Search results for: fault location detection

Authors: Win Zaw Hein, Khin Sandar Linn, Su Su Yi Mon, Yoshitaka Goto

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The Earth ionosphere is located at the altitude of about 70 km to several 100 km from the ground, and it is composed of ions and electrons called plasma. In the ionosphere, these plasma makes delay in GPS (Global Positioning System) signals and reflect in radio waves. The delay along the signal path from the satellite to the receiver is directly proportional to the total electron content (TEC) of plasma, and this delay is the largest error factor in satellite positioning and navigation. Sounding observation from the top and bottom of the ionosphere was popular to investigate such ionospheric plasma for a long time. Recently, continuous monitoring of the TEC using networks of GNSS (Global Navigation Satellite System) observation stations, which are basically built for land survey, has been conducted in several countries. However, in these stations, multi-frequency support receivers are installed to estimate the effect of plasma delay using their frequency dependence and the cost of multi-frequency support receivers are much higher than single frequency support GPS receiver. In this research, single frequency GPS receiver was used instead of expensive multi-frequency GNSS receivers to measure the ionospheric plasma variation such as vertical TEC distribution. In this measurement, single-frequency support ublox GPS receiver was used to probe ionospheric TEC. The location of observation was assigned at Mandalay Technological University in Myanmar. In the method, the ionospheric TEC distribution is represented by polynomial functions for latitude and longitude, and parameters of the functions are determined by least-squares fitting on pseudorange data obtained at a known location under an assumption of thin layer ionosphere. The validity of the method was evaluated by measurements obtained by the Japanese GNSS observation network called GEONET. The performance of measurement results using single-frequency of GPS receiver was compared with the results by dual-frequency measurement.

Keywords: ionosphere, global positioning system, GPS, ionospheric delay, total electron content, TEC

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Authors: João Filipe Papel, Tatsuji Munaka

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With society's aging and the number of elderly with dementia rising, researchers have been actively studying how to support the elderly in the early stages of dementia with the objective of allowing them to have a better life quality and as much as possible independence. To make this possible, most researchers in this field are using the Internet Of Things to monitor the elderly activities and assist them in performing them. The most common sensor used to monitor the elderly activities is the Camera sensor due to its easy installation and configuration. The other commonly used sensor is the sound sensor. However, we need to consider privacy when using these sensors. This research aims to develop a system capable of predicting the early stages of dementia based on monitoring and controlling the elderly activities of daily living. To make this system possible, some issues need to be addressed. First, the issue related to elderly privacy when trying to detect their Activities of Daily Living. Privacy when performing detection and monitoring Activities of Daily Living it's a serious concern. One of the purposes of this research is to achieve this detection and monitoring without putting the privacy of the elderly at risk. To make this possible, the study focuses on using an approach based on using Magnet Sensors to collect binary data. The second is to use the data collected by monitoring Activities of Daily Living to predict the early stages of Dementia. To make this possible, the research team suggests developing a proprietary ontology combined with both data-driven and knowledge-driven.

Keywords: dementia, activity recognition, magnet sensors, ontology, data driven and knowledge driven, IoT, activities of daily living

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Authors: Mardi Falck, André Theron

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Traffic congestion on the Northern side of Table Bay is a major problem. In Gauteng, the implementation of the Gautrain between Pretoria and Johannesburg, solved their traffic congestion. In 2002 two entrepreneurs endeavoured to implement a hovercraft ferry service across the bay from Table View to the Port of Cape Town. However, the EIA process proved that disgruntled residents from the area did not agree with their location for a launch site. 17 years later the traffic problem has not gone away, but instead the congestion has increased. While property prices in the City Bowl of Cape Town are ever increasing, people tend to live more on the outskirts of the CBD and commute to work. This means more vehicles on the road every day and the public transport services cannot keep up with the demand. For this reason, the study area of the previous hovercraft plans is being extended further North. The study’s aim is thus to determine the feasibility of a launch site North of Bloubergstrand to launch and receive a public transport ferry across Table Bay. The feasibility is being established by researching ferry services across the world and on what makes them successful. Different types of ferries and their operational capacities in terms of weather and waves are researched and by establishing the offshore and nearshore wind and wave climate for the area, an appropriate protected launch site is determined. It was concluded that travel time could potentially be halved. A hovercraft proved to be the most feasible ferry type, because it does not require a conventional harbour. Other types of vessels require a protected launch site because of the wave climate. This means large breakwaters that influence the cost substantially. The Melkbos Cultural Centre proved to be the most viable option for the location of the launch site, because it already has buildings and infrastructure. It is recommended that, if a harbour is chosen for the proposed ferry service, it could be used for more services like fishing, eco-tourism and leisure. Further studies are recommended to optimise the feasibility of such a harbour.

Keywords: Cape Town, ferry, public, Table Bay

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Authors: Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves

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Background: Avian influenza, more commonly known as Bird flu, is a viral zoonotic respiratory disease stemming from various species of poultry, including pets and migratory birds. Researchers have purported that the accessibility of health information online, in addition to the low-cost data collection methods the internet provides, has revolutionized the methods in which epidemiological and disease surveillance data is utilized. This paper examines the feasibility of using internet data sources, such as Twitter and livestock forums, for the early detection of the avian flu outbreak, through the use of text mining algorithms and social network analysis. Methods: Social media mining was conducted on Twitter between the period of 01/01/2021 to 31/12/2021 via the Twitter API in Python. The results were filtered firstly by hashtags (#avianflu, #birdflu), word occurrences (avian flu, bird flu, H5N1), and then refined further by location to include only those results from within the UK. Analysis was conducted on this text in a time-series manner to determine keyword frequencies and topic modeling to uncover insights in the text prior to a confirmed outbreak. Further analysis was performed by examining clinical signs (e.g., swollen head, blue comb, dullness) within the time series prior to the confirmed avian flu outbreak by the Animal and Plant Health Agency (APHA). Results: The increased search results in Google and avian flu-related tweets showed a correlation in time with the confirmed cases. Topic modeling uncovered clusters of word occurrences relating to livestock biosecurity, disposal of dead birds, and prevention measures. Conclusions: Text mining social media data can prove to be useful in relation to analysing discussed topics for epidemiological surveillance purposes, especially given the lack of applied research in the veterinary domain. The small sample size of tweets for certain weekly time periods makes it difficult to provide statistically plausible results, in addition to a great amount of textual noise in the data.

Keywords: veterinary epidemiology, disease surveillance, infodemiology, infoveillance, avian influenza, social media

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Authors: Shady Adib, Vladimir Vinogradov, Peter Gosling

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Digital Twin (DT) technology is a new technology that appeared in the early 21st century. The DT is defined as the digital representation of living and non-living physical assets. By connecting the physical and virtual assets, data are transmitted smoothly, allowing the virtual asset to fully represent the physical asset. Although there are lots of studies conducted on the DT concept, there is still limited information about the ability of the DT models for monitoring and detecting unexpected changes in structural behaviour in real time. This is due to the large computational efforts required for the analysis and an excessively large amount of data transferred from sensors. This paper aims to develop the DT concept to be able to detect the abnormal changes in structural behaviour in real time using advanced modelling techniques, deep learning algorithms, and data acquisition systems, taking into consideration model uncertainties. finite element (FE) models were first developed offline to be used with a reduced basis (RB) model order reduction technique for the construction of low-dimensional space to speed the analysis during the online stage. The RB model was validated against experimental test results for the establishment of a DT model of a two-dimensional truss. The established DT model and deep learning algorithms were used to identify the location of damage once it has appeared during the online stage. Finally, the RB model was used again to identify the damage severity. It was found that using the RB model, constructed offline, speeds the FE analysis during the online stage. The constructed RB model showed higher accuracy for predicting the damage severity, while deep learning algorithms were found to be useful for estimating the location of damage with small severity.

Keywords: data acquisition system, deep learning, digital twin, model uncertainties, reduced basis, reduced order model

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Authors: Ebru Aktepe Erkoç, Atilla Uluğ

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In this research which has been prepared to show the relationship between Gökova Bay’s morphotectonic structure and seismicity, it is clear that there are many active faults in the region. The existence of a thick sedimentary accumulation since Late Quaternary times is obvious as a result of the geophysical workings in the region and the interpretation of seismic data which has been planning to be taken from the Bay. In the regions which have been tectonically active according to the interpretation of the taken data, the existence of the successive earthquakes in the last few years is remarkable. By analyzing large earthquakes affecting the areas remaining inside the sediments in West Anatolian Collapse System, this paper aims to reveal the fault systems constituting earthquakes with the information obtained from this study and to determine seismicity of the present residential areas right next to them. It is also aimed to anticipate the measures to be taken against possible earthquake hazards, to identify these areas posing a risk in terms of residential and urban planning and to determine at least partly the characteristics of the basin.

Keywords: Gökova Bay, sedimentation, seismic, West Anatolian

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Authors: Astrid Tannert, Anuradha Ramoji, Christina Ebert, Frederike Gladigau, Lorena Tuchscherr, Jürgen Popp, Ute Neugebauer

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Staphylococcus aureus is a facultative intracellular pathogen, which by entering host cells may evade immunologic host response as well as antimicrobial treatment. In that way, S. aureus can cause persistent intracellular infections which are difficult to treat. Depending on the strain, S. aureus may persist at different intracellular locations like the phagolysosome. The first barrier invading pathogens from the blood stream that they have to cross are the endothelial cells lining the inner surface of blood and lymphatic vessels. Upon proceeding from an acute to a chronic infection, intracellular pathogens undergo certain biochemical and structural changes including a deceleration of metabolic processes to adopt for long-term intracellular survival and the development of a special phenotype designated as small colony variant. In this study, the endothelial cell line Ea.hy 926 was used as a model for acute and chronic S. aureus infection. To this end, Ea.hy 926 cells were cultured on QIAscout™ Microraft Arrays, a special graded cell culture substrate that contains around 12,000 microrafts of 200 µm edge length. After attachment to the substrate, the endothelial cells were infected with GFP-expressing S. aureus for 3 weeks. The acute infection and the development of persistent bacteria was followed by confocal laser scanning microscopy, scanning the whole Microraft Array for the presence and for detailed determination of the intracellular location of fluorescent intracellular bacteria every second day. After three weeks of infection representative microrafts containing infected cells, cells with protruded infections and cells that did never show any infection were isolated and fixed for Raman micro-spectroscopic investigation. For comparison, also microrafts with acute infection were isolated. The acquired Raman spectra are correlated with the fluorescence microscopic images to give hints about a) the molecular alterations in endothelial cells during acute and chronic infection compared to non-infected cells, and b) metabolic and structural changes within the pathogen when entering a mode of persistence within host cells. We thank Dr. Ruth Kläver from QIAGEN GmbH for her support regarding QIAscout technology. Financial support by the BMBF via the CSCC (FKZ 01EO1502) and from the DFG via the Jena Biophotonic and Imaging Laboratory (JBIL, FKZ PO 633/29-1, BA 1601/10-1) is highly acknowledged.

Keywords: correlative image analysis, intracellular infection, pathogen-host adaption, Raman micro-spectroscopy

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Authors: Jigg Pelayo, Ricardo Villar

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Leaning on the development of aerial laser scanning in the Philippine geospatial industry, researches about remote sensing and machine vision technology became a trend. Object detection via template matching is one of its application which characterized to be fast and in real time. The paper purposely attempts to provide application for robust pattern matching algorithm based on the normalized cross correlation (NCC) criterion function subjected in Object-based image analysis (OBIA) utilizing high-resolution aerial imagery and low density LiDAR data. The height information from laser scanning provides effective partitioning order, thus improving the hierarchal class feature pattern which allows to skip unnecessary calculation. Since detection is executed in the object-oriented platform, mathematical morphology and multi-level filter algorithms were established to effectively avoid the influence of noise, small distortion and fluctuating image saturation that affect the rate of recognition of features. Furthermore, the scheme is evaluated to recognized the performance in different situations and inspect the computational complexities of the algorithms. Its effectiveness is demonstrated in areas of Misamis Oriental province, achieving an overall accuracy of 91% above. Also, the garnered results portray the potential and efficiency of the implemented algorithm under different lighting conditions.

Keywords: algorithm, LiDAR, object recognition, OBIA

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Authors: Myungsu Lim, William Xiu Shun Wong, Yoonjin Hyun, Chen Liu, Seongi Choi, Dasom Kim, Namgyu Kim

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The amount of real-time data generated through various mass media has been increasing rapidly. In this study, we had performed topic analysis by using the unstructured text data that is distributed through news article. As one of the most prevalent applications of topic analysis, the issue tracking technique investigates the changes of the social issues that identified through topic analysis. Currently, traditional issue tracking is conducted by identifying the main topics of documents that cover an entire period at the same time and analyzing the occurrence of each topic by the period of occurrence. However, this traditional issue tracking approach has limitation that it cannot discover dynamic mutation process of complex social issues. The purpose of this study is to overcome the limitations of the existing issue tracking method. We first derived core issues of each period, and then discover the dynamic mutation process of various issues. In this study, we further analyze the mutation process from the perspective of the issues categories, in order to figure out the pattern of issue flow, including the frequency and reliability of the pattern. In other words, this study allows us to understand the components of the complex issues by tracking the dynamic history of issues. This methodology can facilitate a clearer understanding of complex social phenomena by providing mutation history and related category information of the phenomena.

Keywords: Data Mining, Issue Tracking, Text Mining, topic Analysis, topic Detection, Trend Detection

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Authors: Murat Sözen

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Today terrorism is gaining momentum again. Parallel to this, it hurts more than before because it has victims from not only its own locations but also remote places. As victims are from outside, militants are likewise from own location and outside. What made these individuals join the terrorist organizations and how these organizations recruit militants are still unanswered. The purpose of this work is to find reasons of joining and power of recruiting. In addition, the role of most popular tool of recruiting, ‘social media’ will be examined.

Keywords: recruitment, social media, recruitment, militants

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Authors: Soukaina Motia, Nadia El Alami El Hassani, Alassane Diouf, Benachir Bouchikhi, Nezha El Bari

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Parabens are the antimicrobial molecules largely used in cosmetic products as a preservative agent. Among them, the methylparaben (MP) is the most frequently used ingredient in cosmetic preparations. Nevertheless, their potential dangers led to the development of sensible and reliable methods for their determination in environmental samples. Firstly, a sensitive and selective molecular imprinted polymer (MIP) based on screen-printed gold electrode (Au-SPE), assembled on a polymeric layer of carboxylated poly(vinyl-chloride) (PVC-COOH), was developed. After the template removal, the obtained material was able to rebind MP and discriminate it among other interfering species such as glucose, sucrose, and citric acid. The behavior of molecular imprinted sensor was characterized by Cyclic Voltammetry (CV), Differential Pulse Voltammetry (DPV) and Electrochemical Impedance Spectroscopy (EIS) techniques. Then, the biosensor was found to have a linear detection range from 0.1 pg.mL^-1 to 1 ng.mL^-1 and a low limit of detection of 0.12 fg.mL^-1 and 5.18 pg.mL^-1 by DPV and EIS, respectively. For applications, this biosensor was employed to determine MP content in four wastewaters in Meknes city and two cosmetic products (shower gel and shampoo). The operational reproducibility and stability of this biosensor were also studied. Secondly, another MIP biosensor based on tungsten trioxide (WO₃) functionalized by gold nanoparticles (Au-NPs) assembled on a polymeric layer of PVC-COOH was developed. The main goal was to increase the sensitivity of the biosensor. The developed MIP biosensor was successfully applied for the MP determination in wastewater samples and cosmetic products.

Keywords: cosmetic products, methylparaben, molecularly imprinted polymer, wastewater

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Authors: N. Varsamis, M. Gerasimou, P. Christodoulou, S. Mantzoukis, G. Kolliopoulou, N. Zotos

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Purpose: Body fluids (pleural, peritoneal, synovial, pericardial, cerebrospinal) are an important element in the detection of microorganisms. For this reason, it is important to examine them in the Intensive Care Unit (ICU) patients. Material and Method: Body fluids are transported through sterile containers and enriched as soon as possible with Tryptic Soy Broth (TSB). After one day of incubation, the broth is poured into selective media: Blood, Mac Conkey No. 2, Chocolate, Mueller Hinton, Chapman and Saboureaud agar. The above selective media are incubated directly for 2 days. After this period, if any number of microbial colonies are detected, gram staining is performed. After that, the isolated organisms are identified by biochemical techniques in the automated Microscan system (Siemens) and followed by a sensitivity test on the same system using the minimum inhibitory concentration MIC technique. The sensitivity test is verified by Kirby Bauer-based plate test. Results: In 2017 the Laboratory of Microbiology received 60 samples of body fluids from the ICU. More specifically the Microbiology Department received 6 peritoneal fluid specimens, 18 pleural fluid specimens and 36 cerebrospinal fluid specimens. 36 positive cultures were tested. S. epidermidis was identified in 18 specimens, S. haemolyticus in 6, and E. faecium in 12. Conclusions: The results show low detection of microorganisms in body fluid cultures.

Keywords: body fluids, culture, intensive care unit, microorganisms

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Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas

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This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).

Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems

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Authors: S. Shankar Bharathi

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Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

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Authors: Praveena Nair Sivasankaran, Thomas Arthur Ward, Rubentheren Viyapuri

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Paper describes a methodology to fabricate a simplified dragonfly wing structure using balsa wood and red prepreg fibre glass. These simplified wing structures were created for use in Biomimetic Micro Air Vehicles (BMAV). Dragonfly wings are highly corrugated and possess complex vein structures. In order to mimic the wings function and retain its properties, a simplified version of the wing was designed. The simplified dragonfly wing structure was created using a method called spatial network analysis which utilizes Canny edge detection method. The vein structure of the wings were carved out in balsa wood and red prepreg fibre glass. Balsa wood and red prepreg fibre glass was chosen due to its ultra- lightweight property and hence, highly suitable to be used in our application. The fabricated structure was then immersed in a nanocomposite solution containing chitosan as a film matrix, reinforced with chitin nanowhiskers and tannic acid as a crosslinking agent. These materials closely mimic the membrane of a dragonfly wing. Finally, the wings were subjected to a bending test and comparisons were made with previous research for verification. The results had a margin of difference of about 3% and thus the structure was validated.

Keywords: dragonfly wings, simplified, Canny edge detection, balsa wood, red prepreg, chitin, chitosan, tannic acid

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Authors: Vu Hoang Thu Huong

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Background: Surface electrical stimulation (SES) is a popular non-invasive approach that offers a wide range of treatments for many diseases of physical therapy. It involves applying electrical stimulation to the skin via surface electrodes to stimulate nerve fibers. SES was regularly used to treat the back and upper or lower extremities, but it was rarely used to treat the chest and abdomen. SES on the thorax and abdomen should be administered with more attention because crucial organs are under those areas (i.e., heart, lungs, liver, etc.). In these areas, safety precautions are suggested, and some SES applications might even be a contraindication. The fact that physical therapists have less experience with SES in these situations can also be attributed to these. Although a few earlier studies applied it to these settings and discovered hopeful results, none of them highlight the relationship between the intensity of SES and its depth of impact for safety considerations. Objective: To assure feasibility when using SES in these areas, the purpose of this study is to summarize the common location and intensity of those areas that have been conducted in previous studies. Method: A thorough systematic review was conducted to determine the common surface electrode position for the thorax and abdomen areas. The studies with the randomized controlled design were systematically searched using inclusion and exclusion criteria through nine electronic databases, including Pubmed, Scopus, etc., between 1975 and Dec 2021. Results: Thirty-three studies with over 1800 participants and 4 types of SES (TENS, IFC, NMES, and FES) with various categories of department hospitals were found. Following an anterior, lateral, and posterior observation, the particular SES positions found that it concentrated on 6 regions (the thoracic, abdomen, upper lateral, lower lateral, upper back, and lower back regions), and its intensity for each region was also summarized. Conclusion: This systematic review figured out the popular locations of SES in the thorax and abdominal areas as well as a summarized maximum of intensity that was found in previous studies with outstanding outcomes.

Keywords: surface electrical stimulation, electrical stimulation, thoracic, abdomen, abdominal.

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Jian-Heng Wu, Bor-Shen Lin

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The temperature-salinity relationship is one of the most important characteristics used for identifying water masses in marine research. Temperature-salinity characteristics, however, may change dynamically with respect to the geographic location and is quite sensitive to the depth at the same location. When depth is taken into consideration, however, it is not easy to compare the characteristics of different water masses efficiently for a wide range of areas of the ocean. In this paper, the Gaussian mixture model was proposed to analyze the temperature-salinity-depth characteristics of water masses, based on which comparison between water masses may be conducted. Gaussian mixture model could model the distribution of a random vector and is formulated as the weighting sum for a set of multivariate normal distributions. The temperature-salinity-depth data for different locations are first used to train a set of Gaussian mixture models individually. The distance between two Gaussian mixture models can then be defined as the weighting sum of pairwise Bhattacharyya distances among the Gaussian distributions. Consequently, the distance between two water masses may be measured fast, which allows the automatic and efficient comparison of the water masses for a wide range area. The proposed approach not only can approximate the distribution of temperature, salinity, and depth directly without the prior knowledge for assuming the regression family, but may restrict the complexity by controlling the number of mixtures when the amounts of samples are unevenly distributed. In addition, it is critical for knowledge discovery in marine research to represent, manage and share the temperature-salinity-depth characteristics flexibly and responsively. The proposed approach has been applied to a real-time visualization system of ocean data, which may facilitate the comparison of water masses by aggregating the data without degrading the discriminating capabilities. This system provides an interface for querying geographic locations with similar temperature-salinity-depth characteristics interactively and for tracking specific patterns of water masses, such as the Kuroshio near Taiwan or those in the South China Sea.

Keywords: water mass, Gaussian mixture model, data visualization, system framework

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Authors: Lin Wenli, Fu Linchun, Zhang Yi, Wu Ming

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To meet the demands of long-life and high-intelligence for satellites, the electrical power system should be provided with self-health condition evaluation capability. Any over-stress events in operations should be recorded. Based on Linear stresses accumulation damage theory, accumulative damage analysis was performed on thermal-mechanical-electrical united stresses for three components including the solar array, the batteries and the power conditioning unit. Then an overall health percentage evaluation model for satellite electrical power system was built. To obtain the accurate quantity for system health percentage, an automatic feedback closed-loop correction method for all coefficients in the evaluation model was present. The evaluation outputs could be referred as taking earlier fault-forecast and interventions for Ground Control Center or Satellites self.

Keywords: satellite electrical power system, health percentage, linear stresses accumulation damage, evaluation model

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Authors: Chia-Yi Liu, Bo-Bin Xiao, Wen-Bin Lin, Hsiang-Ning Wu, Liang-Hsun Huang

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In order to reduce the risk of radiation damage that personnel may suffer during operations in the radiation environment, the use of automated guided vehicles to assist or replace on-site personnel in the radiation environment has become a key technology and has become an important trend. In this paper, we demonstrate our proof of concept for autonomous self-learning radiation source searcher in an unknown environment without a map. The research uses GPU version of Asynchronous Advantage Actor-Critic network (GA3C) of deep reinforcement learning to search for radiation sources. The searcher network, based on GA3C architecture, has self-directed learned and improved how search the anomalous radiation source by training 1 million episodes under three simulation environments. In each episode of training, the radiation source position, the radiation source intensity, starting position, are all set randomly in one simulation environment. The input for searcher network is the fused data from a 2D laser scanner and a RGB-D camera as well as the value of the radiation detector. The output actions are the linear and angular velocities. The searcher network is trained in a simulation environment to accelerate the learning process. The well-performance searcher network is deployed to the real unmanned vehicle, Dashgo E2, which mounts LIDAR of YDLIDAR G4, RGB-D camera of Intel D455, and radiation detector made by Institute of Nuclear Energy Research. In the field experiment, the unmanned vehicle is enable to search out the radiation source of the 18.5MBq Na-22 by itself and avoid obstacles simultaneously without human interference.

Keywords: deep reinforcement learning, GA3C, source searching, source detection

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Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

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Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: computer-aided system, detection, image segmentation, morphology

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Authors: C. A. Roberts, J. G. Andrae, S. R. Smith, M. H. Poore, C. A. Young, D. W. Hancock, G. J. Pent

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To the author’s best knowledge, there are no published reports of effective methods for teaching fescue toxicosis and grass endophyte technology in the USA. To address this need, a group of university scientists, industry representatives, government agents, and livestock producers formed an organization called the Alliance for Grassland Renewal. One goal of the Alliance was to develop a teaching method that could be employed across all regions in the USA and all sectors of the agricultural community. The first step in developing this method was identification of experts who were familiar with the science and management of fescue toxicosis. The second step was curriculum development. Experts wrote a curriculum that addressed all aspects of toxicosis and management, including toxicology, animal nutrition, pasture management, economics, and mycology. The curriculum was created for presentation in lectures, laboratories, and in the field. The curriculum was in that it could be delivered across state lines, regardless of peculiar, in-state recommendations. The curriculum was also unique as it was unanimously supported by private companies otherwise in competition with each other. The final step in developing this teaching method was formulating a delivery plan. All experts, including university, industry, government, and production, volunteered to travel from any state in the USA, converge in one location, teach a 1-day workshop, then travel to the next location. The results of this teaching method indicate widespread success. Since 2012, experts across the entire USA have converged to teach Alliance workshops in Kansas, Oklahoma, Missouri, Kentucky, Georgia, South Carolina, North Carolina, and Virginia, with ongoing workshops in Arkansas and Tennessee. Data from post-workshop surveys indicate that instruction has been effective, as at least 50% of the participants stated their intention to adopt the endophyte technology presented in these workshops. The teaching method developed by the Alliance for Grassland Renewal has proved to be effective, and the Alliance continues to expand across the USA.

Keywords: endophyte, Epichloe coenophiala, ergot alkaloids, fescue toxicosis, tall fescue

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Authors: Samaneh Nabavi, Hadi Shirzad, Arash Ghoorchian, Maryam Shanesaz, Reza Naderi

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Morphine (MO), the most effective painkiller, is considered the reference by which analgesics are assessed. It is very necessary for the biomedical applications to detect and maintain the MO concentrations in the blood and urine with in safe ranges. To date, there are many expensive techniques for detecting MO. Recently, many electrochemical sensors for direct determination of MO were constructed. The molecularly imprinted polymer (MIP) is a polymeric material, which has a built-in functionality for the recognition of a particular chemical substance with its complementary cavity.This paper reports a sensor for MO using a combination of a molecularly imprinted polymer (MIP) and differential-pulse voltammetry (DPV). Electropolymerization of MO doped polypyrrole yielded poor quality, but a well-doped, nanostructure and increased impregnation has been obtained in the pH=12. Above a pH of 11, MO is in the anionic forms. The effect of various experimental parameters including pH, scan rate and accumulation time on the voltammetric response of MO was investigated. At the optimum conditions, the concentration of MO was determined using DPV in a linear range of 7.07 × 10−6 to 2.1 × 10−4 mol L−1 with a correlation coefficient of 0.999, and a detection limit of 13.3 × 10-8 mol L−1, respectively. The effect of common interferences on the current response of MO namely ascorbic acid (AA) and uric acid (UA) is studied. The modified electrode can be used for the determination of MO spiked into urine samples, and excellent recovery results were obtained. The nanostructured polypyrrole films were characterized by field emission scanning electron microscopy (FESEM) and furrier transforms infrared (FTIR).

Keywords: morphine detection, sensor, polypyrrole, nanostructure, molecularly imprinted polymer

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Authors: Sanchari Mukhopadhyay

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Today, almost the entire realm of human activities are becoming increasingly dependent on the power of information, where through ICTs it is now possible to cater distances and avail various services at a few clicks. In principle, ICTs are thus expected to blur location-specific differences and affiliations of development and bring in an inclusive society at the wake of globalization. However, eventually researchers and policy analysts realized that ICTs are also generating inequality in spite of the hope for an integrated world and widespread social well-being. Regarding this unevenness, location plays a major role as often ICT development is seen to be concentrated into pockets, leaving behind large tracks as underprivileged. Thus, understanding the spatial pattern of ICT development and distribution is significant in relation to exploring the extent to which ICTs are fulfilling the promises or reassuring the existing divisions. In addition, it is also profoundly crucial to investigate how regions are connecting and competing both locally and globally. The focus of the research paper is to evaluate the spatial structure of ICT led development in India. Thereby, it attempts to understand the state level (four selected states) pattern of ICT penetration, the pattern of diffusion across districts with respect to large urban centres and the rural-urban disparity of technology adoption. It also tries to assess the changes in access dynamisms of ICTs as one move away from a large metropolitan city towards the periphery. In brief, the analysis investigates into the tendency towards the uneven growth of development through the identification of the core areas of ICT advancement within the country and its diffusion from the core to the periphery. In order to assess the level of ICT development and rural-urban disparity across the districts of selected states, two indices named ICT Development Index and Rural-Urban Digital Divide Index have been constructed. The study mostly encompasses the latest Census (2011) of the country and TRAI (Telecom Regulatory Authority of India) in some cases.

Keywords: ICT development, diffusion, core-periphery, digital divide

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Authors: Madushani Rodrigo, Banuka Athuraliya

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In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

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Authors: M. F. Abreu, A. I. Aguiar, R. Gaio, R. Duarte

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Background: Prison establishments constitute high-risk environments for the transmission and spread of tuberculosis (TB), given their epidemiological context and the difficulty of implementing preventive and control measures. Guidelines for control and prevention of tuberculosis in prisons have been described as incomplete and heterogeneous internationally, due to several identified obstacles, for example scarcity of human resources and funding of prisoner health services. In Portugal, a protocol was created in 2014 with the aim to define and standardize procedures of detection and prevention of tuberculosis within prisons. Objective: The main objective of this study was to identify and describe barriers to tuberculosis detection in prisons of Porto and Lisbon districts in Portugal. Methods: A cross-sectional study was conducted from 2ⁿᵈ January 2018 till 30ᵗʰ June 2018. Semi-structured questionnaires were applied to health care professionals working in the prisons of the districts of Porto (n=6) and Lisbon (n=8). As inclusion criteria we considered having work experience in the area of tuberculosis (either in diagnosis, treatment, or follow up). The questionnaires were self-administered, in paper format. Descriptive analyses of the questionnaire variables were made using frequencies and median. Afterwards, a hierarchical agglomerative clusters analysis was performed. After obtaining the clusters, the chi-square test was applied to study the association between the variables collected and the clusters. The level of significance considered was 0.05. Results: From the total of 186 health professionals, 139 met the criteria of inclusion and 82 health professionals were interviewed (62,2% of participation). Most were female, nurses, with a median age of 34 years, with term employment contract. From the cluster analysis, two groups were identified with different characteristics and behaviors for the procedures of this protocol. Statistically significant results were found in: elements of cluster 1 (78% of the total participants) work in prisons for a longer time (p=0.003), 45,3% work > 4 years while 50% of the elements of cluster 2 work for less than a year, and more frequently answered they know and apply the procedures of the protocol (p=0.000). Both clusters answered frequently the need of having theoretical-practical training for TB (p=0.000), especially in the areas of diagnosis, treatment and prevention and that there is scarcity of funding to prisoner health services (p=0.000). Regarding procedures for TB screening (periodic and contact screening) and procedures for transferring a prisoner with this disease, cluster 1 also answered more frequently to perform them (p=0.000). They also referred that the material/equipment for TB screening is accessible and available (p=0.000). From this clusters we identified as barriers scarcity of human resources, the need to theoretical-practical training for tuberculosis, inexperience in working in health services prisons and limited knowledge of protocol procedures. Conclusions: The barriers found in this study are the same described internationally. This protocol is mostly being applied in portuguese prisons. The study also showed the need to invest in human and material resources. This investigation bridged gaps in knowledge that could help prison health services optimize the care provided for early detection and adherence of prisoners to treatment of tuberculosis.

Keywords: barriers, health care professionals, prisons, protocol, tuberculosis

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Authors: Kathryn L. Duncan, Charles A. Kuntz, Alessandra C. Santamaria, James O. Simcock

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Access to megavoltage radiation therapy for small animals is limited in many locations around the world. This can preclude the use of palliative radiation therapy for the treatment of appendicular osteosarcoma in dogs. The objective of this study was to retrospectively assess the adverse effects and survival times of dogs with appendicular osteosarcoma that were treated with hypofractionated orthovoltage radiation therapy and adjunctive carboplatin chemotherapy administered via a single subcutaneous infusion. Medical records were reviewed retrospectively to identify client-owned dogs with spontaneously occurring appendicular osteosarcoma that was treated with palliative orthovoltage radiation therapy and a single subcutaneous infusion of carboplatin. Data recorded included signalment, tumour location, results of diagnostic imaging, haematologic and serum biochemical analyses, adverse effects of radiation therapy and chemotherapy, and survival times. Kaplan-Meier survival analysis was performed, and log-rank analysis was used to determine the impact of specific patient variables on survival time. Twenty-three dogs were identified that met the inclusion criteria. Median survival time for dogs was 182 days. Eleven dogs had adverse haematologic effects, 3 had adverse gastrointestinal effects, 6 had adverse effects at the radiation site and 7 developed infections at the carboplatin infusion site. No statistically significant differences were identified in survival times based on sex, tumour location, development of infection, or pretreatment serum alkaline phosphatase. Median survival time and incidence of adverse effects were comparable to those previously reported in dogs undergoing palliative radiation therapy with megavoltage or cobalt radiation sources and conventional intravenous carboplatin chemotherapy. The use of orthovoltage palliative radiation therapy may be a reasonable alternative to megavoltage radiation in locations where access is limited.

Keywords: radiotherapy, veterinary oncology, chemotherapy, osteosarcoma

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Authors: Priya Saha, Sourav Dey Roy Jr., Debotosh Bhattacharjee, Mita Nasipuri, Barin Kumar De, Mrinal Kanti Bhowmik

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Biometric face recognition technology (FRT) has gained a lot of attention due to its extensive variety of applications in both security and non-security perspectives. It has come into view to provide a secure solution in identification and verification of person identity. Although other biometric based methods like fingerprint scans, iris scans are available, FRT is verified as an efficient technology for its user-friendliness and contact freeness. Accurate facial feature localization plays an important role for many facial analysis applications including biometrics and emotion recognition. But, there are certain factors, which make facial feature localization a challenging task. On human face, expressions can be seen from the subtle movements of facial muscles and influenced by internal emotional states. These non-rigid facial movements cause noticeable alterations in locations of facial landmarks, their usual shapes, which sometimes create occlusions in facial feature areas making face recognition as a difficult problem. The paper proposes a new hybrid based technique for automatic landmark detection in both neutral and expressive frontal and near frontal face images. The method uses the concept of thresholding, sequential searching and other image processing techniques for locating the landmark points on the face. Also, a Graphical User Interface (GUI) based software is designed that could automatically detect 16 landmark points around eyes, nose and mouth that are mostly affected by the changes in facial muscles. The proposed system has been tested on widely used JAFFE and Cohn Kanade database. Also, the system is tested on DeitY-TU face database which is created in the Biometrics Laboratory of Tripura University under the research project funded by Department of Electronics & Information Technology, Govt. of India. The performance of the proposed method has been done in terms of error measure and accuracy. The method has detection rate of 98.82% on JAFFE database, 91.27% on Cohn Kanade database and 93.05% on DeitY-TU database. Also, we have done comparative study of our proposed method with other techniques developed by other researchers. This paper will put into focus emotion-oriented systems through AU detection in future based on the located features.

Keywords: biometrics, face recognition, facial landmarks, image processing

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Authors: Lee Tasker, Ali Karrech, Jeffrey Shragge, Matthew Josh

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Monitoring for the deterioration of concrete infrastructure is an important assessment tool for an engineer and difficulties can be experienced with monitoring for deterioration within an infrastructure. If a failure crack, or fluid seepage through such a crack, is observed from the surface often the source location of the deterioration is not known. Geophysical methods are used to assist engineers with assessing the subsurface conditions of materials. Techniques such as Ground Penetrating Radar (GPR) provide information on the location of buried infrastructure such as pipes and conduits, positions of reinforcements within concrete blocks, and regions of voids/cavities behind tunnel lining. This experiment underlines the application of GPR as an infrastructure-monitoring tool to highlight and monitor regions of possible deterioration within a concrete test wall due to an increase in the generation of fractures; in particular, during a time period of applied load to a concrete wall up to and including structural failure. A three-point load was applied to a concrete test wall of dimensions 1700 x 600 x 300 mm³ in increments of 10 kN, until the wall structurally failed at 107.6 kN. At each increment of applied load, the load was kept constant and the wall was scanned using GPR along profile lines across the wall surface. The measured radar amplitude responses of the GPR profiles, at each applied load interval, were reconstructed into depth-slice grids and presented at fixed depth-slice intervals. The corresponding depth-slices were subtracted from each data set to compare the radar amplitude response between datasets and monitor for changes in the radar amplitude response. At lower values of applied load (i.e., 0-60 kN), few changes were observed in the difference of radar amplitude responses between data sets. At higher values of applied load (i.e., 100 kN), closer to structural failure, larger differences in radar amplitude response between data sets were highlighted in the GPR data; up to 300% increase in radar amplitude response at some locations between the 0 kN and 100 kN radar datasets. Distinct regions were observed in the 100 kN difference dataset (i.e., 100 kN-0 kN) close to the location of the final failure crack. The key regions observed were a conical feature located between approximately 3.0-12.0 cm depth from surface and a vertical linear feature located approximately 12.1-21.0 cm depth from surface. These key regions have been interpreted as locations exhibiting an increased change in pore-space due to increased mechanical loading, or locations displaying an increase in volume of micro-cracks, or locations showing the development of a larger macro-crack. The experiment showed that GPR is a useful geophysical monitoring tool to assist engineers with highlighting and monitoring regions of large changes of radar amplitude response that may be associated with locations of significant internal structural change (e.g. crack development). GPR is a non-destructive technique that is fast to deploy in a production setting. GPR can assist with reducing risk and costs in future infrastructure maintenance programs by highlighting and monitoring locations within the structure exhibiting large changes in radar amplitude over calendar-time.

Keywords: 4D GPR, engineering geophysics, ground penetrating radar, infrastructure monitoring

Procedia PDF Downloads 166