With the continuous application and development of marine navigation technology, the accuracy requirements of navigation information for ships are becoming higher and higher. The Inertial Navigation ...System (INS) has been widely used due to their advantages in accuracy and stability. Star sensor, which can provide high-precision carrier attitude quaternion information, has gradually gained more and more applications. With the continuous application and development of various integrated navigation systems, based on the above two sensors, the Star sensor/INS combined attitude positioning system has the advantages in high-precision, anti-interference and autonomy. However, sensor errors and system initial errors will affect the performance of this integrated navigation system significantly. To compensate the above errors is a common way to improved the accuracy of the integrated navigation system, and the error calibration is the premise of error compensation. Different from the traditional Star sensor/INS integrated navigation system which carries out data fusion on the computer, this study is based on the actual installation in which the Star sensor is installed directly and fixedly on the INS. In this case, not only the error sources within the INS, but also the installation error between the INS and the Star sensor should be considered. Thus, on the basis of considering the traditional error terms, introduced the new installation error term and established the extended INS error model, the errors can be estimated and compensated in real time by utilizing the state estimation method, improving the accuracy of the Star sensor/INS integrated navigation system greatly. Simulations are carried out and the results verify the effectiveness and availability of the improved on-line calibration method proposed in this manuscript.
For an underwater Strapdown Inertial Navigation System/Doppler velocity log (SINS/DVL) integrated navigation system, the short-term failure of DVL may lead to the loss of reliable external velocity ...information from DVL, which will cause the SINS errors to accumulate. To circumvent this problem, this paper proposes a velocity predictor based on fuzzy multi-output least squares support vector machine (FMLS-SVM) to predict DVL measurements when DVL malfunctions occur. Firstly, the single-output least squares support vector machine (LS-SVM) model is extended to the multi-output LS-SVM model (MLS-SVM), and the self-adaptive fuzzy membership is introduced to fuzzify the input samples to overcome the over-fitting problem caused by the excessive sensitivity to the outlier points. Secondly, the fuzzy membership function is designed from the idea of the K nearest neighbor (KNN) algorithm. Finally, considering the influence of vehicle maneuver on the prediction model of DVL, the dynamic attitude angles are extended to the input samples of the prediction model to improve the adaptability of the DVL prediction model under large maneuver conditions. The performance of the method is verified by lake experiments. The comparison results show that the velocity predictor based on FMLS-SVM can correctly provide the estimated DVL measurements, effectively prolong the fault tolerance time of DVL faults, and improve the accuracy and reliability of the SINS/DVL integrated navigation system.
Enhanced accuracy and long-term predictions of ship motion during sea operations can effectively mitigate safety risks associated with aircraft takeoff and landing on board. This article proposes a ...transformer-based ship motion attitude prediction model. Our work leverages a novel self-attention mechanism (AM) with adaptive position encoding and learnable attention weights to improve long-term prediction accuracy. Furthermore, we also incorporate a pretraining phase using a random masking strategy to enhance the model's training capability and reduce prediction phase duration. The proposed model is evaluated using data from a ship undergoing constant speed and Z-word motion to predict the roll and pitch angles of the ship. The model is compared with autoregressive moving average (ARMA), EMD-ARMA, long-short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and traditional transformer models. The experimental results demonstrate that the proposed method outperforms these models in multistep prediction scenarios.
The ship deformation is an inevitable and severe problem for surface ships, especially when the ship is large or the sea condition is rough. Moreover, the positioning or detecting precision of the ...shipboard equipment will significantly decreases due to the ship deformation. So it is of great significance that estimating the ship deformation angle and restraining its effect in practice. Since ship deformation measurement technologies are mostly under theory simulation phases at present, we proposed a novel measurement technology based on actual ship test. In this method, the Quasi-static model of the ship deformation angle was presented taken the measuring velocity and the slowly varying feature of the static deforming angle into consideration. Established Markov model and Quasi-static model based on the actual ship experiments, the ship deformation angle can be estimated with the Kalman Filter (KF). And the experiment results showed that the ship deformation angle, including the dynamic deformation angle and the slowly changing deformation angle, can be estimated commendably with the Quasi-static model and angular rate matching. Thus, this proposed method can not only improve the estimated accuracy of the deformation angle in various application environments, verified its effectiveness and superiority, but also prove powerful support for the practical application of the ship deformation measurement.
Accurate prediction of ship attitude is crucial for ensuring maritime safety. However, the complexity of the marine environment and the long-term dependency of ship motion pose significant challenges ...to this task. In this study, we propose a hybrid model that combines bidirectional long short-term memory (Bi-LSTM) neural networks with a self-attention mechanism. The proposed model leverages bidirectional LSTM to capture temporal dependencies in ship motion data while incorporating a self-attention mechanism to dynamically focus on key information within the data sequence. To evaluate our model's predictive accuracy, we conduct experiments using real-world ship motion datasets for predicting ship roll and pitch. Performance metrics such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are employed for comparison against two traditional methods: ARMA and LSTM. Experimental results demonstrate that our hybrid model outperforms these traditional methods by effectively capturing complex attitude variations in long time series data.
For underwater Strapdown Inertial Navigation System/Doppler Velocity Log(SINS/DVL) integrated navigation system, it is a crucial factor for the performance of integrated navigation system to estimate ...the scale factor of DVL and its installation relationship with SINS accurately. Firstly, the theoretical model of DVL influenced by scale factor error, installation error between SINS and DVL and dynamic attitude are established. Due to the effect of dynamic attitude angle, the velocity measurement model of DVL presents great complexity and strong nonlinearity. Hence, this paper proposes a novel DVL model training approach using relevance vector machine (RVM), in which all error sources are considered in this model without separated DVL velocity measurement models. The Artificial Bee Colony (ABC) algorithm is used to optimize the key parameters of RVM. Secondly, the constraint information from GNSS and depth sensor is applied to strengthen the accuracy and generalization of the model. Finally, the performance of this method is verified with experiment in the Yellow Sea. Compared with the traditional least square estimation method based on SVD (SVD-LS), the DVL measurement model shows higher accuracy, and the positioning error of SINS/DVL integrated navigation system has been reduced from 3.1‰ of the voyage to 1.5‰ of the voyage.
•A new seq2seq rainfall-runoff model named LSTM-MSV-S2S is proposed.•LSTM-MSV-S2S has promising performance on the CAMELS data set.•LSTM-MSV-S2S is more appropriate for multi-day-ahead runoff ...predictions.
Rainfall-runoff modeling is a challenging and important nonlinear time series problem in hydrological sciences. Recently, among the data-driven rainfall-runoff models, those ones based on the long short-term memory (LSTM) network show good performance. Furthermore, LSTM-based sequence-to-sequence (LSTM-S2S) models achieve promising performance for multi-step-ahead runoff predictions. In this paper, for multi-day-ahead runoff predictions, we propose a novel data-driven model named LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) rainfall-runoff model, which contains m multiple state vectors for m-step-ahead runoff predictions. It differs from the existing LSTM-S2S rainfall-runoff models using only one state vector and is more appropriate for multi-day-ahead runoff predictions. To show its performance and advantages, we compare it with two LSTM-S2S models by testing them on 673 basins of the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) data set. The results show that our LSTM-MSV-S2S model has better performance in general and thus using multiple state vectors is more appropriate for multi-day-ahead runoff predictions.
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•GO-TiO2 modified membranes presented high rejection and degradation efficiencies.•Membrane filtration enhanced the degradation efficiency and decreased the EEO.•The 300 nm UV-LED ...achieved a high removal efficiency with low energy consumption.•High membrane flux and high degradation rate co-occurred in dead-end filtration.•UV-LED-based photocatalytic ceramic membrane is promising for water treatment.
The development of ultraviolet light-emitting diode (UV-LED) technology provides more potential for the application of photocatalytic membranes. In this study, graphene oxide-titanium dioxide (GO-TiO2) photocatalytic ceramic membranes were prepared and activated by UV-LED. We systematically evaluated the removal of naproxen (NAP), carbamazepine (CBZ) and diclofenac (DCF) under 365 nm, 300 nm and 275 nm UV-LED irradiation in photocatalytic ceramic membrane reactors (PCMRs). Our results showed that this GO-TiO2 modified ceramic membrane achieved simultaneous separation and photocatalytic degradation for tested pharmaceuticals. Through fluence-based and energy-based efficiencies analysis, the 300 nm UV-LED is a good option for achieving a high removal efficiency with low energy consumption. Membrane filtration enhanced the degradation efficiency by four times through enhancing the mass transfer effect in membrane pores, and reduced the electrical energy per order (EEO) by a factor of ten. In addition, high membrane flux, high degradation rate and strong catalytic stability could be simultaneously achieved in closed-loop dead-end filtration. The addition of H2O2 could further increased the removal efficiency. PCMRs equipped with UV-LEDs are promising for pharmaceuticals removal as well as solving the issue of membrane concentrated water.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
Context and Objective:
Paraoxonase 1 (PON1), an enzyme associated with high-density lipoprotein (HDL-PON1), is reported to have antioxidant and cardioprotective properties. The aim of the present ...study was to investigate the effects of anthocyanins on the HDL-PON1 activity and cholesterol efflux capacity in hypercholesterolemic subjects.
Design and Participants:
A total of 122 hypercholesterolemic subjects were given 160 mg of anthocyanins twice daily or placebo (n = 61 of each group) for 24 weeks in a double-blind, randomized, placebo-controlled trial. Participants and investigators were masked to treatment allocation.
Results:
Anthocyanin consumption significantly increased HDL cholesterol and decreased low -density lipoprotein cholesterol concentrations compared with placebo (P < .018 and P < .001, respectively). Anthocyanin supplementation also increased the activity of HDL-PON1 compared with placebo (P < .001). Furthermore, cholesterol efflux capacity was increased more in the anthocyanin group (20.0% increase) than in the placebo group (0.2% increase) (P < .001). The negative correlations established between HDL-PON1 activity and the levels of lipid hydroperoxides associated with HDL confirm the relationship between PON1 activity and lipid peroxidation of lipoproteins. Furthermore, a strong positive correlation was noted between increased HDL-PON1 activity and improved cholesterol efflux capacity both before and after adjustment for HDL cholesterol and apolipoprotein AI in anthocyanin-treated subjects (both P < .001). Inhibition of HDL-PON1 activity strongly prevented the antioxidant ability of HDL and attenuated the cholesterol efflux capacity of subjects from anthocyanin group.
Conclusions:
Our observations suggest that the alterations of PON1 activity by anthocyanin observed in hypercholesterolemic HDL reflect a shift to an improvement of cholesterol efflux capacity of HDL and may provide a link between anthocyanin and cardioprotective effects.
•A new framework named LSTM-SS is proposed to deal with rainfall-runoff modeling.•LSTM-SS improves performance significantly on the CAMELS data set.•LSTM-SS achieves good performance for ...multi-day-ahead runoff predictions.•LSTM-SS has good performance with short input sequence length.
Rainfall-runoff modeling, a nonlinear time series process, is challenging and important in hydrological sciences. Among the data-driven approaches, those ones based on the long short-term memory (LSTM) network show their promising performance. In this paper, for rainfall-runoff modeling, we propose a novel data-driven framework named long short-term memory based step-sequence (LSTM-SS) framework, which contains m specific models for m-step-ahead runoff predictions. This model uses the sequential information of runoff series and follows the causality in practice: the current runoff is not affected by the later meteorological data. To show its performance and advantages, we test it on 241 basins of the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) data set and predict the 7-day-ahead runoff. The results show that our rainfall-runoff models outperform the benchmark (physically-based or data-driven) models significantly employing for the CAMELS data set, including the Sacramento Soil Moisture Accounting Model (SAC-SMA) coupled with the Snow-17 snow routine, a two-layer LSTM network, and a LSTM-based sequence-to-sequence network. For 1-day-ahead runoff predictions, the median of Nash–Sutcliffe model efficiency for the 241 basins provided by our model is 0.85, while that provided by the two-layer LSTM network is 0.65. Furthermore, the results also show that our proposed LSTM-SS framework not only can significantly improve the performance of a single daily runoff prediction, but also has good performance for multiple-step-ahead runoff predictions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
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