Characteristics Of Time Series Research Articles

Application of artificial bee colony algorithm based on homogenization mapping and collaborative acquisition control in network communication security.

In order to optimize the spectrum allocation strategy of existing wireless communication networks and improve information transmission efficiency and data transmission security, this study uses the independent correlation characteristics of chaotic time series to simulate the collection and control strategy of bees, and proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection control. Furthermore, it proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection and control. The method begins by establishing a composite system of uniformly distributed Chebyshev maps. In the neighborhood intervals where the nectar sources are firmly connected and relatively independent, the algorithm then conducts a chaotic traversal search. The research results demonstrated the great performance of the suggested algorithm in each test function as well as the positive effects of the optimization search. The network throughput rate was over 300 kbps, the quantity of security service eavesdropping was below 0.1, and the spectrum utilization rate of the algorithm-based allocation method could be enhanced to 0.8 at the most. Overall, the performance of the proposed algorithm outperformed the comparison algorithm, with high optimization accuracy and a significant amount of optimization. This is favorable for the efficient use of spectrum resources and the secure transmission of communication data, and it encourages the development of spectrum allocation technology in wireless communication networks.

Interpretable and efficient RUL prediction of turbofan engines using EM-enhanced Bi-LSTM with TCN and attention mechanism

Remaining useful life (RUL) prediction for turbofan engines is important in prognostics and health management (PHM) for the maintenance and operation of critical equipment. With continuous innovations in deep learning techniques, the complexity of models continues to increase, but the interpretability and comprehensibility of the prediction results become particularly important in industrial applications. Therefore, in this study, an improved bidirectional long and short-term memory network (Bi-LSTM) based interpretable hybrid deep learning model for RUL prediction of turbofan engines is proposed, which ingeniously integrates time series convolutional networks (TCNs), expectation maximization (EM), Bi-LSTMs, and attention mechanisms. By capturing time-series features at different levels, the model adapts to the complex dynamics of turbofan engine performance evolution in an efficient and cost-effective manner. Experimental validation on the C-MAPSS dataset demonstrated that the model significantly outperforms other methods in terms of RUL prediction performance, especially in improving prediction accuracy and coping with the degradation of complex system dynamics. The largest contribution of key metrics to the model is validated through consistent results from multiple interpretable tools, providing comprehensive and consistent support for understanding and trusting prediction results in industrial applications. This study further enhances the robustness of the model and the reliability of the interpretable results by delving into the dynamic relationships between the properties of the different life stages, which not only reveal the importance of these characteristics in engine life prediction but also provide more comprehensive information about the engine performance variations by observing the dynamic relationships.

Carbon price forecasting using leaky integrator echo state networks with the framework of decomposition-reconstruction-integration

Carbon trading is one of the strategies to achieve the goal of dual carbon. However, carbon prices exhibit non-stationary and nonlinear characteristics, posing significant challenges for accurate forecasting. Inspired by the ideas of “division and conquest” and “granularity reconstruction”, a decomposition-reconstruction-integration framework is built for carbon price forecasting. The proposed model leverages a comprehensive ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sample entropy (SE) to extract features from two perspectives of time and complexity, and thus the one-dimensional time series of carbon price is reconstructed into different items. Subsequently, leaky integrator echo state networks (LiESNs) are utilized to forecast each of these items individually. These individual forecasts are then integrated to generate the final point forecast. To evaluate the uncertainty of the point forecast, a Gaussian process model is applied to interval forecast. The proposed model comprehensively considers the time series features of carbon price including the temporal modal features in the decomposition stage, the entropy probability features in the reconstruction stage, and the essential temporal dependencies via dynamic reservoir of interconnected neurons in the integration forecasting stage. Experimental results demonstrate the model's exceptional forecasting performance, surpassing the effectiveness of alternative approaches.

Ultrasound fuzzy entropy imaging based on time-series signal for tissue characterization

This study proposed a parametric imaging method of fuzzy entropy based on time-series signal, which can enhance the contrast and augment the accuracy of the detection of lesions in soft tissue. Fuzzy entropy assesses the irregularity of ultrasound time-series data through the detection of similarities within the signal waveform. Applying fuzzy entropy in ultrasound tissue characterization can provide more tissue information. The sliding windows are performed to traverse across the whole image with the step of one pixel. The signal in the sliding window calculates the fuzzy entropy (FE), and the FE value is assigned to the window’s center pixel. The entropy image will be constructed after obtaining all fuzzy entropies. The performance of the proposed FE imaging was compared with weighted Shannon entropy (WSE) imaging and B-mode imaging by simulations, ex vivo experiments, and clinical data. FE imaging got superior lesion area prediction with the AUC of 0.959 ± 0.042 (p < 0.0001), the accuracy of 99.88 ± 0.09 % (p < 0.0001), and the contrast-to-noise ratio of 7.806 ± 4.476 (p < 0.001) in the thyroid nodule diagnostic tests. The results show that FE imaging significantly increases the contrast of the images. Compared with WSE, the FE imaging method has better sensitivity to disorder degree and better recovery ability of tissue information when the amplitude of the signal decreases or the change of signal properties is small. FE imaging considers the characteristics of ultrasound time series. It has a better recovery ability for ultrasound signals, which is a promising technique for soft tissue diagnosis and thermal ablation monitoring.

SARIMA Model-Based Monte Carlo Simulation of Option Contract Design for Maize Seasonal Heavy Precipitation in Shenyang

Agricultural production is highly dependent on weather conditions such as temperature, light, water and heat. The huge loss caused by extreme disaster weather makes the global insurance and reinsurance market overwhelmed, and seriously affects the enthusiasm of agricultural investment and the development of agricultural economy. With unique topography and vast territory, China is vulnerable to complex and diverse climate disasters. In recent years, the annual average economic loss caused by extreme weather disasters has reached about 200-300 billion yuan, among which floods caused by extreme heavy rainfall are the main agricultural disasters in China. In order to enhance the "farmer - insurance company - government" interest association to resist the risk of flood disaster caused by extreme heavy rainfall, financial hedging derivatives of weather disaster risk have gradually become a new hedging tool besides traditional insurance and reinsurance. This paper takes Shenyang, the main grain producing area in northeast China, as the sample of the study area, and uses the SARIMA model to obtain the distribution characteristics of seasonal rainfall time series. Different seasonal rainfall index option products (call option and put option contracts) are designed respectively, and the final option pricing is obtained by Monte Carlo stochastic simulation. The seasonal rainfall call option and put option contracts have opened up a new hedging model of agricultural extreme weather catastrophe outside the insurance and reinsurance market, which has enriched the varieties of weather financial derivatives market in China, reduced the severe impact of extreme weather disasters on agriculture, and enhanced the ability of agricultural stakeholders to resist the risk of extreme weather disasters.

Predicting abrupt depletion of dissolved oxygen in Chaohu lake using CNN-BiLSTM with improved attention mechanism

Depletion of dissolved oxygen (DO) is a significant incentive for biological catastrophic events in freshwater lakes. Although predicting the DO concentrations in lakes with high-frequency real-time data to prevent hypoxic events is effective, few related experimental studies were made. In this study, a short-term predicting model was developed for DO concentrations in three problematic areas in China's Chaohu Lake. To predict the DO concentrations at these representative sites, which coincide with biological abnormal death areas, water quality indicators at the three sampling sites and hydrometeorological features were adopted as input variables. The monitoring data were collected every 4 h between 2020 and 2023 and applied separately to train and test the model at a ratio of 8:2. A new AC-BiLSTM coupling model of the convolution neural network (CNN) and the bidirectional long short-term memory (BiLSTM) with the attention mechanism (AM) was proposed to tackle characteristics of discontinuous dynamic change of DO concentrations in long time series. Compared with the BiLSTM and CNN-BiLSTM models, the AC-BiLSTM showed better performance in the evaluation criteria of MSE, MAE, and R2 and a stronger ability to capture global dependency relationships. Although the prediction accuracy of hypoxic events was slightly worse, the general time series characteristics of abrupt DO depletion were captured. Water temperature regularly affects DO concentrations due to its periodic variations. The high correlation and the universal importance of total nitrogen (TN) and total phosphorus (TP) with DO reveals that point source pollution are critical cause of DO depletion in the freshwater lake. The importance of NTU at the Zhong Miao Station indicates the self-purification capacity of the lake is affected by the flow rate changes brought by the tributaries. Calculating linear correlations of variables in conjunction with a permutation variable importance analysis enhanced the interpretability of the proposed model results. This study demonstrates that the AC-BiLSTM model can complete the task of short-term prediction of DO concentration of lakes and reveal its response features of timing and magnitude of abrupt DO depletion.

Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model

Effective air quality prediction models are crucial for the timely prevention and control of air pollution. However, previous models often fail to fully consider air quality’s temporal and spatial distribution characteristics. In this study, Xi’an City is used as the study area. Data from 1 January 2019 to 31 October 2020 are used as the training set, while data from 1 November 2020 to 31 December 2020 are used as the test set. This paper proposes a multi-time and multi-site air quality prediction model for Xi’an, leveraging a deep learning network model based on APSO-CNN-Bi-LSTM. The CNN model extracts the spatial features of the input data, the Bi-LSTM model extracts the time series features, and the PSO algorithm with adaptive inertia weight (APSO) optimizes the model’s hyperparameters. The results show that the model achieves the best results in terms of MAE and RMSE. Compared to the PSO-SVR, BPTT, CNN-LSTM, and GA-ACO-BP models, the MAE improved by 9.375%, 6.667%, 2.276%, and 4.975%, while the RMSE improved by 8.371%, 8.217%, 6.327%, and 5.293%. These significant improvements highlight the model’s accuracy and its promising application prospects.

Spatial weighting EMD-LSTM based approach for short-term PM2.5 prediction research

Given the significant health and environmental risks posed by atmospheric PM2.5 pollution, accurately predicting its concentration changes is especially important. Current models fall short in researching time-series feature extraction from pollutants and spatial correlations among monitoring stations. In this study, a spatiotemporal prediction model is introduced to address these issues. The model combines spatial weighting, empirical mode decomposition (EMD), and a long short-term memory (LSTM) network. First, weights are allocated to sites using Pearson correlation analysis and distance weighting methods. Next, the pollutant time series is decomposed using the EMD method. The highly correlated intrinsic mode function (IMF) component is selected for signal reconstruction, enhancing denoising. Finally, the model uses an LSTM network to capture nonlinear and dynamic time series traits, which significantly improves the PM2.5 prediction accuracy. The model utilizes data collected from 10 monitoring stations across Hefei city during 2018-2019, employing the previous 24 h of observations to forecast PM2.5 concentrations for the subsequent hour. By comparing with RNN, HPO-RNN, GRU, LSTM, and CBAM-CNN-Bi LSTM, the results show that our model surpasses five benchmark models in terms of prediction accuracy. Relative to the best-performing CBAM-CNN-Bi LSTM model, our model reduces RMSE and MAE by 73.91% and 72.99%, respectively, and improves R2 by 8.15%. In summary, the proposed spatial weighting EMD-LSTM model offers an efficient new approach for predicting atmospheric PM2.5 pollution. It integrates spatial and time series analysis, significantly enhancing the prediction accuracy.

거시경제 및 인구특성의 변화가 수도권 및 비수도권 주택 매매시장에 미치는 영향에 관한 연구

This study attempted to compare and derive the impact on the housing sales market in the metropolitan area and local cities and to present implications in terms of the real estate brokerage market. The Vector Error Correction Model was used as the analysis method in consideration of the time series characteristics and cointegration relationship of the used macroeconomic and demographic variables. The analysis results and implications are summarized as follows. In the case of the metropolitan area model, interest rates, consumer price indices, population growth rates, and fertility rates were the main factors influencing the housing sales market. The five metropolitan city models are the main factors influencing interest rates, economic growth rates, and consumer price indices, while the explanatory power of the fertility rate is very low. Considering these results, housing can be classified as an investment product in the metropolitan area, and local cities can be classified as a housing sales market with strong characteristics as a consumer product. Long-term housing sales are not expected to increase significantly due to the recent decline in investment and real demand due to the stabilization of housing prices due to high-interest rates. The housing brokerage market needs to be developed into a convergence field by combining highly relevant management and consulting.

Analysis of multi-factors affecting resource consumption efficiency in Vietnam

Theprimary objective of this study is todelve into the factors potentially influencing resource utilisationefficiency at the provincial level in Vietnam. We leverage 185 data points gathered from 63 Vietnamese cities or provinces, obtained from two key sources: the General Statistical Office of Vietnam and the Ministry of Information and Communications Vietnam. The collected data was subjected to analysis using an empirical econometric model that incorporates time-series characteristics to explore the connections between these potential determinants of resource consumption efficiency. The analysis revealed a strong correlation between resource utilisationefficiency and the quality of digital transformation, logistics performance, and various aspects of public governance,including accountability to higher authorities, control of corruption, administrative procedures, and public service delivery. Additionally, the provincial competitiveness index, encompassing factors like entry costs, policy bias, and labourpolicy, also demonstrated a significant association. These findings highlight a clear link between effective public governance and efficient resource utilisationin Vietnamese provinces. This suggests that robust public governance systems, combined with advancements in digitalisationand logistics, can play a crucial role in optimisingresource utilisationwithin Vietnamese provinces.

Solar Power Generation Forecast Using Multivariate Convolution Gated Recurrent Unit Network

For the advancement of smart grids, solar power generation predictions have become an important research topic. In the case of using traditional modeling methods, excessive computational costs may be incurred and it is difficult for these methods to learn the multi-variable dependencies of the data. Therefore, in this paper, a deep learning model was used to combine convolutional neural networks and long short-term memory recurrent network predictions. This method enables hourly power generation one day into the future. Convolutional neural networks are used to extract the features of multiple time series, while long short-term memory neural networks predict multivariate outcomes simultaneously. In order to obtain more accurate prediction results, we performed feature selection on meteorological features and combined the selected weather features to train the prediction model. We further distinguished sunny- and rainy-day models according to the predicted daily rainfall conditions. In the experiment, it was shown that the method of combining meteorological features further reduced the error. Finally, taking into account the differences in climate conditions between the northern and southern regions of Taiwan, the experimental results of case studies involving multiple regions were evaluated to verify the proposed method. The results showed that training combined with selected meteorological features can be widely used in regions with different climates in Taiwan.

Effects of deep coal mining on groundwater hydrodynamic and hydrochemical processes in a multi-aquifer system: Insights from a long-term study of mining areas in ecologically fragile western China

The groundwater hydrodynamic and hydrochemical process of the multi-aquifer system will experience complicated and serious influence under deep coal mining disturbance. There is relatively little research that has integrated hydrodynamic and hydrochemical properties of groundwater to investigate the spatiotemporal distribution characteristics and evolution patterns of hydrogeochemistry and hydrodynamic information in deep multi-aquifer systems. The study of the groundwater hydrodynamic and hydrochemical spatiotemporal coupling response of multi-aquifer systems under the deep and special thick coal seam mining-motivated effect in ecologically fragile western mining areas is of great significance for the safe mining of coal resources and ecological environment protection. In this research, the hydrochemical analysis data composed of 218 groundwater samples from Tangjiahui coalfield, Northwest China with 1526 measurements and a 6-year (2016–2021) sampling period were collected for studying the hydrogeochemical spatiotemporal evolution process and governing mechanism of the multi-aquifer system using hierarchical cluster analysis, ion-ratio method, saturation index and multidimensional statistical analysis. Additionally, wavelet analysis and cross-wavelet coherence analysis were implemented to quantitatively recognize the spatiotemporal variation characteristics of hydrodynamic information and analyze the coherence relationships between time series. The results demonstrate that the hydrochemical characteristics exhibit significant spatial differences, while the temporal variation of hydrochemical characteristics in the Permian Shanxi Formation fractured sandstone aquifer (PSFFA), mine water (MW), and Ordovician karst limestone aquifer (OKA) is not significant. The water-rock interaction is the predominant control mechanism for the spatial evolution of hydrogeochemistry in the research area. Moreover, the large-scale mining of deep coal seams controls the type and degree of water-rock interactions by damaging the structure of aquifers and altering the hydrodynamic conditions of groundwater. The period from 2016 to 2021 exhibits multi-time scale characteristics in time series of precipitation, mine water discharge, and the water level of PSFFA and OKA. The mine water discharge has a positive correlation with the water level of PSFFA and OKA, whereas the significant period of precipitation and the water level of PSFFA coherence is not obvious. The research findings not only provide in-depth insights to protect the groundwater resources in water-shortage mining areas but also promote the secure mining of deep coal resources.

TodyNet: Temporal dynamic graph neural network for multivariate time series classification

Multivariate time series classification (MTSC) is a crucial data mining task that can be effectively tackled using prevalent deep learning technology. However, current methods often overlook hidden dependencies across dimensions and struggle to capture dynamic time series features adequately, leading to poor accuracy. To tackle this challenge, we propose TodyNet, a novel dynamic temporal graph neural network. TodyNet extracts latent spatio-temporal dependencies without predefined graph structures, facilitating information flow among isolated but implicitly interdependent variables. It captures associations between time slots through dynamic graphs, boosting classification performance. Furthermore, to address the limitation that the hierarchical representations of graphs are challenging to learn with graph neural networks (GNNs), we introduce a temporal graph pooling layer for obtaining a global graph-level representation. The dynamic graph, graph information propagation, and temporal convolution are jointly learned within an end-to-end framework. Experimental results on 26 UEA benchmark datasets indicate that compared to current state-of-the-art deep learning methods in the MTSC task, the proposed Todynet achieved a performance improvement of 5.5%.

Forecasting and uncertainty analysis of tailings dam system safety based on data mining techniques

Tailings dams, as critical infrastructure, play a vital role in ensuring the safety and reliability of tailings pond systems. Predicting the trend of tailings dam monitoring parameters helps monitor its operational status and support safety management and decision-making. The traditional time series prediction model focuses on point prediction while neglecting the nonlinear feature of data and the uncertainty of prediction results, which is far from meeting the requirements for risk monitoring and safety management of tailings dam systems. To address this, Deep Auto-Encoder is used to extract and optimize features of multi-dimensional time series; Whale Optimization Algorithm, Long Short Term Memory, and Kernel Density Estimation are used to build a monitoring parameters trend interval prediction model. It can effectively predict and analyze the uncertainty of tailings dam monitoring parameters. Time series data of monitoring parameters are collected from a tailings dam in Anhui, China and modeled to verify the effectiveness of the model. The results show that the hybrid prediction model performs the best in monitoring parameter prediction accuracy and trend. The Root Mean Square Error values of the Whale Optimization Algorithm and Long Short Term Memory prediction for reservoir water level, surface displacement, internal displacement, phreatic line, and beach width are 0.0160, 1.0405, 0.0389, 0.0176, and 0.7525, with errors reduced by 0.37 % to 17.92 % compared to other models. The uncertainty analysis confirms the high reliability of the model. The Kernel Density Estimation effectively forecasts the fluctuation range of predicted values, significantly improving the prediction effectiveness of the monitoring parameters.

Unsteady flow-field forecasting leveraging a hybrid deep-learning architecture

With the rapid development of data-driven technology, deep learning has been widely used to study unsteady flow phenomena, owing to its high-dimensional, nonlinear, and excellent big-data processing capabilities. Rapidly and accurately predicting unsteady flow fields has been a major challenge in fluid mechanics. Therefore, we designed a new U-shaped hybrid deep neural network (UDNN) framework using a multilayer convolution. Through the multilayer learning interaction of high-dimensional flow-field data, the temporal and spatial characteristics of the flow-field flow are captured, and the flow-field characteristics are predicted in an end-to-end form. The UDNN comprises a convolutional neural layer, deconvolutional layer, convolutional long-term and short-term layers, and attention-mechanism layer. First, based on computational fluid dynamics, we generated unsteady flow-field datasets of the flow around fixed and rotating cylinders at different Reynolds numbers, which were used as training samples for the network framework. Second, we designed a U-shaped convolutional layer, added horizontal time-series feature processing and attention-mechanism units, and fused the deep feature information predicted by the model with shallow semantic information to predict the flow-field features. In addition, we compared the UDNN, proper orthogonal decomposition – long short-term memory, and traditional convolutional autoencoder – long short-term memory models in terms of the flow-field prediction error, model training time, and inference speed. The final results showed that the proposed UDNN framework achieved high accuracy and strong robustness in predicting unsteady flow fields.

Improving algorithmic trading consistency via human alignment and imitation learning

Research on algorithmic trading using reinforcement learning has become increasingly popular in recent years. Although most of the current reinforcement learning methods are employed to train the agent for some kind of modeling or data problem, it is worthwhile to explore in aligning agents with human behavior in applications as crucial as financial trading. Achieving such consistency by incorporating human expert experience into agent behavior is a key for potential improvements in this field. Imitation learning learns directly from examples of humans or other agents performing tasks. However, using imitation learning alone suffers from the problem of transitionally fitting expert example data. By combining the advantages of imitation learning and the Advantage Actor–Critic method, the Human Alignment Advantage Actor–Critic (HA3C) algorithm is proposed, to enhance single-asset trading strategy. First, by adding daily and weekly frequency trading data as input features to TimesNet, which is specifically designed to extract correlated temporal patterns from time-series data, it can capture both short-term and long-term features, thus capturing time-series features more comprehensively. Second, an expert action labeling method is proposed to train a strategy prediction network through supervised learning of behavior imitation. Third, a pre-trained strategy network is transferred to balance the exploration and exploitation of the agent’s behavior. Imitation learning techniques leverage finance-specific knowledge to enhance algorithmic trading consistency. This approach enables algorithms to mimic and adapt human decision-making patterns in finance, ultimately improving overall performance. This paper introduces a novel return-based function that efficiently balances short-term and long-term returns over flexible time horizons. It considers the maximum return from different positions and uses flexible time windows to capture trends while maximizing returns. Finally, evaluation on six commonly used datasets, such as DJI and SP500, demonstrates the advantages of the proposed HA3C algorithm compared with other classical and reinforcement learning-based strategies. Notably, on the HSI dataset, the HA3C strategy significantly outperforms other methods, achieving an impressive cumulative return of 681.55% and a Sharpe ratio of 5.07. These results show the superior performance of the HA3C algorithm in enhancing stock trading strategies and its potential to impact algorithmic trading consistency through aligning agent behavior with human expertise.

Analysis of Periodic and Seasonal Characteristics in Time Series: Taking the Dow Jones Industrial Average as an Example

The objective of this research is to conduct a comprehensive analysis of the Dow Jones Industrial Average (DJIA) with a focus on identifying cyclical and seasonal patterns observed in long-term time series data. In order to accurately predict future market trends, this research employs a combination of fundamental and advanced forecasting techniques, including mean, naive, snaive, drift, Holt, Holt-Winters, and ARIMA models. Furthermore, the research explores the impact of return ratios on market investment opportunities. Customized portfolio optimization strategies are developed based on the risk profiles of different investors, along with quantitative trading strategies that effectively manage market risks using the Capital Asset Pricing Model (CAPM). The research shows that the price of Dow Jones Industrial Index has a gradual upward trend, and its return rate has a certain periodicity and seasonality. The aim of this research is to provide valuable insights into the fundamental framework of the American stock market, enabling economists, investors, and policymakers to make informed decisions.

Soundscape mapping: understanding regional spatial and temporal patterns of soundscapes incorporating remotely-sensed predictors and wildfire disturbance

Increased environmental threats require proper monitoring of animal communities to understand where and when changes occur. Ecoacoustic tools that quantify natural acoustic environments use a combination of biophony (animal sound) and geophony (wind, rain, and other natural phenomena) to represent the natural soundscape and, in comparison to anthropophony (technological human sound) can highlight valuable landscapes to both human and animal communities. However, recording these sounds requires intensive deployment of recording devices and storage and interpretation of large amounts of data, resulting in large data gaps across the landscape and periods in which recordings are absent. Interpolating ecoacoustic metrics like biophony, geophony, anthropophony, and acoustic indices can bridge these gaps in observations and provide insight across larger spatial extents and during periods of interest. Here, we use seven ecoacoustic metrics and acoustically-derived bird species richness across a heterogeneous landscape composed of densely urbanized, suburban, rural, protected, and recently burned lands in Sonoma County, California, U.S.A., to explore spatiotemporal patterns in ecoacoustic measurements. Predictive models of ecoacoustic metrics driven by land-use/land-cover, remotely-sensed vegetation structure, anthropogenic impact, climate, geomorphology, and phenology variables capture landscape and daily differences in ecoacoustic patterns with varying performance (avg. R 2 = 0.38 ± 0.11) depending on metric and period-of-day and provide interpretable patterns in sound related to human activity, weather phenomena, and animal activity. We also offer a case study on the use of the data-driven prediction of biophony to capture changes in soniferous species activity before (1–2 years prior) and after (1–2 years post) wildfires in our study area and find that biophony may depict the reorganization of acoustic communities following wildfires. This is demonstrated by an upward trend in activity 1–2 years post-wildfire, particularly in more severely burned areas. Overall, we provide evidence of the importance of climate, spaceborne-lidar-derived forest structure, and phenological time series characteristics when modeling ecoacoustic metrics to upscale site observations and map ecoacoustic biodiversity in areas without prior acoustic data collection. Resulting maps can identify areas of attention where changes in animal communities occur at the edge of human and natural disturbances.

An Efficient Method for Detecting Abnormal Electricity Behavior

The non-technical losses caused by abnormal power consumption behavior of power users seriously affect the revenue of power companies and the quality of power supply. To assist electric power companies in improving the efficiency of power consumption audit and regulating the power consumption behavior of users, this paper proposes a power consumption anomaly detection method named High-LowDAAE (Autoencoder model for dual adversarial training of high low-level temporal features). High-LowDAAE adds an extra “discriminator” named AE3 to USAD (UnSupervised Anomaly Detection on Multivariate Time Series), which performs the same function as AE2 in USAD. AE3 performs the same function as AE2 in USAD, i.e., it is trained against AE1 to enhance its ability to reconstruct average data. However, AE3 differs from AE2 because the two “discriminators” correspond to the high-level and low-level time series features output from the shared encoder network. By utilizing different levels of temporal features to reconstruct the data and conducting adversarial training, AE1 can reconstruct the time-series data more efficiently, thus improving the accuracy of detecting abnormal electricity usage. In addition, to enhance the model’s feature extraction ability for time-series data, the self-encoder is constructed with a long short-term memory (LSTM) network, and the fully connected layer in the USAD model is no longer used. This modification improves the extraction of temporal features and provides richer hidden features for the adversarial training of the dual “discriminators”. Finally, the ablation and comparison experiments are conducted using accurate electricity consumption data from users, and the results show that the proposed method has higher accuracy.

Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer

The mud pump water sealing system (MPWSS) is important in the efficient operation and prolonged service life of the cutter suction dredger’s (CSD) mud pump. Considering that the underwater pump operates underwater and the shaft seal water pressure sensor is prone to failure, a hybrid deep learning model MCNN transformer is proposed to predict the underwater pump shaft seal water pressure in the event of sensor failure. This paper uses big data from the dredging project to deeply excavate the relationship between the shaft end sealing water pressure and other construction data by combining experience and artificial intelligence, and then uses multi-scale convolutional neural network (MCNN) to reconstruct the data, highlighting the time series characteristics of the multi-scale data were then input into the transformer model for prediction, and compared with a single MCNN, transformer model and four other neural networks. Finally, the cutter suction dredger “Hua An Long” was selected as an application research case; experimental comparisons were conducted on seven different models to verify the accuracy and applicability of the MCNN-transformer model.