scholarly journals An Optimization Method for the Layout of Soil Humidity Sensors Based on Compressed Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yunsong Jia ◽  
Xueyun Tian ◽  
Xin Chen ◽  
Xiang Li
Keyword(s):  
Compressed Sensing ◽  
Structural Characteristics ◽  
Optimization Method ◽  
Layout Optimization ◽  
Small Error ◽  
Average Error ◽  
Small Data ◽  
Agricultural Field ◽  
Measurement Matrix ◽  
Experimental Conditions

In the farmland Internet of Things, to achieve precise control of production, it is necessary to obtain more data support, which requires the deployment of many sensors, and this will inevitably bring about high investment and high-cost problems. This paper mainly studies the optimization of sensor placement in the agricultural field. Through compressed sensing and algorithm optimization, the number of sensors used is reduced and the cost is reduced on the premise of ensuring the effect. At present, there are many mature sensor layout optimization methods, but these methods will have incomplete parameters due to experimental conditions and environmental factors. They are more suitable for structural health monitoring and lack research in agricultural applications. Considering that the sensor layout optimization can be converted into the characteristics of image compression selection and the compression effect of the compressed sensing theory is better, therefore, this paper proposes a sensor layout optimization method based on compressed sensing. Due to the structural characteristics of the existing measurement matrix in the compressed sensing theory, the specific position distribution of the optimized sensor layout cannot be obtained directly. This paper improves the existing sparse random measurement matrix to determine the number of sensors required for a given region and the function of the specific location of each sensor. The experimental results show that soil moisture can be measured with a small error of 0.91 by using 1/3 of the original sensor number. The result of data reconstruction using 1/6 of the original sensor is average, and the average error is up to 1.68, which is suitable for the environment with small data fluctuation.

SEM analysis of the change in the reaction rates with deposit structures in the copper-iron cementation system

1978 ◽  
Vol 36 (1) ◽  
pp. 456-457
Author(s):  
V. Annamalai ◽  
L.E. Murr
Keyword(s):  
Structural Characteristics ◽  
Secondary Electron Emission ◽  
Copper Deposit ◽  
Reaction Rates ◽  
Sem Analysis ◽  
Experimental Conditions ◽  
Major Drawback ◽  
Emission Mode ◽  
Scrap Iron ◽  
Image Position

Economical recovery of copper metal from leach liquors has been carried out by the simple process of cementing copper onto a suitable substrate metal, such as scrap-iron, since the 16th century. The process has, however, a major drawback of consuming more iron than stoichiometrically needed by the reaction.Therefore, many research groups started looking into the process more closely. Though it is accepted that the structural characteristics of the resultant copper deposit cause changes in reaction rates for various experimental conditions, not many systems have been systematically investigated. This paper examines the deposit structures and the kinetic data, and explains the correlations between them.A simple cementation cell along with rotating discs of pure iron (99.9%) were employed in this study to obtain the kinetic results The resultant copper deposits were studied in a Hitachi Perkin-Elmer HHS-2R scanning electron microscope operated at 25kV in the secondary electron emission mode.

scholarly journals Compressed sensing image restoration algorithm based on improved SURF operator

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 1033-1045
Author(s):  
Guodong Zhou ◽  
Huailiang Zhang ◽  
Raquel Martínez Lucas
Keyword(s):  
Compressed Sensing ◽  
Image Restoration ◽  
Feature Vector ◽  
Optimization Method ◽  
Image Feature ◽  
Diffusion Method ◽  
Local Similarity ◽  
Weighting Method ◽  
Data Smoothing ◽  
Restoration Algorithm

Abstract Aiming at the excellent descriptive ability of SURF operator for local features of images, except for the shortcoming of global feature description ability, a compressed sensing image restoration algorithm based on improved SURF operator is proposed. The SURF feature vector set of the image is extracted, and the vector set data is reduced into a single high-dimensional feature vector by using a histogram algorithm, and then the image HSV color histogram is extracted.MSA image decomposition algorithm is used to obtain sparse representation of image feature vectors. Total variation curvature diffusion method and Bayesian weighting method perform image restoration for data smoothing feature and local similarity feature of texture part respectively. A compressed sensing image restoration model is obtained by using Schatten-p norm, and image color supplement is performed on the model. The compressed sensing image is iteratively solved by alternating optimization method, and the compressed sensing image is restored. The experimental results show that the proposed algorithm has good restoration performance, and the restored image has finer edge and texture structure and better visual effect.

Research on Compressed Sensing Matrix Based on Data Compressing of Insulator Leakage Current

2013 ◽  
Vol 475-476 ◽  
pp. 451-454
Author(s):  
Xue Ming Zhai ◽  
Xiao Bo You ◽  
Ruo Chen Li ◽  
Yu Jia Zhai ◽  
De Wen Wang
Keyword(s):  
Compressed Sensing ◽  
Data Compression ◽  
Leakage Current ◽  
Sparse Matrix ◽  
Circulant Matrix ◽  
Measurement Matrix ◽  
Sensing Matrix ◽  
Significant Step ◽  
On Line

Insulator fault may lead to the accident of power network,thus the on-line monitoring of insulator is very significant. Low rates wireless network is used for data transmission of leakage current. Making data compression and reconstruction of leakage current with the compressed sensing theory can achieve pretty good results. Determination of measurement matrix is the significant step for realizing the compressed sensing theory. This paper compares multiple measurement matrix of their effect via experiments, putting forward to make data compression and reconstruction of leakage current using Toeplitz matrix, circulant matrix and sparse matrix as measurement matrix, of which the reconstitution effect is almost the same as classical measurement matrix and depletes computational complexity and workload.

Simulation-Based Hybrid Optimization Method for the Digital Twin of Garment Production Lines

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Woo-Kyun Jung ◽  
Young-Chul Park ◽  
Jae-Won Lee ◽  
Eun Suk Suh
Keyword(s):  
Expert Knowledge ◽  
Structural Characteristics ◽  
Optimization Method ◽  
Hybrid Optimization ◽  
Optimization Process ◽  
Production Lines ◽  
Distribution Method ◽  
Workload Analysis ◽  
Hybrid Optimization Method ◽  
Simulation Based

AbstractImplementing digital transformation in the garment industry is very difficult, owing to its labor-intensive structural characteristics. Further, the productivity of a garment production system is considerably influenced by a combination of processes and operators. This study proposes a simulation-based hybrid optimization method to maximize the productivity of a garment production line. The simulation reflects the actual site characteristics, i.e., process and operator level indices, and the optimization process reflects constraints based on expert knowledge. The optimization process derives an optimal operator sequence through a genetic algorithm (GA) and sequentially removes bottlenecks through workload analysis based on the results. The proposed simulation optimization (SO) method improved productivity by ∼67.4%, which is 52.3% higher than that obtained by the existing meta-heuristic algorithm. The correlation between workload and production was verified by analyzing the workload change trends. This study holds significance because it presents a new simulation-based optimization model that further applies the workload distribution method by eliminating bottlenecks and digitizing garment production lines.

scholarly journals Sizing and shape optimization material use in 10 bar trusses

2021 ◽  
Vol 343 ◽  
pp. 04004
Author(s):  
Nenad Petrović ◽  
Nenad Kostić ◽  
Vesna Marjanović ◽  
Ileana Ioana Cofaru ◽  
Nenad Marjanović
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Cross Sections ◽  
Optimization Model ◽  
Structural Characteristics ◽  
Optimization Method ◽  
Truss Optimization ◽  
Algorithm Optimization ◽  
Sizing Optimization ◽  
Bill Of Materials

Truss optimization has the goal of achieving savings in costs and material while maintaining structural characteristics. In this research a 10 bar truss was structurally optimized in Rhino 6 using genetic algorithm optimization method. Results from previous research where sizing optimization was limited to using only three different cross-sections were compared to a sizing and shape optimization model which uses only those three cross-sections. Significant savings in mass have been found when using this approach. An analysis was conducted of the necessary bill of materials for these solutions. This research indicates practical effects which optimization can achieve in truss design.

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