Search results for: Data analysis

Authors: Shahin Dezdar, Sulaiman Ainin

Abstract:

The aim of this paper is to examine factors related to system environment (namely, system quality and vendor support) that influences ERP implementation success in Iranian companies. Implementation success is identified using user satisfaction and organizational impact perspective. The study adopts the survey questionnaire approach to collect empirical data. The questionnaire was distributed to ERP users and a total of 384 responses were used for analysis. The results illustrated that both system quality and vendor support have significant effect on ERP implementation success. This implies that companies must ensure they source for the best available system and a vendor that is dependable, reliable and trustworthy.

Keywords: Enterprise resource planning (ERP), Iran, system quality, vendor support.

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Authors: Lenka Ševelová, Aleš Florian

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Appropriate and progressive tool for analyzing behavior of low volume roads are probabilistic models used in reliability analyses. The necessary part of the probabilistic model is the deterministic model of structural behavior. The FE model of low volume roads is created in the ANSYS software. It is able to determine the state of stress and deformation in any point of the structure and thus generate data required for the reliability analysis. The paper compares two material constitutive models used for modeling of unbound non-homogenous materials used in low volume roads. The first model is linear elastic model according to Hook theory (H model), the second one is nonlinear elastic-plastic Drucker-Prager model (D-P model).

Keywords: FEA, FEM, geotechnical materials, low volume roads, material constitutive models, pavement.

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Authors: Tian Hai, Yang Shengsheng, Jr., Wang Yi

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Based on an analysis of the mechanism of degradation of optical characteristics of the ZnO-pigmented white paint by electron irradiation, a model of single molecular color centers is built. An equation that explains the relationship between the changes of variation of the ZnO-pigmented white paint-s spectrum absorptance and electron fluence is derived. The uncertain parameters in the equation can be calculated using the curve fitting by experimental data. The result indicates that the model can be applied to predict the degradation of optical characteristics of ZnO-pigmented white paint by electron radiation.

Keywords: ZnO-pigmented white pain, effects of electron radiation, optical characteristics degradation, prediction model.

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Authors: Amir Alani, Mojtaba Mahmoodian, Anna Romanova, Asaad Faramarzi

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Pipelines are extensively used engineering structures which convey fluid from one place to another. Most of the time, pipelines are placed underground and are encumbered by soil weight and traffic loads. Corrosion of pipe material is the most common form of pipeline deterioration and should be considered in both the strength and serviceability analysis of pipes. The study in this research focuses on concrete pipes in sewage systems (concrete sewers). This research firstly investigates how to involve the effect of corrosion as a time dependent process of deterioration in the structural and failure analysis of this type of pipe. Then three probabilistic time dependent reliability analysis methods including the first passage probability theory, the gamma distributed degradation model and the Monte Carlo simulation technique are discussed and developed. Sensitivity analysis indexes which can be used to identify the most important parameters that affect pipe failure are also discussed. The reliability analysis methods developed in this paper contribute as rational tools for decision makers with regard to the strengthening and rehabilitation of existing pipelines. The results can be used to obtain a cost-effective strategy for the management of the sewer system.

Keywords: Reliability analysis, service life prediction, Monte Carlo simulation method, first passage probability theory, gamma distributed degradation model.

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Authors: Biao Qin, Jin Huang, Jiaojiao Ren, Wei Kang

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This paper deals with the problem of passivity analysis for stochastic neural networks with leakage, discrete and distributed delays. By using delay partitioning technique, free weighting matrix method and stochastic analysis technique, several sufficient conditions for the passivity of the addressed neural networks are established in terms of linear matrix inequalities (LMIs), in which both the time-delay and its time derivative can be fully considered. A numerical example is given to show the usefulness and effectiveness of the obtained results.

Keywords: Passivity, Stochastic neural networks, Multiple time delays, Linear matrix inequalities (LMIs).

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Authors: N. Dumitru, S. Dumitru, C. Copilusi, N. Ploscaru

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On this research, experimental analyses have been performed in order to determine the oil pump mechanism dynamics and stability from an oil unit mechanical structure. The experimental tests were focused on the vibrations which occur inside of the rod element during functionality of the oil pump unit. The oil pump mechanism dynamic parameters were measured and also determined through numerical computations. Entire research is based on the oil pump unit mechanical system virtual prototyping. For a complete analysis of the mechanism, the frequency dynamic response was identified, mainly for the mechanism driven element, based on two methods: processing and virtual simulations with MSC Adams aid and experimental analysis. In fact, through this research, a complete methodology is presented where numerical simulations of a mechanism with deformed elements are developed on a dynamic mode and these can be correlated with experimental tests.

Keywords: Modal dynamic analysis, oil pump, vibrations, flexible elements, frequency response.

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Authors: Zunaira Asif, Zhi Chen

Abstract:

The sustainable measures on air quality management are recognized as one of the most serious environmental concerns in the mining region. The mining operations emit various types of pollutants which have significant impacts on the environment. This study presents a stochastic control strategy by developing the air pollution control model to achieve a cost-effective solution. The optimization method is formulated to predict the cost of treatment using linear programming with an objective function and multi-constraints. The constraints mainly focus on two factors which are: production of metal should not exceed the available resources, and air quality should meet the standard criteria of the pollutant. The applicability of this model is explored through a case study of an open pit metal mine, Utah, USA. This method simultaneously uses meteorological data as a dispersion transfer function to support the practical local conditions. The probabilistic analysis and the uncertainties in the meteorological conditions are accomplished by Monte Carlo simulation. Reasonable results have been obtained to select the optimized treatment technology for PM_2.5, PM₁₀, NO_x, and SO₂. Additional comparison analysis shows that baghouse is the least cost option as compared to electrostatic precipitator and wet scrubbers for particulate matter, whereas non-selective catalytical reduction and dry-flue gas desulfurization are suitable for NO_x and SO₂ reduction respectively. Thus, this model can aid planners to reduce these pollutants at a marginal cost by suggesting control pollution devices, while accounting for dynamic meteorological conditions and mining activities.

Keywords: Air pollution, linear programming, mining, optimization, treatment technologies.

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Authors: Kyung Bae Park, Sung Ho Ha

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Online user-generated contents (UGC) significantly change the way customers behave (e.g., shop, travel), and a pressing need to handle the overwhelmingly plethora amount of various UGC is one of the paramount issues for management. However, a current approach (e.g., sentiment analysis) is often ineffective for leveraging textual information to detect the problems or issues that a certain management suffers from. In this paper, we employ text mining of Latent Dirichlet Allocation (LDA) on a popular online review site dedicated to complaint from users. We find that the employed LDA efficiently detects customer complaints, and a further inspection with the visualization technique is effective to categorize the problems or issues. As such, management can identify the issues at stake and prioritize them accordingly in a timely manner given the limited amount of resources. The findings provide managerial insights into how analytics on social media can help maintain and improve their reputation management. Our interdisciplinary approach also highlights several insights by applying machine learning techniques in marketing research domain. On a broader technical note, this paper illustrates the details of how to implement LDA in R program from a beginning (data collection in R) to an end (LDA analysis in R) since the instruction is still largely undocumented. In this regard, it will help lower the boundary for interdisciplinary researcher to conduct related research.

Keywords: Latent Dirichlet allocation, R program, text mining, topic model, user generated contents, visualization.

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Authors: Graziele Zwielewski, Suzana R. Tolfo

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The globalization of markets, the need to develop competitive advantages and core competencies, among other things, lead organizations to increasingly cross borders to operate in other countries. The expatriation of professionals who go to work in another country besides their own becomes increasingly common. In order to generate data about this issue, research was conducted concerning the perception of expatriate employees concerning expatriation success. The research method used was case study through a qualitative approach. This research was done through interviews with five India expatriates and five China expatriates, interview with expatriate department heads and analysis of company documents. It was found that there are differences between the organizational perception and perception of expatriates of what constitutes mission success. The paper also provides suggestions for further research and suggestions for future expatriates.

Keywords: Expatriation success, International assignments, Success factors, Success for expatriates.

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Authors: Ion Miciu

Abstract:

The system is made with main distributed components: First Level: Industrial Computers placed in Control Room (monitors thermal and electrical processes based on the data provided by the second level); Second Level: PLCs which collects data from process and transmits information on the first level; also takes commands from this level which are further, passed to execution elements from third level; Third Level: field elements consisting in 3 categories: data collecting elements; data transfer elements from the third level to the second; execution elements which take commands from the second level PLCs and executes them after which transmits the confirmation of execution to them. The purpose of the automatic functioning is the optimization of the co-generative electrical energy commissioning in the national energy system and the commissioning of thermal energy to the consumers. The integrated system treats the functioning of all the equipments and devices as a whole: Gas Turbine Units (GTU); MT 20kV Medium Voltage Station (MVS); 0,4 kV Low Voltage Station (LVS); Main Hot Water Boilers (MHW); Auxiliary Hot Water Boilers (AHW); Gas Compressor Unit (GCU); Thermal Agent Circulation Pumping Unit (TPU); Water Treating Station (WTS).

Keywords: Automation System, Cogenerative Power Plant, Control, Monitoring, Real Time

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Authors: Praveen Boinee, Alessandro De Angelis, Gian Luca Foresti

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Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques with classifiers such as random forests, neural networks and support vector machines. The data sets are from MAGIC, a Cherenkov telescope experiment. The task is to classify gamma signals from overwhelmingly hadron and muon signals representing a rare class classification problem. We compare the individual classifiers with their ensemble counterparts and discuss the results. WEKA a wonderful tool for machine learning has been used for making the experiments.

Keywords: Ensembles, WEKA, Neural networks [NN], SupportVector Machines [SVM], Random Forests [RF].

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Authors: Jaemoon Lim

Abstract:

To construct the lumped spring-mass model considering the occupants for the offset frontal crash, the SISAME software and the NHTSA test data were used. The data on 56 kph 40% offset frontal vehicle to deformable barrier crash test of a MY2007 Mazda 6 4-door sedan were obtained from NHTSA test database. The overall behaviors of B-pillar and engine of simulation models agreed very well with the test data. The trends of accelerations at the driver and passenger head were similar but big differences in peak values. The differences of peak values caused the large errors of the HIC36 and 3 ms chest g’s. To predict well the behaviors of dummies, the spring-mass model for the offset frontal crash needs to be improved.

Keywords: Chest g’s, HIC36, lumped spring-mass model, offset frontal impact, SISAME.

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Authors: Sofia Stoica

Abstract:

In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices over the past five years of 10 major tech companies: Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We implemented and tested three models – a linear regressor model, a k-nearest neighbor model (KNN), and a sequential neural network – and two algorithms – Multiplicative Weight Update and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: Finance, machine learning, opening price, stock market.

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Authors: M.K.Jeya Kumar

Abstract:

Many advanced Routing protocols for wireless sensor networks have been implemented for the effective routing of data. Energy awareness is an essential design issue and almost all of these routing protocols are considered as energy efficient and its ultimate objective is to maximize the whole network lifetime. However, the introductions of video and imaging sensors have posed additional challenges. Transmission of video and imaging data requires both energy and QoS aware routing in order to ensure efficient usage of the sensors and effective access to the gathered measurements. In this paper, the performance of the energy-aware QoS routing Protocol are analyzed in different performance metrics like average lifetime of a node, average delay per packet and network throughput. The parameters considered in this study are end-to-end delay, real time data generation/capture rates, packet drop probability and buffer size. The network throughput for realtime and non-realtime data was also has been analyzed. The simulation has been done in NS2 simulation environment and the simulation results were analyzed with respect to different metrics.

Keywords: Cluster nodes, end-to-end delay, QoS routing, routing protocols, sensor networks, least-cost-path.

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Authors: Favour Makuochukwu Orji, Yingkui Zhao

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A myriad of environmental issues face the Nigerian industrial region, resulting from; oil and gas production, mining, manufacturing and domestic wastes. Amidst these, much effort has been directed by stakeholders in the Nigerian oil producing regions, because of the impacts of the region on the wider Nigerian economy. Although collaborative environmental management has been noted as an effective approach in managing environmental issues, little attention has been given to the roles and practices of stakeholders in effecting a collaborative environmental management framework for the Nigerian oil-producing region. This paper produces a framework to expand and deepen knowledge relating to stakeholders aspects of collaborative roles in managing environmental issues in the Nigeria oil-producing region. The knowledge is derived from analysis of stakeholders’ practices – studied through multiple case studies using document analysis. Selected documents of key stakeholders – Nigerian government agencies, multi-national oil companies and host communities, were analyzed. Open and selective coding was employed manually during document analysis of data collected from the offices and websites of the stakeholders. The findings showed that the stakeholders have a range of roles, practices, interests, drivers and barriers regarding their collaborative roles in managing environmental issues. While they have interests for efficient resource use, compliance to standards, sharing of responsibilities, generating of new solutions, and shared objectives; there is evidence of major barriers and these include resource allocation, disjointed policy, ineffective monitoring, diverse socio- economic interests, lack of stakeholders’ commitment and limited knowledge sharing. However, host communities hold deep concerns over the collaborative roles of stakeholders for economic interests, particularly, where government agencies and multi-national oil companies are involved. With these barriers and concerns, a genuine stakeholders’ collaboration is found to be limited, and as a result, optimal environmental management practices and policies have not been successfully implemented in the Nigeria oil-producing region. A framework is produced that describes practices that characterize collaborative environmental management might be employed to satisfy the stakeholders’ interests. The framework recommends critical factors, based on the findings, which may guide a collaborative environmental management in the oil producing regions. The recommendations are designed to re-define the practices of stakeholders in managing environmental issues in the oil producing regions, not as something wholly new, but as an approach essential for implementing a sustainable environmental policy. This research outcome may clarify areas for future research as well as to contribute to industry guidance in the area of collaborative environmental management.

Keywords: Collaborative environmental management framework, document analysis, case studies, multinational oil companies, Nigerian oil-producing region, stakeholders analysis.

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Authors: A. Sivakumar, S. S. Darun Prakash, P. Navaneethakrishnan

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In and around Erode District, it is estimated that more than 1250 chemical and allied textile processing fabric industries are affected, partially closed and shut off for various reasons such as poor management, poor supplier performance, lack of planning for productivity, fluctuation of output, poor investment, waste analysis, labor problems, capital/labor ratio, accumulation of stocks, poor maintenance of resources, deficiencies in the quality of fabric, low capacity utilization, age of plant and equipment, high investment and input but low throughput, poor research and development, lack of energy, workers’ fear of loss of jobs, work force mix and work ethic. The main objective of this work is to analyze the existing conditions in textile fabric sector, validate the break even of Total Productivity (TP), analyze, design and implement fuzzy sets and mathematical programming for improvement of productivity and quality dimensions in the fabric processing industry. It needs to be compatible with the reality of textile and fabric processing industries. The highly risk events from productivity and quality dimension were found by fuzzy systems and results are wrapped up among the textile fabric processing industry.

Keywords: Break Even Point, Fuzzy Crisp Data, Fuzzy Sets, Productivity, Productivity Cycle, Total Productive Maintenance.

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Authors: Zhaoan Wang

Abstract:

Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: Automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation.

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Authors: N. Khleifia, A. Hannachi, N. Abbes

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In the present paper, an experimental investigation was conducted to study the thermodynamic of uranium extraction from Tunisian wet phosphoric acid using the synergistic solvent mixture of di-2-ethylhexyl phosphoric acid (DEHPA) and trioctyl phosphine oxid (TOPO) diluted in kerosene. The effect of different factors affecting the extraction process (temperature, TOPO and DEHPA concentrations) has been investigated. The obtained data of temperature effect on the extraction showed that the enthalpy change is -35.8 kJ.mol^-1. The slope analysis method was used for determining the stoichiometry of the extracted species.

Keywords: DEHPA-TOPO, extraction, phosphoric acid, stoichiometry, uranium.

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Authors: Ibrahim A. Khorwat, Nabil Naas

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In this paper, we present a simple circuit for Manchester decoding and without using any complicated or programmable devices. This circuit can decode 90kbps of transmitted encoded data; however, greater than this transmission rate can be decoded if high speed devices were used. We also present a new method for extracting the embedded clock from Manchester data in order to use it for serial-to-parallel conversion. All of our experimental measurements have been done using simulation.

Keywords: High threshold level, level segregation, lowthreshold level, smoothing circuit synchronization..

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Authors: Natália B. Pompermayer, Mariana B. Porto, Elizabeth F. Souza

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Nanophotocatalysts such as titanium (TiO2), zinc (ZnO), and iron (Fe2O3) oxides can be used in organic pollutants oxidation, and in many other applications. But among the challenges for technological application (scale-up) of the nanotechnology scientific developments two aspects are still little explored: research on environmental risk of the nanomaterials preparation methods, and the study of nanomaterials properties and/or performance variability. The environmental analysis was performed for six different methods of ZnO nanoparticles synthesis, and showed that it is possible to identify the more environmentally compatible process even at laboratory scale research. The obtained ZnO nanoparticles were tested as photocatalysts, and increased the degradation rate of the Rhodamine B dye up to 30 times.

Keywords: Environmental impact analysis, inorganic nanoparticles, photocatalysts.

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Authors: Ashagrie Getnet Flattie

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Line-of-sight (LOS) information, data rates, good quality, and flexible network service are limited by the fact that, for the duration of any given connection, they experience severe variation in signal strength due to fading and path loss. Wireless system faces major challenges in achieving wide coverage and capacity without affecting the system performance and to access data everywhere, all the time. In this paper, the cell coverage and edge rate of different Multiple-input multiple-output (MIMO) schemes in 20 MHz Long Term Evolution (LTE) system under Unmanned Air Vehicles (UAV) platform are investigated. After some background on the enormous potential of UAV, MIMO, and LTE in wireless links, the paper highlights the presented system model which attempts to realize the various benefits of MIMO being incorporated into UAV platform. The performances of the three MIMO LTE schemes are compared with the performance of 4x4 MIMO LTE in UAV scheme carried out to evaluate the improvement in cell radius, BER, and data throughput of the system in different morphology. The results show that significant performance gains such as bit error rate (BER), data rate, and coverage can be achieved by using the presented scenario.

Keywords: BER, LTE, MIMO, path loss, UAV.

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Authors: Mahnaz Shafakhah, Marzieh Moattari, Rahelae Sabet Sarvestani

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Regular physical activity contributes positively to physiological and psychological health. This study aimed to identify exercise behavior changes, self efficacy and decisional balance in nursing and midwifery students. This was a cross-sectional study carried out in Iran.300undergraduate nursing and midwifery students participated in this study. Data were collected using a questionnaire including demographic information, exercise stages of change, exercise self efficacy and pros and cons exercise decisional balance. The analysis was performed using the SPSS.A p-value of less than 0.05 was considered as statistically significant.

Keywords: Exercise, Behavior, Student, Self efficacy.

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Authors: A. Al-Rashidi, A. El-Hamalawi

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In this paper, a performance study was conducted to investigate single and dual-axis PV systems to generate electricity in five different sites in Kuwait. Relevant data were obtained by using two sources for validation purposes. A commercial software, PVsyst, was used to analyse the data, such as metrological data and other input parameters, and compute the performance parameters such as capacity factor (CF) and final yield (YF). The results indicated that single and dual-axis PV systems would be very beneficial to electricity generation in Kuwait as an alternative source to conventional power plants, especially with the increased demand over time. The ranges were also found to be competitive in comparison to leading countries using similar systems. A significant increase in CF and YF values around 24% and 28.8% was achieved related to the use of single and dual systems, respectively.

Keywords: Single-axis and dual-axis photovoltaic systems, capacity factor, final yield, renewable energy, Kuwait.

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Authors: Mohammad Javad Dehghani

Abstract:

In the power quality analysis non-stationary nature of voltage distortions require some precise and powerful analytical techniques. The time-frequency representation (TFR) provides a powerful method for identification of the non-stationary of the signals. This paper investigates a comparative study on two techniques for analysis and visualization of voltage distortions with time-varying amplitudes. The techniques include the Discrete Wavelet Transform (DWT), and the S-Transform. Several power quality problems are analyzed using both the discrete wavelet transform and S–transform, showing clearly the advantage of the S– transform in detecting, localizing, and classifying the power quality problems.

Keywords: Power quality, S-Transform, Short Time FourierTransform , Wavelet Transform, instantaneous sag, swell.

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Authors: T. S. Chou, K. K. Yen, J. Luo

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The network traffic data provided for the design of intrusion detection always are large with ineffective information and enclose limited and ambiguous information about users- activities. We study the problems and propose a two phases approach in our intrusion detection design. In the first phase, we develop a correlation-based feature selection algorithm to remove the worthless information from the original high dimensional database. Next, we design an intrusion detection method to solve the problems of uncertainty caused by limited and ambiguous information. In the experiments, we choose six UCI databases and DARPA KDD99 intrusion detection data set as our evaluation tools. Empirical studies indicate that our feature selection algorithm is capable of reducing the size of data set. Our intrusion detection method achieves a better performance than those of participating intrusion detectors.

Keywords: Intrusion detection, feature selection, k-nearest neighbors, fuzzy clustering, Dempster-Shafer theory

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Authors: Han-Taw Chen, Chung-Hou Lai, Tzu-Hsiang Lin, Ge-Jang He

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This study applies the inverse method and three- dimensional CFD commercial software in conjunction with the experimental temperature data to investigate the heat transfer and fluid flow characteristics of the plate-fin heat sink in a closed rectangular enclosure for various values of fin height. The inverse method with the finite difference method and the experimental temperature data is applied to determine the heat transfer coefficient. The k-ε turbulence model is used to obtain the heat transfer and fluid flow characteristics within the fins. To validate the accuracy of the results obtained, the comparison of the average heat transfer coefficient is made. The calculated temperature at selected measurement locations on the plate-fin is also compared with experimental data.

Keywords: Inverse method, FLUENT, k-ε model, Heat transfer characteristics, Plate-fin heat sink.

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Authors: Sorin Valcan, Mihail Găianu

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

Keywords: Labeling automation, infrared camera, driver monitoring, eye detection, Convolutional Neural Networks.

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Authors: Sachin Man Bajimaya, SangChul Park, Gi-Nam Wang

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The objective of this paper is to estimate realistic principal extrusion process parameters by means of artificial neural network. Conventionally, finite element analysis is used to derive process parameters. However, the finite element analysis of the extrusion model does not consider the manufacturing process constraints in its modeling. Therefore, the process parameters obtained through such an analysis remains highly theoretical. Alternatively, process development in industrial extrusion is to a great extent based on trial and error and often involves full-size experiments, which are both expensive and time-consuming. The artificial neural network-based estimation of the extrusion process parameters prior to plant execution helps to make the actual extrusion operation more efficient because more realistic parameters may be obtained. And so, it bridges the gap between simulation and real manufacturing execution system. In this work, a suitable neural network is designed which is trained using an appropriate learning algorithm. The network so trained is used to predict the manufacturing process parameters.

Keywords: Artificial Neural Network (ANN), Indirect Extrusion, Finite Element Analysis, MES.

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Authors: Che-Wei Lee, Bay-Erl Lai

Abstract:

A new steganographic method via the use of numeric data on public websites with a self-authentication capability is proposed. The proposed technique transforms a secret message into partial shares by Shamir’s (k, n)-threshold secret sharing scheme with n = k + 1. The generated k+1 partial shares then are embedded into the numeric items to be disguised as part of the website’s numeric content, yielding the stego numeric content. Afterward, a receiver links to the website and extracts every k shares among the k+1 ones from the stego numeric content to compute k+1 copies of the secret, and the phenomenon of value consistency of the computed k+1 copies is taken as an evidence to determine whether the extracted message is authentic or not, attaining the goal of self-authentication of the extracted secret message. Experimental results and discussions are provided to show the feasibility and effectiveness of the proposed method.

Keywords: Steganography, data hiding, secret authentication, secret sharing.

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Authors: Yoshifumi Ishizaki, Naoki Kanatani, Masaki Ito, Toshihiko Sasama, Takao Kawamura, Kazunori Sugahara

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This paper demonstrates the bus location system for the route bus through the experiment in the real environment. A bus location system is a system that provides information such as the bus delay and positions. This system uses actual services and positions data of buses, and those information should match data on the database. The system has two possible problems. One, the system could cost high in preparing devices to get bus positions. Two, it could be difficult to match services data of buses. To avoid these problems, we have developed this system at low cost and short time by using the smart phone with GPS and the bus route system. This system realizes the path planning considering bus delay and displaying position of buses on the map. The bus location system was demonstrated on route buses with smart phones for two months.

Keywords: Route Bus, Path Planning System, GPS, Smart Phone.

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