Search results for: panel data analysis.

Authors: Natalia Rudeli, Elisabeth Viles, Adrian Santilli

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Delays in the construction industry are a global phenomenon. Many construction projects experience extensive delays exceeding the initially estimated completion time. The main purpose of this study is to identify construction projects typical behaviors in order to develop a prognosis and management tool. Being able to know a construction projects schedule tendency will enable evidence-based decision-making to allow resolutions to be made before delays occur. This study presents an innovative approach that uses Cluster Analysis Method to support predictions during Earned Value Analyses. A clustering analysis was used to predict future scheduling, Earned Value Management (EVM), and Earned Schedule (ES) principal Indexes behaviors in construction projects. The analysis was made using a database with 90 different construction projects. It was validated with additional data extracted from literature and with another 15 contrasting projects. For all projects, planned and executed schedules were collected and the EVM and ES principal indexes were calculated. A complete linkage classification method was used. In this way, the cluster analysis made considers that the distance (or similarity) between two clusters must be measured by its most disparate elements, i.e. that the distance is given by the maximum span among its components. Finally, through the use of EVM and ES Indexes and Tukey and Fisher Pairwise Comparisons, the statistical dissimilarity was verified and four clusters were obtained. It can be said that construction projects show an average delay of 35% of its planned completion time. Furthermore, four typical behaviors were found and for each of the obtained clusters, the interim milestones and the necessary rhythms of construction were identified. In general, detected typical behaviors are: (1) Projects that perform a 5% of work advance in the first two tenths and maintain a constant rhythm until completion (greater than 10% for each remaining tenth), being able to finish on the initially estimated time. (2) Projects that start with an adequate construction rate but suffer minor delays culminating with a total delay of almost 27% of the planned time. (3) Projects which start with a performance below the planned rate and end up with an average delay of 64%, and (4) projects that begin with a poor performance, suffer great delays and end up with an average delay of a 120% of the planned completion time. The obtained clusters compose a tool to identify the behavior of new construction projects by comparing their current work performance to the validated database, thus allowing the correction of initial estimations towards more accurate completion schedules.

Keywords: Cluster analysis, construction management, earned value, schedule.

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Authors: Sandya Mummullage, Prasanna Egodawatta, Ashantha Goonetilleke, Godwin A. Ayoko

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Urban road dust comprises of a range of potentially  toxic metal elements and plays a critical role in degrading urban  receiving water quality. Hence, assessing the metal composition and  concentration in urban road dust is a high priority. This study  investigated the variability of metal composition and concentrations  in road dust in 4 different urban land uses in Gold Coast, Australia.  Samples from 16 road sites were collected and tested for selected 12  metal species. The data set was analyzed using both univariate and  multivariate techniques. Outcomes of the data analysis revealed that  the metal concentrations inroad dust differs considerably within and  between different land uses. Iron, aluminum, magnesium and zinc are  the most abundant in urban land uses. It was also noted that metal  species such as titanium, nickel, copper and zinc have the highest  concentrations in industrial land use. The study outcomes revealed  that soil and traffic related sources as key sources of metals deposited  on road surfaces.

 

Keywords: Metals build-up, Pollutant accumulation, Stormwater quality, Urban road dust.

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Authors: Ramy H. Mohammed

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In heat sinks, the flow within the core exhibits separation and hence does not lend itself to simple analytical boundary layer or duct flow analysis of the wall friction. In this paper, we present some findings from an experimental and numerical study aimed to obtain physical insight into the influence of the presence of the shield and its position on the hydraulic and thermal performance of square pin fin heat sink without top by-pass. The variations of the Nusselt number and friction factor are obtained under varied parameters, such as the Reynolds number and the shield position. The numerical code is validated by comparing the numerical results with the available experimental data. It is shown that, there is a good agreement between the temperature predictions based on the model and the experimental data. Results show that, as the presence of the shield, the heat transfer of fin array is enhanced and the flow resistance increased. The surface temperature distribution of the heat sink base is more uniform when the dimensionless shield position equals to 1/3 or 2/3. The comprehensive performance evaluation approach based on identical pumping power criteria is adopted and shows that the optimum shield position is at x/l=0.43.

Keywords: Shield, Fin array, Performance evaluation, Heat transfer, Validation.

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Authors: Mustafa Bin Yusof, Mohammad Amirul Affiz Bin Afripin

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Safety of bus journey is a fundamental concern. Risk of injuries and fatalities is severe when bus superstructure fails during rollover accident. Adequate design and sufficient strength of bus superstructure can reduce the number of injuries and fatalities. This paper deals with structural analysis of bus superstructure undergoes rollover event. Several value of mass will be varied in multiple simulations. The purpose of this work is to analyze structural response of bus superstructure in terms of deformation, stress and strain under several loading and constraining conditions. A complete bus superstructure with forty four passenger-s capability was developed using finite element analysis software. Simulations have been conducted to observe the effect of total mass of bus on the strength of superstructure. These simulations are following United Nation Economic Commission of Europe regulation 66 which focuses on strength of large vehicle superstructure. Validation process had been done using simple box model experiment and results obtained are comparing with simulation results. Inputs data from validation process had been used in full scale simulation. Analyses suggested that, the failure of bus superstructure during rollover situation is basically dependent on the total mass of bus and on the strength of bus superstructure.

Keywords: Bus, rollover, superstructure strength, UNECE regulation 66.

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Authors: Alireza Ansariyar, Mansoureh Jeihani

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Light Detection and Ranging (LiDAR) sensors is capable of recording traffic data including the number of passing vehicles and bicyclists, the speed of vehicles and bicyclists, and the number of conflicts among both road users. In order to collect real-time traffic data and investigate the safety of different road users, a LiDAR sensor was installed at Cold Spring Ln – Hillen Rd intersection in Baltimore city. The frequency and severity of collected real-time conflicts were analyzed and the results highlighted that 122 conflicts were recorded over a 10-month time interval from May 2022 to February 2023. By employing an image-processing algorithm, a safety Measure of Effectiveness (MOE) aims to identify critical zones for bicyclists upon entering each respective zone at the signalized intersection. Considering the trajectory of conflicts, the results of analysis demonstrated that conflicts in the northern approach (zone N) are more frequent and severe. Additionally, sunny weather is more likely to cause severe vehicle-bike conflicts.

Keywords: LiDAR sensor, Post Encroachment Time threshold, vehicle-bike conflicts, measure of effectiveness, weather condition.

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Authors: Omer K. Jasim, Safia Abbas, El-Sayed M. El-Horbaty, Abdel-Badeeh M. Salem

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Cloud computing technology is very useful in present day to day life, it uses the internet and the central remote servers to provide and maintain data as well as applications. Such applications in turn can be used by the end users via the cloud communications without any installation. Moreover, the end users’ data files can be accessed and manipulated from any other computer using the internet services. Despite the flexibility of data and application accessing and usage that cloud computing environments provide, there are many questions still coming up on how to gain a trusted environment that protect data and applications in clouds from hackers and intruders. This paper surveys the “keys generation and management” mechanism and encryption/decryption algorithms used in cloud computing environments, we proposed new security architecture for cloud computing environment that considers the various security gaps as much as possible. A new cryptographic environment that implements quantum mechanics in order to gain more trusted with less computation cloud communications is given.

Keywords: Cloud Computing, Cloud Encryption Model, Quantum Key Distribution.

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Authors: Martin Grootveld, Benita Percival, Sarah Moumtaz, Kerry L. Grootveld

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Objectives/Hypotheses: The adverse health effect potential of dietary lipid oxidation products (LOPs) has evoked much clinical interest. Therefore, we employed a ¹H NMR-linked Principal Component Regression (PCR) chemometrics modelling strategy to explore relationships between data matrices comprising (1) aldehydic LOP concentrations generated in culinary oils/fats when exposed to laboratory-simulated shallow frying practices, and (2) the prior saturated (SFA), monounsaturated (MUFA) and polyunsaturated fatty acid (PUFA) contents of such frying media (FM), together with their heating time-points at a standard frying temperature (180 ^oC). Methods: Corn, sunflower, extra virgin olive, rapeseed, linseed, canola, coconut and MUFA-rich algae frying oils, together with butter and lard, were heated according to laboratory-simulated shallow-frying episodes at 180 ^oC, and FM samples were collected at time-points of 0, 5, 10, 20, 30, 60, and 90 min. (n = 6 replicates per sample). Aldehydes were determined by ¹H NMR analysis (Bruker AV 400 MHz spectrometer). The first (dependent output variable) PCR data matrix comprised aldehyde concentration scores vectors (PC1* and PC2*), whilst the second (predictor) one incorporated those from the fatty acid content/heating time variables (PC1-PC4) and their first-order interactions. Results: Structurally complex trans,trans- and cis,trans-alka-2,4-dienals, 4,5-epxy-trans-2-alkenals and 4-hydroxy-/4-hydroperoxy-trans-2-alkenals (group I aldehydes predominantly arising from PUFA peroxidation) strongly and positively loaded on PC1*, whereas n-alkanals and trans-2-alkenals (group II aldehydes derived from both MUFA and PUFA hydroperoxides) strongly and positively loaded on PC2*. PCR analysis of these scores vectors (SVs) demonstrated that PCs 1 (positively-loaded linoleoylglycerols and [linoleoylglycerol]:[SFA] content ratio), 2 (positively-loaded oleoylglycerols and negatively-loaded SFAs), 3 (positively-loaded linolenoylglycerols and [PUFA]:[SFA] content ratios), and 4 (exclusively orthogonal sampling time-points) all powerfully contributed to aldehydic PC1* SVs (p 10^-3 to < 10^-9), as did all PC1-3 x PC4 interaction ones (p 10^-5 to < 10^-9). PC2* was also markedly dependent on all the above PC SVs (PC2 > PC1 and PC3), and the interactions of PC1 and PC2 with PC4 (p < 10^-9 in each case), but not the PC3 x PC4 contribution. Conclusions: NMR-linked PCR analysis is a valuable strategy for (1) modelling the generation of aldehydic LOPs in heated cooking oils and other FM, and (2) tracking their unsaturated fatty acid (UFA) triacylglycerol sources therein.

Keywords: Frying oils, frying episodes, lipid oxidation products, cytotoxic/genotoxic aldehydes, chemometrics, principal component regression, NMR Analysis.

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Authors: S. J. Matlan, M. Mukhlisin, M. R. Taha

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Characterization of the engineering behavior of unsaturated soil is dependent on the soil-water characteristic curve (SWCC), a graphical representation of the relationship between water content or degree of saturation and soil suction. A reasonable description of the SWCC is thus important for the accurate prediction of unsaturated soil parameters. The measurement procedures for determining the SWCC, however, are difficult, expensive, and timeconsuming. During the past few decades, researchers have laid a major focus on developing empirical equations for predicting the SWCC, with a large number of empirical models suggested. One of the most crucial questions is how precisely existing equations can represent the SWCC. As different models have different ranges of capability, it is essential to evaluate the precision of the SWCC models used for each particular soil type for better SWCC estimation. It is expected that better estimation of SWCC would be achieved via a thorough statistical analysis of its distribution within a particular soil class. With this in view, a statistical analysis was conducted in order to evaluate the reliability of the SWCC prediction models against laboratory measurement. Optimization techniques were used to obtain the best-fit of the model parameters in four forms of SWCC equation, using laboratory data for relatively coarse-textured (i.e., sandy) soil. The four most prominent SWCCs were evaluated and computed for each sample. The result shows that the Brooks and Corey model is the most consistent in describing the SWCC for sand soil type. The Brooks and Corey model prediction also exhibit compatibility with samples ranging from low to high soil water content in which subjected to the samples that evaluated in this study.

Keywords: Soil-water characteristic curve (SWCC), statistical analysis, unsaturated soil.

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Authors: Sidharth Talan, Devendra Kumar Yadav, Yuvraj Singh Rajput, Subhajit Bhattacharjee

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Mining industry is known for its appetite to spend sizeable capital on mine equipment. However, in the current scenario, the mining industry is challenged by daunting factors of non-uniform geological conditions, uneven ore grade, uncontrollable and volatile mineral commodity prices and the ever increasing quest to optimize the capital and operational costs. Thus, the role of equipment reliability and maintenance planning inherits a significant role in augmenting the equipment availability for the operation and in turn boosting the mine productivity. This paper presents the Risk Based Maintenance (RBM) planning conducted on mine loading equipment namely Load Haul Dumpers (LHDs) at Vedanta Resources Ltd subsidiary Hindustan Zinc Limited operated Sindesar Khurd Mines, an underground zinc and lead mine situated in Dariba, Rajasthan, India. The mining equipment at the location is maintained by the Original Equipment Manufacturers (OEMs) namely Sandvik and Atlas Copco, who carry out the maintenance and inspection operations for the equipment. Based on the downtime data extracted for the equipment fleet over the period of 6 months spanning from 1^st January 2017 until 30^th June 2017, it was revealed that significant contribution of three downtime issues related to namely Engine, Hydraulics, and Transmission to be common among all the loading equipment fleet and substantiated by Pareto Analysis. Further scrutiny through Bubble Matrix Analysis of the given factors revealed the major influence of selective factors namely Overheating, No Load Taken (NTL) issues, Gear Changing issues and Hose Puncture and leakage issues. Utilizing the equipment wise analysis of all the downtime factors obtained, spares consumed, and the alarm logs extracted from the machines, technical design changes in the equipment and pre shift critical alarms checklist were proposed for the equipment maintenance. The given analysis is beneficial to allow OEMs or mine management to focus on the critical issues hampering the reliability of mine equipment and design necessary maintenance strategies to mitigate them.

Keywords: Bubble matrix analysis, LHDs, OEMs, pareto chart analysis, spares consumption matrix, critical alarms checklist.

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Authors: Hocine Hammoum, Karima Bouzelha, Lounis Ziani, Lounis Hamitouche

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In this paper, we study a complex structure which is an apartment building surmounted by a reinforced concrete water tank. The tank located on the top floor of the building is a container with capacity of 1000 m³. The building is complex in its design, its calculation and by its behavior under earthquake effect. This structure located in Algiers and aged of 53 years has been subjected to several earthquakes, but the earthquake of May 21^st, 2003 with a magnitude of 6.7 on the Richter scale that struck Boumerdes region at 40 Kms East of Algiers was fatal for it. It was downgraded after an investigation study because the central core sustained serious damage. In this paper, to estimate the degree of its damages, the seismic performance of the structure will be evaluated taking into account the hydrodynamic effect, using a static equivalent nonlinear analysis called pushover.

Keywords: Performance analysis, building, reinforced concrete tank, seismic analysis, nonlinear analysis, hydrodynamic, pushover.

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Authors: Sharath Kumar T., S.Vinodh

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Over the course of the past century, the global automotive industry-s stance towards safety has evolved from one of contempt to one nearing reverence. A suspension system that provides safe handling and cornering capabilities can, with the help of an efficient braking system, improve safety to a large extent. The aim of this research is to propose a new automotive brake rotor design and to compare it with automotive vented disk rotor. Static structural and transient thermal analysis have been carried out on the vented disk rotor and proposed rotor designs to evaluate and compare their performance. Finite element analysis was employed for both static structural and transient thermal analysis. Structural analysis was carried out to study the stress and deformation pattern of the rotors under extreme loads. Time varying temperature load was applied on the rotors and the temperature distribution was analysed considering cooling parameters (convection and radiation). This dissertation illustrates the use of Finite Element Methods to examine models, concluding with a comparative study of the proposed rotor design and the conventional vented disk rotor for structural stability and thermal efficiency.

Keywords: Disk brakes, CAD model, rotor design, structural and thermal analysis

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Authors: Ren-Junn Hwang, Chung-Chien Lu, Jain-Shing Wu

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Cloud outsource storage is one of important services in cloud computing. Cloud users upload data to cloud servers to reduce the cost of managing data and maintaining hardware and software. To ensure data confidentiality, users can encrypt their files before uploading them to a cloud system. However, retrieving the target file from the encrypted files exactly is difficult for cloud server. This study proposes a protocol for performing multikeyword searches for encrypted cloud data by applying k-nearest neighbor technology. The protocol ranks the relevance scores of encrypted files and keywords, and prevents cloud servers from learning search keywords submitted by a cloud user. To reduce the costs of file transfer communication, the cloud server returns encrypted files in order of relevance. Moreover, when a cloud user inputs an incorrect keyword and the number of wrong alphabet does not exceed a given threshold; the user still can retrieve the target files from cloud server. In addition, the proposed scheme satisfies security requirements for outsourced data storage.

Keywords: Fault-tolerance search, multi-keywords search, outsource storage, ranked search, searchable encryption.

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Authors: Qais M. Yousef, Yasmeen A. Alshaer

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Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Keywords: Artificial intelligence, data contents search, human active memory, mind wave, multi-objective optimization.

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Authors: Estela Macedo Alves

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This paper is an overview on public policy analysis focused on the study of Brazilian public policy making process. The methodology is based on the review of some theories on the subject, linking them to Brazilian reality. The study presents basic policy analysis concepts, such as policy, polity and politics. It is emphasized John Kingdon's Multiple Stream Model, because of its clarifying aspects concerning public policies formulation process in democratic countries. In this path it was possible to establish interpretations on environmental public policies in Brazil and understand its methods, instead of presenting only a case study. At the end, it is possible to connect theory with Brazilian reality, identifying negative and positive points of its political processes and structure.

Keywords: Brazilian policies, environmental public policy, multiple stream model, public policy analysis.

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Authors: Martina Kanciruk, Jac W. Andrews, Tyrone Donnon

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The purpose of this study was to determine the significance of maternal smoking for the development of childhood overweight and/or obesity. Accordingly, a systematic literature review of English-language studies published from 1980 to 2012 using the following data bases: MEDLINE, PsychINFO, Cochrane Database of Systematic Reviews, and Dissertation Abstracts International was conducted. The following terms were used in the search: pregnancy, overweight, obesity, smoking, parents, childhood, risk factors. Eighteen studies of maternal smoking during pregnancy and obesity conducted in Europe, Asia, North America, and South America met the inclusion criteria. A meta-analysis of these studies indicated that maternal smoking during pregnancy is a significant risk factor for overweight and obesity; mothers who smoke during pregnancy are at a greater risk for developing obesity or overweight; the quantity of cigarettes consumed by the mother during pregnancy influenced the odds of offspring overweight and/or obesity. In addition, the results from moderator analyses suggest that part of the heterogeneity discovered between the studies can be explained by the region of world that the study occurred in and the age of the child at the time of weight assessment.

Keywords: Childhood obesity, overweight, smoking, parents, risk factors.

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Authors: S. L. Tulasi Devi, R. N. Rao

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This paper focuses attention on specific aspects of entrepreneurial decisions relating to investment, both in the total fixed investments and plant & machinery (separately). Demand and financial factors, internal and external, are considered in the investment analysis. Finally the influence of determinants of fixed investment and investment plans are examined in Electric Power industry in India.

Keywords: Determinants, Electric Power Industry, Fixed Assets, Econometric Analysis.

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Authors: Rasheed O. Oloyede, Joseph O. Adelusi, Peter O. Akinbile

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This study was conducted to investigate the extent with which knowledge of results influences the performance of cricket players. A sample of 160 fresh students in the Department of Physical and Health Education who are novice in the game were randomly assigned into two groups. The first group of eighty (80) subjects was classified as experimental group while the second group of eighty (80) subjects was the control group. Subjects in both groups were asked to bowl and bat ten times each for a period of six weeks. After the first round, the subjects in the experimental group were allowed feedback on their performance in the first trial while those in the control group were denied feedback. Two null hypotheses generated for the study were tested using percentages and chi-square statistical analysis at 0.05 level of significance. Analysis of data showed that knowledge of results influenced the performance of cricket players. It was concluded that knowledge of results is pertinent for effective skill acquisition and could enhance better performance among unskilled cricket players. Hence, it is suggested that immediate feedback on the level of skill acquisition by the prospective and unskilled cricket players would inspire them for better performance in cricket tournaments.

Keywords: Batting, Bowling, Knowledge of Results, Performance, Skill Acquisition.

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Authors: Vladimir Simovic, Matija Varga, Tonco Marusic

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This research paper shows matrix technology models and examples of information systems in education (in the Republic of Croatia and in the Germany) in support of business, education (when learning and teaching) and e-learning. Here we researched and described the aims and objectives of the main process in education and technology, with main matrix classes of data. In this paper, we have example of matrix technology with detailed description of processes related to specific data classes in the processes of education and an example module that is support for the process: ‘Filling in the directory and the diary of work’ and ‘evaluation’. Also, on the lower level of the processes, we researched and described all activities which take place within the lower process in education. We researched and described the characteristics and functioning of modules: ‘Fill the directory and the diary of work’ and ‘evaluation’. For the analysis of the affinity between the aforementioned processes and/or sub-process we used our application model created in Visual Basic, which was based on the algorithm for analyzing the affinity between the observed processes and/or sub-processes.

Keywords: Designing, education management, information systems, matrix technology, process affinity.

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Authors: Hatem A. Khater, A. Baith Mohamed, Sara M. Kamel

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Software Development Risks Identification (SDRI), using Fault Tree Analysis (FTA), is a proposed technique to identify not only the risk factors but also the causes of the appearance of the risk factors in software development life cycle. The method is based on analyzing the probable causes of software development failures before they become problems and adversely affect a project. It uses Fault tree analysis (FTA) to determine the probability of a particular system level failures that are defined by A Taxonomy for Sources of Software Development Risk to deduce failure analysis in which an undesired state of a system by using Boolean logic to combine a series of lower-level events. The major purpose of this paper is to use the probabilistic calculations of Fault Tree Analysis approach to determine all possible causes that lead to software development risk occurrence

Keywords: Software Development Risks Identification (SDRI), Fault Tree Analysis (FTA), Taxonomy for Software Development Risks (TSDR), Probabilistic Risk Assessment (PRA).

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Authors: Mohamed Amine Ben Henni, Taher Hassaine Daouadji, Boussad Abbes, Yu Ming Li, Fazilay Abbes

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The reinforcement and repair of concrete structures by bonding composite materials have become relatively common operations. Different types of composite materials can be used: carbon fiber reinforced polymer (CFRP), glass fiber reinforced polymer (GFRP) as well as functionally graded material (FGM). The development of analytical and numerical models describing the mechanical behavior of structures in civil engineering reinforced by composite materials is necessary. These models will enable engineers to select, design, and size adequate reinforcements for the various types of damaged structures. This study focuses on the free vibration behavior of orthotropic laminated composite plates using a refined shear deformation theory. In these models, the distribution of transverse shear stresses is considered as parabolic satisfying the zero-shear stress condition on the top and bottom surfaces of the plates without using shear correction factors. In this analysis, the equation of motion for simply supported thick laminated rectangular plates is obtained by using the Hamilton’s principle. The accuracy of the developed model is demonstrated by comparing our results with solutions derived from other higher order models and with data found in the literature. Besides, a finite-element analysis is used to calculate the natural frequencies of laminated composite plates and is compared with those obtained by the analytical approach.

Keywords: Composites materials, laminated composite plate, shear deformation theory of plates, finite element analysis, free vibration.

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Authors: Eiad Yafi, Ahmed Sultan Al-Hegami, M. A. Alam, Ranjit Biswas

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Association rules are an important problem in data mining. Massively increasing volume of data in real life databases has motivated researchers to design novel and incremental algorithms for association rules mining. In this paper, we propose an incremental association rules mining algorithm that integrates shocking interestingness criterion during the process of building the model. A new interesting measure called shocking measure is introduced. One of the main features of the proposed approach is to capture the user background knowledge, which is monotonically augmented. The incremental model that reflects the changing data and the user beliefs is attractive in order to make the over all KDD process more effective and efficient. We implemented the proposed approach and experiment it with some public datasets and found the results quite promising.

Keywords: Knowledge discovery in databases (KDD), Data mining, Incremental Association rules, Domain knowledge, Interestingness, Shocking rules (SHR).

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Authors: Seema Chopra, R. Mitra, Vijay Kumar

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In this paper, a neural network tuned fuzzy controller is proposed for controlling Multi-Input Multi-Output (MIMO) systems. For the convenience of analysis, the structure of MIMO fuzzy controller is divided into single input single-output (SISO) controllers for controlling each degree of freedom. Secondly, according to the characteristics of the system-s dynamics coupling, an appropriate coupling fuzzy controller is incorporated to improve the performance. The simulation analysis on a two-level mass–spring MIMO vibration system is carried out and results show the effectiveness of the proposed fuzzy controller. The performance though improved, the computational time and memory used is comparatively higher, because it has four fuzzy reasoning blocks and number may increase in case of other MIMO system. Then a fuzzy neural network is designed from a set of input-output training data to reduce the computing burden during implementation. This control strategy can not only simplify the implementation problem of fuzzy control, but also reduce computational time and consume less memory.

Keywords: Fuzzy Control, Neural Network, MIMO System, Optimization of Membership functions.

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Authors: Y. N. Phang, E. F. Loh

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Zero inflated strict arcsine model is a newly developed model which is found to be appropriate in modeling overdispersed count data. In this study, we extend zero inflated strict arcsine model to zero inflated strict arcsine regression model by taking into consideration the extra variability caused by extra zeros and covariates in count data. Maximum likelihood estimation method is used in estimating the parameters for this zero inflated strict arcsine regression model.

Keywords: Overdispersed count data, maximum likelihood estimation, simulated annealing.

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Authors: Ma Ya-li, Tang Fu-jun, Xue Yu-xiong, Chen Yi-feng, Gao Xin, Wang Yi, Tian Kai, Yan Ze-dong

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Plasma plume will be produced and arrive at spacecraft when the electric thruster operates on orbit. It-s important to characterize the thruster plasma parameters because the plume has significant effects or hazards on spacecraft sub-systems and parts. Through the ground test data of the desired parameters, the major characteristics of the thruster plume will be achieved. Also it is very important for optimizing design of Ion thruster. Retarding Potential Analyzer (RPA) is an effective instrument for plasma ion energy per unit charge distribution measurement. Special RPA should be designed according to certain plume plasma parameters range and feature. In this paper, major principles usable for good RPA design are discussed carefully. Conform to these principles, a four-grid planar electrostatic energy analyzer RPA was designed to avoid false data, and details were discussed including construction, materials, aperture diameter and so on. At the same time, it was designed more suitable for credible and long-duration measurements in the laboratory. In the end, RPA measurement results in the laboratory were given and discussed.

Keywords: Thruster plume ion energy distributions, retarding potential analyzer, ground test.

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Authors: Panikos Heracleous

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In this study, the use of silicon NAM (Non-Audible Murmur) microphone in automatic speech recognition is presented. NAM microphones are special acoustic sensors, which are attached behind the talker-s ear and can capture not only normal (audible) speech, but also very quietly uttered speech (non-audible murmur). As a result, NAM microphones can be applied in automatic speech recognition systems when privacy is desired in human-machine communication. Moreover, NAM microphones show robustness against noise and they might be used in special systems (speech recognition, speech conversion etc.) for sound-impaired people. Using a small amount of training data and adaptation approaches, 93.9% word accuracy was achieved for a 20k Japanese vocabulary dictation task. Non-audible murmur recognition in noisy environments is also investigated. In this study, further analysis of the NAM speech has been made using distance measures between hidden Markov model (HMM) pairs. It has been shown the reduced spectral space of NAM speech using a metric distance, however the location of the different phonemes of NAM are similar to the location of the phonemes of normal speech, and the NAM sounds are well discriminated. Promising results in using nonlinear features are also introduced, especially under noisy conditions.

Keywords: Speech recognition, unvoiced speech, nonlinear features, HMM distance measures

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Authors: Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, William Templier, Costin Vasile

Abstract:

Industrial end users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both Machine Learning and First Principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed from breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems and humans – including asset maintenance operations – in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.

Keywords: Degradation models, ageing, anomaly detection, soft sensor, incremental learning.

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Authors: Monika Kulikowska-Pawlak, Katarzyna Bratnicka-Myśliwiec, Tomasz Ingram

Abstract:

This study seeks to contribute to the literature on firm competitiveness by advancing the perspective of organizational politics that views this process as a driver which creates identifiable differences in firm performance. The hypothesized relationships were tested on the basis of data from 355 Polish medium and large-sized enterprises. Data were analyzed using correlation analysis, EFA and robustness tests. The main result of the conducted analyses proved the coexistence, previously examined in the literature, of corporate entrepreneurship and firm performance. The obtained research findings made it possible to add organizational politics to a wide range of elements determining corporate entrepreneurship, followed by competitive advantage, in addition to antecedents such as strategic leadership, corporate culture, opportunity-oriented resource-based management, etc. Also, the empirical results suggest that four dimensions of organizational politics (dominant coalition, influence exertion, making organizational changes, and information openness) are positively related to firm competitiveness. In addition, these findings seem to underline a supposition that corporate entrepreneurship is an important mediator which strengthens the competitive effects of organizational politics.

Keywords: Corporate entrepreneurship, firm competitiveness organizational politics, sensemaking.

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

Abstract:

A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: Big data, k-NN, machine learning, traffic speed prediction.

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Authors: Kirsi Korkealehto

Abstract:

In Finland the transformation from homogenous culture into multicultural one as a result of heavy immigration has been rapid in the recent decades. As multilingualism and multiculturalism are growing features in our society, teachers in all educational levels need to be competent for encounters with students from diverse cultural backgrounds. Consequently, also the number of multicultural and multilingual vocational school students has increased which has not been taken into consideration in teacher education enough. To bridge this gap between teachers’ competences and the requirements of the contemporary school world, Finnish Ministry of Culture and Education established the DivEd-project. The aim of the project is to prepare all teachers to work in the linguistically and culturally diverse world they live in, to develop and increase culturally sustaining and linguistically responsive pedagogy in Finland, increase awareness among Teacher Educators working with preservice teachers and to increase awareness and provide specific strategies to in-service teachers. The partners in the nationwide project are 6 universities and 2 universities of applied sciences. In this research, the linguistically and culturally sustainable teaching practices developed within the DivEd-project are tested in practice. This research aims to explore vocational teachers’ perceptions of these multilingualism and multilingual educational practices. The participants of this study are vocational teachers in of different fields. The data were collected by individual, face-to-face interviews. The data analysis was conducted through content analysis. The findings indicate that the vocational teachers experience that they lack knowledge on linguistically and culturally responsive pedagogy. Moreover, they regard themselves in some extent incompetent in incorporating multilingually and multiculturally sustainable pedagogy in everyday teaching work. Therefore, they feel they need more training pertaining multicultural and multilingual knowledge, competences and suitable pedagogical methods for teaching students from diverse linguistic and cultural backgrounds.

Keywords: Multicultural, multilingual, teacher competences, vocational school.

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Authors: Neamat El Gayar

Abstract:

Learning using labeled and unlabelled data has received considerable amount of attention in the machine learning community due its potential in reducing the need for expensive labeled data. In this work we present a new method for combining labeled and unlabeled data based on classifier ensembles. The model we propose assumes each classifier in the ensemble observes the input using different set of features. Classifiers are initially trained using some labeled samples. The trained classifiers learn further through labeling the unknown patterns using a teaching signals that is generated using the decision of the classifier ensemble, i.e. the classifiers self-supervise each other. Experiments on a set of object images are presented. Our experiments investigate different classifier models, different fusing techniques, different training sizes and different input features. Experimental results reveal that the proposed self-supervised ensemble learning approach reduces classification error over the single classifier and the traditional ensemble classifier approachs.

Keywords: Multiple Classifier Systems, classifier ensembles, learning using labeled and unlabelled data, K-nearest neighbor classifier, Bayes classifier.

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