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Research on the motion control of overhead cranes, constrained by underactuated characteristics, helps improve the efficiency of payload transportation. Most studies require all system state variables (trolley displacement, payload swing angle, and their velocities). In practice, sensors measure and transmit these variables, but noise affects their accuracy, reducing control performance. Additionally, uncertainties in crane parameters, unmodeled friction, and unknown disturbances threaten the system's stability. Traditional methods struggle to address these issues effectively. To address these challenges, this article proposes an adaptive discrete sliding mode control (DSMC) method with a Kalman filter. By extending the state system and considering disturbances as new variables, the Kalman filter effectively eliminates signal noise, accurately estimates disturbances, and estimates system states simultaneously. The proposed method incorporates disturbance compensators into the adaptive DSMC, utilizing exponential terms to suppress oscillations caused by excessively high or low control gains, thus increasing control speed. Experimental comparisons demonstrate the superiority and robustness of the proposed control method under various disturbance conditions. [ABSTRACT FROM AUTHOR]

Investigation of rarely studied gravel layers found in the loess in Shandong Province, eastern China, reveals the fabric characteristics of two gravel layers (G1, G2) and the sedimentary characteristics of loess at the typical and well‐preserved Heiyu section (HY), where, to determine the paleoclimatic changes during Marine Isotope Stage 3a. Optically stimulated luminescence dates of the HY formation range from 0.26 ± 0.02 ka to 39.00 ± 2.00 ka. In addition, the ages of G1 and G2 were estimated using the Bayesian model to be 39.60–40.50 and 29.00–29.50 ka. G1 and G2 are mainly composed of fine and medium gravel, both of which were subangular to subrounded limestone, with gravel directions to NE and E. The average flow velocity, average depth, and flood peak flow of G1 are 1.10 m/s, 0.49 m, and 37.04 m3/s, respectively, calculated using the flow energy method, whereas those of G2 are 0.98 m/s, 0.38 m, and 18.38 m3/s, respectively. Analysis of climate proxy indices show that the sedimentary environment of the gravel and loess in HY might be a regional response to global change. [ABSTRACT FROM AUTHOR]

Summary: Due to the long‐term operation of the tower crane, various faults will inevitably occur, such as actuator faults, which further increases the difficulty of controller design. In this paper, a fault‐tolerant control method with adaptive gain is proposed for the double‐pendulum tower crane with distributed payload beam (DTCDPB) system in the existence of external disturbances and actuator failures. First, a nonlinear fault‐tolerant observer is designed for fault diagnosis. Second, an S‐shaped trajectory functions are coupled online and embedded into the system target trajectories for payload swing suppression. Then, feedforward compensation is applied in the controller for the observed faults. Moreover, the adaptive suppression term to be activated is also designed in the controller taking into account the errors existing in the observation. Finally, the effectiveness of the proposed observer and controller is experimentally verified. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motors (PMSMs) are widely applied in industry, and proportional integral (PI) controllers are often used to control PMSMs. Aiming at the characteristics of the poor anti-disturbance ability and speed ripple of traditional PI controllers, a novel composite speed controller for PMSMs is proposed in this paper that uses a novel sliding mode control (SMC). To improve the chattering problem of traditional SMC, a high-order approaching law super-twisting algorithm (STA) is applied. Considering the internal and external disturbance of motor driver systems, such as motor parameter drifts and load torque changes, a disturbance estimator based on an extended state observer (ESO) is proposed, and it is used for the feed-forward compensation of the current. The composite super-twisting integral sliding mode controller (ST-ISMC) with a nonlinear ESO is tested by simulations and experiments, and the comparative results verify that the proposed controller has the higher control accuracy, smaller speed ripple and stronger robustness. [ABSTRACT FROM AUTHOR]

Accurately estimating the state of health (SOH) of lithium-ion batteries (LIBs) is one of the pivotal technologies to ensure the safe and dependable operation of electric vehicles (EVs). To tackle the challenges related to the intricate preprocessing procedures and extensive data prerequisites of conventional SOH estimation approaches, this paper proposes an improved LSTNet network model. Firstly, the discharged battery sequence data are divided into long-term and short-term sequences. A spatially convolutional long short-term memory network (ConvLSTM) is then introduced to extract multidimensional capacity features. Next, an autoregressive (AR) component is employed to enhance the model's robustness while incorporating a shortcut connection structure to enhance its convergence speed. Finally, the results of the linear and nonlinear components are fused to make predictive judgments. Experimental comparisons on two datasets are conducted in this study to demonstrate that the method fits the electric capacity recession curve well, even without the preprocessing step. For the data of four NASA batteries, the maximum root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) of the prediction results were maintained at 0.65%, 0.58%, and 0.435% when the proportion of the training set was 40%, which effectively validates the model's feasibility and accuracy. [ABSTRACT FROM AUTHOR]

Compressors are important components in various power systems in the field of energy and power. In practical applications, compressors often operate under non-design conditions. Therefore, accurate calculation on performance under various operating conditions is of great significance for the development and application of certain power systems equipped with compressors. To calculate and predict the performance of a compressor under all operating conditions through limited data, the interpolation method was combined with a support vector machine (SVM). Based on the known data points of compressor design conditions, the interpolation method was adopted to obtain training samples of the SVM. In the calculation process, preliminary screening was conducted on the kernel functions of the SVM. Two interpolation methods, including linear interpolation and cubic spline interpolation, were used to obtain sample data. In the subsequent training process of the SVM, the genetic algorithm (GA) was used to optimize its parameters. After training, the available data were compared with the predicted data of the SVM. The results show that the SVM uses the Gaussian kernel function to achieve the highest prediction accuracy. The prediction accuracy of the SVM trained with the data obtained from linear interpolation was higher than that of cubic spline interpolation. Compared with the back propagation neural network optimized by the genetic algorithm (GA-BPNN), the genetic algorithm optimization of extreme learning machine neural network (GA-ELMNN), and the genetic algorithm optimization of generalized regression neural network (GA-GRNN), the support vector machine optimized by the genetic algorithm (GA-SVM) has a better generalization, and GA-SVM is more accurate in predicting boundary data than the GA-BPNN. In addition, reducing the number of original data points still enables the GA-SVM to maintain a high level of predictive accuracy. [ABSTRACT FROM AUTHOR]

Gaining insight into the characteristics of past interglacials enhances our understanding of the current warm period and improves predictions of future climate. In this study, we analyze precipitation records of the past 900 ka and find that precipitation intensity during interglacials varied across Asia, especially between interglacials that occurred before and after the mid‐Brunhes transition. To clarify the mechanism of precipitation intensity variation during interglacials, we present a high‐resolution eolian record documenting the past 900 ka, which is from the thickest loess core (202.49 m) drilled in Ili Basin, Central Asia and conduct modeling with Community Earth System Model 1.2.2. We propose that sorting coefficient of Ili loess is a sensitive proxy for wind intensity, and it indirectly reflects Northern Hemisphere ice volume. Supported by numerical modeling experiments, we suggest that strong asymmetry of interhemispheric ice volume during marine isotope stages 11, 9, and 7 drives heterogeneity of interglacial precipitation intensity over Asia. Plain Language Summary: Interglacials after 430 ka (ka mean millennium) exhibit lower global ice volumes, higher global CO2 concentrations, and higher temperatures in the Southern Hemisphere than interglacials before 430 ka. This transition has become known as the mid‐Brunhes transition (MBT). However, interglacial precipitation intensities reconstructed in different regions over Asia show conflicting patterns before and after MBT. The uplift of Tibetan Plateau and changes in global ice volume fail to account for documented spatial variations of precipitation intensities during interglacials. To explain this spatial discrepancy, we present grain size and palaeomagnetic analysis of the 202.49‐m long loess drilling core obtained from Ili Basin, documenting environmental history of the past 900 ka, and performed modeling with Community Earth System Model 1.2.2. The sorting coefficient of grain size is used as an indicator for wind intensity in Ili Basin and further Northern Hemisphere ice volume (NHIV). We found an increased NHIV during marine isotope stage 11, 9, and 7 relative to pre‐MBT interglacials, in contrast to variation of Southern Hemisphere ice volume. Supported by numerical modeling experiments, we suggest that asymmetric inter‐hemisphere ice volume could lead to spatial variation in precipitation intensities during interglacials over Asia. Key Points: The thickest loess borehole in the Ili Basin, Central Asia is studiedA new record reflecting indirectly Northern Hemisphere ice volume (NHIV) covering the past 900 ka is presentedRelatively increased NHIV during marine isotope stage 11 (MIS 11), MIS 9, and MIS 7 caused heterogeneity of interglacial precipitation intensity over Asia [ABSTRACT FROM AUTHOR]

Eastern Central Asia has an arid environment and experiences frequent dust storms. Modern observations indicate that the dust storm frequency in eastern Central Asia is modulated mainly by the intensity of the Siberian High (SH). However, a lack of knowledge about the process influencing the variation of the SH limits our ability to understand the genesis of dust storms and their predictions. In this study, we present data on heavy mineral assemblages and sediment grain size in a loess section in eastern Central Asia, spanning the last glacial. Combined with previously published results, we propose that the sorting coefficient in loess‐paleosol sequences is a more sensitive proxy for wind intensity—and thus for the intensity of the SH—than the commonly used proxies of the mean grain size and grain size fractions, which are also influenced by changes in dust source and vegetation cover. Our sorting coefficient series clearly reveals Heinrich events, as well as the prominent ∼5 kyr Bond cycle. Larger sorting coefficients correspond to more depleted Sanbao Cave stalagmite δ18O values, revealing an antiphase relationship between the intensity of the SH and the East Asian Summer Monsoon during the last glacial. The sorting coefficient series also support the significant influence of Arctic sea‐ice cover on the intensity of the SH. Plain Language Summary: Central Asia is characterized by an arid environment. Dust storms occur frequently in Central Asia. Modern observations indicate that the frequency of dust storms in eastern Central Asia is mainly related to the near‐surface wind intensity, which is modulated by the Siberian High (SH). Therefore, understanding the change process of the SH intensity and its driving mechanism is helpful to effectively predict dust storms in the future. Bulk grain size proxies, such as mean/median grain size or grain size fraction, are usually used to reflect changes in the intensity of the SH, but they will also be affected by other factors such as vegetation coverage and dust provenance. In this study, we first theoretically discuss the potential of sorting coefficient as an indicator of near‐surface wind intensity. The sorting coefficient is then applied to a Central Asian loess section to infer the variation of the SH intensity. In contrast to commonly used mean grain size, sorting coefficients not only clearly reveal Heinrich events but also demonstrate very strong ∼5 kyr cycles, known as the "Bond Cycle". Larger sorting coefficient corresponding to more depleted Sanbao cave stalagmite δ18O values indicates an antiphase relationship between the SH and East Asian Summer Monsoon during the last glacial. Key Points: Sorting coefficient is a more sensitive proxy for wind intensity than commonly used proxies of mean grain size and grain size fractionAntiphase relationship between the Siberian High (SH) and East Asian Summer Monsoon during the last glacialOur results support the great influence of the extent of Arctic sea ice on the intensity of the SH [ABSTRACT FROM AUTHOR]

Dune fields and loess deposits of the Great Plains of North America contain stratigraphic records of eolian activity that can be used to extend the short observational record of drought. We present a 10,000 yr reconstruction of dune activity and dust production in the central Great Plains region, based on 95 optically stimulated luminescence ages. The integration of data from both eolian sand and loess is an important new aspect of this record. Clusters of ages define episodes of extensive eolian activity, which we interpret as a response to frequent severe drought, at 1.0-0.7 ka and 2.3-4.5 ka (with peaks centered on 2.5 and 3.8 ka); sustained eolian activity occurred from 9.6 to 6.5 ka. Parts of this record may be consistent with hypotheses linking Holocene drought to sea surface temperature anomalies in the Pacific or Atlantic oceans, or to the El Nino-Southern Oscillation phenomenon, but the record as a whole is difficult to reconcile with any of these hypotheses. Keywords: drought, eolian sand, loess, OSL dating, Great Plains, Holocene, Medieval Climate Anomaly.