Search results for: mean squared error

Authors: Sonali Banrjee, Swarup Kumar Mitra, Aritra Acharyya

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A piano note recognition method has been proposed by the authors in this paper. The authors have used a comprehensive method for onset detection of each note present in a piano piece followed by segmented short-time Fourier transform (STFT) for the identification of piano notes. The performance evaluation of the proposed method has been carried out in different harsh noisy environments by adding different levels of additive white Gaussian noise (AWGN) having different signal-to-noise ratio (SNR) in the original signal and evaluating the note detection error rate (NDER) of different piano pieces consisting of different number of notes at different SNR levels. The NDER is found to be remained within 15% for all piano pieces under consideration when the SNR is kept above 8 dB.

Keywords: AWGN, onset detection, piano note, STFT

Procedia PDF Downloads 150

Authors: Gustavo Cruz-Bello

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Progreso is a coastal community in Yucatan, Mexico, highly exposed to floods produced by severe storms and tropical cyclones. A participatory cartography approach was conducted to help to reduce floods disasters and assess social vulnerability within the community. The first step was to engage local authorities in risk management to facilitate the process. Two workshop were conducted, in the first, a poster size printed high spatial resolution satellite image of the town was used to gather information from the participants: eight women and seven men, among them construction workers, students, government employees and fishermen, their ages ranged between 23 and 58 years old. For the first task, participants were asked to locate emblematic places and place them in the image to familiarize with it. Then, they were asked to locate areas that get flooded, the buildings that they use as refuges, and to list actions that they usually take to reduce vulnerability, as well as to collectively come up with others that might reduce disasters. The spatial information generated at the workshops was digitized and integrated into a GIS environment. A printed version of the map was reviewed by local risk management experts, who validated feasibility of proposed actions. For the second workshop, we retrieved the information back to the community for feedback. Additionally a survey was applied in one household per block in the community to obtain socioeconomic, prevention and adaptation data. The information generated from the workshops was contrasted, through T and Chi Squared tests, with the survey data in order to probe the hypothesis that poorer or less educated people, are less prepared to face floods (more vulnerable) and live near or among higher presence of floods. Results showed that a great majority of people in the community are aware of the hazard and are prepared to face it. However, there was not a consistent relationship between regularly flooded areas with people’s average years of education, house services, or house modifications against heavy rains to be prepared to hazards. We could say that the participatory cartography intervention made participants aware of their vulnerability and made them collectively reflect about actions that can reduce disasters produced by floods. They also considered that the final map could be used as a communication and negotiation instrument with NGO and government authorities. It was not found that poorer and less educated people are located in areas with higher presence of floods.

Keywords: climate change, floods, Mexico, participatory mapping, social vulnerability

Procedia PDF Downloads 99

Authors: E. Shahsavari, R. Ghasemi, A. Akramizadeh

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This paper presents a new method to design nonlinear feedback linearization controller for polymer electrolyte membrane fuel cells (PEMFCs). A nonlinear controller is designed based on nonlinear model to prolong the stack life of PEM fuel cells. Since it is known that large deviations between hydrogen and oxygen partial pressures can cause severe membrane damage in the fuel cell, feedback linearization is applied to the PEM fuel cell system so that the deviation can be kept as small as possible during disturbances or load variations. To obtain an accurate feedback linearization controller, tuning the linear parameters are always important. So in proposed study NSGA_II method was used to tune the designed controller in aim to decrease the controller tracking error. The simulation result showed that the proposed method tuned the controller efficiently.

Keywords: nonlinear dynamic model, polymer electrolyte membrane fuel cells, feedback linearization, optimal control, NSGA_II

Procedia PDF Downloads 501

Authors: Rafid A. Al-Khannak, Fawzi M. Al-Naima

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Power load is one of the most important controlling keys which decide power demands and illustrate power usage to shape power market. Hence, power load forecasting is the parameter which facilitates understanding and analyzing all these aspects. In this paper, power load forecasting is solved under MATLAB environment by constructing a neural network for the power load to find an accurate simulated solution with the minimum error. A developed algorithm to achieve load forecasting application with faster technique is the aim for this paper. The algorithm is used to enable MATLAB power application to be implemented by multi machines in the Grid computing system, and to accomplish it within much less time, cost and with high accuracy and quality. Grid Computing, the modern computational distributing technology, has been used to enhance the performance of power applications by utilizing idle and desired Grid contributor(s) by sharing computational power resources.

Keywords: DeskGrid, Grid Server, idle contributor(s), grid computing, load forecasting

Procedia PDF Downloads 459

Authors: Marlon Gallo, Eduardo Miguel, Laura Oca, Eneko Gonzalez, Unai Iraola

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The heat generation of an energy storage system is an essential topic when designing a battery pack and its cooling system. Heat generation estimation is used together with thermal models to predict battery temperature in operation and adapt the design of the battery pack and the cooling system to these thermal needs guaranteeing its safety and correct operation. In the present work, a comparison between the use of a heat flux sensor (HFS) for indirect measurement of heat losses in a cell and the widely used and simplified version of Bernardi’s equation for estimation is presented. First, a Li-ion cell is thermally characterized with an HFS to measure the thermal parameters that are used in a first-order lumped thermal model. These parameters are the equivalent thermal capacity and the thermal equivalent resistance of a single Li-ion cell. Static (when no current is flowing through the cell) and dynamic (making current flow through the cell) tests are conducted in which HFS is used to measure heat between the cell and the ambient, so thermal capacity and resistances respectively can be calculated. An experimental platform records current, voltage, ambient temperature, surface temperature, and HFS output voltage. Second, an equivalent circuit model is built in a Matlab-Simulink environment. This allows the comparison between the generated heat predicted by Bernardi’s equation and the HFS measurements. Data post-processing is required to extrapolate the heat generation from the HFS measurements, as the sensor records the heat released to the ambient and not the one generated within the cell. Finally, the cell temperature evolution is estimated with the lumped thermal model (using both HFS and Bernardi’s equation total heat generation) and compared towards experimental temperature data (measured with a T-type thermocouple). At the end of this work, a critical review of the results obtained and the possible mismatch reasons are reported. The results show that indirectly measuring the heat generation with HFS gives a more precise estimation than Bernardi’s simplified equation. On the one hand, when using Bernardi’s simplified equation, estimated heat generation differs from cell temperature measurements during charges at high current rates. Additionally, for low capacity cells where a small change in capacity has a great influence on the terminal voltage, the estimated heat generation shows high dependency on the State of Charge (SoC) estimation, and therefore open circuit voltage calculation (as it is SoC dependent). On the other hand, with indirect measuring the heat generation with HFS, the resulting error is a maximum of 0.28ºC in the temperature prediction, in contrast with 1.38ºC with Bernardi’s simplified equation. This illustrates the limitations of Bernardi’s simplified equation for applications where precise heat monitoring is required. For higher current rates, Bernardi’s equation estimates more heat generation and consequently, a higher predicted temperature. Bernardi´s equation accounts for no losses after cutting the charging or discharging current. However, HFS measurement shows that after cutting the current the cell continues generating heat for some time, increasing the error of Bernardi´s equation.

Keywords: lithium-ion battery, heat flux sensor, heat generation, thermal characterization

Procedia PDF Downloads 361

Authors: Nadia Fatima Zahra Malki

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Quality is one of the most important modern management patterns that organizations seek to achieve in all areas and sectors in order to meet the needs and desires of customers and to remain and continuity, as they constitute a competitive advantage for the organization. and among the most prominent organizations that must be available on the quality factor are health organizations as they relate to the most valuable component of production. It is a person, and his health, and any error in it threatens his life and may lead to death, so she must provide health services of high quality to achieve the highest degree of satisfaction for the patient. This research aims to study the quality of health services and the extent of their impact on patient satisfaction, and this is through an applied study that relied on measuring the level of quality of health services in the university hospital center of Algeria and the extent of their impact on patient satisfaction according to the dimensions of the quality of health services, and we reached a conclusion that the determinants of the quality of health services It affects patient satisfaction, which necessitates developing health services according to patients' requirements and improving their quality to obtain patient satisfaction.

Keywords: health service, health quality, quality determinants, patient satisfaction

Procedia PDF Downloads 31

Authors: Yixun Shi

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Many interior point methods for convex programming solve an (n+m)x(n+m)linear system in each iteration. Many implementations solve this system in each iteration by considering an equivalent mXm system (4) as listed in the paper, and thus the job is reduced into solving the system (4). However, the system(4) has to be solved exactly since otherwise the error would be entirely passed onto the last m equations of the original system. Often the Cholesky factorization is computed to obtain the exact solution of (4). One Cholesky factorization is to be done in every iteration, resulting in higher computational costs. In this paper, two iterative methods for solving linear systems using vector division are combined together and embedded into interior point methods. Instead of computing one Cholesky factorization in each iteration, it requires only one Cholesky factorization in the entire procedure, thus significantly reduces the amount of computation needed for solving the problem. Based on that, a hybrid algorithm for solving convex programming problems is proposed.

Keywords: convex programming, interior point method, linear systems, vector division

Procedia PDF Downloads 388

Authors: Ousmane Youme, Jean Marie Dembele, Eugene Ezin, Christophe Cambier

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In recent years, the convolutional neural networks (CNN) architectures designed by evolution algorithms have proven to be competitive with handcrafted architectures designed by experts. However, these algorithms need a lot of computational power, which is beyond the capabilities of most researchers and engineers. To overcome this problem, we propose an evolution architecture under length constraints. It consists of two algorithms: a search length strategy to find an optimal space and a search architecture strategy based on a genetic algorithm to find the best individual in the optimal space. Our algorithms drastically reduce resource costs and also keep good performance. On the Cifar-10 dataset, our framework presents outstanding performance with an error rate of 5.12% and only 4.6 GPU a day to converge to the optimal individual -22 GPU a day less than the lowest cost automatic evolutionary algorithm in the peer competition.

Keywords: CNN architecture, genetic algorithm, evolution algorithm, length constraints

Procedia PDF Downloads 109

Authors: Amhimmid Mohammed Saffour, Abdelkader Salama

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Filtering is a part of image enhancement techniques, it is used to enhance certain details such as edges in the image that are relevant to the application. Additionally, filtering can even be used to eliminate unwanted components of noise. Medical images typically contain salt and pepper noise and Poisson noise. This noise appears to the presence of minute grey scale variations within the image. In this paper, different filters techniques namely (Median, Wiener, Rank order3, Rank order5, and Average) were applied on CT medical images (Brain and chest). We using all these filters to remove salt and pepper noise from these images. This type of noise consists of random pixels being set to black or white. Peak Signal to Noise Ratio (PSNR), Mean Square Error r(MSE) and Histogram were used to evaluated the quality of filtered images. The results, which we have achieved shows that, these filters, are more useful and they prove to be helpful for general medical practitioners to analyze the symptoms of the patients with no difficulty.

Keywords: CT imaging, median filter, adaptive filter and average filter, MATLAB

Procedia PDF Downloads 301

Authors: Chaouch Souad, Abdou Latifa, Larbi Chrifi Alaoui

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This paper deals with sensorless backstepping control of induction motor using MRAS technique associated to sliding mode approach. A high order genetic algorithm structure is used to approximate a control law designed by the Backstepping technique, and to find the best parameters globally optimized. However, the Backstepping control approach is unsuitable for high performance applications because the need of a speed sensor for increased accuracy and the absence of any error decay mechanism. In this paper a nonlinear observer, obtained by combining sliding mode structure and model reference adaptive system (MRAS), is designed for the rotor flux and rotor speed estimations. To validate the proposed method, the results are presented for showing the improved drive characteristics and performances.

Keywords: Backstepping Control, Induction Motor, Genetic Algorithm, Sliding Mode observer

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Authors: Yuta Moriyama, Etsuo Morishita

Abstract:

Plasma actuator is very effective to recover stall flows over an upper airfoil surface. We first manufacture the actuator, test the stability of the device by trial and error basis and find the conditions for steady operations. We visualize the flow around an airfoil in the smoke tunnel and observe the stall recovery. The plasma actuator is stationary device and has no moving parts, and it might be an ideal device to control a model aircraft. We can use the actuator not only as a stall recovery device but also as a spoiler. We put the actuator near the leading edge of an elevator of a model aircraft as a spoiler, and measure the aerodynamic forces by a three-component balance. We observe the effect of the plasma actuator on the aerodynamic forces and the device effectiveness changes depending on the angle of attack whether it is positive or negative. We also visualize the flow caused by the plasma actuator by a desk-top Schlieren photography which is otherwise very difficult in a low-speed wind tunnel experiment.

Keywords: aerodynamics, plasma actuator, model aircraft, wind tunnel

Procedia PDF Downloads 355

Authors: Hiesik Kim, Yong-Beom Kim, Jaheon Gu

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Internet of Things (IoT) applications are widely serviced and spread worldwide. Local wireless data transmission technique must be developed to rate up with some technique. Bluetooth wireless data communication is wireless technique is technique made by Special Inter Group (SIG) using the frequency range 2.4 GHz, and it is exploiting Frequency Hopping to avoid collision with a different device. To implement experiment, equipment for experiment transmitting measured data is made by using Arduino as open source hardware, gas sensor, and Bluetooth module and algorithm controlling transmission rate is demonstrated. Experiment controlling transmission rate also is progressed by developing Android application receiving measured data, and controlling this rate is available at the experiment result. It is important that in the future, improvement for communication algorithm be needed because a few error occurs when data is transferred or received.

Keywords: Arduino, Bluetooth, gas sensor, IoT, transmission

Procedia PDF Downloads 262

Authors: S. Sabatino, V. Calderaro, V. Galdi, G. Graber, L. Ippolito

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Climate change is a rapidly growing global threat caused mainly by increased emissions of carbon dioxide (CO₂) into the atmosphere. These emissions come from multiple sources, including industry, power generation, and the transport sector. The need to tackle climate change and reduce CO₂ emissions is indisputable. A crucial solution to achieving decarbonization in the transport sector is the adoption of electric vehicles (EVs). These vehicles use lithium (Li-Ion) batteries as an energy source, making them extremely efficient and with low direct emissions. However, Li-Ion batteries are not without problems, including the risk of overheating and performance degradation. To ensure its safety and longevity, it is essential to use a battery management system (BMS). The BMS constantly monitors battery status, adjusts temperature and cell balance, ensuring optimal performance and preventing dangerous situations. From the monitoring carried out, it is also able to optimally manage the battery to increase its life. Among the parameters monitored by the BMS, the main ones are State of Charge (SoC), State of Health (SoH), and State of Power (SoP). The evaluation of these parameters can be carried out in two ways: offline, using benchtop batteries tested in the laboratory, or online, using batteries installed in moving vehicles. Online estimation is the preferred approach, as it relies on capturing real-time data from batteries while operating in real-life situations, such as in everyday EV use. Actual battery usage conditions are highly variable. Moving vehicles are exposed to a wide range of factors, including temperature variations, different driving styles, and complex charge/discharge cycles. This variability is difficult to replicate in a controlled laboratory environment and can greatly affect performance and battery life. Online estimation captures this variety of conditions, providing a more accurate assessment of battery behavior in real-world situations. In this article, a hybrid approach based on a neural network and a statistical method for real-time estimation of SoC, SoH, and SoP parameters of interest is proposed. These parameters are estimated from the analysis of a one-day driving profile of an electric vehicle, assumed to be divided into the following four phases: (i) Partial discharge (SoC 100% - SoC 50%), (ii) Partial discharge (SoC 50% - SoC 80%), (iii) Deep Discharge (SoC 80% - SoC 30%) (iv) Full charge (SoC 30% - SoC 100%). The neural network predicts the values of ohmic resistance and incremental capacity, while the statistical method is used to estimate the parameters of interest. This reduces the complexity of the model and improves its prediction accuracy. The effectiveness of the proposed model is evaluated by analyzing its performance in terms of square mean error (RMSE) and percentage error (MAPE) and comparing it with the reference method found in the literature.

Keywords: electric vehicle, Li-Ion battery, BMS, state-of-charge, state-of-health, state-of-power, artificial neural networks

Procedia PDF Downloads 49

Authors: Wasila Lawan Gadanya

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This paper examines the problems of learning English vowel pronunciation. The objective is to identify some of the factors that affect the learning of English vowel sounds and their proper realization in words. The theoretical framework adopted is based on both error analysis and contrastive analysis. The data collection instruments used in the study are questionnaire and word list for the respondents (students) and observation of some of their lecturers. All the data collected were analyzed using simple percentage. The findings show that it is not a single factor that affects the learning of English vowel pronunciation rather many factors concurrently do so. Among the factors examined, it has been found that lack of correlation between English orthography and its pronunciation, not mother-tongue (which most people consider as a factor affecting learning of the pronunciation of a second language), has the greatest influence on students’ learning and realization of English vowel sounds since the respondents in this study are from different ethnic groups of Nigeria and thus speak different languages but having the same or almost the same problem when pronouncing the English vowel sounds.

Keywords: English vowels, learning, Nigeria, pronunciation

Procedia PDF Downloads 422

Authors: H. J. Wattimanela, U. S. Passaribu, N. T. Puspito, S. W. Indratno

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Banda Sea collision zone (BSCZ) of is the result of the interaction and convergence of Indo-Australian plate, Eurasian plate and Pacific plate. This location in the eastern part of Indonesia. This zone has a very high seismic activity. In this research, we will be calculated rate (λ) and Mean Square Eror (MSE). By this result, we will identification of Poisson distribution of earthquakes in the BSCZ with the point process approach. Chi-square test approach and test Anscombe made in the process of identifying a Poisson distribution in the partition area. The data used are earthquakes with Magnitude ≥ 6 SR and its period 1964-2013 and sourced from BMKG Jakarta. This research is expected to contribute to the Moluccas Province and surrounding local governments in performing spatial plan document related to disaster management.

Keywords: molluca banda sea collision zone, earthquakes, mean square error, poisson distribution, chi-square test, anscombe test

Procedia PDF Downloads 290

Authors: Emmanuel Temitope Adaranijo

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Medical error is on the increase in many nations and like many developing nations, Nigeria is not excluded and more importantly, Lafia, Nasarawa state, where the study was carried. The study was undertaken to explore Patients' knowledge and their reactions to medical errors in hospitals in Lafia Local Government Area; therefore, five objectives were formulated to guide the study. The survey research design was employed and triangulation of quantitative and qualitative instruments was used to collect data. The total population for the study was 330,712 and the sample size was 400; however, only 343 patients and three doctors responded to the quantitative and qualitative study, respectively. Frequency distribution, simple percentage, and r test were used to analyze the data obtained from respondents. The findings revealed that medical errors are prevalent in hospitals in Lafia and the patients are neither aware nor willing to report such occurrence. The study recommends that social workers, hospital management, and governments should take up their roles in reducing the occurrence of medical errors.

Keywords: health, hospital, medical errors, social work

Procedia PDF Downloads 116

Authors: Muhammad Ahnaf Zahin, Yaw Adu-Gyamfi

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Bus transit is a crucial component of transportation networks, especially in urban areas. Any intelligent transportation system must have accurate real-time information on bus travel times since it minimizes waiting times for passengers at different stations along a route, improves service reliability, and significantly optimizes travel patterns. Bus agencies must enhance the quality of their information service to serve their passengers better and draw in more travelers since people waiting at bus stops are frequently anxious about when the bus will arrive at their starting point and when it will reach their destination. For solving this issue, different models have been developed for predicting bus travel times recently, but most of them are focused on smaller road networks due to their relatively subpar performance in high-density urban areas on a vast network. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. In this study, Gated Recurrent Unit (GRU) neural network was followed to predict the mean vehicle travel times for different hours of the day for multiple stations along multiple routes. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. The spatial and temporal information and the historical average travel times were captured from the dataset for model input parameters. As adjacency matrices for the spatial input parameters, the station distances and sequence numbers were used, and the time of day (hour) was considered for the temporal inputs. Other inputs, including volatility information such as standard deviation and variance of journey durations, were also included in the model to make it more robust. The model's performance was evaluated based on a metric called mean absolute percentage error (MAPE). The observed prediction errors for various routes, trips, and stations remained consistent throughout the day. The results showed that the developed model could predict travel times more accurately during peak traffic hours, having a MAPE of around 14%, and performed less accurately during the latter part of the day. In the context of a complicated transportation network in high-density urban areas, the model showed its applicability for real-time travel time prediction of public transportation and ensured the high quality of the predictions generated by the model.

Keywords: gated recurrent unit, mean absolute percentage error, single-step forecasting, travel time prediction.

Procedia PDF Downloads 57

Authors: Pitak Keawbunsong, Sathaporn Promwong

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This paper presents an investigation on the efficiency of the optimized Davison path loss model in order to look for a suitable path loss model to design and planning DTTV propagation for small and medium urban areas in southern Thailand. Hadyai City in Songkla Province is chosen as the case study to collect the analytical data on the electric field strength. The optimization is conducted through the least square method while the efficiency index is through the statistical value of relative error (RE). The result of the least square method is the offset and slop of the frequency to be used in the optimized process. The statistical result shows that RE of the old Davidson model is at the least when being compared with the optimized Davison and the Hata models. Thus, the old Davison path loss model is the most accurate that further becomes the most optimized for the plan on the propagation network design.

Keywords: DTTV propagation, path loss model, Davidson model, least square method

Procedia PDF Downloads 325

Authors: Derya Karagöz

Abstract:

A Shewhart chart based on normality assumption is not appropriate for skewed distributions since its Type-I error rate is inflated. This study presents X̄ and S control charts for monitoring the process variability for skewed distributions. We propose Weighted Standard Deviation (WSD) X̄ and S control charts. Standard deviation estimator is applied to monitor the process variability for estimating the process standard deviation, in the case of the W SD X̄ and S control charts as this estimator is simple and easy to compute. Unlike the Shewhart control chart, the proposed charts provide asymmetric limits in accordance with the direction and degree of skewness to construct the upper and lower limits. The performances of the proposed charts are compared with other heuristic charts for skewed distributions by using Simulation study. The Simulation studies show that the proposed control charts have good properties for skewed distributions and large sample sizes.

Keywords: weighted standard deviation, MAD, skewed distributions, S control charts

Procedia PDF Downloads 381

Authors: Guorong Sui, Xingwei Jia, Chenhui Yin, Xiumin Gao

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In the processing of camera-based high precision and non-contact measurement, the geometric-optical aberration is always inevitably disturbing the measuring system. Moreover, the aberration is different with the different focal length, which will increase the difficulties of the system’s calibration. Therefore, to understand the relationship between the focal length as a function of aberration properties is a very important issue to the calibration of the measuring systems. In this study, we propose a new mathematics model, which is based on the plane calibration method by Zhang Zhengyou, and establish a relationship between the focal length and aberration coefficient. By using the mathematics model and carefully modified compensation templates, the calibration precision of the system can be dramatically improved. The experiment results show that the relative error is less than 1%. It is important for optoelectronic imaging systems that apply to measure, track and position by changing the camera’s focal length.

Keywords: camera calibration, aberration coefficient, vision measurement, focal length, mathematics model

Procedia PDF Downloads 342

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 302

Authors: Jing Qingfeng, Wu Jiajia

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Due to the crowded orbits and shortage of frequency resources, utilizing of MIMO technology to improve spectrum efficiency and increase the capacity has become a necessary trend of broadband satellite communication. We analyze the main influenced factors and compare the BER performance of space-time block code (STBC) scheme and space-time trellis code (STTC) scheme. This paper emphatically studies the bit error rate (BER) performance of STTC and STBC under Rayleigh channel. The main emphasis is placed on the effects of the factors, such as terminal environment and elevation angles, on the BER performance of STBC and STTC schemes. Simulation results indicate that performance of STTC under Rayleigh channel is obviously improved with the increasing of transmitting and receiving antennas numbers, but the encoder state has little impact on the performance. Under Rayleigh channel, performance of Alamouti code is better than that of STTC.

Keywords: MIMO, space time block code (STBC), space time trellis code (STTC), Rayleigh channel

Procedia PDF Downloads 331

Authors: Ajay Kumar Sarathe, Abhinay Kumar

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Production of quality products required for specific engineering applications is an important issue. The roughness of the surface plays an important role in the quality of the product by using appropriate machining parameters to eliminate wastage due to over machining. To increase the quality of the surface, the optimum machining parameter setting is crucial during the machining operation. The effect of key machining parameters- spindle speed, feed rate, and depth of cut on surface roughness has been evaluated. Experimental work was carried out using High Speed Steel tool and AlSI 1018 as workpiece material. In this study, the predictive model has been developed using Box-Behnken Design. An experimental investigation has been carried out for this work using BBD for three factors and observed that the predictive model of Ra value is closed to predictive value with a marginal error of 2.8648 %. Developed model establishes a correlation between selected key machining parameters that influence the surface roughness in a AISI 1018. F

Keywords: ANOVA, BBD, optimisation, response surface methodology

Procedia PDF Downloads 142

Authors: Bharti Gupta, V. K. Kukreja

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A comprehensive numerical study is presented for the solution of time-dependent advection diffusion problems by using cubic B-spline collocation method. The linear combination of cubic B-spline basis, taken as approximating function, is evaluated using the zeros of shifted Chebyshev polynomials as collocation points in each element to obtain the best approximation. A comparison, on the basis of efficiency and accuracy, with the previous techniques is made which confirms the superiority of the proposed method. An asymptotic convergence analysis of technique is also discussed, and the method is found to be of order two. The theoretical analysis is supported with suitable examples to show second order convergence of technique. Different numerical examples are simulated using MATLAB in which the 3-D graphical presentation has taken at different time steps as well as different domain of interest.

Keywords: cubic B-spline basis, spectral norms, shifted Chebyshev polynomials, collocation points, error estimates

Procedia PDF Downloads 207

Authors: Yihua Wang, Yunru Lai

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Statistical process control has been successfully applied in a variety of industries. In some applications, the quality of a process or product is better characterized and summarized by a functional relationship between a response variable and one or more explanatory variables. A collection of this type of data is called a profile. Profile monitoring is used to understand and check the stability of this relationship or curve over time. The independent assumption for the error term is commonly used in the existing profile monitoring studies. However, in many applications, the profile data show correlations over time. Therefore, we focus on a general linear regression model with a first-order autocorrelation between profiles in this study. We propose an exponentially weighted moving average charting scheme to monitor this type of profile. The simulation study shows that our proposed methods outperform the existing schemes based on the average run length criterion.

Keywords: autocorrelation, EWMA control chart, general linear regression model, profile monitoring

Procedia PDF Downloads 442

Authors: E. O. Clarke, O. J. Aderinola, O. A. Adeboyejo, M. A. Anetekhai

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Aims: The study is aimed at establishing the influence of some physical and chemical parameters on the abundance, distribution pattern and seasonal variations of the planktonic crustacean populations. Place and Duration of Study: A premier investigation into the dynamics of planktonic crustacean populations in Ologe lagoon was carried out from January 2011 to December 2012. Study Design: The study covered identification, temporal abundance, spatial distribution and diversity of the planktonic crustacea. Methodology: Standard techniques were used to collect samples from eleven stations covering five proximal satellite towns (Idoluwo, Oto, Ibiye, Obele, and Gbanko) bordering the lagoon. Data obtained were statistically analyzed using linear regression and hierarchical clustering. Results:Thirteen (13) planktonic crustacean populations were identified. Total percentage abundance was highest for Bosmina species (20%) and lowest for Polyphemus species (0.8%). The Pearson’s correlation coefficient (“r” values) between total planktonic crustacean population and some physical and chemical parameters showed that positive correlations having low level of significance occurred with salinity (r = 0.042) (sig = 0.184) and with surface water dissolved oxygen (r = 0.299) (sig = 0.155). Linear regression plots indicated that, the total population of planktonic crustacea were mainly influenced and only increased with an increase in value of surface water temperature (Rsq = 0.791) and conductivity (Rsq = 0.589). The total population of planktonic crustacea had a near neutral (zero correlation) with the surface water dissolved oxygen and thus, does not significantly change with the level of the surface water dissolved oxygen. The correlations were positive with NO3-N (midstream) at Ibiye (Rsq =0.022) and (downstream) Gbanko (Rsq =0.013), PO4-P at Ibiye (Rsq =0.258), K at Idoluwo (Rsq =0.295) and SO4-S at Oto (Rsq = 0.094) and Gbanko (Rsq = 0.457). The Berger-Parker Dominance Index (BPDI) showed that the most dominant species was Bosmina species (BPDI = 1.000), followed by Calanus species (BPDI = 1.254). Clusters by squared Euclidan distances using average linkage between groups showed proximities, transcending the borders of genera. Conclusion: The results revealed that planktonic crustacean population in Ologe lagoon undergo seasonal perturbations, were highly influenced by nutrient, metal and organic matter inputs from river Owoh, Agbara industrial estate and surrounding farmlands and were patchy in spatial distribution.

Keywords: diversity, dominance, perturbations, richness, crustacea, lagoon

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Authors: Jacquelene Swanepoel, J. C. Visagie, H. M. Linde

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Purpose: The aim of this article is to investigate the psychosocial risk environment influencing employee behaviour, and subsequently the trust relationship between employer and employee. Design/methodology/approach: The unique nature and commonness of negative acts, such as unsafe behaviour, human errors, poor performance and negligence, also referred to as unsafe practice, are explored. A literature review is formulated to investigate the nature of negative acts or unsafe behaviour. The findings of this study are used to draw comparisons between unsafe behaviour/misconduct and accidents in the workplace and finally conclude how it should be addressed from a labour relations point of view. Findings: The results indicate comparisons between unsafe practice/misconduct and occupational injuries and accidents, as a result of system flaws, human error or psychosocial risk.

Keywords: occupational risks, unsafe practice, misconduct, organisational safety culture, ergonomics, management commitment and leadership, labour relations

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Authors: Jiaying Zhang, Xin Mu, Chang Ni, Jeff L. Zhang

Abstract:

Magnetic resonance elastography (MRE) non-invasively assesses tissue elastic properties, such as shear modulus, by measuring tissue’s displacement in response to mechanical waves. The estimated metrics on tissue elasticity or stiffness have been shown to be valuable for monitoring physiologic or pathophysiologic status of tissue, such as a tumor or fatty liver. To quantify tissue shear modulus from MRE-acquired displacements (essentially an inverse problem), multiple approaches have been proposed, including Local Frequency Estimation (LFE) and Direct Inversion (DI). However, one common problem with these methods is that the estimates are severely noise-sensitive due to either the inverse-problem nature or noise propagation in the pixel-by-pixel process. With the advent of deep learning (DL) and its promise in solving inverse problems, a few groups in the field of MRE have explored the feasibility of using DL methods for quantifying shear modulus from MRE data. Most of the groups chose to use real MRE data for DL model training and to cut training images into smaller patches, which enriches feature characteristics of training data but inevitably increases computation time and results in outcomes with patched patterns. In this study, simulated wave images generated by Finite Differential Time Domain (FDTD) simulation are used for network training, and U-Net is used to extract features from each training image without cutting it into patches. The use of simulated data for model training has the flexibility of customizing training datasets to match specific applications. The proposed method aimed to estimate tissue shear modulus from MRE data with high robustness to noise and high model-training efficiency. Specifically, a set of 3000 maps of shear modulus (with a range of 1 kPa to 15 kPa) containing randomly positioned objects were simulated, and their corresponding wave images were generated. The two types of data were fed into the training of a U-Net model as its output and input, respectively. For an independently simulated set of 1000 images, the performance of the proposed method against DI and LFE was compared by the relative errors (root mean square error or RMSE divided by averaged shear modulus) between the true shear modulus map and the estimated ones. The results showed that the estimated shear modulus by the proposed method achieved a relative error of 4.91%±0.66%, substantially lower than 78.20%±1.11% by LFE. Using simulated data, the proposed method significantly outperformed LFE and DI in resilience to increasing noise levels and in resolving fine changes of shear modulus. The feasibility of the proposed method was also tested on MRE data acquired from phantoms and from human calf muscles, resulting in maps of shear modulus with low noise. In future work, the method’s performance on phantom and its repeatability on human data will be tested in a more quantitative manner. In conclusion, the proposed method showed much promise in quantifying tissue shear modulus from MRE with high robustness and efficiency.

Keywords: deep learning, magnetic resonance elastography, magnetic resonance imaging, shear modulus estimation

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Authors: Temiloluwa J. Akinyemi, Denis W. Aheto, Wisdom Akpalu

Abstract:

Capture fisheries in developing countries provide significant animal protein and directly supports the livelihoods of several communities. However, the misperception of biophysical dynamics owing to a lack of adequate scientific data has contributed to the suboptimal management in marine capture fisheries. This is because yield and catch potentials are sensitive to the quality of catch and effort data. Yet, studies on fisheries data collection practices in developing countries are hard to find. This study investigates the data collection methods utilized by fisheries technical officers within the four fishing regions of Ghana. We found that the officers employed data collection and sampling procedures which were not consistent with the technical guidelines curated by FAO. For example, 50 instead of 166 landing sites were sampled, while 290 instead of 372 canoes were sampled. We argue that such sampling errors could result in the over-capitalization of capture fish stocks and significant losses in resource rents.

Keywords: Fisheries data quality, fisheries management, Ghana, Sustainable Fisheries

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Authors: L. D. Upegui, Isabel Alvarez-Solorza, Karina Garduno-Ulloa, Maren Boecker

Abstract:

Introduction: The increasing prevalence rate of resistant and multiresistant bacterial strains to antibiotics is a threat to public health and requires a rapid multifunctional answer. Individuals that are affected by resistant strains present a higher morbidity and mortality than individuals that are infected with the same species of bacteria but with sensitive strains. There have been identified risk factors that are related to the misuse and overuse of antibiotics, like socio-demographic characteristics and psychological aspects of the individuals that have not been explored objectively due to a lack of valid and reliable instruments for their measurement. Objective: To validate a questionnaire for the evaluation of the levels of knowledge related to the use of antibiotics in a Mexican population. Materials and Methods: Analytical cross-sectional observational study. The questionnaire consists of 12 items to evaluated knowledge (1=no, 2=not sure, 3=yes) regarding the use of antibiotics, with higher scores corresponding to a higher level of knowledge. Data are collected in a sample of students. Data collection is still ongoing. In this abstract preliminary results of 30 respondents are reported which were collected during pilot-testing. The validation of the instrument was done using the Rasch model. Fit to the Rasch model was tested checking overall fit to the model, unidimensionality, local independence and evaluating the presence of Differential Item Functioning (DIF) by age and gender. The software Rumm2030 and the SPSS were used for the analyses. Results: The participants of the pilot-testing presented an average age of 32 years ± 12.6 and 53% were women. The preliminary results indicated that the items showed good fit to the Rasch model (chi-squared=12.8 p=0.3795). Unidimensionality (number of significant t-tests of 3%) could be proven, the items were locally independent, and no DIF was observed. Knowledge was the smallest regarding statements on the role of antibiotics in treating infections, e.g., most of the respondents did not know that antibiotics would not work against viral infections (70%) and that they could also cause side effects (87%). The knowledge score ranged from 0 to 100 points with a transformed measurement (mean of knowledge 27.1 ± 4.8). Conclusions: The instrument showed good psychometric proprieties. The low scores of knowledge about antibiotics suggest that misinterpretations on the use of these medicaments were prevalent, which could influence the production of antibiotic resistance. The application of this questionnaire will allow the objective identification of 'Hight risk groups', which will be the target population for future educational campaigns, to reduce the knowledge gaps on the general population as an effort against antibiotic resistance.

Keywords: antibiotics, knowledge, misuse, overuse, questionnaire, Rasch model, validation

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