Search results for: automated fraud detection

Authors: A. Sohouli, A. Suleman

Abstract:

This study focuses on reliability analysis of variable stiffness composite laminate structures to investigate the potential structural improvement compared to conventional (straight fibers) composite laminate structures. A computational framework was developed which it consists of a deterministic design step and reliability analysis. The optimization part is Discrete Material Optimization (DMO) and the reliability of the structure is computed by Monte Carlo Simulation (MCS) after using Stochastic Response Surface Method (SRSM). The design driver in deterministic optimization is the maximum stiffness, while optimization method concerns certain manufacturing constraints to attain industrial relevance. These manufacturing constraints are the change of orientation between adjacent patches cannot be too large and the maximum number of successive plies of a particular fiber orientation should not be too high. Variable stiffness composites may be manufactured by Automated Fiber Machines (AFP) which provides consistent quality with good production rates. However, laps and gaps are the most important challenges to steer fibers that effect on the performance of the structures. In this study, the optimal curved fiber paths at each layer of composites are designed in the first step by DMO, and then the reliability analysis is applied to investigate the sensitivity of the structure with different standard deviations compared to the straight fiber angle composites. The random variables are material properties and loads on the structures. The results show that the variable stiffness composite laminate structures are much more reliable, even for high standard deviation of material properties, than the conventional composite laminate structures. The reason is that the variable stiffness composite laminates allow tailoring stiffness and provide the possibility of adjusting stress and strain distribution favorably in the structures.

Keywords: material optimization, Monte Carlo simulation, reliability analysis, response surface method, variable stiffness composite structures

Procedia PDF Downloads 522

Authors: Ahmad Aslizadeh, Farid Ghaderi

Abstract:

Mapping organizational knowledge is an innovative concept and useful instrument of representation, capturing and visualization of implicit and explicit knowledge. There are a diversity of methods, instruments and techniques presented by different researchers following mapping organizational knowledge to reach determined goals. Implicating of these methods, it is necessary to know their exigencies and conditions in which those can be used. Integrating identified methods of knowledge mapping and comparing them would help knowledge managers to select the appropriate methods. This research conducted to presenting a model and framework to map organizational knowledge. At first, knowledge maps, their applications and necessity are introduced because of extracting comparative framework and detection of their structure. At the next step techniques of researchers such as Eppler, Kim, Egbu, Tandukar and Ebner as knowledge mapping models are presented and surveyed. Finally, they compare and a superior model would be introduced.

Keywords: knowledge mapping, knowledge management, comparative study, business and management

Procedia PDF Downloads 405

Authors: Odaleia de O. Farias, Suzelene C. Marinho, Ecleidson B. Fragoso, Daniel S. Lima, Francisco R. S. Lira, Lara S. Araújo, Gabriel dos S. D. Soares

Abstract:

In populous cities, prehospital care services that use vehicles alternative to ambulances are needed in order to reduce costs and improve response time to occurrences in areas with large concentration of people, such as leisure and tourism spaces. In this context, it was implanted a program called BIKE VIDA, that is innovative quick access and assistance program. The aim of this study is to describe the implantation and initial profile of occurrences performed by an urgency/emergency pre-hospital care service through paramedics on bicycles. It is a cross-sectional, descriptive study carried out in the city of Fortaleza, Ceara, Brazil. The data included service records from July to August 2017. Ethical aspects were respected. The service covers a perimeter of 4.5 km, divided into three areas with perimeter of 1.5 km for each paramedic, attending from 5 am to 9 pm. Materials transported by bicycles include External Automated Defibrillator - DEA, portable oxygen, oximeter, cervical collar, stethoscope, sphygmomanometer, dressing and immobilization materials and personal protective equipment. Occurrences are requested directly by calling the emergency number 192 or through direct approach to the professional. In the first month of the program, there were 93 emergencies/urgencies, mainly in the daytime period (71,0%), in males (59,7%), in the age range of 26 to 45 years (46,2%). The main nature was traumatic incidents (53.3%). Most of the cases (88,2%) did not require ambulance transport to the hospital, and there were two deaths. Pre-hospital service through bicycles is an innovative strategy in Brazil and has shown to be promising in terms of reducing costs and improving the quality of the services offered.

Keywords: emergency, response time, prehospital care, urgency

Procedia PDF Downloads 201

Authors: Antonio Ruiz Gonzalez, Kwang-Leong Choy

Abstract:

The measurement of electrolytes has a high value in the clinical routine. Ions are present in all body fluids with variable concentrations and are involved in multiple pathologies such as heart failures and chronic kidney disease. In the case of dissolved potassium, although a high concentration in the blood (hyperkalemia) is relatively uncommon in the general population, it is one of the most frequent acute electrolyte abnormalities. In recent years, the integration of thin films technologies in this field has allowed the development of highly sensitive biosensors with ultra-low limits of detection for the assessment of metals in liquid samples. However, despite the current efforts in the miniaturization of sensitive devices and their integration into portable systems, only a limited number of successful examples used commercially can be found. This fact can be attributed to a high cost involved in their production and the sustained degradation of the electrodes over time, which causes a signal drift in the measurements. Thus, there is an unmet necessity for the development of low-cost and robust sensors for the real-time monitoring of analyte concentrations in patients to allow the early detection and diagnosis of diseases. This paper reports a thin film ion-selective sensor for the evaluation of potassium ions in aqueous samples. As an alternative for this fabrication method, aerosol assisted chemical vapor deposition (AACVD), was applied due to cost-effectivity and fine control over the film deposition. Such a technique does not require vacuum and is suitable for the coating of large surface areas and structures with complex geometries. This approach allowed the fabrication of highly homogeneous surfaces with well-defined microstructures onto 50 nm thin gold layers. The degradative processes of the ubiquitously employed poly (vinyl chloride) membranes in contact with an electrolyte solution were studied, including the polymer leaching process, mechanical desorption of nanoparticles and chemical degradation over time. Rational design of a protective coating based on an organosilicon material in combination with cellulose to improve the long-term stability of the sensors was then carried out, showing an improvement in the performance after 5 weeks. The antifouling properties of such coating were assessed using a cutting-edge quartz microbalance sensor, allowing the quantification of the adsorbed proteins in the nanogram range. A correlation between the microstructural properties of the films with the surface energy and biomolecules adhesion was then found and used to optimize the protective film.

Keywords: hyperkalemia, drift, AACVD, organosilicon

Procedia PDF Downloads 129

Authors: K. Khelil, H. Ammar, K. Saouchi

Abstract:

Fiber Bragg Grating (FBG) structure is an periodically modulated optical fiber. It acts as a selective filter of wavelength whose reflected peak is called Bragg wavelength and it depends on the period of the fiber and the refractive index. The simulation of FBG is based on solving the Coupled Mode Theory equation by using the Transfer Matrix Method which is carried out using MATLAB. It is found that spectral reflectivity is shifted when the change of temperature and strain is uniform. Under non-uniform temperature or strain perturbation, the spectrum is both shifted and destroyed. In case of transverse loading, reflectivity spectrum is split into two peaks, the first is specific to X axis, and the second belongs to Y axis. FBGs are used in civil engineering to detect perturbations applied to buildings.

Keywords: Bragg wavelength, coupled mode theory, optical fiber, temperature measurement

Procedia PDF Downloads 498

Authors: Daoudi Abdelaziz, Mahmoudi Saïd, Chikh Mohamed Amine

Abstract:

This paper presents a fully automatic segmentation method of the left ventricle at End Systolic (ES) and End Diastolic (ED) in the ultrasound images by means of an implicit deformable model (level set) based on Geodesic Active Contour model. A pre-processing Gaussian smoothing stage is applied to the image, which is essential for a good segmentation. Before the segmentation phase, we locate automatically the area of the left ventricle by using a detection approach based on the Hough Transform method. Consequently, the result obtained is used to automate the initialization of the level set model. This initial curve (zero level set) deforms to search the Endocardial border in the image. On the other hand, quantitative evaluation was performed on a data set composed of 15 subjects with a comparison to ground truth (manual segmentation).

Keywords: level set method, transform Hough, Gaussian smoothing, left ventricle, ultrasound images.

Procedia PDF Downloads 468

Authors: Ali Kassem, Yhea Kishik, Ali Hassan, Mohamed Abdelwahab

Abstract:

Introduction: Accelerated coronary and peripheral vascular atherosclerosis is one of the most common and chronic complications of diabetes mellitus. A recent aspect of coronary artery disease in this condition is its silent nature. The aim of the work: Detection of the prevalence of silent myocardial ischemia (SMI) in Upper Egypt type 2 diabetic males and to select male diabetic population who should be screened for SMI. Patients and methods: 100 type 2 diabetic male patients with a negative history of angina or anginal equivalent symptoms and 30 healthy control were included. Full medical history and thorough clinical examination were done for all participants. Fasting and post prandial blood glucose level, lipid profile, (HbA1c), microalbuminuria, and C-reactive protein were done for all participants Resting ECG, trans-thoracic echocardiography, treadmill exercise ECG, myocardial perfusion imaging were done for all participants and patients positive for one or more NITs were subjected for coronary angiography. Results Twenty nine patients (29%) were positive for one or more NITs in the patients group compared to only one case (3.3%) in the controls. After coronary angiography, 20 patients were positive for significant coronary artery stenosis in the patients group, while it was refused to be done by the patient in the controls. There were statistical significant difference between the two groups regarding, hypertension, dyslipidemia and obesity, family history of DM and IHD with higher levels of microalbuminuria, C-reactive protein, total lipids in patient group versus controls According to coronary angiography, patients were subdivided into two subgroups, 20 positive for SMI (positive for coronary angiography) and 80 negative for SMI (negative for coronary angiography). No statistical difference regarding family history of DM and type of diabetic therapy was found between the two subgroups. Yet, smoking, hypertension, obesity, dyslipidemia and family history of IHD were significantly higher in diabetics positive versus those negative for SMI. 90% of patients in subgroup positive for SMI had two or more cardiac risk factors while only two patients had one cardiac risk factor (10%). Uncontrolled DM was detected more in patients positive for SMI. Diabetic complications were more prevalent in patients positive for SMI versus those negative for SMI. Most of the patients positive for SMI have DM more than 5 years duration. Resting ECG and resting Echo detected only 6 and 11 cases, respectively, of the 20 positive cases in group positive for SMI compared to treadmill exercise ECG and myocardial perfusion imaging that detected 16 and 18 cases respectively, Conclusion: Type 2 diabetic male patients should be screened for detection of SMI when aged above 50 years old, diabetes duration is more than 5 years, presence of two or more cardiac risk factors and/or patients suffering from one or more of the chronic diabetic complications. CRP, is an important parameter for selection of type 2 diabetic male patients who should be screened for SMI. Non invasive cardiac tests are reliable for screening of SMI in these patients in our locality.

Keywords: C-reactive protein, Silent myocardial ischemia, Stress tests, type 2 DM

Procedia PDF Downloads 389

Authors: Rindra Rantoson, Hichem Nouira, Nabil Anwer, Charyar Mehdi-Souzani

Abstract:

Multiple measurements by means of various data acquisition systems are generally required to measure the shape of freeform workpieces for accuracy, reliability and holisticity. The obtained data are aligned and fused into a common coordinate system within a registration technique involving coarse and fine registrations. Standardized iterative methods have been established for fine registration such as Iterative Closest Points (ICP) and its variants. For coarse registration, no conventional method has been adopted yet despite a significant number of techniques which have been developed in the literature to supply an automatic rough matching between data sets. Two main issues are addressed in this paper: the coarse registration and the fine registration. For coarse registration, two novel automated methods based on the exploitation of discrete curvatures are presented: an enhanced Hough Transformation (HT) and an improved Ransac Transformation. The use of curvature features in both methods aims to reduce computational cost. For fine registration, a new variant of ICP method is proposed in order to reduce registration error using curvature parameters. A specific distance considering the curvature similarity has been combined with Euclidean distance to define the distance criterion used for correspondences searching. Additionally, the objective function has been improved by combining the point-to-point (P-P) minimization and the point-to-plane (P-Pl) minimization with automatic weights. These ones are determined from the preliminary calculated curvature features at each point of the workpiece surface. The algorithms are applied on simulated and real data performed by a computer tomography (CT) system. The obtained results reveal the benefit of the proposed novel curvature-based registration methods.

Keywords: discrete curvature, RANSAC transformation, hough transformation, coarse registration, ICP variant, point-to-point and point-to-plane minimization combination, computer tomography

Procedia PDF Downloads 429

Authors: Shaomei Li, Yawen Wang, Chao Gao

Abstract:

To improve tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, object tracking is posed as a binary classification problem and is modeled by partial least squares (PLS) analysis. Secondly, tracking object frame by frame via particle filtering. Thirdly, validating the tracking reliability based on both positive and negative models matching. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm cannot only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this algorithm outperforms state-of-the-art algorithms on many challenging sequences.

Keywords: object tracking, tracking drift, partial least squares analysis, positive and negative models matching

Procedia PDF Downloads 537

Authors: Byungsik Moon

Abstract:

Electron beam inspection (EBI) has played an important role in detecting defects during the Fab process. The study focused on capturing buried Cu metal voids for 5nm technology nodes in Qualcomm Snapdragon mass production. This paper illustrates a case study where Cu metal voids can be detected without side effects with optimized EBI scanning conditions. The voids were buried in the VIA and not detected effectively by bright field inspection. EBI showed higher detectability, about 10 times that of bright fields, and a lower landing energy of EBI can avoid film damage. A comparison of detectability between EBI and bright field inspection was performed, and TEM confirmed voids that were detected by EBI. Therefore, a much higher detectability of buried Cu metal voids can be achieved without causing film damage.

Keywords: electron beam inspection, EBI, landing energy, Cu metal voids, bright field inspection

Procedia PDF Downloads 80

Authors: Alhaji Musa Abdullahi, Usman Abdullahi, Adamu Lawan, Aminu Maidala

Abstract:

Abstract 16 healthy lactating cows will be randomly selected for the trial and will be randomly divided in to 4 groups with 4 cows in each. They will be kept under similar management condition (conventional management system). Animals of relatively same weight and age will be used. After 11days for adaptation, feed intake and performance of the experimental animals will be determine. Milk sample will be collected at each milking in the morning and afternoon to determine; Milk yield, Milk fat percentage, Solid not fat percentage, Total solid percentage of milk. Cows dung will be observe to determine; Score 1 very loose watery stool, Score 2 semi solid with undigested raw material, Score 3 semi solid with less undigested raw material, Score 4 solid with very less undigested raw material, Score 5 good dung no undigested raw material. At the end of the experiment, blood samples will be analyzed for full blood counts and differentials {White Blood Cells (WBC), Red Blood Cells (RBC), Hemoglobin (Hb), Packed Cell Volume (PCV), Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), Mean Corpuscular Hemoglobin Concentration (MCHC), Platelets (PLT), Lymphocytes (LYM), Basophils, Eosinophils and Monocytes Proportion (MXD) and Neutrophils (NEUT)} using automated hematology analyzer. Serum samples will be analyzed for heat shock transcription factors, heat shock proteins and hormones (Serum glucocorticoid, prolactin and cortisol). Moreover, biochemical analysis will also be conducted to check for Total protein (TP), Albumen (ALB), Globulin (GBL), Total cholesterol (TCH), glucose (G), sodium (Na+), potassium (K+), chloride (Cl-) and pH. Keywords: Lactating cows, milk composition, dung score and blood parameters.

Keywords: Lactating cows , Milk yield , Dung score , Blood parameters

Procedia PDF Downloads 190

Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

Abstract:

Emotions classification of text documents is applied to reveal if the document expresses a determined emotion from its writer. As different supervised methods are previously used for emotion documents’ classification, in this research we present a novel model that supports the classification algorithms for more accurate results by the support of TF-IDF measure. Different experiments have been applied to reveal the applicability of the proposed model, the model succeeds in raising the accuracy percentage according to the determined metrics (precision, recall, and f-measure) based on applying the refinement of the lexicon, integration of lexicons using different perspectives, and applying the TF-IDF weighting measure over the classifying features. The proposed model has also been compared with other research to prove its competence in raising the results’ accuracy.

Keywords: emotion detection, TF-IDF, WEKA tool, classification algorithms

Procedia PDF Downloads 490

Authors: H. Elgubbi, A. Mlitan, A. Alzridy

Abstract:

Ninhydrin is the most well known spray reagent for identification of amino acids. Spring with Ninhydrin as a non-specific reagent is well-known and widely used for its remarkable high sensitivity. Using Ninhydrin reagent alone to detect amino acid on thin layer chromatography (TLA) paper is not advisable due to its lower sensitivity. A new spray reagent, Stannus chloride solution (Sn CL2) has been used to detect amino acids on filtter paper (witman 14) and TLC paper, silica Gel, 60 F254 TLC Aluminium Sheet 20x20cm Merck- Germany. Also, modified TLC pre-staining method was used, which only consisted of 3 steps: spotting, separating and color. The improved method was rapid and inexpensive and the results obtained were clear and reliable. In addition, it is suitable for screening different amino acid.

Keywords: amino acid, ninhydrin, modified ninhydrin reagent, stannus chloride reagent, thin-layer chromatography (TLC), TLC pre-staining

Procedia PDF Downloads 417

Authors: George R. Puno, Eric N. Bruno

Abstract:

The natural landscape of the Philippine archipelago plus the current realities of climate change make the country vulnerable to flood hazards. Flooding becomes the recurring natural disaster in the country resulting to lose of lives and properties. Musimusi is among the rivers which exhibited inundation particularly at the inhabited floodplain portion of its watershed. During the event, rescue operations and distribution of relief goods become a problem due to lack of high resolution flood maps to aid local government unit identify the most affected areas. In the attempt of minimizing impact of flooding, hydrologic modelling with high resolution mapping is becoming more challenging and important. This study focused on the analysis of flood extent as a function of different geomorphologic characteristics of Musimusi watershed. The methods include the delineation of morphometric parameters in the Musimusi watershed using Geographic Information System (GIS) and geometric calculations tools. Digital Terrain Model (DTM) as one of the derivatives of Light Detection and Ranging (LiDAR) technology was used to determine the extent of river inundation involving the application of Hydrologic Engineering Center-River Analysis System (HEC-RAS) and Hydrology Modelling System (HEC-HMS) models. The digital elevation model (DEM) from synthetic Aperture Radar (SAR) was used to delineate watershed boundary and river network. Datasets like mean sea level, river cross section, river stage, discharge and rainfall were also used as input parameters. Curve number (CN), vegetation, and soil properties were calibrated based on the existing condition of the site. Results showed that the drainage density value of the watershed is low which indicates that the basin is highly permeable subsoil and thick vegetative cover. The watershed’s elongation ratio value of 0.9 implies that the floodplain portion of the watershed is susceptible to flooding. The bifurcation ratio value of 2.1 indicates higher risk of flooding in localized areas of the watershed. The circularity ratio value (1.20) indicates that the basin is circular in shape, high discharge of runoff and low permeability of the subsoil condition. The heavy rainfall of 167 mm brought by Typhoon Seniang last December 29, 2014 was characterized as high intensity and long duration, with a return period of 100 years produced 316 m3s-1 outflows. Portion of the floodplain zone (1.52%) suffered inundation with 2.76 m depth at the maximum. The information generated in this study is helpful to the local disaster risk reduction management council in monitoring the affected sites for more appropriate decisions so that cost of rescue operations and relief goods distribution is minimized.

Keywords: flooding, geomorphology, mapping, watershed

Procedia PDF Downloads 233

Authors: Sinarwati Mohamad Suhaili, Naomie Salim, Mohamad Nazim Jambli

Abstract:

Sequence-to-sequence (seq2seq) models augmented with attention mechanisms are playing an increasingly important role in automated customer service. These models, which are able to recognize complex relationships between input and output sequences, are crucial for optimizing chatbot responses. Central to these mechanisms are neural attention weights that determine the focus of the model during sequence generation. Despite their widespread use, there remains a gap in the comparative analysis of different attention weighting functions within seq2seq models, particularly in the domain of chatbots using the Customer Support Twitter (CST) dataset. This study addresses this gap by evaluating four distinct attention-scoring functions—dot, multiplicative/general, additive, and an extended multiplicative function with a tanh activation parameter — in neural generative seq2seq models. Utilizing the CST dataset, these models were trained and evaluated over 10 epochs with the AdamW optimizer. Evaluation criteria included validation loss and BLEU scores implemented under both greedy and beam search strategies with a beam size of k=3. Results indicate that the model with the tanh-augmented multiplicative function significantly outperforms its counterparts, achieving the lowest validation loss (1.136484) and the highest BLEU scores (0.438926 under greedy search, 0.443000 under beam search, k=3). These results emphasize the crucial influence of selecting an appropriate attention-scoring function in improving the performance of seq2seq models for chatbots. Particularly, the model that integrates tanh activation proves to be a promising approach to improve the quality of chatbots in the customer support context.

Keywords: attention weight, chatbot, encoder-decoder, neural generative attention, score function, sequence-to-sequence

Procedia PDF Downloads 79

Authors: Rohin Rameswarapu, Sameer Valsangkar

Abstract:

Background: Globally, musculoskeletal disorders are the largest single cause of work-related illnesses accounting for over 33% of all newly reported occupational illnesses. Risk factors for MSD need to be delineated to suggest means for amelioration. Material and methods: In this current cross-sectional study, the prevalence of MSDs among workers in an electrical company assembly line, the socio-demographic and job characteristics associated with MSD were obtained through a semi-structured questionnaire. A quantitative assessment of the physical risk factors through the Rapid Upper Limb Assessment (RULA) tool, and measurement of psychosocial risk factors through a Likert scale was obtained. Statistical analysis was conducted using Epi-info software and descriptive and inferential statistics including chi-square and unpaired t test were obtained. Results: A total of 263 workers consented and participated in the study. Among these workers, 200 (76%) suffered from MSD. Most of the workers were aged between 18–27 years and majority of the workers were women with 198 (75.2%) of the 263 workers being women. A chi square test was significant for association between male gender and MSD with a P value of 0.007. Among the MSD positive group, 4 (2%) had a grand score of 5, 10 (5%) had a grand score of 6 and 186 (93%) had a grand score of 7 on RULA. There were significant differences between the non-MSD and MSD group on five out of the seven psychosocial domains, namely job demand, job monotony, co-worker support, decision control and family and environment domains. Discussion: The current cross-sectional study demonstrates a high prevalence of MSD among assembly line works with inherent physical and psychosocial risk factors and recommends that not only physical risk factors, addressing psychosocial risk factors through proper ergonomic means is also essential to the well-being of the employee.

Keywords: musculoskeletal disorders, India, occupational health, Rapid Upper Limb Assessment (RULA)

Procedia PDF Downloads 352

Authors: Lae-Jeong Park, Kyusoo Chung, Jungho Moon

Abstract:

This paper introduces an effective method of segmenting Korean text (place names in Korean) from a Korean road sign image. A Korean advanced directional road sign is composed of several types of visual information such as arrows, place names in Korean and English, and route numbers. Automatic classification of the visual information and extraction of Korean place names from the road sign images make it possible to avoid a lot of manual inputs to a database system for management of road signs nationwide. We propose a series of problem-specific heuristics that correctly segments Korean place names, which is the most crucial information, from the other information by leaving out non-text information effectively. The experimental results with a dataset of 368 road sign images show 96% of the detection rate per Korean place name and 84% per road sign image.

Keywords: segmentation, road signs, characters, classification

Procedia PDF Downloads 446

Authors: Nikhat Nawaz, S. Srinivasulu, P. Kesava Rao

Abstract:

Feature extraction plays an important role in many remote sensing applications. Automatic extraction of water bodies is of great significance in many remote sensing applications like change detection, image retrieval etc. This paper presents a procedure for automatic extraction of water information from remote sensing images. The algorithm uses the relative location of R-colour component of the chromaticity diagram. This method is then integrated with the effectiveness of the spatial scale transformation of whole method. The whole method is based on water index fitted from spectral library. Experimental results demonstrate the improved accuracy and effectiveness of the integrated method for automatic extraction of water bodies.

Keywords: feature extraction, remote sensing, image retrieval, chromaticity, water index, spectral library, integrated method

Procedia PDF Downloads 392

Authors: Julian Jaegers, Siegmar Wirtz, Viktor Scherer

Abstract:

Wood pellets belong to the most established trade formats of wood-based fuels. Especially, because of the transportability and the storage properties, but also due to low moisture content, high energy density, and the homogeneous particle size and shape, wood pellets are well suited for power generation in power plants and for the use in automated domestic firing systems. Before they are thermally converted, wood pellets pass various transport and storage procedures. There they undergo different mechanical impacts, which leads to pellet breakage and abrasion and to an increase in fines. The fines lead to operational problems during storage, charging, and discharging of pellets, they can increase the risk of dust explosions and can lead to pollutant emissions during combustion. In the current work, the dependence of the formation of fines caused by breakage during pneumatic transport is analyzed experimentally and numerically. The focus lies on the influence of conveying velocity, pellet loading, pipe diameter, and the shape of pipe components like bends or couplings. A test rig has been built, which allows the experimental evaluation of the pneumatic transport varying the above-mentioned parameters. Two high-speed cameras are installed for the quantitative optical access to the particle-particle and particle-wall contacts. The particle size distribution of the bulk before and after a transport process is measured as well as the amount of fines produced. The experiments will be compared with results of corresponding DEM/CFD simulations to provide information on contact frequencies and forces. The contribution proposed will present experimental results and report on the status of the DEM/CFD simulations. The final goal of the project is to provide a better insight into pellet breakage during pneumatic transport and to develop guidelines ensuring a more gentle transport.

Keywords: DEM/CFD-simulation of pneumatic conveying, mechanical impact on wood pellets during transportation, pellet breakage, pneumatic transport of wood pellets

Procedia PDF Downloads 154

Authors: Sorin Valcan, Mihail Gaianu

Abstract:

Labeling is a very costly and time consuming process which aims to generate datasets for training neural networks in several functionalities and projects. For driver monitoring system projects, the need for labeled images has a significant impact on the budget and distribution of effort. This paper presents the modifications done to an algorithm used for the generation of ground truth data for 2D eyes location on infrared images with drivers in order to improve the quality of the data and performance of the trained neural networks. The algorithm restrictions become tougher, which makes it more accurate but also less constant. The resulting dataset becomes smaller and shall not be altered by any kind of manual label adjustment before being used in the neural networks training process. These changes resulted in a much better performance of the trained neural networks.

Keywords: labeling automation, infrared camera, driver monitoring, eye detection, convolutional neural networks

Procedia PDF Downloads 123

Authors: Dinku Seyoum Zeleke, Rong Fung Huang, Ching Min Hsu

Abstract:

Comparison of flow and mixing characteristics between non-oscillating jet and transversely oscillating jet was investigated experimentally. Flow evolution process was detected by using high-speed digital camera, and jet spread width was calculated using binary edge detection techniques by using the long-exposure images. The velocity characteristics of transversely oscillating jet induced by a V-shaped fluidic oscillator were measured using single component hot-wire anemometer. The jet spread width of non-oscillating jet was much smaller than the jet exit gap because of behaving natural jet behaviors. However, the transversely oscillating jet has a larger jet spread width, which was associated with the excitation of the flow by self-induced oscillation. As a result, the flow mixing characteristics desperately improved both near-field and far-field. Therefore, this transversely oscillating jet has a better turbulence intensity, entrainment, and spreading width so that it augments flow-mixing characteristics desperately.

Keywords: flow mixing, transversely oscillating, spreading width, velocity characteristics

Procedia PDF Downloads 255

Authors: Michael Naderhirn, Marco Pavone

Abstract:

Problem statement, motivation, and aim of work: So far, the development of control algorithms was done by control engineers in a way that the controller would fit a specification by testing. When it comes to the certification of an autonomous car in highly complex scenarios, the challenge is much higher since such a controller must mathematically guarantee to implement the rules of the road while on the other side guarantee aspects like safety and real time executability. What if it becomes reality to solve this demanding problem by combining Formal Verification and System Theory? The aim of this work is to present a workflow to solve the above mentioned problem. Summary of the presented results / main outcomes: We show the usage of an English like language to transform the rules of the road into system specification for an autonomous car. The language based specifications are used to define system functions and interfaces. Based on that a formal model is developed which formally correctly models the specifications. On the other side, a mathematical model describing the systems dynamics is used to calculate the systems reachability set which is further used to determine the system input boundaries. Then a motion planning algorithm is applied inside the system boundaries to find an optimized trajectory in combination with the formal specification model while satisfying the specifications. The result is a control strategy which can be applied in real time independent of the scenario with a mathematical guarantee to satisfy a predefined specification. We demonstrate the applicability of the method in simulation driving scenarios and a potential certification. Originality, significance, and benefit: To the authors’ best knowledge, it is the first time that it is possible to show an automated workflow which combines a specification in an English like language and a mathematical model in a mathematical formal verified way to synthesizes a controller for potential real time applications like autonomous driving.

Keywords: formal system verification, reachability, real time controller, hybrid system

Procedia PDF Downloads 244

Authors: Olais A. Ingrid, Peraza G. Leydi, Estrella C. Damaris

Abstract:

Breast cancer is the most common among women worldwide; the different treatments can bring sequels that directly affect the quality of life, such as lymphedema. The objective was to determine if there is presence of lymphedema secondary to breast cancer in Yucatecan women. It was an observational, analytical, cross-sectional study, 92 women were included who met the following criteria: women with surgical treatment for unilateral: breast cancer, aged between 25 and 65 years old, minimum 6 weeks after unilateral breast surgery and have completed any type of chemotherapy or adjuvant radiotherapy treatment for breast cancer. The evaluation was through indirect measurement volume by circometry to determine the presence of lymphedema. 23% of women had lymphedema grade I. It related to the presence of some of the symptoms like stiffness, swelling, decreased range of motion and feeling of heaviness in the arm of the operated side of the breast. It is important to determine the presence of lymphedema to perform physical therapy treatment.

Keywords: breast cancer, lymphedema, physical therapy, Yucatan

Procedia PDF Downloads 354

Authors: Amira Dhokar, Lobna Hlaoua, Lotfi Ben Romdhane

Abstract:

Tweet contextualization (TC) is a new issue that aims to answer questions of the form 'What is this tweet about?' The idea of this task was imagined as an extension of a previous area called multi-document summarization (MDS), which consists in generating a summary from many sources. In both TC and MDS, the summary should ideally contain the most relevant information of the topic that is being discussed in the source texts (for MDS) and related to the query (for TC). Furthermore of being informative, a summary should be coherent, i.e. well written to be readable and grammatically compact. Hence, coherence is an essential characteristic in order to produce comprehensible texts. In this paper, we propose a new approach to improve readability and coherence for tweet contextualization based on bipartite graphs. The main idea of our proposed method is to reorder sentences in a given paragraph by combining most expressive words detection and HITS (Hyperlink-Induced Topic Search) algorithm to make up a coherent context.

Keywords: bipartite graphs, readability, summarization, tweet contextualization

Procedia PDF Downloads 197

Authors: Dona Grace Jeyaseeli

Abstract:

Water quality conditions in a pond are controlled by both natural processes and human influences. Natural factors such as the source of the pond water and the types of rock and soil in the pond watershed will influence some water quality characteristics. These factors are difficult to control but usually cause few problems. Instead, most serious water quality problems originate from land uses or other activities near or in the pond. The effects of these activities can often be minimized through proper management and early detection of problems through testing. In the present study a survey of three ponds in Coimbatore city, Tamilnadu, India were analyzed and found that water quality problems in their ponds, ranging from muddy water to fish kills. Unfortunately, most pond owners have never tested their ponds, and water quality problems are usually only detected after they cause a problem. Hence the present study discusses some common water quality parameters that may cause problems in ponds and how to detect through testing for better management of pond.

Keywords: water quality, pond, test, problem

Procedia PDF Downloads 515

Authors: Ng Liang Shen, Ngahzaifa Abdul Ghani

Abstract:

This research aimed at developing an early warning system for pre-diabetic and diabetics by analyzing simple and easily determinable signs and symptoms of diabetes among the people living in Malaysia using Particle Swarm Optimized Artificial. With the skyrocketing prevalence of Type 2 diabetes in Malaysia, the system can be used to encourage affected people to seek further medical attention to prevent the onset of diabetes or start managing it early enough to avoid the associated complications. The study sought to find out the best predictive variables of Type 2 Diabetes Mellitus, developed a system to diagnose diabetes from the variables using Artificial Neural Networks and tested the system on accuracy to find out the patent generated from diabetes diagnosis result in machine learning algorithms even at primary or advanced stages.

Keywords: diabetes diagnosis, Artificial Neural Networks, artificial intelligence, soft computing, medical diagnosis

Procedia PDF Downloads 341

Authors: Axelle Bertrand, Tom Bauer, Carole Charbuillet, Martin Bonte, Marie Voyer, Nicolas Perry

Abstract:

The complexity of Life Cycle Assessment (LCA) can be identified as the ultimate obstacle to massification. Due to these obstacles, the diffusion of eco-design and LCA methods in the manufacturing sectors could be impossible. This article addresses the research question: How to adapt the LCA method to generalize it massively and improve its performance? This paper aims to develop an approach for automating LCA in order to carry out assessments on a massive scale. To answer this, we proceeded in three steps: First, an analysis of the literature to identify existing automation methods. Given the constraints of large-scale manual processing, it was necessary to define a new approach, drawing inspiration from certain methods and combining them with new ideas and improvements. In a second part, our development of automated construction is presented (reconciliation and implementation of data). Finally, the LCA case study of a conduit is presented to demonstrate the feature-based approach offered by the developed tool. A computerized environment supports effective and efficient decision-making related to materials and processes, facilitating the process of data mapping and hence product modeling. This method is also able to complete the LCA process on its own within minutes. Thus, the calculations and the LCA report are automatically generated. The tool developed has shown that automation by code is a viable solution to meet LCA's massification objectives. It has major advantages over the traditional LCA method and overcomes the complexity of LCA. Indeed, the case study demonstrated the time savings associated with this methodology and, therefore, the opportunity to increase the number of LCA reports generated and, therefore, to meet regulatory requirements. Moreover, this approach also presents the potential of the proposed method for a wide range of applications.

Keywords: automation, EEE, life cycle assessment, life cycle inventory, massively

Procedia PDF Downloads 93

Authors: Ines Elouedi, Atef Hammouda

Abstract:

This paper presents a new fast version of the generalized Multi-Directional Radon Transform method. The new method uses the inverse Fast Fourier Transform to lead to a faster Generalized Radon projections. We prove in this paper that the fast algorithm leads to almost the same results of the eldest one but with a considerable lower time computation cost. The projection end result of the fast method is a parameterized Radon space where a high valued pixel allows the detection of a curve from the original image. The proposed fast inversion algorithm leads to an exact reconstruction of the initial image from the Radon space. We show examples of the impact of this algorithm on the pattern recognition domain.

Keywords: fast generalized multi-directional Radon transform, curve, exact reconstruction, pattern recognition

Procedia PDF Downloads 281

Authors: Sai Maniveer Adapa, Sai Guptha Perla, Adithya Reddy P.

Abstract:

Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection.

Keywords: thyroid tumor diagnosis, ultrasound images, deep learning, machine learning, convolutional auto-encoder, support vector machine

Procedia PDF Downloads 62

Authors: Robert Caulk

Abstract:

A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

Procedia PDF Downloads 96