Measurement Error Detection, Elimination Method and its Application

2012 ◽  
Vol 239-240 ◽  
pp. 990-993
Author(s):  
Xiao Ping Ren ◽  
Zhi Chao Xue ◽  
Ke Zhao
Keyword(s):  
Measurement Error ◽  
Cost Function ◽  
Error Detection ◽  
Comparison Method ◽  
Raw Data ◽  
Elimination Method ◽  
Matching Process ◽  
Set Up ◽  
The Cost ◽  
Key Techniques

Bullet mark comparison is one of the key techniques in the discovery of evidences. A bullet mark comparison method was presented in this paper. First the bullet marks data are gathered according to generally used equipment. There are many coarse data cannot be used in matching process, so a preprocessing on the raw data is need. A statistics modeling is set up to construct a cost function, which can be used to evaluation choose of error until the cost function get minimum value. Experiment showed this comparison method can solve comparison problems of bullet mark.

scholarly journals Automatic Registration and Error Detection of Multiple Slices Using Landmarks

2001 ◽  
Vol 23 (3-4) ◽  
pp. 159-165
Author(s):  
Hans Frimmel ◽  
Lars Egevad ◽  
Christer Busch ◽  
Ewert Bengtsson
Keyword(s):  
Cost Function ◽  
Error Detection ◽  
3D Structure ◽  
Automatic Registration ◽  
Registration Method ◽  
Automated Method ◽  
Current State ◽  
Manual Registration ◽  
The Cost

Objectives. When analysing the 3D structure of tissue, serial sectioning and staining of the resulting slices is sometimes the preferred option. This leads to severe registration problems. In this paper, a method for automatic registration and error detection of slices using landmark needles has been developed. A cost function takes some parameters from the current state of the problem to be solved as input and gives a quality of the current solution as output. The cost function used in this paper, is based on a model of the slices and the landmark needles. The method has been used to register slices of prostates in order to create 3D computer models. Manual registration of the same prostates has been undertaken and compared with the results from the algorithm. Methods. Prostates from sixteen men who underwent radical prostatectomy were formalin fixed with landmark needles, sliced and the slices were computer reconstructed. The cost function takes rotation and translation for each prostate slice, as well as slope and offset for each landmark needle as input. The current quality of fit of the model, using the input parameters given, is returned. The function takes the built‐in instability of the model into account. The method uses a standard algorithm to optimize the prostate slice positions. To verify the result, s standard method in statistics was used. Results. The methods were evaluated for 16 prostates. When testing blindly, a physician could not determine whether the registration shown to him were created by the automated method described in this paper, or manually by an expert, except in one out of 16 cases. Visual inspection and analysis of the outlier confirmed that the input data had been deformed. The automatic detection of erroneous slices marked a few slices, including the outlier, as suspicious. Conclusions. The model based registration performs better than traditional simple slice‐wise registration. In the case of prostate slice registration, other aspects, such as the physical slicing method used, may be more important to the final result than the selection of registration method to use.

Research on the Method of Reliability Allocation of Diesel Engine Base on the Cost Function

2012 ◽  
Vol 271-272 ◽  
pp. 1115-1120
Author(s):  
Jia Li ◽  
Ji Ze Guo ◽  
Hai Qing Zhou ◽  
You Wen Wei
Keyword(s):  
Diesel Engine ◽  
Cost Function ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Minimum Cost ◽  
Copula Function ◽  
Reliability Allocation ◽  
Cost Coefficient ◽  
Set Up ◽  
The Cost

In this paper, cost coefficient is introduced by using the technology of FMECA and FTA, and the DM model is proposed, the parameters of cost function are determined by applying the comprehensive evaluation method, the system reliability correlation model is set up by using copula function. the model is nonlinear programming, and the minimum cost is the goal of the model. The reliability allocation for diesel engine is completed by use of genetic algorithm. Finally, the feasibility and effectiveness of the model are verified through example.

REKAYASA NILAI HARGA PEMBUATAN TERHADAP EFEKTIFITAS PENGGUNAAN BAHAN PADA PEMBANGUNAN PERUMAHAN TIRTO PENATARAN ASRI TIPE 70/135 KECAMATAN NGLEGOK KABUPATEN BLITAR

2017 ◽  
Vol 7 (1) ◽  
pp. 43-52
Author(s):  
Mochamad Tamim Ma’ruf
Keyword(s):  
Engineering Application ◽  
Cost Savings ◽  
Comparison Method ◽  
Value Engineering ◽  
Design Work ◽  
Housing Projects ◽  
Initial Design ◽  
A Value ◽  
The Cost ◽  
Design Proposal

One-solving methods and techniques necessary to avoid inefficiencies and not economic costs as well as reduce the cost of housing construction is the method of Value Engineering. Value engineering is a method and cost control techniques to analyze a function to its value at the lowest cost alternative (most economical) without reducing the quality desired.At the writing of this study used a comparison method by comparing the initial design to the design proposal of the author. In the housing projects Upgrading Tirto Penataran Asri type 70, the application of Value Engineering conducted on the job a couple walls and roofs pair by replacing some work items with a more economical alternative but does not change the original function and high aesthetic level and still qualify safe. For that performed the step of determining a work item, the alternative stage, the analysis stage, and the stage of recommendations to get a Value Engineering application and cost savings against the wall a couple of work items and partner roof.The proposed design as compared to the initial design. Work items discussed was the work of a couple wall having analyzed obtained savings of Rp. 2,747,643.56 and the work of the roof pair obtained savings of Rp. 2,363,446.80. Thus the total overall savings gained is Rp 5,111,090.36 or savings of 0048%.

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

Sorting permutations by fragmentation-weighted operations

2020 ◽  
Vol 18 (02) ◽  
pp. 2050006 ◽  
Author(s):  
Alexsandro Oliveira Alexandrino ◽  
Carla Negri Lintzmayer ◽  
Zanoni Dias
Keyword(s):  
Approximation Algorithms ◽  
Computational Biology ◽  
Cost Function ◽  
Traditional Approach ◽  
Upper Bounds ◽  
Evolutionary Distance ◽  
Lower And Upper Bounds ◽  
Approximation Factor ◽  
New Type ◽  
The Cost

One of the main problems in Computational Biology is to find the evolutionary distance among species. In most approaches, such distance only involves rearrangements, which are mutations that alter large pieces of the species’ genome. When we represent genomes as permutations, the problem of transforming one genome into another is equivalent to the problem of Sorting Permutations by Rearrangement Operations. The traditional approach is to consider that any rearrangement has the same probability to happen, and so, the goal is to find a minimum sequence of operations which sorts the permutation. However, studies have shown that some rearrangements are more likely to happen than others, and so a weighted approach is more realistic. In a weighted approach, the goal is to find a sequence which sorts the permutations, such that the cost of that sequence is minimum. This work introduces a new type of cost function, which is related to the amount of fragmentation caused by a rearrangement. We present some results about the lower and upper bounds for the fragmentation-weighted problems and the relation between the unweighted and the fragmentation-weighted approach. Our main results are 2-approximation algorithms for five versions of this problem involving reversals and transpositions. We also give bounds for the diameters concerning these problems and provide an improved approximation factor for simple permutations considering transpositions.

Maximum Likelihood Ensemble Filter: Theoretical Aspects

2005 ◽  
Vol 133 (6) ◽  
pp. 1710-1726 ◽  
Author(s):  
Milija Zupanski
Keyword(s):  
Maximum Likelihood ◽  
Data Assimilation ◽  
Cost Function ◽  
Ensemble Data Assimilation ◽  
Error Covariance ◽  
Ensemble Data ◽  
Analysis Error ◽  
Nonlinear Observation ◽  
The Cost ◽  
Maximum Likelihood Ensemble Filter

Abstract A new ensemble-based data assimilation method, named the maximum likelihood ensemble filter (MLEF), is presented. The analysis solution maximizes the likelihood of the posterior probability distribution, obtained by minimization of a cost function that depends on a general nonlinear observation operator. The MLEF belongs to the class of deterministic ensemble filters, since no perturbed observations are employed. As in variational and ensemble data assimilation methods, the cost function is derived using a Gaussian probability density function framework. Like other ensemble data assimilation algorithms, the MLEF produces an estimate of the analysis uncertainty (e.g., analysis error covariance). In addition to the common use of ensembles in calculation of the forecast error covariance, the ensembles in MLEF are exploited to efficiently calculate the Hessian preconditioning and the gradient of the cost function. A sufficient number of iterative minimization steps is 2–3, because of superior Hessian preconditioning. The MLEF method is well suited for use with highly nonlinear observation operators, for a small additional computational cost of minimization. The consistent treatment of nonlinear observation operators through optimization is an advantage of the MLEF over other ensemble data assimilation algorithms. The cost of MLEF is comparable to the cost of existing ensemble Kalman filter algorithms. The method is directly applicable to most complex forecast models and observation operators. In this paper, the MLEF method is applied to data assimilation with the one-dimensional Korteweg–de Vries–Burgers equation. The tested observation operator is quadratic, in order to make the assimilation problem more challenging. The results illustrate the stability of the MLEF performance, as well as the benefit of the cost function minimization. The improvement is noted in terms of the rms error, as well as the analysis error covariance. The statistics of innovation vectors (observation minus forecast) also indicate a stable performance of the MLEF algorithm. Additional experiments suggest the amplified benefit of targeted observations in ensemble data assimilation.

Using the cost function to generate Marshallian demand systems

2000 ◽  
Vol 25 (2) ◽  
pp. 209-227 ◽  
Author(s):  
Keith R. McLaren ◽  
Peter D. Rossitter ◽  
Alan A. Powell
Keyword(s):  
Cost Function ◽  
Demand Systems ◽  
The Cost

Optimal actuator placement in adaptive optics systems

2021 ◽  
pp. 107754632110324
Author(s):  
Berk Altıner ◽  
Bilal Erol ◽  
Akın Delibaşı
Keyword(s):  
Cost Function ◽  
Adaptive Optics ◽  
Disturbance Attenuation ◽  
Linear Quadratic ◽  
Placement Problem ◽  
Convex Optimization Problem ◽  
Wavefront Aberrations ◽  
The Cost ◽  
Actuator Placement ◽  
Quadratic Cost Function

Adaptive optics systems are powerful tools that are implemented to degrade the effects of wavefront aberrations. In this article, the optimal actuator placement problem is addressed for the improvement of disturbance attenuation capability of adaptive optics systems due to the fact that actuator placement is directly related to the enhancement of system performance. For this purpose, the linear-quadratic cost function is chosen, so that optimized actuator layouts can be specialized according to the type of wavefront aberrations. It is then considered as a convex optimization problem, and the cost function is formulated for the disturbance attenuation case. The success of the presented method is demonstrated by simulation results.

Sparse Incremental Delta-Bar-Delta for System Identification

2014 ◽  
Vol 665 ◽  
pp. 643-646
Author(s):  
Ying Liu ◽  
Yan Ye ◽  
Chun Guang Li
Keyword(s):  
System Identification ◽  
Cost Function ◽  
Learning Algorithm ◽  
Learning System ◽  
The Other ◽  
Sparse System ◽  
Speed Up ◽  
Sparse System Identification ◽  
The Cost ◽  
Zero Attractor

Metalearning algorithm learns the base learning algorithm, targeted for improving the performance of the learning system. The incremental delta-bar-delta (IDBD) algorithm is such a metalearning algorithm. On the other hand, sparse algorithms are gaining popularity due to their good performance and wide applications. In this paper, we propose a sparse IDBD algorithm by taking the sparsity of the systems into account. Thenorm penalty is contained in the cost function of the standard IDBD, which is equivalent to adding a zero attractor in the iterations, thus can speed up convergence if the system of interest is indeed sparse. Simulations demonstrate that the proposed algorithm is superior to the competing algorithms in sparse system identification.

The Mitigation of the French Nuclear Option: New Industrial Realism and Technical Democracy

2002 ◽  
Vol 13 (2) ◽  
pp. 263-279 ◽  
Author(s):  
Dominique Finon
Keyword(s):  
Waste Management ◽  
Electricity Markets ◽  
Social Preferences ◽  
Public Inquiry ◽  
The Future ◽  
Parliamentary Committee ◽  
Set Up ◽  
The Stability ◽  
The Cost ◽  
Phase Out

Nuclear phase-out policies and the European obligation to liberalise electricity markets could put the French nuclear option dramatically at risk by influencing social preferences or by constraining power producers' investment choices in the future. So far, the particular institutional set-up which has allowed the efficient build-up and operation of several series of standardised reactors preserves the stability of the main elements of the option. However, important adaptations to the evolving industrial and political environment occur and contribute to changing the option. Some institutional changes (such as local public inquiry, creation of a Parliamentary committee, independence of safety authorities) and divergence between industrial interests already allow debates on internal options such as reprocessing, type of waste management deposits, ordering of an advanced PWR. These changes improve the cost transparency, even if internalisation of nuclear externalities (cost of insurance, provisions for waste management) is still incomplete. However, when effective, this internalisation would not affect definitively the competitive position of the nuclear production because of the parallel internalisation of CO2 externalities from fossil fuel power generation in the official rationale. Consequently the real issue for the future of the nuclear option in France remains the preservation of social acceptability in the perception of nuclear risks.

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