Search results for: fault detection and isolation

Authors: Liping Qiu, Xiaofeng Zhang

Abstract:

The ion chromatography method for the rapid determination of four anions (F⁻、Cl⁻、SO₄²⁻、NO₃⁻) in burial ground poles was optimized. The L₉(₃⁴) orthogonal test was used to determine the optimal parameters of sample pretreatment: accurately weigh 2.000g of sample, add 10mL of ultrapure water, and extract for 40min under the conditions of shaking temperature 40℃ and shaking speed 180 r·min-1. The eluent was 25 mmol/L KOH solution, the analytical column was Ion Pac® AS11-SH (250 mm × 4.0 mm), and the purified filtrate was measured by a conductivity detector. Under this method, the detection limit of each ion is 0.066～0.078mg/kg, the relative standard deviation is 0.86%～2.44% (n=7), and the recovery rate is 94.6～101.9.

Keywords: ion chromatography, tomb, anion (F⁻, Cl⁻, SO₄²⁻, NO₃⁻), environmental protection

Procedia PDF Downloads 86

Authors: Jean Pierre Kabue, Emma Meader, Afsatou Ndama Traore, Paul R. Hunter, Natasha Potgieter

Abstract:

Norovirus is now considered the most common cause of outbreaks of nonbacterial gastroenteritis. Limited data are available for Norovirus strains in Africa, especially in rural and peri-urban areas. Despite the excessive burden of diarrhea disease in developing countries, Norovirus infections have been to date mostly reported in developed countries. There is a need to investigate intensively the role of viral agents associated with diarrhea in different settings in Africa continent. To determine the prevalence and genetic diversity of Norovirus strains circulating in the rural communities in the Limpopo Province, South Africa and investigate the genetic relationship between Norovirus strains, a cross-sectional study was performed on human stools collected from rural communities. Between July 2014 and April 2015, outpatient children under 5 years of age from rural communities of Vhembe District, South Africa, were recorded for the study. A total of 303 stool specimens were collected from those with diarrhea (n=253) and without (n=50) diarrhea. NoVs were identified using real-time one-step RT-PCR. Partial Sequence analyses were performed to genotype the strains. Phylogenetic analyses were performed to compare identified NoVs genotypes to the worldwide circulating strains. Norovirus detection rate was 41.1% (104/253) in children with diarrhea. There was no significant difference (OR=1.24; 95% CI 0.66-2.33) in Norovirus detection between symptomatic and asymptomatic children. Comparison of the median CT values for NoV in children with diarrhea and without diarrhea revealed significant statistical difference of estimated GII viral load from both groups, with a much higher viral burden in children with diarrhea. To our knowledge, this is the first study reporting on the differences in estimated viral load of GII and GI NoV positive cases and controls. GII.Pe (n=9) were the predominant genotypes followed by GII.Pe/GII.4 Sydney 2012 (n=8) suspected recombinant and GII.4 Sydney 2012 variants(n=7). Two unassigned GII.4 variants and an unusual RdRp genotype GII.P15 were found. With note, the rare GIIP15 identified in this study has a common ancestor with GIIP15 strain from Japan previously reported as GII/untypeable recombinant strain implicated in a gastroenteritis outbreak. To our knowledge, this is the first report of this unusual genotype in the African continent. Though not confirmed predictive of diarrhea disease in this study, the high detection rate of NoV is an indication of subsequent exposure of children from rural communities to enteric pathogens due to poor sanitation and hygiene practices. The results reveal that the difference between asymptomatic and symptomatic children with NoV may possibly be related to the NoV genogroups involved. The findings emphasize NoV genetic diversity and predominance of GII.Pe/GII.4 Sydney 2012, indicative of increased NoV activity. An uncommon GII.P15 and two unassigned GII.4 variants were also identified from rural settings of the Vhembe District/South Africa. NoV surveillance is required to help to inform investigations into NoV evolution, and to support vaccine development programmes in Africa.

Keywords: asymptomatic, common, outpatients, norovirus genetic diversity, sporadic gastroenteritis, South African rural communities, symptomatic

Procedia PDF Downloads 175

Authors: Gabriela Borilova, Monika Petrakova, Petr Kralik

Abstract:

Nowadays, European producers are increasingly interested in the production of halal meat products. Halal meat has been increasingly appearing in the EU's market network and meat products from European producers are being exported to Islamic countries. Halal criteria are mainly related to the origin of muscle used in production, and also to the way products are obtained and processed. Although the EU has legislatively addressed the question of food authenticity, the circumstances of previous years when products with undeclared horse or poultry meat content appeared on EU markets raised the question of the effectiveness of control mechanisms. Replacement of expensive or not-available types of meat for low-priced meat has been on a global scale for a long time. Likewise, halal products may be contaminated (falsified) by pork or food components obtained from pigs. These components include collagen, offal, pork fat, mechanically separated pork, emulsifier, blood, dried blood, dried blood plasma, gelatin, and others. These substances can influence sensory properties of the meat products - color, aroma, flavor, consistency and texture or they are added for preservation and stabilization. Food manufacturers sometimes access these substances mainly due to their dense availability and low prices. However, the use of these substances is not always declared on the product packaging. Verification of the presence of declared ingredients, including the detection of undeclared ingredients, are among the basic control procedures for determining the authenticity of food. Molecular biology methods, based on DNA analysis, offer rapid and sensitive testing. The PCR method and its modification can be successfully used to identify animal species in single- and multi-ingredient raw and processed foods and qPCR is the first choice for food analysis. Like all PCR-based methods, it is simple to implement and its greatest advantage is the absence of post-PCR visualization by electrophoresis. qPCR allows detection of trace amounts of nucleic acids, and by comparing an unknown sample with a calibration curve, it can also provide information on the absolute quantity of individual components in the sample. Our study addresses a problem that is related to the fact that the molecular biological approach of most of the work associated with the identification and quantification of animal species is based on the construction of specific primers amplifying the selected section of the mitochondrial genome. In addition, the sections amplified in conventional PCR are relatively long (hundreds of bp) and unsuitable for use in qPCR, because in DNA fragmentation, amplification of long target sequences is quite limited. Our study focuses on finding a suitable genomic DNA target and optimizing qPCR to reduce variability and distortion of results, which is necessary for the correct interpretation of quantification results. In halal products, the impact of falsification of meat products by the addition of components derived from pigs is all the greater that it is not just about the economic aspect but above all about the religious and social aspect. This work was supported by the Ministry of Agriculture of the Czech Republic (QJ1530107).

Keywords: food fraud, halal food, pork, qPCR

Procedia PDF Downloads 233

Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

Abstract:

Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

Procedia PDF Downloads 60

Authors: Bilal Murtaza, Xiaoyu Li, Liming Dong, Muhammad Kashif Saleemi, Gen Li, Bowen Jin, Lili Wang, Yongping Xu

Abstract:

Fusarium spp. produce numerous mycotoxins, such as zearalenone (ZEN), deoxynivalenol (DON), and its acetylated compounds, 3-acetyl-deoxynivalenol (3-ADON) and 15-acetyl-deoxynivalenol (15-ADON) (15-ADON). In a co-culture system, the soil-derived Escherichia marmotae strain degrades ZEN and DON into 3-keto-DON and DOM-1 via enzymatic deep-oxidation. When pure mycotoxins were subjected to Escherichia marmotae in culture flasks, degradation, and detoxification were also attained. DON and ZEN concentrations, ambient pH, incubation temperatures, bacterium concentrations, and the impact of acid treatment on degradation were all evaluated. The results of the ELISA and high-performance liquid chromatography-electrospray ionization-high resolution mass spectrometry (HPLC-ESI-HRMS) tests demonstrated that the concentration of mycotoxins exposed to Escherichia marmotae was significantly lower than the control. ZEN levels were reduced by 43.9%, while zearalenone sulfate ([M/z 397.1052 C18H21O8S1) was discovered as a derivative of ZEN converted by microbes to a less toxic molecule. Furthermore, Escherichia marmotae appeared to metabolize DON 35.10% into less toxic derivatives (DOM-1 at m/z 281 of [DON - O]+ and 3-keto-DON at m/z 295 of [DON - 2H]+). These results show that Escherichia marmotae can reduce Fusarium mycotoxins production, degrade pure mycotoxins, and convert them to less harmful compounds, opening up new possibilities for study and innovation in mycotoxin detoxification.

Keywords: mycotoxins, zearalenone, deoxynivalenol, bacterial degradation

Procedia PDF Downloads 74

Authors: Ugochukwu Ejike Akpudo, Jank-Wook Hur

Abstract:

The increasing demand for improved productivity, maintainability, and reliability has prompted rapidly increasing research studies on the emerging condition-based maintenance concept- Prognostics and health management (PHM). Varieties of fuel pumps serve critical functions in several hydraulic systems; hence, their failure can have daunting effects on productivity, safety, etc. The need for condition monitoring and assessment of these pumps cannot be overemphasized, and this has led to the uproar in research studies on standard feature extraction techniques for optimized condition assessment of fuel pumps. By extracting time-based, frequency-based and the more robust time-frequency based features from these vibrational signals, a more comprehensive feature assessment (and selection) can be achieved for a more accurate and reliable condition assessment of these pumps. With the aid of emerging deep classification and regression algorithms like the locally linear embedding (LLE), we propose a method for comprehensive condition assessment of electromagnetic fuel pumps (EMFPs). Results show that the LLE as a comprehensive feature extraction technique yields better feature fusion/dimensionality reduction results for condition assessment of EMFPs against the use of single features. Also, unlike other feature fusion techniques, its capabilities as a fault classification technique were explored, and the results show an acceptable accuracy level using standard performance metrics for evaluation.

Keywords: electromagnetic fuel pumps, comprehensive feature extraction, condition assessment, locally linear embedding, feature fusion

Procedia PDF Downloads 105

Authors: Ummi Sholihah Pertiwi Abidin, Eva Fadhilah

Abstract:

Social changes are more and more growing and having many different forms as the time passed and thought methods in the society. One of many forms of that social changes is the emancipation of women that is flourishing by the inception of gender equality perception between men and women in all aspects including education. It’s not anymore found the distinction between genders in learning and the education achieving right at this globalized era. But, it is still many perceptions which are against that equality of education achieving right, either come from the women’s selves or many external factors. They assumed that they are going to be a mother in the future, and a wife, someone with responsible for taking care of the household and everything inside, while the husband is the one who has the responsible for looking for the living. So comes from this kind of assumption, the perception against the education equality between genders, which means there is no need for them –women- to achieve the high education because they will still end up as housewives. Except those working or career women that need high education to support their works. These women are not aware that even a mother needs the high and capable education. Because, as the 'mother to be,' they surely need broad knowledge from the education to educate their children in the future. It is such a big fault to say the kind of thing, 'It is no matter that I am not educated, in case I’m just a housewife. The important thing is my children get a great education'. Unfortunately, it is still often found, saying 'A housewife job is not a big deal to do with high education.' This qualitative method paper raises a theme about the importance of education for women, no matter what will they be in the future. Because however, and whatever is the woman’s career outside the house, or even not working outside, she’s still a mother for her children, and 'educational provision' is a great need. And so forth, this educational provision is a big deal to do with future generation’s foredoom, regarding the first source of children’s knowledge and the first school for them is their mother.

Keywords: women education, mother to be, educational provision, first school, future generation’s foredoom

Procedia PDF Downloads 254

Authors: Young-Ji Kim, Jin-Hyeong Park, Hong-Seok Kim, Jung-Whan Chon, Kwang-Yeop Kim, Dong-Hyeon Kim, Il-Byeong Kang, Da-Na Jeong, Jin-Hyeok Yim, Ho-Seok Jang, Kwang-Young Song, Kun-Ho Seo

Abstract:

Stenotrophomonas maltophilia is one of the emerging opportunistic pathogens, and also known to have extensive drug resistance intrinsically including carbepenems which is last resort for most serious infections. One possible way for S. maltophilia to infect human is via wastewater treatment plant (WWTP). In the period between October 2016 and February 2017, effluent samples of WWTP from 3 different pig farms were collected once a month and screened for isolation of S. maltophilia. Total 16 strains of S. maltophilia were isolated and, the antibiotic susceptibility phenotypes were determined by Vitek 2 system for 16 antibiotics, ampicillin (AMP), amoxicillin/clavulanic acid (AMC), piperacillin/tazobactam (TZP), cefazolin (CZ), cefoxitin (FOX), cefotaxime (CTX), ceftazidime (CAZ), cefepime (FEP), aztreonam (AZT), ertapenem (ETP), imipenem (IMP), amikacin (AK), gentamicin (GN), ciprofloxacin (CIP), tigecycline (TGC) and trimethoprim/sulfamethoxazole (SXT). All isolates showed high resistance to AMP (100%), CZ (100%), FOX (100%), CTX (100%), CAZ (100%), FEP (94%), AZT (100%), ETP (100%), IMP (100%), AK (100%), GN (100%) whereas were susceptible to CIP (0%), TGC (0%), SXT (6%). All strains harbored at least one of the antibiotic resistance determinant such as spgM, rmlA, and rpfF. Some isolates had similar MLST (multilocus sequence typing) types with clinical isolates, suggesting WWTP could have potential role in the transmission of S. maltophilia to aquatic environment and, possibly, to humans.

Keywords: Stenotrophomonas maltophilia, Carbapenem resistance, waste water treatment plant, pig farm

Procedia PDF Downloads 449

Authors: Ali Ghorbanipour

Abstract:

The aim of this study is to investigate the drug resistance pattern and the value of the MIC (minimum inhibitory concentration)method to reduce the impact of infectious diseases and the slow development of resistance. Method: The study was conducted on clinical specimens collected between 2018 to 2021. identification of isolates and antibiotic susceptibility testing were performed using conventional biochemical tests. Antibiotic resistance was determined using kibry-Bauer disk diffusion and MIC by E-test methods comparative with microdilution plate elisa method. Results were interpreted according to CLSI. Results: Out of 249600 different clinical specimens, 18720 different pathogenic bacteria by overall detection ratio 7.7% were detected. Among pathogen bacterial were Gram negative bacteria (70%,n=13000) and Gram positive bacteria(30%,n=5720).Medically relevant gram-negative bacteria include a multitude of species such as E.coli , Klebsiella .spp , Pseudomonas .aeroginosa , Acinetobacter .spp , Enterobacterspp ,and gram positive bacteria Staphylococcus.spp , Enterococcus .spp , Streptococcus .spp was isolated . Conclusion: Our results highlighted that the resistance ratio among Gram Negative bacteria and Gram positive bacteria with different infection is high it suggest constant screening and follow-up programs for the detection of antibiotic resistance and the value of MIC drug susceptibility reporting that provide a new way to the usage of resistant antibiotic in combination with other antibiotics or accurate weight of antibiotics that inhibit or kill bacteria. Evaluation of wrong medication in the expansion of resistance and side effects of over usage antibiotics are goals. Ali ghorbanipour presently working as a supervision at the microbiology department of Massoud medical laboratory. Iran. Earlier, he worked as head department of pulmonary infection in firoozgarhospital, Iran. He received master degree in 2012 from Fergusson College. His research prime objective is a biologic wound dressing .to his credit, he has Published10 articles in various international congresses by presenting posters.

Keywords: antimicrobial profile, MIC & MBC Method, microplate antimicrobial assay, E-test

Procedia PDF Downloads 121

Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

Abstract:

Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

Procedia PDF Downloads 52

Authors: Dmytro D. Buiadzhy

Abstract:

The modern psychiatric discourse proves the existence of the direct ties between the children's mental health and their success in life as adults. The unresolved mental health problems in childhood are likely to lead individuals to poverty, isolation, and social exclusion as stated by Marcus Richards. Such an approach justifies the involvement of children in the view of supervision and control of power. The discourse, related to the mental health of children, provides a tight impact of family, educational institutions and medical authorities on the child through any manifestations of his psychic, having signs of "abnormality.” Throughout the adult life, the individual continues to feel the pressure of power through legal, political, and economic institutions that also appeal to the mental health regulation. The juvenile law declares the equality of a child and an adult, but in fact simply delegates the powers of parents to impersonal social institutions of the guardianship, education, and social protection. The psychiatric power in this study is considered in accordance with the Michel Foucault’s concept of power as a manifestation of "positive" technologies of power, which include various manifestations of subjectivity, in particular children’s one, in a view of supervision and control of the state power. The main issue disclosed in this paper is how weakening of the parental authority, in the context of legislative ratification of the child rights, strengthens the other forms of power over children, especially the psychiatric power, which justifies and affects the children mancipation.

Keywords: child rights, psychiatric power, discourse, parental authority

Procedia PDF Downloads 331

Authors: Naima Nur, Safa Islam, Saeema Islam, Faridul Alam

Abstract:

Background: Drug-resistant pulmonary tuberculosis (DR-PTB), particularly multidrug-resistant tuberculosis (MDR-TB) and pre-extensive drug-resistant (pre-XDR), is a major challenge in effectively controlling TB, especially in developing. This study aimed to identify the strains of M. tuberculosis complex (MTC) and drug resistance patterns among the pulmonary tuberculosis patients. Methods: The study used a cross-sectional design, and 815 patients were recruited randomly in three study periods. In the first-period, 210 treated PTB patients, who were completed their treatment, received their diagnoses using light microscopy, fluorescence microscopy and cultured on Lowenstein-Jensen (L-J) slant, and then strains were identified as MTC by biochemical tests, and then sensitivity test in National Institute of Diseases of the Chest and Hospital. In the second-period, 220 re-treated PTB patients, who were completed their treatment, received their diagnoses using culture on L-J slant, line probe assay (LPA), and GeneXpert in the same hospital. In the last-period, during treatment, 385 MDR-PTB patients received their diagnoses using culture on L-J slant and LPA in the same hospital. Results: Among sixty-two (29.5%) PTB patients, 13% were sensitive to all first-line anti-TB drugs, 26% were MDR-TB patients, and 14.2% were pre-XDR-TB among 14 MDR-TB patients. After three years, 31% were MDR-TB among 220 re-treated PTB patients. After five years, 16.4% was pre-XDR-TB among 385 MDR-TB patients. Compared to females, male patients were significantly higher at all times. Conclusion: The current study demonstrated that in three study periods, the proportions of DR-TB, MDR-TB, and pre-XDR patients were an alarming issue and increasing daily.

Keywords: multi-drug resistant, drug-resistant, pre-extensive drug resistant, pulmonary tuberculosis

Procedia PDF Downloads 36

Authors: Bitewulign Mekonnen

Abstract:

Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

Procedia PDF Downloads 71

Authors: Ali Bahkali, Rayan Yousef Booq, Mohammad Khiyami

Abstract:

Soil samples were collected from five different regions in the Kingdom of Saudi Arabia. Microbiological methods included dilution methods and pour plates to isolate and purify bacteria soil. The ability of isolates to develop biopolymer was investigated on petri dishes containing elements and substance concentrations stimulating developing biopolymer. Fluorescent stains, Nile red and Nile blue were used to stain the bacterial cells developing biopolymers. In addition, Sudan black was used to detect biopolymers in bacterial cells. The isolates which developed biopolymers were identified based on their gene sequence of 1 6sRNA and their ability to grow and synthesize PHAs on mineral medium supplemented with 1% dates molasses as the only carbon source under nitrogen limitation. During the study 293 bacterial isolates were isolated and detected. Through the initial survey on the petri dishes, 84 isolates showed the ability to develop biopolymers. These bacterial colonies developed a pink color due to accumulation of the biopolymers in the cells. Twenty-three isolates were able to grow on dates molasses, three strains of which showed the ability to accumulate biopolymers. These strains included Bacillus sp., Ralstonia sp. and Microbacterium sp. They were detected by Nile blue A stain with fluorescence microscopy (OLYMPUS IX 51). Among the isolated strains Ralstonia sp. was selected after its ability to grow on molasses dates in the presence of a limited nitrogen source was detected. The optimum conditions for formation of biopolymers by isolated strains were investigated. Conditions studied included, best incubation duration (2 days), temperature (30°C) and pH (7-8). The maximum PHB production was raised by 1% (v1v) when using concentrations of dates molasses 1, 2, 3, 4 and 5% in MSM. The best inoculated with 1% old inoculum (1= OD). The ideal extraction method of PHA and PHB proved to be 0.4% sodium hypochlorite solution, producing a quantity of polymer 98.79% of the cell's dry weight. The maximum PHB production was 1.79 g/L recorded by Ralstonia sp. after 48 h, while it was 1.40 g/L produced by R.eutropha ATCC 17697 after 48 h.

Keywords: bacteria forming polyhydroxyalkanoate, detection, molecular, Saudi Arabia

Procedia PDF Downloads 329

Authors: Reham Banwair, Lana Alshehri, Sara Hadrawi

Abstract:

The purpose of this project is to identify user satisfaction with the AI functions on Snapchat, in order to generate improvement proposals that allow its development within the app. To achieve this, a qualitative analysis was carried out through interviews to people who usually use the application, revealing their satisfaction or dissatisfaction with the usefulness of the AI. In addition, the background of the company and its introduction in these algorithms were analyzed. Furthermore, the characteristics of the three main functions of AI were explained: identify songs, solve mathematical problems, and recognize plants. As a result, it was obtained that 50% still do not know the characteristics of AI, 50% still believe song recognition is not always correct, 41.7% believe that math problems are usually accurate and 91.7% believes the plant detection tool is working properly.

Keywords: artificial intelligence, scanning, Snapchat, machine learning

Procedia PDF Downloads 114

Authors: Syed Amer Mahmood, Rao Mansor Ali Khan

Abstract:

This study deals with the remote sensing analysis of tectonic deformation and its implications to understand the regional uplift conditions in the lower Jhelum and eastern Potwar. Identification and mapping of active structures is an important issue in order to assess seismic hazards and to understand the Quaternary deformation of the region. Digital elevation models (DEMs) provide an opportunity to quantify land surface geometry in terms of elevation and its derivatives. Tectonic movement along the faults is often reflected by characteristic geomorphological features such as elevation, stream offsets, slope breaks and the contributing drainage area. The river profile analysis in this region using SRTM digital elevation model gives information about the tectonic influence on the local drainage network. The steepness and concavity indices have been calculated by power law of scaling relations under steady state conditions. An uplift rate map is prepared after carefully analysing the local drainage network showing uplift rates in mm/year. The active faults in the region control local drainages and the deflection of stream channels is a further evidence of the recent fault activity. The results show variable relative uplift conditions along MBT and Riasi and represent a wonderful example of the recency of uplift, as well as the influence of active tectonics on the evolution of young orogens.

Keywords: quaternary deformation, SRTM DEM, geomorphometric indices, active tectonics and MBT

Procedia PDF Downloads 335

Authors: Deike Janssen

Abstract:

In the scenery of the Tyrolean mountains, at an altitude of 1300 meters, stands a building. Residents and activists call it a prison. However, it is not a prison -according to authorities, it is a 'Return Counseling Facility' where migrants and refugees should be "motivated" to return "voluntary" to their countries of origin. This paper argues that the geographical location of the camp functions as a site of exclusion, isolation, and coercion where no one can decide “voluntary” to return, but where people are brought to despair to leave Austria. Through a qualitative case study, this paper documents the heavy impact of offshore detention on the mental, physical and social state of the residents and a variety of human rights problems in the centre. Different developments at the Return Counselling Facility and the law that back up the centre uncover a worrying dynamic that deliberately accepts human rights problems in order to enforce borders, a policy that disregards humanitarian, legal, and ethical stands in order to deport people at all hazards. It, therefore, can be seen as a creative and ultimate exercise of state power, which uses isolated locations to control migration. While the analysis revises the micro and macro implications of the facility and, therefore, the legal and political facets, it also sheds light on the role of the civil society, which tries to increase through constant and collective efforts the human rights efforts of the government.

Keywords: deportation, human rights, migration, refugee detention, voluntary return

Procedia PDF Downloads 124

Authors: Linjie Zhang, Baifen Ren, Xi Zhang, Genwang Liu

Abstract:

Ship detection is of great significance for both military and civilian applications. Synthetic aperture radar (SAR) with all-day, all-weather, ultra-long-range characteristics, has been used widely. In view of the low time resolution of low orbit SAR and the needs for high time resolution SAR data, GEO (Geosynchronous orbit) SAR is getting more and more attention. Since GEO SAR has short revisiting period and large coverage area, it is expected to be well utilized in marine ship targets monitoring. However, the height of the orbit increases the time of integration by almost two orders of magnitude. For moving marine vessels, the utility and efficacy of GEO SAR are still not sure. This paper attempts to find the feasibility of GEO SAR by giving a GEO SAR simulator of moving ships. This presented GEO SAR simulator is a kind of geometrical-based radar imaging simulator, which focus on geometrical quality rather than high radiometric. Inputs of this simulator are 3D ship model (.obj format, produced by most 3D design software, such as 3D Max), ship's velocity, and the parameters of satellite orbit and SAR platform. Its outputs are simulated GEO SAR raw signal data and SAR image. This simulating process is accomplished by the following four steps. (1) Reading 3D model, including the ship rotations (pitch, yaw, and roll) and velocity (speed and direction) parameters, extract information of those little primitives (triangles) which is visible from the SAR platform. (2) Computing the radar scattering from the ship with physical optics (PO) method. In this step, the vessel is sliced into many little rectangles primitives along the azimuth. The radiometric calculation of each primitive is carried out separately. Since this simulator only focuses on the complex structure of ships, only single-bounce reflection and double-bounce reflection are considered. (3) Generating the raw data with GEO SAR signal modeling. Since the normal ‘stop and go’ model is not available for GEO SAR, the range model should be reconsidered. (4) At last, generating GEO SAR image with improved Range Doppler method. Numerical simulation of fishing boat and cargo ship will be given. GEO SAR images of different posture, velocity, satellite orbit, and SAR platform will be simulated. By analyzing these simulated results, the effectiveness of GEO SAR for the detection of marine moving vessels is evaluated.

Keywords: GEO SAR, radar, simulation, ship

Procedia PDF Downloads 161

Authors: S. M. Moustafa, A. Ezzat, Y. S. Taha, G. H. Hassib, S. Hamada

Abstract:

After the occurrence of 14, Nov, 1981 earthquake (M = 5.3), on Kalabska fault, south of Egypt, seismic stations distributed in and around the Kalabsha area, in order to monitoring, recording and studying the seismic activity in the area. In addition of that, from 1989 a number of piezometer wells drilled in the same area, distribed on at the both side of the active faults area and in different water bearing formations, in order to measuring the groundwater parameters (level, temperature, ph, and conductivity) to monitoring the relationship between those parameters and the seismic activity at Kalabsha area. The behavior of groundwater due to seismic activity over the world studied by several scientists i.e. H. Wakita (1979) on Izu-Oshima earthquake (M= 7.0) at Japan, M. E. Contadakis & G.asteriadis (1972), and Evans (1966), they found an anomalies on groundwater measurements prior, co, and post the occurrence of bigger earthquakes, referring to the probability of precursory evidence of impending earthquakes. In Kalabsha area south of Egypt, this study has been done using recorded seismic data, and the measurements of underground water parameters. same phenomena of anomalies founded on groundwater measurements pre, co. and post the occurrence of earthquakes with magnitude bigger than 3, and no systematic regularity exists for epicenter distance, duration of anomalies or time lag between anomalies appear and occurrence of events. Also the results found present strong relation between the groundwater in the upper unconfined aquifer Nubian Sandstone formation, and Kalabsha seismic activity, otherwise no relation between the seismic activities in the area with the deep groundwater in the lower confined aquifer Sandstone.

Keywords: seismicity, groundwater, Aswan, Egypt

Procedia PDF Downloads 368

Authors: Tesfaye Mengistu

Abstract:

Power cables play a crucial role in the transmission and distribution of electrical energy. As the electricity generation, transmission, distribution, and storage systems become smarter, there is a growing emphasis on incorporating intelligent approaches to ensure the reliability of power cables. Various types of electrical cables are employed for transmitting and distributing electrical energy, with cross-linked polyethylene (XLPE) cables being widely utilized due to their exceptional electrical and mechanical properties. However, insulation defects can occur in XLPE cables due to subpar manufacturing techniques during production and cable joint installation. To address this issue, experts have proposed different methods for monitoring XLPE cables. Some suggest the use of interdigital capacitive (IDC) technology for online monitoring, while others propose employing continuous wave (CW) terahertz (THz) imaging systems to detect internal defects in XLPE plates used for power cable insulation. In this study, we have developed models that employ a custom dataset collected locally to classify the physical safety status of individual power cables. Our models aim to replace physical inspections with computer vision and image processing techniques to classify defective power cables from non-defective ones. The implementation of our project utilized the Python programming language along with the TensorFlow package and a convolutional neural network (CNN). The CNN-based algorithm was specifically chosen for power cable defect classification. The results of our project demonstrate the effectiveness of CNNs in accurately classifying power cable defects. We recommend the utilization of similar or additional datasets to further enhance and refine our models. Additionally, we believe that our models could be used to develop methodologies for detecting power cable defects from live video feeds. We firmly believe that our work makes a significant contribution to the field of power cable inspection and maintenance. Our models offer a more efficient and cost-effective approach to detecting power cable defects, thereby improving the reliability and safety of power grids.

Keywords: artificial intelligence, computer vision, defect detection, convolutional neural net

Procedia PDF Downloads 89

Authors: Ifigeneia V. Mavragani, Zacharenia Nikitaki, George Kalantzis, George Iliakis, Alexandros G. Georgakilas

Abstract:

Complex DNA damage consisting of a combination of DNA lesions, such as Double Strand Breaks (DSBs) and non-DSB base lesions occurring in a small volume is considered as one of the most important biological endpoints regarding ionizing radiation (IR) exposure. Strong theoretical (Monte Carlo simulations) and experimental evidence suggests an increment of the complexity of DNA damage and therefore repair resistance with increasing linear energy transfer (LET). Experimental detection of complex (clustered) DNA damage is often associated with technical deficiencies limiting its measurement, especially in cellular or tissue systems. Our groups have recently made significant improvements towards the identification of key parameters relating to the efficient detection of complex DSBs and non-DSBs in human cellular systems exposed to IR of varying quality (γ-, X-rays 0.3-1 keV/μm, α-particles 116 keV/μm and 36Ar ions 270 keV/μm). The induction and processing of DSB and non-DSB-oxidative clusters were measured using adaptations of immunofluorescence (γH2AX or 53PB1 foci staining as DSB probes and human repair enzymes OGG1 or APE1 as probes for oxidized purines and abasic sites respectively). In the current study, Relative Biological Effectiveness (RBE) values for DSB and non-DSB induction have been measured in different human normal (FEP18-11-T1) and cancerous cell lines (MCF7, HepG2, A549, MO59K/J). The experimental results are compared to simulation data obtained using a validated microdosimetric fast Monte Carlo DNA Damage Simulation code (MCDS). Moreover, this simulation approach is implemented in two realistic clinical cases, i.e. prostate cancer treatment using X-rays generated by a linear accelerator and a pediatric osteosarcoma case using a 200.6 MeV proton pencil beam. RBE values for complex DNA damage induction are calculated for the tumor areas. These results reveal a disparity between theory and experiment and underline the necessity for implementing highly precise and more efficient experimental and simulation approaches.

Keywords: complex DNA damage, DNA damage simulation, protons, radiotherapy

Procedia PDF Downloads 304

Authors: Kahsay Tadesse Mawcha, Na Zhang, Xu Yiying, Chang Jiaying, Wenxiang Yang

Abstract:

Fusarium crown rot (FCR) diseased wheat seedlings were collected from different wheat-growing counties in seven different regions (Baoding, Cangzhou, Handan, Hengshui, Langfang, Shijiazhuang, and Xingtai) in Hebei province, China from 2019 to 2020. One-hundred twenty-two Fusarium isolates were isolated from crown rot diseased wheat seedlings and identified morphologically, confirmation was undertaken molecularly, and species-specific PCR was utilized to verify the morphological identification of F. psuedograminearum, F. graminearum, F. asiaticum, and F. culmorum. The predominant Fusarium species associated with wheat crown rot in the Hebei province were F. psuedograminearum, F. graminearum, F. asiaticum, and F. culmorum with isolation frequency of 85.25%, 12.30%, 1.64%, and 0.81%, respectively. All the Fusarium strains isolated from the different wheat-growing fields were qualitatively tested for toxigenic chemotypes using toxin-specific primers and chemotaxonomically classified into DON, 3-ADON, 15-ADON, and NIV. Among F. psuedograminearum identified, 84.62% were classified as DON chemotypes, 6.73% as 15-ADON chemotypes, 3.84% as 3-ADON chemotypes, and 4.81% of them had NIV as detected by the toxin-specific PCR results. Most of the F. graminearum isolates produced 15-ADON, and only two isolates had NIV chemotypes. F. asiaticum and F. culmorum produce chemotype of 15-ADON and 3-ADON, respectively. Pathogenicity test results showed that F. pseudograminearum and F. graminearum had strong pathogenicity, and F. asiaticum and F. culmorum had moderate pathogenicity to wheat in Hebei province.

Keywords: crown rot, pathogen, wheat, Fusarium species, mycotoxin

Procedia PDF Downloads 111

Authors: Iván Castelló, Georgiana Stoica, Emilio Palomares, Fernando Bravo

Abstract:

We present herein two colour encoded silica nanospheres (2nanoSi) for the fluorescence quantitative ratiometric determination of trypsin in humans. The system proved to be a faster (minutes) method, with two times higher sensitivity than the state-of-the-art biomarkers based sensors for cystic fibrosis (CF), allowing the quantification of trypsin concentrations in a wide range (0-350 mg/L). Furthermore, as trypsin is directly related to the development of cystic fibrosis, different human genotypes, i.e. healthy homozygotic (> 80 mg/L), CF homozygotic (< 50 mg/L), and heterozygotic (> 50 mg/L), respectively, can be determined using our 2nanoSi nanospheres.

Keywords: cystic fibrosis, trypsin, quantum dots, biomarker, homozygote, heterozygote

Procedia PDF Downloads 466

Authors: June Tay, Ivy Chia

Abstract:

There are numerous changes, both mental and physical, taking place when people age. We need to understand the different aspects required for healthy living, including meeting nutritional needs, regular physical activities to keep agility, sufficient rest and sleep to have physical and mental well-being, social engagement to avoid the risk of social isolation and depression, and access to healthcare to detect and manage chronic conditions. Promoting physical activities for an ageing population is necessary as many may have enjoyed sedentary lifestyles for some time. In our study, we evaluate the considerations when designing a computer vision video game for the elderly. We need to design some low-impact activities, such as stretching and gentle movements, because some elderly individuals may have joint pains or mobility issues. The exercise game should consist of simple movements that are easy to follow and remember. It should be fun and enjoyable so that they can be motivated to do some exercise. Social engagement can keep the elderly motivated and competitive, and they are more willing to engage in game exercises. Elderly citizens can compare their game scores and try to improve them. We propose a computer vision-based video game for the elderly that will capture and track the movement of the elderly hand pushing a ball on the screen into a circle. It can be easily set up using a PC laptop with a webcam. Our video game adhered to the design framework we employed, and it encompassed ease of use, a simple graphical interface, easy-to-play game exercise, and fun gameplay.

Keywords: about computer vision, video games, gerontology technology, caregiving

Procedia PDF Downloads 59

Authors: R. L. Ujjwal, Abhishek Jain

Abstract:

In the field of computer vision, most facial variations such as identity, expression, emotions and gender have been extensively studied. Automatic age estimation has been rarely explored. With age progression of a human, the features of the face changes. This paper is providing a new comparable study of different type of algorithm to feature extraction [Hybrid features using HAAR cascade & HOG features] & classification [KNN & SVM] training dataset. By using these algorithms we are trying to find out one of the best classification algorithms. Same thing we have done on the feature selection part, we extract the feature by using HAAR cascade and HOG. This work will be done in context of age group classification model.

Keywords: computer vision, age group, face detection

Procedia PDF Downloads 353

Authors: M. Fathy, I. Maarouf, S. El-Sayary

Abstract:

Our buildings are responsible for around 50% of energy consumption and most of this consumption because of spaces design, low heat isolation building material and occupant presence and behavior in buildings beside non-efficient architectural treatments. It has been shown to have large impact on heating, cooling and ventilation demand, energy consumption of lighting and appliances, and building controls. This paper aims to focus on passive treatments in Wakala Buildings in Cairo and how far it meets the LEED Criteria as the LEED – Leadership in Energy and Environmental Design – considered the widest spread rating system in the world. By studying Wakala buildings in Cairo, there are a lot of environmental potentials in it in the field of passive treatments and energy efficiency that could be found in examples by surveying and analyzing Wakala buildings. Besides the environmental treatments through the natural materials and façade architectural treatments, there is a measuring phase to declare the efficiency of the Wakala building through temperature decline between outdoor and indoor the Wakala building. Also, measuring how far the indoor conditions matched the thermal comfort for occupants. After measuring the Wakala buildings, it is the role of applying the criteria of LEED rating system to find out how fare Wakala buildings meet the LEED rating system criteria. After all, the building technologies used in Wakala buildings in the field of passive design and caused that energy efficiency would be clear and what is needed for Wakala buildings to have a LEED Certification.

Keywords: energy awareness, historical commercial buildings, LEED, Wakala buildings

Procedia PDF Downloads 185

Authors: H. T. Le Luong, C. Hénaux, F. Messine, G. Bueno-Mariani, S. Mollov, N. Voyer

Abstract:

This paper presents a comparative study of different modular brushless wound rotor synchronous machine (MB-WRSM). The goal of the study is to highlight the structure which offers the best fault tolerant capability and the highest output performances. The fundamental winding factor is calculated by using the method based on EMF phasors as a significant criterion to select the preferred number of phases, stator slots, and poles. With the limited number of poles for a small machine (3.67kW/7000rpm), 15 different machines for preferred phase/slot/pole combinations are analyzed using two-dimensional (2-D) finite element method and compared according to three criteria: torque density, torque ripple and efficiency. The 7phase/7slot/6pole machine is chosen with the best compromise of high torque density, small torque ripple (3.89%) and high nominal efficiency (95%). This machine is then compared with a reference design surface permanent magnet synchronous machine (SPMSM). In conclusion, this paper provides an electromagnetic analysis of a new brushless wound-rotor synchronous machine using multiphase non-overlapping fractional slot double layer winding. The simulation results are discussed and demonstrate that the MB-WRSM presents interesting performance features, with overall performance closely matching that of an equivalent SPMSM.

Keywords: finite element method (FEM), machine performance, modular wound rotor synchronous machine, non-overlapping concentrated winding

Procedia PDF Downloads 274

Authors: Hassan Saffari;, Reza Askari Moghadam

Abstract:

In modern communication systems, it is highly demanded to achieve high performance with minimal power consumption. Low actuation voltage RF MEMS (Micro-Electro-Mechanical Systems) switches represent a significant advancement in this regard. These switches, with their ability to operate at lower voltages, offer promising solutions for enhancing connectivity while minimizing energy consumption. Microelectromechanical switches are good alternatives for electronic and mechanical switches due to their low insertion loss, high isolation, and fast switching speeds. They have attracted more attention in recent years. Most of the presented RF MEMS switches use electrostatic actuators due to their low power consumption. Low actuation voltage RF MEMS switches are among the important issues that have been investigated in research articles. The actuation voltage can be reduced by different methods. One usually implemented method is low spring constant structures. However, despite their numerous benefits, challenges remain in the widespread adoption of low-actuation voltage RF MEMS switches. Issues related to reliability, durability, and manufacturing scalability need to be addressed to realize their full potential in commercial applications. While overcoming certain challenges, their exceptional performance characteristics and compatibility with miniaturized electronic systems make them a promising choice for next-generation wireless communication and RF applications. In this paper, some previous works that proposed low-voltage actuation RF MEMS switches are investigated and analyzed.

Keywords: RF MEMS switches, low actuation voltage, small spring constant structures, electrostatic actuation

Procedia PDF Downloads 29

Authors: Seynabou Toure, Oumar Diop, Kidiyo Kpalma, Amadou S. Maiga

Abstract:

Color and texture are the two most determinant elements for perception and recognition of the objects in an image. For this reason, color and texture analysis find a large field of application, for example in image classification and segmentation. But, the pioneering work in texture analysis was conducted on grayscale images, thus discarding color information. Many grey-level texture descriptors have been proposed and successfully used in numerous domains for image classification: face recognition, industrial inspections, food science medical imaging among others. Taking into account color in the definition of these descriptors makes it possible to better characterize images. Color texture is thus the subject of recent work, and the analysis of color texture images is increasingly attracting interest in the scientific community. In optical remote sensing systems, sensors measure separately different parts of the electromagnetic spectrum; the visible ones and even those that are invisible to the human eye. The amounts of light reflected by the earth in spectral bands are then transformed into grayscale images. The primary natural colors Red (R) Green (G) and Blue (B) are then used in mixtures of different spectral bands in order to produce RGB images. Thus, good color texture discrimination can be achieved using RGB under controlled illumination conditions. Some previous works investigate the effect of using different color space for color texture classification. However, the selection of the best performing color space in land-sea segmentation is an open question. Its resolution may bring considerable improvements in certain applications like coastline detection, where the detection result is strongly dependent on the performance of the land-sea segmentation. The aim of this paper is to present the results of a study conducted on different color spaces in order to show the best-performing color space for land-sea segmentation. In this sense, an experimental analysis is carried out using five different color spaces (RGB, XYZ, Lab, HSV, YCbCr). For each color space, the Haar wavelet decomposition is used to extract different color texture features. These color texture features are then used for Fusion of Over Segmentation (FOOS) based classification; this allows segmentation of the land part from the sea one. By analyzing the different results of this study, the HSV color space is found as the best classification performance while using color and texture features; which is perfectly coherent with the results presented in the literature.

Keywords: classification, coastline, color, sea-land segmentation

Procedia PDF Downloads 230

Authors: D. Heinemann, S. Schramm, S. Knabner, D. Baumgarten

Abstract:

Purpose: Applying solder paste to printed circuit boards (PCB) with stencils has been the method of choice over the past years. A new method uses a jet printer to deposit tiny droplets of solder paste through an ejector mechanism onto the board. This allows for more flexible PCB layouts with smaller components. Due to the viscosity of the solder paste, air blisters can be trapped in the cartridge. This can lead to missing solder joints or deviations in the applied solder volume. Therefore, a built-in and real-time inspection of the printing process is needed to minimize uncertainties and increase the efficiency of the process by immediate correction. The objective of the current study is the design of an optimal imaging system and the development of an automatic algorithm for the detection of applied solder joints from optical from the captured images. Methods: In a first approach, a camera module connected to a microcomputer and LED strips are employed to capture images of the printed circuit board under four different illuminations (white, red, green and blue). Subsequently, an improved system including a ring light, an objective lens, and a monochromatic camera was set up to acquire higher quality images. The obtained images can be divided into three main components: the PCB itself (i.e., the background), the reflections induced by unsoldered positions or screw holes and the solder joints. Non-uniform illumination is corrected by estimating the background using a morphological opening and subtraction from the input image. Image sharpening is applied in order to prevent error pixels in the subsequent segmentation. The intensity thresholds which divide the main components are obtained from the multimodal histogram using three probability density functions. Determining the intersections delivers proper thresholds for the segmentation. Remaining edge gradients produces small error areas which are removed by another morphological opening. For quantitative analysis of the segmentation results, the dice coefficient is used. Results: The obtained PCB images show a significant gradient in all RGB channels, resulting from ambient light. Using different lightings and color channels 12 images of a single PCB are available. A visual inspection and the investigation of 27 specific points show the best differentiation between those points using a red lighting and a green color channel. Estimating two thresholds from analyzing the multimodal histogram of the corrected images and using them for segmentation precisely extracts the solder joints. The comparison of the results to manually segmented images yield high sensitivity and specificity values. Analyzing the overall result delivers a Dice coefficient of 0.89 which varies for single object segmentations between 0.96 for a good segmented solder joints and 0.25 for single negative outliers. Conclusion: Our results demonstrate that the presented optical imaging system and the developed algorithm can robustly detect solder joints on printed circuit boards. Future work will comprise a modified lighting system which allows for more precise segmentation results using structure analysis.

Keywords: printed circuit board jet-printing, inspection, segmentation, solder paste detection

Procedia PDF Downloads 322