Optimal method for auto-body closure panel fitting using Hausdorff distance criteria

We evaluated the effects of presurgical nasoalveolar molding (NAM) with an orthopedic appliance and compared them with a passive orthopedic method (Hotz plate, HP), focusing on the naris morphology and width of the alveolar and palate cleft gap. The subjects were 28 unilateral complete cleft lip and palate patients treated with primary cheiloplasty at Tsukuba University Hospital from 2004 to 2011. Thirteen patients were treated preoperatively with NAM (NAM group), and 15 with HP (HP group). The surgical outcome was assessed according to left-right naris symmetry, as measured by the area ratio, perimeter ratio, aspect a/u ratio (aspect ratio of the affected side/aspect ratio of the unaffected side), and Hausdorff distance. In addition, the alveolar and palate cleft width was measured at the times of orthopedic plate setting and primary cheiloplasty. The aspect ratio was significantly smaller in the NAM group than in the HP group before the operation. In both groups, the aspect ratio, perimeter ratio, and Hausdorff distance were significantly smaller after the operation than before. The width of the alveolar and palate cleft gap was significantly narrowed in the NAM group, and the cleft gap at the initiation of NAM correlated significantly with the Hausdorff distance after cheiloplasty. We found that NAM improved the form of the naris after primary cheiloplasty and decreased the palate cleft gap more effectively than HP and that the width of the palate cleft gap was correlated with the surgical outcome of the naris.

Response surface methodology's steepest ascent and step size revisited

This work presents a low-complexity high-performance motion estimation method using a one-dimensional (1-D) gradient descent search. The proposed method consists of initial-point determination, initial-direction determination, a gradient descent search using variable step sizes and conjugate directions, and convergence checking. For each block, one of the original and predictive points with the best block-matching performance is the initial point where the predictive ones are motion vectors of its neighboring four searched blocks. In addition, the searching path is optimized by determining the initial direction based on the vector from the original point to the initial point. After determining the initial point and direction, the gradient descent search using variable step sizes and conjugate directions is performed until the searched point attains a better performance than its neighboring points. Moreover, variable step sizes for moving and slowly moving, or stationary blocks, are well addressed to improve the convergence performance of a gradient descent search. Simulation results demonstrate that our method yields a superior performance in terms of computational complexity and picture quality as compared to the three-step search, 1-D full search, block-based gradient descent search, and one-at-a-time search methods.

A new measure for evaluating fit quality of mating parts via Fit Capability Indices (FCIs) is introduced as an interface between fit quality and process control of the mating parts. Fit Capability Analysis method is discussed based on the relationship between FCIs and Process Capability Indices (PCIs). A new statistical tolerancing approach for assuring predetermined fit quality is developed on the basis of fit quality evaluation measures and fit capability analysis, which can be used as a guideline to process design and process monitoring of the mating parts.

Recent advances in the flash memory technology call for more efficient error-correction codes (ECCs) than the conventional, yet very popular, ones such as BCH codes. The ability to make multiple voltage reads allows one to estimate soft values at the time of decoding, which in turn makes soft-decision ECCs such as LDPC codes a suitable candidate for implementation in flash memories. On the other hand, fully utilizing the potential of soft decision based codes demands higher precision memory sensing, which introduces a trade-off between the read latency and the error probability. In this paper, we explore and compares two approaches to optimize the positioning as well as the number of read (word-line) voltages for a specified program/erase (PE) cycle.In the first approach, we aim for selecting those read thresholds that maximize the mutual information (MMI) of the equivalent discrete memoryless channel. By utilizing conventional optimization methods such as the gradient descent (GD), we are able to find the optimal read locations for any number of read probes. Our simulation results show that ~20 reads are effective. Next, we redesign our optimization problem to take the LDPC code structure into account. To do so, we use discretized density evolution (DDE) as a proxy for bit error rate (BER), which serves as our cost function in the GD search. To overcome the problem of local minima, we propose a two-step optimization: MMI for coarse optimization, followed by DDE for fine optimization. Simulation results confirm the effectiveness of this method. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

One of the earliest and the best method to minimize a function is the classical steepest descent (SD) method. In this paper, a new modification of SD method is suggested using a new search direction, d k. The numerical results are presented based on number of iterations and CPU time. It shows that the new d k are efficient when compared to the classical SD.

This paper proposes a new method for voltage and reactive power control in power systems. The proposed method is based on tabu search that is effective for combinatorial problems. Tabu search makes use of the idea of the hill-climbing method, but some search directions are fixed to escape from local minima. To improve the solution accuracy, a parallel scheme for tabu search is developed so that the neighborhood is computed in a parallel way with different tabu lengths. In this paper, the proposed method is applied to voltage and reactive power control in power systems. The efficiency of the proposed method is demonstrated in a sample system.

This paper presents a benchmarking study on the steepest descent (SD) method considering three different line search conditions including Backtracking (BC), Armijo-Backtracking (ABC) and Goldstein (GC) in nonlinear least squares fitting of measured data obtained from coordinate measuring machine (CMM). Within this scope, five primitive geometries such as circle, square, rectangle, triangle and ellipse were built via 3D printer. Those geometries were then scanned with CMM to acquire their 2D profiles to be fitted. To find best fitting parameters for each geometry, the nonlinear least squares approach along with the above-mentioned optimization method-line search condition combinations were employed. During the fitting process, the total number of function evaluations, when the combination converges to required tolerance, were used as a performance metric of the combination in question. With those data, the performance and data profiles for each combination were created to be able to carry out a reliable performance evaluation. Based on those profiles, it has been seen that the SD-ABC combination is the fastest one. In addition, it is successful on all the geometries while the others are not. For the second fastest combination, the SD-BC combination stands out. However, its successful rate is only 80%, which means it fails on a geometry. On the other hand, the SD-GC combination takes the last place in this study. All those results have shown that the line search conditions have a great contribution to the success and performance of the optimization algorithm being used. Besides, their performance may differ from problem-to-problem. The end-users should consider these facts to find best optimization method-line search condition combination for their problems.

The uncertainty in the new power system has increased, leading to limitations in traditional stability analysis methods. Therefore, considering the perspective of the three-dimensional static security region (SSR), we propose a novel approach for system static stability analysis. To address the slow training speed of traditional deep learning algorithms using batch gradient descent (BGD), we introduce an improved stochastic–batch gradient descent (S-BGD) search method that combines the advantages of stochastic gradient descent (SGD) in fast training. This method ensures both speed and precision in parameter training. Moreover, to tackle the problem of getting trapped in local optima and saddle points during parameter training, we draw inspiration from kinematic theory and propose a gradient pile (GP) training method. By utilizing accumulated gradients as parameter corrections, this method effectively avoids getting stuck in local optima and saddle points, thereby enhancing precision. Finally, we apply the proposed methods to construct the static security region for the IEEE-118 new power system using its data as samples, demonstrating the effectiveness of our approach.

This paper proposes a three-parameter gearshift scheduling strategy that has been implemented on both large and small electric vehicles with two-speed transmission systems. The new strategy evaluates vehicle performance under varying driving conditions on flat and hilly roads by assessing the vehicle speed, acceleration, and road grade. A heuristic approach is used to develop two gearshift schedules for vehicle acceleration and road grade, and gradient descent and pattern search methods are applied to optimize the gear ratios and primary gearshift schedules. The results show that the proposed gearshift strategy saves 16.5% of energy on hilly roads compared to conventional approaches. Optimal gearshift schedules for acceleration provide more room for second gear operation, while optimized gearshift schedules for the road grade increase the buffer zone for larger vehicles and allow more space for the second gear operating area. The experimental results validate the proposed approach’s performance for both large and small electric vehicles.

In this study, we tend to propose a replacement hybrid algorithmic rule which mixes the search directions like Steepest Descent (SD) and Quasi-Newton (QN). First, we tend to develop a replacement search direction for combined conjugate gradient (CG) and QN strategies. Second, we tend to depict a replacement positive CG methodology that possesses the adequate descent property with sturdy Wolfe line search. We tend to conjointly prove a replacement theorem to make sure global convergence property is underneath some given conditions. Our numerical results show that the new algorithmic rule is powerful as compared to different standard high scale CG strategies.

This paper proposes a recursive Levenberg-Marquardt (LM) search direction as the training algorithm for non-linear adaptive filters, which use multi-layer feed forward neural nets as the filter structures. The neural nets can be considered as a class of non-linear adaptive filters with transversal or recursive filter structures. In the off-line training, the LM method is regarded as an intermediate method between the steepest descent (SD) and Gauss-Newton (GN) methods, and it has better convergence properties than the other two methods. In the echo cancellation experiments, the recursive LM algorithm converges faster and gives higher echo return loss enhancement (ERLE) than the recursive SD and GN algorithms.

Due to large dimensionality of typical field optimization problems (in terms of either the number of the optimization variables or the number of equations that needs to be solved at every simulation time-step), most approaches for field optimization are local iterative methods. Due to their local nature, achievable optima by these methods are directly impacted by the choice of the initial guess for the sought optimal profiles. Here we propose a simple, low-cost framework to efficiently initialize localsearch optimization algorithms, with the focus on waterflooding optimization under a standard net-present value (NPV) objective. The procedure relies on accepted reservoir engineering concepts and consists of two sequential steps. First, a common economical 'shut-in' water-cut (WCT) is determined by rewriting the (assumed) positive NPV contribution of the individual completion in terms of WCT. If the WCT reached during the production from a given completion is lower than the economical 'shut-in' WCT the objective function is insensitive to that particular control variable. An adjusted shut-in WCT is therefore defined for each completion as the minimum of the economic WCT and the 'reached' WCT. Then, using these shut-in WCT values in a (rather limited) number of reservoir simulation runs, coefficients in a group production guide rate formula for the preferred phase are determined yielding a maximized NPV. Such a guide rate formula is normally implemented in all state of the art reservoir simulators. The guide rate coefficients and the economic and adjusted WCT values are used to initialize the WCT optimization problem. The initialization strategy was applied to history matched models of the Brugge field [1]. The optimization algorithms were Hooke-Jeeves (HJ), Nelder-Mead and Sequential Quadratic Programming. With any of these methods, the optimized shut-in WCTs in terms of the achieved NPV clearly outperformed the best NPV solution obtained so far on these history matched models (HJ being the best choice). The combined economic and adjusted WCT gave additional improvement in NPV. The injecting strategy was chosen to be the unity voidage replacement ratio, which decreased the number of variables considerably. Introduction Finding an optimal depletion strategy for hydrocarbon production has naturally always been a key subject in reservoir management, the underlying problem to be solved generally being maximization of a key quantity as oil production, net present value (NPV), etc. In the past, optimal settings of the optimization parameters were almost exclusively determined in a manual fashion, which generally is a quite time consuming procedure with a high likelihood of obtaining suboptimal results. While manual approaches are still predominant strategies in the reservoir management practice, due to maturity of most existing major oilfields and a gradual decrease in large oil discoveries, research for more systematic optimization approaches has been initiated. Not surprisingly, most 'advances' have so far been made in systematic optimization, at least in theory, of waterflooding recovery processes, see e.g. Yeten et al. (2003); Brouwer and Jansen (2004); Sarma et al. (2005); Lorentzen et al. (2006); Wang et al. (2007); Chen et al. (2008); Sarma et al. (2008), Sarma and Chen (2008) [2–9]. The spectrum of the proposed (waterflooding) optimization techniques is already quite broad. Most of the proposed methods are local-search iterative methods, ranging from basic steepest descent iterative updates of the control variables involving either deterministic or stochastic objectivefunction gradient/sensitivity approximations, e.g. Wang et al. (2007); Chen et al. (2008) [6–7] to 'direct search' methods, as e.g. Nelder-Mead or Hooke-Jeeves [10–11], not requiring any derivative information.

SUMMARY A fully automated method for obtaining multimode phase speed measurements from a single seismogram has been developed and applied to a large data set of three-component longperiod seismograms in North America, constructing high-resolution phase speed maps on a continental scale. The method of our phase speed estimation is based on a fully non-linear waveform inversion by Yoshizawa & Kennett working with a global search method (the Neighbourhood Algorithm). The entire process of waveform fitting and the evaluation of the estimated phase speed have been fully automated employing several empirical quantitative measures, assessing the quality of waveform fit and the relative contributions of each mode in a chosen time window. The measured phase speed data undergo automatic screening for quality control, comprising the threshold evaluation of their reliability and outlier detection and removal. This new automated method has been applied to a large data set recorded at North American stations, including the latest transportable stations of USArray. Using long-period three-component seismograms recorded during the past eight years, we have successfully retrieved large numbers of regional surface wave paths, including over 20000 paths for the fundamental-mode Rayleigh waves over a wide range of frequencies, and over 10000 paths for the higher mode Rayleigh as well as the fundamental-mode Love waves. The consistentresultsoftheautomatedmeasurementproceduresuggestthatthemethodworkswell at regional distances, allowing us to perform a high-resolution mapping of multimode phase speeds in North America. The results of the automated waveform analysis also indicate some intrinsic limitations in the higher mode phase speed measurements from a single seismogram particularly in the short period range, mainly due to the overlapping of higher mode arrivals as well as coupling between mode branches. Despite such an innate difficulty in the higher mode dispersion measurements, the automated method allows us to construct reliable multimode phase speed maps. The current data set of ray paths is significantly biased towards the western half of North America, resulting in non-uniform horizontal resolution across the continent. This issue will be resolved by the future migration of the USArray stations to cover the central and eastern United States. The new automated method can be a useful tool for high-resolution mapping of regional 3-D shear wave structure including possible anisotropy.

Internet of Things applications can be represented as workflows in which stream and batch processing are combined to accomplish data analytics objectives in many application domains such as smart home, health care, bioinformatics, astronomy, and education. The main challenge of this combination is the differentiation of service quality constraints between batch and stream computations. Stream processing is highly latency-sensitive while batch processing is more likely resource-intensive. In this work, we propose an end-to-end hybrid workflow scheduling on an edge cloud system as a two-stage framework. In the first stage, we propose a resource estimation algorithm based on a linear optimization approach, gradient descent search (GDS), and in the second stage, we propose a cluster-based provisioning and scheduling technique for hybrid workflows on heterogeneous edge cloud resources. We provide a multi-objective optimization model for execution time and monetary cost under constraints of deadline and throughput. Results demonstrate the framework performance in controlling the execution of hybrid workflows by efficiently tuning several parameters including stream arrival rate, processing throughput, and workflow complexity. In comparison to a meta-heuristics technique using Particle Swarm Optimization (PSO), the proposed scheduler provides significant improvement for large-scale hybrid workflows in terms of execution time and cost with an average of 8% and 35%, respectively.