Combine Assembly Quality Detection Based on Multi-Entropy Data Fusion and Optimized LSSVM

Zhao, Sixia; Xu, Longya; Zhang, Jiaming; Zhang, Shuai; Chen, Xiaoliang; Wei, Jun

doi:10.1109/access.2021.3074255

Cited by 4 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By changing the value of p between − ∞ and + ∞, the value between end and start can be obtained. The overall flow of the algorithm is as follows [24,25]:…”

Section: Ipso's Data Fusion Weight Factor Estimationmentioning

confidence: 99%

Creating Smart House via IoT and Intelligent Computation

Sung¹,

Hsiao²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

This study mainly uses the concept of the Internet of Things (IoT) to establish a smart house with an indoor, comfortable, environmental, and real-time monitoring system. In the smart house, this investigation employed the temperature-and humidity-sensing module and the lightness module to monitor any condition for an intelligent-living house. The data of temperature, humidity, and lightness are transmitted wirelessly to the human-machine interface. The correlation of the weight of the extension theory is used to analyze the ideal and comfortable environment so that people in the indoor environment can feel better thermal comfort and lightness. In this study, improved particle swarm optimization (IPSO) is employed-an effective evolutionary method used to search the function extreme. It is simple and has a fast convergence. The convergence accuracy of this algorithm is not high at the beginning, and it can easily fall into the local extreme points. The effect of the inertia weight in mix extension theory and PSO becomes IPSO-Extension Neural Network (ENN), which was analyzed and found reliable. Motivated by the idea of power function, a new non-linear strategy for decreasing inertia weight (DIW) was proposed based on the existing linear DIW. Then, a novel hierarchical multi-sensor data fusion algorithm adopting this strategy was presented, and the weight factor of the data fusion was estimated. The distinctive feature of this algorithm is its capability of fusing data in a near-optimal manner when there is no available information about the reliability of the information sources, the degree of redundancy/complementarities of the information sources, and the structure of the hierarchy. It obtained effective information from the fusion data, successfully removed the noise disturbance, and achieved favorable results.

show abstract

“…By changing the value of p between − ∞ and + ∞, the value between end and start can be obtained. The overall flow of the algorithm is as follows [24,25]:…”

Section: Ipso's Data Fusion Weight Factor Estimationmentioning

confidence: 99%

Creating Smart House via IoT and Intelligent Computation

Sung¹,

Hsiao²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…Using the trained model can effectively determine the specific assembly failure type. Reference [8] proposed a vibration detection method of combined assembly quality detection based on multi-entropy feature fusion and optimized least squares support vector machine.…”

Section: Related Workmentioning

confidence: 99%

Assembly Status Detection Based on Step-by-step Strategy for Robotic Assembly

Chen

Bie

Yao

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Assembly status detection is of great significance in the automated assembly of industrial robots. However, in addition to the traditional manual inspection, the existing methods for assembly status detection are mainly through methods such as torque sensors, etc., which are difficult to implement in practical assembly and lack the detection of the entire assembly cycle. To solve this problem, this paper proposes a method for assembly statute detection based on a step-by-step strategy. The first step is to use fast bounding box collision detection to determine whether the robot enters the correct assembly neighborhood. The second step is to use accurate pose information to determine whether the product is assembled correctly. The experiments show that the proposed method can accurately detect the assembly status for the entire assembly cycle with high assembly efficiency.

show abstract

“…It is used in a variety of security applications as well as in a variety areas like marketing, healthcare, etc. A face recognition system [1] is a technology that allows you to match a human face in a digital image or video frame with a database of faces. Researchers are currently developing several methods of how facial recognition systems work.…”

Section: Introductionmentioning

confidence: 99%

Comparision of Face Detection Tools

Amirgaliyev¹,

Садыкова

Kenshimov

2021

Bullet. Ser. of Phys. & Math. Sc.

View full text Add to dashboard Cite

This article examines the assessment of tools such as Dlib, OpenCV, MTCNN, FaceNet for face recognition. In the process of work, the execution time and the count of detecting of each tool were determined and calculated. The results pictured in graph choose the right tool according to the data obtained in the article that was optimal for next research works. The choice was made for the ease of writing a parallel algorithm. The rationale for the choice of the tool is also given according to the parameters of the use of machine resources, which makes it possible to optimally select a machine without additional and large costs. A comparative analysis of each instrument was performed and the results were identified accordingly. Based on the test results, we divided two cases and tried to give recommendations for each of them. The first case is triggered if only quick face detection is considered in the video. The second case is triggered if more faces are viewed in the video. It turned out that in the first case, we need to use the Dlib tool. In the second case, we can choose tools like Facenet or Mtcnn. The results obtained in the process of the research are presented in the form of graphs, tables and recorded in the conclusion section of this article.

show abstract

Combine Assembly Quality Detection Based on Multi-Entropy Data Fusion and Optimized LSSVM

Cited by 4 publications

References 32 publications

Creating Smart House via IoT and Intelligent Computation

Creating Smart House via IoT and Intelligent Computation

Assembly Status Detection Based on Step-by-step Strategy for Robotic Assembly

Comparision of Face Detection Tools

Contact Info

Product

Resources

About