Search results for: data driven diagnosis

Authors: Marjila Burhanzoi, Kenta Onohara, Tomoaki Ikegami

Abstract:

Faults in photovoltaic (PV) modules should be detected to the greatest extent as early as possible. For that conventional fault detection methods such as electrical characterization, visual inspection, infrared (IR) imaging, ultraviolet fluorescence and electroluminescence (EL) imaging are used, but they either fail to detect the location or category of fault, or they require expensive equipment and are not convenient for onsite application. Hence, these methods are not convenient to use for monitoring small-scale PV systems. Therefore, low cost and efficient inspection techniques with the ability of onsite application are indispensable for PV modules. In this study in order to establish efficient inspection technique, correlation between faults and magnetic flux density on the surface is of crystalline PV modules are investigated. Magnetic flux on the surface of normal and faulted PV modules is measured under the short circuit and illuminated conditions using two different sensor devices. One device is made of small integrated sensors namely 9-axis motion tracking sensor with a 3-axis electronic compass embedded, an IR temperature sensor, an optical laser position sensor and a microcontroller. This device measures the X, Y and Z components of the magnetic flux density (Bx, By and Bz) few mm above the surface of a PV module and outputs the data as line graphs in LabVIEW program. The second device is made of a laser optical sensor and two magnetic line sensor modules consisting 16 pieces of magnetic sensors. This device scans the magnetic field on the surface of PV module and outputs the data as a 3D surface plot of the magnetic flux intensity in a LabVIEW program. A PC equipped with LabVIEW software is used for data acquisition and analysis for both devices. To show the effectiveness of this method, measured results are compared to those of a normal reference module and their EL images. Through the experiments it was confirmed that the magnetic field in the faulted areas have different profiles which can be clearly identified in the measured plots. Measurement results showed a perfect correlation with the EL images and using position sensors it identified the exact location of faults. This method was applied on different modules and various faults were detected using it. The proposed method owns the ability of on-site measurement and real-time diagnosis. Since simple sensors are used to make the device, it is low cost and convenient to be sued by small-scale or residential PV system owners.

Keywords: fault diagnosis, fault location, integrated sensors, PV modules

Procedia PDF Downloads 221

Authors: R. Loukil, M. Chtourou, T. Damak

Abstract:

In this work, we use the Fault detection and isolation and the Fault tolerant control based on sliding mode observer in order to introduce the well diagnosis of a nonlinear system. The robustness of the proposed observer for the two techniques is tested through a physical example. The results in this paper show the interaction between the Fault tolerant control and the Diagnosis procedure.

Keywords: fault detection and isolation FDI, fault tolerant control FTC, sliding mode observer, nonlinear system, robustness, stability

Procedia PDF Downloads 370

Authors: Boumediene Hamzi, Turky N. AlOtaiby, Saleh AlShebeili, Arwa AlAnqary

Abstract:

We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.

Keywords: kernel methods, maximum mean discrepancy, seizure detection, machine learning

Procedia PDF Downloads 229

Authors: Marchant Van Der Schyff

Abstract:

South Africa remains one of the most dangerous places for women – lesbians in particular – to live freely and safely, where a culture of patriarchy and a lack of socio-economic opportunity are ubiquitous throughout its communities. While the Internet has given a wider platform to provide insights to issues plaguing lesbians, very little information exists regarding the elements used in the construction of these online reports. This is not only due to the lack of language required to contextualise lesbian issues, but also persistent institutional and societal homophobia. This article describes the technical frames used in the online news reporting of four case studies of ‘queercide’. Through using a thematic coding sheet, data was collected from 70 online articles purposively selected based on priori population characteristics. The study found technical elements, such as the length of online reports, credible sources used, ‘code driven’-, and ‘user driven’ elements which were identified in the coded online articles. From the conclusions some clear trends emerged enabling the construction of a Venn-type diagram which present insights to how the murder of lesbians (referred to as ‘queercide’ in the article) is being reported on by online news media compared to the contemporary theoretical discussions on how these cases should be reported on.

Keywords: journalism, lesbian murder, queercide, technical frames, reporting, online

Procedia PDF Downloads 65

Authors: P. Bharathiraja

Abstract:

Computer Aided decision making system is used to enhance diagnosis and prognosis of diseases and also to assist clinicians and junior doctors in clinical decision making. Medical Data used for decision making should be definite and consistent. Data Mining and soft computing techniques are used for cleaning the data and for incorporating human reasoning in decision making systems. Fuzzy rule based inference technique can be used for classification in order to incorporate human reasoning in the decision making process. In this work, missing values are imputed using the mean or mode of the attribute. The data are normalized using min-ma normalization to improve the design and efficiency of the fuzzy inference system. The fuzzy inference system is used to handle the uncertainties that exist in the medical data. Equal-width-partitioning is used to partition the attribute values into appropriate fuzzy intervals. Fuzzy rules are generated using Class Based Associative rule mining algorithm. The system is trained and tested using heart disease data set from the University of California at Irvine (UCI) Machine Learning Repository. The data was split using a hold out approach into training and testing data. From the experimental results it can be inferred that classification using fuzzy inference system performs better than trivial IF-THEN rule based classification approaches. Furthermore it is observed that the use of fuzzy logic and fuzzy inference mechanism handles uncertainty and also resembles human decision making. The system can be used in the absence of a clinical expert to assist junior doctors and clinicians in clinical decision making.

Keywords: decision making, data mining, normalization, fuzzy rule, classification

Procedia PDF Downloads 507

Authors: M. Bilal Ishfaq, Adnan N. Qureshi

Abstract:

COVID-19 is the most infectious disease these days, it was first reported in Wuhan, the capital city of Hubei in China then it spread rapidly throughout the whole world. Later on 11 March 2020, the World Health Organisation (WHO) declared it a pandemic. Since COVID-19 is highly contagious, it has affected approximately 219M people worldwide and caused 4.55M deaths. It has brought the importance of accurate diagnosis of respiratory diseases such as pneumonia and COVID-19 to the forefront. In this paper, we propose a hybrid approach for the automated detection of COVID-19 using medical imaging. We have presented the hybridization of manually extracted and convolutional features. Our approach combines Haralick texture features and convolutional features extracted from chest X-rays and CT scans. We also employ a minimum redundancy maximum relevance (MRMR) feature selection algorithm to reduce computational complexity and enhance classification performance. The proposed model is evaluated on four publicly available datasets, including Chest X-ray Pneumonia, COVID-19 Pneumonia, COVID-19 CTMaster, and VinBig data. The results demonstrate high accuracy and effectiveness, with 0.9925 on the Chest X-ray pneumonia dataset, 0.9895 on the COVID-19, Pneumonia and Normal Chest X-ray dataset, 0.9806 on the Covid CTMaster dataset, and 0.9398 on the VinBig dataset. We further evaluate the effectiveness of the proposed model using ROC curves, where the AUC for the best-performing model reaches 0.96. Our proposed model provides a promising tool for the early detection and accurate diagnosis of COVID-19, which can assist healthcare professionals in making informed treatment decisions and improving patient outcomes. The results of the proposed model are quite plausible and the system can be deployed in a clinical or research setting to assist in the diagnosis of COVID-19.

Keywords: COVID-19, feature engineering, artificial neural networks, radiology images

Procedia PDF Downloads 71

Authors: Yao Cheng, Weihua Zhang

Abstract:

Although the measured vibration signal contains rich information on machine health conditions, the white noise interferences and the discrete harmonic coming from blade, shaft and mash make the fault diagnosis of rolling element bearings difficult. In order to overcome the interferences of useless signals, a new fault diagnosis method combining Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) and Multipoint Optimal Minimum Entropy Deconvolution (MOMED) is proposed for the fault diagnosis of high-speed train bearings. Firstly, the CEEMDAN technique is applied to adaptively decompose the raw vibration signal into a series of finite intrinsic mode functions (IMFs) and a residue. Compared with Ensemble Empirical Mode Decomposition (EEMD), the CEEMDAN can provide an exact reconstruction of the original signal and a better spectral separation of the modes, which improves the accuracy of fault diagnosis. An effective sensitivity index based on the Pearson's correlation coefficients between IMFs and raw signal is adopted to select sensitive IMFs that contain bearing fault information. The composite signal of the sensitive IMFs is applied to further analysis of fault identification. Next, for propose of identifying the fault information precisely, the MOMED is utilized to enhance the periodic impulses in composite signal. As a non-iterative method, the MOMED has better deconvolution performance than the classical deconvolution methods such Minimum Entropy Deconvolution (MED) and Maximum Correlated Kurtosis Deconvolution (MCKD). Third, the envelope spectrum analysis is applied to detect the existence of bearing fault. The simulated bearing fault signals with white noise and discrete harmonic interferences are used to validate the effectiveness of the proposed method. Finally, the superiorities of the proposed method are further demonstrated by high-speed train bearing fault datasets measured from test rig. The analysis results indicate that the proposed method has strong practicability.

Keywords: bearing, complete ensemble empirical mode decomposition with adaptive noise, fault diagnosis, multipoint optimal minimum entropy deconvolution

Procedia PDF Downloads 364

Authors: Toufik Bensana, Slimane Mekhilef, Kamel Tadjine

Abstract:

The use of vibration analysis has been established as the most common and reliable method of analysis in the field of condition monitoring and diagnostics of rotating machinery. Rolling bearings cover a broad range of rotary machines and plays a crucial role in the modern manufacturing industry. Unfortunately, the vibration signals collected from a faulty bearing are generally non-stationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that, the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. the results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing.

Keywords: fault diagnosis, local mean decomposition, rolling element bearing, vibration analysis

Procedia PDF Downloads 400

Authors: Ssembatya Joseph Mary

Abstract:

Introduction: Cryptococcal infection in the Central Nervous System (CNS) is frequently seen in human immunodeficiency virus (HIV) patients and others with low immunity as an opportunistic fungal infection. However, CNS cryptococcal granuloma (cryptococcoma) in immunocompetent patients is rare. We present a case of CNS cryptococcoma in an immunocompetent patient and review the literature to illustrate the diagnosis and treatment of such lesions. Case presentation: A 62-year-old, HIV-negative, immunocompetent female patient with no known chronic illness presented with 5 months history of a progressive headache associated with on and off episodic generalized tonic-clonic convulsions. She had been to several hospitals before she was referred to our center with a diagnosis of a brain tumor. Before referral and despite a negative CSF analysis result, she had received treatment for bacterial meningitis with no success. At Mbarara Regional Referral Hospital (MRRH), she had surgery with an excision biopsy which showed features consistent with cryptococcosis on histology. The patient had a successful adjuvant treatment with antifungal drugs following surgery. Conclusion: The diagnosis of a parasitic CNS infection, particularly cryptococcal infection mimicking neoplastic lesions in an immunocompetent patient, was unusual. Surgical management of such lesions from different reports has a bad outcome and management remains totally conservative.

Keywords: Cryptococcal meningitis, immunocompetent patient, Uganda, low resource setting

Procedia PDF Downloads 78

Authors: A. Ferasat, S. Rostampour Yasouri

Abstract:

Seasonal flu is a highly contagious infection caused by influenza viruses. These viruses undergo genetic changes that result in new epidemics across the globe. Medical attention is crucial in severe cases, particularly for the elderly, frail, and those with chronic illnesses, as their immune systems are often weaker. The purpose of this study was to detect new subtypes of the influenza A virus rapidly using a specific RT-PCR method based on the HA gene (hemagglutinin). In the winter and spring of 2022_2023, 120 embryonated egg samples were cultured, suspected of seasonal influenza. RNA synthesis, followed by cDNA synthesis, was performed. Finally, the PCR technique was applied using a pair of specific primers designed based on the HA gene. The PCR product was identified after purification, and the nucleotide sequence of purified PCR products was compared with the sequences in the gene bank. The results showed a high similarity between the sequence of the positive samples isolated from the patients and the sequence of the new strains isolated in recent years. This RT-PCR technique is entirely specific in this study, enabling the detection and multiplication of influenza and its subspecies from clinical samples. The RT-PCR technique based on the HA gene, along with sequencing, is a fast, specific, and sensitive diagnostic method for those infected with influenza viruses and its new subtypes. Rapid molecular diagnosis of influenza is essential for suspected people to control and prevent the spread of the disease to others. It also prevents the occurrence of secondary (sometimes fatal) pneumonia that results from influenza and pathogenic bacteria. The critical role of rapid diagnosis of new strains of influenza is to prepare a drug vaccine against the latest viruses that did not exist in the community last year and are entirely new viruses.

Keywords: influenza, molecular diagnosis, patients, RT-PCR technique

Procedia PDF Downloads 63

Authors: Yayun Hsu, Henry Horng-Shing Lu

Abstract:

This paper proposes a data-driven, biology-inspired neural segmentation method of 3D drosophila Brainbow images. We use Bayesian Sequential Partitioning algorithm for probabilistic modeling, which can be used to detect somas and to eliminate cross talk effects. This work attempts to develop an automatic methodology for neuron image segmentation, which nowadays still lacks a complete solution due to the complexity of the image. The proposed method does not need any predetermined, risk-prone thresholds since biological information is inherently included in the image processing procedure. Therefore, it is less sensitive to variations in neuron morphology; meanwhile, its flexibility would be beneficial for tracing the intertwining structure of neurons.

Keywords: brainbow, 3D imaging, image segmentation, neuron morphology, biological data mining, non-parametric learning

Procedia PDF Downloads 480

Authors: Eda Jazexhiu-Postoli, Gladiola Hoxha, Ada Simeoni, Sonila Biba

Abstract:

Background Neonatal hypotonia represents a commonly encountered issue in the Neonatal Intensive Care Unit and newborn nursery. The differential diagnosis is broad, encompassing chromosome abnormalities, primary muscular dystrophies, neuropathies and inborn errors of metabolism. Aim of study Our study describes some of the main clinical features of hypotonia in newborns and presents clinical cases of neonatal hypotonia we treated in our Neonatal unit in the last 3 years. Case reports Four neonates born in our hospital presented with hypotonia after birth, one preterm newborn 35-36 weeks of gestational age and three other term newborns (38-39 weeks of gestational age). Prenatal data revealed a decrease in fetal movements in both cases. Intrapartum meconium-stained amniotic fluid was found in 75% of our hypotonic newborns. Clinical features included inability to establish effective respiratory movements and need for resuscitation in the delivery room, respiratory distress syndrome, feeding difficulties and need for oro-gastric tube feeding, dysmorphic features, hoarse voice and moderate to severe muscular hypotonia. The genetic workup revealed the diagnosis of Autosomal Recessive Congenital Myasthenic Syndrome 1-B, Sotos Syndrome, Spinal Muscular Atrophy Type 1 and Transient Hypotonia of the Newborn. Two out of four hypotonic neonates were transferred to the Pediatric Intensive Care Unit and died at the age of three to five months old. Conclusion Hypotonia is a concerning finding in neonatal care and it is suggested by decreased intrauterine fetal movements, failure to establish first breaths, respiratory distress and feeding difficulties in the neonate. Prognosis is determined by its etiology and time of diagnosis and intervention.

Keywords: hypotonic neonate, respiratory distress, feeding difficulties, fetal movements

Procedia PDF Downloads 107

Authors: Shuaili Ji

Abstract:

The correct use of verbs is an important element of high-quality research articles, and thus for Chinese EFL learners, it is significant to master characteristics of verbs and to precisely use verbs. However, some researches have shown that there are differences in using verbs between learners and native speakers and learners have difficulty in using English verbs. This corpus-based quantitative research can enhance learners’ knowledge of English verbs and promote the quality of research article abstracts even of the whole academic writing. The aim of this study is to find the differences between learners’ and native speakers’ use of verbs and to study the factors that contribute to those differences. To this end, the research question is as follows: What are the differences between most frequently used verbs by learners and those by native speakers? The research question is answered through a study that uses corpus-based data-driven approach to analyze the verbs used by learners in their abstract writings in terms of collocation, colligation and semantic prosody. The results show that: (1) EFL learners obviously overused ‘be, can, find, make’ and underused ‘investigate, examine, may’. As to modal verbs, learners obviously overused ‘can’ while underused ‘may’. (2) Learners obviously overused ‘we find + object clauses’ while underused ‘nouns (results, findings, data) + suggest/indicate/reveal + object clauses’ when expressing research results. (3) Learners tended to transfer the collocation, colligation and semantic prosody of shǐ and zuò to make. (4) Learners obviously overused ‘BE+V-ed’ and used BE as the main verb. They also obviously overused the basic forms of BE such as be, is, are, while obviously underused its inflections (was, were). These results manifested learners’ lack of accuracy and idiomatic property in verb usage. Due to the influence of the concept transfer of Chinese, the verbs in learners’ abstracts showed obvious transfer of mother language. In addition, learners have not fully mastered the use of verbs, avoiding using complex colligations to prevent errors. Based on these findings, the present study has implications for English teaching, seeking to have implications for English academic abstract writing in China. Further research could be undertaken to study the use of verbs in the whole dissertation to find out whether the characteristic of the verbs in abstracts can apply in the whole dissertation or not.

Keywords: academic writing abstracts, Chinese EFL learners, corpus-based, data-driven, verbs

Procedia PDF Downloads 323

Authors: Seong Woo Kwak, Jung-Min Yang

Abstract:

A strategy of fault diagnosis and tolerance for asynchronous sequential machines is discussed in this paper. With no synchronizing clock, it is difficult to diagnose an occurrence of permanent or stuck-in faults in the operation of asynchronous machines. In this paper, we present a fault detector comprised of a timer and a set of static functions to determine the occurrence of faults. In order to realize immediate fault tolerance, corrective control theory is applied to designing a dynamic feedback controller. Existence conditions for an appropriate controller and its construction algorithm are presented in terms of reachability of the machine and the feature of fault occurrences.

Keywords: asynchronous sequential machines, corrective control, fault diagnosis and tolerance, fault detector

Procedia PDF Downloads 340

Authors: Si Mon Kueh, Tom J. Kazmierski

Abstract:

There are about 1% of the world population suffering from the hidden disability known as epilepsy and major developing countries are not fully equipped to counter this problem. In order to reduce the inconvenience and danger of epilepsy, different methods have been researched by using a artificial neural network (ANN) classification to distinguish epileptic waveforms from normal brain waveforms. This paper outlines the aim of achieving massive ANN parallelization through a dedicated hardware using bit-serial processing. The design of this bit-serial Neural Processing Element (NPE) is presented which implements the functionality of a complete neuron using variable accuracy. The proposed design has been tested taking into consideration non-idealities of a hardware ANN. The NPE consists of a bit-serial multiplier which uses only 16 logic elements on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a look-up table. Arrays of NPEs can be driven by a single controller which executes the neural processing algorithm. In conclusion, the proposed compact NPE design allows the construction of complex hardware ANNs that can be implemented in a portable equipment that suits the needs of a single epileptic patient in his or her daily activities to predict the occurrences of impending tonic conic seizures.

Keywords: Artificial Neural Networks (ANN), bit-serial neural processor, FPGA, Neural Processing Element (NPE)

Procedia PDF Downloads 314

Authors: Theoktisti Makridou, Konstantinos Tsaprailis, George Arvanitakis, Charalampos Kontoes

Abstract:

In the literature, the data-driven approaches for mosquito abundance prediction relaying on supervised machine learning models that get trained with historical in-situ measurements. The counterpart of this approach is once the model gets trained on pointlevel (specific x,y coordinates) measurements, the predictions of the model refer again to point-level. These point-level predictions reduce the applicability of those solutions once a lot of early warning and mitigation actions applications need predictions for an area level, such as a municipality, village, etc... In this study, we apply a data-driven predictive model, which relies on public-open satellite Earth Observation and geospatial data and gets trained with historical point-level in-Situ measurements of mosquito abundance. Then we propose a methodology to extract information from a point-level predictive model to a broader area-level prediction. Our methodology relies on the randomly spatial sampling of the area of interest (similar to the Poisson hardcore process), obtaining the EO and geomorphological information for each sample, doing the point-wise prediction for each sample, and aggregating the predictions to represent the average mosquito abundance of the area. We quantify the performance of the transformation from the pointlevel to the area-level predictions, and we analyze it in order to understand which parameters have a positive or negative impact on it. The goal of this study is to propose a methodology that predicts the mosquito abundance of a given area by relying on point-level prediction and to provide qualitative insights regarding the expected performance of the area-level prediction. We applied our methodology to historical data (of Culex pipiens) of two areas of interest (Veneto region of Italy and Central Macedonia of Greece). In both cases, the results were consistent. The mean mosquito abundance of a given area can be estimated with similar accuracy to the point-level predictor, sometimes even better. The density of the samples that we use to represent one area has a positive effect on the performance in contrast to the actual number of sampling points which is not informative at all regarding the performance without the size of the area. Additionally, we saw that the distance between the sampling points and the real in-situ measurements that were used for training did not strongly affect the performance.

Keywords: mosquito abundance, supervised machine learning, culex pipiens, spatial sampling, west nile virus, earth observation data

Procedia PDF Downloads 140

Authors: Aileen F. Wang

Abstract:

Mammography has been one of the most reliable methods for early detection and diagnosis of breast cancer. However, mammography misses about 17% and up to 30% of breast cancers due to the subtle and unstable appearances of breast cancer in their early stages. Recent computer-aided diagnosis (CADx) technology using Zernike moments has improved detection accuracy. However, it has several drawbacks: it uses manual segmentation, Zernike moments are not robust, and it still has a relatively high false negative rate (FNR)–17.6%. This project will focus on the development of a novel breast cancer detection algorithm to automatically segment the breast mass and further reduce FNR. The algorithm consists of automatic segmentation of a single breast mass using Point Region Growing Segmentation, reconstruction of the segmented breast mass using Pseudo-Zernike moments, and classification of the breast mass using the root mean square (RMS). A comparative study among the various algorithms on the segmentation and reconstruction of breast masses was performed on randomly selected mammographic images. The results demonstrated that the newly developed algorithm is the best in terms of accuracy and cost effectiveness. More importantly, the new classifier RMS has the lowest FNR–6%.

Keywords: computer aided diagnosis, mammography, point region growing segmentation, pseudo-zernike moments, root mean square

Procedia PDF Downloads 444

Authors: E. Irankhah, M. Zarif, E. Mazrooei Rad, K. Ghandehari

Abstract:

Alzheimer's prevalence is on the rise, and the disease comes with problems like cessation of treatment, high cost of treatment, and the lack of early detection methods. The pathology of this disease causes the formation of protein deposits in the brain of patients called plaque amyloid. Generally, the diagnosis of this disease is done by performing tests such as a cerebrospinal fluid, CT scan, MRI, and spinal cord fluid testing, or mental testing tests and eye tracing tests. In this paper, we tried to use the Medial Temporal Atrophy (MTA) method and the Leave One Out (LOO) cycle to extract the statistical properties of the three Fz, Pz, and Cz channels of ERP signals for early diagnosis of this disease. In the process of CT scan images, the accuracy of the results is 81% for the healthy person and 88% for the severe patient. After the process of ERP signaling, the accuracy of the results for a healthy person in the delta band in the Cz channel is 81% and in the alpha band the Pz channel is 90%. In the results obtained from the signal processing, the results of the severe patient in the delta band of the Cz channel were 89% and in the alpha band Pz channel 92%.

Keywords: Alzheimer's disease, image and signal processing, LOO cycle, medial temporal atrophy

Procedia PDF Downloads 192

Authors: Yi-Fei Yang, Nai-Bao He, Shao-Bang Xing

Abstract:

This study investigates the permanent magnet synchronous linear motor (PMSLM) chaotic motion under the specific physical parameters, the stability and the security of motor-driven system will be unavoidably influenced. Therefore, it is really necessary to investigate the methods of controlling or suppressing chaos in PMSLM. Firstly, we derive a chaotic model of PMSLM in the closed-loop system. Secondly, in order to realize the local asymptotic stabilization of the mechanical subsystem and the global stabilization of the motor-driven system including electrical subsystem, we propose an improved uniting control lyapunov functions by introducing backstepping approach. Finally, an illustrated example is also given to show the electiveness of the obtained results.

Keywords: linear motor, lyapunov functions, chao control, hybrid controller

Procedia PDF Downloads 329

Authors: Ian Omung'a

Abstract:

Breast cancer remains to be one of the deadliest cancers for women worldwide, with the risk of developing tumors being as high as 50 percent in Sub-Saharan African countries like Kenya. With as many as 42 percent of these cases set to be diagnosed late when cancer has metastasized and or the prognosis has become terminal, Full Field Digital [FFD] Mammography remains an effective screening technique that leads to early detection where in most cases, successful interventions can be made to control or eliminate the tumors altogether. FFD Mammograms have been proven to multiply more effective when used together with Computer-Aided Detection and Diagnosis [CADe] systems, relying on algorithmic implementations of Deep Learning techniques in Computer Vision to carry out deep pattern recognition that is comparable to the level of a human radiologist and decipher whether specific areas of interest in the mammogram scan image portray abnormalities if any and whether these abnormalities are indicative of a benign or malignant tumor. Within this paper, we review emergent Deep Learning techniques that will prove relevant to the development of State-of-The-Art FFD Mammogram CADe systems. These techniques will span self-supervised learning for context-encoded occlusion, self-supervised learning for pre-processing and labeling automation, as well as the creation of a standardized large-scale mammography dataset as a benchmark for CADe systems' evaluation. Finally, comparisons are drawn between existing practices that pre-date these techniques and how the development of CADe systems that incorporate them will be different.

Keywords: breast cancer diagnosis, computer aided detection and diagnosis, deep learning, whole mammogram classfication, ultrasound classification, computer vision

Procedia PDF Downloads 85

Authors: Maria Neagu

Abstract:

This paper presents the heat and mass driven natural convection succession in a Darcy thermally stratified porous medium that embeds a vertical semi-infinite impermeable wall of constant heat flux and concentration. The scale analysis of the system determines the two possible maps of the heat and mass driven natural convection sequence along the wall as a function of the process parameters. These results are verified using the finite differences method applied to the conservation equations.

Keywords: finite difference method, natural convection, porous medium, scale analysis, thermal stratification

Procedia PDF Downloads 325

Authors: Livia Eisler, Cristiane C. B. Alves, Cristina L. F. Ortolani, Kurt Faltin Jr.

Abstract:

Purpose: This paper proposes a technically simple diagnosis method among orthodontists and maxillofacial surgeons in order to evaluate discrete bone alterations. The methodology consists of a protocol to optimize the diagnosis and minimize the possibility for orthodontic and ortho-surgical retreatment. Materials and Methods: A protocol of image processing and analysis, through ImageJ software and its plugins, was applied to 20 pairs of lateral cephalometric images obtained from cone beam computerized tomographies, before and 1 year after undergoing orthognathic surgery. The optical density of the images was analyzed in the condylar region to determine possible bone alteration after surgical correction. Results: Image density was shown to be altered in all image pairs, especially regarding the condyle contours. According to measures, condyle had a gender-related density reduction for p=0.05 and condylar contours had their alterations registered in mm. Conclusion: A simple, viable and cost-effective technique can be applied to achieve the more detailed image-based diagnosis, not depending on the human eye and therefore, offering more reliable, quantitative results.

Keywords: bone resorption, computer-assisted image processing, orthodontics, orthognathic surgery

Procedia PDF Downloads 147

Authors: V. Kuzhandai Velu, R. Ramesh

Abstract:

Background and objectives: Acute myocardial infarction (AMI) is the most common cause of mortality and morbidity. Over the last decade, microRNAs (miRs) have emerged as a potential marker for detecting AMI. The current study evaluates the kinetics and importance of miRs in the differential diagnosis of ST-segment elevated MI (STEMI) and non-STEMI (NSTEMI) and its correlation to conventional biomarkers and to predict the immediate outcome of AMI for arrhythmias and left ventricular (LV) dysfunction. Materials and Method: A total of 100 AMI patients were recruited for the study. Routine cardiac biomarker and miRNA levels were measured during diagnosis and serially at admission, 6, 12, 24, and 72hrs. The baseline biochemical parameters were analyzed. The expression of miRs was compared between STEMI and NSTEMI at different time intervals. Diagnostic utility of miR-1, miR-133, miR-208, and miR-499 levels were analyzed by using RT-PCR and with various diagnostics statistical tools like ROC, odds ratio, and likelihood ratio. Results: The miR-1, miR-133, and miR-499 showed peak concentration at 6 hours, whereas miR-208 showed high significant differences at all time intervals. miR-133 demonstrated the maximum area under the curve at different time intervals in the differential diagnosis of STEMI and NSTEMI which was followed by miR-499 and miR-208. Evaluation of miRs for predicting arrhythmia and LV dysfunction using admission sample demonstrated that miR-1 (OR = 8.64; LR = 1.76) and miR-208 (OR = 26.25; LR = 5.96) showed maximum odds ratio and likelihood respectively. Conclusion: Circulating miRNA showed a highly significant difference between STEMI and NSTEMI in AMI patients. The peak was much earlier than the conventional biomarkers. miR-133, miR-208, and miR-499 can be used in the differential diagnosis of STEMI and NSTEMI, whereas miR-1 and miR-208 could be used in the prediction of arrhythmia and LV dysfunction, respectively.

Keywords: myocardial infarction, cardiac biomarkers, microRNA, arrhythmia, left ventricular dysfunction

Procedia PDF Downloads 122

Authors: Ehsan Gholamalizadeh, Jae Dong Chung

Abstract:

Solar chimney power plant is a feasible solar thermal system which produces electricity from the Sun. The objective of this study is to investigate buoyancy-driven flow and heat transfer through a built pilot solar chimney system called 'Kerman Project'. The system has a chimney with the height and diameter of 60 m and 3 m, respectively, and the average radius of its solar collector is about 20 m, and also its average collector height is about 2 m. A three-dimensional simulation was conducted to analyze the system, using computational fluid dynamics (CFD). In this model, radiative transfer equation was solved using the discrete ordinates (DO) radiation model taking into account a non-gray radiation behavior. In order to modelling solar irradiation from the sun’s rays, the solar ray tracing algorithm was coupled to the computation via a source term in the energy equation. The model was validated with comparing to the experimental data of the Manzanares prototype and also the performance of the built pilot system. Then, based on the numerical simulations, velocity and temperature distributions through the system, the temperature profile of the ground surface and the system performance were presented. The analysis accurately shows the flow and heat transfer characteristics through the pilot system and predicts its performance.

Keywords: buoyancy-driven flow, computational fluid dynamics, heat transfer, renewable energy, solar chimney power plant

Procedia PDF Downloads 250

Authors: Sajjad Baghernezhad

Abstract:

Meta-heuristic and hybrid algorithms have high adeer in modeling medical problems. In this study, a neural network was used to predict covid-19 among high-risk and low-risk patients. This study was conducted to collect the applied method and its target population consisting of 550 high-risk and low-risk patients from the Kerman University of medical sciences medical center to predict the coronavirus. In this study, the memetic algorithm, which is a combination of a genetic algorithm and a local search algorithm, has been used to update the weights of the neural network and develop the accuracy of the neural network. The initial study showed that the accuracy of the neural network was 88%. After updating the weights, the memetic algorithm increased by 93%. For the proposed model, sensitivity, specificity, positive predictivity value, value/accuracy to 97.4, 92.3, 95.8, 96.2, and 0.918, respectively; for the genetic algorithm model, 87.05, 9.20 7, 89.45, 97.30 and 0.967 and for logistic regression model were 87.40, 95.20, 93.79, 0.87 and 0.916. Based on the findings of this study, neural network models have a lower error rate in the diagnosis of patients based on individual variables and vital signs compared to the regression model. The findings of this study can help planners and health care providers in signing programs and early diagnosis of COVID-19 or Corona.

Keywords: COVID-19, decision support technique, neural network, genetic algorithm, memetic algorithm

Procedia PDF Downloads 63

Authors: Ioannis Panagiotopoulos, Aristidis Anastasakis, Konstantinos Toutouzas, Ioannis Iakovou, Charalampos Vlachopoulos, Vasilis Voudris, Georgios Tziomalos, Konstantinos Tsioufis, Efstathios Kastritis, Alexandros Briassoulis, Kimon Stamatelopoulos, Alexios Antonopoulos, Paraskevi Exadaktylou, Evanthia Giannoula, Anastasia Katinioti, Maria Kalantzi, Evangelos Leontiadis, Eftychia Smparouni, Ioannis Malakos, Nikolaos Aravanis, Argyrios Doumas, Maria Koutelou

Abstract:

Introduction: Cardiac amyloidosis from Transthyretin (ATTR-CA) is an infiltrative disease characterized by the deposition of pathological transthyretin complexes in the myocardium. This study describes the characteristics of patients diagnosed with ATTR-CA from 2019 until present at the Nuclear Medicine Department of Onassis Cardiac Surgery Center and AHEPA Hospital. These centers have extensive experience in amyloidosis and modern technological equipment for its diagnosis. Materials and Methods: Records of consecutive patients (N=73) diagnosed with any type of amyloidosis were collected, analyzed, and prospectively followed. The diagnosis of amyloidosis was made using specific myocardial scintigraphy with Tc-99m DPD. Demographic characteristics, including age, gender, marital status, height, and weight, were collected in a database. Clinical characteristics, such as amyloidosis type (ATTR and AL), serum biomarkers (BNP, troponin), electrocardiographic findings, ultrasound findings, NYHA class, aortic valve replacement, device implants, and medication history, were also collected. Some of the most significant results are presented. Results: A total of 73 cases (86% male) were diagnosed with amyloidosis over four years. The mean age at diagnosis was 82 years, and the main symptom was dyspnea. Most patients suffered from ATTR-CA (65 vs. 8 with AL). Out of all the ATTR-CA patients, 61 were diagnosed with wild-type and 2 with two rare mutations. Twenty-eight patients had systemic amyloidosis with extracardiac involvement, and 32 patients had a history of bilateral carpal tunnel syndrome. Four patients had already developed polyneuropathy, and the diagnosis was confirmed by DPD scintigraphy, which is known for its high sensitivity. Among patients with isolated cardiac involvement, only 6 had left ventricular ejection fraction below 40%. The majority of ATTR patients underwent tafamidis treatment immediately after diagnosis. Conclusion: In conclusion, the experiences shared by the two centers and the continuous exchange of information provide valuable insights into the diagnosis and management of cardiac amyloidosis. Clinical suspicion of amyloidosis and early diagnostic approach are crucial, given the availability of non-invasive techniques. Cardiac scintigraphy with DPD can confirm the presence of the disease without the need for a biopsy. The ultimate goal still remains continuous education and awareness of clinical cardiologists so that this systemic and treatable disease can be diagnosed and certified promptly and treatment can begin as soon as possible.

Keywords: amyloidosis, diagnosis, myocardial scintigraphy, Tc-99m DPD, transthyretin

Procedia PDF Downloads 73

Authors: Madeleine Cox

Abstract:

Objective: discussion of diagnosis of vernix caseosa peritonitis, recovery and subsequent caesarean seciton Case: 30 year old G4P1 presented in labour at 40 weeks, planning a vaginal birth afterprevious caesarean section. She underwent an emergency caesarean section due to concerns for fetal wellbeing on CTG. She was found to have a thin lower segment with a very small area of dehiscence centrally. The operation was uncomplicated, and she recovered and went home 2 days later. She then represented to the emergency department day 6 post partum feeling very unwell, with significant abdominal pain, tachycardia as well as urinary retention. Raised white cell count of 13.7 with neutrophils of 11.64, CRP of 153. An abdominal ultrasound was poorly tolerated by the patient and did not aide in the diagnosis. Chest and abdominal xray were normal. She underwent a CT chest and abdomen, which found a small volume of free fluid with no apparent collection. Given no obvious cause of her symptoms were found and the patient did not improve, she had a repeat CT 2 days later, which showed progression of free fluid. A diagnostic laparoscopy was performed with general surgeons, which reveled turbid fluid, an inflamed appendix which was removed. The patient improved remarkably post operatively. The histology showed periappendicitis with acute appendicitis with marked serosal inflammatory reaction to vernix caseosa. Following this, the patient went on to recover well. 4 years later, the patient was booked for an elective caesarean section, on entry into the abdomen, there were very minimal adhesions, and the surgery and her subsequent recovery was uncomplicated. Discussion: this case represents the diagnostic dilemma of a patient who presents unwell without a clear cause. In this circumstance, multiple modes of imaging did not aide in her diagnosis, and so she underwent diagnostic surgery. It is important to evaluate if a patient is or is not responding to the typical causes of post operative pain and adjust management accordingly. A multiteam approach can help to provide a diagnosis for these patients. Conclusion: Vernix caseosa peritonitis is a rare cause of acute abdomen post partum. There are few reports in the literature of the initial presentation and no reports on the possible effects on future pregnancies. This patient did not have any complications in her following pregnancy or delivery secondary to her diagnosis of vernix caseosa peritonitis. This may assist in counselling other women who have had this uncommon diagnosis.

Keywords: peritonitis, obstetrics, caesarean section, pain

Procedia PDF Downloads 95

Authors: Rashmi Gupta

Abstract:

Rewarding stimuli capture attention involuntarily as a result of an association process that develops quickly during value learning, referred to as the reward or value-driven attentional capture. It is essential to compare reward with punishment processing to get a full picture of value-based modulation in visual attention processing. Hence, the present study manipulated both valence/value (reward as well as punishment) and motivational salience (probability of an outcome: high vs. low) together. Series of experiments were conducted, and there were two phases in each experiment. In phase 1, participants were required to learn to associate specific face stimuli with a high or low probability of winning or losing points. In the second phase, these conditioned stimuli then served as a distractor or prime in a speeded letter search task. Faces with high versus low outcome probability, regardless of valence, slowed the search for targets (specifically the left visual field target) and suggesting that the costs to performance on non-emotional cognitive tasks were only driven by motivational salience (high vs. loss) associated with the stimuli rather than the valence (gain vs. loss). It also suggests that the processing of motivationally salient stimuli is right-hemisphere biased. Together, results of these studies strengthen the notion that our visual attention system is more sensitive to affected by motivational saliency rather than valence, which termed here as motivational-driven attentional capture.

Keywords: attention, distractors, motivational salience, valence

Procedia PDF Downloads 216

Authors: Daniel Bulanda, Janusz A. Starzyk, Adrian Horzyk

Abstract:

The electrocardiogram (ECG) is one of the most commonly used medical tests, essential for correct diagnosis and treatment of the patient. While ECG devices generate a huge amount of data, only a small part of them carries valuable medical information. To deal with this problem, many compression algorithms and filters have been developed over the past years. However, the rapid development of new machine learning techniques poses new challenges. To address this class of problems, we created the FlexPoints algorithm that searches for characteristic points on the ECG signal and ignores all other points that do not carry relevant medical information. The conducted experiments proved that the presented algorithm can significantly reduce the number of data points which represents ECG signal without losing valuable medical information. These sparse but essential characteristic points (flex points) can be a perfect input for some modern machine learning models, which works much better using flex points as an input instead of raw data or data compressed by many popular algorithms.

Keywords: characteristic points, electrocardiogram, ECG, machine learning, signal compression

Procedia PDF Downloads 156

Authors: Maximilian Winkens, Peter Nyhuis

Abstract:

Components with sensory properties such as gentelligent components developed at the Collaborative Research Center 653 offer a new angle on the full utilization of the remaining service life in case of a preventive maintenance. The developed methodology of component status driven maintenance analyses the stress data obtained during the component's useful life and on the basis of this knowledge assesses the type of maintenance called for in this case. The procedure is derived from the case-based reasoning method and will be elucidated in detail. The method's functionality is demonstrated with real-life data obtained during test runs of a racing car prototype.

Keywords: gentelligent component, preventive maintenance, case-based reasoning, sensory

Procedia PDF Downloads 357