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Aiming at the problems of non-line-of-sight delay and low positioning accuracy in the location of transport vehicles in the complex environment of the non-coal opencast mine, an improved adaptive H ∞ CKF and IAGA-TDOA positioning algorithm for transport vehicles in non-coal opencast mine is proposed, a Lora + 4G positioning platform is built, and the effectiveness of the algorithm is verified through simulation and field test. Improved Adaptive Genetic Algorithm (IAGA) updates the optimal preservation strategy of traditional genetic algorithm and redefines the adaptive cross rate and variation rate. The improved adaptive H ∞ Cubature Kalman Filter (CKF) algorithm reduces the effect of noise by weighting new and old noise. The simulation results show that the relative error of the improved adaptive H ∞ CKF algorithm is reduced by 85% compared with the traditional Kalman Filter. Comparing IAGA with other four algorithms to solve the TDOA positioning model, the IAGA algorithm has the smallest positioning error, and the minimum error is only 0.8 m. The field test shows that the average error of the system is within 7.4 m, and it can display the historical running track of the vehicle, which has a certain guiding significance for the automatic production of the mining area.

In order to improve the convergence time and stabilization accuracy of the real-time state estimation of the power batteries for electric vehicles, a fuzzy unscented Kalman filtering algorithm (F-UKF) of a new type is proposed in this paper, with an improved second-order resistor-capacitor (RC) equivalent circuit model established and an online parameter identification used by Bayes. Ohmic resistance is treated as a battery state of health (SOH) characteristic parameter, F-UKF algorithms are used for the joint estimation of battery state of charge (SOC) and SOH. The experimental data obtained from the ITS5300-based battery test platform are adopted for the simulation verification under discharge conditions with constant-current pulses and urban dynamometer driving schedule (UDDS) conditions in the MATLAB environment. The experimental results show that the F-UKF algorithm is insensitive to the initial value of the SOC under discharge conditions with constant-current pulses, and the SOC and SOH estimation accuracy under UDDS conditions reaches 1.76% and 1.61%, respectively, with the corresponding convergence time of 120 and 140 s, which proves the superiority of the joint estimation algorithm.

Dynamic displacement plays an essential role in structural health monitoring. To overcome the shortcomings of displacement measured directly, such as installation difficulty of monitoring devices, this paper proposes a smart reconstruction method, which can realize real-time intelligent online reconstruction of structural displacement. Unlike the existing approaches, the proposed algorithm combines the improved mode superposition methods that is suitable for complex beam structures with the Kalman filtering approach using acceleration and strain data. The effectiveness of the proposed multi-rate data fusion method for dynamic displacement reconstruction is demonstrated by both numerical simulation and model vibration experiment. Parametric analysis shows that the reconstruction error is only 5% when the noise signal to noise ratio is 5 dB, illustrating that the proposed algorithm has excellent anti-noise performance. The results also indicate that both the high-frequency and low-frequency components of the dynamic displacements can be accurately reconstructed through the proposed method, which has good robustness.

The dynamic management of sensor nodes and advanced information fusion are necessary technologies to enhance the comprehensive performance of sensor networks. This paper presents a cascaded sensor dynamic activation and information fusion algorithm to simultaneously optimize the energy and sensing performance of wireless sensing networks. The proposed algorithm dynamically activates nodes that are most suitable for the current sensing task through a joint event-driven and state ranking activation algorithm that achieves a better sensing performance with lower energy costs. In addition, it further utilizes the sensing information of all the activated nodes with maximum efficiency, through an improved distributed Kalman information fusion, which achieves an extra improvement in sensing accuracy as measured by the minimum variance. Finally, the superiority of the proposed cascaded algorithm is verified by a simulation comparison, achieving almost zero dead nodes in terms of energy, and a 62.1% decrease in average error in terms of sensing.

The high-cycle bending fatigue experiment is one of the most important necessary steps in guiding the crankshaft manufacturing process, especially for high-power engines. In this paper, an accelerated method was proposed to shorten the time period of this experiment. First, the loading period was quickened through the prediction of the residual fatigue life based on the unscented Kalman filtering algorithm approach and the crack growth speed. Then, the accuracy of the predictions was improved obviously based on the modified training section based on the theory of fracture mechanics. Finally, the fatigue limit load analysis result was proposed based on the predicted fatigue life and the modified SAFL (statistical analysis for the fatigue limit) method. The main conclusion proposed from this paper is that compared with the conventional training sections, the modified training sections based on the theory of fracture mechanics can obviously improve the accuracy of the remaining fatigue life prediction results, which makes this approach more suitable for the application. In addition, compared with the system’s inherent natural frequency, the fatigue crack can save the experiment time more effectively and thus is superior to the former factor as the failure criterion parameter.

Lithium battery state of charge (SOC) estimation is an important part of the battery management system and is of great significance to the safe and efficient operation of the battery. This paper first analyzes the hysteresis characteristics of battery charging and discharging through the hysteresis main loop and small loop characteristic tests, and constructs a hysteresis model that can correct the hysteresis voltage. Then, the principle of fractional-order calculus was introduced into the traditional integer-order model, and a constant phase element (CPE) was used to describe the fractional-order dynamic characteristics of the battery. Combined with the hysteretic model, a fractional-order hysteretic equivalent circuit model was constructed., and use genetic algorithm to identify the model parameters. Improvements are proposed to address the estimation bias and filter divergence of the extended Kalman filter algorithm. Correlation coefficients and adaptive factors are added to adaptively update the noise and Kalman gains to estimate battery SOC. Finally, the DST working condition experiment shows that the SOC error of the method proposed in this article is about 1.53%, the calculation time is 0.6 s, and the absolute correlation coefficient is 0.9953.

In order to improve the efficiency of the intelligent control system of the port tipping line, this paper builds a port tipping line simulation intelligent control system based on WITHNESS software. Firstly, through ARTG (Konecranes Automation System) control switch, the simulation model of single-vessel loading and unloading transportation of the road network from the port front to the yard is established. Secondly, the Kalman filter algorithm and genetic algorithm are used to informally manage the port loading and unloading truck entry and exit data, and the assembly balance in the genetic algorithm is used to improve the data mining efficiency, and finally, the simulation system is tested for data. The results show that: the simulation system WITHNESS model constructed in this paper has an average time of 20.34 min for external collector trucks in the port dwell time, among which the number of vehicles with dwell time in [17/22] and [22/30] minutes is the majority accounting for a total of 60.7% of the total. Furthermore, the comparison of yard area reduction shows that the dwell time is 72.6%, 70.9%, and 87.5%, respectively, for yard area at different times. It can be seen that the simulation system WITHNESS model constructed in this paper helps to promote the development of port loading and unloading and can improve the effectiveness of loading and unloading work.

Exploring the evolution of translation of contemporary Chinese literature better presents Chinese literary charm to foreign readers. This paper starts with the Kalman filter algorithm and introduces its cost function that satisfies the error minimization and the Kalman filter gain. Then the BP neural network is illustrated, its minimization of mean square error is solved using the backpropagation algorithm, and the momentum factor is introduced to update the weight function of the BP neural network. The correlation between the Kalman gain and the filtering error is fitted using BP neural network to optimize the Kalman filtering algorithm, and the algorithm flow chart of the BP-KF algorithm is given. Finally, the BP-KF algorithm is used to analyze the data on the evolution of translation strategies and translation dissemination channels of contemporary Chinese literature on the Internet. From the evolution of the translation strategy, the number of translated works of additive French literature decreased by 5.28% year-on-year from 2016 to 2020, and the number of translated works of annotated French literature increased by nearly 11 times year-on-year from 2016 to 2020. In terms of the evolution of the translation and dissemination channels, the percentage of using the Internet to disseminate literary translation and mediation works reviews increased from 16.29% in 2017 to 41.65% in 2021, an increase of 25.36 percentage points. Based on the BP-KF algorithm, the evolution of translation of contemporary Chinese literature can be effectively analyzed, and the data can visually show the direction of the evolution of literary translation, thus expanding the influence of contemporary Chinese literature.

Accurate state of charge (SOC) estimation of lithium-ion batteries can effectively help battery management system better manage the charging and discharging process of batteries, providing important reference basis for the use planning of power vehicles. In this paper, an improved chaotic particle butterfly optimization-cubature Kalman filtering (CPBO-CKF) algorithm is proposed for accurate SOC estimation of lithium-ion batteries. Considering the hysteresis characteristics and polarization effects, an improved hysteresis characteristics-dual polarization (HC-DP) equivalent circuit model is established, which can more accurately characterize the internal characteristics of battery. To achieve high-precision SOC estimation, an improved chaotic particle butterfly optimization algorithm is introduced for dynamic optimization of noise in the cubature Kalman filtering algorithm, and the proposed CPBO-CKF algorithm can more accurately describe the actual noise characteristics, thereby reducing estimation errors. The proposed algorithm is validated under complex working conditions at different temperatures, and the results show that it has good accuracy. Under BBDST condition at 15 °C, 25 °C, and 35 °C, the mean absolute errors (MAEs) are 0.80%, 0.56%, and 0.71%, while the root mean square errors (RMSEs) are 1.09%, 0.70%, and 0.88%. Under DST condition, the MAEs are 0.73%, 0.49%, and 0.52%, and the RMSEs are 0.86%, 0.67%, and 0.63%.

The Internet of Things (IoT) aims to meet the needs of smart services by combining multiple information intelligence tools and network technologies. In terms of technology, management, cost, policy, and security, the development of the IoT still faces many challenges. This study proposes an intelligent image detection and transmission system based on IoT communication, with the following primary objectives: First, this study aims to provide an early warning model based on Kalman Filtering Fast Fourier Transform Support Vector Machine (KF-FFT-SVM). It delivers early warning signals by analyzing and extracting historical features from surface motion data for a given period. The step size for spectrum analysis is determined by the signal frequency and is used to create a training dataset and train the SVM model. The use of a trained model for early warning can improve the accuracy of early warning evaluation. Second, the forward line of the video image is used as the necessary information in the content symbol retrieval process, and the information required for the structure is used to improve the quality as much as possible. Because of the search and influence of the transmission quality of digital components, the important data in the digital transmission space is used to ensure the accuracy of digital components while transmitting a small amount of energy. Third, when the device is connected to the network, other users can obtain information about the device via security breaches. The data must be transmitted by both parties using their own identities, which increases transmission security. The design idea of ZigBee technology follows the method of distributing information, such as food space through a zigzag-shaped dance, and low power consumption with low cost. Finally, to increase system efficiency, non-orthogonal multiple access technologies realize and transmit data from multiple users in time, frequency, and code zones while using various channels for each user. The wireless signal-enabled environment is changed by varying the reflection coefficient of the passive reflector units, which are components of the same mapping set as the smart reflector. To improve the effectiveness and performance of the system transmission, the wireless signal may have the effect of boosting the signal or removing interference.