How Does El Niño Affect Predictability Barrier of Its Accompanied Positive Indian Ocean Dipole Event?

The effects of El Niño on the predictability of positive Indian Ocean dipole (pIOD) events are investigated by using the GFDL CM2p1 coupled model from the perspective of error growth. The results show that, under the influence of El Niño, the summer predictability barrier (SPB) for pIOD tends to intensify and the winter predictability barrier (WPB) is weakened. Since the reason for the weakening of WPB has been explained in a previous study, the present study attempts to explore why the SPB is enhanced. The results demonstrate that the initial sea temperature errors, which are most likely to induce SPB for pIOD with El Niño, possess patterns similar to those for pIOD without El Niño, whose dominant errors concentrate in the tropical Pacific Ocean (PO), with a pattern of negative SST errors occurring in the eastern and central PO and subsurface sea temperature errors being negative in the eastern PO and positive in the western PO. By tracking the development of such initial errors, it is found that the initial errors over PO lead to anomalous westerlies in the southeastern Indian Ocean (IO) through the effect of double-cell Walker circulation. Such westerly anomalies are inhibited by the strongest climatological easterly wind and the southeasterlies related to the pIOD event itself in summer, while they are enhanced by El Niño. This competing effect causes the intensified seasonal variation in latent heat flux, with much less loss in summer under the effect of El Niño. The greater suppression of the loss of latent heat flux favors the positive sea surface temperature (SST) errors developing much faster in the eastern Indian Ocean in summer, and eventually induces an enhanced SPB for pIOD due to El Niño.

Enhancing Stability and Robustness of State-of-Charge Estimation for Lithium-Ion Batteries by Using Improved Adaptive Kalman Filter Algorithms

The traditional Kalman filter algorithms have disadvantages of poor stability (the program cannot converge or crash), robustness (sensitive to the initial errors) and accuracy, partially resulted from the fact that noise covariance matrices in the algorithms need to be set artificially. To overcome the above problems, some adaptive Kalman filter (AKF) algorithms are studied, but the problems still remain unsolved. In this study, two improved AKF algorithms, the improved Sage-Husa and innovation-based adaptive estimation (IAE) algorithms, are proposed. Under the different operating conditions, the estimation accuracy, filter stability, and robustness of the two proposed algorithms are analyzed. Results show that the state of charge (SOC) Max error based on the improved Sage-Husa and the improved IAE is less than 3% and 1.5%, respectively, while the Max errors of the original algorithms is larger than 16% and 4% The two proposed algorithms have higher filter stability than the traditional algorithms. In addition, analyses of the robustness of the two proposed algorithms are carried out by changing the initial parameters, proving that neither are sensitive to the initial errors.

Design Hybrid Iterative Learning Controller for Directly Driving the Wheels of Mobile Platform against Uncertain Parameters and Initial Errors

In this paper, we develop a hybrid iterative learning controller (HILC) for a non-holonomic wheeled mobile platform to achieve trajectory tracking with actual complex constraints, such as physical constraints, uncertain parameters, and initial errors. Unlike the traditional iterative learning controller (ILC), the control variable selects the rotation speed of two driving wheels instead of the forward speed and the rotation speed. The hybrid controller considers the physical constraints of the robot’s motors and can effectively handle the uncertain parameters and initial errors of the system. Without the initial errors, the hybrid controller can improve the convergence speed for trajectory tracking by adding other types of error signals; otherwise, the hybrid controller achieves trajectory tracking by designing a signal compensation for the initial errors. Then, the effectiveness of the proposed hybrid controller is proven by the relationship between the input, output, and status signals. Finally, the simulations demonstrate that the proposed hybrid iterative learning controller effectively tracked various trajectories by directly controlling the two driving wheels under various constraints. Furthermore, the results show that the controller did not significantly depend on the system’s structural parameters.

Interlaced Attitude Estimation for Spacecraft Using Vector Observations

This paper proposes an interlaced attitude estimation method for spacecraft using vector observations, which can simultaneously estimate the constant attitude at the very start and the attitude of the body frame relative to its initial state. The arbitrary initial attitude, described by constant attitude at the very start, is determined using quaternion estimator which requires no prior information. The multiplicative extended Kalman filter (EKF) is competent for estimating the attitude of the body frame relative to its initial state since the initial value of this attitude is exactly known. The simulation results show that the proposed algorithms could achieve better performance compared with the state-of-the-art algorithms even with extreme large initial errors. Meanwhile, the computational burden is also much less than that of the advanced nonlinear attitude estimators.

A mixed-integer least-squares formulation of the GNSS snapshot positioning problem

This paper presents a formulation of snapshot positioning as a mixed-integer least-squares problem. In snapshot positioning, one estimates a position from code-phase (and possibly Doppler-shift) observations of global navigation satellite system (GNSS) signals without knowing the time of departure (timestamp) of the codes. Solving the problem allows a receiver to determine a fix from short radio-frequency snapshots missing the timestamp information embedded in the GNSS data stream. This is used to reduce the time to first fix in some receivers, and it is used in certain wildlife trackers. This paper presents two new formulations of the problem and an algorithm that solves the resulting mixed-integer least-squares problems. We also show that the new formulations can produce fixes even with huge initial errors, much larger than permitted in Van Diggelen's widely-cited coarse-time navigation method.

A kinetic error filtering mechanism for enzyme-free copying of nucleic acid sequences

Accurate copying of nucleic acid sequences is essential for self-replicating systems. Modern cells achieve error ratios as low as 10-9 with sophisticated enzymes capable of kinetic proofreading. In contrast, experiments probing enzyme-free copying of RNA and DNA as potential prebiotic replication processes find error ratios on the order of 10%. Given this low intrinsic copying fidelity, plausible scenarios for the spontaneous emergence of molecular evolution require an accuracy-enhancing mechanism. Here, we study a 'kinetic error filtering' scenario that dramatically boosts the likelihood of producing exact copies of nucleic acid sequences. The mechanism exploits the observation that initial errors in template-directed polymerization of both DNA and RNA are likely to trigger a cascade of consecutive errors and significantly stall downstream extension. We incorporate these characteristics into a mathematical model with experimentally estimated parameters, and leverage this model to probe to what extent accurate and faulty polymerization products can be kinetically discriminated. While limiting the time window for polymerization prevents completion of erroneous strands, resulting in a pool in which full-length products show an enhanced accuracy, this comes at the price of a concomitant reduction in yield. We show that this fidelity-yield trade-off can be circumvented via repeated copying attempts in cyclically varying environments such as the temperature cycles occurring naturally in the vicinity of hydrothermal systems. This setting could produce exact copies of sequences as long as 50mers within their lifetime, facilitating the emergence and maintenance of catalytically active oligonucleotides.

One-Step Predictive H2 FIR Tracking under Persistent Disturbances and Data Errors

Information loss often occurs in industrial processes under unspecified impacts and data errors. Therefore robust predictors are required to assure the performance. We design a one-step H2 optimal finite impulse response (H2-OFIR) predictor under persistent disturbances, measurement errors, and initial errors by minimizing the squared weighted Frobenius norms for each error. The H2-OFIR predictive tracker is tested by simulations assuming Gauss-Markov disturbances and data errors. It is shown that the H2-OFIR predictor has a better robustness than the Kalman and unbiased FIR predictor. An experimental verification is provided based on the moving robot tracking problem

An experimental study on the effects of compression on the acoustic performance of porous fibrous materials

The current investigation experimentally studied the effects of compression on the acoustic performance of porous fibrous material. Two inch and four inch thick samples of fiberglass and three varying densities of mineral wool were tested using two different impedance tube sizes at compression rates of 1, 1.3 and 2. The absorption coefficient was measured using Chung and Blaser’s method. The flow resistivity was measured using Tao et al.’s method. Overall, the 4” samples resulted in steadier results than the 2” samples. Compression generally led to a decrease in absorption coefficient and an increase in flow resistivity. These effects were most evident in the lower frequency range. Although there were some experimental errors in sample preparation, sample variation, compression technique, testing order and other initial errors, the current study demonstrated that the effects of compression on insulation should be not be overlooked.

An experimental study on the effects of compression on the acoustic performance of porous fibrous materials

The current investigation experimentally studied the effects of compression on the acoustic performance of porous fibrous material. Two inch and four inch thick samples of fiberglass and three varying densities of mineral wool were tested using two different impedance tube sizes at compression rates of 1, 1.3 and 2. The absorption coefficient was measured using Chung and Blaser’s method. The flow resistivity was measured using Tao et al.’s method. Overall, the 4” samples resulted in steadier results than the 2” samples. Compression generally led to a decrease in absorption coefficient and an increase in flow resistivity. These effects were most evident in the lower frequency range. Although there were some experimental errors in sample preparation, sample variation, compression technique, testing order and other initial errors, the current study demonstrated that the effects of compression on insulation should be not be overlooked.