scholarly journals Using Haar-like Features and SVM Classifier for Quality Assurance in a Surgical Mask Production Line

2021 ◽  
Vol 10 (2) ◽  
pp. 148
Author(s):  
Laszlo Marak
Keyword(s):  
Quality Assurance ◽  
Feature Selection ◽  
Production Line ◽  
Low Cost ◽  
Classification Method ◽  
Svm Classifier ◽  
Production Lines ◽  
Camera System ◽  
Surgical Masks ◽  
Human Operators

With the recent increase for demand of surgical masks, the design and development of mask production lines has become an ever pressing issue. These production lines produce low cost high quantity products. As there are errors during the production, it is important to be able to detect invalid masks to assure that the produced masks are of consistent quality. Manual quality assurance using human operators is an error prone and a costly solution. In this article we describe an image classification method, which is using a low-cost Commercial Camera System and relies on Haar-like features combined with Maximum Relevance, Minimum Redundancy feature selection to detect the invalid masks at the end of the production process. The classification method consists of Preprocessing, Feature Selection and SVM Training. We have tested the method on a database of 150 000 images and it provides a high accuracy method which we use in the Production Line.

Tackling Means for Balancing the Production Lines

2011 ◽  
Vol 383-390 ◽  
pp. 4620-4628
Author(s):  
Olga Ioana Amariei ◽  
Codruţa Oana Hamat ◽  
Liviu Coman ◽  
Cristian Fănică ◽  
Cristian Rudolf
Keyword(s):  
Production Line ◽  
Idle Time ◽  
Computer Method ◽  
Basic Method ◽  
Assembly Lines ◽  
Production Lines ◽  
Heuristic Techniques ◽  
Human Operators

Balancing a production line means to organize the activity of the human operators, to establish the production flux and designing the line, minimizing the idle time for the machines and the operators, through an optimal charge bestowed upon them. WinQSB software offers three methods of solving this type of problem, namely: heuristic techniques (a basic method is specified and an alternative one from all the available ones), Optimizing Best Bud Search and Computer Method of Sequencing Operations for Assembly Lines, presented all in the present paper.

Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis

2020 ◽  
Vol 20 ◽  
Author(s):  
Hongwei Zhang ◽  
Steven Wang ◽  
Tao Huang
Keyword(s):  
Feature Selection ◽  
Expression Analysis ◽  
Hypersensitivity Pneumonitis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Great Promise ◽  
Support Vector ◽  
Svm Classifier ◽  
Clinical Tool ◽  
Chronic Hypersensitivity Pneumonitis

Aims: We would like to identify the biomarkers for chronic hypersensitivity pneumonitis (CHP) and facilitate the precise gene therapy of CHP. Background: Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the tasks of differentiating between CHP and other interstitial lungs diseases, especially idiopathic pulmonary fibrosis (IPF), were challenging. Objective: In this study, we analyzed the public available gene expression profile of 82 CHP patients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers. Method: The CHP biomarkers were selected with advanced feature selection methods: Monte Carlo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrichment analysis and differential co-expression analysis. Result: There were 674 identified CHP biomarkers. The co-expression network of these biomarkers in CHP included more negative regulations and the network structure of CHP was quite different from the network of IPF and control. Conclusion: The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential co-expression network showed great promise in revealing the underlying mechanisms of CHP.

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

scholarly journals Ears in the Sky: Potential of Drones for the Bioacoustic Monitoring of Birds and Bats

Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 9
Author(s):  
Adrien Michez ◽  
Stéphane Broset ◽  
Philippe Lejeune
Keyword(s):  
Forest Canopy ◽  
Low Cost ◽  
Population Monitoring ◽  
Biodiversity Loss ◽  
Active Species ◽  
Terrestrial Mammals ◽  
Technical Developments ◽  
High Resolution Imagery ◽  
Human Operators ◽  
The Impact

In the context of global biodiversity loss, wildlife population monitoring is a major challenge. Some innovative techniques such as the use of drones—also called unmanned aerial vehicle/system (UAV/UAS)—offer promising opportunities. The potential of UAS-based wildlife census using high-resolution imagery is now well established for terrestrial mammals or birds that can be seen on images. Nevertheless, the ability of UASs to detect non-conspicuous species, such as small birds below the forest canopy, remains an open question. This issue can be solved with bioacoustics for acoustically active species such as bats and birds. In this context, UASs represent an interesting solution that could be deployed on a larger scale, at lower risk for the operator, and over hard-to-reach locations, such as forest canopies or complex topographies, when compared with traditional protocols (fixed location recorders placed or handled by human operators). In this context, this study proposes a methodological framework to assess the potential of UASs in bioacoustic surveys for birds and bats, using low-cost audible and ultrasound recorders mounted on a low-cost quadcopter UAS (DJI Phantom 3 Pro). The proposed methodological workflow can be straightforwardly replicated in other contexts to test the impact of other UAS bioacoustic recording platforms in relation to the targeted species and the specific UAS design. This protocol allows one to evaluate the sensitivity of UAS approaches through the estimate of the effective detection radius for the different species investigated at several flight heights. The results of this study suggest a strong potential for the bioacoustic monitoring of birds but are more contrasted for bat recordings, mainly due to quadcopter noise (i.e., electronic speed controller (ESC) noise) but also, in a certain manner, to the experimental design (use of a directional speaker with limited call intensity). Technical developments, such as the use of a winch to safely extent the distance between the UAS and the recorder during UAS sound recordings or the development of an innovative platform, such as a plane–blimp hybrid UAS, should make it possible to solve these issues.

Low-cost Smart Camera System for Water Stress Detection in Crops

2020 ◽  
Author(s):  
Paula Ramos-Giraldo ◽  
S. Chris Reberg-Horton ◽  
Steven Mirsky ◽  
Edgar Lobaton ◽  
Anna M. Locke ◽  
...  
Keyword(s):  
Water Stress ◽  
Low Cost ◽  
Smart Camera ◽  
Stress Detection ◽  
Camera System

Kameraunterstützte Verschwendungsreduktion*/Computer-vision supported waste reduction in lean production

2017 ◽  
Vol 107 (09) ◽  
pp. 572-577
Author(s):  
B. Prof. Lorenz ◽  
I. Kaltenmark
Keyword(s):  
Computer Vision ◽  
Lean Manufacturing ◽  
Lean Production ◽  
Low Cost ◽  
Industrie 4.0 ◽  
Waste Reduction ◽  
Camera System ◽  
New Developments ◽  
Neue Entwicklungen

In modernen Produktionen ist Lean Manufacturing einer der wichtigsten Treiber für Produktivitätssteigerungen. Durch neue Entwicklungen im Bereich Industrie 4.0 können Impulse im Lean Manufacturing gegeben werden. An der OTH Regensburg wird getestet, wie kostengünstige Kamerasysteme helfen können, Verschwendungen sichtbar zu machen und zu minimieren. Es zeigt sich, dass auch mit geringen Investitionskosten neue Potentiale zur Verschwendungsreduktion aufgedeckt werden können.   In modern production lean manufacturing is one of the most effective drivers for productivity. Due to new developments in the Industrie 4.0-campaign new impulses can be given into lean manufacturing. Experiments at OTH Regensburg indicate that a low-cost camera system can help to make waste visible and minimize it. This shows that with low invest costs, new potentials for waste reduction can be revealed.

Feature Selection Method Based on Mutual Information and Support Vector Machine

2021 ◽  
pp. 2150021
Author(s):  
Gang Liu ◽  
Chunlei Yang ◽  
Sen Liu ◽  
Chunbao Xiao ◽  
Bin Song
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Mutual Information ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Standard Data ◽  
Feature Dimension

A feature selection method based on mutual information and support vector machine (SVM) is proposed in order to eliminate redundant feature and improve classification accuracy. First, local correlation between features and overall correlation is calculated by mutual information. The correlation reflects the information inclusion relationship between features, so the features are evaluated and redundant features are eliminated with analyzing the correlation. Subsequently, the concept of mean impact value (MIV) is defined and the influence degree of input variables on output variables for SVM network based on MIV is calculated. The importance weights of the features described with MIV are sorted by descending order. Finally, the SVM classifier is used to implement feature selection according to the classification accuracy of feature combination which takes MIV order of feature as a reference. The simulation experiments are carried out with three standard data sets of UCI, and the results show that this method can not only effectively reduce the feature dimension and high classification accuracy, but also ensure good robustness.

scholarly journals Systematic Innovation for Manufacturing Quality Improvement

2018 ◽  
Vol 221 ◽  
pp. 02005
Author(s):  
Swee Shu Luing Nikalus ◽  
Guan Toh Guat ◽  
Mum Wai Yip ◽  
See Chew Tai
Keyword(s):  
Quality Control ◽  
Production Line ◽  
Function Analysis ◽  
Low Cost ◽  
Innovation Process ◽  
Root Cause ◽  
Latex Gloves ◽  
Chain Analysis ◽  
Air Blowing ◽  
The Times

This paper provides a detailed analysis on the systematic innovation process in improving the quality control of latex gloves production. The systematic innovation tool such as TRIZ is applied in this case study. Function analysis, cause and effect chain analysis, physical contradiction, By-separation model and 40 Inventive Principles are applied in order to derive some feasible and low cost solutions to alleviate the problem. Findings revealed that the rejected (leaking) gloves on the production line will be manually monitored by a checker during the air blowing test and will be discarded by the same checker instantly. The main root cause is that the quality control worker is not able to concentrate all the times to detect the torn gloves, mainly is due to the fast speed production line and other distractions. The problem is solved by applying function analysis, physical contradiction, by-separation tool and Inventive Principles to generate low cost but elegant solutions within the defined scope of several constraints and without making the production line more complex. Therefore, it can be concluded that TRIZ is a systematic and innovative problem solving methodology.

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