Search results for: motor for washing machine

Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

Procedia PDF Downloads 290

Authors: Mekamı Hayet, Bounoua Nacer, Benabderrahmane Sidahmed, Taleb Ahmed

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Pose estimation is an important task in computer vision. Though the majority of the existing solutions provide good accuracy results, they are often overly complex and computationally expensive. In this perspective, we propose the use of dimensionality reduction techniques to address the problem of facial pose estimation. Firstly, a face image is converted into one-dimensional time series using Hilbert space filling curve, then the approach converts these time series data to a symbolic representation. Furthermore, a distance matrix is calculated between symbolic series of an input learning dataset of images, to generate classifiers of frontal vs. profile face pose. The proposed method is evaluated with three public datasets. Experimental results have shown that our approach is able to achieve a correct classification rate exceeding 97% with K-NN algorithm.

Keywords: machine learning, pattern recognition, facial pose classification, time series

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Authors: Liu Canqi, Zeng Junsheng

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This study identifies two typical bottlenecks that occur when a vehicle cannot change lanes: car following and car stopping. The ideas of traffic field and traffic mass are presented in this work. When there are other vehicles in front of the target vehicle within a particular distance, a force is created that affects the target vehicle's driving speed. The characteristics of the driver and the vehicle collectively determine the traffic mass; the driving speed of the vehicle and external variables have no bearing on this. From a physical level, this study examines the vehicle's bottleneck when following a car, identifies the outside factors that have an impact on how it drives, takes into account that the vehicle will transform kinetic energy into potential energy during deceleration, and builds a calculation model for traffic mass. The energy-time conversion coefficient is created from an economic standpoint utilizing the social average wage level and the average cost of motor fuel. Vissim simulation program measures the vehicle's deceleration distance and delays under the Wiedemann car-following model. The difference between the measured value of deceleration delay acquired by simulation and the theoretical value calculated by the model is compared using the conversion calculation model of traffic mass and deceleration delay. The experimental data demonstrate that the model is reliable since the error rate between the theoretical calculation value of the deceleration delay obtained by the model and the measured value of simulation results is less than 10%. The article's conclusion is that the traffic field has an impact on moving cars on the road and that physical and socioeconomic factors should be taken into account while studying vehicle-following behavior. The deceleration delay value of a vehicle's driving and traffic mass have a socioeconomic relationship that can be utilized to calculate the energy-time conversion coefficient when dealing with the bottleneck of cars stopping and starting.

Keywords: traffic field, social economics, traffic mass, bottleneck, deceleration delay

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Authors: Sneha Kumari, Ravi Krishnan Elangovan

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This abstract describes the development of a high-speed tracking method by modification in motor components for nanoparticle attachment. Myosin motors are nano-sized protein machines powering movement that defines life. These miniature molecular devices serve as engines utilizing chemical energy stored in ATP to produce useful mechanical energy in the form of a few nanometre displacement events leading to force generation that is required for cargo transport, cell division, cell locomotion, translated to macroscopic movements like running etc. With the advent of in vitro motility assay (IVMA), detailed functional studies of the actomyosin system could be performed. The major challenge with the currently available IVMA for tracking actin filaments is a resolution limitation of ± 50nm. To overcome this, we are trying to develop Single Molecule IVMA in which nanoparticle (GNP/QD) will be attached along or on the barbed end of actin filaments using CapZ protein and visualization by a compact TIRF module called ‘cTIRF’. The waveguide-based illumination by cTIRF offers a unique separation of excitation and collection optics, enabling imaging by scattering without emission filters. So, this technology is well equipped to perform tracking with high precision in temporal resolution of 2ms with significantly improved SNR by 100-fold as compared to conventional TIRF. Also, the nanoparticles (QD/GNP) attached to actin filament act as a point source of light coffering ease in filament tracking compared to conventional manual tracking. Moreover, the attachment of cargo (QD/GNP) to the thin filament paves the way for various nano-technological applications through their transportation to different predetermined locations on the chip

Keywords: actin, cargo, IVMA, myosin motors and single-molecule system

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Authors: Ramanpreet Kaur, Upasana Sharma

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The purpose of this paper is to address the challenges facing the water supply for the Machine Learning Database (MLDB) system at the piston foundry plant. In the MLDB system, one main unit, i.e., robotic, is connected by two sub-units. The functioning of the system depends on the robotic and water supply. Lack of water supply causes system failure. The system operates at full capacity with the help of two sub-units. If one sub-unit fails, the system runs at a low capacity. Reliability modeling is performed using semi-Markov processes and regenerative point techniques. Several system effects such as mean time to system failure, availability at full capacity, availability at reduced capacity, busy period for repair and expected number of visits have been achieved. Benefits have been analyzed. The graphical study is designed for a specific case using programming in C++ and MS Excel.

Keywords: MLDB system, robotic, semi-Markov process, regenerative point technique

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Authors: Mehmet Kale, Ramazan Sencan, Sibel Yavru, Ahmet Ak, Nuri Mamak, Sibel Hasırcıoglu, Mesih Kocamuftuoglu, Yakup Yıldırım, Hasbi Sait Saltık

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Bovine Papilloma Virus (BPV)-induced warts can cause mastitis, teat blindness, reduction of milk yield, udder deformities, and a difficulty in getting the teats into the milking machine. Especially, surgical operations cannot be performed in BPV-induced teat warts because of the increased sensitivity of the breast region and small-sized papillomas. Thus, there is a need to find new topical treatment methods. We have developed a pomade for treatment of BPV in cattle. The pomade is consists of lanoline, snakeskin (two special kind of snake), alcohol, vaseline, and ether. Firstly, we determined 46 cattle with teat warts. In the study, BPV antigen was detected in 28 cattle blood samples (61%) by ELISA. The pomade was applied to all BPV infected animals. The regression and recovery of warts were 100% in all animals. We advised using the pomade for treatment of BPV-induced warts in teats.

Keywords: bovine papilloma virus, pomade, teat, udder

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Authors: Sajad Shokohuyar, Mahsa Zomorrodi Anbaji, Saghar Pouyan Shad

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According to fast technology growth, modern banking tries to decrease going to banks’ branches and increase customers’ consent. One of the problems which banks face is securing customer’s password. The banks’ solution is one time password creation system. In this research by adapting from acceptance of technology model theory, assesses factors that are effective on banking in Iran especially in using one time password machine by one of the private banks of Iran customers. The statistical population is all of this bank’s customers who use electronic banking service and one time password technology and the questionnaires were distributed among members of statistical population in 5 selected groups of north, south, center, east and west of Tehran. Findings show that confidential preservation, education, ease of utilization and advertising and informing has positive relations and distinct hardware and age has negative relations.

Keywords: security, electronic banking, one time password, information technology

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Authors: Zatovska T. V., Nesterova N. V., Baranova G. V., Zagorodnya S. D.

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In recent years, biosensor technologies based on the phenomenon of surface plasmon resonance (SPR) are becoming increasingly used in biology and medicine. Their application facilitates exploration in real time progress of binding of biomolecules and identification of agents that specifically interact with biologically active substances immobilized on the biosensor surface (biochips). Special attention is paid to the use of Biosensor analysis in determining the antibody-antigen interaction in the diagnostics of diseases caused by viruses and bacteria. According to WHO, the diseases that are caused by the herpes simplex virus (HSV), take second place (15.8%) after influenza as a cause of death from viral infections. Current diagnostics of HSV infection include PCR and ELISA assays. The latter allows determination the degree of immune response to viral infection and respective stages of its progress. In this regard, the searches for new and available diagnostic methods are very important. This work was aimed to develop Biosensor chip for detection of specific antibodies to HSV-1 in the human blood serum. The proteins of HSV1 (strain US) were used as antigens. The viral particles were accumulated in cell culture MDBK and purified by differential centrifugation in cesium chloride density gradient. Analysis of the HSV1 proteins was performed by polyacrylamide gel electrophoresis and ELISA. The protein concentration was measured using De Novix DS-11 spectrophotometer. The device for detection of antigen-antibody interactions was an optoelectronic two-channel spectrometer ‘Plasmon-6’, using the SPR phenomenon in the Krechman optical configuration. It was developed at the Lashkarev Institute of Semiconductor Physics of NASU. The used carrier was a glass plate covered with 45 nm gold film. Screening of human blood serums was performed using the test system ‘HSV-1 IgG ELISA’ (GenWay, USA). Development of Biosensor chip included optimization of conditions of viral antigen sorption and analysis steps. For immobilization of viral proteins 0.2% solution of Dextran 17, 200 (Sigma, USA) was used. Sorption of antigen took place at 4-8°C within 18-24 hours. After washing of chip, three times with citrate buffer (pH 5,0) 1% solution of BSA was applied to block the sites not occupied by viral antigen. It was found direct dependence between the amount of immobilized HSV1 antigen and SPR response. Using obtained biochips, panels of 25 positive and 10 negative for the content of antibodies to HSV-1 human sera were analyzed. The average value of SPR response was 185 a.s. for negative sera and from 312 to. 1264 a.s. for positive sera. It was shown that SPR data were agreed with ELISA results in 96% of samples proving the great potential of SPR in such researches. It was investigated the possibility of biochip regeneration and it was shown that application of 10 mM NaOH solution leads to rupture of intermolecular bonds. This allows reuse the chip several times. Thus, in this study biosensor chip for detection of specific antibodies to HSV1 was successfully developed expanding a range of diagnostic methods for this pathogen.

Keywords: biochip, herpes virus, SPR

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Authors: Khenfer-Koummich Fatma, Hendel Fatiha, Mesbahi Larbi

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this paper presents an approach based on Hidden Markov Models (HMM: Hidden Markov Model) using HTK tools. The goal is to create a man-machine interface with a voice recognition system that allows the operator to tele-operate a mentor robot to execute specific tasks as rotate, raise, close, etc. This system should take into account different levels of environmental noise. This approach has been applied to isolated words representing the robot commands spoken in two languages: French and Arabic. The recognition rate obtained is the same in both speeches, Arabic and French in the neutral words. However, there is a slight difference in favor of the Arabic speech when Gaussian white noise is added with a Signal to Noise Ratio (SNR) equal to 30 db, the Arabic speech recognition rate is 69% and 80% for French speech recognition rate. This can be explained by the ability of phonetic context of each speech when the noise is added.

Keywords: voice command, HMM, TIMIT, noise, HTK, Arabic, speech recognition

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Authors: Pavel Zlabek, Vaclav Mentl

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The fatigue crack growth rate evaluation is a basic experimental characteristic when assessment o f the remaining lifetime is needed. Within the repair welding technology project, the crack growth rate at cyclic loading was measured in base and weld metals and in the situation when cracks were initiated in base metal and grew into the weld metal through heat-affected zone and back to the base metal. Two welding technologies were applied and specimens in as-welded state and after heat treatment were tested. Fatigue crack growth rate measurement was performed on CrMoV pressure vessel steel and the tests were performed at room temperature. The crack growth rate was measured on CCT test specimens (see figure) for both the base and weld metals and also in the case of crack subsequent transition through all the weld zones. A 500 kN MTS controlled electro-hydraulic testing machine and Model 632.13C-20 MTS extensometer were used to perform the tests.

Keywords: cracks, fatigue, steels, weldments

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Authors: F. L. Bernardo, R. A. S. Zacarias

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The ways to translate are manifold since textual genres undergoing translations are diverse. In this essay, our goal is to give special attention to the literary genre and to the online translation tool Google Translate (GT), widely used either by nonprofessionals or by scholars, in order to show evidence of the indispensability of human wit in a good translation. Our study has its basis on a literary review of prominent authors, with emphasis on translation categories. Also highlighting the issue of polysemous literary translation, we aim to shed light on the translator’s craft and the fallible nature of online translation. To better illustrate these principles, the methodology consisted on performing a comparative analysis involving the original text Moll Flanders by Daniel Defoe in English to its online translation given by GT and to a translation into Brazilian Portuguese performed by a human. We proceeded to identifying and analyzing the degrees of textual equivalence according to the following categories: volume, levels and order. The results have attested the unsuitability in a translation done by a computer connected to the World Wide Web.

Keywords: Google Translator, human translation, literary translation, Moll Flanders

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Authors: LuoJiaoyang, Yu Hongyang

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In this paper, we experimented with a new approach to multimodal identification using RGB, RGB-D and voice data. The multimodal combination of RGB and voice data has been applied in tasks such as emotion recognition and has shown good results and stability, and it is also the same in identity recognition tasks. We believe that the data of different modalities can enhance the effect of the model through mutual reinforcement. We try to increase the three modalities on the basis of the dual modalities and try to improve the effectiveness of the network by increasing the number of modalities. We also implemented the single-modal identification system separately, tested the data of these different modalities under clean and noisy conditions, and compared the performance with the multimodal model. In the process of designing the multimodal model, we tried a variety of different fusion strategies and finally chose the fusion method with the best performance. The experimental results show that the performance of the multimodal system is better than that of the single modality, especially in dealing with noise, and the multimodal system can achieve an average improvement of 5%.

Keywords: multimodal, three modalities, RGB-D, identity verification

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Authors: Maamar Ali Saud Al Tobi, Geraint Bevan, K. P. Ramachandran, Peter Wallace, David Harrison

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Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated.

Keywords: centrifugal pump setup, vibration analysis, artificial intelligence, genetic algorithm

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Authors: Hikmet Cicek

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Transition metal nitrides are widely used as protective coatings on machine parts and cutting tools to protect the surfaces from abrasion and corrosion for decades. In this study, the ternary TiNbVN thin coatings were produced with closed field unbalanced magnetron sputtering system and their structural, mechanical and fatigue-like (multi-pass scratch test) properties were investigated. Two different substrates (M2 and H13 steels) were used to explore substrates effects. X-Ray diffractometer, scanning electron microscope, and energy dispersive spectroscopy were used for the structural and chemical analysis of the coatings. Nanohardness tests were proceed for mechanical properties. The fatigue-like properties of the coatings obtained from the multi-scratch test under three different cycle passes. The results showed that TiNbVN films have excellent fatigue resistance and the coatings deposited on M2 steel substrate have higher hardness and better fatigue resistance.

Keywords: physical vapor deposition, fatigue, metal nitride, multipass scratch test

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Authors: Shuge Lei, Haonan Hu, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Yan Tong

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It remains challenging to measure reliability from classification results from different machine learning models. This paper proposes a reliable soup optimization algorithm based on the model weight fusion algorithm Model Soup, aiming to improve reliability by using dual-channel reliability as the objective function to fuse a series of weights in the breast ultrasound classification models. Experimental results on breast ultrasound clinical datasets demonstrate that reliable soup significantly enhances the reliability of breast ultrasound image classification tasks. The effectiveness of the proposed approach was verified via multicenter trials. The results from five centers indicate that the reliability optimization algorithm can enhance the reliability of the breast ultrasound image classification model and exhibit low multicenter correlation.

Keywords: breast ultrasound image classification, feature attribution, reliability assessment, reliability optimization

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Authors: Li Ni, Pen ManMan, Li KenLi

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Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. So the property embedded in CRO is used to solve optimization problems. This paper presents an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm:k-means and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems.

Keywords: chemical reaction optimization, expection maimization, initia, objective function clustering

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Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

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Authors: R. B. Patil, B. S. Kothavale, L. Y. Waghmode

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Today, the CNC turning center becomes an important machine tool for manufacturing industry worldwide. However, as the breakdown of a single CNC turning center may result in the production of an entire plant being halted. For this reason, operations and preventive maintenance have to be minimized to ensure availability of the system. Indeed, improving the availability of the CNC turning center as a whole, objectively leads to a substantial reduction in production loss, operating, maintenance and support cost. In this paper, fault tree analysis (FTA) method is used for reliability analysis of CNC turning center. The major faults associated with the system and the causes for the faults are presented graphically. Boolean algebra is used for evaluating fault tree (FT) diagram and for deriving governing reliability model for CNC turning center. Failure data over a period of six years has been collected and used for evaluating the model. Qualitative and quantitative analysis is also carried out to identify critical sub-systems and components of CNC turning center. It is found that, at the end of the warranty period (one year), the reliability of the CNC turning center as a whole is around 0.61628.

Keywords: fault tree analysis (FTA), reliability analysis, risk assessment, hazard analysis

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Authors: W. C. Lucas, S. Titus, M. E. M. Young

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Gymnastics is the umbrella term that represents seven different and unique disciplines of gymnastics. Men and women of all ages and abilities practice this sport, and participation in gymnastics can develop both gross and fine motor skills, strength, flexibility, coordination and balance. There are various social factors, such as a family’s socioeconomic status or accessibility to sports facilities that may play a role in affecting levels of participation. The aim of this study is to investigate the social factors that have an influence on gymnastics participation in the Western Cape. To this end, a qualitative approach is adopted to collect data. This study also adopts the ecological systems theory as the theoretical framework, and is used to analyze and interpret current social factors that directly or indirectly influence participation in gymnastics. The study’s objectives were to ascertain which social factors hinder participation, and which social factors promote participation, thus, coaches, parents and gymnasts participated in focus group discussions. Key informant interviews took place with experts in the field of gymnastics in the Western Cape. A thematic analysis was conducted on transcriptions from the focus group discussions and key informant interviews. Social factors investigated in this study occurred in the chronosystem, macrosystem, exosystem, mesosystem, and microsystem, and had both a direct and indirect influence on the gymnast’s continued participation. These systems are defined as the environment of the individual, in which they grow and develop. The research findings of this paper are used to draw conclusions and make specific recommendations for practice and further research. The information gathered in this study can assist all stakeholders within the field of gymnastics, such as parents, judges, coaches, gymnasts, and the supporting community which surround the participating gymnast.

Keywords: developing child, ecological systems theory, facilities, federation, gymnastics, influence, participation, social factors, socioeconomic status, sport

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Authors: Amarnath M., Ramachandra C. G., H. Chelladurai, P..Sateesh Kumar, K. Santhosh Kumar

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Journal bearings are extensively used sliding contact machine elements to support radial/axial loaded rotors used in various applications viz. automobile crankshaft, turbine propeller shaft, rope conveyer, heavy duty electric motors. The primary reasons for the failures of these bearings include unstable lubricant film, oil degradation, misalignment, etc. This paper describes the results of experimental investigations carried out to detect surface fatigue wear developed on load bearing the contact surfaces of journal bearing. The test bearing was subjected to fatigue load cycles over a period of 600 hours. The vibration signals were acquired from the journal bearing at regular intervals of 100 hrs. These signals were post-processed by using the vibration analysis technique to obtain diagnostic information of wear propagated in the journal-bearing system.

Keywords: fatigue, journal bearing, sound signals, vibration signals, wear

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Authors: B. G. Patel, A. K. Desai, S. G. Shah

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The main of objective of this paper is to develop new measurement technique without touching the object. DIC is advance measurement technique use to measure displacement of particle with very high accuracy. This powerful innovative technique which is used to correlate two image segments to determine the similarity between them. For this study, nine geometrically similar beam specimens of different sizes with (steel fibers and glass fibers) and without fibers were tested under three-point bending in a closed loop servo-controlled machine with crack mouth opening displacement control with a rate of opening of 0.0005 mm/sec. Digital images were captured before loading (unreformed state) and at different instances of loading and were analyzed using correlation techniques to compute the surface displacements, crack opening and sliding displacements, load-point displacement, crack length and crack tip location. It was seen that the CMOD and vertical load-point displacement computed using DIC analysis matches well with those measured experimentally.

Keywords: Digital Image Correlation, fibres, self compacting concrete, size effect

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Authors: Boughedaoui Rachid, Belaidi Idir, Ouali Mohamed

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The formatting by removal of material remains an indispensable means for obtaining different forms of pieces. The objective of this work is to study the influence of parameters of the vibratory regime of the system PTM 'Piece-Tool-Machine, in the case of the machining of the thin pieces on the surface finish. As a first step, an analytical study of essential dynamic models 2D slice will be presented. The stability lobes will be thus obtained. In a second step, a characterization of PTM system will be realized. This system will be instrumented with accelerometric sensors but also a laser vibrometer so as to have the information closer to the cutting area. Dynamometers three components will be used for the analysis of cutting forces. Surface states will be measured and the condition of the cutting edge will be visualized thanks to a binocular microscope coupled to a data acquisition system. This information will allow quantifying the influence of chatter on the dimensional quality of the parts. From lobes stabilities previously determined experimental validation allow for the development a method for detecting of the phenomenon of chatter and so an approach will be proposed.

Keywords: chatter, dynamic, milling, lobe stability

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Authors: Daniel Benson, Yundan Gong, Hannah Kirk

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Advancement in national language processing techniques has led to increased data processing speeds, and reduced the need for cumbersome, manual data processing that is often required when processing data from multilateral organizations for specific purposes. As such, using named entity recognition (NER) modeling and the Organisation of Economically Developed Countries (OECD) Creditor Reporting System database, we present the first geotagged dataset of OECD donor Official Development Assistance (ODA) projects on a global, subnational basis. Our resulting data contains 52,086 ODA projects geocoded to subnational locations across 115 countries, worth a combined $87.9bn. This represents the first global, OECD donor ODA project database with geocoded projects. We use this new data to revisit old questions of how ‘well’ donors allocate ODA to the developing world. This understanding is imperative for policymakers seeking to improve ODA effectiveness.

Keywords: international aid, geocoding, subnational data, natural language processing, machine learning

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Authors: Kevin Smith

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Publicly traded company in the US are legally obligated to host earnings calls that discuss their most recent financial reports. During these calls, investors are able to ask these companies questions about these financial reports and on the future direction of the company. This paper examines whether common institutional owners use these calls as a way to indirectly signal to companies in their portfolio to not take actions that could hurt the common owner's interests. This paper uses transcripts taken from the earnings calls of the six largest health insurance companies in the US from 2014 to 2019. This data is analyzed using text analysis and sentiment analysis to look for patterns in the statements made by common owners. The analysis found that common owners where more likely to recommend against direct price competition and instead redirect the insurance companies towards more passive actions, like investing in new technologies. This result indicates a mechanism that common owners use to reduce competition in the health insurance market.

Keywords: common ownership, text analysis, sentiment analysis, machine learning

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Authors: Sungyani Tripathy, Atul Desai

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Turbo machinery Frame Foundation is very important for power generation, gas, steam, hydro, geothermal and nuclear power plants. The Turbo machinery Foundation system was simulated in SAP: 2000 software and dynamic response of foundation was analysed. In this paper, the detailed study of turbo machinery foundation with different running speed has considered. The different revolution per minute considered in this study is 4000 rpm, 6000 rpm, 8000 rpm, 1000 rpm and 12000 rpm. The above analysis has been carried out considering Winkler spring soil model, solid finite element modelling and dynamic analysis of Turbo machinery foundations. The comparison of frequency and time periods at various mode shapes are addressed in this study. Current work investigates the effect of damping on the response spectra curve at the foundation top deck, considering the dynamic machine load. It has been found that turbo generator foundation with haunches remains more elastic during seismic action for different running speeds.

Keywords: turbo machinery, SAP: 2000, response spectra, running speeds

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Authors: Catarina Isabel Ramos Pereira, Jorge Machado, Begona Alonso Criado, Maria João Santos

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Introduction: Gait disorders are one of the symptoms that have severe implications on the quality of life in Parkinson's disease (PD). Currently, there is no therapy to reverse or treat this condition. None of the drugs used in conventional medical treatment is entirely efficient, and all have a high incidence of side effects. Acupuncture treatment is believed to improve motor ability, but there is still little scientific evidence in individuals with PD. Aim: The aim of the study is to investigate the acute effect of acupuncture on gait parameters in Parkinson's disease. Methods: This is a randomized and controlled crossover study. The same individual patient was part of both the experimental (real acupuncture) and control group (false acupuncture/sham), and the sequence was randomized. Gait parameters were measured at two different moments, before and after treatment, using four force platforms as well as the collection of 3D markers positions taken by 11 cameras. Images were quantitatively analyzed using Qualisys Track Manager software that let us extract data related to the quality of gait and balance. Seven patients with the diagnosis of Parkinson's disease were included in the study. Results: Statistically significant differences were found in gait speed (p = 0.016), gait cadence (p = 0.006), support base width (p = 0.0001), medio-lateral oscillation (p = 0.017), left-right step length (p = 0.0002), and stride length: right-right (p = 0.0000) and left-left (p = 0.0018), time of left support phase (p = 0.029), right support phase (p = 0.025) and double support phase (p = 0.015), between the initial and final moments for the experimental group. Differences in right-left stride length were found for both groups. Conclusion: Our results show that acupuncture could enhance gait in Parkinson's disease patients. Deep research involving a larger number of volunteers should be accomplished to validate these encouraging findings.

Keywords: acupuncture, traditional Chinese medicine, Parkinson's disease, gait

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Authors: Donghee Noh, Seonhyeong Kim, Junhwan Choi, Heegon Kim, Sooho Jung, Keunho Park

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In this paper, we describe an image post-processing method to enhance the performance of the parcel segmentation method using deep learning-based aerial images conducted in previous studies. The study results were evaluated using a confusion matrix, IoU, Precision, Recall, and F1-Score. In the case of the confusion matrix, it was observed that the false positive value, which is the result of misclassification, was greatly reduced as a result of image post-processing. The average IoU was 0.9688 in the image post-processing, which is higher than the deep learning result of 0.8362, and the F1-Score was also 0.9822 in the image post-processing, which was higher than the deep learning result of 0.8850. As a result of the experiment, it was found that the proposed technique positively complements the deep learning results in segmenting the parcel of interest.

Keywords: aerial image, image process, machine vision, open field smart farm, segmentation

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Authors: Arezou Javadi

Abstract:

The use of algorithmic models dependent on software calculations and simulation of new government management assays with the help of specialized software had increased the productivity and efficiency of the government management system recently. This has caused the management approach to change from the old bitch & fix model, which has low efficiency and less usefulness, to the capable management model with higher efficiency called the partnership with resident model. By using Govpilot TM software, the relationship between people in a system and the government was examined. The method of two tailed interaction was the outsourcing of a goal in a system, which is formed in the order of goals, qualified executive people, optimal executive model, and finally, summarizing additional activities at the different statistical levels. The results showed that the participation of people in a financial implementation system with a statistical potential of P≥5% caused a significant increase in investment and initial capital in the government system with maximum implement project in a smart government.

Keywords: machine learning, financial income, statistical potential, govpilot

Procedia PDF Downloads 67

Authors: Arezou Javadi

Abstract:

The use of algorithmic models dependent on software calculations and simulation of new government management assays with the help of specialized software had increased the productivity and efficiency of the government management system recently. This has caused the management approach to change from the old bitch & fix model, which has low efficiency and less usefulness, to the capable management model with higher efficiency called the partnership with resident model. By using Govpilot TM software, the relationship between people in a system and the government was examined. The method of two tailed interaction was the outsourcing of a goal in a system, which is formed in the order of goals, qualified executive people, optimal executive model, and finally, summarizing additional activities at the different statistical levels. The results showed that the participation of people in a financial implementation system with a statistical potential of P≥5% caused a significant increase in investment and initial capital in the government system with maximum implement project in a smart government.

Keywords: machine learning, financial income, statistical potential, govpilot

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Authors: Changqiao Wu, Guoqing Ding, Xin Chen

Abstract:

In this paper, ways of modeling dynamic measurement systems are discussed. Specially, for linear system with single-input single-output, it could be modeled with shallow neural network. Then, gradient based optimization algorithms are used for searching the proper coefficients. Besides, method with normal equation and second order gradient descent are proposed to accelerate the modeling process, and ways of better gradient estimation are discussed. It shows that the mathematical essence of the learning objective is maximum likelihood with noises under Gaussian distribution. For conventional gradient descent, the mini-batch learning and gradient with momentum contribute to faster convergence and enhance model ability. Lastly, experimental results proved the effectiveness of second order gradient descent algorithm, and indicated that optimization with normal equation was the most suitable for linear dynamic models.

Keywords: dynamic system modeling, neural network, normal equation, second order gradient descent

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