Search results for: fault detection and diagnosis

Authors: R. Sanchez-Salcedo, N. H. Voelcker

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Currently, low breast milk production in women is one of the leading health complications in infants. Recently, It has been demonstrated that exclusive breastfeeding, especially up to a minimum of 6 months, significantly reduces respiratory and gastrointestinal infections, which are the main causes of death in infants. However, the current data shows that a high percentage of women stop breastfeeding their children because they perceive an inadequate supply of milk, and only 45% of children are breastfeeding under 6 months. It is, therefore, clear the necessity to design and develop a biosensor that is sensitive and selective enough to identify and validate a panel of milk biomarkers that allow the early diagnosis of this condition. In this context, electrochemical biosensors could be a powerful tool for assessing all the requirements in terms of reliability, selectivity, sensitivity, cost efficiency and potential for multiplex detection. Moreover, they are suitable for the development of POC devices and wearable sensors. In this work, we report the development of two types of sensing platforms towards several biomarkers, including miRNAs and hormones present in breast milk and dysregulated in this pathological condition. The first type of sensing platform consists of an enzymatic sensor for the detection of lactose, one of the main components in milk. In this design, we used gold surface as an electrochemical transducer due to the several advantages, such as the variety of strategies available for its rapid and efficient functionalization with bioreceptors or capture molecules. For the second type of sensing platform, nanoporous silicon film (pSi) was chosen as the electrode material for the design of DNA sensors and aptasensors targeting miRNAs and hormones, respectively. pSi matrix offers a large superficial area with an abundance of active sites for the immobilization of bioreceptors and tunable characteristics, which increase the selectivity and specificity, making it an ideal alternative material. The analytical performance of the designed biosensors was not only characterized in buffer but also validated in minimally treated breastmilk samples. We have demonstrated the potential of an electrochemical transducer on pSi and gold surface for monitoring clinically relevant biomarkers associated with the heightened risk of low milk production in women. This approach, in which the nanofabrication techniques and the functionalization methods were optimized to increase the efficacy of the biosensor highly provided a foundation for further research and development of targeted diagnosis strategies.

Keywords: biosensors, electrochemistry, early diagnosis, clinical markers, miRNAs

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Authors: Muhammad Rosli Abdullah, Noor Hasmiza Harun

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A microorganism detection system has a potential to be used with the advancement in a biosensor development. The detection system requires an optical sensing system, microfluidic device and biological reagent. Although, the biosensors are available in the market, a label free and a lab-on-chip approach will promote a flexible solution. As a preliminary study of microorganism detection, three mechanisms such as Total Internal Reflection (TIR), Micro Fluidic Channel (MFC) and magnetic-electric field propagation were study and simulated. The objective are to identify the TIR angle, MFC parabolic flow and the wavelength for the microorganism detection. The simulation result indicates that evanescent wave is achieved when TIR angle > 42°, the corner and centre of a parabolic velocity are 0.02 m/s and 0.06 m/s respectively, and a higher energy distribution of a perfect electromagnetic scattering with dipole resonance radiation occurs at 500 nm. This simulation is beneficial to determine the components of the microorganism detection system that does not rely on classical microbiological, immunological and genetic methods which are laborious, time-consuming procedures and confined to specialized laboratories with expensive instrumentation equipment.

Keywords: microorganism, microfluidic, total internal reflection, lab on chip

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Authors: Vijaya Yuvaram Singh V M, Kameshwar Rao J V

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The challenge with development of neural network based methods for medical is the availability of data. Anatomical landmark detection in the medical domain is a process to find points on the x-ray scan report of the patient. Most of the time this task is done manually by trained professionals as it requires precision and domain knowledge. Traditionally object detection based methods are used for landmark detection. Here, we utilize reinforcement learning and query based method to train a single agent capable of detecting multiple landmarks. A deep Q network agent is trained to detect single and multiple landmarks present on hip and shoulder from x-ray scan of a patient. Here a single agent is trained to find multiple landmark making it superior to having individual agents per landmark. For the initial study, five images of different patients are used as the environment and tested the agents performance on two unseen images.

Keywords: reinforcement learning, medical landmark detection, multi target detection, deep neural network

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Authors: Mohd Noah A. Rahman, Afzaal H. Seyal, Norhafilah Bara

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Developing a software application using an interface with computers and peripheral devices using gestures of human body such as hand movements keeps growing in interest. A review on this hand gesture detection and recognition based on computer vision technique remains a very challenging task. This is to provide more natural, innovative and sophisticated way of non-verbal communication, such as sign language, in human computer interaction. Nevertheless, this paper explores hand detection and hand gesture recognition applying a vision based approach. The hand detection and recognition used skin color spaces such as HSV and YCrCb are applied. However, there are limitations that are needed to be considered. Almost all of skin color space models are sensitive to quickly changing or mixed lighting circumstances. There are certain restrictions in order for the hand recognition to give better results such as the distance of user’s hand to the webcam and the posture and size of the hand.

Keywords: hand detection, hand gesture, hand recognition, sign language

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Authors: Buse Bahitli, Samiye Mete

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The aim of the study was to evaluate the quality of sexual life of women with diagnosed gynecological cancer and receive treatment. The study was a descriptive and cross-sectional type, and it was carried out with 276 women. Information Form and Sexual Quality of Life Scale-Female (SQOL) form was used in the study. The data was evaluated using Mann-Whitney U and Kruskal-Wallis test. In the study, Sexual Quality of Life Scale-Female average score was 68.83 ± 21.17. The %43.1 of women was endometrial cancer, %30.8 was cervical cancer, %24.6 was ovarian cancer, and %1.4 was vulvar cancer. The average time to diagnosis of patients is 41.80 ± 47.64 months. There was no significant difference mean SQOL according to individual/sociodemographic characteristics like age, education. Gynecological cancer-related characteristics like gynaecological cancer type, treatment type, surgery type were found not to affect the mean score of SQOL. However, it was found that the difference was due to the higher SQOL score in the group with a diagnosis time of 25 months and over (X²KW= 6.356, p= 0.046). The reason of significant difference means SQOL according to diagnosis over time might be that women adapted to cancer diagnosis. While women with gynaecologic cancer are evaluating their sexual lives, it is necessary to evaluate them with good evaluation tools.

Keywords: gynecological cancers, sexuality, quality of sexual life, SQOL

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Authors: Elise M. Shauf

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Beryllium can be found naturally in some fruits and vegetables (carrots, garden peas, kidney beans, pears) at very low concentrations, but is typically not clinically significant due to the low-level exposure and limited absorption of beryllium by the stomach and intestines. However, acute or chronic beryllium exposure can result in harmful toxic and carcinogenic biological effects. Beryllium can be both a workplace hazard and an environmental pollutant, therefore determining the presence of beryllium at trace levels can be essential to protect workers as well as the environment. Analysis of gallein, C₂₀H₁₂O₇, to determine if it is usable as a fluorescent dye for beryllium detection. The primary detection method currently in use includes hydroxybenzoquinoline sulfonates (HBQS), for which alternative indicators are desired. Unfortunately, gallein does not have the desired aspects needed as a dye for beryllium detection due to the peak shift properties.

Keywords: beryllium detection, fluorescent, gallein dye, indicator, spectroscopy

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Authors: Frederick Huie, Lauren Fusfeld, William Burchenal, Scott Howell, Alyssa McVey, Thomas F. Goss

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Alzheimer’s Disease (AD) is a degenerative brain disease characterized by memory loss and cognitive decline that presents a substantial economic burden for patients and health insurers in the US. This study evaluates the payer budget impact of the DISCERN™ test in the diagnosis and management of patients with symptoms of dementia evaluated for AD. DISCERN™ comprises three assays that assess critical factors related to AD that regulate memory, formation of synaptic connections among neurons, and levels of amyloid plaques and neurofibrillary tangles in the brain and can provide a quicker, more accurate diagnosis than tests in the current diagnostic pathway (CDP). An Excel-based model with a three-year horizon was developed to assess the budget impact of DISCERN™ compared with CDP in a Medicare Advantage plan with 1M beneficiaries. Model parameters were identified through a literature review and were verified through consultation with clinicians experienced in diagnosis and management of AD. The model assesses direct medical costs/savings for patients based on the following categories: •Diagnosis: costs of diagnosis using DISCERN™ and CDP. •False Negative (FN) diagnosis: incremental cost of care avoidable with a correct AD diagnosis and appropriately directed medication. •True Positive (TP) diagnosis: AD medication costs; cost from a later TP diagnosis with the CDP versus DISCERN™ in the year of diagnosis, and savings from the delay in AD progression due to appropriate AD medication in patients who are correctly diagnosed after a FN diagnosis.•False Positive (FP) diagnosis: cost of AD medication for patients who do not have AD. A one-way sensitivity analysis was conducted to assess the effect of varying key clinical and cost parameters ±10%. An additional scenario analysis was developed to evaluate the impact of individual inputs. In the base scenario, DISCERN™ is estimated to decrease costs by $4.75M over three years, equating to approximately $63.11 saved per test per year for a cohort followed over three years. While the diagnosis cost is higher with DISCERN™ than with CDP modalities, this cost is offset by the higher overall costs associated with CDP due to the longer time needed to receive a TP diagnosis and the larger number of patients who receive a FN diagnosis and progress more rapidly than if they had received appropriate AD medication. The sensitivity analysis shows that the three parameters with the greatest impact on savings are: reduced sensitivity of DISCERN™, improved sensitivity of the CDP, and a reduction in the percentage of disease progression that is avoided with appropriate AD medication. A scenario analysis in which DISCERN™ reduces the utilization for patients of computed tomography from 21% in the base case to 16%, magnetic resonance imaging from 37% to 27% and cerebrospinal fluid biomarker testing, positive emission tomography, electroencephalograms, and polysomnography testing from 4%, 5%, 10%, and 8%, respectively, in the base case to 0%, results in an overall three-year net savings of $14.5M. DISCERN™ improves the rate of accurate, definitive diagnosis of AD earlier in the disease and may generate savings for Medicare Advantage plans.

Keywords: Alzheimer’s disease, budget, dementia, diagnosis.

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Authors: Olena Mayboroda, Angel Gonzalez Benito, Jonathan Sabate Del Rio, Marketa Svobodova, Sandra Julich, Herbert Tomaso, Ciara K. O'Sullivan, Ioanis Katakis

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DNA amplification is required for most molecular diagnostic applications but conventional PCR has disadvantages for field testing. Isothermal amplification techniques are being developed to respond to this problem. One of them is the Recombinase Polymerase Amplification (RPA) that operates at isothermal conditions without sacrificing specificity and sensitivity in easy-to-use formats. In this work RPA was used for the optical detection of solid-phase amplification of the potential biowarfare agent Yersinia pestis. Thiolated forward primers were immobilized on the surface of maleimide-activated microtitre plates for the quantitative detection of synthetic and genomic DNA, with elongation occurring only in the presence of the specific template DNA and solution phase reverse primers. Quantitative detection was achieved via the use of biotinylated reverse primers and post-amplification addition of streptavidin-HRP conjugate. The overall time of amplification and detection was less than 1 hour at a constant temperature of 37oC. Single-stranded and double-stranded DNA sequences were detected achieving detection limits of 4.04*10-13 M and 3.14*10-16 M, respectively. The system demonstrated high specificity with negligible responses to non-specific targets.

Keywords: recombinase polymerase amplification, Yersinia pestis, solid-phase detection, ELONA

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Authors: Komal Babbar

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Multiuser Detection is the intelligent estimation/demodulation of transmitted bits in the presence of Multiple Access Interference. The authors have presented the Bit-error rate (BER) achieved by linear multi-user detectors: Matched filter (which treats the MAI as AWGN), Decorrelating and MMSE. In this work, authors investigate the bit error probability analysis for Matched filter, decorrelating, and MMSE. This problem arises in several practical CDMA applications where the receiver may not have full knowledge of the number of active users and their signature sequences. In particular, the behavior of MAI at the output of the Multi-user detectors (MUD) is examined under various asymptotic conditions including large signal to noise ratio; large near-far ratios; and a large number of users. In the last section Authors also shows Matlab Simulation results for Multiuser detection techniques i.e., Matched filter, Decorrelating, MMSE for 2 users and 10 users.

Keywords: code division multiple access, decorrelating, matched filter, minimum mean square detection (MMSE) detection, multiple access interference (MAI), multiuser detection (MUD)

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Authors: Kalaumari Mayoral-Peña, Ana I. Montejano-Montelongo, Josué Reyes-Muñoz, Gonzalo A. Ortiz-Mancilla, Mayrin Rodríguez-Cruz, Víctor Hernández-Villalobos, Jesús A. Guzmán-López, Santiago García-Jacobo, Iván Licona-Vázquez, Grisel Fierros-Romero, Rosario Flores-Vallejo

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The group B-lactamic antibiotics include some of the most frequently used small drug molecules against bacterial infections. Nevertheless, an alarming decrease in their efficacy has been reported due to the emergence of antibiotic-resistant bacteria. Infections caused by bacteria expressing extended Spectrum B-lactamases (ESBLs) are difficult to treat and account for higher morbidity and mortality rates, delayed recovery, and high economic burden. According to the Global Report on Antimicrobial Resistance Surveillance, it is estimated that mortality due to resistant bacteria will ascend to 10 million cases per year worldwide. These facts highlight the importance of developing low-cost and readily accessible detection methods of drug-resistant ESBLs bacteria to prevent their spread and promote accurate and fast diagnosis. Bacterial detection is commonly done using molecular diagnostic techniques, where PCR stands out for its high performance. However, this technique requires specialized equipment not available everywhere, is time-consuming, and has a high cost. Loop-Mediated Isothermal Amplification (LAMP) is an alternative technique that works at a constant temperature, significantly decreasing the equipment cost. It yields double-stranded DNA of several lengths with repetitions of the target DNA sequence as a product. Although positive and negative results from LAMP can be discriminated by colorimetry, fluorescence, and turbidity, there is still a large room for improvement in the point-of-care implementation. DNA origami is a technique that allows the formation of 3D nanometric structures by folding a large single-stranded DNA (scaffold) into a determined shape with the help of short DNA sequences (staples), which hybridize with the scaffold. This research aimed to generate DNA origami structures using LAMP products as scaffolds to improve the sensitivity to detect ESBLs in point-of-care diagnosis. For this study, the coding sequence of the CTM-X-15 ESBL of E. coli was used to generate the LAMP products. The set of LAMP primers were designed using PrimerExplorerV5. As a result, a target sequence of 200 nucleotides from CTM-X-15 ESBL was obtained. Afterward, eight different DNA origami structures were designed using the target sequence in the SDCadnano and analyzed with CanDo to evaluate the stability of the 3D structures. The designs were constructed minimizing the total number of staples to reduce costs and complexity for point-of-care applications. After analyzing the DNA origami designs, two structures were selected. The first one was a zig-zag flat structure, while the second one was a wall-like shape. Given the sequence repetitions in the scaffold sequence, both were able to be assembled with only 6 different staples each one, ranging between 18 to 80 nucleotides. Simulations of both structures were performed using scaffolds of different sizes yielding stable structures in all the cases. The generation of the LAMP products were tested by colorimetry and electrophoresis. The formation of the DNA structures was analyzed using electrophoresis and colorimetry. The modeling of novel detection methods through bioinformatics tools allows reliable control and prediction of results. To our knowledge, this is the first study that uses LAMP products and DNA-origami in combination to delect ESBL-producing bacterial strains, which represent a promising methodology for diagnosis in the point-of-care.

Keywords: beta-lactamases, antibiotic resistance, DNA origami, isothermal amplification, LAMP technique, molecular diagnosis

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Authors: Junling Li, Fang Meng, Yichun Zhang

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Image saliency detection has been long studied, while several challenging problems are still unsolved, such as detecting saliency inaccurately in complex scenes or suppressing salient objects in the image borders. In this paper, we propose a new saliency detection algorithm in order to solving these problems. We represent the image as a graph with superixels as nodes. By considering appearance similarity between the boundary and the background, the proposed method chooses non-saliency boundary nodes as background priors to construct the background probability model. The probability that each node belongs to the model is computed, which measures its similarity with backgrounds. Thus we can calculate saliency by the transformed probability as a metric. We compare our algorithm with ten-state-of-the-art salient detection methods on the public database. Experimental results show that our simple and effective approach can attack those challenging problems that had been baffling in image saliency detection.

Keywords: visual saliency, background probability, boundary knowledge, background priors

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Authors: Yeongyu Choi, Ju H. Park, S. M. Lee, Ho-Youl Jung

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In this paper, an improved method for estimating fundamental matrix is proposed. The method is applied effectively to monocular camera based moving object detection. The method consists of corner points detection, moving object’s motion estimation and fundamental matrix calculation. The corner points are obtained by using Harris corner detector, motions of moving objects is calculated from pyramidal Lucas-Kanade optical flow algorithm. Through epipolar geometry analysis using RANSAC, the fundamental matrix is calculated. In this method, we have improved the performances of moving object detection by using two threshold values that determine inlier or outlier. Through the simulations, we compare the performances with varying the two threshold values.

Keywords: corner detection, optical flow, epipolar geometry, RANSAC

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Authors: Ibrahim Aly, Rabab Zalat, Bahaa EL Deen W. El Aswad, Ismail M. Moharm, Basam M. Masoud, Tarek Diab

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The objective of the present study was to evaluate the novel nanomagnetic beads based–latex agglutination assay (NMB-LAT) as a simple test for diagnosis of S. haematobium as well as standardize the novel nanomagnetic beads based –ELISA (NMB-ELISA). According to urine examination this study included 85 S. haematobium infected patients, 30 other parasites infected patients and 25 negative control samples. The sensitivity of novel NMB-LAT was 82.4% versus 96.5% and 88.2% for NMB-ELISA and currently used sandwich ELISA respectively. The specificity of NMB-LAT was 83.6% versus 96.3% and 87.3% for NMB-ELISA and currently used sandwich ELISA respectively. In conclusion, the novel NMB-ELISA is a valuable applicable diagnostic technique for diagnosis of human schistosomiasis haematobium. The novel NMB-ELISA assay is a suitable applicable diagnostic method in field survey especially when followed by ELISA as a confirmatory test in query false negative results. Trials are required to increase the sensitivity and specificity of NMB-ELISA assay.

Keywords: diagnosis, iatex agglutination, nanomagnetic beads, sandwich ELISA

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Authors: Michael Dole, Pierre Ninin, Denis Raffourt

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Most of the CERN’s facilities hosting particle accelerators are large, underground and radioactive areas. All fire detection systems installed in such areas, shall be carefully studied to cope with the particularities of this stringent environment. The detection equipment usually chosen by CERN to secure these underground facilities are based on air sampling technology. The electronic equipment is located in non-radioactive areas whereas air sampling networks are deployed in radioactive areas where fire detection is required. The air sampling technology provides very good detection performances and prevent the "radiation-to-electronic" effects. In addition, it reduces the exposure to radiations of maintenance workers and is permanently available during accelerator operation. In order to protect the Super Proton Synchrotron and its 7 km tunnels, a specific long distance aspirating smoke detector has been developed to detect smoke at up to 700 meters between electronic equipment and the last air sampling hole. This paper describes the architecture, performances and return of experience of the long distance fire detection system developed and installed to secure the CERN Super Proton Synchrotron tunnels.

Keywords: air sampling, fire detection, long distance, radioactive areas

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Authors: Oluwaponmile D. Alao

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In this paper, two models have been developed to ascertain the best network needed for diagnosis of breast cancer. Breast cancer has been a disease that required the attention of the medical practitioner. Experience has shown that misdiagnose of the disease has been a major challenge in the medical field. Therefore, designing a system with adequate performance for will help in making diagnosis of the disease faster and accurate. In this paper, two models: backpropagation neural network and support vector machine has been developed. The performance obtained is also compared with other previously obtained algorithms to ascertain the best algorithms.

Keywords: breast cancer, data mining, neural network, support vector machine

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

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Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet

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Authors: Dilip K. Prasad, C. Krishna Prasath, Deepu Rajan, Lily Rachmawati, Eshan Rajabally, Chai Quek

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This paper discusses the technical challenges in maritime image processing and machine vision problems for video streams generated by cameras. Even well documented problems of horizon detection and registration of frames in a video are very challenging in maritime scenarios. More advanced problems of background subtraction and object detection in video streams are very challenging. Challenges arising from the dynamic nature of the background, unavailability of static cues, presence of small objects at distant backgrounds, illumination effects, all contribute to the challenges as discussed here.

Keywords: autonomous maritime vehicle, object detection, situation awareness, tracking

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Authors: Frazer Kirk, Rohen Skiba, Pankaj Saxena

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The natural history of the QAV is poorly understood due to the exceeding rarity of the condition. Incidence rates vary between 0.00028-1%. Classically patients present with Aortic Regurgitation (AR) between 40-60 years of age experiencing palpitations, chest pain, or heart failure. (1, 2) Echocardiography is the mainstay of diagnosis for this condition; however, given the rarity of this condition, it can easily be overlooked, as demonstrated here. The case report that follows serves as a reminder of the condition to reduce the innate cognitive bias to overlook the diagnosis due to the availability heuristic. Intraoperative photography, echocardiographic and magnetic resonance imaging from this case for reference to demonstrate that while the diagnosis of Aortic regurgitation was recognized early, the valve morphology was underappreciated.

Keywords: quadricuspid aortic valve, cardiac surgery, echocardiography, congenital

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Authors: Saleh Alshehri

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Human skin detection is a vital step in many applications. Some of the applications are critical especially those related to security. This leverages the importance of a high-performance detection algorithm. To validate the accuracy of the algorithm, image databases are usually used. However, the suitability of these image databases is still questionable. It is suggested that the suitability can be measured mainly by the span the database covers of the color space. This research investigates the validity of three famous image databases.

Keywords: image databases, image processing, pattern recognition, neural networks

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Authors: Tawfig Alghbaili

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LP3D Field is located near the center of the Sirt Basin in the Marada Trough approximately 215 km south Marsa Al Braga City. The Marada Trough is bounded on the west by a major fault, which forms the edge of the Beda Platform, while on the east, a bounding fault marks the edge of the Zelten Platform. The main reservoir in the LP3D Field is Upper Paleocene Beda Formation. The Beda Formation is mainly limestone interbedded with shale. The reservoir average thickness is 117.5 feet. To develop a better understanding of the characterization and distribution of the Beda reservoir, quantitative seismic data interpretation has been done, and also, well logs data were analyzed. Six reflectors corresponding to the tops of the Beda, Hagfa Shale, Gir, Kheir Shale, Khalifa Shale, and Zelten Formations were picked and mapped. Special work was done on fault interpretation part because of the complexities of the faults at the structure area. Different attribute analyses were done to build up more understanding of structures lateral extension and to view a clear image of the fault blocks. Time to depth conversion was computed using velocity modeling generated from check shot and sonic data. The simplified stratigraphic cross-section was drawn through the wells A1, A2, A3, and A4-LP3D. The distribution and the thickness variations of the Beda reservoir along the study area had been demonstrating. Petrophysical analysis of wireline logging also was done and Cross plots of some petrophysical parameters are generated to evaluate the lithology of reservoir interval. Structure and Stratigraphic Framework was designed and run to generate different model like faults, facies, and petrophysical models and calculate the reservoir volumetric. This study concluded that the depth structure map of the Beda formation shows the main structure in the area of study, which is north to south faulted anticline. Based on the Beda reservoir models, volumetric for the base case has been calculated and it has STOIIP of 41MMSTB and Recoverable oil of 10MMSTB. Seismic attributes confirm the structure trend and build a better understanding of the fault system in the area.

Keywords: LP3D Field, Beda Formation, reservoir models, Seismic attributes

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Authors: Fikremariam Beyene, Getachew Bekele

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Ethiopia is currently on the move with various projects to raise the amount of power generated in the country. The progress observed in recent years indicates this fact clearly and indisputably. The rural electrification program, the modernization of the power transmission system, the development of wind farm is some of the main accomplishments worth mentioning. As it is well known, currently, wind power is globally embraced as one of the most important sources of energy mainly for its environmentally friendly characteristics, and also that once it is installed, it is a source available free of charge. However, integration of wind power plant with an existing network has many challenges that need to be given serious attention. In Ethiopia, a number of wind farms are either installed or are under construction. A series of wind farm is planned to be installed in the near future. Ashegoda Wind farm (13.2°, 39.6°), which is the subject of this study, is the first large scale wind farm under construction with the capacity of 120 MW. The first phase of 120 MW (30 MW) has been completed and is expected to be connected to the grid soon. This paper is concerned with the investigation of the wind farm interaction with the national grid under transient operating condition. The main concern is the fault ride through (FRT) capability of the system when the grid voltage drops to exceedingly low values because of short circuit fault and also the active and reactive power behavior of wind turbines after the fault is cleared. On the wind turbine side, a detailed dynamic modelling of variable speed wind turbine of a 1 MW capacity running with a squirrel cage induction generator and full-scale power electronics converters is done and analyzed using simulation software DIgSILENT PowerFactory. On the Ethiopian electric power corporation side, after having collected sufficient data for the analysis, the grid network is modeled. In the model, a fault ride-through (FRT) capability of the plant is studied by applying 3-phase short circuit on the grid terminal near the wind farm. The results show that the Ashegoda wind farm can ride from voltage deep within a short time and the active and reactive power performance of the wind farm is also promising.

Keywords: squirrel cage induction generator, active and reactive power, DIgSILENT PowerFactory, fault ride-through capability, 3-phase short circuit

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Authors: Murat Yazici

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Anomalies are irregularities found in data that do not adhere to a well-defined standard of normal behavior. The identification of outliers or anomalies in data has been a subject of study within the statistics field since the 1800s. Over time, a variety of anomaly detection techniques have been developed in several research communities. The cluster analysis can be used to detect anomalies. It is the process of associating data with clusters that are as similar as possible while dissimilar clusters are associated with each other. Many of the traditional cluster algorithms have limitations in dealing with data sets containing categorical properties. To detect anomalies in categorical data, fuzzy clustering approach can be used with its advantages. The fuzzy k-Mode (FKM) clustering algorithm, which is one of the fuzzy clustering approaches, by extension to the k-means algorithm, is reported for clustering datasets with categorical values. It is a form of clustering: each point can be associated with more than one cluster. In this paper, anomaly detection is performed on two simulated data by using the FKM cluster algorithm. As a significance of the study, the FKM cluster algorithm allows to determine anomalies with their abnormality degree in contrast to numerous anomaly detection algorithms. According to the results, the FKM cluster algorithm illustrated good performance in the anomaly detection of data, including both one anomaly and more than one anomaly.

Keywords: fuzzy k-mode clustering, anomaly detection, noise, categorical data

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Authors: Laicheng Cao, Xiangqian Su, Youxiao Wu

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Feature selection is one of the important problems in network security, pattern recognition, data mining and other fields. In order to remove redundant features, effectively improve the detection speed of intrusion detection system, proposes a new feature selection method, which is based on the invasive weed optimization (IWO) algorithm and tabu search algorithm(TS). Use IWO as a global search, tabu search algorithm for local search, to improve the results of IWO algorithm. The experimental results show that the feature selection method can effectively remove the redundant features of network data information in feature selection, reduction time, and to guarantee accurate detection rate, effectively improve the speed of detection system.

Keywords: intrusion detection, feature selection, iwo, tabu search

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Authors: Alireza Mirrashid, Mohammad Khoshbin, Ali Atghaei, Hassan Shahbazi

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In various industries, smoke and fire are two of the most important threats in the workplace. One of the common methods for detecting smoke and fire is the use of infrared thermal and smoke sensors, which cannot be used in outdoor applications. Therefore, the use of vision-based methods seems necessary. The problem of smoke and fire detection is spatiotemporal and requires spatiotemporal solutions. This paper presents a method that uses spatial features along with temporal-based features to detect smoke and fire in the scene. It consists of three main parts; the task of each part is to reduce the error of the previous part so that the final model has a robust performance. This method also uses transformer modules to increase the accuracy of the model. The results of our model show the proper performance of the proposed approach in solving the problem of smoke and fire detection and can be used to increase workplace safety.

Keywords: attention, fire detection, smoke detection, spatio-temporal

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Chowduru Ramachandra Sharma, Shatunjay Rawat

Abstract:

A false positive state is when the IDS/IPS identifies an activity as an attack, but the activity is acceptable behavior in the system. False positives in a Network Intrusion Detection System ( NIDS ) is an issue because they desensitize the administrator. It wastes computational power and valuable resources when rules are not tuned properly, which is the main issue with anomaly NIDS. Furthermore, most false positives reduction techniques are not performed during the real-time of attempted intrusions; instead, they have applied afterward on collected traffic data and generate alerts. Of course, false positives detection in ‘offline mode’ is tremendously valuable. Nevertheless, there is room for improvement here; automated techniques still need to reduce False Positives in real-time. This paper uses the Snort signature detection model to redirect the alerted attacks to Honeypots and verify attacks.

Keywords: honeypot, TPOT, snort, NIDS, honeybird, iptables, netfilter, redirection, attack detection, docker, snare, tanner

Procedia PDF Downloads 156

Authors: Mamidi Ramakrishna Rao

Abstract:

Doubly-fed induction generators (DFIG) due to their advantages like speed variation and four-quadrant operation, find its application in wind turbines. DFIG besides supplying power to the grid has to support reactive power (kvar) under grid voltage variations, should contribute minimum fault current during faults, have high efficiency, minimum weight, adequate rotor protection during crow-bar-operation from +20% to -20% of rated speed.  To achieve the optimum performance, a good electromagnetic design of DFIG is required. In this paper, a simple and heuristic global optimization – Differential Evolution has been used. Variables considered are lamination details such as slot dimensions, stack diameters, air gap length, and generator stator and rotor stack length. Two operating conditions have been considered - voltage and speed variations. Constraints included were reactive power supplied to the grid and limiting fault current and torque. The optimization has been executed separately for three objective functions - maximum efficiency, weight reduction, and grid fault stator currents. Subsequent calculations led to the conclusion that designs determined through differential evolution help in determining an optimum electrical design for each objective function.

Keywords: design optimization, performance, DFIG, differential evolution

Procedia PDF Downloads 150

Authors: Yan Zhang

Abstract:

Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

Procedia PDF Downloads 386

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 238

Authors: Anoop T. R., Otman Basir, Robert F. Hess, Ben Thompson

Abstract:

Strabismus refers to a misalignment of the eyes. Early detection and treatment of strabismus in childhood can prevent the development of permanent vision loss due to abnormal development of visual brain areas. Currently, many children with strabismus remain undiagnosed until school entry because current automated screening methods have limited success in the preschool age range. A method for strabismus detection using eye alignment stability (EAS) is proposed. This method starts with face detection, followed by facial landmark detection, eye region segmentation, eye gaze extraction, and eye alignment stability estimation. Binarization and morphological operations are performed for segmenting the pupil region from the eye. After finding the EAS, its absolute value is used to differentiate the strabismic eye from the non-strabismic eye. If the value of the eye alignment stability is greater than a particular threshold, then the eyes are misaligned, and if its value is less than the threshold, the eyes are aligned. The method was tested on 175 strabismic and non-strabismic images obtained from Kaggle and Google Photos. The strabismic eye is taken as a positive class, and the non-strabismic eye is taken as a negative class. The test produced a true positive rate of 100% and a false positive rate of 7.69%.

Keywords: strabismus, face detection, facial landmarks, eye segmentation, eye gaze, binarization

Procedia PDF Downloads 77