A multi-objective optimization framework for aerosol jet customized line width printing via small data set and prediction uncertainty

2020 ◽  
Vol 285 ◽  
pp. 116779
Author(s):  
Haining Zhang ◽  
Joon Phil Choi ◽  
Seung Ki Moon ◽  
Teck Hui Ngo
Keyword(s):  
Line Width ◽  
Small Data ◽  
Multi Objective Optimization ◽  
Data Set ◽  
Prediction Uncertainty ◽  
Multi Objective ◽  
Optimization Framework ◽  
Small Data Set

The Lyapunov Exponent Variance of an Electronic Manufacturing Enterprise’s Daily Qualified Rate Time Series by Improved Small Data Sets Approach

2012 ◽  
Vol 197 ◽  
pp. 271-277
Author(s):  
Zhu Ping Gong
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Chaotic Time Series ◽  
Quality System ◽  
Quality Level ◽  
Largest Lyapunov Exponent ◽  
Small Data ◽  
Data Set ◽  
Electronic Manufacturing ◽  
Small Data Set

Small data set approach is used for the estimation of Largest Lyapunov Exponent (LLE). Primarily, the mean period drawback of Small data set was corrected. On this base, the LLEs of daily qualified rate time series of HZ, an electronic manufacturing enterprise, were estimated and all positive LLEs were taken which indicate that this time series is a chaotic time series and the corresponding produce process is a chaotic process. The variance of the LLEs revealed the struggle between the divergence nature of quality system and quality control effort. LLEs showed sharp increase in getting worse quality level coincide with the company shutdown. HZ’s daily qualified rate, a chaotic time series, shows us the predictable nature of quality system in a short-run.

scholarly journals Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2775
Author(s):  
Tsubasa Takano ◽  
Takumi Nakane ◽  
Takuya Akashi ◽  
Chao Zhang
Keyword(s):  
Optimization Problem ◽  
Line Pair ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Framework ◽  
Visually Impaired People ◽  
Comprehensive Comparison ◽  
Basic Characteristics ◽  
Support Devices

In this paper, we propose a method to detect Braille blocks from an egocentric viewpoint, which is a key part of many walking support devices for visually impaired people. Our main contribution is to cast this task as a multi-objective optimization problem and exploits both the geometric and the appearance features for detection. Specifically, two objective functions were designed under an evolutionary optimization framework with a line pair modeled as an individual (i.e., solution). Both of the objectives follow the basic characteristics of the Braille blocks, which aim to clarify the boundaries and estimate the likelihood of the Braille block surface. Our proposed method was assessed by an originally collected and annotated dataset under real scenarios. Both quantitative and qualitative experimental results show that the proposed method can detect Braille blocks under various environments. We also provide a comprehensive comparison of the detection performance with respect to different multi-objective optimization algorithms.

Auto-sharing parameters for transfer learning based on multi-objective optimization

2021 ◽  
pp. 1-13
Author(s):  
Hailin Liu ◽  
Fangqing Gu ◽  
Zixian Lin
Keyword(s):  
Transfer Learning ◽  
Optimization Problem ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Real World Data ◽  
Data Set ◽  
Target Task ◽  
Main Research ◽  
Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

scholarly journals Research on urban public bicycle dispatching optimization method

2019 ◽  
Author(s):  
Lin Fei ◽  
Yang Yang ◽  
Wang Shihua ◽  
Xu Yudi ◽  
Ma Hong
Keyword(s):  
Clustering Algorithm ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Data Set ◽  
Community Discovery ◽  
The Public ◽  
Multi Objective ◽  
Scheduling Scheme ◽  
Theoretical Support ◽  
The Matrix

Unreasonable public bicycle dispatching area division seriously affects the operational efficiency of the public bicycle system. To solve this problem, this paper innovatively proposes an improved community discovery algorithm based on multi-objective optimization (CDoMO). The data set is preprocessed into a lease/return relationship, thereby it calculated a similarity matrix, and the community discovery algorithm Fast Unfolding is executed on the matrix to obtain a scheduling scheme. For the results obtained by the algorithm, the workload indicators (scheduled distance, number of sites, and number of scheduling bicycles) should be adjusted to maximize the overall benefits, and the entire process is continuously optimized by a multi-objective optimization algorithm NSGA2. The experimental results show that compared with the clustering algorithm and the community discovery algorithm, the method can shorten the estimated scheduling distance by 20%-50%, and can effectively balance the scheduling workload of each area. The method can provide theoretical support for the public bicycle dispatching department, and improve the efficiency of public bicycle dispatching system.

A Multi-variable & Multi-objective Optimization Framework for LID Layout in Sponge City

2021 ◽  
Author(s):  
Sijie Tang ◽  
Jiping Jiang ◽  
Yi Zheng
Keyword(s):  
Ecological Factors ◽  
Green Roofs ◽  
Design Tool ◽  
Distribution Area ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Sponge City ◽  
Multi Objective ◽  
Optimization Framework ◽  
City Construction

<p>Practitioners usually design the plan of Sponge City construction (SCC) by combining LID facilities (e.g., rain garden, rain barrels, green roofs, and grassed swales) according to their personal experiences or general guidelines. The layout (including selection, connection and distribution area) of LID facilities is subjective, in the risk of far from optimal combination. Previous researchers have developed some LID optimization tools, which only consider the dimension and number of LIDs in a given scenario. Therefore, it is necessary to develop a flexible and extensible design tool with the support of urban hydrological model to conduct the facilities layout optimization. This study introduced a SWMM-based multi-variable and multi-objective optimization framework called CAFID (Comprehensive Assessment and Fine Design Model of Sponge City) to meet this end. The assessment module with multi-objective couples diverse controlling end-points (e.g., total runoff, peak runoff, pollutant concentration, cost, and customized social-ecological factors) as the candidates of assessment criteria. The optimization module with multi-variable is implemented by SWMM, starting with three steps: 1) Full allocation. Based on the availability, list the candidates of LID facility for each sub-catchment; 2) Full connection. Order the potential stream direction of surface runoff from rainfall to municipal network, based on possible hierarchical structure of sub-catchments and LID facilities; 3) Full coverage. Identify all the suitable area for LID facility in sub-catchment. The optimization on the 3 variables, the selection, connection, and area, is powered by NSGA-II and TOPSIS algorithms, which make it possible that we choose a final result from the set of nondominated solutions according to special weight distribution. The effectiveness of CAFID was illustrated through a case of Sponge City in Fenghuangcheng of Shenzhen City, one of 30 national pilot sponge cities in China. As well, this new framework is expected to be widely verified and applied in Sponge City construction in China or similar concepts all over the world.</p>

Sign in / Sign up

Export Citation Format

Share Document