Search results for: gate diffusion input
274 Interference Management in Long Term Evolution-Advanced System
Authors: Selma Sbit, Mohamed Bechir Dadi, Belgacem Chibani Rhaimi
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Incorporating Home eNodeB (HeNB) in cellular networks, e.g. Long Term Evolution Advanced (LTE-A), is beneficial for extending coverage and enhancing capacity at low price especially within the non-line-of sight (NLOS) environments such as homes. HeNB or femtocell is a small low powered base station which provides radio coverage to the mobile users in an indoor environment. This deployment results in a heterogeneous network where the available spectrum becomes shared between two layers. Therefore, a problem of Inter Cell Interference (ICI) appears. This issue is the main challenge in LTE-A. To deal with this challenge, various techniques based on frequency, time and power control are proposed. This paper deals with the impact of carrier aggregation and higher order MIMO (Multiple Input Multiple Output) schemes on the LTE-Advanced performance. Simulation results show the advantages of these schemes on the system capacity (4.109 b/s/Hz when bandwidth B=100 MHz and when applying MIMO 8x8 for SINR=30 dB), maximum theoretical peak data rate (more than 4 Gbps for B=100 MHz and when MIMO 8x8 is used) and spectral efficiency (15 b/s/Hz and 30b/s/Hz when MIMO 4x4 and MIMO 8x8 are applying respectively for SINR=30 dB).
Keywords: LTE-Advanced, carrier aggregation, MIMO, capacity, peak data rate, spectral efficiency.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 897273 Neural Networks-Based Acoustic Annoyance Model for Laptop Hard Disk Drive
Authors: Yi Chao Ma, Cheng Siong Chin, Wai Lok Woo
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Since the last decade, there has been a rapid growth in digital multimedia, such as high-resolution media files and threedimentional movies. Hence, there is a need for large digital storage such as Hard Disk Drive (HDD). As such, users expect to have a quieter HDD in their laptop. In this paper, a jury test has been conducted on a group of 34 people where 17 of them are students who are the potential consumer, and the remaining are engineers who know the HDD. A total 13 HDD sound samples have been selected from over hundred HDD noise recordings. These samples are selected based on an agreed subjective feeling. The samples are played to the participants using head acoustic playback system, which enabled them to experience as similar as possible the same environment as have been recorded. Analysis has been conducted and the obtained results have indicated different group has different perception over the noises. Two neural network-based acoustic annoyance models are established based on back propagation neural network. Four psychoacoustic metrics, loudness, sharpness, roughness and fluctuation strength, are used as the input of the model, and the subjective evaluation results are taken as the output. The developed models are reasonably accurate in simulating both training and test samples.Keywords: Hard disk drive noise, jury test, neural network model, psychoacoustic annoyance.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1527272 Optimum Design of an 8x8 Optical Switch with Thermal Compensated Mechanisms
Authors: Tien-Tung Chung, Chin-Te Lin, Chung-Yun Lee, Kuang-Chao Fan, Shou-Heng Chen
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This paper studies the optimum design for reducing optical loss of an 8x8 mechanical type optical switch due to the temperature change. The 8x8 optical switch is composed of a base, 8 input fibers, 8 output fibers, 3 fixed mirrors and 17 movable mirrors. First, an innovative switch configuration is proposed with thermal-compensated design. Most mechanical type optical switches have a disadvantage that their precision and accuracy are influenced by the ambient temperature. Therefore, the thermal-compensated design is to deal with this situation by using materials with different thermal expansion coefficients (α). Second, a parametric modeling program is developed to generate solid models for finite element analysis, and the thermal and structural behaviors of the switch are analyzed. Finally, an integrated optimum design program, combining Autodesk Inventor Professional software, finite element analysis software, and genetic algorithms, is developed for improving the thermal behaviors that the optical loss of the switch is reduced. By changing design parameters of the switch in the integrated design program, the final optimum design that satisfies the design constraints and specifications can be found.Keywords: Optical switch, finite element analysis, thermal-compensated design, optimum design.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1538271 Determining Fire Resistance of Wooden Construction Elements through Experimental Studies and Artificial Neural Network
Authors: Sakir Tasdemir, Mustafa Altin, Gamze Fahriye Pehlivan, Ismail Saritas, Sadiye Didem Boztepe Erkis, Selma Tasdemir
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Artificial intelligence applications are commonly used in industry in many fields in parallel with the developments in the computer technology. In this study, a fire room was prepared for the resistance of wooden construction elements and with the mechanism here, the experiments of polished materials were carried out. By utilizing from the experimental data, an artificial neural network (ANN) was modelled in order to evaluate the final cross sections of the wooden samples remaining from the fire. In modelling, experimental data obtained from the fire room were used. In the developed system, the first weight of samples (ws-gr), preliminary cross-section (pcs-mm2), fire time (ft-minute), and fire temperature (t-oC) as input parameters and final cross-section (fcs-mm2) as output parameter were taken. When the results obtained from ANN and experimental data are compared after making statistical analyses, the data of two groups are determined to be coherent and seen to have no meaning difference between them. As a result, it is seen that ANN can be safely used in determining cross sections of wooden materials after fire and it prevents many disadvantages.
Keywords: Artificial neural network, final cross-section, fire retardant polishes, fire safety, wood resistance.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1949270 An Image Enhancement Method Based on Curvelet Transform for CBCT-Images
Authors: Shahriar Farzam, Maryam Rastgarpour
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Image denoising plays extremely important role in digital image processing. Enhancement of clinical image research based on Curvelet has been developed rapidly in recent years. In this paper, we present a method for image contrast enhancement for cone beam CT (CBCT) images based on fast discrete curvelet transforms (FDCT) that work through Unequally Spaced Fast Fourier Transform (USFFT). These transforms return a table of Curvelet transform coefficients indexed by a scale parameter, an orientation and a spatial location. Accordingly, the coefficients obtained from FDCT-USFFT can be modified in order to enhance contrast in an image. Our proposed method first uses a two-dimensional mathematical transform, namely the FDCT through unequal-space fast Fourier transform on input image and then applies thresholding on coefficients of Curvelet to enhance the CBCT images. Consequently, applying unequal-space fast Fourier Transform leads to an accurate reconstruction of the image with high resolution. The experimental results indicate the performance of the proposed method is superior to the existing ones in terms of Peak Signal to Noise Ratio (PSNR) and Effective Measure of Enhancement (EME).
Keywords: Curvelet transform, image enhancement, CBCT, image denoising.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1249269 Human Action Recognition Using Variational Bayesian HMM with Dirichlet Process Mixture of Gaussian Wishart Emission Model
Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park
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In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods.
Keywords: Human action recognition, Bayesian HMM, Dirichlet process mixture model, Gaussian-Wishart emission model, Variational Bayesian inference, Prior distribution and approximate posterior distribution, KTH dataset.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 991268 Factory Virtual Environment Development for Augmented and Virtual Reality
Authors: M. Gregor, J. Polcar, P. Horejsi, M. Simon
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Machine visualization is an area of interest with fast and progressive development. We present a method of machine visualization which will be applicable in real industrial conditions according to current needs and demands. Real factory data were obtained in a newly built research plant. Methods described in this paper were validated on a case study. Input data were processed and the virtual environment was created. The environment contains information about dimensions, structure, disposition, and function. Hardware was enhanced by modular machines, prototypes, and accessories. We added functionalities and machines into the virtual environment. The user is able to interact with objects such as testing and cutting machines, he/she can operate and move them. Proposed design consists of an environment with two degrees of freedom of movement. Users are in touch with items in the virtual world which are embedded into the real surroundings. This paper describes development of the virtual environment. We compared and tested various options of factory layout virtualization and visualization. We analyzed possibilities of using a 3D scanner in the layout obtaining process and we also analyzed various virtual reality hardware visualization methods such as: Stereoscopic (CAVE) projection, Head Mounted Display (HMD) and augmented reality (AR) projection provided by see-through glasses.
Keywords: Augmented reality, spatial scanner, virtual environment, virtual reality.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2046267 Application of Extreme Learning Machine Method for Time Series Analysis
Authors: Rampal Singh, S. Balasundaram
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In this paper, we study the application of Extreme Learning Machine (ELM) algorithm for single layered feedforward neural networks to non-linear chaotic time series problems. In this algorithm the input weights and the hidden layer bias are randomly chosen. The ELM formulation leads to solving a system of linear equations in terms of the unknown weights connecting the hidden layer to the output layer. The solution of this general system of linear equations will be obtained using Moore-Penrose generalized pseudo inverse. For the study of the application of the method we consider the time series generated by the Mackey Glass delay differential equation with different time delays, Santa Fe A and UCR heart beat rate ECG time series. For the choice of sigmoid, sin and hardlim activation functions the optimal values for the memory order and the number of hidden neurons which give the best prediction performance in terms of root mean square error are determined. It is observed that the results obtained are in close agreement with the exact solution of the problems considered which clearly shows that ELM is a very promising alternative method for time series prediction.Keywords: Chaotic time series, Extreme learning machine, Generalization performance.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3507266 Energy Efficiency Analysis of Discharge Modes of an Adiabatic Compressed Air Energy Storage System
Authors: Shane D. Inder, Mehrdad Khamooshi
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Efficient energy storage is a crucial factor in facilitating the uptake of renewable energy resources. Among the many options available for energy storage systems required to balance imbalanced supply and demand cycles, compressed air energy storage (CAES) is a proven technology in grid-scale applications. This paper reviews the current state of micro scale CAES technology and describes a micro-scale advanced adiabatic CAES (A-CAES) system, where heat generated during compression is stored for use in the discharge phase. It will also describe a thermodynamic model, developed in EES (Engineering Equation Solver) to evaluate the performance and critical parameters of the discharge phase of the proposed system. Three configurations are explained including: single turbine without preheater, two turbines with preheaters, and three turbines with preheaters. It is shown that the micro-scale A-CAES is highly dependent upon key parameters including; regulator pressure, air pressure and volume, thermal energy storage temperature and flow rate and the number of turbines. It was found that a micro-scale AA-CAES, when optimized with an appropriate configuration, could deliver energy input to output efficiency of up to 70%.
Keywords: CAES, adiabatic compressed air energy storage, expansion phase, micro generation, thermodynamic.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1105265 Artificial Intelligence Model to Predict Surface Roughness of Ti-15-3 Alloy in EDM Process
Authors: Md. Ashikur Rahman Khan, M. M. Rahman, K. Kadirgama, M.A. Maleque, Rosli A. Bakar
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Conventionally the selection of parameters depends intensely on the operator-s experience or conservative technological data provided by the EDM equipment manufacturers that assign inconsistent machining performance. The parameter settings given by the manufacturers are only relevant with common steel grades. A single parameter change influences the process in a complex way. Hence, the present research proposes artificial neural network (ANN) models for the prediction of surface roughness on first commenced Ti-15-3 alloy in electrical discharge machining (EDM) process. The proposed models use peak current, pulse on time, pulse off time and servo voltage as input parameters. Multilayer perceptron (MLP) with three hidden layer feedforward networks are applied. An assessment is carried out with the models of distinct hidden layer. Training of the models is performed with data from an extensive series of experiments utilizing copper electrode as positive polarity. The predictions based on the above developed models have been verified with another set of experiments and are found to be in good agreement with the experimental results. Beside this they can be exercised as precious tools for the process planning for EDM.Keywords: Ti-15l-3, surface roughness, copper, positive polarity, multi-layered perceptron.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1891264 A Finite Precision Block Floating Point Treatment to Direct Form, Cascaded and Parallel FIR Digital Filters
Authors: Abhijit Mitra
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This paper proposes an efficient finite precision block floating point (BFP) treatment to the fixed coefficient finite impulse response (FIR) digital filter. The treatment includes effective implementation of all the three forms of the conventional FIR filters, namely, direct form, cascaded and par- allel, and a roundoff error analysis of them in the BFP format. An effective block formatting algorithm together with an adaptive scaling factor is pro- posed to make the realizations more simple from hardware view point. To this end, a generic relation between the tap weight vector length and the input block length is deduced. The implementation scheme also emphasises on a simple block exponent update technique to prevent overflow even during the block to block transition phase. The roundoff noise is also investigated along the analogous lines, taking into consideration these implementational issues. The simulation results show that the BFP roundoff errors depend on the sig- nal level almost in the same way as floating point roundoff noise, resulting in approximately constant signal to noise ratio over a relatively large dynamic range.
Keywords: Finite impulse response digital filters, Cascade structure, Parallel structure, Block floating point arithmetic, Roundoff error.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1637263 Machine Learning for Music Aesthetic Annotation Using MIDI Format: A Harmony-Based Classification Approach
Authors: Lin Yang, Zhian Mi, Jiacheng Xiao, Rong Li
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Swimming with the tide of deep learning, the field of music information retrieval (MIR) experiences parallel development and a sheer variety of feature-learning models has been applied to music classification and tagging tasks. Among those learning techniques, the deep convolutional neural networks (CNNs) have been widespreadly used with better performance than the traditional approach especially in music genre classification and prediction. However, regarding the music recommendation, there is a large semantic gap between the corresponding audio genres and the various aspects of a song that influence user preference. In our study, aiming to bridge the gap, we strive to construct an automatic music aesthetic annotation model with MIDI format for better comparison and measurement of the similarity between music pieces in the way of harmonic analysis. We use the matrix of qualification converted from MIDI files as input to train two different classifiers, support vector machine (SVM) and Decision Tree (DT). Experimental results in performance of a tag prediction task have shown that both learning algorithms are capable of extracting high-level properties in an end-to end manner from music information. The proposed model is helpful to learn the audience taste and then the resulting recommendations are likely to appeal to a niche consumer.
Keywords: Harmonic analysis, machine learning, music classification and tagging, MIDI.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 738262 BIDENS: Iterative Density Based Biclustering Algorithm With Application to Gene Expression Analysis
Authors: Mohamed A. Mahfouz, M. A. Ismail
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Biclustering is a very useful data mining technique for identifying patterns where different genes are co-related based on a subset of conditions in gene expression analysis. Association rules mining is an efficient approach to achieve biclustering as in BIMODULE algorithm but it is sensitive to the value given to its input parameters and the discretization procedure used in the preprocessing step, also when noise is present, classical association rules miners discover multiple small fragments of the true bicluster, but miss the true bicluster itself. This paper formally presents a generalized noise tolerant bicluster model, termed as μBicluster. An iterative algorithm termed as BIDENS based on the proposed model is introduced that can discover a set of k possibly overlapping biclusters simultaneously. Our model uses a more flexible method to partition the dimensions to preserve meaningful and significant biclusters. The proposed algorithm allows discovering biclusters that hard to be discovered by BIMODULE. Experimental study on yeast, human gene expression data and several artificial datasets shows that our algorithm offers substantial improvements over several previously proposed biclustering algorithms.Keywords: Machine learning, biclustering, bi-dimensional clustering, gene expression analysis, data mining.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1948261 Adaptive Fuzzy Control for Air-Fuel Ratio of Automobile Spark Ignition Engine
Authors: Ali Ghaffari, A. Hosein Shamekhi, Akbar Saki, Ehsan Kamrani
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In order to meet the limits imposed on automotive emissions, engine control systems are required to constrain air/fuel ratio (AFR) in a narrow band around the stoichiometric value, due to the strong decay of catalyst efficiency in case of rich or lean mixture. This paper presents a model of a sample spark ignition engine and demonstrates Simulink-s capabilities to model an internal combustion engine from the throttle to the crankshaft output. We used welldefined physical principles supplemented, where appropriate, with empirical relationships that describe the system-s dynamic behavior without introducing unnecessary complexity. We also presents a PID tuning method that uses an adaptive fuzzy system to model the relationship between the controller gains and the target output response, with the response specification set by desired percent overshoot and settling time. The adaptive fuzzy based input-output model is then used to tune on-line the PID gains for different response specifications. Experimental results demonstrate that better performance can be achieved with adaptive fuzzy tuning relative to similar alternative control strategies. The actual response specifications with adaptive fuzzy matched the desired response specifications.Keywords: Modelling, Air–fuel ratio control, SI engine, Adaptive fuzzy Control.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2510260 Disaggregation the Daily Rainfall Dataset into Sub-Daily Resolution in the Temperate Oceanic Climate Region
Authors: Mohammad Bakhshi, Firas Al Janabi
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High resolution rain data are very important to fulfill the input of hydrological models. Among models of high-resolution rainfall data generation, the temporal disaggregation was chosen for this study. The paper attempts to generate three different rainfall resolutions (4-hourly, hourly and 10-minutes) from daily for around 20-year record period. The process was done by DiMoN tool which is based on random cascade model and method of fragment. Differences between observed and simulated rain dataset are evaluated with variety of statistical and empirical methods: Kolmogorov-Smirnov test (K-S), usual statistics, and Exceedance probability. The tool worked well at preserving the daily rainfall values in wet days, however, the generated data are cumulated in a shorter time period and made stronger storms. It is demonstrated that the difference between generated and observed cumulative distribution function curve of 4-hourly datasets is passed the K-S test criteria while in hourly and 10-minutes datasets the P-value should be employed to prove that their differences were reasonable. The results are encouraging considering the overestimation of generated high-resolution rainfall data.
Keywords: DiMoN tool, disaggregation, exceedance probability, Kolmogorov-Smirnov Test, rainfall.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 981259 Forecast of Polyethylene Properties in the Gas Phase Polymerization Aided by Neural Network
Authors: Nasrin Bakhshizadeh, Ashkan Forootan
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A major problem that affects the quality control of polymer in the industrial polymerization is the lack of suitable on-line measurement tools to evaluate the properties of the polymer such as melt and density indices. Controlling the polymerization in ordinary method is performed manually by taking samples, measuring the quality of polymer in the lab and registry of results. This method is highly time consuming and leads to producing large number of incompatible products. An online application for estimating melt index and density proposed in this study is a neural network based on the input-output data of the polyethylene production plant. Temperature, the level of reactors' bed, the intensity of ethylene mass flow, hydrogen and butene-1, the molar concentration of ethylene, hydrogen and butene-1 are used for the process to establish the neural model. The neural network is taught based on the actual operational data and back-propagation and Levenberg-Marquart techniques. The simulated results indicate that the neural network process model established with three layers (one hidden layer) for forecasting the density and the four layers for the melt index is able to successfully predict those quality properties.
Keywords: Polyethylene, polymerization, density, melt index, neural network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 672258 Testing Loaded Programs Using Fault Injection Technique
Authors: S. Manaseer, F. A. Masooud, A. A. Sharieh
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Fault tolerance is critical in many of today's large computer systems. This paper focuses on improving fault tolerance through testing. Moreover, it concentrates on the memory faults: how to access the editable part of a process memory space and how this part is affected. A special Software Fault Injection Technique (SFIT) is proposed for this purpose. This is done by sequentially scanning the memory of the target process, and trying to edit maximum number of bytes inside that memory. The technique was implemented and tested on a group of programs in software packages such as jet-audio, Notepad, Microsoft Word, Microsoft Excel, and Microsoft Outlook. The results from the test sample process indicate that the size of the scanned area depends on several factors. These factors are: process size, process type, and virtual memory size of the machine under test. The results show that increasing the process size will increase the scanned memory space. They also show that input-output processes have more scanned area size than other processes. Increasing the virtual memory size will also affect the size of the scanned area but to a certain limit.Keywords: Complex software systems, Error detection, Fault tolerance, Injection and testing methodology, Memory faults, Process and virtual memory.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1874257 Design and Performance of Adaptive Polarized MIMO MC-SS-CDMA System for Downlink Mobile Communications
Authors: Joseph V. M. Halim, Hesham El-Badawy, Hadia M. El-Hennawy
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In this paper, an adaptive polarized Multiple-Input Multiple-Output (MIMO) Multicarrier Spread Spectrum Code Division Multiple Access (MC-SS-CDMA) system is designed for downlink mobile communications. The proposed system will be examined in Frequency Division Duplex (FDD) mode for both macro urban and suburban environments. For the same transmission bandwidth, a performance comparison between both nonoverlapped and orthogonal Frequency Division Multiplexing (FDM) schemes will be presented. Also, the proposed system will be compared with both the closed loop vertical MIMO MC-SS-CDMA system and the synchronous vertical STBC-MIMO MC-SS-CDMA system. As will be shown, the proposed system introduces a significant performance gain as well as reducing the spatial dimensions of the MIMO system and simplifying the receiver implementation. The effect of the polarization diversity characteristics on the BER performance will be discussed. Also, the impact of excluding the cross-polarization MCSS- CDMA blocks in the base station will be investigated. In addition, the system performance will be evaluated under different Feedback Information (FBI) rates for slowly-varying channels. Finally, a performance comparison for vehicular and pedestrian environments will be presentedKeywords: Closed loop technique, MC-SS-CDMA, Polarized MIMO systems, Transmit diversity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1615256 LFC Design of a Deregulated Power System with TCPS Using PSO
Authors: H. Shayeghi, H.A. Shayanfar, A. Jalili
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In the LFC problem, the interconnections among some areas are the input of disturbances, and therefore, it is important to suppress the disturbances by the coordination of governor systems. In contrast, tie-line power flow control by TCPS located between two areas makes it possible to stabilize the system frequency oscillations positively through interconnection, which is also expected to provide a new ancillary service for the further power systems. Thus, a control strategy using controlling the phase angle of TCPS is proposed for provide active control facility of system frequency in this paper. Also, the optimum adjustment of PID controller's parameters in a robust way under bilateral contracted scenario following the large step load demands and disturbances with and without TCPS are investigated by Particle Swarm Optimization (PSO), that has a strong ability to find the most optimistic results. This newly developed control strategy combines the advantage of PSO and TCPS and has simple stricture that is easy to implement and tune. To demonstrate the effectiveness of the proposed control strategy a three-area restructured power system is considered as a test system under different operating conditions and system nonlinearities. Analysis reveals that the TCPS is quite capable of suppressing the frequency and tie-line power oscillations effectively as compared to that obtained without TCPS for a wide range of plant parameter changes, area load demands and disturbances even in the presence of system nonlinearities.
Keywords: LFC, TCPS, Dregulated Power System, PowerSystem Control, PSO.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2059255 Food Safety Management: Concerns from EU Tourists in Thailand
Authors: Kevin Wongleedee
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Culinary culture differences can cause health problems for international tourists in Thailand. This paper drew upon data collected from an international tourist survey conducted in Bangkok, Thailand during summer of 2012. Summer is the period that a variety food safety issues and incidents are often publicized in Thailand. The survey targeted European Union tourists- concerns toward a variety of food safety issues that they encountered during their trip in Thailand. A total of 400 respondents were elicited as data input for t-test, and one way ANOVA test. The findings revealed an astonishing result that up to 46.5 percent of respondents were sick at least one time or more in Thailand. However, the majority of respondents trusted that the Thai hotel and Thai restaurants would ensure food safety, but they did not trust street vendors to ensure food safety. The level of food safety concern can be ranked from most concern to least concern by using the value of mean scores as follows: 1) artificial coloring, 2) use of preservatives, 3) antibiotics, 4) growth hormones, 5) chemical residues, and 6) bacterial contamination. The overall mean score for level of concerns was 3.493 with standard deviation of 1.677 which did not indicate a very high level of concern. In addition, the result for t-test and one way ANOVA test revealed that there was not much effect from the demographic differences to level of food safety concerns.Keywords: Concerns, European Union Tourists, Food Safety Management.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2862254 Creation of Greater Mekong Subregion Regional Competitiveness through Cluster Mapping
Authors: Danuvasin Charoen
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This research investigates cluster development in the area called the Greater Mekong Subregion (GMS), which consists of Thailand, the People’s Republic of China (PRC), the Yunnan Province and Guangxi Zhuang Autonomous Region, Myanmar, the Lao People’s Democratic Republic (Lao PDR), Cambodia, and Vietnam. The study utilized Porter’s competitiveness theory and the cluster mapping approach to analyze the competitiveness of the region. The data collection consists of interviews, focus groups, and the analysis of secondary data. The findings identify some evidence of cluster development in the GMS; however, there is no clear indication of collaboration among the components in the clusters. GMS clusters tend to be stand-alone. The clusters in Vietnam, Lao PDR, Myanmar, and Cambodia tend to be labor intensive, whereas the clusters in Thailand and the PRC (Yunnan) have the potential to successfully develop into innovative clusters. The collaboration and integration among the clusters in the GMS area are promising, though it could take a long time. The most likely relationship between the GMS countries could be, for example, suppliers of the low-end, labor-intensive products will be located in the low income countries such as Myanmar, Lao PDR, and Cambodia, and these countries will be providing input materials for innovative clusters in the middle income countries such as Thailand and the PRC.Keywords: Greater Mekong Subregion, competitiveness, cluster, development.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1054253 Metallurgical Analysis of Surface Defect in Telescopic Front Fork
Authors: Souvik Das, Janak Lal, Arthita Dey, Goutam Mukhopadhyay, Sandip Bhattacharya
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Telescopic Front Fork (TFF) used in two wheelers, mainly motorcycle, is made from high strength steel, and is manufactured by high frequency induction welding process wherein hot rolled and pickled coils are used as input raw material for rolling of hollow tubes followed by heat treatment, surface treatment, cold drawing, tempering, etc. The final application demands superior quality TFF tubes w.r.t. surface finish and dimensional tolerances. This paper presents the investigation of two different types of failure of fork during operation. The investigation consists of visual inspection, chemical analysis, characterization of microstructure, and energy dispersive spectroscopy. In this paper, comprehensive investigations of two failed tube samples were investigated. In case of Sample #1, the result revealed that there was a pre-existing crack, known as hook crack, which leads to the cracking of the tube. Metallographic examination exhibited that during field operation the pre-existing hook crack was surfaced out leading to crack in the pipe. In case of Sample #2, presence of internal oxidation with decarburised grains inside the material indicates origin of the defect from slab stage.
Keywords: Telescopic front fork, induction welding, hook crack, internal oxidation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 811252 Measuring Relative Efficiency of Korean Construction Company using DEA/Window
Authors: Jung-Lo Park, Sung-Sik Kim, Sun-Young Choi, Ju-Hyung Kim, Jae-Jun Kim
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Sub-prime mortgage crisis which began in the US is regarded as the most economic crisis since the Great Depression in the early 20th century. Especially, hidden problems on efficient operation of a business were disclosed at a time and many financial institutions went bankrupt and filed for court receivership. The collapses of physical market lead to bankruptcy of manufacturing and construction businesses. This study is to analyze dynamic efficiency of construction businesses during the five years at the turn of the global financial crisis. By discovering the trend and stability of efficiency of a construction business, this study-s objective is to improve management efficiency of a construction business in the ever-changing construction market. Variables were selected by analyzing corporate information on top 20 construction businesses in Korea and analyzed for static efficiency in 2008 and dynamic efficiency between 2006 and 2010. Unlike other studies, this study succeeded in deducing efficiency trend and stability of a construction business for five years by using the DEA/Window model. Using the analysis result, efficient and inefficient companies could be figured out. In addition, relative efficiency among DMU was measured by comparing the relationship between input and output variables of construction businesses. This study can be used as a literature to improve management efficiency for companies with low efficiency based on efficiency analysis of construction businesses.Keywords: Construction Company, DEA, DEA/Window, Efficiency Analysis
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1973251 Productivity and Performance of Barangays: The Case of the Heritage City of Vigan, Philippines
Authors: Edelyn Alicar-Cadorna
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This study assessed the productivity and performance of the barangays in the Heritage City of Vigan in terms of the barangays- resource requirements, management of resources, produced goods and services, and outcomes of service delivery. The descriptive research design was used in the study employing the input-process-output-outcomes model. Findings of this study showed that the barangays were strong in terms of resource requirements which enabled them to produce goods and services. The barangays were also strong in terms of management of resources in development planning. They also showed great potential along fiscal administration, and had a moderately high capability in organization and management. However, the barangays appeared to be most wanting in the area of barangay legislation, but they were strong in community mobilization and they had strong linkages with POs, NGOs and educational institutions. In the delivery of social services, the barangays favored the maintenance of day care centers. However, the barangays seem to be weak in the delivery of economic services. They fared well along providing protective services such as in establishing a Barangay Disaster Coordinating Council and organizing a group of Barangay Tanod. In terms of environmental services, the barangays performed garbage collection and disposal; however, garbage still found their way in the streets in some barangays. The services delivered had effected an improved status of the barangays. However, the barangays are still facing some problems.
Keywords: Barangays, Performance and Productivity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13469250 A Comparison of Marginal and Joint Generalized Quasi-likelihood Estimating Equations Based On the Com-Poisson GLM: Application to Car Breakdowns Data
Authors: N. Mamode Khan, V. Jowaheer
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In this paper, we apply and compare two generalized estimating equation approaches to the analysis of car breakdowns data in Mauritius. Number of breakdowns experienced by a machinery is a highly under-dispersed count random variable and its value can be attributed to the factors related to the mechanical input and output of that machinery. Analyzing such under-dispersed count observation as a function of the explanatory factors has been a challenging problem. In this paper, we aim at estimating the effects of various factors on the number of breakdowns experienced by a passenger car based on a study performed in Mauritius over a year. We remark that the number of passenger car breakdowns is highly under-dispersed. These data are therefore modelled and analyzed using Com-Poisson regression model. We use the two types of quasi-likelihood estimation approaches to estimate the parameters of the model: marginal and joint generalized quasi-likelihood estimating equation approaches. Under-dispersion parameter is estimated to be around 2.14 justifying the appropriateness of Com-Poisson distribution in modelling underdispersed count responses recorded in this study.
Keywords: Breakdowns, under-dispersion, com-poisson, generalized linear model, marginal quasi-likelihood estimation, joint quasi-likelihood estimation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1453249 Complex-Valued Neural Network in Signal Processing: A Study on the Effectiveness of Complex Valued Generalized Mean Neuron Model
Authors: Anupama Pande, Ashok Kumar Thakur, Swapnoneel Roy
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A complex valued neural network is a neural network which consists of complex valued input and/or weights and/or thresholds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in signal processing. In Neural networks, generalized mean neuron model (GMN) is often discussed and studied. The GMN includes a new aggregation function based on the concept of generalized mean of all the inputs to the neuron. This paper aims to present exhaustive results of using Generalized Mean Neuron model in a complex-valued neural network model that uses the back-propagation algorithm (called -Complex-BP-) for learning. Our experiments results demonstrate the effectiveness of a Generalized Mean Neuron Model in a complex plane for signal processing over a real valued neural network. We have studied and stated various observations like effect of learning rates, ranges of the initial weights randomly selected, error functions used and number of iterations for the convergence of error required on a Generalized Mean neural network model. Some inherent properties of this complex back propagation algorithm are also studied and discussed.Keywords: Complex valued neural network, Generalized Meanneuron model, Signal processing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1720248 Educational Experiences in Engineering in the COVID-19 Era and Their Comparative Analysis: Spain, March-June 2020
Authors: Borja Bordel, Ramón Alcarria, Marina Pérez
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In March 2020, in Spain, a sanitary and unexpected crisis caused by COVID-19 was declared. All of a sudden, all degrees, classes and evaluation tests and projects had to be transformed into online activities. However, the chaotic situation generated by a complex operation like that, executed without any well-established procedure, led to very different experiences and, finally, results. In this paper, we are describing three experiences in two different Universities in Madrid. On the one hand, the Technical University of Madrid, a public university with little experience in online education was considered. On the other hand, Alfonso X el Sabio University, a private university with more than five years of experience in online teaching was involved. All analyzed subjects were related to computer engineering. Professors and students answered a survey and personal interviews were also carried out. Besides, the professors’ workload and the students’ academic results were also compared. From the comparative analysis of all these experiences, we are extracting the most successful strategies, methodologies, and activities. The recommendations in this paper will be useful for courses during the next months when the sanitary situation is still affecting an educational organization. While, at the same time, they will be considered as input for the upcoming digitalization process of higher education.
Keywords: educational experience, online education, higher education digitalization, COVID, Spain
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 442247 An Ant-based Clustering System for Knowledge Discovery in DNA Chip Analysis Data
Authors: Minsoo Lee, Yun-mi Kim, Yearn Jeong Kim, Yoon-kyung Lee, Hyejung Yoon
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Biological data has several characteristics that strongly differentiate it from typical business data. It is much more complex, usually large in size, and continuously changes. Until recently business data has been the main target for discovering trends, patterns or future expectations. However, with the recent rise in biotechnology, the powerful technology that was used for analyzing business data is now being applied to biological data. With the advanced technology at hand, the main trend in biological research is rapidly changing from structural DNA analysis to understanding cellular functions of the DNA sequences. DNA chips are now being used to perform experiments and DNA analysis processes are being used by researchers. Clustering is one of the important processes used for grouping together similar entities. There are many clustering algorithms such as hierarchical clustering, self-organizing maps, K-means clustering and so on. In this paper, we propose a clustering algorithm that imitates the ecosystem taking into account the features of biological data. We implemented the system using an Ant-Colony clustering algorithm. The system decides the number of clusters automatically. The system processes the input biological data, runs the Ant-Colony algorithm, draws the Topic Map, assigns clusters to the genes and displays the output. We tested the algorithm with a test data of 100 to1000 genes and 24 samples and show promising results for applying this algorithm to clustering DNA chip data.
Keywords: Ant colony system, biological data, clustering, DNA chip.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1962246 Classifier Based Text Mining for Neural Network
Authors: M. Govindarajan, R. M. Chandrasekaran
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Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the network performance, set the rate of adjustments. This paper describes a proposed back propagation neural net classifier that performs cross validation for original Neural Network. In order to reduce the optimization of classification accuracy, training time. The feasibility the benefits of the proposed approach are demonstrated by means of five data sets like contact-lenses, cpu, weather symbolic, Weather, labor-nega-data. It is shown that , compared to exiting neural network, the training time is reduced by more than 10 times faster when the dataset is larger than CPU or the network has many hidden units while accuracy ('percent correct') was the same for all datasets but contact-lences, which is the only one with missing attributes. For contact-lences the accuracy with Proposed Neural Network was in average around 0.3 % less than with the original Neural Network. This algorithm is independent of specify data sets so that many ideas and solutions can be transferred to other classifier paradigms.Keywords: Back propagation, classification accuracy, textmining, time complexity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4206245 Pilot Induced Oscillations Adaptive Suppression in Fly-By-Wire Systems
Authors: Herlandson C. Moura, Jorge H. Bidinotto, Eduardo M. Belo
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The present work proposes the development of an adaptive control system which enables the suppression of Pilot Induced Oscillations (PIO) in Digital Fly-By-Wire (DFBW) aircrafts. The proposed system consists of a Modified Model Reference Adaptive Control (M-MRAC) integrated with the Gain Scheduling technique. The PIO oscillations are detected using a Real Time Oscillation Verifier (ROVER) algorithm, which then enables the system to switch between two reference models; one in PIO condition, with low proneness to the phenomenon and another one in normal condition, with high (or medium) proneness. The reference models are defined in a closed loop condition using the Linear Quadratic Regulator (LQR) control methodology for Multiple-Input-Multiple-Output (MIMO) systems. The implemented algorithms are simulated in software implementations with state space models and commercial flight simulators as the controlled elements and with pilot dynamics models. A sequence of pitch angles is considered as the reference signal, named as Synthetic Task (Syntask), which must be tracked by the pilot models. The initial outcomes show that the proposed system can detect and suppress (or mitigate) the PIO oscillations in real time before it reaches high amplitudes.
Keywords: Adaptive control, digital fly-by-wire, oscillations suppression, PIO.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 732