SQUEAK AND RATTLE PREVENTION BY GEOMETRIC VARIATION MANAGEMENT USING A TWO-STAGE EVOLUTIONARY OPTIMISATION APPROACH

2021 ◽  
pp. 1-40
Author(s):  
Mohsen Bayani ◽  
Casper Wickman ◽  
Lars Lindkvist ◽  
Rikard Söderberg
Keyword(s):  
Lower Order ◽  
Uniform Space ◽  
Order Effect ◽  
Objective Functions ◽  
Two Stage ◽  
Second Stage ◽  
Evolutionary Optimisation ◽  
Space Filler ◽  
Computationally Expensive ◽  
Geometric Variation

Abstract Squeak and rattle are annoying sounds that are often regarded as the failure indicators by car users. Geometric variation is a key contributor to the generation of squeak and rattle sounds. Optimisation of the connection configuration in assemblies can be a provision to minimise this risk. However, the optimisation process for large assemblies can be computationally expensive. The focus of this work is to propose a two-stage evolutionary optimisation scheme to find the fittest connection configurations that minimise the risk for squeak and rattle. This was done by defining the objective functions as the measured variation and deviation in the rattle direction and the squeak plane. In the first stage, the location of the fasteners primarily contributing to the rattle direction measures are identified. In the second stage, fasteners primarily contributing to the squeak plane measures are added to the fittest configuration from phase one. It was assumed that the fasteners from the squeak group plane have a lower-order effect on the rattle direction measures, compared to the fasteners from the rattle direction group. This assumption was falsified for a set of simplified geometries. Also, a new uniform space filler algorithm was introduced to efficiently generate an inclusive and feasible starting population for the optimisation process by incorporating the problem constraints in the algorithm. For two industrial cases, it was shown that by using the proposed two-stage optimisation scheme the variation and deviation measures in critical interfaces for squeak and rattle improved compared to the baseline results.

Squeak and Rattle Prevention by Geometric Variation Management Using a Two-Stage Evolutionary Optimisation Approach

2020 ◽  
Author(s):  
Mohsen Bayani ◽  
Casper Wickman ◽  
Lars Lindkvist ◽  
Rikard Söderberg
Keyword(s):  
Objective Function ◽  
Management Approach ◽  
Two Stage ◽  
Second Stage ◽  
Evolutionary Optimisation ◽  
Dimensional Variation ◽  
Minimum Variation ◽  
Geometric Variation ◽  
Fine Tune ◽  
Variation Management

Abstract Squeak and rattle are annoying sounds that often are regarded as the indicators for defects and quality issues by the automotive customers. Among the major causes for the generation of squeak and rattle sounds, geometric variation or tolerance stack-up is a key contributor. In the assembly process, the dimensional variation in critical interfaces for generating squeak and rattle events can be magnified due to tolerance stack-up. One provision to manage the tolerance stack-up in these critical interfaces is to optimise the location of connectors between parts in an assembly. Hence, the focus of this work is to prevent squeak and rattle by introducing a geometric variation management approach to be used in the design phase in the automotive industry. The objective is to identify connection configurations that result in minimum variation and deviation in selected measure points from the critical interfaces for squeak and rattle. In this study, a two-stage evolutionary optimisation scheme, based on the genetic algorithm employing the elitism pool, is introduced to fine-tune the connectors’ configuration in an assembly. The objective function was defined as the variation and the deviation in the normal direction and the squeak plane. In the first stage, the location of one-dimensional connectors was found by minimising the objective function in the rattle direction. In the second stage, the best combination of some of the connectors from the first stage was found to define planar fasteners to optimise the objective function both in the rattle direction and the squeak plane. It was shown that by using the proposed two-stage optimisation scheme, the variation and deviation results in critical interfaces for squeak and rattle improved compared to the baseline results.

scholarly journals A New Two-Stage Algorithm for Solving Optimization Problems

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 491
Author(s):  
Sajjad Amiri Doumari ◽  
Hadi Givi ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Victor Leiva ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Optimization Methods ◽  
Objective Functions ◽  
Two Stage ◽  
Second Stage ◽  
Marine Predators ◽  
Good Population ◽  
Living Organisms ◽  
Teaching Learning ◽  
And Behavior

Optimization seeks to find inputs for an objective function that result in a maximum or minimum. Optimization methods are divided into exact and approximate (algorithms). Several optimization algorithms imitate natural phenomena, laws of physics, and behavior of living organisms. Optimization based on algorithms is the challenge that underlies machine learning, from logistic regression to training neural networks for artificial intelligence. In this paper, a new algorithm called two-stage optimization (TSO) is proposed. The TSO algorithm updates population members in two steps at each iteration. For this purpose, a group of good population members is selected and then two members of this group are randomly used to update the position of each of them. This update is based on the first selected good member at the first stage, and on the second selected good member at the second stage. We describe the stages of the TSO algorithm and model them mathematically. Performance of the TSO algorithm is evaluated for twenty-three standard objective functions. In order to compare the optimization results of the TSO algorithm, eight other competing algorithms are considered, including genetic, gravitational search, grey wolf, marine predators, particle swarm, teaching-learning-based, tunicate swarm, and whale approaches. The numerical results show that the new algorithm is superior and more competitive in solving optimization problems when compared with other algorithms.

An Enhanced Two Stage MMSE Equalizer for Coded FBMC/OQAM Systems

2019 ◽  
Vol 10 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Mohammad Rizk Assaf ◽  
Abdel-Nasser Assimi
Keyword(s):  
Error Propagation ◽  
Channel Model ◽  
Error Correcting Codes ◽  
Two Stage ◽  
Probability Of Error ◽  
Second Stage ◽  
Different Types ◽  
Mmse Equalizer ◽  
And Performance ◽  
Ici Cancellation

In this article, the authors investigate the enhanced two stage MMSE (TS-MMSE) equalizer in bit-interleaved coded FBMC/OQAM system which gives a tradeoff between complexity and performance, since error correcting codes limits error propagation, so this allows the equalizer to remove not only ICI but also ISI in the second stage. The proposed equalizer has shown less design complexity compared to the other MMSE equalizers. The obtained results show that the probability of error is improved where SNR gain reaches 2 dB measured at BER compared with ICI cancellation for different types of modulation schemes and ITU Vehicular B channel model. Some simulation results are provided to illustrate the effectiveness of the proposed equalizer.

Personalised attraction recommendation for enhancing topic diversity and accuracy

2021 ◽  
pp. 016555152199980
Author(s):  
Yuanyuan Lin ◽  
Chao Huang ◽  
Wei Yao ◽  
Yifei Shao
Keyword(s):  
Real World ◽  
Information Overload ◽  
Two Stage ◽  
Misclassification Cost ◽  
Second Stage ◽  
Low Visibility ◽  
Recommendation Diversity ◽  
Optimisation Model ◽  
Definition Of ◽  
Improved Methods

Attraction recommendation plays an important role in tourism, such as solving information overload problems and recommending proper attractions to users. Currently, most recommendation methods are dedicated to improving the accuracy of recommendations. However, recommendation methods only focusing on accuracy tend to recommend popular items that are often purchased by users, which results in a lack of diversity and low visibility of non-popular items. Hence, many studies have suggested the importance of recommendation diversity and proposed improved methods, but there is room for improvement. First, the definition of diversity for different items requires consideration for domain characteristics. Second, the existing algorithms for improving diversity sacrifice the accuracy of recommendations. Therefore, the article utilises the topic ‘features of attractions’ to define the calculation method of recommendation diversity. We developed a two-stage optimisation model to enhance recommendation diversity while maintaining the accuracy of recommendations. In the first stage, an optimisation model considering topic diversity is proposed to increase recommendation diversity and generate candidate attractions. In the second stage, we propose a minimisation misclassification cost optimisation model to balance recommendation diversity and accuracy. To assess the performance of the proposed method, experiments are conducted with real-world travel data. The results indicate that the proposed two-stage optimisation model can significantly improve the diversity and accuracy of recommendations.

scholarly journals Two-stage multi-tasking transform framework for large-scale many-objective optimization problems

2021 ◽  
Author(s):  
Lu Chen ◽  
Handing Wang ◽  
Wenping Ma
Keyword(s):  
Large Scale ◽  
Original Problem ◽  
Optimization Problems ◽  
Reference Points ◽  
Optimization Strategy ◽  
Two Stage ◽  
Decision Space ◽  
Second Stage ◽  
Decision Variables ◽  
Objective Space

AbstractReal-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the computation costs of those mentioned algorithms will be unaffordable. To reduce such high computation cost on large-scale many-objective optimization problems, we proposed a two-stage framework. The first stage of the proposed algorithm combines with a multi-tasking optimization strategy and a bi-directional search strategy, where the original problem is reformulated as a multi-tasking optimization problem in the decision space to enhance the convergence. To improve the diversity, in the second stage, the proposed algorithm applies multi-tasking optimization to a number of sub-problems based on reference points in the objective space. In this paper, to show the effectiveness of the proposed algorithm, we test the algorithm on the DTLZ and LSMOP problems and compare it with existing algorithms, and it outperforms other compared algorithms in most cases and shows disadvantage on both convergence and diversity.

scholarly journals A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Alejandra Ríos ◽  
Eusebio E. Hernández ◽  
S. Ivvan Valdez
Keyword(s):  
Genetic Algorithm ◽  
Performance Metrics ◽  
Tracking Error ◽  
Parallel Manipulators ◽  
Optimization Method ◽  
Statistical Hypothesis ◽  
Two Stage ◽  
Multi Objective ◽  
Second Stage ◽  
Conflicting Objectives

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

scholarly journals Fast Two-Stage Computation of an Index Policy for Multi-Armed Bandits with Setup Delays

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 52
Author(s):  
José Niño-Mora
Keyword(s):  
Numerical Study ◽  
Arithmetic Operation ◽  
Bandit Problem ◽  
Index Policy ◽  
Two Stage ◽  
Second Stage ◽  
Whittle Index ◽  
Set Up ◽  
Computing Method ◽  
Special Case

We consider the multi-armed bandit problem with penalties for switching that include setup delays and costs, extending the former results of the author for the special case with no switching delays. A priority index for projects with setup delays that characterizes, in part, optimal policies was introduced by Asawa and Teneketzis in 1996, yet without giving a means of computing it. We present a fast two-stage index computing method, which computes the continuation index (which applies when the project has been set up) in a first stage and certain extra quantities with cubic (arithmetic-operation) complexity in the number of project states and then computes the switching index (which applies when the project is not set up), in a second stage, with quadratic complexity. The approach is based on new methodological advances on restless bandit indexation, which are introduced and deployed herein, being motivated by the limitations of previous results, exploiting the fact that the aforementioned index is the Whittle index of the project in its restless reformulation. A numerical study demonstrates substantial runtime speed-ups of the new two-stage index algorithm versus a general one-stage Whittle index algorithm. The study further gives evidence that, in a multi-project setting, the index policy is consistently nearly optimal.

scholarly journals Multiple Sclerosis: Assessment of Disease Progression and Effects of Treatment

1987 ◽  
Vol 14 (S3) ◽  
pp. 518-520 ◽  
Author(s):  
D.W. Paty
Keyword(s):  
Multiple Sclerosis ◽  
Disease Progression ◽  
Treatment Effects ◽  
Two Stage ◽  
Second Stage ◽  
Sequential Development ◽  
Stage Disease

ABSTRACT:MS could well be a two stage disease. The first stage involves the sequential development of multiple small lesions, mostly inflammatory, that accumulate at a given rate. The second stage could be that of consolidation and confluence of lesions that involves not only demyelination but gliosis. MRI now gives us an opportunity to watch the evolution of these processes and also to monitor treatment effects. It is only after the evolution of this process is understood that we can design rational therapies directed toward the prevention of spasticity in MS.

scholarly journals Two-Stage Mask-RCNN Approach for Detecting and Segmenting the Optic Nerve Head, Optic Disc, and Optic Cup in Fundus Images

2020 ◽  
Vol 10 (11) ◽  
pp. 3833 ◽  
Author(s):  
Haidar Almubarak ◽  
Yakoub Bazi ◽  
Naif Alajlan
Keyword(s):  
Optic Nerve ◽  
Optic Disc ◽  
Optic Nerve Head ◽  
Fine Tuning ◽  
Fundus Images ◽  
Two Stage ◽  
Second Stage ◽  
Retinal Fundus Images ◽  
Tuning Strategy ◽  
Retinal Fundus

In this paper, we propose a method for localizing the optic nerve head and segmenting the optic disc/cup in retinal fundus images. The approach is based on a simple two-stage Mask-RCNN compared to sophisticated methods that represent the state-of-the-art in the literature. In the first stage, we detect and crop around the optic nerve head then feed the cropped image as input for the second stage. The second stage network is trained using a weighted loss to produce the final segmentation. To further improve the detection in the first stage, we propose a new fine-tuning strategy by combining the cropping output of the first stage with the original training image to train a new detection network using different scales for the region proposal network anchors. We evaluate the method on Retinal Fundus Images for Glaucoma Analysis (REFUGE), Magrabi, and MESSIDOR datasets. We used the REFUGE training subset to train the models in the proposed method. Our method achieved 0.0430 mean absolute error in the vertical cup-to-disc ratio (MAE vCDR) on the REFUGE test set compared to 0.0414 obtained using complex and multiple ensemble networks methods. The models trained with the proposed method transfer well to datasets outside REFUGE, achieving a MAE vCDR of 0.0785 and 0.077 on MESSIDOR and Magrabi datasets, respectively, without being retrained. In terms of detection accuracy, the proposed new fine-tuning strategy improved the detection rate from 96.7% to 98.04% on MESSIDOR and from 93.6% to 100% on Magrabi datasets compared to the reported detection rates in the literature.

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