Search

In this paper, we research the dynamic car sequencing problem with car body buffer (DCSPwB) in automotive mixed-flow assembly. The objective is to reorder the sequence of cars in the paint shop using the post-painted body buffers to minimize the violation of constraint rules and the time cost of sequencing in the general assembly shop. We establish a mathematical model of DCSPwB and propose a decomposition-based algorithm based on the dynamic genetic algorithm (DGA) and greedy algorithm for delayed car release (PGDA). Experiments are conducted based on production orders from actual companies, and the results are compared with the solution results of the underlying genetic algorithm (GA) and greedy algorithm (GDA) to verify the effectiveness of the algorithm. In addition, the effect of buffer capacity on DCSPwB is investigated. [ABSTRACT FROM AUTHOR]

As an important technology in 3D vision, point-cloud registration has broad development prospects in the fields of space-based remote sensing, photogrammetry, robotics, and so on. Of the available algorithms, the Iterative Closest Point (ICP) algorithm has been used as the classic algorithm for solving point cloud registration. However, with the point cloud data being under the influence of noise, outliers, overlapping values, and other issues, the performance of the ICP algorithm will be affected to varying degrees. This paper proposes a global structure and adaptive weight aware ICP algorithm (GSAW-ICP) for image registration. Specifically, we first proposed a global structure mathematical model based on the reconstruction of local surfaces using both the rotation of normal vectors and the change in curvature, so as to better describe the deformation of the object. The model was optimized for the convergence strategy, so that it had a wider convergence domain and a better convergence effect than either of the original point-to-point or point-to-point constrained models. Secondly, for outliers and overlapping values, the GSAW-ICP algorithm was able to assign appropriate weights, so as to optimize both the noise and outlier interference of the overall system. Our proposed algorithm was extensively tested on noisy, anomalous, and real datasets, and the proposed method was proven to have a better performance than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Accurate prostate segmentation in ultrasound images is crucial for the clinical diagnosis of prostate cancer and for performing image-guided prostate surgery. However, it is challenging to accurately segment the prostate in ultrasound images due to their low signal-to-noise ratio, the low contrast between the prostate and neighboring tissues, and the diffuse or invisible boundaries of the prostate. In this paper, we develop a novel hybrid method for segmentation of the prostate in ultrasound images that generates accurate contours of the prostate from a range of datasets. Our method involves three key steps: (1) application of a principal curve-based method to obtain a data sequence comprising data coordinates and their corresponding projection index; (2) use of the projection index as training input for a fractional-order-based neural network that increases the accuracy of results; and (3) generation of a smooth mathematical map (expressed via the parameters of the neural network) that affords a smooth prostate boundary, which represents the output of the neural network (i.e., optimized vertices) and matches the ground truth contour. Experimental evaluation of our method and several other state-of-the-art segmentation methods on datasets of prostate ultrasound images generated at multiple institutions demonstrated that our method exhibited the best capability. Furthermore, our method is robust as it can be applied to segment prostate ultrasound images obtained at multiple institutions based on various evaluation metrics. [ABSTRACT FROM AUTHOR]

This paper presents a method for determination of heat transfer coefficients for finite-element method housing model of medium-voltage switchgear cell. Suggested method is based on the particle swarm optimization algorithm. For that purpose, a real test model of partition wall has been created as well as an equivalent numerical FE model. Since the preciseness of thermal model is dependent on the preciseness of thermal coefficients, the goal of the optimization procedure is to assure even better match between the measured and the FE model calculated temperature values. Because of the fact that the temperature field is a consequence of eddy currents that appear in metal parts of switchgear devices, this paper deals with a coupled problem. Nonlinear calculation of magnetic field is also considered. [ABSTRACT FROM AUTHOR]

As the most important core process in the dyeing and finishing workshop of knitting companies, the dyeing process has the characteristics of multi-variety, small-batch, parallel machine processing of multiple types, and high cost in equipment cleaning, which render the dyeing scheduling problem a bottleneck in the production management of a dyeing and finishing workshop. In this paper, the dyeing process scheduling problem in dyeing and finishing workshops is described and abstracted, and an optimized mathematical model of dyeing scheduling is constructed with the goal of minimizing the delay cost and switching cost. Constraints such as multiple types of equipment, equipment capacity, weights of orders and equipment cleaning time are considered. For the sub-problem of equipment scheduling in the dyeing scheduling problem, a heuristic rule that considers equipment utilization and order delay is proposed. For the sub-problem of order sorting of the equipment in the dyeing scheduling problem, a hybrid genetic algorithm with a variable neighbourhood search strategy has been designed to optimize sorting. The algorithm proposed in this paper has been demonstrated via case simulation to be effective in solving the scheduling problem in dyeing and finishing workshops. [ABSTRACT FROM AUTHOR]

A fast flash calculation of the Rachford-Rice equation is very much needed in many engineering applications, especially in multi-component mixtures. This paper suggests a direct and hands-on calculation by a pocket calculator or a Chinese abacus. The calculation is based on an ancient Chinese mathematics called as He Chengtian average. An example is given to show the simplicity and effectiveness of the ancient Chinese algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a new hybrid optimization method for the phase-frequency characteristics of the double input power system stabilizer (PSS) based on the improved active set algorithm. This method takes the effect of the filtering section optimization on the parameter improvement into account, and the optimized model focuses on the minimum residual sum of squares between the actual and the target phase-frequency characteristics. The result shows that the improved parameters obtained from the proposed method provide much better phase-frequency characteristics than the widely used engineering parameters. The comparison between the proposed method and the typical commercial software indicates the universal superiority of the proposed method. And the studies on the impact of considering the filter section optimization on the phase-frequency improvement show that taking the filter section optimization into account will be beneficial for the phase-frequency improvement, though in application to the PSS2A model and the PSS2B model there are some differences. The achievements obtained in this paper provide a significant reference for the practical PSS parameter modification and improvement. [ABSTRACT FROM AUTHOR]

The Unlimited Sensing Framework (USF) was recently introduced to overcome the sensor saturation bottleneck in conventional digital acquisition systems. At its core, the USF converts a continuous-time high-dynamic-range (HDR) signal into folded, low-dynamic-range, modulo samples and allows the recovery of the HDR signal via algorithmic unfolding. In hardware, however, implementing ideal modulo folding requires careful calibration, analog design and high precision. At the interface of theory and practice, this paper explores a computational sampling strategy that relaxes strict hardware requirements via a novel, mathematically guaranteed reconstruction. We start with a generalized model for USF with two new parameters modeling hysteresis and folding transients in addition to the modulo threshold. Hysteresis accounts for mismatches between the reset threshold and the amplitude displacement at the folding time and the folding transient is a continuous transition period in the implementation of a reset. Both these effects are motivated by our hardware experiments and also occur in previous, domain-specific applications. We show that hysteresis is beneficial for USF and leverage it to derive the first recovery guarantees in the context of our generalized USF model for a certain sampling rate regime. Additionally, we show how the sampling rate requirement can be greatly reduced via a direct generalization of the proposed recovery. Our theoretical work is corroborated by hardware experiments with a hysteresis enabled, modulo ADC testbed comprising off-the-shelf electronic components. Thus, by capitalizing on a collaboration between hardware and algorithms, our paper enables an end-to-end pipeline for HDR sampling allowing more flexible hardware implementations. [ABSTRACT FROM AUTHOR]

In this paper, we propose to extend the standard Convolutional Dictionary Learning problem to a tensor representation where the activations are constrained to be “low-rank” through a Canonical Polyadic decomposition. We show that this additional constraint increases the robustness of the CDL with respect to noise and improve the interpretability of the final results. In addition, we discuss in detail the advantages of this representation and introduce two algorithms, based on ADMM or FISTA, that efficiently solve this problem. We show that by exploiting the low rank property of activations, they achieve lower complexity than the main CDL algorithms. Finally, we evaluate our approach on a wide range of experiments, highlighting the modularity and the advantages of this tensorial low-rank formulation. [ABSTRACT FROM AUTHOR]

Earth observation satellite (EOS) scheduling is critical for improving the performance of Earth observation system. Most of the existing studies on satellite observation scheduling problems focus on static tasks and real-time dynamic tasks also known as emergency tasks while few attempts have been made for common dynamic tasks yet. The common dynamic tasks are characterized by uncertain arrival times and delayable observations while the emergency tasks require immediate observations. The number of common dynamic tasks is rapidly increasing with the expansion of satellite observation applications, which poses great challenges to EOS observation scheduling. To address this issue, this paper investigates the satellite observation scheduling problem with common dynamic tasks. Firstly, a centralized onboard dynamic scheduling framework based on a periodic-triggered rolling horizontal optimization (RHO) strategy is proposed and a novel two-stage mathematical model is established for the periodic rescheduling problem. Secondly, we propose a low-complexity onboard periodic rescheduling algorithm (OPR), which consists of a greedy-based task allocation algorithm, a pointer network (Ptr-network) based task scheduling algorithm and an iterative local search algorithm. In the greedy-based task allocation algorithm, we define a Task-EOS Fitness Indicator (TEFI) and each task is greedily allocated to the EOS with maximum TEFI. In the Ptr-network based task scheduling algorithm, the allocated task sets of all EOSs are fed into the Ptr-network in parallel to generate the observation scheduling result. Afterward, an iterative local search algorithm is proposed to further improve the quality of the observation scheduling result. Finally, extensive experiments are conducted to demonstrate the superiority of the OPR algorithm for the satellite observation scheduling problem with common dynamic tasks. [ABSTRACT FROM AUTHOR]

Ribonucleic acid (RNA) structure alignment is an important problem in computational biology to identify structural similarity of RNAs. Obtaining an efficient method for this problem is challenging due to the high computational time for the optimal solution and the low accuracy of a heuristic solution. In this paper, an efficient algorithm is proposed based on a mathematical model called longest arc-preserving common subsequence. The proposed algorithm uses a heuristic technique and high-performance computing to optimize the solution of RNA structure alignment, both in terms of the running time and the accuracy of the output. Extensive experimental studies on a multicore system are conducted to show the effectiveness of the proposed algorithm on two types of data. The first is simulated data that consists of 450 comparisons of RNA structures, while the second is real biological data that consists of 357 comparisons of RNA structures. The results show that the proposed algorithm outperforms the best-known heuristic algorithm in terms of execution time, with a percentage improvement of 71% and increasing the length of the output, i.e., accuracy, by approximately 45% in all studied cases. Finally, future approaches are discussed. [ABSTRACT FROM AUTHOR]

An efficient and more accurate millimeter-wave imaging algorithm, applied to a close-range monostatic personnel screening system, with consideration of dual path propagation loss, is presented in this paper. The algorithm is developed in accordance with a more rigorous physical model for the monostatic system. The physical model treats incident waves and scattered waves as spherical waves with a more rigorous amplitude term as per electromagnetic theory. As a result, the proposed method can achieve a better focusing effect for multiple targets in different range planes. Since the mathematical methods in classical algorithms, such as spherical wave decomposition and Weyl identity, cannot handle the corresponding mathematical model, the proposed algorithm is derived through the method of stationary phase (MSP). The algorithm has been validated by numerical simulations and laboratory experiments. Good performance in terms of computational efficiency and accuracy has been observed. The synthetic reconstruction results show that the proposed algorithm has significant advantages compared with the classical algorithms, and the reconstruction by using full-wave data generated by FEKO further verifies the validity of the proposed algorithm. Finally, the proposed algorithm performs as expected over real data acquired by our laboratory prototype. [ABSTRACT FROM AUTHOR]

Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics. In this study, two different algorithms, the single-dimensional perturbation descent algorithm (SDPD) and the second-order stochastic parallel gradient descent algorithm (2SPGD), are proposed for wavefront sensorless adaptive optics, and a theoretical analysis of the algorithms' convergence rates is presented. The results demonstrate that the single-dimensional perturbation descent algorithm outperforms the stochastic parallel gradient descent (SPGD) and 2SPGD algorithms in terms of convergence speed. Then, a 32-unit deformable mirror is constructed as the wavefront corrector, and the SPGD, single-dimensional perturbation descent, and 2SPSA algorithms are used in an adaptive optics numerical simulation model of the wavefront controller. Similarly, a 39-unit deformable mirror is constructed as the wavefront controller, and the SPGD and single-dimensional perturbation descent algorithms are used in an adaptive optics experimental verification device of the wavefront controller. The outcomes demonstrate that the convergence speed of the algorithm developed in this paper is more than twice as fast as that of the SPGD and 2SPGD algorithms, and the convergence accuracy of the algorithm is 4% better than that of the SPGD algorithm. [ABSTRACT FROM AUTHOR]

The theory of variational inequalities is an important tool in physics, engineering, finance, and optimization theory. The projection algorithm and its variants are useful tools for determining the approximate solution to the variational inequality problem. This paper introduces three distinct extragradient algorithms for dealing with variational inequality problems involving quasi-monotone and semistrictly quasi-monotone operators in infinite-dimensional real Hilbert spaces. This problem is a general mathematical model that incorporates a set of applied mathematical models as an example, such as equilibrium models, optimization problems, fixed point problems, saddle point problems, and Nash equilibrium point problems. The proposed algorithms employ both fixed and variable stepsize rules that are iteratively transformed based on previous iterations. These algorithms are based on the fact that no prior knowledge of the Lipschitz constant or any line-search framework is required. To demonstrate the convergence of the proposed algorithms, some simple conditions are used. Numerous experiments have been conducted to highlight the numerical capabilities of algorithms. [ABSTRACT FROM AUTHOR]

Stochastic configuration network (SCN) is a mathematical model of incremental generation under a supervision mechanism, which has universal approximation property and advantages in data modeling. However, the efficiency of SCN is affected by some network parameters. An optimized searching algorithm for the input weights and biases is proposed in this paper. An optimization model with constraints is first established based on the convergence theory and inequality supervision mechanism of SCN; Then, a hybrid bat-particle swarm optimization algorithm (G-BAPSO) based on gradient information is proposed under the framework of PSO algorithm, which mainly uses gradient information and local adaptive adjustment mechanism characterized by pulse emission frequency to improve the searching ability. The algorithm optimizes the input weights and biases to improve the convergence rate of the network. Simulation results over some datasets demonstrate the feasibility and validity of the proposed algorithm. The training RMSE of G-BAPSO-SCN increased by 5.57 × 10 − 5 and 3.2 × 10 − 3 compared with that of SCN in the two regression experiments, and the recognition accuracy of G-BAPSO-SCN increased by 0.07% on average in the classification experiments. [ABSTRACT FROM AUTHOR]

The generation of mathematical models for the analysis of buildings with multiple thermal zones is a large and complex task. Furthermore, the order and complexity of the dynamical model are increased by the number of included thermal zones. To overcome this problem, this paper presents an algorithm to define the mathematical model automatically, using the geometric and physics parameters as inputs. Additionally, the spatial position of each thermal zone must be recorded in an arrangement called a contact matrix. The algorithm for modeling systems with multiple thermal zones is the main contribution of this work. This algorithm is presented in pseudocode format and as an annex, an implementation in MATLAB software. One of the advantages of this methodology is that it allows us to work with parallelepipeds and not necessarily cubic thermal zones. The algorithm allows us to generate mathematical models with symbolic variables, starting from the knowledge of how many thermal zones compose the system and its geometric organization. This information must be organized in a matrix arrangement called a contact matrix. Different arrays of thermal zones were constructed with wooden boxes to verify the functionality of the models generated with the algorithm. Each case provided information that allowed us to adjust the mathematical models and their simulations, obtaining a range of errors between experimental and simulated temperatures from 2.08 to 5.6 , depending on the number of thermal zones studied. [ABSTRACT FROM AUTHOR]

In this paper, a flow shop scheduling problem with minimal and maximal machine idle time with the goal of minimizing makespan is considered. The mathematical model of the problem is presented. A generalization of the prefix sum, called the job shift scan, for computing required shifts for overlapping jobs is proposed. A work-efficient algorithm for computing the job shift scan in parallel for the PRAM model with n processors is proposed and its time complexity of O (log n) is proven. Then, an algorithm for computing the makespan in time O (m log n) in parallel using the prefix sum and job shift scan is proposed. Computer experiments on GPU were conducted using the CUDA platform. The results indicate multi-thread GPU vs. single-thread GPU speedups of up to 350 and 1000 for job shift scan and makespan calculation algorithms, respectively. Multi-thread GPU vs. single-thread CPU speedups up to 4.5 and 14.7, respectively, were observed as well. The experiments on the Taillard-based problem instances using a simulated annealing solving method and employing the parallel makespan calculation show that the method is able to perform many more iterations in the given time limit and obtain better results than the non-parallel version. [ABSTRACT FROM AUTHOR]

This paper proposes a new decentralized data-driven load restoration (DDLR) scheme for transmission and distribution (TD) systems with high penetration of wind power. Robust DDLR models are constructed in order to handle uncertainties and ensure the feasibility of decentralized schemes. The Wasserstein metric is used to describe the ambiguity sets of probability distributions in order to build the complete DDLR model and realize computationally tractable formulation. A data-driven model-nested analytical target cascading (DATC) algorithm is developed to obtain the final load restoration result by iteratively solving small-scale mathematical models. The proposed DDLR scheme provides load restoration results with adjustable robustness, and performance efficiency is independent from the amount of data. The DDLR scheme makes full use of the available data while respecting information privacy requirements of independent operated systems, and ensures the feasibility of a decentralized load restoration strategy even in the worst-case condition. The effectiveness of the proposed method is validated using a small-scale TD system and a large-scale system with the IEEE 118-bus TS and thirty IEEE-33 DSs, showing high computational efficiency and superior restoration performance. [ABSTRACT FROM AUTHOR]

Most existing ICP (Iterative Closet Point)-based 3D ear recognition approaches resort to the coarse-to-fine ICP algorithms to match 3D ear models. With such an approach, the gallery-probe pairs are coarsely aligned based on a few local feature points and then finely matched using the original ear point cloud. However, such an approach ignores the fact that not all the points in the coarsely segmented ear data make positive contributions to recognition. As such, the coarsely segmented ear data which contains a lot of redundant and noisy data could lead to a mismatch in the recognition scenario. Additionally, the fine ICP matching can easily trap in local minima without the constraint of local features. In this paper, an efficient and fully automatic 3D ear recognition system is proposed to address these issues. The system describes the 3D ear surface with a local feature--the Local Surface Variation (LSV), which is responsive to the concave and convex areas of the surface. Instead of being used to extract discrete key points, the LSV descriptor is utilized to eliminate redundancy flat non-ear data and get normalized and refined ear data. At the stage of recognition, only one-step modified iterative closest points using local surface variation (ICP-LSV) algorithm is proposed, which provides additional local feature information to the procedure of ear recognition to enhance both the matching accuracy and computational efficiency. On an Inter®Xeon®W3550, 3.07 GHz work station (DELL T3500, Beijing, China), the authors were able to extract features from a probe ear in 2.32 s match the ear with a gallery ear in 0.10 s using the method outlined in this paper. The proposed algorithm achieves rank-one recognition rate of 100% on the Chinese Academy of Sciences' Institute of Automation 3D Face database (CASIA-3D FaceV1, CASIA, Beijing, China, 2004) and 98.55% with 2.3% equal error rate (EER) on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2, University of Notre Dame, South Bend, IN, USA, between 2003 and 2005). [ABSTRACT FROM AUTHOR]

This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems. [ABSTRACT FROM AUTHOR]

Stereophonic audio devices employ two loudspeakers and two microphones in order to create a bidirectional sound effect. In this context, the stereophonic acoustic echo cancellation (SAEC) setup requires the estimation of four echo paths, each one corresponding to a loudspeaker-to-microphone pair. The widely linear (WL) model was proposed in recent literature in order to simplify the handling of the SAEC mathematical model. Moreover, low complexity recursive least- squares (RLS) adaptive algorithms were developed within the WL framework and successfully tested for SAEC scenarios. This paper proposes to apply a data-reuse (DR) approach for the combination between the RLS algorithm and the dichotomous coordinate descent (DCD) iterative method. The resulting WL-DR-RLS-DCD algorithm inherits the convergence properties of the RLS family and requires an amount of mathematical operations attractive for practical implementations. Simulation results show that the DR approach improves the tracking capabilities of the RLS-DCD algorithm, with an acceptable surplus in terms of computational workload. [ABSTRACT FROM AUTHOR]

Numerical methods have gained momentum among specific engineering problems that must be solved in such a manner that accuracy and speed are the two most important aspects to consider regarding the output. This paper presents a fast, semi-analytical method (SAM) and original mathematical algorithms to determine the pressure distribution and von Mises stress for spur gears' meshing teeth. The SAM begins with the Hartnett approach, based on Boussinesq's equation for the half-space theory of linear elasticity, which implicitly means an infinite width of the gear flank. To simulate more realistic quarter-space conditions, corrections based on virtual mirror pressure are introduced in the computational algorithm. Mathematical surfaces modeling is an important aspect for spur gears as an intermediate stage to determine the pressure distribution and von Mises stress. Shaft misalignment changes the contact problem from symmetric, in which the half- or quarter-space model can be used, to asymmetric. In the latter case, the model must determine the entire contact area. The obtained output is validated by comparisons between our original FEA results and results from the literature using SAMs and FEA. [ABSTRACT FROM AUTHOR]

Aiming at the low accuracy or inability to solve the problem when the traditional reliability optimization method deals with the practical engineering reliability analysis problem, this paper proposes a structural reliability analysis method using the improved wolf swarm algorithm (IWSA). Firstly, the reliability optimization mathematical model is established with the minimum reliability index as the objective function and the limit state equation as the equality constraint. Then the augmented multiplier method is used to transform the constrained optimization mathematical model into an unconstrained optimization mathematical model. Finally, IWSA is used to solve the problem. The proposed method is applied to a numerical example and an engineering example. The computation results of the proposed method in solving the numerical example are compared with the Monte Carlo simulation (MCS), which verifies that the proposed method in this paper has good calculation accuracy. The maximum deformation under the working condition is used as the target to establish a limit state function, and the reliability index is obtained by the proposed method combining with the finite element analysis software, which provides a certain theoretical guiding significance for the reliability analysis of engineering structures. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicle (USV) formation is a hot topic of current research. Path planning is the core technology for USV formation. This paper focuses on a USV formation path planning problem considering kinetic constraints. Firstly, an improved A* algorithm is proposed to solve the point-to-point path planning of a USV considering kinetic constraints. In this algorithm, the yaw constraint is introduced on top of the position constraint to extend the state space of the USV to three dimensions, and the convergence speed is accelerated by building a heuristic map. The dynamics model of the USV is used to generate the minimum trajectory elements to ensure that the path conforms to the kinetic constraints. Secondly, the mathematical model of USV formation based on the virtual structure method is established, and the path planning scheme of formation navigation and formation reconfiguration is given according to the improved A* algorithm. Finally, we carry out a USV model identification experiment for SL900 USV and simulation experiments based on the model. The experimental results show that the output path of the proposed method is smoother compared with the traditional method. This method can provide a globally safe path with kinetic constraints for USV formation navigation and formation reconstruction. [ABSTRACT FROM AUTHOR]

Brain tumors (BTs) are an uncommon but fatal kind of cancer. Therefore, the development of computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic resonance imaging (MRI) has been the subject of many research papers so far. However, research in this sector is still in its early stage. The ultimate goal of this research is to develop a lightweight effective implementation of the U-Net deep network for use in performing exact real-time segmentation. Moreover, a simplified deep convolutional neural network (DCNN) architecture for the BT classification is presented for automatic feature extraction and classification of the segmented regions of interest (ROIs). Five convolutional layers, rectified linear unit, normalization, and max-pooling layers make up the DCNN's proposed simplified architecture. The introduced method was verified on multimodal brain tumor segmentation (BRATS 2015) datasets. Our experimental results on BRATS 2015 acquired Dice similarity coefficient (DSC) scores, sensitivity, and classification accuracy of 88.8%, 89.4%, and 88.6% for high-grade gliomas. When it comes to segmenting BRATS 2015 BT images, the performance of our proposed CAD framework is on par with existing state-of-the-art methods. However, the accuracy achieved in this study for the classification of BT images has improved upon the accuracy reported in prior studies. Image classification accuracy for BRATS 2015 BT has been improved from 88% to 88.6%. [ABSTRACT FROM AUTHOR]

For the quantitative applications of the Suomi National Polar-orbiting Partnership (SNPP) Advanced Technology Microwave Sounder (ATMS), the geolocation accuracy of its sensor data records must be quantified during its on-orbit operation. In this paper, a refined coastline inflection point method is used to evaluate the on-orbit geolocation accuracy of SNPP ATMS. It is disclosed that for SNPP ATMS, the static error term with scan-angle-dependent feature is a dominant part among all the geolocation error sources. A mathematical model is then developed to convert the in-track and cross-track geolocation errors to the beam pointing Euler angles defined in the spacecraft coordinate system, which can be further used to construct the correction matrix for on-orbit geolocation process. By using the correction matrix built in this paper, the geolocation error is obviously reduced both at nadir and at the edge of the scan. The total geolocation error at nadir before/after correction is 3.8/0.8 km at K-band, 5.6/0.8 km at Ka-band, 3.3/0.4 km at V-band, and 1.5/0.1 km at W-band. The geolocation bias at the edge of the scan line before/after correction is 4.6/1.3 km at K-band, 9.4/1.8 km at Ka-band, 4.4/2.4 km at V-band, and 3.2/0.8 km at W-band. After correction, the scan-angle-dependent feature in geolocation error is also largely reduced. [ABSTRACT FROM AUTHOR]

Generalized linear models (GLMs) are used in high-dimensional machine learning, statistics, communications, and signal processing. In this paper we analyze GLMs when the data matrix is random, as relevant in problems such as compressed sensing, error-correcting codes, or benchmark models in neural networks. We evaluate the mutual information (or "free entropy") from which we deduce the Bayes-optimal estimation and generalization errors. Our analysis applies to the high-dimensional limit where both the number of samples and the dimension are large and their ratio is fixed. Nonrigorous predictions for the optimal errors existed for special cases of GLMs, e.g., for the perceptron, in the field of statistical physics based on the socalled replica method. Our present paper rigorously establishes those decades-old conjectures and brings forward their algorithmic interpretation in terms of performance of the generalized approximate message-passing algorithm. Furthermore, we tightly characterize, for many learning problems, regions of parameters for which this algorithm achieves the optimal performance and locate the associated sharp phase transitions separating learnable and nonlearnable regions. We believe that this random version of GLMs can serve as a challenging benchmark for multipurpose algorithms. [ABSTRACT FROM AUTHOR]

This paper which depended on the foundation of back strip method has established mathematic model to simulate stratum burial history, which considered the computation of stratum skeleton depth based on max depth and the equation-depth point on the procedure to recover stratum by stripping each layer, and put forward the enhanced restoration method taking layers group as studying object. Finally, the method has been applied to build the computer model and program automatic back strip algorithm successfully. Experiments have proved that the method is accurate, applicable, and extensible under the conditions of various complex strata. [ABSTRACT FROM AUTHOR]

The wheel slide protection control system for rail vehicles plays a crucial role in ensuring a consistent braking performance in all operating environments, making it a vital factor in the safety and efficiency of rail transportation. In this paper, a hybrid approach to wheel slide protection control is presented, which combines the rule-based control strategy and the model-based control methods using adhesion force estimation. Model-based control usually relies on mathematical models to characterize the vehicle dynamics, requiring online estimators to be designed or extra sensors to be added for practical application. Rule-based control operates on predefined rules and thresholds and the available data from vehicles in service. A comparative test was conducted between the traditional rule-based control strategy and the proposed combined control strategy using a semi-physical simulation test bench. The performance differences of the control strategies were analyzed from two perspectives: adhesion utilization and air consumption. It was observed that among the traditional 2-phase, 3-phase, 4-phase and the optimized 4-phase combined control method, the combined control strategy has the best adhesion utilization and the traditional 4-phase control strategy has the least air consumption. [ABSTRACT FROM AUTHOR]

Exploring special multi-scroll chaotic systems is meaningful work. This paper studies an expanded multi-scroll chaotic system consisting of eight terms with one nonlinearity. It is generated by modifying the nonlinear term of the newly constructed chaotic system by a polynomial function. The unique mathematical model makes the unstable index-2 equilibria increase in four dimensions, which contributes to the number of scrolls expanding in each phase plane unidirectionally. Dynamic analysis finds that the system can yield complex nested multi-scroll attractors and has single-parameter-based synchronization control of amplitude and offset boosting behavior. Moreover, circuit implementation verifies the physical existence of the proposed system. Also, an image encryption algorithm for remote sensing images is established. Permutation and diffusion operations are performed using new pixel coordinates derived from the data of the chaotic matrix, which comes from the same position as the pixel matrix. Relevant tests suggest that the algorithm is secure and able to resist undesirable interference. • Simple multiscroll chaotic system with various signal control features is proposed. • Complex dynamics, circuit implementation of the proposed system is studied. • New chaos-based image encryption algorithm with high security is designed. [ABSTRACT FROM AUTHOR]

This paper extends the notion of weak convergence in metric spaces to the case of S-metric spaces. Moreover, some results on the weak convergence of fixed point iterations of Banach’s, Kannan’s, Chatterjea’s, Reich’s, Hardy and Roger’s types of contractions on S-metric spaces are obtained. In addition, an example is presented to demonstrate our primary result. [ABSTRACT FROM AUTHOR]

Higher utilization of existing resources and facilities in order to increase efficiency and profitability is always one of the basic challenges for parallel processing systems and environments, and this challenge becomes more complicated when the system resources are heterogeneous. One way to achieve high efficiency and profitability of heterogeneous parallel systems is to schedule tasks optimally. In this paper, an extended tabu search-based scheduling algorithm (ESTS) is presented to improve the profitability of heterogeneous parallel systems, which can achieve suitable solutions in a short computational time. To evaluate the efficiency of the proposed solution, due to the lack of a suitable criterion to evaluate this problem, the obtained results are compared with both the results of an extended scheduling based on a genetic algorithm (ESGA) with a large number of chromosomes and a high number of generations, as well as an extended scheduling based on a simulated annealing algorithm (ESSA) with a linear temperature reduction. The benchmark files of different sizes were tested under the same conditions, and the comparison of results shows the superiority of the proposed solution in terms of profitability and computational time. [ABSTRACT FROM AUTHOR]

A huge amount of construction waste is generated in construction sites every day that needs to be transported by vehicle to disposal facilities for processing. Unlike in most typical transportation problems, once these vehicles are loaded with construction waste, they must travel directly to the disposal facility. Moreover, there are different types of construction waste that may require handling by different disposal facilities. In this paper, we develop a model and algorithm for identifying the optimal transportation routes specific to construction waste transportation. Our results can not only minimize the overall costs for both the logistics company and the contractor but also minimize the distance traveled, thus reducing urban traffic emissions. [ABSTRACT FROM AUTHOR]

A new improved three-dimensional continuous chaotic system (ImproBsys) is designed in this paper. It can achieve from an ordinary chaotic state to a hyperchaotic state, that is, chaotic behavior tends to become more complex. Furthermore, by using ImproBsys, this paper proposes a double image encryption algorithm based on compressive sensing and public key elliptic curve. First, the two plain images of the same size are excuted by discrete wavelet transformation (DWT), and then the DWT coefficients are thresholded. Second, the quantization matrix is compressed by compressive sensing, and the size is reduced to half of the original one, and then the two compressed matrices are spliced together to form a new matrix. Finally, the new matrix is encrypted by elliptic curve cipher to get the cipher image. Our contributions are: (1) A new ImproBsys is designed with better chaotic behavior, which has two positive Lyapunov exponents to show hyperchaos phenomenon. (2) Compressive sensing technique is employed to reduce the amount of data transmission for two single images, and then we use ImproBsys to control the measurement matrix. (3) The initial values of the ImproBsys depend on the information entropy of the plain image by a new constructed mathematical model. [ABSTRACT FROM AUTHOR]

In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise the collected rewards while interacting with their environment while using some prior knowledge that is accessed beforehand. Many BRL algorithms have already been proposed, but the benchmarks used to compare them are only relevant for specific cases. The paper addresses this problem, and provides a new BRL comparison methodology along with the corresponding open source library. In this methodology, a comparison criterion that measures the performance of algorithms on large sets of Markov Decision Processes (MDPs) drawn from some probability distributions is defined. In order to enable the comparison of non-anytime algorithms, our methodology also includes a detailed analysis of the computation time requirement of each algorithm. Our library is released with all source code and documentation: it includes three test problems, each of which has two different prior distributions, and seven state-of-the-art RL algorithms. Finally, our library is illustrated by comparing all the available algorithms and the results are discussed. [ABSTRACT FROM AUTHOR]

This paper deals with signal reconstruction for multichannel high-resolution wide-swath synthetic aperture radar (HRWS-SAR) systems. One of the commonly used algorithms for processing HRWS-SAR data is Krieger's digital beamforming (DBF) algorithm. This algorithm works quite well for cases with uniform space–time sampling as well as moderately nonuniform sampling. However, for cases with highly nonuniform sampling, Krieger's DBF algorithm in its original form suffers from a severe degradation in the signal-to-noise ratio, and for cases with coinciding sampling, this algorithm even fails to function. In this paper, we will solve these problems such that an improved DBF (IDBF) algorithm is developed. The key of the IDBF algorithm consists of two procedures: The first one implements an operation called ambiguity-index screening, which is based on two novel quantities, termed the equivalent sampling spacing and the effective reconstructed Doppler bandwidth; and the other key procedure implements a Doppler-spectrum weighting based on a newly defined parameter that is termed the maximum-to-minimum ratio of the noise spectrum. Simulation results show that the newly proposed IDBF algorithm shares a pronounced improvement in the imaging performance over Krieger's DBF algorithm. Moreover, simulation results also demonstrate that the IDBF algorithm exhibits a more robust and consistent overall performance than several other existing reconstruction algorithms. [ABSTRACT FROM PUBLISHER]

This paper presents a closed-form noniterative transformation of the Maxwell-Garnett mixing rule for biphased mixtures to the triple-pole Debye relaxation formula. For the first time, it is formally proven that such a transformation is complete for conductive constituent materials. In other words, the Maxwell-Garnett representation of any biphased mixture of any conductive materials always has its formal equivalent in the Debye form with three poles at most. For specific aspect ratios of ellipsoidal inclusions, the number of poles reduces to one or two, which is formally proven herein, while in previous studies, a single-pole Debye model was arbitrarily assumed. The proposed transformation provides Debye parameters as an explicit function of a mixture composition, which is competitive to alternative techniques based on laborious curve-fitting algorithms. The newly proposed approach is of particular importance to time-domain modeling of dilute mixtures, where the Maxwell-Garnett mixing rule is usually approximated with available dispersive models. Computational examples given in this paper show advantages of the presented method over previous Maxwell-Garnett to Debye conversion algorithms, in terms of accuracy, robustness, and computational cost. [ABSTRACT FROM AUTHOR]

Abstract  The models for finite element analysis usually have a number of complex geometric features such as holes, slots, and curved surfaces, etc. The exact description of these features is crucial to the accuracy of finite element analysis. This paper presents an algorithm for the adaptive generation of the initial hexahedral element mesh based on the geometric features of the solid model. This algorithm is a modified grid-based method, in which a new geometry-adaptive hexahedral mesh generation method and a new mesh boundary matching method called threading method are established and employed. Firstly, an initial grid structure, which is uniformly sized or locally refined and encompasses the solid model completely, is generated based on the geometric features of the solid model. Then, an initial indention mesh is generated after removing all the elements in the exterior of the solid model using the even-odd rule. The surface of the indention mesh, which is adaptively generated based on the geometric features, is so complicated and different in size that the match of the edges and surface to the surface of the solid model becomes inconvenient. In this case, the closest position tracing approach used widely is difficult to realize an accurate match. The threading method proposed in this paper can accurately match the boundaries of the indention mesh to the boundaries of the solid model. Especially, it is fit for the generation of the geometry-adaptive mesh and avoids the recurrent search process of characteristic boundary nodes in the closest position tracing method. So the efficiency of the finite element mesh generation is enhanced remarkably. The principles and procedures of the algorithm are illustrated through the geometry-adaptive hexahedral mesh generation of a typical three-dimensional solid model. The effectiveness and robustness of the algorithm are tested by several applications in the complex solid models. [ABSTRACT FROM AUTHOR]

This paper continues formalization in the Mizar system [2, 1] of basic notions of the composition-nominative approach to program semantics [14] which was started in [8, 12, 10]. The composition-nominative approach studies mathematical models of computer programs and data on various levels of abstraction and generality and provides tools for reasoning about their properties. In particular, data in computer systems are modeled as nominative data [15]. Besides formalization of semantics of programs, certain elements of the composition-nominative approach were applied to abstract systems in a mathematical systems theory [4, 6, 7, 5, 3]. In the paper we give a formal definition of the notions of a binominative function over given sets of names and values (i.e. a partial function which maps simple-named complex-valued nominative data to such data) and a nominative predicate (a partial predicate on simple-named complex-valued nominative data). The sets of such binominative functions and nominative predicates form the carrier of the generalized Glushkov algorithmic algebra for simple-named complex-valued nominative data [15]. This algebra can be used to formalize algorithms which operate on various data structures (such as multidimensional arrays, lists, etc.) and reason about their properties. In particular, we formalize the operations of this algebra which require a specification of a data domain and which include the existential quantifier, the assignment composition, the composition of superposition into a predicate, the composition of superposition into a binominative function, the name checking predicate. The details on formalization of nominative data and the operations of the algorithmic algebra over them are described in [11, 13, 9]. [ABSTRACT FROM AUTHOR]

This paper continues formalization in Mizar [2, 1] of basic notions of the composition-nominative approach to program semantics [13] which was started in [8, 11]. The composition-nominative approach studies mathematical models of computer programs and data on various levels of abstraction and generality and provides tools for reasoning about their properties. Besides formalization of semantics of programs, certain elements of the composition-nominative approach were applied to abstract systems in a mathematical systems theory [4, 6, 7, 5, 3]. In the paper we introduce a definition of the notion of a binominative function over a set D understood as a partial function which maps elements of D to D. The sets of binominative functions and nominative predicates [11] over given sets form the carrier of the generalized Glushkov algorithmic algebra [14]. This algebra can be used to formalize algorithms which operate on various data structures (such as multidimensional arrays, lists, etc.) and reason about their properties. We formalize the operations of this algebra (also called compositions) which are valid over uninterpretated data and which include among others the sequential composition, the prediction composition, the branching composition, the monotone Floyd-Hoare composition, and the cycle composition. The details on formalization of nominative data and the operations of the algorithmic algebra over them are described in [10, 12, 9]. [ABSTRACT FROM AUTHOR]

Critical process loads need to be safeguarded against power system disturbances and erroneous operations caused by human factors. During such events, a fast synchronism-supervised transfer scheme is required to ensure continuous operation. This paper discusses the implementation of a fast motor bus transfer scheme using existing protective relays. The scheme is designed to respond to local and remote (upstream) events in the substation. This makes possible an attempt to initiate a fast transfer for events that are remote to the motor bus. On a failure to achieve a fast transfer, the scheme resorts to an in-phase transfer and, if that fails, finally a residual transfer. Real-time dynamic modeling was performed to simulate motor bus decay and reacceleration. This paper provides the results of the simulations and the response of the transfer scheme. [ABSTRACT FROM AUTHOR]

Terahertz (THz) band communication is envisioned as a key technology to satisfy the increasing demand for ultrabroadband wireless systems, thanks to its ultrabroad bandwidth. Tailored for the unique properties of pulse-based communications in the THz band, two timing acquisition algorithms are proposed and analyzed thoroughly in this paper. First, a low-sampling-rate (LSR) synchronization algorithm is proposed, by extending the theory of sampling signals with finite rate of innovation in the communication context and exploiting the properties of the annihilating filter. The simulation results show that the timing accuracy at an order of ten picoseconds is achievable. In particular, the LSR algorithm has high performance with uniform sampling at $1/20$ of the Nyquist rate when the signal-to-noise ratio (SNR) is high (i.e., greater than 18 dB). Complementary to this, a maximum likelihood (ML) approach for timing acquisition is developed, which searches for the timing offsets by adopting a two-step acquisition procedure based on the ML criterion. The simulation results show that the ML-based algorithm is well suitable in the low SNR case with a half-reduced search space. For further evaluation, the error performance and the resulting bit-error-rate sensitivity to the timing errors in the LSR and the ML algorithms are both analytically and numerically studied. This paper provides very different and promising angles to efficiently and reliably solve the timing acquisition problem for pulse-based THz band wireless systems. [ABSTRACT FROM PUBLISHER]

The nanoindentation technique is employed to characterize the behaviors of biomaterials. Nevertheless, there is a lack of development of a miniaturized precise positioner for in situ nanoindentation. Besides, modeling behaviors of the positioner are restricted due to its complex kinematic characteristics. Therefore, this paper proposes a novel compliant two degrees of freedom (dof) stage for positioning a biomaterial sample in in situ nanoindentation. In addition, a new modeling and dimensional optimization synthesis method of the stage is developed. The proposed effective methodology is developed based on a kinetostatic analysis-based calculation method, the Lagrange approach, and a neural network algorithm. With an increased advance in artificial intelligence, a neural network algorithm is proposed to extend the applicability of artificial neural networks in optimizing the parameters of the stage. First, the 2-dof stage is built via a combination of an eight-lever displacement amplifier and a symmetric parallelogram mechanism. Second, a chain of mathematical equations of the 2-dof stage is constructed using a kinetostatic analysis-based method to calculate the ratio of displacement amplification and the input stiffness of the 2-dof stage. Then, the Lagrange method is utilized to formulate the dynamic equation of the 2-dof stage. Finally, a neural network algorithm is adopted to maximize the natural first frequency of the proposed stage. The optimal results determined that the frequency of the stage can achieve a high value of 112.0995 Hz. Besides, the formed mathematical models were relatively precise by comprising the simulation verifications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vehicle and airborne navigation users are facing more and more complex signal interference and even spoofing. If vehicles and aircraft do not strengthen their anti-spoofing ability, their navigation performance is bound to be adversely affected. global navigation satellite system (GNSS) spoofing technology has gradually become a preferred interference method for spoofer because of its high concealment and great harm. For spoofer, user terminal increasingly adopts GNSS with inertial measurement unit (IMU): tightly-coupled GNSS/IMU, on this basis, user also configures a variety of anti-spoofing techniques to effectively deal with spoofing. Even if spoofer slowly changes user's positioning, if spoofing strategy is unreasonable, it will lead to the violation of parameter rationality of coupled filter output parameters and spoofing observation, which greatly increases the difficulty of spoofing. In view of the above problems, from the perspective of spoofer, in order to effectively counter the non cooperative target of assembling tightly-coupled GNSS/IMU by using GNSS spoofing, this paper establishes GNSS spoofing mathematical model, and proposes a slowly varying spoofing algorithm to avoid tightly-coupled GNSS/IMU with multiple anti-spoofing techniques based on the analysis of the influence mechanism of spoofing on the positioning of tightly-coupled GNSS/IMU, the algorithm proposes a measurement deviation determination method to avoid a variety of anti-spoofing techniques, which can gradually pull the positioning results of coupled system, and successfully avoid anti-spoofing techniques detection of least squares residual receiver autonomous integrity monitoring (RAIM) and parameter rationality check. The experimental results show that the algorithm can gradually change positioning of tightly-coupled GNSS/IMU within 30 s, and the north, east and down displacements basically achieve the spoofing effect, the errors with the expected offset are −0.5 m, 1.9 m and 12.7 m respectively. At the same time, the detection of the above anti-spoofing techniques is avoided. The mean value of test statistics for tightly-coupled system is reduced by 75.4% and does not exceed the alarm threshold, so as to achieve the purpose of spoofing, the effectiveness and high concealment of the spoofing algorithm are proved. [ABSTRACT FROM AUTHOR]

As the competition between enterprises intensifies, employees have witnessed a decline of psychological health year by year and severe anxiety and depression. To ensure the normal work of employees, it is important to implement psychological occupational therapy intervention (POTI). The previous studies on corporate occupational values focus on the behavioral variables and attitude variables related to occupations. The research paradigm does not suit a specific group of employees. Therefore, this paper explores the individual differences in POTI guided by the realization of occupational values. For a specific group of employees, the correlations of the psychological problems with job attitude, job involvement, and psychological health level were reasoned logically, a theoretical model was established, and the full-model map was plotted to illustrate the influence of the realization of occupational values over POTI. Then, the evaluation data on the psychological intervention effect of 2,800 employees were explored based on multikernel learning, and an individual difference prediction model was constructed through multikernel learning. Finally, the predicted results of different groups of employees were compared, revealing the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a fault-tolerant control method is proposed for a hexacopter under uncertainties. The proposed method is based on adaptive-sliding-mode control (ASMC) and a control allocation scheme. First, a mathematical model of the hexacopter is employed with model uncertainties. Next, the control allocation strategy is combined with ASMC to handle actuator faults, which can distribute the virtual control signal to redundant actuators. A modified fault-tolerant control is proposed to overcome this virtual input saturation. Finally, the system stability is validated using the Lyapunov theory. The performance of the proposed method is compared with that of normal ASMC. The simulation results show that the suggested strategy can realize quicker compensation under faulty conditions. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyse, model, and solve the rescheduling problem in dynamic permutation flow shop environments while considering several criteria to optimize. Searching optimal solutions in multiobjective optimization problems may be difficult as these objectives are expressing different concepts and are not directly comparable. Thus, it is not possible to reduce the problem to a single-objective optimization, and a set of efficient (nondominated) solutions, a so-called Pareto front, must be found. Moreover, in manufacturing environments, disruptive changes usually emerge in scheduling problems, such as machine breakdowns or the arrival of new jobs, causing a need for fast schedule adaptation. In this paper, a mathematical model for this type of problem is proposed and a restarted iterated Pareto greedy (RIPG) metaheuristic is used to find the optimal Pareto front. To demonstrate the appropriateness of this approach, the algorithm is applied to a benchmark specifically designed in this study, considering three objective functions (makespan, total weighted tardiness, and steadiness) and three classes of disruptions (appearance of new jobs, machine faults, and changes in operational times). Experimental studies indicate the proposed approach can effectively solve rescheduling tasks in a multiobjective environment. [ABSTRACT FROM AUTHOR]

In view of the poor performance of the original mathematical model of assembly construction project precost budget, a mathematical model of assembly construction project precost budget based on improved neural network algorithm is proposed. This paper investigates the cost content of assembly construction project and analyzes its early cost. It finds that the early cost of assembly construction project includes component production cost, transportation component cost, and installation component cost. Based on the improved neural network algorithm to build an improved neural network model, the improved neural network model to mine the cost data in the early stage of assembly construction project is used. In this paper, the earned value variable is introduced to transform the project duration and project cost in the early stage of the prefabricated construction project into quantifiable cost data, and the earned value analysis method is used to estimate the implementation cost of the prefabricated construction project. According to the result of cost estimation, the mathematical model of precost budget of prefabricated construction project is built based on the project parameters. In order to prove that the cost budget performance of the mathematical model based on the improved neural network algorithm in the early stage of assembly construction project is better, the original mathematical model is compared with the mathematical model, the experimental results show that the cost budget performance of the model is better than the original model, and the cost budget performance is improved. [ABSTRACT FROM AUTHOR]

An issue of improving flight safety during landing with an inertial navigation system (INS) and a failed barometric altimeter is considered. In this paper, we propose a specific algorithm for in-flight calibration of the vertical channel of INS. Accordingly, the baro-inertial integration algorithm using a discrete five-state Kalman filter will be performed during a particular flight maneuver before landing. As a result, it is possible to estimate not only the bias of vertical accelerometer but also its scale factor, which is too small to be defined by a usual in-flight calibration algorithm. After applying the proposed algorithm, the flight management system can provide a safe landing with a standalone INS. The algorithm's performance is assessed by simulating complete mathematical models of aircraft motion and control systems. The impact of calibrated bias and scale factor of vertical accelerometer on the altitude estimation error is provided through an analysis. [ABSTRACT FROM AUTHOR]