Search results for: uncertainty and error visualisation

Authors: Amulya P. Rao, Prathima S., Sreedevi N.

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Speech sound disorders (SSD) encompass the major concern in younger population of India with highest prevalence rate among the speech disorders. Children with SSD if not identified and rehabilitated at the earliest, are at risk for academic difficulties. This necessitates early identification using screening tools assessing the frequently misarticulated speech sounds. The literature on frequently misarticulated speech sounds is ample in English and other western languages targeting individuals with various communication disorders. Articulation is language specific, and there are limited studies reporting the same in Kannada, a Dravidian Language. Hence, the present study aimed to identify the frequently misarticulated consonants in Kannada and also to examine the error type. A retrospective, descriptive study was carried out using secondary data analysis of 41 participants (34-phonetic type and 7-phonemic type) with SSD in the age range 3-to 12-years. All the consonants of Kannada were analyzed by considering three words for each speech sound from the Kannada Diagnostic Photo Articulation test (KDPAT). Picture naming task was carried out, and responses were audio recorded. The recorded data were transcribed using IPA 2018 broad transcription. A criterion of 2/3 or 3/3 error productions was set to consider the speech sound to be an error. Number of error productions was calculated for each consonant in each participant. Then, the percentage of participants meeting the criteria were documented for each consonant to identify the frequently misarticulated speech sound. Overall results indicated that velar /k/ (48.78%) and /g/ (43.90%) were frequently misarticulated followed by voiced retroflex /ɖ/ (36.58%) and trill /r/ (36.58%). The lateral retroflex /ɭ/ was misarticulated by 31.70% of the children with SSD. Dentals (/t/, /n/), bilabials (/p/, /b/, /m/) and labiodental /v/ were produced correctly by all the participants. The highly misarticulated velars /k/ and /g/ were frequently substituted by dentals /t/ and /d/ respectively or omitted. Participants with SSD-phonemic type had multiple substitutions for one speech sound whereas, SSD-phonetic type had consistent single sound substitutions. Intra- and inter-judge reliability for 10% of the data using Cronbach’s Alpha revealed good reliability (0.8 ≤ α < 0.9). Analyzing a larger sample by replicating such studies will validate the present study results.

Keywords: consonant, frequently misarticulated, Kannada, SSD

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Authors: T. Nishimura, K. Doi, H. Fujimoto, T. Wada

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We conducted perception experiments on tactile symbols to elucidate the impact of the size of these letters on the level of perceptibility. This study was based on the accessible design perspective and aimed at expanding the availability of tactile symbols for the visually impaired who are unable to read Braille characters. In particular, this study targeted people with acquired visual impairments as users of the tactile symbols. The subjects (young and elderly individuals) in this study had normal vision. They were asked to participate in the experiments to identify tactile symbols while unable to see their hand during the experiments. This study investigated the relation between the size and perceptibility of tactile symbols based on an examination using test pieces of these letters in different sizes. The results revealed that the error rates for both young and elderly subjects converged to almost 0% when 12 mm size tactile symbols were used. The findings also showed that the error rate was low and subjects could identify the symbols in 5 s when 16 mm size tactile symbols were introduced.

Keywords: accessible design, tactile sense, tactile symbols, bioinformatic

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Authors: Hendri Purwadi, Christine McCloud

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Triage is a complex decision-making process that aims to categorize a victim’s level of acuity and the need for medical assistance. Two common triage systems have been widely used in Mass Casualty Incidents (MCIs) and disaster situation are START (Simple triage algorithm and rapid treatment) and SALT (sort, asses, lifesaving, intervention, and treatment/transport). There is currently controversy regarding the effectiveness of SALT over START triage system. This systematic review aims to investigate and compare the effectiveness between SALT and START triage system in disaster and MCIs setting. Literatures were searched via systematic search strategy from 2009 until 2019 in PubMed, Cochrane Library, CINAHL, Scopus, Science direct, Medlib, ProQuest. This review included simulated-based and medical record -based studies investigating the accuracy and applicability of SALT and START triage systems of adult and children population during MCIs and disaster. All type of studies were included. Joana Briggs institute critical appraisal tools were used to assess the quality of reviewed studies. As a result, 1450 articles identified in the search, 10 articles were included. Four themes were identified by review, they were accuracy, under-triage, over-triage and time to triage per individual victim. The START triage system has a wide range and inconsistent level of accuracy compared to SALT triage system (44% to 94. 2% of START compared to 70% to 83% of SALT). The under-triage error of START triage system ranged from 2.73% to 20%, slightly lower than SALT triage system (7.6 to 23.3%). The over-triage error of START triage system was slightly greater than SALT triage system (START ranged from 2% to 53% compared to 2% to 22% of SALT). The time for applying START triage system was faster than SALT triage system (START was 70-72.18 seconds compared to 78 second of SALT). Consequently; The START triage system has lower level of under-triage error and faster than SALT triage system in classifying victims of MCIs and disaster whereas SALT triage system is known slightly more accurate and lower level of over-triage. However, the magnitude of these differences is relatively small, and therefore the effect on the patient outcomes is not significance. Hence, regardless of the triage error, either START or SALT triage system is equally effective to triage victims of disaster and MCIs.

Keywords: disaster, effectiveness, mass casualty incidents, START triage system, SALT triage system

Procedia PDF Downloads 133

Authors: Zhuang Guo

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In the field of GaN-based resonant tunneling diodes (RTDs) simulations, the traditional Tsu-Esaki formalism failed to predict the values of peak currents and peak voltages in the simulated current-voltage(J-V) characteristics. The main reason is that due to the strong internal polarization fields, two-dimensional electron gas(2DEG) accumulates at emitters, resulting in 2D-2D resonant tunneling currents, which become the dominant parts of the total J-V characteristics. By comparison, based on the 3D-2D resonant tunneling mechanism, the traditional Tsu-Esaki formalism cannot predict the J-V characteristics correctly. To overcome this shortcoming, we develop a new analytical model for the 2D-2D resonant tunneling currents generated in GaN-based RTDs. Compared with Tsu-Esaki formalism, the new model has made the following modifications: Firstly, considering the Heisenberg uncertainty, the new model corrects the expression of the density of states around the 2DEG eigenenergy levels at emitters so that it could predict the half width at half-maximum(HWHM) of resonant tunneling currents; Secondly, taking into account the effect of bias on wave vectors on the collectors, the new model modifies the expression of the transmission coefficients which could help to get the values of peak currents closer to the experiment data compared with Tsu-Esaki formalism. The new analytical model successfully predicts the J-V characteristics of GaN-based RTDs, and it also reveals more detailed mechanisms of resonant tunneling happened in GaN-based RTDs, which helps to design and fabricate high-performance GaN RTDs.

Keywords: GaN-based resonant tunneling diodes, tsu-esaki formalism, 2D-2D resonant tunneling, heisenberg uncertainty

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Authors: Ebrahim Ghanbari, Mohammad Reza Nematollahi

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Integrating deterministic and probabilistic safety assessment (IDPSA) is one of the most commonly used issues in the field of safety analysis of power plant accident. It has also been recognized today that the role of human error in creating these accidents is not less than systemic errors, so the human interference and system errors in fault and event sequences are necessary. The integration of these analytical topics will be reflected in the frequency of core damage and also the study of the use of water resources in an accident such as the loss of all electrical power of the plant. In this regard, the SBO accident was simulated for the pressurized water reactor in the deterministic analysis issue, and by analyzing the operator's behavior in controlling the accident, the results of the combination of deterministic and probabilistic assessment were identified. The results showed that the best performance of the plant operator would reduce the risk of an accident by 10%, as well as a decrease of 6.82 liters/second of the water sources of the plant.

Keywords: IDPSA, human error, SBO, risk

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Authors: P. Venu, Joeju M. Issac

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Quality Function Deployment (QFD) is a systematic technique that creates a platform where the customer responses can be positively converted to design attributes. The accuracy of a QFD process heavily depends on the data that it is handling which is captured from customers or QFD team members. Customized computer programs that perform Quality Function Deployment within a stipulated time have been used by various companies across the globe. These programs heavily rely on storage and retrieval of the data on a common database. This database must act as a perfect source with minimum missing values or error values in order perform actual prioritization. This paper introduces a missing/error data handler module which uses Genetic Algorithm and Fuzzy numbers. The prioritization of customer requirements of sesame oil is illustrated and a comparison is made between proposed data handler module-based deployment and manual deployment.

Keywords: hybrid data handler, QFD, prioritization, module-based deployment

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Authors: Paramjit Kaur, Harish Kundra

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In the recent years, swarm intelligence based firefly algorithm has become a great focus for the researchers to solve the real time optimization problems. Here, firefly algorithm is used for the application of satellite image classification. For experimentation, Alwar area is considered to multiple land features like vegetation, barren, hilly, residential and water surface. Alwar dataset is considered with seven band satellite images. Firefly Algorithm is based on the attraction of less bright fireflies towards more brightener one. For the evaluation of proposed concept accuracy assessment parameters are calculated using error matrix. With the help of Error matrix, parameters of Kappa Coefficient, Overall Accuracy and feature wise accuracy parameters of user’s accuracy & producer’s accuracy can be calculated. Overall results are compared with BBO, PSO, Hybrid FPAB/BBO, Hybrid ACO/SOFM and Hybrid ACO/BBO based on the kappa coefficient and overall accuracy parameters.

Keywords: image classification, firefly algorithm, satellite image classification, terrain classification

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Authors: Yi-Hsiu Lai

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The present study aimed to elucidate the lexical-semantic processing for Chinese as second language (CSL) learners. Twenty L1 speakers of Chinese and twenty CSL learners in Taiwan participated in a picture naming task and a category fluency task. Based on their Chinese proficiency levels, these CSL learners were further divided into two sub-groups: ten CSL learners of elementary Chinese proficiency level and ten CSL learners of intermediate Chinese proficiency level. Instruments for the naming task were sixty black-and-white pictures: thirty-five object pictures and twenty-five action pictures. Object pictures were divided into two categories: living objects and non-living objects. Action pictures were composed of two categories: action verbs and process verbs. As in the naming task, the category fluency task consisted of two semantic categories – objects (i.e., living and non-living objects) and actions (i.e., action and process verbs). Participants were asked to report as many items within a category as possible in one minute. Oral productions were tape-recorded and transcribed for further analysis. Both error types and error frequency were calculated. Statistical analysis was further conducted to examine these error types and frequency made by CSL learners. Additionally, category effects, pictorial effects and L2 proficiency were discussed. Findings in the present study helped characterize the lexical-semantic process of Chinese naming in CSL learners of different Chinese proficiency levels and made contributions to Chinese vocabulary teaching and learning in the future.

Keywords: lexical-semantic processing, Mandarin Chinese, naming, category effects

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Authors: Muhammad Ilyas, Awais Khan, Syed Ali Raza Shah

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DC motors are widely used in industries to provide mechanical power in speed and torque. The position and speed control of DC motors is getting the interest of the scientific community in robotics, especially in the robotic arm, a flexible joint manipulator. The current research work is based on position control of DC motors using experimental investigations in LabVIEW. The linear control strategy is applied to track the position and speed of the DC motor with comparative analysis in the LabVIEW platform and simulation analysis in MATLAB. The tracking error in hardware setup based on LabVIEW programming is slightly greater than simulation analysis in MATLAB due to the inertial load of the motor during steady-state conditions. The controller output shows the input voltage applied to the dc motor varies between 0-8V to ensure minimal steady error while tracking the position and speed of the DC motor.

Keywords: DC motor, labview, proportional integral derivative control, position tracking, speed tracking

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Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

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In this paper, we propose an approach of detecting the behavior of the viewers of a TV program in a non-controlled environment. The experiment we propose is based on the use of three types of connected objects (smartphone, smart watch, and a connected remote control). 23 participants were observed while watching their TV programs during three phases: before, during and after watching a TV program. Their behaviors were detected using an approach based on The Dempster Shafer Theory (DST) in two phases. The first phase is to approximate dynamically the mass functions using an approach based on the correlation coefficient. The second phase is to calculate the approximate mass functions. To approximate the mass functions, two approaches have been tested: the first approach was to divide each features data space into cells; each one has a specific probability distribution over the behaviors. The probability distributions were computed statistically (estimated by empirical distribution). The second approach was to predict the TV-viewing behaviors through the use of classifiers algorithms and add uncertainty to the prediction based on the uncertainty of the model. Results showed that mixing the fusion rule with the computation of the initial approximate mass functions using a classifier led to an overall of 96%, 95% and 96% success rate for the first, second and third TV-viewing phase respectively. The results were also compared to those found in the literature. This study aims to anticipate certain actions in order to maintain the attention of TV viewers towards the proposed TV programs with usual connected objects, taking into account the various uncertainties that can be generated.

Keywords: Iot, TV-viewing behaviors identification, automatic classification, unconstrained environment

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Authors: Ju-Hong Lee, Ching-Wei Liao

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Adaptive beamforming using antenna array of sensors is useful in the process of adaptively detecting and preserving the presence of the desired signal while suppressing the interference and the background noise. For conventional adaptive array beamforming, we require a prior information of either the impinging direction or the waveform of the desired signal to adapt the weights. The adaptive weights of an antenna array beamformer under a steered-beam constraint are calculated by minimizing the output power of the beamformer subject to the constraint that forces the beamformer to make a constant response in the steering direction. Hence, the performance of the beamformer is very sensitive to the accuracy of the steering operation. In the literature, it is well known that the performance of an adaptive beamformer will be deteriorated by any steering angle error encountered in many practical applications, e.g., the wireless communication systems with massive antennas deployed at the base station and user equipment. Hence, developing effective signal processing techniques to deal with the problem due to steering angle error for array beamforming systems has become an important research work. In this paper, we present an effective signal processing technique for constructing an adaptive beamformer against the steering angle error. The proposed array beamformer adaptively estimates the actual direction of the desired signal by using the presumed steering vector and the received array data snapshots. Based on the presumed steering vector and a preset angle range for steering mismatch tolerance, we first create a matrix related to the direction vector of signal sources. Two projection matrices are generated from the matrix. The projection matrix associated with the desired signal information and the received array data are utilized to iteratively estimate the actual direction vector of the desired signal. The estimated direction vector of the desired signal is then used for appropriately finding the quiescent weight vector. The other projection matrix is set to be the signal blocking matrix required for performing adaptive beamforming. Accordingly, the proposed beamformer consists of adaptive quiescent weights and partially adaptive weights. Several computer simulation examples are provided for evaluating and comparing the proposed technique with the existing robust techniques.

Keywords: adaptive beamforming, robustness, signal blocking, steering angle error

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Authors: Nadezhda Kvatashidze

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The International Accounting Standards Board updated the conceptual framework for financial reporting. The main reason behind it is to resolve the tasks of the accounting, which are caused by the market development and business-transactions of a new economic content. Also, the investors call for higher transparency of information and responsibility for the results in order to make a more accurate risk assessment and forecast. All these make it necessary to further develop the conceptual framework for financial reporting so that the users get useful information. The market development and certain shortcomings of the conceptual framework revealed in practice require its reconsideration and finding new solutions. Some issues and concepts, such as disclosure and supply of information, its qualitative characteristics, assessment, and measurement uncertainty had to be supplemented and perfected. The criteria of recognition of certain elements (assets and liabilities) of reporting had to be updated, too and all this is set out in the updated edition of the conceptual framework for financial reporting, a comprehensive collection of concepts underlying preparation of the financial statement. The main objective of conceptual framework revision is to improve financial reporting and development of clear concepts package. This will support International Accounting Standards Board (IASB) to set common “Approach & Reflection” for similar transactions on the basis of mutually accepted concepts. As a result, companies will be able to develop coherent accounting policies for those transactions or events that are occurred from particular deals to which no standard is used or when standard allows choice of accounting policy.

Keywords: conceptual framework, measurement basis, measurement uncertainty, neutrality, prudence, stewardship

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Authors: Emad A. Mohammed

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The concept of hydraulic flow units (HFU) has been used for decades in the petroleum industry to improve the prediction of permeability. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir rock quality index (RQI). Both indices are based on reservoir porosity and permeability of core samples. It is assumed that core samples with similar FZI values belong to the same HFU. Thus, after dividing the porosity-permeability data based on the HFU, transformations can be done in order to estimate the permeability from the porosity. The conventional practice is to use the power law transformation using conventional HFU where percentage of error is considerably high. In this paper, neural network technique is employed as a soft computing transformation method to predict permeability instead of power law method to avoid higher percentage of error. This technique is based on HFU identification where Amaefule et al. (1993) method is utilized. In this regard, Kozeny and Carman (K–C) model, and modified K–C model by Hasan and Hossain (2011) are employed. A comparison is made between the two transformation techniques for the two porosity-permeability models. Results show that the modified K-C model helps in getting better results with lower percentage of error in predicting permeability. The results also show that the use of artificial intelligence techniques give more accurate prediction than power law method. This study was conducted on a heterogeneous complex carbonate reservoir in Oman. Data were collected from seven wells to obtain the permeability correlations for the whole field. The findings of this study will help in getting better estimation of permeability of a complex reservoir.

Keywords: permeability, hydraulic flow units, artificial intelligence, correlation

Procedia PDF Downloads 136

Authors: Byeongcheol Kang, Junyi Kim, Hyungsik Jung, Hyungjun Yang, Jaewoo An, Jonggeun Choe

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It is important to figure out reservoir properties for better production management. Due to the limited information, there are geological uncertainties on very heterogeneous or channel reservoir. One of the solutions is to generate multiple equi-probable realizations using geostatistical methods. However, some models have wrong properties, which need to be excluded for simulation efficiency and reliability. We propose a novel method of model selection scheme, based on distance-based clustering for reliable application of production optimization algorithm. Distance is defined as a degree of dissimilarity between the data. We calculate Hausdorff distance to classify the models based on their similarity. Hausdorff distance is useful for shape matching of the reservoir models. We use multi-dimensional scaling (MDS) to describe the models on two dimensional space and group them by K-means clustering. Rather than simulating all models, we choose one representative model from each cluster and find out the best model, which has the similar production rates with the true values. From the process, we can select good reservoir models near the best model with high confidence. We make 100 channel reservoir models using single normal equation simulation (SNESIM). Since oil and gas prefer to flow through the sand facies, it is critical to characterize pattern and connectivity of the channels in the reservoir. After calculating Hausdorff distances and projecting the models by MDS, we can see that the models assemble depending on their channel patterns. These channel distributions affect operation controls of each production well so that the model selection scheme improves management optimization process. We use one of useful global search algorithms, particle swarm optimization (PSO), for our production optimization. PSO is good to find global optimum of objective function, but it takes too much time due to its usage of many particles and iterations. In addition, if we use multiple reservoir models, the simulation time for PSO will be soared. By using the proposed method, we can select good and reliable models that already matches production data. Considering geological uncertainty of the reservoir, we can get well-optimized production controls for maximum net present value. The proposed method shows one of novel solutions to select good cases among the various probabilities. The model selection schemes can be applied to not only production optimization but also history matching or other ensemble-based methods for efficient simulations.

Keywords: distance-based clustering, geological uncertainty, particle swarm optimization (PSO), production optimization

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Authors: Kudzanayi Chiteka, Wellington Makondo

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The whole world is on the drive to go green owing to the negative effects of burning fossil fuels. Therefore, there is immediate need to identify and utilise alternative renewable energy sources. Among these energy sources solar energy is one of the most dominant in Zimbabwe. Solar power plants used to generate electricity are entirely dependent on solar radiation. For planning purposes, solar radiation values should be known in advance to make necessary arrangements to minimise the negative effects of the absence of solar radiation due to cloud cover and other naturally occurring phenomena. This research focused on the prediction of Global Horizontal Irradiation values for the sixth day given values for the past five days. Artificial intelligence techniques were used in this research. Three models were developed based on Support Vector Machines, Radial Basis Function, and Feed Forward Back-Propagation Artificial neural network. Results revealed that Support Vector Machines gives the best results compared to the other two with a mean absolute percentage error (MAPE) of 2%, Mean Absolute Error (MAE) of 0.05kWh/m²/day root mean square (RMS) error of 0.15kWh/m²/day and a coefficient of determination of 0.990. The other predictive models had prediction accuracies of MAPEs of 4.5% and 6% respectively for Radial Basis Function and Feed Forward Back-propagation Artificial neural network. These two models also had coefficients of determination of 0.975 and 0.970 respectively. It was found that prediction of GHI values for the future days is possible using artificial intelligence-based predictive models.

Keywords: solar energy, global horizontal irradiation, artificial intelligence, predictive models

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Authors: Sizar Abed Mohammed, Zahraa Ghazi Sadeeq

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The use of the classic method, least squares (OLS) to estimate the linear regression parameters, when they are available assumptions, and capabilities that have good characteristics, such as impartiality, minimum variance, consistency, and so on. The development of alternative statistical techniques to estimate the parameters, when the data are contaminated with outliers. These are powerful methods (or resistance). In this paper, three of robust methods are studied, which are: Maximum likelihood type estimate M-estimator, Modified Maximum likelihood type estimate MM-estimator and Least Trimmed Squares LTS-estimator, and their results are compared with OLS method. These methods applied to real data taken from Duhok company for manufacturing furniture, the obtained results compared by using the criteria: Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Sum of Absolute Error (MSAE). Important conclusions that this study came up with are: a number of typical values detected by using four methods in the furniture line and very close to the data. This refers to the fact that close to the normal distribution of standard errors, but typical values in the doors line data, using OLS less than that detected by the powerful ways. This means that the standard errors of the distribution are far from normal departure. Another important conclusion is that the estimated values of the parameters by using the lifeline is very far from the estimated values using powerful methods for line doors, gave LTS- destined better results using standard MSE, and gave the M- estimator better results using standard MAPE. Moreover, we noticed that using standard MSAE, and MM- estimator is better. The programs S-plus (version 8.0, professional 2007), Minitab (version 13.2) and SPSS (version 17) are used to analyze the data.

Keywords: Robest, LTS, M estimate, MSE

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Authors: Maria Bonolo Mathevula

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Background: Children are important members of communities playing major role in the future of any given country (Pera, Fails, Gelsomini, &Garzotto, 2018). Visual Perceptual Skills (VPSs) in children are important health aspect of early childhood development through the Foundation Phases in school. Subsequently, children should undergo visual screening before commencement of schooling for early diagnosis ofVPSs anomalies because the primary role of VPSs is to capacitate children with academic performance in general. Aim : The aim of this study was to determine the anomalies of visual VPSs amongst school children in Foundation Phase. The study’s objectives were to determine the prevalence of VPSs anomalies amongst school children in Foundation Phase; Determine the relationship between children’s academic and VPSs anomalies; and to investigate the relationship between VPSs anomalies and refractive error. Methodology: This study was a mixed method whereby triangulated qualitative (interviews) and quantitative (questionnaire and clinical data) was used. This was, therefore, descriptive by nature. The study’s target population was school children in Foundation Phase. The study followed purposive sampling method. School children in Foundation Phase were purposively sampled to form part of this study provided their parents have given a signed the consent. Data was collected by the use of standardized interviews; questionnaire; clinical data card, and TVPS standard data card. Results: Although the study is still ongoing, the preliminary study outcome based on data collected from one of the Foundation Phases have suggested the following:While VPSs anomalies is not prevalent, it, however, have indirect relationship with children’s academic performance in Foundation phase; Notably, VPSs anomalies and refractive error are directly related since majority of children with refractive error, specifically compound hyperopic astigmatism, failed most subtests of TVPS standard tests. Conclusion: Based on the study’s preliminary findings, it was clear that optometrists still have a lot to do in as far as researching on VPSs is concerned. Furthermore, the researcher recommends that optometrist, as the primary healthcare professionals, should also conduct the school-readiness pre-assessment on children before commencement of their grades in Foundation phase.

Keywords: foundation phase, visual perceptual skills, school children, refractive error

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

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

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

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Authors: Biruhi Tesfaye, Avinash M. Potdar

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The laboratory test process to determine the California bearing ratio (CBR) of black cotton soils is not only overpriced but also time-consuming as well. Hence advanced prediction of CBR plays a significant role as it is applicable In pavement design. The prediction of CBR of treated soil was executed by Artificial Neural Networks (ANNs) which is a Computational tool based on the properties of the biological neural system. To observe CBR values, combined eggshell and waste glass was added to soil as 4, 8, 12, and 16 % of the weights of the soil samples. Accordingly, the laboratory related tests were conducted to get the required best model. The maximum CBR value found at 5.8 at 8 % of eggshell waste glass powder addition. The model was developed using CBR as an output layer variable. CBR was considered as a function of the joint effect of liquid limit, plastic limit, and plastic index, optimum moisture content and maximum dry density. The best model that has been found was ANN with 5, 6 and 1 neurons in the input, hidden and output layer correspondingly. The performance of selected ANN has been 0.99996, 4.44E-05, 0.00353 and 0.0067 which are correlation coefficient (R), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE) respectively. The research presented or summarized above throws light on future scope on stabilization with waste glass combined with different percentages of eggshell that leads to the economical design of CBR acceptable to pavement sub-base or base, as desired.

Keywords: CBR, artificial neural network, liquid limit, plastic limit, maximum dry density, OMC

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Authors: Mohammad J. Rezai, Fereshteh Davarpanah

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Producing a text in a language which is not one’s mother tongue can be a demanding task for language learners. Examining lexical errors committed by EFL learners is a challenging area of investigation which can shed light on the process of second language acquisition. Despite the considerable number of investigations into grammatical errors, few studies have tackled formal and semantic errors of lexis committed by EFL learners. The current study aimed at examining Persian learners’ formal and semantic errors of lexis in English. To this end, 60 students at three different proficiency levels were asked to write on 10 different topics in 10 separate sessions. Finally, 600 essays written by Persian EFL learners were collected, acting as the corpus of the study. An error taxonomy comprising formal and semantic errors was selected to analyze the corpus. The formal category covered misselection and misformation errors, while the semantic errors were classified into lexical, collocational and lexicogrammatical categories. Each category was further classified into subcategories depending on the identified errors. The results showed that there were 2583 errors in the corpus of 9600 words, among which, 2030 formal errors and 553 semantic errors were identified. The most frequent errors in the corpus included formal error commitment (78.6%), which were more prevalent at the advanced level (42.4%). The semantic errors (21.4%) were more frequent at the low intermediate level (40.5%). Among formal errors of lexis, the highest number of errors was devoted to misformation errors (98%), while misselection errors constituted 2% of the errors. Additionally, no significant differences were observed among the three semantic error subcategories, namely collocational, lexical choice and lexicogrammatical. The results of the study can shed light on the challenges faced by EFL learners in the second language acquisition process.

Keywords: collocational errors, lexical errors, Persian EFL learners, semantic errors

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Authors: Fang Ye, Han Yu, Yibing Li

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Radio interference is one of the major concerns in using the global positioning system (GPS) for civilian and military applications. Interference signals are produced not only through all electronic systems but also illegal jammers. Among different types of interferences, continuous wave (CW) interference has strong adverse impacts on the quality of the received signal. In this paper, we make more detailed analysis for CW interference effects on GPS signal quality. Based on the C/A code spectrum lines, the influence of CW interference on the acquisition performance of GPS receivers is further analysed. This influence is supported by simulation results using GPS software receiver. As the most important user parameter of GPS receivers, the mathematical expression of bit error probability is also derived in the presence of CW interference, and the expression is consistent with the Monte Carlo simulation results. The research on CW interference provides some theoretical gist and new thoughts on monitoring the radio noise environment and improving the anti-jamming ability of GPS receivers.

Keywords: GPS, CW interference, acquisition performance, bit error probability, Monte Carlo

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Authors: Bharatendra Rai

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Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error

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Authors: Dharshini M., Hema N. S.

Abstract:

The project for the production of aniline is done by providing 295.46 tons per day of nitrobenzene as feed. The material and energy balance calculations for the different equipment like distillation column, heat exchangers, reactor and mixer are carried out with simulation via DWSIM. The conversion of nitrobenzene to aniline by hydrogenation process is considered to be 96% and the total production of the plant was found to be 215 TPD. The cost estimation of the process is carried out to estimate the feasibility of the plant. The net profit and percentage return of investment is estimated to be ₹27 crores and 24.6%. The payback period was estimated to be 4.05 years and the unit production cost is ₹113/kg. A techno-economic analysis was performed for the production of aniline; the result includes economic analysis and sensitivity analysis of critical factors. From economic analysis, larger the plant scale increases the total capital investment and annual operating cost, even though the unit production cost decreases. Uncertainty analysis was performed to predict the influence of economic factors on profitability and the scenario analysis is one way to quantify uncertainty. In scenario analysis the best-case scenario and the worst-case scenario are compared with the base case scenario. The best-case scenario was found at a feed rate of 120 kmol/hr with a unit production cost of ₹112.05/kg and the worst-case scenario was found at a feed rate of 60 kmol/hr with a unit production cost of ₹115.9/kg. The base case is closely related to the best case by 99.2% in terms of unit production cost. since the unit production cost is less and the profitability is more with less payback time, it is feasible to construct a plant at this capacity.

Keywords: aniline, nitrobenzene, economic analysis, unit production cost

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Authors: Uyi Kizito Ehigiamusoe

Abstract:

The paper examines the impact of money market on economic growth in Nigeria using data for the period 1980-2012. Econometrics techniques such as Ordinary Least Squares Method, Johanson’s Co-integration Test and Vector Error Correction Model were used to examine both the long-run and short-run relationship. Evidence from the study suggest that though a long-run relationship exists between money market and economic growth, but the present state of the Nigerian money market is significantly and negatively related to economic growth. The link between the money market and the real sector of the economy remains very weak. This implies that the market is not yet developed enough to produce the needed growth that will propel the Nigerian economy because of several challenges. It was therefore recommended that government should create the appropriate macroeconomic policies, legal framework and sustain the present reforms with a view to developing the market so as to promote productive activities, investments, and ultimately economic growth.

Keywords: economic growth, investments, money market, money market challenges, money market instruments

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Authors: Maharani Yulisti, Amin Mugera, James Fogarty

Abstract:

Food safety standards have received significant attention in the fisheries global market due to health issues, free trade agreements, and increasing aquaculture production. Vertical coordination throughout the supply chain of fish producing and exporting countries is needed to meet food safety demands imposed by importing countries. However, the complexities of the supply chain governance structures and difficulties in standard implementation can generate safety uncertainty and high transaction costs. Using a Transaction Cost Economics framework, this paper examines the alignment between food safety standards and the governance structures in the shrimp supply chain in Indonesia. We find the supply chain is organized closer to the hierarchy-like governance structure where private standard (organic standard) are implemented and more towards a market-like governance structure where public standard (IndoGAP certification) are more prevalent. To verify the statements, two cases are examined from Sidoarjo district as a centre of shrimp production in Indonesia. The results show that public baseline FSS (Food Safety Standards) need additional mechanism to achieve a coordinated chain-wide response because uncertainty, asset specificity, and performance measurement problems are high in this chain. Organic standard as private chain-wide FSS is more efficient because it has been achieved by hierarchical-like type of governance structure.

Keywords: governance structure, shrimp value chain, food safety standards, transaction costs economics

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Authors: Roger L. Goodwin

Abstract:

The US Consumer Price Indices (CPIs) measures hundreds of items in the US economy. Many social programs and government benefits index to the CPIs. In mid to late 1990, much research went into changes to the CPI by a Congressional Advisory Committee. One thing can be said from the research is that, aside from there are alternative estimators for the CPI; any fundamental change to the CPI will affect many government programs. The purpose of this project is to modernize an existing process. This paper will show the development of a small, visual, software product that documents the Economic Price Adjustment (EPA) for long-term contracts. The existing workbook does not provide the flexibility to calculate EPAs where the base-month and the option-month are different. Nor does the workbook provide automated error checking. The small, visual, software product provides the additional flexibility and error checking. This paper presents the feedback to project.

Keywords: Consumer Price Index, Economic Price Adjustment, contracts, visualization tools, database, reports, forms, event procedures

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Authors: Prathuang Usaborisut, Dithaporn Thungsotanon

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Soil compaction induced by a tractor towing trailer becomes a major problem associated to sugarcane productivity. Soil beneath the tractor’s tire is not only under compressing stress but also shearing stress. Therefore, in order to help to understand such effects on soil, this research aimed to determine stress state in soil and predict compaction of soil under a tractive tire. The octahedral stress ratios under the tires were higher than one and much higher under higher draft forces. Moreover, the ratio was increasing with increase of number of tire’s passage. Soil compaction model was developed using data acquired from triaxial tests. The model was then used to predict soil bulk density under tractive tire. The maximum error was about 4% at 15 cm depth under lower draft force and tended to increase with depth and draft force. At depth of 30 cm and under higher draft force, the maximum error was about 16%.

Keywords: draft force, soil compaction model, stress state, tractive tire

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Authors: Sfiso Radebe

Abstract:

The design models dealing with flawless composite structures are in abundance, where the mechanical properties of composite structures are assumed to be known a priori. However, if the worst case scenario is assumed, where material defects combined with processing anomalies in composite structures are expected, a different solution is attained. Furthermore, if the system being designed combines in series hybrid elements, individually affected by material constant variations, it implies that a different approach needs to be taken. In the body of literature, there is a compendium of research that investigates different modes of failure affecting hybrid metal-composite structures. It covers areas pertaining to the failure of the hybrid joints, structural deformation, transverse displacement, the suppression of vibration and noise. In the present study a system employing a combination of two or more hybrid power transmitting elements will be explored for the least favourable dynamic loads as well as weight minimization, subject to uncertain material properties. Elastic constants are assumed to be uncertain-but-bounded quantities varying slightly around their nominal values where the solution is determined using convex models of uncertainty. Convex analysis of the problem leads to the computation of the least favourable solution and ultimately to a robust design. This approach contrasts with a deterministic analysis where the average values of elastic constants are employed in the calculations, neglecting the variations in the material properties.

Keywords: convex modelling, hybrid, metal-composite, robust design

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Authors: Luis C. Parra

Abstract:

The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm.

Keywords: significant wave height, machine learning optimization, multilayer perceptron neural networks, evolutionary algorithms

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Authors: Sanjai Sureshkumar, Sathish G. Kumar, P. V. V. Sathyanarayana

Abstract:

For e-gear drivetrain, the transmission error and the resulting variation in mesh stiffness is one of the main source of excitation in High performance Electric Vehicle. These vibrations are transferred through the shaft to the bearings and then to the e-Gear drive housing eventually radiating noise. A parametrical model developed in 1-D system simulation by optimizing the micro and macro geometry along with bearing properties and oil filtration to achieve least transmission error and high contact ratio. Histogram analysis is performed to condense the actual road load data into condensed duty cycle to find the bearing forces. The structural vibration generated by these forces will be simulated in a nonlinear solver obtaining the normal surface velocity of the housing and the results will be carried forward to Acoustic software wherein a virtual environment of the surrounding (actual testing scenario) with accurate microphone position will be maintained to predict the sound pressure level of radiated noise and directivity plot of the e-Gear Drive. Order analysis will be carried out to find the root cause of the vibration and whine noise. Broadband spectrum will be checked to find the rattle noise source. Further, with the available results, the design will be optimized, and the next loop of simulation will be performed to build a best e-Gear Drive on NVH aspect. Structural analysis will be also carried out to check the robustness of the e-Gear Drive.

Keywords: 1-D system simulation, contact ratio, e-Gear, mesh stiffness, micro and macro geometry, transmission error, radiated noise, NVH

Procedia PDF Downloads 149