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Purpose: To investigate surrogate optimisation (SO) as a modern, purely data‐driven, nonlinear adaptive iterative strategy for lens formula constant optimisation in intraocular lens power calculation. Methods: A SO algorithm was implemented for optimising the root mean squared formula prediction error (rmsPE, defined as predicted refraction minus achieved refraction) for the SRKT, Hoffer Q, Holladay, Haigis and Castrop formulae in a dataset of N = 888 cataractous eyes with implantation of the Hoya Vivinex hydrophobic acrylic aspheric lens. A Gaussian Process estimator was used as the model, and the SO was initialised with equidistant datapoints within box constraints, and the number of iterations restricted to either 200 (SRKT, Hoffer Q, Holladay) or 700 (Haigis, Castrop). The performance of the algorithm was compared to the classical gradient‐based Levenberg‐Marquardt algorithm. Results: The SO algorithm showed stable convergence after fewer than 50/150 iterations (SRKT, HofferQ, Holladay, Haigis, Castrop). The rmsPE was reduced systematically to 0.4407/0.4288/0.4265/0.3711/0.3449 dioptres. The final constants were A = 119.2709, pACD = 5.7359, SF = 1.9688, −a0 = 0.5914/a1 = 0.3570/a2 = 0.1970, C = 0.3171/H = 0.2053/R = 0.0947 for the SRKT, Hoffer Q, Holladay, Haigis and Castrop formula and matched the respective constants optimised in previous studies. Conclusion: The SO proves to be a powerful adaptive nonlinear iteration algorithm for formula constant optimisation, even in formulae with one or more constants. It acts independently of a gradient and is in general able to search within a (box) constrained parameter space for the best solution, even where there are multiple local minima of the target function. [ABSTRACT FROM AUTHOR]

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Purpose: To evaluate the effects of exposure protocol, voxel sizes, and artifact removal algorithms on the trueness of segmentation in various mandible regions using an artificial intelligence (AI)‐based system. Materials and methods: Eleven dry human mandibles were scanned using a cone beam computed tomography (CBCT) scanner under differing exposure protocols (standard and ultra‐low), voxel sizes (0.15 mm, 0.3 mm, and 0.45 mm), and with or without artifact removal algorithm. The resulting datasets were segmented using an AI‐based system, exported as 3D models, and compared to reference files derived from a white‐light laboratory scanner. Deviation measurement was performed using a computer‐aided design (CAD) program and recorded as root mean square (RMS). The RMS values were used as a representation of the trueness of the AI‐segmented 3D models. A 4‐way ANOVA was used to assess the impact of voxel size, exposure protocol, artifact removal algorithm, and location on RMS values (α = 0.05). Results: Significant effects were found with voxel size (p < 0.001) and location (p < 0.001), but not with exposure protocol (p = 0.259) or artifact removal algorithm (p = 0.752). Standard exposure groups had significantly lower RMS values than the ultra‐low exposure groups in the mandible body with 0.3 mm (p = 0.014) or 0.45 mm (p < 0.001) voxel sizes, the symphysis with a 0.45 mm voxel size (p = 0.011), and the whole mandible with a 0.45 mm voxel size (p = 0.001). Exposure protocol did not affect RMS values at teeth and alveolar bone (p = 0.544), mandible angles (p = 0.380), condyles (p = 0.114), and coronoids (p = 0.806) locations. Conclusion: This study informs optimal exposure protocol and voxel size choices in CBCT imaging for true AI‐based automatic segmentation with minimal radiation. The artifact removal algorithm did not influence the trueness of AI segmentation. When using an ultra‐low exposure protocol to minimize patient radiation exposure in AI segmentations, a voxel size of 0.15 mm is recommended, while a voxel size of 0.45 mm should be avoided. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for predicting the remaining usability of rolling bearings. The method combines a fully adaptive ensemble empirical modal decomposition of noise (CEEMDAN), convolutional neural network (CNN), and attention bidirectional long short-term memory network (ABiLSTM). Firstly, a finite number of intrinsic mode functions (IMFs) are obtained from breaking down the initial vibration signals using CEEMDAN. The IMFs are further screened by combining the correlation criterion and the craggy criterion. Then, time-frequency domain features, which are extracted from the screened IMFs, are reconstructed into a feature set. The SPT is recognized through some features, like the root mean square (RMS), variance, and kurtosis. Secondly, the deterioration character of rolling bearings was extracted using CNN and used to train the ABiLSTM network. Based on the output of the ABiLSTM network, it forecasts how long rolling bearings will last during use. Finally, the XJTU-SY rolling bearing dataset validated the validity of the suggested rolling bearing remaining life prediction method. We compare our algorithm with other algorithms, such as GRU, LSTM, and CNN–BiLSTM, in which the accuracy of MAE, MSE, RMSE, MAPE, and R2_score is significantly improved. Thus, the results of the validation experiments demonstrate that our proposed algorithm has excellent prediction accuracy. [ABSTRACT FROM AUTHOR]

The Moho is the interface between crust and mantle, and accurate location of the Moho is important for both resource exploration and deep earth condition and structural change investigations. The theory of the traditional Parker‐Oldenburg (P‐O) method is quite simple and it is widely applied in the frequency domain of Moho depth inversion. However, Moho fluctuation simulations using the P‐O method are not reliable because of the lack of field geographic data constraints during the inversion process and the excessively smoothing of data details caused by using filters to correct the source data signals. To solve those problems, we propose an improved iteration P‐O method with a variable density contrast model, the iterative process is constrained by geological data in the inversion parameters, and the variable depth of the gravity interface is iterated using an equivalent form of upward continuation in the Fourier domain, which is more stable and convergent than downward continuation term in original P‐O method. Synthetic experiments indicate that improved method has the better consistency among the simulations than original method, and our improved method has the smallest root mean square (RMS) of 0.59 km. In a real case, we employed the improved method to invert for the Moho depth of the South China Sea (SCS), and the RMS between our Moho depth model and the seismological data is the smallest value of 2.27 km. The synthetic experiments and application of the model to the SCS further prove that our method is practical and efficient. Key Points: We developed an improved Parker‐Oldenburg method using an equivalent form of upward continuation in the Fourier domain instead of applying a low‐pass filter, which achieves a better stable performanceGravity inversion combines the actual density variations between layersMoho inversion in the South China Sea fits well with the seismic data, and our Moho depth model have the smallest root mean square (2.27 km) compared to other gravimetric Moho models [ABSTRACT FROM AUTHOR]

In order to ensure the attitude determination accuracy and speed of a fine guidance sensor (FGS) in a space telescope with limited onboard hardware computing resources, an adaptive absolute attitude determination algorithm was proposed. The more stars involved in the attitude determination, the higher the attitude accuracy, but more hardware resources will be consumed. By analyzing the relationship between the attitude determination accuracy and the number of stars (NOS) in the field of view (FOV), and the relationship between the detector exposure time and the NOS, an adaptive method of adjusting the NOS in the FOV was proposed to keep the number of observed stars in the FOV of the detector at a target value. The star map recognition algorithm based on improved log-polar transformation has a higher recognition speed than the traditional algorithm but cannot accurately identify and match the corresponding guide star when the number of observed stars is less than the number of guide stars. Thus, a comparison-AND star identification algorithm based on polar coordinates was proposed. In the case of a given line-of-sight pointing and 100-frame image simulation calculation, the root mean square (RMS) value of the line-of-sight pointing error was less than 37 mas in the direction of a right ascension, and less than 25 mas in the direction of declination, as concluded from the experimental simulation. [ABSTRACT FROM AUTHOR]

The new generation of Intrusion Detection Systems (IDS) progressively requires automatic and intellectual network ID strategies for managing security risks made by a rising amount of advanced attackers from the cyber environment. Specifically, there were huge demands for autonomous agent-related IDS solution which need some human intervention as possible whereas can progress and enhance themselves (by making suitable acts for a presented environment) and turns out to be highly powerful to effective threats that were not seen before (for instance, zero-day attacks). Recently, DRL methods were presented that can learns from the environment with uncontrollable massive amount of states for addressing the major drawbacks of prevailing RL methods. This article introduces a Binary Bat Algorithm-based Feature Selection with Deep Reinforcement Learning (BBAFS-DRL) system for IDSs. The major intention of the BBAFS-DRL approach is the recognition and classification of intrusion systems. In the projected BBAFS-DRL method, data pre-processed was primarily executed to change the data as suitable format. Furthermore, the BBAFS system can be utilized for the effectual feature selection. Next, deep Q-network is applied for the effectual identification and classification of intrusion. At last, the root means square propagation (RMSProp) optimizer is utilized for the effectual hyperparameter tuning procedure. The experimental analysis of the BBAFS-DRL algorithm can be tested by utilize of benchmark database and the outcome can be analyzed on many measures. The comparison outcome demonstrated the improvement of the BBAFS-DRL methodology over other existing approaches. [ABSTRACT FROM AUTHOR]

This paper proposes the usage of the fuzzy rule-based Bayesian algorithm to determine which residential appliances can be considered for the Demand Response program. In contrast with other related studies, this research recognizes both randomness and fuzziness in appliance usage. Moreover, the input data for usage prediction consists of nodal price values (which represent the actual power system conditions), appliance operation time, and time of day. The case study of residential power consumer behavior modeling was implemented to show the functionality of the proposed methodology. The results of applying the suggested algorithm are presented as colored 3D control surfaces. In addition, the performance of the model was verified using R squared coefficient and root mean square error. The conducted studies show that the proposed approach can be used to predict when the selected appliances can be used under specific circumstances. Research of this type may be useful for evaluation of the demand response programs and support residential load forecasting. [ABSTRACT FROM AUTHOR]

This paper introduces methods for parallelizing the algorithm to enhance the efficiency of event recovery in Spin Physics Detector (SPD) experiments at the Nuclotron-based Ion Collider Facility (NICA). The problem of eliminating false tracks during the particle trajectory detection process remains a crucial challenge in overcoming performance bottlenecks in processing collider data generated in high volumes and at a fast pace. In this paper, we propose and show fast parallel false track elimination methods based on the introduced criterion of a clustering-based thresholding approach with a chi-squared quality-of-fit metric. The proposed strategy achieves a good trade-off between the effectiveness of track reconstruction and the pace of execution on today's advanced multicore computers. To facilitate this, a quality benchmark for reconstruction is established, using the root mean square (rms) error of spiral and polynomial fitting for the datasets identified as the subsequent track candidate by the neural network. Choosing the right benchmark enables us to maintain the recall and precision indicators of the neural network track recognition performance at a level that is satisfactory to physicists, even though these metrics will inevitably decline as the data noise increases. Moreover, it has been possible to improve the processing speed of the complete program pipeline by 6 times through parallelization of the algorithm, achieving a rate of 2000 events per second, even when handling extremely noisy input data. [ABSTRACT FROM AUTHOR]

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets. [ABSTRACT FROM AUTHOR]

The correlation filtering algorithm of infrared spectral data for dim and small target tracking is studied to improve the tracking accuracy of small and weak targets and to track small and weak targets in real time. After the image noise reduction processing by the mean shift filtering algorithm, the infrared small and weak target image data model is constructed by using the denoised infrared small and weak target image. And the brightness value and position of unknown small and weak targets are obtained. The tracking and measurement model of small and weak targets is built. The joint probabilistic data association algorithm is used to calculate the probability that each measurement is associated with its possible source targets, and the particle filter is used to update the tracking status of small and weak targets to achieve real-time tracking of small and weak targets. The experimental results show that the algorithm can enhance the edge contour information of small and weak images, so as to accurately track small and weak targets moving in any track, and has good real-time tracking and accuracy. There is a small deviation between the tracking track of weak and small targets tracked by the algorithm and the actual track, and the root mean square difference of tracking weak and small targets is within 2 pixels. In addition, the detection probability of detecting weak and small targets is less affected by SNR environmental factors. [ABSTRACT FROM AUTHOR]

Purpose: To measure the influence of best‐fit (BF) algorithms (entire dataset, 3 or 6 points landmark‐based, or section‐based BF) on virtual casts and their alignment discrepancies. Material and methods: A mandibular typodont was obtained and digitized by using an industrial scanner (GOM Atos Q 3D 12M). A control mesh was acquired. The typodont was digitized by using an intraoral scanner (TRIOS 4). Based on the alignment procedures, four groups were created: BF of the entire dataset (BF group), landmark‐based BF using 3 reference points (LBF‐3 group), or 6 reference points (LBF‐6 group), and section‐based BF (SBF group). The root mean square (RMS) error was calculated. One‐way ANOVA and post hoc pairwise multi‐comparison Tukey were used to analyze the data (α = 0.05). Results: Significant RMS error mean value differences were found across the groups (p < 0.001). Tukey test revealed significant RMS error mean value differences between the BF and LBF‐3 groups (p = 0.022), BF and LBF‐6 groups (p < 0.001), LB‐3 and LB‐6 groups (p < 0.001), LBF‐3 and SBF groups (p < 0.001), and LBF‐6 and SBF groups (p < 0.001). The LBF‐6 group had the lowest trueness, while SBF and BF groups obtained the highest trueness values. Furthermore, significant SD differences were revealed across the groups tested (p < 0.001). Tukey test revealed significant SD differences between the BF and LBF‐6 groups (p < 0.001), LBF‐3 and LB‐6 groups (p < 0.001), LBF‐3 and SBF groups (p = 0.004), and LBF‐6 and SBF groups (p < 0.001). The BF and SBF groups showed equal and highest precision, while the LBF‐6 group had the lowest precision. Conclusions: The best‐fit algorithms tested influenced the virtual casts' alignment discrepancy. Entire dataset or section‐based best‐fit algorithms obtained the highest virtual casts' alignment trueness and precision compared with the landmark‐based method. [ABSTRACT FROM AUTHOR]

Glass fiber reinforced polymer (GFRP) is a typical difficult-to-process material. Its drilling quality is directly affected by the processing technology and tool life; burrs, tearing, delamination and other defects will reduce the service life of GFRP structural parts. Through drilling damage and tool wear experiments of GFRP, the thrust force, vibration amplitude, the number of processed holes, feed rate and cutting speed were found to be the main factors in drilling damage and tool wear. Using those main factors as the input layer, a tool wear and delamination factors prediction model was established based on an improved circle chaotic mapping (CCM) Grey Wolf algorithm for a back propagation (BP) neural network. Compared with the original BP neural network, the maximum prediction error of the improved BP neural network model was reduced by 71.2% and the root mean square (RMS) prediction error was reduced by 63.82%. The maximum prediction error of the delamination factor at the entrance was less than 3%, and the maximum prediction error of the delamination factor at the exit was less than 1%. The prediction results showed that the BP neural network model optimized by an improved circle chaotic mapping Grey Wolf algorithm can better predict the GFRP drilling quality and tool wear, and had higher accuracy, optimization efficiency and better robustness than the ordinary BP neural network. [ABSTRACT FROM AUTHOR]

For a pilot, the angle of attack (AoA) estimation may represent a possibility to cross-check the traditional cockpit indicators and also to obtain information whose benefits are widely recognized in many inflight situations. This paper proposes an AoA algorithm—named ACF—capable of detecting fast changes in the actual signal, that is a useful stall prevention capability, for example. The solution is based on the complementary filtering methodology and does not involve any airdata sensor output, with consequent saving of weight and cost reduction. In particular, it does not use the airspeed measurement, easily subject to failure and has been identified as the root cause of several fatal accidents. Consequently, the ACF shows the same (high) reliability as the used inertial sensors. Furthermore, the ACF algorithm requires neither information on the aircraft aerodynamic coefficients nor forecast wind information to be preloaded onboard, thus being time and cost effective. The ACF is also characterized by a low computational load, only needing a few dozen of algebraic operations. In this work, the ACF performances have been assessed using both a detailed simulation environment and real flight data. In the former case, an airliner following a sample maneuver is assumed, and a mean root mean square (RMS) less than 3° is guaranteed under all possible conditions of wind, gust, and turbulence. In the latter case, data collected in two different general aviation flights are used, providing a mean RMS of less than 0.3°. During piloting, the angle of attack (AoA) readings may represent a possibility to cross-check the traditional indicators and to obtain information whose benefits are recognized in many inflight situations. Unfortunately, the AoA sensors are exposed to several possible failures, often common to the other airdata such as airspeed and altitude. In addition, especially for small aerial vehicles, the systems cannot accommodate direct sensors due to size, weight, and power constraints. For these reasons, having an AoA sensorless estimation would be extremely important. A software solution (ACF) is proposed to estimate the AoA without using any external sensor, saving weight and costs, and with a strong improvement of reliability since the angle vanes—and especially the air probes—are generally identified as the root cause of several inflight accidents. Due to the fact that the ACF algorithm relies on inertial measurements only, it could even be implemented in a pilot's personal device because it does not require any tuning or flight calibration. Furthermore, in spite of its simplicity, it can achieve good accuracy by detecting—in any weather and regardless of particular airplane or maneuver—fast changes in the actual AoA that may endanger the safety of the flight. [ABSTRACT FROM AUTHOR]

In this article, we present a synthetic inertia (SI) algorithm that allows for the simulation of the inertia response of a traditional generator to an electrical power system. To obtain the algorithm, detailed dynamic calculations were performed using a large real-system dynamic model in Siemens PSS/E modeling packages (PSS/E). Output error (OE), autoregressive moving average model with exogenous inputs (ARMAX), and Box–Jenkins (BJ) models of parametric identification were used to obtain the SI algorithm. The dynamic calculation results such as active power output, frequency variation in the presence of the active power deficit, surplus, and short circuit in the power system were used to compare the algorithm accuracy with comparable generator results. For this purpose, the power system stabilizer (PSS) and the turbine governor were not evaluated to obtain the most accurate possible active power change due to the characteristics of the generator. The errors were evaluated by using the models to determine the error estimates for the correlation coefficient (Ryŷ), root mean square deviation (RMSE), and coefficient of determination (R2). Based on the obtained results, we established that the OE mathematical model should be used, as it is more efficient compared to the ARMAX and BJ models. [ABSTRACT FROM AUTHOR]

A contra-rotating propfan consists of inner and outer propellers, having a dual inter-shaft bearing structure. The operating conditions are characterized by Slight Rotating Speed Differences (SRSDs) between the inner and outer rotors. These speed differences generate complex vibration transmission paths and harmonic cross-coupling between the dual rotors. Consequently, it becomes challenging to identify unbalanced vibration, leading to decreased low dynamic balance efficiency. Focusing on the prominent issues of low identification accuracy and poor stability in existing algorithms, mainly Least Square Method (LSM), Non-whole Beat Correlation Method (NWBCM), and Whole Beat Correlation Method (WBCM), when dealing with SRSDs under harmonic and noise interference, this paper introduces the Kalman Filter-Based Least Squares Identification Method for Filtering Harmonic Coupling (HLSM-KF). This method achieves precise, stable, and fast identification of unbalanced responses in contra-rotating rotors with SRSDs ranging from 1 to 30 rpm. Comparing the errors of the HLSM-KF algorithm to the other existing algorithms through simulations and experiments, the findings demonstrated that the identification accuracy and stability of the HLSM-KF algorithm are significantly dominant. Specifically, the Root Mean Square Error (RMSE) of the HLSM-KF algorithm is 47 % lower than that of the LSM algorithm. Therefore, a balancing experiment is conducted on the contra-rotating dual-rotor test rig, using the HLSM-KF algorithm. Consequently, the unbalanced vibrations of the inner and outer rotors are reduced by 86 % and 91 %, respectively, and the Root Mean Square (RMS) value of the whole system vibration is reduced by 87 %. [ABSTRACT FROM AUTHOR]

A method to estimate modal frequency based on empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) is proposed. This method can decrease the difficulties in identifying modal frequency of combine harvesters. First, we used 16 acceleration sensors installed at different test points to collect vibration signals of a corn combine harvester under operating conditions (mass time-varying conditions). Second, we calculated mean value, variance and root mean square (RMS) value of the vibration signals, and analyzed its stationarity of vibration signals. Third, the main frequencies of the 16 points were extracted using the EMD and EEMD methods. Finally, we considered modal frequencies identified by the SSI algorithm as standard, and calculated the fitting degrees of the EMD and EEMD methods. The results show that in different time periods (0~60 s and 60~120 s), the maximum differences of the mean value, variance and RMS value of signals were 0.8633, 171.1629 and 11.3767, and the vibration signal under the operating condition of field harvesting can be regarded as a typical non-stationary random vibration signal. The EMD method had more modal aliasing than EEMD, and when we obtained the fitting equations of EMD, EEMD and SSI methods, the value of the Euler distance between the EMD fitting equation and the SSI fitting equation was 446.7883, while that for EEMD and SSI was 417.2845. The vibration frequencies calculated by the EEMD method is closer to the modal frequencies identified by SSI algorithm. The proposed method provides a reference for modal frequency identification and vibration control in a complex working environment. [ABSTRACT FROM AUTHOR]

With the increasing number of available satellites from multi‐global navigation satellite systems (GNSS) and their applications in complicated environments, an increased number of faults in measurements are inevitable. Fault detection and exclusion (FDE) is an effective way to reject observation faults in order to guarantee the integrity of a GNSS positioning and navigation system. We propose a modified between‐receiver single‐differencing (SD)‐based FDE algorithm for real‐time kinematic (RTK) positioning. First, a modified between‐receiver SD model‐based FDE is proposed for testing the estimated float ambiguities. This is helpful in rejecting observations with unreliable float ambiguities and obtaining reliable double‐differencing (DD) integer ambiguities, denoted as the SD float model in the sequel. Second, DD ambiguities are resolved by the DD model and used to update the previous SD float ambiguity. After that, a modified SD fix model, taking the DD ambiguities as the known parameters, is further developed to detect any unreliable or fault‐resolved ambiguities. To verify the modified SD model‐based FDE algorithm, a kinematic vehicle test is conducted. The fault detection test statistics of the SD float model and the SD fix model, time‐to‐first‐fix, ambiguity‐fix rate, SD residuals, and positioning performances are examined. The results show that the modified FDE method can detect and exclude fault satellites accurately and effectively, which is verified by the SD residuals of fault satellites. The ambiguity‐fix rate of the proposed modified FDE algorithm is improved by approximately 3% with 70 more epochs of fixes. Regarding the 3D positioning performance, the modified proposed SD‐based FDE algorithm can achieve 54.11%, 57.78%, and 73.11% improvements for standard deviation, root mean square, and mean bias, respectively, when compared with the processing strategy without the use of the proposed FDE. [ABSTRACT FROM AUTHOR]

Featured Application: Adaptive optics in large aperture telescopes for wide band imaging. To realize the decoupling control of the dual corrector of liquid crystal (LC)–deformable mirror (DM) adaptive optics (AO) systems and prevent the loss of correction ability induced by coupling aberrations, a wavefront decoupling method based on DM eigenmodes was proposed. The coupling relationship of two correctors was studied, and the distribution of aberrations corrected using the DM and LC was investigated. First, a DM eigenmode matrix was derived according to the DM influence function. Then, aberrations were decomposed according to the Strehl ratio, and the projection of the LC response matrix on the DM eigenmode matrix, that is, the coupling term, was derived. Before using the LC corrector, the coupling item was eliminated for the decoupling of the two correctors, and the decoupling effect was verified through simulations. Finally, a 2 m telescope LC–DM AO system was developed to validate the proposed method. The simulation and experimental results showed that the root mean square of the DM-corrected residual after 55 eigenmodes' correction was 0.09 μm. After decoupling correction, the resolution of the 700–1700 nm wide band improved from 1.1 to 1 times the diffraction limit, which is 1.2 times better than that obtained using the traditional Zernike mode correction. [ABSTRACT FROM AUTHOR]

We present a simple iterative pre-distortion algorithm for achieving a rapid linear frequency sweep of semiconductor lasers. The algorithm achieves the desired frequency swept linearity with only four iterations. We derive a general formula for iterative pre-distortion by establishing the relationship between the laser output frequency and the drive current. The linear frequency-swept laser source obtained by this algorithm can be used in FMCW LiDAR systems. Experimentally, we investigated the algorithm using a 1550 nm distributed feedback (DFB) laser, achieving frequency swept excursion of 30.26 GHz, and frequency swept slope of 504 THz/s. We analyzed the linearity of the frequency swept results for the fourth iteration, achieving less than 5 MHz root mean square (RMS) value of frequency swept nonlinearity. [ABSTRACT FROM AUTHOR]

In this paper, a new fractional-order fuzzy controller named proportional fuzzy ا integral fuzzy + deferential fuzzy (FP + Fla + FDp) is proposed. The proposed controller is single gain, integrate, derivative and fractional order in its structure. The controller has a structure with two adjustable fractions and is simply designed. This controller performs the stability process in a short time. The response time can also be changed by adjusting the coefficient of time scale a and p, which makes it more flexible and efficient compared to the classic PID controller. On the other hand, in the design of the proposed controller, the optimal adjustment of the controller gain and fuzzy members has turned into an optimization problem, which is done by a hybrid algorithm based on the vcs and the HBC colony (ABC) based on a function in the time domain. In the proposed hybrid algorithm, attempts have been made to take advantage of local and final search as the strengths of these controllers to dramatically reduce the possibility of locating in local points. Investigations of different load variation scenarios, better performance of the proposed controller facing load disturbances from the root mean square, rise and fall time, will show the number of oscillations, and the fall time of the frequency changes. [ABSTRACT FROM AUTHOR]

Due to factors such as indoor multipath effects and noise, the positioning signal becomes unstable, posing a challenge for visible light positioning systems relying on a single photodetector (PD) to achieve accurate position estimation. To enhance localization precision and ranging accuracy, a positioning algorithm based on spatial constraints of the PD array is introduced in this paper. By leveraging spatial information from the PD array and the channel state information (CSI) from multiple PDs, cross-matching among multiple PDs is performed to alleviate the limitation of unstable positioning information of a single PD during the positioning process. Moreover, the distances obtained through cross-matching are fused with those based on CSI, and the measured distance is iteratively updated to reduce the interference of reflection paths on ranging. This algorithm utilizes the spatial arrangement of the PD array to constrain positioning information received by multiple PDs, rather than relying on a single PD for positioning. This method can achieve joint optimization of positioning accuracy and ranging accuracy without requiring additional equipment or modifying the transceiver structure. Simulation results within a 4 m × 4 m × 3 m indoor space showcase the proposed method's effectiveness. The average positioning error has been reduced to 1.28 cm, and the root mean square error has been reduced to 1.51 cm. In comparison to the least squares (LS) algorithm based on CSI and the nonlinear LS algorithm based on CSI, the new approach reduces average localization errors by approximately 50.4% and 41.3%, respectively. Additionally, the algorithm attains a ranging accuracy of 3.86 cm, outperforming CSI-based distance estimation accuracy by 41.9%. • A high-accuracy positioning method based on spatial constraint of the PD array is proposed. • It visibly reduces positioning errors caused by unstable positioning information. • The proposed algorithm significantly reduces the impact of multipath effects on ranging. • The mean positioning error is 1.28 cm and 95% of the positioning errors are within 2.8 cm. [ABSTRACT FROM AUTHOR]

• Numerical methods used for the estimation of the RMS. • Simpson's 1/3 rule and Simpson's 3/8 rule modified for the purpose of general application. • Modified Simpson's rules do not necessitate more complex mathematical calculations than those used in the existing methods. • Modifications provide better measurement results for some a lower ratio of the frequency of sampling and the frequency of the signal. • Modified methods do not require the manufacturers of commercial measurement equipment to additionally invest in it. This paper demonstrates that direct changes in the algorithm for the estimation of the root mean square value of a voltage signal of an arbitrary waveform can lead to improved performances and lower measurement uncertainty of commercially available instruments without requiring any upgrade of their existing hardware. The research conducted and presented here is an original contribution to the development of estimation techniques and mathematical models for measurement oriented purposes regardless of the number of samples in the given period relying on mathematical calculation of the equal complexity as in the methods already in use. The theoretical approach examines the problem of numerical integration focusing on modified Simpson's 1/3 rule and modified Simpson's 3/8 rule used for the purpose of the estimation of the root mean square value when a small number of samples per period is available. It highlights the limitations of Simpson's 1/3 rule and Simpson's 3/8 rule, and shows that the newly proposed algorithm is optimal with respect to measurement accuracy and precision even in cases when the ratio of the sampling frequency and the signal's fundamental frequency is low. All theoretical results have been validated experimentally. [ABSTRACT FROM AUTHOR]

Background: The Electrocardiogram (ECG) is an important measure for diagnosing the presence or absence of heart arrhythmias. Premature ventricular contractions (PVC) is a relatively large arrhythmia occurring outside the normal tract and being triggered outside the Sino atrial (SA) node of heart. Objective: This study has focused on tunable Q-factor wavelet transform (TQWT) algorithm and statistical methods to detect PVC. Material and Methods: In this analytical and statistical study, 22 ECGs records were selected from the MIT/BIH arrhythmia database. In the first stage the noise of signal remove and then five sub-bands create by TQWT. In the second stage nine features (minimum, maximum, root mean square, mean, interquartile range, standard deviation (SD), skewness, and variance) extracted of ECG and then the best features selected by using analysis of variance (ANOVA) test. Finally, the system is evaluated by using the learning machines of support vector machine (SVM), the K-Nearest Neighbor (KNN), and artificial neural network (ANN). Results: The best results were verified with KNN learning machine: the sensitivity Se= 98.23% and accuracy Ac= 97.81%. Conclusion: A comparative analysis with the related existing methods shows the method proposed in this study is higher than the other method for classification PVC and can help physicians to classify normal and PVC heart signals in the screening of the patients with coronary artery diseases (CADs). [ABSTRACT FROM AUTHOR]

Purpose: Current diagnostic and treatment modalities for pancreatic cysts (PCs) are invasive and are associated with patient morbidity. The purpose of this study is to develop and evaluate machine learning algorithms to delineate mucinous from non-mucinous PCs using non-invasive CT-based radiomics. Methods: A retrospective, single-institution analysis of patients with non-pseudocystic PCs, contrast-enhanced computed tomography scans within 1 year of resection, and available surgical pathology were included. A quantitative imaging software platform was used to extract radiomics. An extreme gradient boosting (XGBoost) machine learning algorithm was used to create mucinous classifiers using texture features only, or radiomic/radiologic and clinical combined models. Classifiers were compared using performance scoring metrics. Shapely additive explanation (SHAP) analyses were conducted to identify variables most important in model construction. Results: Overall, 99 patients and 103 PCs were included in the analyses. Eighty (78%) patients had mucinous PCs on surgical pathology. Using multiple fivefold cross validations, the texture features only and combined XGBoost mucinous classifiers demonstrated an area under the curve of 0.72 ± 0.14 and 0.73 ± 0.14, respectively. By SHAP analysis, root mean square, mean attenuation, and kurtosis were the most predictive features in the texture features only model. Root mean square, cyst location, and mean attenuation were the most predictive features in the combined model. Conclusion: Machine learning principles can be applied to PC texture features to create a mucinous phenotype classifier. Model performance did not improve with the combined model. However, specific radiomic, radiologic, and clinical features most predictive in our models can be identified using SHAP analysis. [ABSTRACT FROM AUTHOR]

A novel Power Line Interference (PLI) detection and suppression algorithm is proposed to preprocess Electrocardiogram (ECG) signals. A distinctive feature of this algorithm is the ability to detect the presence or absence of PLI in the ECG signal before applying the PLI suppression algorithm in the ECG signal. Only after the detection of PLI, it can be suppressed. We propose a PLI detector employing a classifier designed based on observations of the Root Mean Square (RMS) values of noise and reference ECG signals. An efficient recursive Least Mean Square (LMS) adaptive notch filter was also developed to serve the purpose of PLI suppression. An adaptive filter consists of a digital filter with adjustable coefficients and an LMS algorithm to modify the coefficient values to filter each sample. Experimental results demonstrate superior performance of this proposed algorithm. [ABSTRACT FROM AUTHOR]

For GNSS coordinate time series analysis with missing data, we propose an improved comprehensive scheme without any pre-interpolation of missing data, and we perform a simulation experiment to evaluate the validity of this method. The coordinate series of more than 260 reference stations in the CMONOC (Crustal Movement Observation Network of China) from 2011 to 2018 are employed to verify this method further. First, the moving median method is applied to detect outliers and determine the occurrence times and amplitudes of all offsets. The results of the offsets indicate that the discontinuities caused by large earthquakes can be located and adjusted accurately. Second, a method based on MTLS (Multivariate Total Least Squares) and ILSP (Improved Lomb–Scargle Periodogram) is applied in a weighted iterative procedure. Using the ILSP-based algorithm, not only can the influence of both observation noise and the mutual modulation between different frequency signals be eliminated, but the periods of all actual periodic signals can also be accurately determined even if there are false spectral peaks. Analysis of the velocity results shows that the crustal movement in the eastern region of mainland China is weaker than that in the west, where the most intense crustal activities usually happened (such as Tibet, Sichuan, and Yunnan). For the periodic signals in the coordinate series of each station, the annual (365.25 d) and semi-annual (182.26 d) signals are dominant, followed by other quad-annual (91.3 d) signals, and the amplitude of each periodic signal is associated with specific geographical or hydrological causes. The reliability of the MTLS + ILSP method is further verified by a comparative analysis of three sets of velocity field products, where the RMS (Root Mean Square) of the residual velocity is reduced by more than 70% on average, and high precision velocity field is conducive to realize and maintain regional reference frame. [ABSTRACT FROM AUTHOR]

In order to improve the surveillance effect of the aviation target surveillance radar, this paper improves the traditional filtering algorithm and builds the channel optimization system of the ADS-B aviation target surveillance radar based on the improved filtering algorithm. Moreover, this paper uses algorithm improvement to ensure the positive definite or semipositive definiteness of the state covariance and uses the root mean square volume Kalman filter to avoid the filter divergence or tracking interruption caused by the nonpositive definiteness of the matrix; the filtering principle of the interactive multimodel is to use multiple filters for parallel processing and achieve the adaptive adjustment algorithm residual error by adjusting the one-step prediction covariance in the adjustment algorithm. In addition, this paper combines the actual needs to construct a system functional structure to optimize the channel of the ADS-B aviation target surveillance radar and uses software engineering methods to model and analyze the requirements. Finally, this paper designs experiments to verify system performance. The research results show that the performance of the system constructed in this paper meets actual needs. [ABSTRACT FROM AUTHOR]

The geeSEBAL application estimates and displays evapotranspiration maps and times series based on Landsat images and global meteorological data from ERA5 Land reanalysis. Codes and applications are available at https://github.com/et-brasil/geesebal and https://etbrasil.org/geesebal , respectively. [Display omitted] Accurate estimation of evapotranspiration (ET) is essential for several applications in water resources management. ET models using remote sensing data have flourished in recent years allowing spatial and temporal assessments at unprecedented resolutions. This study presents geeSEBAL, a new tool for automated estimation of ET , based on the Surface Energy Balance Algorithm for Land (SEBAL) and a simplified version of the CIMEC (Calibration using Inverse Modeling at Extreme Conditions) process for the endmembers selection, developed within the Google Earth Engine (GEE) environment. The tool framework is introduced, and case studies across multiple biomes in Brazil are presented by comparing daily ET estimates with eddy covariance (EC) data from 10 flux towers. Based on 224 Landsat images using ERA5 Land as meteorological inputs, daily ET estimates of geeSEBAL yielded an average root mean squared difference (RMSD) of 0.67 mm day−1 when compared to EC data corrected for the energy balance closure. Additional analyses indicate a low geeSEBAL sensitivity to meteorological inputs, yielding an average RMSD of 0.71 mm day−1 when driven by in situ meteorological measurements. On the other hand, we found a higher sensitivity of the automated CIMEC algorithm to the selection of endmembers for internal calibration. For instance, by adjusting the endmembers percentiles to tropical biomes we found an error that was 36% lower compared to the standard CIMEC percentiles. Finally, we assessed the long-term effects (1984–2020) of land cover changes on surface energy fluxes and water use in agriculture for key areas in Brazil, from deforested areas in the Amazon to irrigated crops in the Pampas and Cerrado biomes. A comparison with a land surface temperature-based (SSEBop) and a vegetation-based (MOD16) model was also performed to assess relative advantages and disadvantages. This analysis showed that geeSEBAL has a significant potential for long-term assessment of ET in data-scarce areas, due to its lower sensitivity to meteorological inputs. geeSEBAL codes are written in Python and JavaScript and are freely available on GitHub (https://github.com/et-brasil/geesebal). geeSEBAL also includes a graphical user interface (https://etbrasil.org/geesebal), allowing important advances in water resources management at regional scales. [ABSTRACT FROM AUTHOR]

• Combine VMD and whale optimization algorithm for noise reduction of MCG signal. • Adaptive optimization of the variational mode decomposition is performed. • Prevention of local optimum problems in optimization is performed. • Adaptive thresholding is used to denoise the decomposition results. • The algorithm was evaluated with simulated and measured signals. In the diagnosis and treatment of cardiac diseases, magnetocardiography (MCG) technology is characterized by non-invasive, non-contact, and high precision. Therefore, it has currently become a research hotspot in the field of new medical technologies. However, due to the weak signal of the magnetocardiography, the noise needs to be filtered after acquisition. In this paper, an optimized variational mode decomposition algorithm based on the improved threshold method and the improved whale optimization algorithm (WOA) is proposed to process the MCG signal. In order to improve the denoising accuracy, the improved whale optimization algorithm, VMD algorithm, and the improved threshold method are combined. Firstly, the correlation coefficients are obtained by the improved whale optimization algorithm to decompose the IMFs, then the baseline drift is removed by using the moving average method for the low-frequency IMFs, and then the improved thresholding algorithm is applied to each IMF. Finally, the denoised signal is obtained by integration. Experimental tests show that the algorithm has good denoising performance compared with similar algorithms and can filter environmental noise as much as possible without changing the original signal information. The proposed method has the highest Signal-to-Noise Ratio improvement (SNR imp) and Correlation Coefficient (CC) and the lowest Percentage Root Mean Square Difference (PDR). Also, the method is validated in real MCG signal processing. The proposed algorithm can be applied in the field of signal denoising, and it has a wide range of application backgrounds. [ABSTRACT FROM AUTHOR]

In this study, we used the ARIMA time series model, the fuzzy-neural inference network, multi-layer perceptron artificial neural network, and ARIMA-ANN, ARIMA-ANFIS hybrid models for the modeling and prediction of the daily electrical conductivity parameter of daily teleZang hydrometric station over the statistical period of 49 years. For this purpose, the daily data for the 1996-2004 period were used for model training and data for the 1996-2006 period were applied for testing. In order to verify the validity of the fitted ARIMA models, the residual autocorrelation and partial autocorrelation functions and Port Manteau statistics were used. PMI algorithm were then used to model and predict electrical conductivity for selecting the effective input parameter of the neural fuzzy inference network and the artificial neural network. The daily parameters of magnesium (with two days delay) and sodium (with one day delay), heat (with one day delay), flow rate (with two months delay), and acidity (with one day delay) were obtained with the lowest values of Akaike and highest values of hempel statistics as the input of the neural fuzzy inference network and the artificial neural network for modelling daily electric conductivity predictions; then predictions were made. Also, models evaluation criteria confirmed the superiority of the ARIMA-ANFIS hybrid model with the trapezoidal membership function and with two membership numbers, as compared to other models with a coefficient of determination of 0.86 and the root mean square of 29 dS / m. Also, the Arima model had the weakest performance. So, it could be applied to modeling and forecasting the daily quality parameter of the tele Zang hydrometer station. [ABSTRACT FROM AUTHOR]

We optimize Hockney and Eastwood’s particle-particle particle-mesh algorithm to achieve maximal accuracy in the electrostatic energies (instead of forces) in three-dimensional periodic charged systems. To this end we construct an optimal influence function that minimizes the root-mean-square (rms) errors of the energies. As a by-product we derive a new real-space cutoff correction term, give a transparent derivation of the systematic errors in terms of Madelung energies, and provide an accurate analytical estimate for the rms error of the energies. This error estimate is a useful indicator of the accuracy of the computed energies and allows an easy and precise determination of the optimal values of the various parameters in the algorithm (Ewald splitting parameter, mesh size, and charge assignment order). [ABSTRACT FROM AUTHOR]

Breast cancer is the only type of cancer which is affecting the woman on a large scale because the tumors which are developed in the early stage of cancer or the origin of cancer these tumor cells or lumps are divided into two different kinds and both are dangerous in their own away as one of the lump is cancerous and other is not but both of them are one of the main reason behind the increasing numbers of death among the women. The purpose of the proposed research is control the rising tally of death day by day all over the world because there no efficient way of predicting or diagnosing the tumor of breast cancer whether the lumps developed in the patients are cancerous or not so, the objective of the proposed research is to overcome this major problem by using machine learning and deep learning as machine learning (ML) can provide certain tools which can be used in addition with hyper parametric for diagnosing the tumor in an efficient way that is the main reason behind the use of six supervised Machine Learning(ML) algorithm involving. The KNN, SVM, DT, and DP methods like Adam Gradient learning were utilized due to their adaptive gradient algorithm and root mean square propagation benefits; as in each model is has been proved that it shows more accuracy individually in the models or on comparing them with each other as the accuracy shown by these method was 98.50%. as described in result and discussion section. [ABSTRACT FROM AUTHOR]

This paper presents a method for estimating the land surface temperature (LST) from Himawari-8 data. The Advanced Himawari Imager onboard Himawari-8 has three thermal infrared bands in the spectral range of 10 - 12.5 µm. We developed a nonlinear three-band algorithm (NTB) that makes the best use of these bands to estimate the LST. The formula of the algorithm includes 10 coefficients. The optimum values of these coefficients were derived using a statistical regression method from the simulated data, as obtained by a radiative transfer model. The simulated data sets correspond to a variety of values of LST, as well as surface emissivity, type and season of temperature and water vapor profiles. Viewing zenith angles (VZAs) from 0° to 60° were considered. For the coefficients obtained in this way, we verified the root-mean-square error (RMSE) in terms of the VZA, LST and precipitable water dependence. We showed that the NTB can accurately estimate the LST with an RMSE less than 0.9 K compared with the nonlinear split-window algorithm developed by Sobrino and Romaguera (2004). Moreover, we evaluated the sensitivities of the LST algorithms to the uncertainties in input data by using the dataset independent of the dataset used to obtain coefficients. Consequently, we showed that the NTB has the highest robustness against the uncertainties in input data. Finally, the stepwise LST retrieval method was constructed. This method includes a simple cloud mask procedure and the land surface emissivity estimation. The LST product was evaluated using in-situ data over the Tibetan Plateau, and the validity was confirmed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel methodology for very short term forecasting of hourly global solar irradiance (GSI). The proposed methodology is based on meteorology data, especially for optimizing the operation of power generating electricity from photovoltaic (PV) energy. This methodology is a combination of k-nearest neighbor (k-NN) algorithm modelling and artificial neural network (ANN) model. The k-NN-ANN method is designed to forecast GSI for 60 min ahead based on meteorology data for the target PV station which position is surrounded by eight other adjacent PV stations. The novelty of this method is taking into account the meteorology data. A set of GSI measurement samples was available from the PV station in Taiwan which is used as test data. The first method implements k-NN as a preprocessing technique prior to ANN method. The error statistical indicators of k-NN-ANN model the mean absolute bias error (MABE) is 42 W/m2 and the root-mean-square error (RMSE) is 242 W/m2. The models forecasts are then compared to measured data and simulation results indicate that the k-NN-ANN-based model presented in this research can calculate hourly GSI with satisfactory accuracy. [ABSTRACT FROM AUTHOR]

An interferometric synthetic aperture radar (InSAR) phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double- l1 norm regularization model that enforces the local and nonlocal sparsity constraints was used. Taking advantages of coefficients of the nonlocal similarity between group blocks for the wavelet shrinkage, the proposed algorithm effectively filtered the phase noise. Applying the method to simulated and acquired InSAR data, we obtained satisfactory results. In comparison, the algorithm outperformed several widely-used InSAR phase denoising approaches in terms of the number of residues, root-mean-square errors and other edge preservation indexes. [ABSTRACT FROM AUTHOR]

Purpose To develop and implement an efficient reconstruction technique to improve accelerated multi-channel multi-contrast MRI. Theory and Methods The vectorial total generalized variation (TGV) operator is used as a regularizer for the sensitivity encoding (SENSE) technique to improve image quality of multi-channel multi-contrast MRI. The alternating direction method of multipliers (ADMM) is used to efficiently reconstruct the data. The performance of the proposed method (MC-TGV-SENSE) is assessed on two healthy volunteers at several acceleration factors. Results As demonstrated on the in vivo results, MC-TGV-SENSE had the lowest root-mean-square error (RMSE), highest structural similarity index, and best visual quality at all acceleration factors, compared to other methods under consideration. MC-TGV-SENSE yielded up to 17.3% relative RMSE reduction compared to the widely used total variation regularized SENSE. Furthermore, we observed that the reconstruction time of MC-TGV-SENSE is reduced by approximately a factor of two with comparable RMSEs by using the proposed ADMM-based algorithm as opposed to the more commonly used Chambolle–Pock primal-dual algorithm for the TGV-based reconstruction. Conclusion MC-TGV-SENSE is a better alternative than the existing reconstruction methods for accelerated multi-channel multi-contrast MRI. The proposed method exploits shared information among the images (MC), mitigates staircasing artifacts (TGV), and uses the encoding power of multiple receiver coils (SENSE). [ABSTRACT FROM AUTHOR]

The global monthly equivalent water height (EWH) anomaly (ΔEWH), derived from the Gravity Recovery and Climate Experiment (GRACE) level-2 data, helps understand the terrestrial water storage variation. Unfortunately, the ΔEWH data consist of stripes or noise. Numerous de-noising approaches have been studied. However, the following issues exist, possible overly smoothed results, two-step (de-striping and Gaussian-based smoothing) procedures introducing additional uncertainty and error propagation, requiring multi-temporal datasets or prior knowledge, and possible computational inefficiency. Thus, after analyzing the spectrum of the ΔEWH data, a novel low-pass-filtering algorithm is proposed to remove the noise and resolve the issues. Furthermore, without the global in situ measurements, an alternative assessment method is studied based on the additive characteristics of the signal and noise in the GRACE data. The method consists of the residual analysis and root mean square (RMS) value of the de-noised signal. Then, the proposed algorithm was applied to de-noising the ΔEWH datasets between 2002 and 2015. De-noised results are satisfactory qualitatively and quantitatively. Compared with well-known two-step de-noising methods, data blurring does not occur after the proposed algorithm. The algorithm removes noise the most assessed by the residual analysis and preserves the signal the most evaluated by large RMS values. • Significantly removing the stripes and noise without Gaussian-like smoothening. • Requiring no prior information or the time series. • The time complexity is noticeably low. • Developing a novel performance analysis method to assess global GRACE data. [ABSTRACT FROM AUTHOR]

• We propose a more aggressive algorithm for generating adversarial examples, namely, the accelerated gradient iterative fast gradient sign method(AI-FGSM), and we prove that AI-FGSM can improve the transferability of adversarial examples. • From the perspective of the deep neural network training optimization process, we introduce the gradient update method of the adaptive sub-gradient and root mean square prop into the iterative fast gradient sign method to obtain AG-FGSM and RMS-FGSM, respectively. We analyse the attack results of AG-FGSM and RMS-FGSM, and the results show that they are better than the traditional iterative gradient sign method. • We introduce invariance of image scaling into a deep neural network. With this property,we can replace the training processes of a series of models by scaling the images in different proportions. We introduce this property into our methods and obtain SAI-FGSM, SAG-FGSM and SRMS-FGSM. Experimental results show that the integrated combat training model with strong defense ability is also vulnerable to blackbox attacks. Most existing deep neural networks are susceptible to the influence of adversarial examples, which may cause them to output incorrect prediction results. An adversarial example is the addition of small noise disturbances to the input data samples, which will deceive the classification. Generally, these modifications are very small noise disturbances, which are not easily noticed by the human eye. However, most existing adversarial attacks achieve only low success rates in typical black-box settings, where the attackers have no prior knowledge about the model structures and/or model parameters. To tackle this problem, we propose an iterative algorithm based on an acceleration gradient to enhance the adversary attack. Our method accumulates the gradient of the loss function in each iteration and uses the next gradient information to influence the future gradient. We also introduce the scaling invariance principle of a deep neural network to optimize the input images for black-box attacks. In addition, to handle the drawbacks of a traditional iterative fast gradient sign method, we further present two gradient optimization methods. Experimental results on the ImageNet dataset show that our attack methods can achieve good transferability. In addition, those models obtained by integrated adversarial training with strong defense capability are also quite vulnerable to our black-box attack. [ABSTRACT FROM AUTHOR]

Massive availability of multimedia content has given rise to numerous recommendation algorithms that tackle the associated information overload problem. Because of their growing popularity, selecting the best one is becoming an overload problem in itself. Hybrid algorithms, combining multiple individual algorithms, offer a solution, but often require manual configuration and power only a few individual recommendation algorithms. In this work, we regard the problem of configuring hybrid recommenders as an optimization problem that can be trained in an offline context. Focusing on the switching and weighted hybridization techniques, we compare and evaluate the resulting performance boosts for hybrid configurations of up to 10 individual algorithms. Results showed significant improvement and robustness for the weighted hybridization strategy which seems promising for future self-adapting, user-specific hybrid recommender systems. [ABSTRACT FROM AUTHOR]

The surface electromyogram (sEMG) signal reveals the electrical neuromuscular activities and offers theoretical and clinical information. Recently, exploring motoneuron discharge events at the microscopic level through decomposition has been proposed as a promising analysis approach, which can surpass traditional global sEMG-based analysis method in some aspects. However, computational efficiency is an essential issue when discharge events of individual motor unit are decomposed using blind source separation algorithms. Therefore, choosing proper parameters of decomposition algorithms for different research purposes is important. Accordingly, we have systematically investigated the influences between computation time and decomposition performance for fast independent component analysis (FastICA)-based sEMG decomposition algorithm under different value selections of five decomposition parameters, namely the percentage of eliminated channels, extension factor, the number of decomposition iteration loops, the number of maximum inner loops in each iteration and sampling frequency. We employed high-density sEMG signals from 14 intact subjects during muscle contractions of four-digit extension and flexion at different force levels (20% and 50% maximum voluntary contraction) and a public dataset for sEMG decomposition. According to obtained results, we offer four preference suggestions (less computation time, more motor units, higher accuracy and trade-off). Results show that the trade-off values with consideration of decomposition performance and computation time are recommended as 25% of channels with minimal root mean square, extension factor of 4, 200 iteration loop numbers of decomposition, 20 maximum inner loop numbers and 2048 sampling frequency. Overall, this paper provides a guide for researchers to determine proper decomposition parameters for sEMG decomposition-related works. [ABSTRACT FROM AUTHOR]

The deviation-angle variance technique (DAV-T), which was introduced in the North Atlantic basin for tropical cyclone (TC) intensity estimation, is adapted for use in the North Pacific Ocean using the 'best-track center' application of the DAV. The adaptations include changes in preprocessing for different data sources [Geostationary Operational Environmental Satellite-East (GOES-E) in the Atlantic, stitched GOES-E-Geostationary Operational Environmental Satellite-West (GOES-W) in the eastern North Pacific, and the Multifunctional Transport Satellite (MTSAT) in the western North Pacific], and retraining the algorithm parameters for different basins. Over the 2007-11 period, DAV-T intensity estimation in the western North Pacific results in a root-mean-square intensity error (RMSE, as measured by the maximum sustained surface winds) of 14.3 kt (1 kt ≈ 0.51 m s−1) when compared to the Joint Typhoon Warning Center best track, utilizing all TCs to train and test the algorithm. The RMSE obtained when testing on an individual year and training with the remaining set lies between 12.9 and 15.1 kt. In the eastern North Pacific the DAV-T produces an RMSE of 13.4 kt utilizing all TCs in 2005-11 when compared with the National Hurricane Center best track. The RMSE for individual years lies between 9.4 and 16.9 kt. The complex environment in the western North Pacific led to an extension to the DAV-T that includes two different radii of computation, producing a parametric surface that relates TC axisymmetry to intensity. The overall RMSE is reduced by an average of 1.3 kt in the western North Pacific and 0.8 kt in the eastern North Pacific. These results for the North Pacific are comparable with previously reported results using the DAV for the North Atlantic basin. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning is a promising technique for automatic measurements of tree stems. The objectives of the study were (1) to develop and validate a new method for the detection, classification and measurements of tree stems and canopies using the Hough transformation and the RANSAC algorithm and (2) assess the influence of distance to the scanner on the measurement accuracy. Tree detection and stem diameter estimates were validated for 16 circular plots with 20 m radius. The three dominating tree species were Norway spruce (Picea abies L. Karst.), Scots pine (Pinus sylvestris L.) and birch (Betula spp.). The proportion of detected trees decreased as the distance to the scanner increased and followed the trend of decreasing visible area. Within 10 m from the scanner, the proportion of detected trees was 87% on average for the plots and the diameter at breast height was estimated with a relative root-mean-square-error (RMSE) of 14%. The most accurate diameter measurements were obtained for pine, which had a RMSE of 7% for all the full 20 m radius plots. The RANSAC algorithm reduced noise and made it possible to obtain reliable estimates. [ABSTRACT FROM AUTHOR]

A physical retrieval algorithm has been developed for deriving the legacy atmospheric profile (LAP) product from infrared radiances of the Advanced Baseline Imager (ABI) on board the next-generation Geostationary Operational Environmental Satellite (GOES-R) series. In this study, the GOES-R ABI LAP retrieval algorithm is applied to the GOES-13 sounder radiance measurements (termed the GOES-13 LAP retrieval algorithm in this study) for its validation as well as for potential transition of the GOES-13 LAP retrieval algorithm for the operational processing of GOES sounder data. The GOES-13 LAP retrievals are compared with five different truth measurements: radiosonde observation (raob) and microwave radiometer-measured total precipitable water (TPW) at the Atmospheric Radiation Measurement Cloud and Radiation Testbed site, conventional raob, TPW measurements from the global positioning system-integrated precipitable water NOAA network, and TPW measurements from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The results show that with the GOES-R ABI LAP retrieval algorithm, the GOES-13 sounder provides better water vapor profiles than the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecast fields at the levels between 300 and 700 hPa. The root-mean-square error (RMSE) and standard deviation (STD) of the GOES-13 sounder TPW are consistently reduced from those of the GFS forecast no matter which measurements are used as the truth. These substantial improvements indicate that the GOES-R ABI LAP retrieval algorithm is well prepared to provide continuity of quality to some of the current GOES sounder products, and the algorithm can be transferred to process the current GOES sounder measurements for operational product generation. [ABSTRACT FROM AUTHOR]

This paper presents a method for automatic extraction of ground control points (GCPs) of fully polarimetric synthetic aperture radar (SAR) (PolSAR) images obtained from various satellites with different viewing angles. The scale-invariant feature transform (SIFT) algorithm is applied to extract candidate GCPs, where two-way keypoint matching eliminates improbable correspondence keypoints. Minimizing the root-mean-square error (rmse) also removes matching points with large rmse through a pseudoaffine transformation. In addition, information entropy and spatial dispersion quality constraints enable quantification of the spatial distribution of the GCPs. In accordance with full polarization, applying the SIFT-OCT algorithm (SIFT algorithm with the first scale-space octave skipped) to PolSAR data is examined. The total power (TP) image represents a combination of the characteristics of all four polarization images [horizontal transmitting and horizontal receiving (HH), horizontal transmitting and vertical receiving (HV), vertical transmitting and horizontal receiving (VH), and vertical transmitting and vertical receiving (VV)]. Therefore, GCP extraction using a TP image rather than each polarization image is proposed in order to maximize the accuracy of GCP extraction for all of the polarization data, as the TP image generates the highest signal-to-noise ratio (SNR) value. The SNR in conjunction with the matching correlation surface is used as an indicator of the reliability and accuracy of GCP extraction. After successfully applying the method to Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar and Japanese Earth Resources Satellite-1 SAR images, the GCP matching accuracy is further improved by using geometric calibration. [ABSTRACT FROM AUTHOR]

We examine how the evaluation of modelled sea-ice coverage against reality is affected by uncertainties in the retrieval of sea-ice coverage from satellite, by the usage of sea-ice extent to overcome these uncertainties, and by internal variability. We find that for Arctic summer sea ice, model biases in sea-ice extent can be qualitatively different from biases in sea-ice area. This is because about half of the CMIP5 models and satellite retrievals based on the Bootstrap and the ASI algorithm show a compact ice cover in summer with large areas of high-concentration sea ice, while the other half of the CMIP5 models and satellite retrievals based on the NASA Team algorithm show a loose ice cover. For the Arctic winter sea-ice cover, differences in grid geometry can cause synthetic biases in sea-ice extent that are larger than the observational uncertainty. Comparing the uncertainty arising directly from the satellite retrievals with those that arise from internal variability, we find that the latter by far dominates the uncertainty estimate for trends in sea-ice extent and area: most of the differences between modelled and observed trends can simply be explained by internal variability. For absolute sea-ice area and sea-ice extent, however, internal variability cannot explain the difference between model and observations for about half the CMIP5 models that we analyse here. All models that we examined have regional biases, as expressed by the root-mean-square error in concentration, that are larger than the differences between individual satellite algorithms. [ABSTRACT FROM AUTHOR]

A new passive microwave rainfall retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) that relies on an a priori database derived from matchups between TMI brightness temperatures and precipitation radar (PR)-derived surface rain rates has been developed. In addition to implementing a fairly conventional Bayesian approach to precipitation estimation, it exploits a dimensional reduction technique designed to increase the effective sample density in the database and also to improve the detectability of precipitation over problem surface types. The details of the algorithm itself are described in a companion paper. In this paper, the algorithm is validated against independent PR-TMI matchups from calendar year 2002. The validation results are benchmarked against results obtained for the same scenes from the current standard (version 7) 2A12 rainfall product for TRMM. Validation statistics considered include the biases, correlation coefficients, and root-mean-square (RMS) differences for annual precipitation totals on a 1° grid as well as two-threshold Heidke skill scores (HSS) for instantaneous (pixel level) retrievals, determined separately for each of seven surface classes, including ocean, coast, and five other basic land surface types as well as for cold (<275 K) and warm surface skin temperatures. Overall, the University of Wisconsin (UW) algorithm exhibits markedly reduced RMS error and bias in the annual total rainfall and markedly improved instantaneous skill at delineating light rain rates, especially over land and the coast. To ensure that the improved results were not due to both the training and validation data having been taken from the same calendar year, the validation of the UW algorithm is repeated using 2005 matchup data. [ABSTRACT FROM AUTHOR]

• The activity of muscles is registered during real human jaw motions using sEMG. • The EMG signal is analysed in order to identify an instability disorder in TMJ. • A methodology based on SOM combined with Cross-correlation is proposed. • The approach allows one to classify subjects based on TMJ muscles activity. • The methodology can be used to develop a tool to be applied in clinical practice. The study considers the need for an effective method of classification of patients with a temporomandibular joint disorder (TMD). The self-organising map method (SOM) was applied to group patients and used together with the cross-correlation approach to interpret the processed (rectified and smoothed by using root mean square (RMS) algorithm) surface electromyography signal (sEMG) obtained from testing the muscles (two temporal muscles and two masseters) of the temporomandibular joint (TMJ) during selected jaw movements. SOM's Unified distance matrix (U-matrix) maps consist of formed clusters that correspond to similarities in input datasets. The results showed that SOM was able to encode muscular responses and create clusters. Information about the level of similarity between the activity of right, left, ipsilateral, and contralateral pairs of muscles was provided by intra cross-correlation coefficient (CC). A low intra CC value may indicate instability of the TMJ function. Information about the level of similarity between the sEMG signals of the same muscles tested in two different patients was provided by inter CC. SOM analysis can be used to interpret the activation of muscular systems, and by comparing the results of different individuals also to identify their TMJ health. Using the cross-correlation approach, one can find similarities in the sEMG data of different patients that can be used to provide clinically useful information. Such findings could be used to improve the clinical diagnosis of TMD and assess muscle activity during treatment. [ABSTRACT FROM AUTHOR]

This paper introduces the impact localization algorithm on a composite stiffened plate using an array of four multiplexed fiber Bragg grating (FBG) sensors. For this algorithm, the impact signals are required as the reference data on the baseline points. In order to localize the impact event, the similarities between the obtained and each reference signal have to be evaluated. In this study, such similarities were assessed by calculating the root mean squared (RMS) values between the signals. In the first step, four RMS matrices from each sensor are obtained using the RMS values about each reference location on the test section as the elements. Then, the elements of each matrix are processed to extremely low value excepting a few number of elements with values near the minimum RMS values. From the element-by-element multiplications of these processed matrices, the final matrix could be obtained. Finally, the impact location could be predicted from the location of the high valued element in that matrix. This principle was validated from the localizations of non-baseline points. Also, to check the robustness of this algorithm to dynamic loading conditions, experimental investigations were performed. As a result, this suggested algorithm showed a reasonable localization performance. [ABSTRACT FROM AUTHOR]