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The traction motor realizes the mutual conversion of electrical energy and mechanical energy during the train traction and braking process and is a key component of high-speed trains. The normal operation of the motor is directly related to the safety of high-speed train operation. Changes in temperature signals can reflect faults in the traction motor. By analyzing the internal and external influencing factors of temperature signals, a multi-factor prediction model for traction motors is established based on the maximal information coefficient and improved support vector regression. In this model, highly relevant features selections are performed based on time-delayed sequences and the maximal information coefficient. Using the adaptive particle swarm algorithm to optimize the improved support vector regression algorithm can enhance its accuracy and efficiency. Furthermore, using the K-nearest neighbor algorithm for error prediction will yield more accurate results. By comparing the R M S E , M B E , M A E , and other evaluation metrics of different algorithms under various working conditions, the results show that the prediction method proposed in this paper performs well across different working conditions. This method demonstrates greater adaptability to varying conditions and is more suitable for applications involving high-speed trains. [ABSTRACT FROM AUTHOR]

In air combat missions, unmanned combat aerial vehicles (UCAVs) must take strategic actions to establish combat advantages, enabling effective tracking and attacking of enemy UCAVs. Currently, a lot of reinforcement learning algorithms are applied to the air combat mission of unmanned fighter aircraft. However, most algorithms can only select policies based on the current state of both sides. This leads to the inability to effectively track and attack when the enemy performs large angle maneuvering. Additionally, these algorithms cannot adapt to different situations, resulting in the unmanned fighter aircraft being at a disadvantage in some cases. To solve these problems, this paper proposes predictive air combat decision model with segmented reward allocation for air combat tracking and attacking. On the basis of the air combat environment, we propose the prediction soft actor-critic (Pre-SAC) algorithm, which combines the prediction of enemy states with the states of UCAV for model training. This enables the UCAV to predict the next move of the enemy UCAV in advance and establish a greater air combat advantage for us. Furthermore, by adopting a segmented reward allocation model and combining it with the Pre-SAC algorithm, we propose the segmented reward allocation soft actor-critic (Sra-SAC) algorithm, which solves the problem of UCAVs being unable to adapt to different situations. The results show that the prediction-based segmented reward allocation the Sra-SAC algorithm outperforms the traditional soft actor-critic (SAC) algorithm in terms of overall reward, travel distance, and relative advantage. [ABSTRACT FROM AUTHOR]

Improving the ability and accuracy of intelligent state prediction of large and complex equipment is one of the important directions of current intelligent operation and maintenance technology research. Due to the influence of insufficient analysis of equipment degradation characteristics, single function of traditional prediction model, and difficulty in determining the optimal parameters of the model make the prediction effect poor. In this paper, a state prediction model fusion optimization strategy is proposed for lithium mill equipment as an example. Based on the process flow and vibration mechanism, the inherent vibration characteristics of the roller bearing system are analyzed, and the degradation characteristics of the roller bearing under resonance conditions are explored from the finite element equivalent model, so as to determine the equipment operation stage and the starting point of degradation. The state prediction task is divided into degradation phase and residual life prediction phase, and Time-Convolutional Denoising Autoencoder (TCDAE) and two-layer Sparse Auto Encoder (SAE) are designed for data feature enhancement and degradation feature fusion and dimensionality reduction. Construct BO-BiGRU state prediction model to mine the feature information hidden in the whole time series of data points and adjust the model parameters adaptively using Bayesian Optimization method. The novelty of this study is to analyze the degradation characteristics of key components, correct the theoretical degradation starting point by using the degradation trend formula, and establish a unified framework from monitoring data to condition prediction. Compared with the original model constructed by the above algorithm, the fusion model proposed in this paper has significantly improved performance. The data analysis shows that the prediction accuracy after model fusion is substantially improved, and the accuracy after TCDAE feature enhancement is improved by about 10.2%, the accuracy after two-layer SAE model fusion and dimensionality reduction improved by about 9.8%, and the state accuracy after BO-BiGRU model improved by about 11.6%. The crux to the research depends on the construction of a state prediction model, which is based on the analysis of the bearing degradation process and the effective integration of algorithms. Predictive maintenance of critical components also improves product quality. [ABSTRACT FROM AUTHOR]

Bearings are widely used as a common mechanical component and significantly impacts the reliability and safety of various engineering equipment. In the field of large‐scale equipment such as wind turbines, statistical data for reliability and state prediction of bearings is usually limited, leading to the inapplicability of traditional statistical methods. Besides, individual differences commonly exist among bearings, and the appropriate failure threshold for a specific bearing is difficult to be determined due to the individual uncertainty, which may induce prediction errors. To address these issues, a novel operational reliability and state prediction method based on random threshold degradation hidden Markov model was proposed in the study. The traditional hidden Markov model was improved by considering the effect of performance degradation on state transition probability matrix. Moreover, random failure thresholds of performance degradation were employed to describe individual differences between bearings. The operational reliability and state of bearings was obtained by comprehensive analysis of prediction results under different failure thresholds. Verification of the proposed operational reliability and state prediction method were conducted using PHM2012 bearing operation data. The results underscored the effectiveness of the proposed method in achieving a comparatively high accuracy in operational reliability prediction without the prerequisite of an exact failure threshold, when contrasted with several established reliability and life prediction techniques. [ABSTRACT FROM AUTHOR]

Abstract In air combat missions, unmanned combat aerial vehicles (UCAVs) must take strategic actions to establish combat advantages, enabling effective tracking and attacking of enemy UCAVs. Currently, a lot of reinforcement learning algorithms are applied to the air combat mission of unmanned fighter aircraft. However, most algorithms can only select policies based on the current state of both sides. This leads to the inability to effectively track and attack when the enemy performs large angle maneuvering. Additionally, these algorithms cannot adapt to different situations, resulting in the unmanned fighter aircraft being at a disadvantage in some cases. To solve these problems, this paper proposes predictive air combat decision model with segmented reward allocation for air combat tracking and attacking. On the basis of the air combat environment, we propose the prediction soft actor-critic (Pre-SAC) algorithm, which combines the prediction of enemy states with the states of UCAV for model training. This enables the UCAV to predict the next move of the enemy UCAV in advance and establish a greater air combat advantage for us. Furthermore, by adopting a segmented reward allocation model and combining it with the Pre-SAC algorithm, we propose the segmented reward allocation soft actor-critic (Sra-SAC) algorithm, which solves the problem of UCAVs being unable to adapt to different situations. The results show that the prediction-based segmented reward allocation the Sra-SAC algorithm outperforms the traditional soft actor-critic (SAC) algorithm in terms of overall reward, travel distance, and relative advantage.

Multi-object tracking tasks aim to assign unique trajectory codes to targets in video frames. Most detection-based tracking methods use Kalman filtering algorithms for trajectory prediction, directly utilizing associated target features for trajectory updates. However, this approach often fails, with camera jitter and transient target loss in real-world scenarios. This paper rethinks state prediction and fusion based on target temporal features to address these issues and proposes the SimpleTrackV2 algorithm, building on the previously designed SimpleTrack. Firstly, to address the poor prediction performance of linear motion models in complex scenes, we designed a target state prediction algorithm called LSTM-MP, based on long short-term memory (LSTM). This algorithm encodes the target's historical motion information using LSTM and decodes it with a multilayer perceptron (MLP) to achieve target state prediction. Secondly, to mitigate the effect of occlusion on target state saliency, we designed a spatiotemporal attention-based target appearance feature fusion (TSA-FF) target state fusion algorithm based on the attention mechanism. TSA-FF calculates adaptive fusion coefficients to enhance target state fusion, thereby improving the accuracy of subsequent data association. To demonstrate the effectiveness of the proposed method, we compared SimpleTrackV2 with the baseline model SimpleTrack on the MOT17 dataset. We also conducted ablation experiments on TSA-FF and LSTM-MP for SimpleTrackV2, exploring the optimal number of fusion frames and the impact of different loss functions on model performance. The experimental results show that SimpleTrackV2 handles camera jitter and target occlusion better, achieving improvements of 1.6%, 3.2%, and 6.1% in MOTA, IDF1, and HOTA, respectively, compared to the SimpleTrack algorithm. [ABSTRACT FROM AUTHOR]

Aiming at the problem that predicted data do not reflect the operating status of computer numerical control (CNC) machine tools, this article proposes a new combined model based on SE-ResNet and Transformer for CNC machine tool failure prediction. Firstly, the Transformer model is utilised to build a non-linear temporal feature mapping using the attention mechanism in multidimensional data. Secondly, the predicted data are transformed into 2D features by the SE-ResNet model, which is adept at processing 2D data, and the spatial feature relationships between predicted data are captured, thus enhancing the state recognition capability. Through experiments, data involving the CNC machine tools in different states are collected to build a dataset, and the method is validated. The SE-ResNet-Transformer model can accurately predict the state of CNC machine tools with a recognition rate of 98.56%. Results prove the effectiveness of the proposed method in CNC machine tool failure prediction. The SE-ResNet-Transformer model is a promising approach for CNC machine tool failure prediction. The method shows great potential in improving the accuracy and efficiency of CNC machine tool failure prediction. Feasible methods are provided for precise control of the state of CNC machine tools. [ABSTRACT FROM AUTHOR]

Feed is essential to provide nutrients to dairy cows. An accurate prediction of feed consumption can greatly contribute to ensuring the health of dairy cows and improving production efficiency. However, the feed consumption status often exhibits nonlinear and non-stationary patterns, resulting in low prediction accuracy. In this study, based on the empirical mode decomposition (EMD) and long short-term memory (LSTM), a prediction model of feed consumption was proposed to combine the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), random forest (RF), and improved LSTM (ILSTM), namely ICEEMDAN-RF-ILSTM. Among them, ILSTM, an enhancement of LSTM, was used to adjust the output range of the forgetting gate to retain more data features, thereby improving feature learning. This model decomposed the original data into multiple relatively stationary components. Then, the different features of each component were considered to predict. The first stage was data decomposition. Since ICEEMDAN can decompose nonlinear and nonstationary time sequences into several relatively stationary components, the model decomposed the original data into multiple intrinsic mode function (IMF) components using ICEEMDAN. Each component was arranged from the high to the low frequency. The component with the highest frequency showed complex fluctuation patterns, whereas, the component with the lowest frequency represented the overall change trend of the data, regarded as the trend component. The remaining components reflected the periodic pattern of the data, regarded as the periodic components. The second stage was data prediction for the different components. RF, an ensemble learning method, was used to predict the component with the highest frequency, benefiting from its ability to construct and integrate multiple models. Additionally, the robustness and generalization of the RF model were improved by randomly selecting features. Meanwhile, ILSTM was used to predict the periodic and trend pattern components. The final stage was data integration. The predictions were summed from all components for the final prediction. The model was trained and tested on a self-built feed dataset. The results show that ICEEMDAN-RF-ILSTM achieved high accuracy with the coefficient of determination (R²), mean absolute percentage error (MAPE), and root means square error (RMSE) indicators of 0.993, 2.576%, and 0.596%, respectively, indicating that the methods proposed can effectively predict feed consumption status. At the same time, its performance was better than ICEEMDAN-LSTM and other mainstream models. This research also confirms that LSTM’s learning ability was enhanced by adjusting the output value range of the forgetting gate, in order to retain more features of the data. Meanwhile, by decomposing non-stationary raw data into multiple relatively stationary components, the prediction accuracy was improved. Moreover, by considering the different features of each component and using different methods to predict different components, the prediction accuracy can be further improved. This finding can provide a practical approach to assessing feed consumption, aiding in informed decision-making for intelligent animal husbandry. [ABSTRACT FROM AUTHOR]

Dynamic target tracking is a crucial technique for autonomous underwater vehicles (AUV), enabling key operations such as target detection and reconnaissance. Standard approaches for tracking maneuvering targets often involve the interacting multiple model (IMM) algorithm which integrates the constant velocity (CV) model and the coordinated turn (CT) model. However, these models traditionally utilize fixed transition probabilities and turn rates based on prior information, potentially leading to imprecise state estimations. In response to this, the paper introduces an adaptive parallel IMM (APIMM) based on current adaptive IMM algorithms. This method adaptively adjusts transition probabilities and pairs with the unscented Kalman filter (UKF) algorithm for state prediction of maneuvering targets in a 3D underwater environment. The enhanced algorithm chooses from a model set that encompasses the CV model, the 3D fixed center constant speed and turning rate model (CSCTR) with an adaptive turn rate, and the current statistical (CS) model. Simulation outcomes have demonstrated that this algorithm utilizes posterior information more effectively, possesses an accelerated model switching speed, and improves the prediction accuracy of the underwater dynamic target's state in three-dimensional space by approximately 15%.

The statistical analysis of civil aircraft accidents reveals that the highest incidence of mishaps occurs during the approach and landing stages. Predominantly, these accidents are marked by abnormal energy states, leading to critical situations like stalling and heavy landings. Therefore, it is of great significance to accurately predict the aircraft energy state in the approach and landing stages to ensure a safe landing. In this study, a deep learning method based on time sequence data for the prediction of the aircraft approach and landing energy states is proposed. Firstly, by conducting an extensive overview of the existing literature, three characteristic parameters of altitude, velocity, and glide angle were selected as the indicators to characterize the energy state. Following this, a semi-physical simulation platform for a certain type of aircraft was developed. The approach and landing experiments were carried out with different throttle sizes and flap deflection under different wind speeds and wind directions. Then, a deep learning prediction model based on Long Short-Term Memory (LSTM) was established based on the experimental data to predict the energy state indicators during the approach and landing phases. Finally, the established LSTM model underwent rigorous training and testing under different strategies, and a comparative analysis was carried out. The results demonstrated that the proposed LSTM model exhibited high accuracy and a strong generalization ability in predicting energy states during the approach and landing phases. These results offer a theoretical basis for designing energy early warning systems and formulating the relevant flight control laws in the approach and landing stages. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the lifeline of energy supply for various offshore platforms, submarine cables will have a huge economic and strategic impact in case of failure. Accurate prediction of submarine cable operation status will help to grasp its operation risks in advance, so as to achieve preventive maintenance. On the basis of fully mining the dynamic and static characteristics of submarine cable operation and maintenance data, a method for predicting the operation state of submarine cables based on attention mechanism and convolutional neural network gated cyclic neural network (CNN-GRU) was proposed. Firstly, considering the three key influencing factors of online monitoring, patrol inspection index and static test, the evaluation index system of submarine cable operation status was established. Then, based on the improved analytic hierarchy process (AHP) and the idea of multi-level variable weight evaluation, the evaluation model of submarine cable operation status was constructed. Finally, a combined neural network model based on attention mechanism and CNN-GRU were established, and historical operation parameters and quantitative results of status were taken as input characteristic parameters to realize the evolution trend prediction of submarine cable operation status. The analysis of numerical examples shows that the proposed method can effectively predict the operation state of submarine cables, and the average percentage error is as low as 1. 04% . Compared with full connection neural network, CNN, CNN long short memory neural network ( LSTM), and other algorithms, the proposed method has better prediction accuracy. [ABSTRACT FROM AUTHOR]

Abstract Integrated sensing and communication (ISAC) has been a promising technique in various wireless communication applications. In this paper, we investigate a beamforming design method based on ISAC waveforms in unmanned aerial vehicle (UAV) communications. An integrated state prediction and beamforming design framework is presented. We utilize the target states from sensing algorithms to improve the prediction performance. Based on the predicted states, we formulate the mathematical form of the communication interruption probability. To enhance the beamforming performance, we propose a design approach that satisfies both sensing and communication metrics and the communication interruption constraints. We show that the proposed method achieves robust communication under the integrated state prediction and beamforming design framework. Simulation results show that by using the ISAC signal, our method significantly lowers the communication interruption probability in the beamforming process and achieves better communication performance.

With the continuous proliferation of satellites, accurately determining their operational status is crucial for satellite design and on-orbit anomaly detection. However, existing research overlooks this crucial aspect, falling short in its analysis. Through an analysis of real-time satellite telemetry data, this paper pioneers the introduction of four distinct operational states within satellite attitude control systems and explores the challenges associated with their classification and prediction. Considering skewed data and dimensionality, we propose the Two-Step Graph Convolutional Neural Network (TS-GCN) framework, integrating resampling and a streamlined architecture as the benchmark of the proposed problem. Applying TS-GCN to a specific satellite model yields 98.93% state recognition and 99.13% prediction accuracy. Compared to the Standard GCN, Standard CNN, and ResNet-18, the state recognition accuracy increased by 37.36–75.65%. With fewer parameters, TS-GCN suits on-orbit deployment, enhancing assessment and anomaly detection. [ABSTRACT FROM AUTHOR]

The temperature prediction of hoist motor is one of the effective ways to ensure the safe production of mine hoist. Digital twin technology is a technology that combines the physical system of the real world with the digital model of the virtual world. Through digital twin technology, the physical system in the real world can be monitored and simulated in a virtual environment, and the state information of these systems can be monitored in real time. Recurrent neural network is a kind of neural network suitable for processing sequence data, which can automatically extract and learn the feature information in sequential data. To achieve online monitoring and over-advance perception of the temperature of the mine hoist motor, a temperature prediction and advance sensing method based on digital twins and recurrent neural network is proposed. To begin with, a high-fidelity digital twin monitoring system for mine hoists is constructed, enabling the acquisition of real-time temperature data. These temperature data are then fed into a neural network for feature extraction and precise prediction of the motor's state. Subsequently, based on the temperature prediction module in the digital twin hoist monitoring system, a user interface (UI) is developed, and a fully functional digital twin temperature monitoring system is built and experimentally validated. The experimental results demonstrate that the digital twin system effectively monitors the real-time temperature state of the motor during the operation of the mine hoist. Furthermore, the integration of digital twin and recurrent neural network enables the accurate prediction and proactive detection of temperature variations in the motor of the mine hoist. This innovative approach introduces a novel perspective for implementing predictive maintenance in the mining industry, enhancing the safety and reliability of mine hoists. Additionally, it offers valuable technical support in improving maintenance efficiency and reducing associated costs. [ABSTRACT FROM AUTHOR]

The continuous development of communication technology and various deep learning models has led to the invention and application of many anti-interference technologies in the field of communication countermeasures. The existing communication interference models have defects such as low anti-interference rate and low accuracy in communication spectrum prediction. To solve these problems, this study attempts to construct a Convolutional Neural Networks Long Short Term Memory (CNN-LSTM) and apply it to the communication jamming system for spectrum state prediction. Firstly, the framework of the communication interference system using the USRP RIO radio platform software was designed, and based on it, the communication interference channel was optimized using reinforcement learning Q-learning algorithm. Next, to further predict the signal spectrum state during the communication process, neural networks are utilized to construct a communication spectrum state prediction model. According to the optimization effect of communication interference channel and network spectrum prediction effect tested, the communication model under the Q-learning algorithm can achieve a 100% effective interference probability in fixed communication strategies. The Convolutional Neural Networks-1 Long-Short Term Memory-2 model has a prediction accuracy of 95.2% and can accurately predict changes in the communication spectrum. In summary, the Convolutional Neural Networks-1 Long-Short Term Memory-2 network constructed by this paper can provide new solutions and achieve good results for communication spectrum prediction.

The evolution of partial discharge (PD) with time can provide a deep understanding on the insulation status of power cables. It is of great significance for intelligent operation and maintenance of power cables. In this paper, the PD pulse signal in the 10kV cable joint during accelerated electrical aging under 20kV AC voltage was acquired in the real-time for about 160h. The characteristic parameters of partial discharge–pulse number, average voltage, maximum voltage and energy per second were extracted. The results show that the phase of partial discharge is mainly concentrated at 30°-90° and 200°-270°, which can be characterized as internal discharge. PD characteristic parameters gradually increased after 50h. The pulse number, energy per second and the average voltage of PD pulse reached a peak between 60-80h. Then these parameters reached a steady state between 80-130h and showed a steep rise after 130h. The maximum voltage of PD pulse shows a steep rise at about 70h from 0.1V to 0.3V. It rises sharply from 0.3V to 0.5V after about 120h and then enters a relatively stable oscillation stage. The evolution rules of the PD characteristic parameters comply well with the electrical tree growth states-initiation period, lag period and rapid growth period. Furthermore, model for predicting and evaluating the insulation state based on BP neural network are established and the prediction accuracy is verified. The proposed models can provide early warning for the cable joint before the insulation failure, so as to ensure timely maintenance or replacement.

Integrated sensing and communication (ISAC) has been a promising technique in various wireless communication applications. In this paper, we investigate a beamforming design method based on ISAC waveforms in unmanned aerial vehicle (UAV) communications. An integrated state prediction and beamforming design framework is presented. We utilize the target states from sensing algorithms to improve the prediction performance. Based on the predicted states, we formulate the mathematical form of the communication interruption probability. To enhance the beamforming performance, we propose a design approach that satisfies both sensing and communication metrics and the communication interruption constraints. We show that the proposed method achieves robust communication under the integrated state prediction and beamforming design framework. Simulation results show that by using the ISAC signal, our method significantly lowers the communication interruption probability in the beamforming process and achieves better communication performance. [ABSTRACT FROM AUTHOR]

Aiming at the problem that predicted data do not reflect the operating status of computer numerical control (CNC) machine tools, this article proposes a new combined model based on SE-ResNet and Transformer for CNC machine tool failure prediction. Firstly, the Transformer model is utilised to build a non-linear temporal feature mapping using the attention mechanism in multidimensional data. Secondly, the predicted data are transformed into 2D features by the SE-ResNet model, which is adept at processing 2D data, and the spatial feature relationships between predicted data are captured, thus enhancing the state recognition capability. Through experiments, data involving the CNC machine tools in different states are collected to build a dataset, and the method is validated. The SE-ResNet-Transformer model can accurately predict the state of CNC machine tools with a recognition rate of 98.56%. Results prove the effectiveness of the proposed method in CNC machine tool failure prediction. The SE-ResNet-Transformer model is a promising approach for CNC machine tool failure prediction. The method shows great potential in improving the accuracy and efficiency of CNC machine tool failure prediction. Feasible methods are provided for precise control of the state of CNC machine tools.

Aiming at the problem that the prediction performance of remote operation state of hydro-turbine needs to be improved, this paper uses whale optimization algorithm (WOA) to optimize random forest (RF) . It combines it with Adaboost algorithm to propose the prediction model in this paper. Firstly, the signal of hydro-turbine is analyzed by variational modal decomposition (VMD), and the penalty factor and the number of IMF components K of VMD are optimized by fruit fly optimization algorithm (FOA). The arrangement entropy of intrinsic mode function (IMF) components, kurtosis, mean value of original signal are calculated, and the input eigenvector of hydro-turbine state prediction model is constructed; Secondly, the number of split attribute sets and the optimal number of decision trees of RF are optimized by WOA, and multiple WOA-RF models are iteratively trained by Adaboost algorithm to construct WOA-RF-Adaboost state prediction model. The prediction effect of the proposed model and the traditional model is evaluated by correct rate and confusion matrix. The results show that the prediction accuracy of WOA-RF-Adaboost model proposed in this paper is 99.2%, it has good state prediction performance.

In order to solve the problem of selecting missile health state evaluation indexes and the mapping relationship between the selected indexes and missile health state, a missile health state prediction method based on CA-RBF neural network is proposed. Firstly, the influencing factors of missile health state are analyzed through missile life profile, and the quantitative method is given. Then, the corresponding analysis (CA) method is used to screen the influencing factors of missile health state. Taking the selected factors and the evaluation results of missile health state as the input and output of neural network, the training samples of RBF neural network are established to predict the missile health state. Finally, an example is given to illustrate the practicability and effectiveness of the proposed method. This method can provide a new idea for the selection of missile health state indexes and missile health state prediction, and provide a basis for missile preventive maintenance decision-making.

Aiming at the problem that the path search rules in the traditional path planning methods are divorced from the actual maneuverability of an unmanned surface vehicle (USV), a motion planning method of state prediction rapidly exploring random tree (spRRT) is proposed. This method retains the discrete search of the original rules of RRT while adding the continuity of the motion of USV. Firstly, the state information for each movement (position, yaw angle, velocity, etc.), is calculated based on the mathematical model of USV's motion which takes into account the complete dynamic constraints. Secondly, this information is added to the RRT path search rules to predict the state points that can be reached by the USV. Furthermore, in order to improve search efficiency and reduce cost, spRRT is enhanced by an elliptic sampling domain (spRRT-Informed). The simulation results indicate that spRRT can effectively plan smooth paths for smoothly navigating USV. The inclusion of the USV motion model has improved steering performance by an average of over 40%. Additionally, the spRRT-Informed enhanced with sampling optimization strategy improves performance by at least 10% over spRRT in terms of sailing time and distance of the path. The results of the simulation conducted in a realistic scenario validate that spRRT-Informed can be used as a reference for practical applications. [ABSTRACT FROM AUTHOR]

The statistical analysis of civil aircraft accidents reveals that the highest incidence of mishaps occurs during the approach and landing stages. Predominantly, these accidents are marked by abnormal energy states, leading to critical situations like stalling and heavy landings. Therefore, it is of great significance to accurately predict the aircraft energy state in the approach and landing stages to ensure a safe landing. In this study, a deep learning method based on time sequence data for the prediction of the aircraft approach and landing energy states is proposed. Firstly, by conducting an extensive overview of the existing literature, three characteristic parameters of altitude, velocity, and glide angle were selected as the indicators to characterize the energy state. Following this, a semi-physical simulation platform for a certain type of aircraft was developed. The approach and landing experiments were carried out with different throttle sizes and flap deflection under different wind speeds and wind directions. Then, a deep learning prediction model based on Long Short-Term Memory (LSTM) was established based on the experimental data to predict the energy state indicators during the approach and landing phases. Finally, the established LSTM model underwent rigorous training and testing under different strategies, and a comparative analysis was carried out. The results demonstrated that the proposed LSTM model exhibited high accuracy and a strong generalization ability in predicting energy states during the approach and landing phases. These results offer a theoretical basis for designing energy early warning systems and formulating the relevant flight control laws in the approach and landing stages.

With the rapid development of agile Earth observation satellites (AEOSs), these satellites are able to conduct more high-quality observation missions. Nevertheless, while completing these missions takes up more data transmission and electrical energy resources, it also increases the coupling within each satellite subsystem. To address this problem, we propose a reasoning-based scheduling method for an AEOS under multiple subsystem constraints. First, we defined the AEOS mission scheduling model with multi-subsystem constraints. Second, we put forward a state variable prediction method that reflects the different coupling states of a satellite after analyzing the coupling relationships between various subsystems and identifying the primary limiting coupling states for each subsystem. Third, we established the reasoning rules corresponding to the planning strategies of different coupling states of the satellite by adding two planning strategies based on the planning strategies of existing planning methods. By comparing the proposed method to three heuristic scheduling methods and a meta-heuristic scheduling method, the results show that our method has better performance in terms of scheduling results and efficiency. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The temperature prediction of hoist motor is one of the effective ways to ensure the safe production of mine hoist. Digital twin technology is a technology that combines the physical system of the real world with the digital model of the virtual world. Through digital twin technology, the physical system in the real world can be monitored and simulated in a virtual environment, and the state information of these systems can be monitored in real time. Recurrent neural network is a kind of neural network suitable for processing sequence data, which can automatically extract and learn the feature information in sequential data. To achieve online monitoring and over-advance perception of the temperature of the mine hoist motor, a temperature prediction and advance sensing method based on digital twins and recurrent neural network is proposed. To begin with, a high-fidelity digital twin monitoring system for mine hoists is constructed, enabling the acquisition of real-time temperature data. These temperature data are then fed into a neural network for feature extraction and precise prediction of the motor’s state. Subsequently, based on the temperature prediction module in the digital twin hoist monitoring system, a user interface (UI) is developed, and a fully functional digital twin temperature monitoring system is built and experimentally validated. The experimental results demonstrate that the digital twin system effectively monitors the real-time temperature state of the motor during the operation of the mine hoist. Furthermore, the integration of digital twin and recurrent neural network enables the accurate prediction and proactive detection of temperature variations in the motor of the mine hoist. This innovative approach introduces a novel perspective for implementing predictive maintenance in the mining industry, enhancing the safety and reliability of mine hoists. Additionally, it offers valuable technical support in improving maintenance efficiency and reducing associated costs.

Aiming at the problems of high manual cost, low efficiency, and low precision of the mechanical axis health management in industrial robot applications, this paper proposes a health assessment and state prediction algorithm based on hidden Markov model (HMM) and temporal convolutional networks (TCN). First, the MPdist similarity comparison algorithm is used to construct the mechanical axis health index. Then the hidden Markov model is trained with observable sensor data. After that, the temporal convolution neural network is used to predict state transition time iteratively, and the predicted results are decoded by HMM. The experimental results show that the HMM‐TCN model can accurately assess the health state of the mechanical axis and predict the state transition in real‐time. The prediction accuracy of this method reaches 87.5%, and the error interval locates in [−3,9] time steps. The accuracy, early/late prediction indicators are better than HMM‐RNN, HMM‐LSTM, and HMM‐GRU. [ABSTRACT FROM AUTHOR]

With the development of unmanned systems, more and more attentions are paid to the energy and power systems of data-driven ships. The autonomy of unmanned ships puts forward urgent requirements for the monitoring and prediction of the energy and power system of ships. Aiming at the state prediction for marine diesel engine, an improvement method based on variational modal decomposition (VMD) and long short-term memory (LSTM) is proposed in this paper. The sub signals are obtained by decomposing the signal to be predicted through VMD, the sub signals and resident signal are all predicted with LSTM, and the reconstruction prediction signal is obtained by sum all the predicted sub signals and resident signal. Compared with LSTM, ESN, and SVR methods, the proposed method reduces the prediction errors significantly. Compared with LSTM, the RE errors of the two sensors are reduced by 49.79% and 56.32% respectively, and the RMSE errors are reduced by 34.65% and 27.71% respectively. The performance of this method is better than other methods, and it has sufficient accuracy performance for state prediction of marine diesel engine.

A solid oxide fuel cell (SOFC) is an innovative power generation system that is green, efficient, and promising for a wide range of applications. The prediction and evaluation of the operation state of a solid oxide fuel cell system is of great significance for the stable and long-term operation of the power generation system. Prognostics and Health Management (PHM) technology is widely used to perform preventive and predictive maintenance on equipment. Unlike prediction based on the SOFC mechanistic model, the combination of PHM and deep learning has shown wide application prospects. Therefore, this study first obtains an experimental dataset through short-term degradation experiments of a 1 kW SOFC system, and then proposes an encoder-decoder RNN-based SOFC state prediction model. Based on the experimental dataset, the model can accurately predict the voltage variation of the SOFC system. The prediction results of the four different prediction models developed are compared and analyzed, namely, long short-term memory (LSTM), gated recurrent unit (GRU), encoder–decoder LSTM, and encoder–decoder GRU. The results show that for the SOFC test set, the mean square error of encoder–decoder LSTM and encoder–decoder GRU are 0.015121 and 0.014966, respectively, whereas the corresponding error results of LSTM and GRU are 0.017050 and 0.017456, respectively. The encoder–decoder RNN model displays high prediction precision, which proves that it can improve the accuracy of prediction, which is expected to be combined with control strategies and further help the implementation of PHM in fuel cells. [ABSTRACT FROM AUTHOR]

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Urban traffic signal control can widely use deep reinforcement learning (DRL) technique. However, in existing researches, most DRL agents only use the current traffic state to make decisions and have limited control effects when the traffic flow changes greatly. Aiming at the problem, this paper proposed a state prediction based deep reinforcement learning algorithm for traffic signal control. The algorithm used one-hot coding to design a concise and efficient traffic state, and then used a Long Short-Term Memory（LSTM） to predict the future state. The agent made optimal decisions based on the current state and the predicted state. The experimental results on the simulation platform SUMO show that compared with three typical signal control algorithms, the proposed algorithm has the best performance in terms of average waiting time, travel time, fuel consumption, CO2 emissions and cumulative reward both in a single intersection and multiple intersections under different flow conditions. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used as effective energy storage and have become the main component of power supply systems. Accurate battery state prediction is key to ensuring reliability and has significant guidance for optimizing the performance of battery power systems and replacement. Due to the complex and dynamic operations of lithium-ion batteries, the state parameters change with either the working condition or the aging process. The accuracy of online state prediction is difficult to improve, which is an urgent issue that needs to be solved to ensure a reliable and safe power supply. Currently, with the emergence of artificial intelligence (AI), battery state prediction methods based on data-driven methods have high precision and robustness to improve state prediction accuracy. The demanding characteristics of test time are reduced, and this has become the research focus in the related fields. Therefore, the convolutional neural network (CNN) was improved in the data modeling process to establish a deep convolutional neural network ensemble transfer learning (DCNN-ETL) method, which plays a significant role in battery state prediction. This paper reviews and compares several mathematical DCNN models. The key features are identified on the basis of the modeling capability for the state prediction. Then, the prediction methods are classified on the basis of the identified features. In the process of deep learning (DL) calculation, specific criteria for evaluating different modeling accuracy levels are defined. The identified features of the state prediction model are taken advantage of to give relevant conclusions and suggestions. The DCNN-ETL method is selected to realize the reliable state prediction of lithium-ion batteries. [ABSTRACT FROM AUTHOR]

In view of the difficulty of fault prediction for aviation hydraulic pumps and the poor realtime performance of state monitoring in practical applications, a hydraulic pump pressure signal prediction method is proposed to accomplish the monitoring and prediction of the health status of hydraulic pumps in advance. First, based on the on-line real-time acquisition of time series flight parameters and pressure signal data, the chaotic characteristics of the system are analyzed using chaos theory, so that the time series pressure signal is predictable. Second, phase space reconstruction (PSR) of the one-dimensional time series data is conducted. The embedding dimension m and time delay τ are obtained by the C-C method. The reconstructed matrix is used as the training set and test set of the support vector regression (SVR) algorithm model according to a certain proportion, and the genetic algorithm (GA) is then used to optimize the parameters of the SVR model. Finally, the SVR model optimized by the genetic algorithm based on phase space reconstruction (PSR-GA-SVR) is used to test the test set data. The results show that the prediction accuracy of the proposed method is higher than that of the BP neural network based on phase space reconstruction (PSR-BPNN) and the SVR model based on phase space reconstruction (PSR-SVR). Relative to PSR-BPNN and PSR-SVR, PSR-GA-SVR produces a minimum mean square error (MSE) reduced by 73.40% and 68.0%, respectively, and a mean absolute error (MAE) decreased by 90.41% and 90.87%, respectively. The confidence level for PSR-GA-SVR was increased, and the coefficient of determination was greater than 0.98.

The denial-of-service (DoS) attacks block the communications of the power grids, which affects the availability of the measurement data for monitoring and control. In order to reduce the impact of DoS attacks on measurement data, it is essential to predict missing measurement data. Predicting technique with measurement data depends on the correlation between measurement data. However, it is impractical to install phasor measurement units (PMUs) on all buses owing to the high cost of PMU installment. This paper initializes the study on the impact of PMU placement on predicting measurement data. Considering the data availability, this paper proposes a scheme for predicting states using the LSTM network while ensuring system observability by optimizing phasor measurement unit (PMU) placement. The optimized PMU placement is obtained by integer programming with the criterion of the node importance and the cost of PMU deployment. There is a strong correlation between the measurement data corresponding to the optimal PMU placement. A Long-Short Term Memory neural network (LSTM) is proposed to learn the strong correlation among PMUs, which is utilized to predict the unavailable measured data of the attacked PMUs. The proposed method is verified on an IEEE 118-bus system, and the advantages compared with some conventional methods are also illustrated. [ABSTRACT FROM AUTHOR]

China's tobacco plantation industry includes a large scale and numerous employees. Nevertheless, the current tobacco curing control technology has higher labor intensity, however, it also cannot be adjusted based on the conditions of different tobacco leaves batches reducing the quality of tobacco. The tobacco's quality can be improved after curing and labor intensity can be reduced by modeling the state prediction of the tobacco curing process, accurately predicting the state of the tobacco curing, and making timely adjustments to the curing process. The area, color, weight, and some chemical substances of tobacco leaves significantly change during the tobacco curing process. This can be theoretically used as the input feature of the state prediction model. However, it is difficult to calculate the changes of the area and the changes of chemical substances in real-time owing to the complexity of the intensive curing room environment. Only features such as color and weight are easier to extract indicating that the model has fewer available features, and the accuracy of prediction using a single model is relatively low. Considering this problem, a state prediction fusion model (SPFM) was proposed integrating Long-Short Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost). At the same time, based on the characteristics of the data set, a new data processing is proposed for the tobacco curing data set. By denoising, the image, the characteristic values of the RGB (Red, Green, Blue), HSV (Hue, Saturation, Value) color space were extracted. Then, the data pre-processing such as standardization, data cleaning, and label digitization were performed on the data. Furthermore, an intelligent tobacco curing platform was designed to integrate data collection, online monitoring, data mining, and status prediction, and SPFM was embedded in the platform. A comparative test was conducted, based on the real data collected via a tobacco station in 2019. The results indicate that SPFM has a better performance compared to support vector machines, artificial neural network, and the base models of SPFM such as XGBoost and LSTM. The accuracy of SPFM is 0.974, with an increase of 4.8–59.7%; the macro recall of SPFM is 0.952, with an increase of 8.2%–49.9%; the macro recall of SPFM is 0.936, with an increase of 8.2–75.0%; and the macro F1-score of SPFM is 0.943, with an increase of 9.7–108.6%. [ABSTRACT FROM AUTHOR]

A solid oxide fuel cell (SOFC) system is a kind of green chemical-energy–electric-energy conversion equipment with broad application prospects. In order to ensure the long-term stable operation of the SOFC power-generation system, prediction and evaluation of the system's operating state are required. The mechanism of the SOFC system has not been fully revealed, and data-driven single-step prediction is of little value for practical applications. The state-prediction problem can be regarded as a time series prediction problem. Therefore, an innovative deep learning model for SOFC system state prediction is proposed in this study. The model uses a two-layer LSTM network structure that supports multiple sequence feature inputs and flexible multi-step prediction outputs, which allows multi-step prediction of system states using SOFC system experimental data. Comparing the proposed model with the traditional ARIMA model and LSTM recursive prediction model, it is shown that the multi-step LSTM prediction model performs better than the ARIMA and LSTM recursive prediction models in terms of two evaluation criteria: root mean square error and mean absolute error. Thus, the proposed multi-step LSTM prediction model can effectively and accurately predict and evaluate the SOFC system's state. [ABSTRACT FROM AUTHOR]