measurement method, acoustic tomography (AT) can obtain temperature distribution through reconstruction algorithms. To improve the reconstruction quality, a two-stage algorithm based on virtual observation (VO) and residual network (ResNet) was proposed. This algorithm combines the advantages of VO and ResNet. Aiming at total least squares, the VO method was adopted to reconstruct the ultrasonic time-of-flight (TOF) to obtain the temperature distribution under a coarse grid. Then, ResNet was built to predict the temperature distribution under the refined grid, and the dual-input model was used to improve the network generalization ability. At the same time, the sub-pixel convolution layer was introduced to reduce the network computing dimension, which improves the computing efficiency and the reconstruction quality. The numerical simulation of typical temperature field models was carried out, and the reconstruction results show that the average relative error and root mean square error are less than 0.86% and 1.21%, respectively. The noise immunity and reconstruction spatial resolution are better than traditional algorithms and the previous two-stage algorithm.