Search results for: virtual machine

Authors: Daniel Pinheiro Da Silva Junior, Aline Paes, Daniel De Oliveira, Christiano Lacerda Ghuerren, Marcio Duran

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The Courts of Accounts are institutions responsible for overseeing and point out irregularities of Public Administration expenses. They have a high demand for processes to be analyzed, whose decisions must be grounded on severity laws. Despite the existing large amount of processes, there are several cases reporting similar subjects. Thus, previous decisions on already analyzed processes can be a precedent for current processes that refer to similar topics. Identifying similar topics is an open, yet essential task for identifying similarities between several processes. Since the actual amount of topics is considerably large, it is tedious and error-prone to identify topics using a pure manual approach. This paper presents a tool based on Machine Learning and Natural Language Processing to assists in building a labeled dataset. The tool relies on Topic Modelling with Latent Dirichlet Allocation to find the topics underlying a document followed by Jensen Shannon distance metric to generate a probability of similarity between documents pairs. Furthermore, in a case study with a corpus of decisions of the Rio de Janeiro State Court of Accounts, it was noted that data pre-processing plays an essential role in modeling relevant topics. Also, the combination of topic modeling and a calculated distance metric over document represented among generated topics has been proved useful in helping to construct a labeled base of similar and non-similar document pairs.

Keywords: courts of accounts, data labelling, document similarity, topic modeling

Procedia PDF Downloads 172

Authors: Didem Venus Yildiz, Murat Hocaoglu, Murat Dursun, Taner Akkan

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The shoulder joint is a more complex structure than the hip or knee joints. In addition to the overall complexity of the shoulder joint, two different factors influence the insufficient outcome of shoulder replacement: the shoulder prosthesis design is far from fully developed and it is difficult to place these shoulder prosthesis due to shoulder anatomy. The glenohumeral joint is the most complex joint of the human shoulder. There are various treatments for shoulder failures such as total shoulder arthroplasty, reverse total shoulder arthroplasty. Due to its reverse design than normal shoulder anatomy, reverse total shoulder arthroplasty has different physiological and biomechanical properties. Post-operative achievement of this arthroplasty is depend on improved design of reverse total shoulder prosthesis. Designation achievement can be increased by several biomechanical and computational analysis. In this study, data of human both shoulders with right side fracture was collected by 3D Computer Tomography (CT) machine in dicom format. This data transferred to 3D medical image processing software (Mimics Materilise, Leuven, Belgium) to reconstruct patient’s left and right shoulders’ bones geometry. Provided 3D geometry model of the fractured shoulder was used to constitute of reverse total shoulder prosthesis by 3-matic software. Finite element (FE) analysis was conducted for comparison of intact shoulder and prosthetic shoulder in terms of stress distribution and displacements. Body weight physiological reaction force of 800 N loads was applied. Resultant values of FE analysis was compared for both shoulders. The analysis of the performance of the reverse shoulder prosthesis could enhance the knowledge of the prosthetic design.

Keywords: reverse shoulder prosthesis, biomechanics, finite element analysis, 3D printing

Procedia PDF Downloads 151

Authors: Olanrewaju Rotimi Bodede, Akinlabi Oyetunji

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Modeling the dry compressive strength of sodium silicate bonded kaolin refractory bricks was studied. The materials used for this research work included refractory clay obtained from Ijero-Ekiti kaolin deposit on coordinates 7º 49´N and 5º 5´E, sodium silicate obtained from the open market in Lagos on coordinates 6°27′11″N 3°23′45″E all in the South Western part of Nigeria. The mineralogical composition of the kaolin clay was determined using the Energy Dispersive X-Ray Fluorescence Spectrometer (ED-XRF). The clay samples were crushed and sieved using the laboratory pulveriser, ball mill and sieve shaker respectively to obtain 100 μm diameter particles. Manual pipe extruder of dimension 30 mm diameter by 43.30 mm height was used to prepare the samples with varying percentage volume of sodium silicate 5 %, 7.5 % 10 %, 12.5 %, 15 %, 17.5 %, 20% and 22.5 % while kaolin and water were kept at 50 % and 5 % respectively for the comprehensive test. The samples were left to dry in the open laboratory atmosphere for 24 hours to remove moisture. The samples were then were fired in an electrically powered muffle furnace. Firing was done at the following temperatures; 700ºC, 750ºC, 800ºC, 850ºC, 900ºC, 950ºC, 1000ºC and 1100ºC. Compressive strength test was carried out on the dried samples using a Testometric Universal Testing Machine (TUTM) equipped with a computer and printer, optimum compression of 4.41 kN/mm2 was obtained at 12.5 % sodium silicate; the experimental results were modeled with MATLAB and Origin packages using polynomial regression equations that predicted the estimated values for dry compressive strength and later validated with Pearson’s rank correlation coefficient, thereby obtaining a very high positive correlation value of 0.97.

Keywords: dry compressive strength, kaolin, modeling, sodium silicate

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Authors: Vishal Mahale, Jayashree Bijwe, Sujeet K. Sinha

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Nowadays extensive research is going on in the field of friction materials (FMs) for development of eco-friendly brake-materials by removing copper as it is a proven threat to the aquatic organisms. Researchers are keen to find the solution for copper-free FMs by using different metals or without metals. Steel wool is used as a reinforcement in non-asbestos organic (NAO) FMs mainly for increasing thermal conductivity, and it affects wear adversely, most of the times and also adds friction fluctuations. Copper and brass used to be the preferred choices because of superior performance in almost every aspect except cost. Since these are being phased out because of a proven threat to the aquatic life. Keeping this in view, a series of realistic multi-ingredient FMs containing stainless steel (SS) swarfs as a theme ingredient in increasing amount (0, 5, 10 and 15 wt. %- S₅, S₁₀, and S₁₅) were developed in the form of brake-pads. One more composite containing copper instead of SS swarfs (C₁₀) was developed. These composites were characterized for physical, mechanical, chemical and tribological performance. Composites were tribo-evaluated on a chase machine with various test loops as per SAE J661 standards. Various performance parameters such as normal µ, hot µ, performance µ, fade µ, recovery µ, % fade, % recovery, wear resistance, etc. were used to evaluate the role of amount of SS swarfs in FMs. It was concluded that SS swarfs proved successful in Cu replacement almost in all respects except wear resistance. With increase in amount of SS swarfs, most of the properties improved. Worn surface analysis and wear mechanism were studied using SEM and EDAX techniques.

Keywords: Chase type friction tester, copper-free, non-asbestos organic (NAO) friction materials, stainless steel swarfs

Procedia PDF Downloads 180

Authors: Adenike O. Olatunji-Akioye, Emmanual B Farayola

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In order to improve the animal protein recommended daily intake of Nigerians, there is an upsurge in breeding of hitherto shunned food animals one of which is the rabbit. Radiography and ultrasonography are tools for diagnosing disease and evaluating the anatomical architecture of parts of the body non-invasively. As the rabbit is becoming a more important food animal, to achieve improved breeding of these animals, the best of the species form a breeding stock and will usually depend on breeding soundness which may be evaluated by assessment of the male reproductive organs by these tools. Four male intact rabbits weighing between 1.2 to 1.5 kg were acquired and acclimatized for 2 weeks. Dorsoventral views of the testes were acquired using a digital radiographic machine and a 5 MHz portable ultrasound scanner was used to acquire images of the testes in longitudinal, sagittal and transverse planes. Radiographic images acquired revealed soft tissue images of the testes in all rabbits. The testes lie in individual scrotal sacs sides on both sides of the midline at the level of the caudal vertebrae and thus are superimposed by caudal vertebrae and the caudal limits of the pelvic girdle. The ultrasonographic images revealed mostly homogenously hypoechogenic testes and a hyperechogenic mediastinum testis. The dorsal and ventral poles of the testes were heterogeneously hypoechogenic and correspond to the epididymis and spermatic cord. The rabbit is unique in the ability to retract the testes particularly when stressed and so careful and stressless handling during the procedures is of paramount importance. The imaging of rabbit testes can be safely done using both imaging methods but ultrasonography is a better method of assessment and evaluation of soundness for breeding.

Keywords: breeding soundness, rabbit, radiography, ultrasonography

Procedia PDF Downloads 126

Authors: Mehrnoosh Omati, Mahmod Reza Sahebi

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The information on land use/land cover changing plays an essential role for environmental assessment, planning and management in regional development. Remotely sensed imagery is widely used for providing information in many change detection applications. Polarimetric Synthetic aperture radar (PolSAR) image, with the discrimination capability between different scattering mechanisms, is a powerful tool for environmental monitoring applications. This paper proposes a new boundary-based segmentation algorithm as a fundamental step for land cover change detection. In this method, first, two PolSAR images are segmented using integration of marker-controlled watershed algorithm and coupled Markov random field (MRF). Then, object-based classification is performed to determine changed/no changed image objects. Compared with pixel-based support vector machine (SVM) classifier, this novel segmentation algorithm significantly reduces the speckle effect in PolSAR images and improves the accuracy of binary classification in object-based level. The experimental results on Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric images show a 3% and 6% improvement in overall accuracy and kappa coefficient, respectively. Also, the proposed method can correctly distinguish homogeneous image parcels.

Keywords: coupled Markov random field (MRF), environment, object-based analysis, polarimetric SAR (PolSAR) images

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Authors: Philipp Zopf, Franz Haas

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Manufacturing of very hard and refractory materials like ceramics, glass or carbide poses particular challenges on tools and machines. The company Sauer GmbH developed especially for this application area ultrasonic tool holders working in a frequency range from 15 to 60 kHz and superimpose the common tool movement in the vertical axis. This technique causes a structural weakening in the contact area and facilitates the machining. The possibility of the force reduction for these special materials especially in drilling of carbide with diamond tools up to 30 percent made the authors try to expand the application range of this method. To make the results evaluable, the authors decide to start with existing processes in which the positive influence of the ultrasonic assistance is proven to understand the mechanism. The comparison of a grinding process the Institute use to machine materials mentioned in the beginning and steel could not be more different. In the first case, the authors use tools with geometrically undefined edges. In the second case, the edges are geometrically defined. To get valid results of the tests, the authors decide to investigate two manufacturing methods, drilling and milling. The main target of the investigation is to reduce the cutting force measured with a force measurement platform underneath the workpiece. Concerning to the direction of the ultrasonic assistance, the authors expect lower cutting forces and longer endurance of the tool in the drilling process. To verify the frequencies and the amplitudes an FFT-analysis is performed. It shows the increasing damping depending on the infeed rate of the tool. The reducing of amplitude of the cutting force comes along.

Keywords: drilling, machining, milling, ultrasonic

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Authors: Somayeh Komeylian

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The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE).

Keywords: DoA estimation, Adaptive antenna array, Deep Neural Network, LS-SVM optimization model, Radial basis function, and MSE

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Authors: Pamela R. Jackson, Andrea Hawkins-Daarud, Cassandra R. Rickertsen, Kamala Clark-Swanson, Scott A. Whitmire, Kristin R. Swanson

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Glioblastoma Multiforme (GBM), the most common primary brain tumor, often presents with hyperintensity on T2-weighted or T2-weighted fluid attenuated inversion recovery (T2/FLAIR) magnetic resonance imaging (MRI). This hyperintensity corresponds with vasogenic edema, however there are likely many infiltrating tumor cells within the hyperintensity as well. While MRIs do not directly indicate tumor cells, MRIs do reflect the microenvironmental water abnormalities caused by the presence of tumor cells and edema. The inherent heterogeneity and resulting MRI features of GBMs complicate assessing disease response. To understand how hyperintensity on T2/FLAIR MRI may correlate with edema in the extracellular space (ECS), a multi-compartmental MRI signal equation which takes into account tissue compartments and their associated volumes with input coming from a mathematical model of glioma growth that incorporates edema formation was explored. The reasonableness of two possible extracellular space schema was evaluated by varying the T2 of the edema compartment and calculating the possible resulting T2s in tumor and peripheral edema. In the mathematical model, gliomas were comprised of vasculature and three tumor cellular phenotypes: normoxic, hypoxic, and necrotic. Edema was characterized as fluid leaking from abnormal tumor vessels. Spatial maps of tumor cell density and edema for virtual tumors were simulated with different rates of proliferation and invasion and various ECS expansion schemes. These spatial maps were then passed into a multi-compartmental MRI signal model for generating simulated T2/FLAIR MR images. Individual compartments’ T2 values in the signal equation were either from literature or estimated and the T2 for edema specifically was varied over a wide range (200 ms – 9200 ms). T2 maps were calculated from simulated images. T2 values based on simulated images were evaluated for regions of interest (ROIs) in normal appearing white matter, tumor, and peripheral edema. The ROI T2 values were compared to T2 values reported in literature. The expanding scheme of extracellular space is had T2 values similar to the literature calculated values. The static scheme of extracellular space had a much lower T2 values and no matter what T2 was associated with edema, the intensities did not come close to literature values. Expanding the extracellular space is necessary to achieve simulated edema intensities commiserate with acquired MRIs.

Keywords: extracellular space, glioblastoma multiforme, magnetic resonance imaging, mathematical modeling

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Authors: Bokkasam Sasidhar, Ibrahim Aljasser

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The problem of finding optimal schedules for each equipment in a production process is considered, which consists of a single stage of manufacturing and which can handle different types of products, where changeover for handling one type of product to the other type incurs certain costs. The machine capacity is determined by the upper limit for the quantity that can be processed for each of the products in a set up. The changeover costs increase with the number of set ups and hence to minimize the costs associated with the product changeover, the planning should be such that similar types of products should be processed successively so that the total number of changeovers and in turn the associated set up costs are minimized. The problem of cost minimization is equivalent to the problem of minimizing the number of set ups or equivalently maximizing the capacity utilization in between every set up or maximizing the total capacity utilization. Further, the production is usually planned against customers’ orders, and generally different customers’ orders are assigned one of the two priorities – “normal” or “priority” order. The problem of production planning in such a situation can be formulated into a Multiple Arc Network (MAN) model and can be solved sequentially using the algorithm for maximizing flow along a MAN and the algorithm for maximizing flow along a MAN with priority arcs. The model aims to provide optimal production schedule with an objective of maximizing capacity utilization, so that the customer-wise delivery schedules are fulfilled, keeping in view the customer priorities. Algorithms have been presented for solving the MAN formulation of the production planning with customer priorities. The application of the model is demonstrated through numerical examples.

Keywords: scheduling, maximal flow problem, multiple arc network model, optimization

Procedia PDF Downloads 398

Authors: Juzhong Tan, William Kerr

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Conching is one critical procedure in chocolate processing, where special flavors are developed, and smooth mouse feel the texture of the chocolate is developed due to particle size reduction of cocoa mass and other additives. Therefore, determination of the particle size and volatile compounds profile of cocoa bean is important for chocolate manufacturers to ensure the quality of chocolate products. Currently, precise particle size measurement is usually done by laser scattering which is expensive and inaccessible to small/medium size chocolate manufacturers. Also, some other alternatives, such as micrometer and microscopy, can’t provide good measurements and provide little information. Volatile compounds analysis of cocoa during conching, has similar problems due to its high cost and limited accessibility. In this study, a self-made electronic nose system consists of gas sensors (TGS 800 and 2000 series) was inserted to a conching machine and was used to monitoring the volatile compound profile of chocolate during the conching. A model correlated volatile compounds profiles along with factors including the content of cocoa, sugar, and the temperature during the conching to particle size of chocolate particles by genetic programming was established. The model was used to predict the particle size reduction of chocolates with different cocoa mass to sugar ratio (1:2, 1:1, 1.5:1, 2:1) at 8 conching time (15min, 30min, 1h, 1.5h, 2h, 4h, 8h, and 24h). And the predictions were compared to laser scattering measurements of the same chocolate samples. 91.3% of the predictions were within the range of later scatting measurement ± 5% deviation. 99.3% were within the range of later scatting measurement ± 10% deviation.

Keywords: cocoa bean, conching, electronic nose, genetic programming

Procedia PDF Downloads 249

Authors: Imam Taufiqurrahman, Anugrah Adiwilaga, Egi Hidayat, Bambang Riyanto Trilaksono

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Autonomous Underwater Vehicle glider is one of the renewal of underwater vehicles. This vehicle is one of the autonomous underwater vehicles that are being developed in Indonesia. Glide ability is obtained by controlling the buoyancy and attitude of the vehicle using the movers within the vehicle. The glider motion mechanism is expected to provide energy resistance from autonomous underwater vehicles so as to increase the cruising range of rides while performing missions. The control system on the vehicle consists of three parts: controlling the attitude of the pitch, the buoyancy engine controller and the yaw controller. The buoyancy and pitch controls on the vehicle are sequentially referring to the finite state machine with pitch angle and depth of diving inputs to obtain a gliding cycle. While the yaw control is done through the rudder for the needs of the guide system. This research is focused on design and implementation of control system of Autonomous Underwater Vehicle glider based on PID anti-windup. The control system is implemented on an ARM TS-7250-V2 device along with a mathematical model of the vehicle in MATLAB using the hardware-in-the-loop simulation (HILS) method. The TS-7250-V2 is chosen because it complies industry standards, has high computing capability, minimal power consumption. The results show that the control system in HILS process can form glide cycle with depth and angle of operation as desired. In the implementation using half control and full control mode, from the experiment can be concluded in full control mode more precision when tracking the reference. While half control mode is considered more efficient in carrying out the mission.

Keywords: control system, PID, underwater glider, marine robotics

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Authors: Ouafa Amira, Jiangshe Zhang

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Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

Procedia PDF Downloads 257

Authors: Mostafa Mabrouk, Basma E. Abdel-Ghany, Mona Moaness, Bothaina M. Abdel-Hady, Hanan H. Beherei

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Tissue engineering became a promising field for bone repair and regenerative medicine in which cultured cells, scaffolds and osteogenic inductive signals are used to regenerate tissues. The annual cost of treating bone defects in Egypt has been estimated to be many billions, while enormous costs are spent on imported bone grafts for bone injuries, tumors, and other pathologies associated with defective fracture healing. The current study is aimed at developing a more strategic approach in order to speed-up recovery after bone damage. This will reduce the risk of fatal surgical complications and improve the quality of life of people affected with such fractures. 3D scaffolds loaded with cheap nano-particles that possess an osteogenic effect were prepared by nano-electrospinning. The Microstructure and morphology characterizations of the 3D scaffolds were monitored using scanning electron microscopy (SEM). The physicochemical characterization was investigated using X-ray diffractometry (XRD) and infrared spectroscopy (IR). The Physicomechanical properties of the 3D scaffold were determined by a universal testing machine. The in vitro bioactivity of the 3D scaffold was assessed in simulated body fluid (SBF). The bone-bonding ability of novel 3D scaffolds was also evaluated. The obtained nanofibrous scaffolds demonstrated promising microstructure, physicochemical and physicomechanical features appropriate for enhanced bone regeneration. Therefore, the utilized nanomaterials loaded with the drug are greatly recommended as cheap alternatives to growth factors.

Keywords: bone regeneration, cheap scaffolds, nanomaterials, active molecules

Procedia PDF Downloads 185

Authors: Jude K. Safo

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Knowledge Graphs KG) and their relation to Graph Embeddings (GE) represent a unique data structure in the landscape of machine learning (relative to image, text and acoustic data). Unlike the latter, GEs are the only data structure sufficient for representing hierarchically dense, semantic information needed for use-cases like supply chain data and protein folding where the search space exceeds the limits traditional search methods (e.g. page-rank, Dijkstra, etc.). While GEs are effective for compressing low rank tensor data, at scale, they begin to introduce a new problem of ’data retreival’ which we observe in Large Language Models. Notable attempts by transE, TransR and other prominent industry standards have shown a peak performance just north of 57% on WN18 and FB15K benchmarks, insufficient practical industry applications. They’re also limited, in scope, to next node/link predictions. Traditional linear methods like Tucker, CP, PARAFAC and CANDECOMP quickly hit memory limits on tensors exceeding 6.4 million nodes. This paper outlines a topological framework for linear mapping between concepts in KG space and GE space that preserve cardinality. Most importantly we introduce a traceable framework for composing dense linguistic strcutures. We demonstrate performance on WN18 benchmark this model hits. This model does not rely on Large Langauge Models (LLM) though the applications are certainy relevant here as well.

Keywords: representation theory, large language models, graph embeddings, applied algebraic topology, applied knot theory, combinatorics

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Authors: Behnaz Sohani, Sahand Shahalinezhad, Amir Rahmani, Aliyu Aliyu

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Recently, medical imaging and specifically medical image processing is becoming one of the most dynamically developing areas of medical science. It has led to the emergence of new approaches in terms of the prevention, diagnosis, and treatment of various diseases. In the process of diagnosis of lung cancer, medical professionals rely on computed tomography (CT) scans, in which failure to correctly identify masses can lead to incorrect diagnosis or sampling of lung tissue. Identification and demarcation of masses in terms of detecting cancer within lung tissue are critical challenges in diagnosis. In this work, a segmentation system in image processing techniques has been applied for detection purposes. Particularly, the use and validation of a novel lung cancer detection algorithm have been presented through simulation. This has been performed employing CT images based on multilevel thresholding. The proposed technique consists of segmentation, feature extraction, and feature selection and classification. More in detail, the features with useful information are selected after featuring extraction. Eventually, the output image of lung cancer is obtained with 96.3% accuracy and 87.25%. The purpose of feature extraction applying the proposed approach is to transform the raw data into a more usable form for subsequent statistical processing. Future steps will involve employing the current feature extraction method to achieve more accurate resulting images, including further details available to machine vision systems to recognise objects in lung CT scan images.

Keywords: lung cancer detection, image segmentation, lung computed tomography (CT) images, medical image processing

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Authors: Prodipta Bhowmik, Poulami Saha, Preety Mehra, Yogesh Soni, Triloki Nath Jha

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In India, the smart city concept is growing day by day. So, for smart city development, a better traffic management and monitoring system is a very important requirement. Nowadays, road accidents increase due to more vehicles on the road. Reckless driving is mainly responsible for a huge number of accidents. So, an efficient traffic management system is required for all kinds of roads to control the traffic speed. The speed limit varies from road to road basis. Previously, there was a radar system but due to high cost and less precision, the radar system is unable to become favorable in a traffic management system. Traffic management system faces different types of problems every day and it has become a researchable topic on how to solve this problem. This paper proposed a computer vision and machine learning-based automated system for multiple vehicle detection, tracking, and speed estimation of vehicles using image processing. Detection of vehicles and estimating their speed from a real-time video is tough work to do. The objective of this paper is to detect vehicles and estimate their speed as accurately as possible. So for this, a real-time video is first captured, then the frames are extracted from that video, then from that frames, the vehicles are detected, and thereafter, the tracking of vehicles starts, and finally, the speed of the moving vehicles is estimated. The goal of this method is to develop a cost-friendly system that can able to detect multiple types of vehicles at the same time.

Keywords: OpenCV, Haar Cascade classifier, DLIB, YOLOV3, centroid tracker, vehicle detection, vehicle tracking, vehicle speed estimation, computer vision

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Authors: Meng Su

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High-dimensional data analysis often presents challenges in capturing the complex, nonlinear relationships and manifold structures inherent to the data. This article presents a novel approach that leverages the strengths of two powerful techniques, Diffusion Maps and Diffusion Probabilistic Models (DPMs), to address these challenges. By integrating the dimensionality reduction capability of Diffusion Maps with the data modeling ability of DPMs, the proposed method aims to provide a comprehensive solution for analyzing and generating high-dimensional data. The Diffusion Map technique preserves the nonlinear relationships and manifold structure of the data by mapping it to a lower-dimensional space using the eigenvectors of the graph Laplacian matrix. Meanwhile, DPMs capture the dependencies within the data, enabling effective modeling and generation of new data points in the low-dimensional space. The generated data points can then be mapped back to the original high-dimensional space, ensuring consistency with the underlying manifold structure. Through a detailed example implementation, the article demonstrates the potential of the proposed hybrid approach to achieve more accurate and effective modeling and generation of complex, high-dimensional data. Furthermore, it discusses possible applications in various domains, such as image synthesis, time-series forecasting, and anomaly detection, and outlines future research directions for enhancing the scalability, performance, and integration with other machine learning techniques. By combining the strengths of Diffusion Maps and DPMs, this work paves the way for more advanced and robust data analysis methods.

Keywords: diffusion maps, diffusion probabilistic models (DPMs), manifold learning, high-dimensional data analysis

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Authors: Kristen Emory, Jerry Flores

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Purpose: We explore the unique and underrepresented experiences of Indigenous women living in Toronto, Canada, during the first year of the COVID-19 pandemic. The purpose of this study is to better document the impacts of COVID-19 on the mental health and well-being of Indigenous women in Toronto, Canada, in order to better understand unmet needs, as well as lay the groundwork for more targeted research and potential interventions based on these needs. Background: It has been fairly well documented that the COVID-19 pandemic has increased mental health concerns among various populations globally. There have also been numerous studies indicating increases in substance use and abuse in response to the stress of the pandemic. There is also evidence that the COVID-19 pandemic has disproportionately impacted a variety of historically marginalized populations in Canada, the US, and globally, including Indigenous populations. While these studies provide some insight into how the COVID-19 pandemic is impacting the global population, much less is known about the lived experiences of Indigenous populations during the time of COVID-19. Better understanding these experiences will allow public health professionals, governments, and non-governmental organizations better combat health inequities related to the pandemic. Methods: In-depth qualitative semi-structured virtual (due to COVID-19) interviews with 13 Indigenous women were conducted during the first year of the COVID-19 pandemic (2020). Interviews were recorded, transcribed, and analyzed by team members using Dedoose qualitative analysis software. Findings: COVID-19 negatively affected Indigenous females identifying participants’ mental health and corresponding reported increases in substance use. In addition to the daily stress of the unpredictability of life in the time of the COVID-19 pandemic, participants cited job loss, economic concerns, homeschooling, and lack of access to medical resources as primary factors in increasing their stress and decreasing mental health and wellbeing. In response to these stressors, a majority of participants cited coping mechanisms such as increased substance use to help deal with the uncertainty. In particular, alcohol and tobacco emerged as coping mechanisms to help participants cope with stress related to the pandemic (as well as its social and economic toll on respondents' lives). We will present qualitative data to be presented, including participant direct quotes, explaining their experiences with COVID-19, mental health, and increased substance use, as well as analysis and synthesis with the existing scientific evidence base. Conclusion: This research is among the good studies to our knowledge that scientifically explore the impact of COVID-19 on mental health and well-being and corresponding increases in reported substance use.

Keywords: mental health, covid-19, indigenous, inequity, anxiety, depression, stress

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Authors: Noriyuki Suyama

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The progress and spread of digitalization have led to the provision of a variety of new services. The recent progress in digitization can be attributed to rapid developments in science and technology. First, the research and diffusion of artificial intelligence (AI) has made dramatic progress. Around 2000, the third wave of AI research, which had been underway for about 50 years, arrived. Specifically, machine learning and deep learning were made possible in AI, and the ability of AI to acquire knowledge, define the knowledge, and update its own knowledge in a quantitative manner made the use of big data practical even for commercial PCs. On the other hand, with the spread of social media, information exchange has become more common in our daily lives, and the lending and borrowing of goods and services, in other words, the sharing economy, has become widespread. The scope of this trend is not limited to any industry, and its momentum is growing as the SDGs take root. In addition, the Social Network Service (SNS), a part of social media, has brought about the evolution of the retail business. In the past few years, social network services (SNS) involving users or companies have especially flourished. The People's Republic of China (hereinafter referred to as "China") is a country that is stimulating enormous consumption through its own unique SNS, which is different from the SNS used in developed countries around the world. This paper focuses on the effectiveness and challenges of influencer marketing by focusing on the influence of influencers on users' behavior and satisfaction with Chinese SNSs. Specifically, Conducted was the quantitative survey of Tik Tok users living in China, with the aim of gaining new insights from the analysis and discussions. As a result, we found several important findings and knowledge.

Keywords: customer satisfaction, social networking services, influencer marketing, Chinese consumers’ behavior

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Authors: Ibrahim Umar, Ashiru Rajab, Sumayya Chindo, Emmanuel Olashore

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INTRODUCTION: Kano is the most populous state in Nigeria and one of the two states with the highest TB burden in the country. The state notifies an average of 8,000+ TB cases quarterly and has the highest yearly notification of all the states in Nigeria from 2020 to 2022. The contribution of the state TB program to the National TB notification varies from 9% to 10% quarterly between the first quarter of 2022 and second quarter of 2023. The Kano State TB Situation Room is an innovative platform for timely data collection, collation and analysis for informed decision in health system. During the 2023 second National TB Testing week (NTBTW) Kano TB program aimed at early TB detection, prevention and treatment. The state TB Situation room provided avenue to the state for coordination and surveillance through real time data reporting, review, analysis and use during the NTBTW. OBJECTIVES: To assess the role of innovative information and communication technology platform for real-time TB reporting during second National TB Testing week in Nigeria 2023. To showcase the NTBTW data cascade analysis using TSR as innovative ICT platform. METHODOLOGY: The State TB deployed a real-time virtual dashboard for NTBTW reporting, analysis and feedback. A data room team was set up who received realtime data using google link. Data received was analyzed using power BI analytic tool with statistical alpha level of significance of <0.05. RESULTS: At the end of the week-long activity and using the real-time dashboard with onsite mentorship of the field workers, the state TB program was able to screen a total of 52,054 people were screened for TB from 72,112 individuals eligible for screening (72% screening rate). A total of 9,910 presumptive TB clients were identified and evaluated for TB leading to diagnosis of 445 TB patients with TB (5% yield from presumptives) and placement of 435 TB patients on treatment (98% percentage enrolment). CONCLUSION: The TB Situation Room (TBSR) has been a great asset to Kano State TB Control Program in meeting up with the growing demand for timely data reporting in TB and other global health responses. The use of real time surveillance data during the 2023 NTBTW has in no small measure improved the TB response and feedback in Kano State. Scaling up this intervention to other disease areas, states and nations is a positive step in the right direction towards global TB eradication.

Keywords: tuberculosis (tb), national tb testing week (ntbtw), tb situation rom (tsr), information communication technology (ict)

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Authors: Roman Knizek, Denisa Knizkova

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This paper describes the development of a nanofiber membrane for sport and outdoor use at the Technical University of Liberec (TUL) and the following cooperation with a private Czech company which launched this product onto the market. For making this membrane, Polyurethan was electrospun on the Nanospider spinning machine, and a wire string electrode was used. The created nanofiber membrane with a nanofiber diameter of 150 nm was subsequently hydrophobisied using a low vacuum plasma and Fluorocarbon monomer C6 type. After this hydrophobic treatment, the nanofiber membrane contact angle was higher than 125o, and its oleophobicity was 6. The last step was a lamination of this nanofiber membrane with a woven or knitted fabric to create a 3-layer laminate. Gravure printing technology and polyurethane hot-melt adhesive were used. The gravure roller has a mesh of 17. The resulting 3-layer laminate has a water vapor permeability Ret of 1.6 [Pa.m2.W-1] (– measured in compliance with ISO 11092), it is 100% windproof (– measured in compliance with ISO 9237), and the water column is above 10 000 mm (– measured in compliance with ISO 20811). This nanofiber membrane which was developed in the laboratories of the Technical University of Liberec was then produced industrially by a private company. A low vacuum plasma line and a lamination line were needed for industrial production, and the process had to be fine-tuned to achieve the same parameters as those achieved in the TUL laboratories. The result of this work is a newly developed nanofiber membrane which offers much better properties, especially water vapor permeability, than other competitive membranes. It is an example of product development and the consequent fine-tuning for industrial production; it is also an example of the cooperation between a Czech state university and a private company.

Keywords: nanofiber membrane, start-up, state university, private company, product

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Authors: Arturo Reyes Roman, Daniza Castillo Godoy, Francisca Balarezo Olivares, Francisco Arriagada Castro, Miguel Maulen Tapia

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Mining waste–based geopolymers are a sustainable alternative to conventional cement materials due to their contribution to the valorization of mining wastes as well as to the new construction materials with reduced fingerprints. The objective of this study was to determine the potential of leaching gravel (LG) from hydrometallurgical copper processing to be used as a raw material in the manufacture of geopolymer. NaOH, Na2SiO3 (modulus 1.5), and LG were mixed and then wetted with an appropriate amount of tap water, then stirred until a homogenous paste was obtained. A liquid/solid ratio of 0.3 was used for preparing mixtures. The paste was then cast in cubic moulds of 50 mm for the determination of compressive strengths. The samples were left to dry for 24h at room temperature, then unmoulded before analysis after 28 days of curing time. The compressive test was conducted in a compression machine (15/300 kN). According to the laser diffraction spectroscopy (LDS) analysis, 90% of LG particles were below 500 μm. The X-ray diffraction (XRD) analysis identified crystalline phases of albite (30 %), Quartz (16%), Anorthite (16 %), and Phillipsite (14%). The X-ray fluorescence (XRF) determinations showed mainly 55% of SiO2, 13 % of Al2O3, and 9% of CaO. ICP (OES) concentrations of Fe, Ca, Cu, Al, As, V, Zn, Mo, and Ni were 49.545; 24.735; 6.172; 14.152, 239,5; 129,6; 41,1;15,1, and 13,1 mg kg-1, respectively. The geopolymer samples showed resistance ranging between 2 and 10 MPa. In comparison with the raw material composition, the amorphous percentage of materials in the geopolymer was 35 %, whereas the crystalline percentage of main mineral phases decreased. Further studies are needed to find the optimal combinations of materials to produce a more resistant and environmentally safe geopolymer. Particularly are necessary compressive resistance higher than 15 MPa are necessary to be used as construction unit such as bricks.

Keywords: mining waste, geopolymer, construction material, alkaline activation

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Authors: Yasser F. Hassan

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Social networking involves use of an online platform or website that enables people to communicate, usually for a social purpose, through a variety of services, most of which are web-based and offer opportunities for people to interact over the internet, e.g. via e-mail and ‘instant messaging’, by analyzing the voting behavior and ratings of judges in a popular comments in social networks. While most of the party literature omits the electorate, this paper presents a model where elites and parties are emergent consequences of the behavior and preferences of voters. The research in artificial intelligence and psychology has provided powerful illustrations of the way in which the emergence of intelligent behavior depends on the development of representational structure. As opposed to the classical voting system (one person – one decision – one vote) a new voting system is designed where agents with opposed preferences are endowed with a given number of votes to freely distribute them among some issues. The paper uses ideas from machine learning, artificial intelligence and soft computing to provide a model of the development of voting system response in a simulated agent. The modeled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structure. We employ agent-based computer simulation to demonstrate the formation and interaction of coalitions that arise from individual voter preferences. We are interested in coordinating the local behavior of individual agents to provide an appropriate system-level behavior.

Keywords: voting system, rough sets, multi-agent, social networks, emergence, power indices

Procedia PDF Downloads 391

Authors: Rupita Ghosh, Ritwik Sarkar, Sumit K. Pal, Soumitra Paul

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The use of Hydroxyapatite bioceramics as restorative implants is widely known. These materials can be manufactured by pressing and sintering route to a particular shape. However machining processes are still a basic requirement to give a near net shape to those implants for ensuring dimensional and geometrical accuracy. In this context, optimising the machining parameters is an important factor to understand the machinability of the materials and to reduce the production cost. In the present study a method has been optimized to produce true particulate drilled composite of Hydroxyapatite Yttrium Phosphate. The phosphates are used in varying ratio for a comparative study on the effect of flexural strength, hardness, machining (drilling) parameters and bioactivity.. The maximum flexural strength and hardness of the composite that could be attained are 46.07 MPa and 1.02 GPa respectively. Drilling is done with a conventional radial drilling machine aided with dynamometer with high speed steel (HSS) and solid carbide (SC) drills. The effect of variation in drilling parameters (cutting speed and feed), cutting tool, batch composition on torque, thrust force and tool wear are studied. It is observed that the thrust force and torque varies greatly with the increase in the speed, feed and yttrium phosphate content in the composite. Significant differences in the thrust and torque are noticed due to the change of the drills as well. Bioactivity study is done in simulated body fluid (SBF) upto 28 days. The growth of the bone like apatite has become denser with the increase in the number of days for all the composition of the composites and it is comparable to that of the pure hydroxyapatite.

Keywords: Bioactivity, Drilling, Hydroxyapatite, Yttrium Phosphate

Procedia PDF Downloads 294

Authors: L. Krpalkova, N. O' Mahony, A. Carvalho, S. Campbell, G. Corkery, E. Broderick, J. Walsh

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Farm management and operations will drastically change due to access to real-time data, real-time forecasting, and tracking of physical items in combination with Internet of Things developments to further automate farm operations. Dairy farms have embraced technological innovations and procured vast amounts of permanent data streams during the past decade; however, the integration of this information to improve the whole farm-based management and decision-making does not exist. It is now imperative to develop a system that can collect, integrate, manage, and analyse on-farm and off-farm data in real-time for practical and relevant environmental and economic actions. The developed systems, based on machine learning and artificial intelligence, need to be connected for useful output, a better understanding of the whole farming issue, and environmental impact. Evolutionary computing can be very effective in finding the optimal combination of sets of some objects and, finally, in strategy determination. The system of the future should be able to manage the dairy farm as well as an experienced dairy farm manager with a team of the best agricultural advisors. All these changes should bring resilience and sustainability to dairy farming as well as improving and maintaining good animal welfare and the quality of dairy products. This review aims to provide an insight into the state-of-the-art of big data applications and evolutionary computing in relation to smart dairy farming and identify the most important research and development challenges to be addressed in the future. Smart dairy farming influences every area of management, and its uptake has become a continuing trend.

Keywords: big data, evolutionary computing, cloud, precision technologies

Procedia PDF Downloads 188

Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

Procedia PDF Downloads 201

Authors: Hamidreza Hoshyarmanesh, Benjamin Durante, Alex Irwin, Sanju Lama, Kourosh Zareinia, Garnette R. Sutherland

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This study reports on a proposed method to evaluate the accuracy and precision of Excalibur I, a neurosurgery-specific haptic hand controller, designed and developed at Project neuroArm. Having an efficient and successful robot-assisted telesurgery is considerably contingent on how accurate and precise a haptic hand controller (master/local robot) would be able to interpret the kinematic indices of motion, i.e., position and orientation, from the surgeon’s upper limp to the slave/remote robot. A proposed test rig is designed and manufactured according to standard ASTM F2554-10 to determine the accuracy and precision range of Excalibur I at four different locations within its workspace: central workspace, extreme forward, far left and far right. The test rig is metrologically characterized by a coordinate measuring machine (accuracy and repeatability < ± 5 µm). Only the serial linkage of the haptic device is examined due to the use of the Structural Length Index (SLI). The results indicate that accuracy decreases by moving from the workspace central area towards the borders of the workspace. In a comparative study, Excalibur I performs on par with the PHANToM PremiumTM 3.0 and more accurate/precise than the PHANToM PremiumTM 1.5. The error in Cartesian coordinate system shows a dominant component in one direction (δx, δy or δz) for the movements on horizontal, vertical and inclined surfaces. The average error magnitude of three attempts is recorded, considering all three error components. This research is the first promising step to quantify the kinematic performance of Excalibur I.

Keywords: accuracy, advanced metrology, hand controller, precision, robot-assisted surgery, tele-operation, workspace

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Authors: Yoftahe Nigussie Worku

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This paper explores the utilization of vapor absorption refrigeration systems (VARS) as an alternative to the conventional vapor compression refrigerant systems (VCRS) in vehicle air conditioning (AC) systems. Currently, most vehicles employ VCRS, which relies on engine power to drive the compressor, leading to additional fuel consumption. In contrast, VARS harnesses low-grade heat, specifically from the exhaust of high-power internal combustion engines, reducing the burden on the vehicle's engine. The historical development of vapor absorption technology is outlined, dating back to Michael Faraday's discovery in 1824 and the subsequent creation of the first vapor absorption refrigeration machine by Ferdinand Carre in 1860. The paper delves into the fundamental principles of VARS, emphasizing the replacement of mechanical processes with physicochemical interactions, utilizing heat rather than mechanical work. The study compares the basic concepts of the current vapor compression systems with the proposed vapor absorption systems, highlighting the efficiency gains achieved by eliminating the need for engine-driven compressors. The vapor absorption refrigeration cycle (VARC) is detailed, focusing on the generator's role in separating and vaporizing ammonia, chosen for its low-temperature evaporation characteristics. The project's statement underscores the need for increased efficiency in vehicle AC systems beyond the limitations of VCRS. By introducing VARS, driven by low-grade heat, the paper advocates for a reduction in engine power consumption and, consequently, a decrease in fuel usage. This research contributes to the ongoing efforts to enhance sustainability and efficiency in automotive climate control systems.

Keywords: VCRS, VARS, efficiency, sustainability

Procedia PDF Downloads 69

Authors: Hojjat Mohammadi, Randall Divito, Gary J. E. Kramer

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Tunnelling and pipe jacking in very soft soils (fat clays), even with an Earth Pressure Balance tunnel boring machine (EPBM), can cause large ground displacements. In this study, the short-term and long-term ground and tunnel response is predicted for twin, pipe-jacked EPBM 3 meter diameter tunnels with a narrow pillar width. Initial modelling indicated complete closure of the annulus gap at the tail shield onto the centrifugally cast, glass-fiber-reinforced, polymer mortar jacking pipe (FRP). Numerical modelling was employed to simulate the excavation and support installation sequence, examine the ground response during excavation, confirm the adequacy of the pillar width and check the structural adequacy of the installed pipe. In the numerical models, Mohr-Coulomb constitutive model with the effect of unloading was adopted for the fat clays, while for the bedrock layer, the generalized Hoek-Brown was employed. The numerical models considered explicit excavation sequences and different levels of ground convergence prior to support installation. The well-studied excavation sequences made the analysis possible for this study on a very soft clay, otherwise, obtaining the convergency in the numerical analysis would be impossible. The predicted results indicate that the ground displacements around the tunnel and its effect on the pipe would be acceptable despite predictions of large zones of plastic behaviour around the tunnels and within the entire pillar between them due to excavation-induced ground movements.

Keywords: finite element modeling (FEM), pipe-jacked tunneling, very soft clay, EPBM

Procedia PDF Downloads 77