Search results for: Principle Component Regression

Authors: Kai Häussermann, Christoph Hubig, Paul Levi, Frank Leymann, Oliver Siemoneit, Matthias Wieland, Oliver Zweigle

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Using spatial models as a shared common basis of information about the environment for different kinds of contextaware systems has been a heavily researched topic in the last years. Thereby the research focused on how to create, to update, and to merge spatial models so as to enable highly dynamic, consistent and coherent spatial models at large scale. In this paper however, we want to concentrate on how context-aware applications could use this information so as to adapt their behavior according to the situation they are in. The main idea is to provide the spatial model infrastructure with a situation recognition component based on generic situation templates. A situation template is – as part of a much larger situation template library – an abstract, machinereadable description of a certain basic situation type, which could be used by different applications to evaluate their situation. In this paper, different theoretical and practical issues – technical, ethical and philosophical ones – are discussed important for understanding and developing situation dependent systems based on situation templates. A basic system design is presented which allows for the reasoning with uncertain data using an improved version of a learning algorithm for the automatic adaption of situation templates. Finally, for supporting the development of adaptive applications, we present a new situation-aware adaptation concept based on workflows.

Keywords: context-awareness, ethics, facilitation of system use through workflows, situation recognition and learning based on situation templates and situation ontology's, theory of situationaware systems

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Authors: Fatemeh Behjati Ardakani, Fatemeh Khademian, Abbas Nowzari Dalini, Reza Ebrahimpour

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Human activity is a major concern in a wide variety of applications, such as video surveillance, human computer interface and face image database management. Detecting and recognizing faces is a crucial step in these applications. Furthermore, major advancements and initiatives in security applications in the past years have propelled face recognition technology into the spotlight. The performance of existing face recognition systems declines significantly if the resolution of the face image falls below a certain level. This is especially critical in surveillance imagery where often, due to many reasons, only low-resolution video of faces is available. If these low-resolution images are passed to a face recognition system, the performance is usually unacceptable. Hence, resolution plays a key role in face recognition systems. In this paper we introduce a new low resolution face recognition system based on mixture of expert neural networks. In order to produce the low resolution input images we down-sampled the 48 × 48 ORL images to 12 × 12 ones using the nearest neighbor interpolation method and after that applying the bicubic interpolation method yields enhanced images which is given to the Principal Component Analysis feature extractor system. Comparison with some of the most related methods indicates that the proposed novel model yields excellent recognition rate in low resolution face recognition that is the recognition rate of 100% for the training set and 96.5% for the test set.

Keywords: Low resolution face recognition, Multilayered neuralnetwork, Mixture of experts neural network, Principal componentanalysis, Bicubic interpolation, Nearest neighbor interpolation.

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Authors: Achim Washington, Reece Clothier, Jose Silva

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System Safety Regulations (SSR) are a central component to the airworthiness certification of Unmanned Aircraft Systems (UAS). There is significant debate on the setting of appropriate SSR for UAS. Putting this debate aside, the challenge lies in how to apply the system safety process to UAS, which lacks the data and operational heritage of conventionally piloted aircraft. The limited knowledge and lack of operational data result in uncertainty in the system safety assessment of UAS. This uncertainty can lead to incorrect compliance findings and the potential certification and operation of UAS that do not meet minimum safety performance requirements. The existing system safety assessment and compliance processes, as used for conventional piloted aviation, do not adequately account for the uncertainty, limiting the suitability of its application to UAS. This paper discusses the challenges of undertaking system safety assessments for UAS and presents current and envisaged research towards addressing these challenges. It aims to highlight the main advantages associated with adopting a risk based framework to the System Safety Performance Requirement (SSPR) compliance process that is capable of taking the uncertainty associated with each of the outputs of the system safety assessment process into consideration. Based on this study, it is made clear that developing a framework tailored to UAS, would allow for a more rational, transparent and systematic approach to decision making. This would reduce the need for conservative assumptions and take the risk posed by each UAS into consideration while determining its state of compliance to the SSR.

Keywords: Part 1309 regulations, unmanned aircraft systems, system safety, uncertainty.

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Authors: Carmen Grigoraş, Victor Grigoraş, Daniela Boişteanu

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The standard investigational method for obstructive sleep apnea syndrome (OSAS) diagnosis is polysomnography (PSG), which consists of a simultaneous, usually overnight recording of multiple electro-physiological signals related to sleep and wakefulness. This is an expensive, encumbering and not a readily repeated protocol, and therefore there is need for simpler and easily implemented screening and detection techniques. Identification of apnea/hypopnea events in the screening recordings is the key factor for the diagnosis of OSAS. The analysis of a solely single-lead electrocardiographic (ECG) signal for OSAS diagnosis, which may be done with portable devices, at patient-s home, is the challenge of the last years. A novel artificial neural network (ANN) based approach for feature extraction and automatic identification of respiratory events in ECG signals is presented in this paper. A nonlinear principal component analysis (NLPCA) method was considered for feature extraction and support vector machine for classification/recognition. An alternative representation of the respiratory events by means of Kohonen type neural network is discussed. Our prospective study was based on OSAS patients of the Clinical Hospital of Pneumology from Iaşi, Romania, males and females, as well as on non-OSAS investigated human subjects. Our computed analysis includes a learning phase based on cross signal PSG annotation.

Keywords: Artificial neural networks, feature extraction, obstructive sleep apnea syndrome, pattern recognition, signalprocessing.

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: Deep learning, convolutional neural network, LSTM, housing prediction.

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Authors: Ibrahim H. Kilic, A. B. Tugrul

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Energy is a fundamental component in economic development and energy consumption is an index of prosperity and the standard of living. The consumption of energy per capita has increased significantly over the last decades, as the standard of living has improved. Turkey’s geographical location has several advantages for extensive use of wind power. Among the renewable sources, Turkey has very high wind energy potential. Information such as installation capacity of wind power plants in installation, under construction and license stages in the country are reported in detail. Some suggestions are presented in order to increase the wind power installation capacity of Turkey. Turkey’s economic and social development has led to a massive increase in demand for electricity over the last decades. Since the Turkey has no major oil or gas reserves, it is highly dependent on energy imports and is exposed to energy insecurity in the future. But Turkey does have huge potential for renewable energy utilization. There has been a huge growth in the construction of wind power plants and small hydropower plants in recent years. To meet the growing energy demand, the Turkish Government has adopted incentives for investments in renewable energy production. Wind energy investments evaluated the impact of feed-in tariffs (FIT) based on three scenarios that are optimistic, realistic and pessimistic with APLUS software that is developed for rational evaluation for energy market. Results of the three scenarios are evaluated in the view of electricity market for Turkey.

Keywords: APLUS, energy policy, renewable energy, wind power, Turkey.

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Authors: Robles F, Uribe A, Guadarrama A , Castellanos L, González C.

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The main objective of this study was to demonstrate that differentiation of infected and vaccinated animals (DIVA) strategy using different ELISA tests is possible when a subunit vaccine (Haemagglutinin protein) is used to prevent Avian influenza. Special emphasis was placed on the differentiation in the serological response to different components of the AIV (Nucleoprotein, Neuraminidase, Haemagglutinin, Nucleocapsid) between chickens that were vaccinated with a whole virus kill vaccine and recombinant vaccine. Furthermore, the potential use of this DIVA strategy using ELISA assays to detect Neuraminidase 1 (N1) was analyzed as strategy in countries where the field virus is H5N1 and the vaccine used is formulated with H5N2. Detection of AIV-s antibodies to any component in serum was negative for all animals on the study days 0-13. At study day 14 the titers of antibodies against Nucleoprotein (NP) and Nucleocapsid (NC) rose in the experimental groups vaccinated with Volvac® AI KV and were negatives during all the trial in the experimental groups vaccinated with a subunit H5; significant statistically differences were observed between these groups (p < 0.05). The seroconversion either Haemagglutinin or Neuraminidase was evident after 21 days post-vaccination in the experimental groups vaccinated with the respective viral fraction. Regarding the main aim of this study and according with the results that were obtained, use a combination of different ELISA test as a DIVA strategy is feasible when the vaccination is carry out with a subunit H5 vaccine. Also is possible to use the ELISA kit to detect Neuraminidase (either N1 or N2) as a DIVA concept in countries where H5N1 is present and the vaccination programs are done with H5N2 vaccine.

Keywords: Avian Influenza Virus, "DIVA concept", ELISAassay, subunit H5 vaccine.

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Authors: Liping Jing, Michael K. Ng, Tieyong Zeng

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Microarray data profiles gene expression on a whole genome scale, therefore, it provides a good way to study associations between gene expression and occurrence or progression of cancer. More and more researchers realized that microarray data is helpful to predict cancer sample. However, the high dimension of gene expressions is much larger than the sample size, which makes this task very difficult. Therefore, how to identify the significant genes causing cancer becomes emergency and also a hot and hard research topic. Many feature selection algorithms have been proposed in the past focusing on improving cancer predictive accuracy at the expense of ignoring the correlations between the features. In this work, a novel framework (named by SGS) is presented for stable gene selection and efficient cancer prediction . The proposed framework first performs clustering algorithm to find the gene groups where genes in each group have higher correlation coefficient, and then selects the significant genes in each group with Bayesian Lasso and important gene groups with group Lasso, and finally builds prediction model based on the shrinkage gene space with efficient classification algorithm (such as, SVM, 1NN, Regression and etc.). Experiment results on real world data show that the proposed framework often outperforms the existing feature selection and prediction methods, say SAM, IG and Lasso-type prediction model.

Keywords: Gene Selection, Cancer Prediction, Lasso, Clustering, Classification.

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Authors: Khalid M. Albarkoly, Kenneth S. Park

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The substantial development of the construction industry has forced the cement industry, its major support, to focus on achieving maximum productivity to meet the growing demand for this material. This means that the reliability of a cement production system needs to be at the highest level that can be achieved by good maintenance. This paper studies the extent to which the implementation of RCM is needed as a strategy for increasing the reliability of the production systems component can be increased, thus ensuring continuous productivity. In a case study of four Libyan cement factories, 80 employees were surveyed and 12 top and middle managers interviewed. It is evident that these factories usually breakdown more often than once per month which has led to a decline in productivity. In many times they cannot achieve the minimum level of production amount. This has resulted from the poor reliability of their production systems as a result of poor or insufficient maintenance. It has been found that most of the factories’ employees misunderstand maintenance and its importance. The main cause of this problem is the lack of qualified and trained staff, but in addition it has been found that most employees are not found to be motivated as a result of a lack of management support and interest. In response to these findings, it has been suggested that the RCM strategy should be implemented in the four factories. The results show the importance of the development of maintenance strategies through the implementation of RCM in these factories. The purpose of it would be to overcome the problems that could secure the reliability of the production systems. This study could be a useful source of information for academic researchers and the industrial organizations which are still experiencing problems in maintenance practices.

Keywords: Libyan cement industry, maintenance, production, reliability centered maintenance, reliability.

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Authors: Marshal D. Kafle, Jun H. Kim, Hyun W. Been, Kyoung M. Min, Sung H. Kim

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Fluid viscous damping systems are well suited for many air vehicles subjected to shock and vibration. These damping system work with the principle of viscous fluid throttling through the orifice to create huge pressure difference between compression and rebound chamber and obtain the required damping force. One application of such systems is its use in aircraft door system to counteract the door’s velocity and safely stop it. In exigency situations like crash or emergency landing where the door doesn’t open easily, possibly due to unusually tilting of fuselage or some obstacles or intrusion of debris obstruction to move the parts of the door, such system can be combined with other systems to provide needed force to forcefully open the door and also securely stop it simultaneously within the required time i.e. less than 8 seconds. In the present study, a hydraulic system called snubber along with other systems like actuator, gas bottle assembly which together known as emergency power assist system (EPAS) is designed, built and experimentally studied to check the magnitude of angular velocity, damping force and time required to effectively open the door. Whenever needed, the gas pressure from the bottle is released to actuate the actuator and at the same time pull the snubber’s piston to operate the emergency opening of the door. Such EPAS installed in the suspension arm of the aircraft door is studied explicitly changing parameters like orifice size, oil level, oil viscosity and bypass valve gap and its spring of the snubber at varying temperature to generate the optimum design case. Comparative analysis of the EPAS at several cases is done and conclusions are made. It is found that during emergency condition, the system opening time and angular velocity, when snubber with 0.3mm piston and shaft orifice and bypass valve gap of 0.5 mm with its original spring is used, shows significant improvement over the old ones.

Keywords: Aircraft Door Damper, Bypass Valve, Emergency Power Assist System, Hydraulic Damper, Oil viscosity.

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Authors: P. Manivannan, M. Rajasimman

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Response surface methodology was used for quantitative investigation of water and solids transfer during osmotic dehydration of beetroot in aqueous solution of salt. Effects of temperature (25 – 45oC), processing time (30–150 min), salt concentration (5–25%, w/w) and solution to sample ratio (5:1 – 25:1) on osmotic dehydration of beetroot were estimated. Quadratic regression equations describing the effects of these factors on the water loss and solids gain were developed. It was found that effects of temperature and salt concentrations were more significant on the water loss than the effects of processing time and solution to sample ratio. As for solids gain processing time and salt concentration were the most significant factors. The osmotic dehydration process was optimized for water loss, solute gain, and weight reduction. The optimum conditions were found to be: temperature – 35oC, processing time – 90 min, salt concentration – 14.31% and solution to sample ratio 8.5:1. At these optimum values, water loss, solid gain and weight reduction were found to be 30.86 (g/100 g initial sample), 9.43 (g/100 g initial sample) and 21.43 (g/100 g initial sample) respectively.

Keywords: Optimization, Osmotic dehydration, Beetroot, saltsolution, response surface methodology

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Authors: Heba Mohammed Nagy, Walid Mohamed Aly, Osama Fathy Hegazy

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Educational  data mining  is  a  specific  data   mining field applied to data originating from educational environments, it relies on different  approaches to discover hidden knowledge  from  the  available   data. Among these approaches are   machine   learning techniques which are used to build a system that acquires learning from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems.

In  our  research, we propose  a “Student  Advisory  Framework” that  utilizes  classification  and  clustering  to  build  an  intelligent system. This system can be used to provide pieces of consultations to a first year  university  student to  pursue a  certain   education   track   where  he/she  will  likely  succeed  in, aiming  to  decrease   the  high  rate   of  academic  failure   among these  students.  A real case study  in Cairo  Higher  Institute  for Engineering, Computer  Science  and  Management  is  presented using  real  dataset   collected  from  2000−2012.The dataset has two main components: pre-higher education dataset and first year courses results dataset. Results have proved the efficiency of the suggested framework.

Keywords: Classification, Clustering, Educational Data Mining (EDM), Machine Learning.

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Authors: Alexander G. Yushchenko

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From a multi-science point of view, we analyze threats to security resulting from globalization of international information space and information and communication aggression of Russia. A definition of Ruschism is formulated as an ideology supporting aggressive actions of modern Russia against the Euro-Atlantic community. Stages of the hybrid war Russia is leading against Ukraine are described, including the elements of subversive activity of the special services, the activation of the military phase and the gradual shift of the focus of confrontation to the realm of information and communication technologies. We reveal an emergence of a threat for democratic states resulting from the destabilizing impact of a target state’s mass media and social networks being exploited by Russian secret services under freedom-of-speech disguise. Thus, we underline the vulnerability of cyber- and information security of the network society in regard of hybrid war. We propose to define the latter a synergetic war. Our analysis is supported with a long-term qualitative monitoring of representation of top state officials on popular TV channels and Facebook. From the memetics point of view, we have detected a destructive psycho-information technology used by the Kremlin, a kind of information catastrophe, the essence of which is explained in detail. In the conclusion, a comprehensive plan for information protection of the public consciousness and mentality of Euro-Atlantic citizens from the aggression of the enemy is proposed.

Keywords: Cyber and information security, psycho-information technology, hybrid war, synergetic war, WWIII, Ruschism.

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Authors: Ayşin Sev, Görkem Aslan

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Sustainable tall buildings that provide comfortable, healthy and efficient indoor environments are clearly desirable as the densification of living and working space for the world’s increasing population proceeds. For environmental concerns, these buildings must also be energy efficient. One component of these tasks is the provision of indoor air quality and thermal comfort, which can be enhanced with natural ventilation by the supply of fresh air. Working spaces can only be naturally ventilated with connections to the outdoors utilizing operable windows, double facades, ventilation stacks, balconies, patios, terraces and skygardens. Large amounts of fresh air can be provided to the indoor spaces without mechanical air-conditioning systems, which are widely employed in contemporary tall buildings. This paper tends to present the concept of natural ventilation for sustainable tall office buildings in order to achieve healthy and comfortable working spaces, as well as energy efficient environments. Initially the historical evolution of ventilation strategies for tall buildings is presented, beginning with natural ventilation and continuing with the introduction of mechanical airconditioning systems. Then the emergence of natural ventilation due to the health and environmental concerns in tall buildings is handled, and the strategies for implementing this strategy are revealed. In the next section, a number of case studies that utilize this strategy are investigated. Finally, how tall office buildings can benefit from this strategy is discussed.

Keywords: Tall office building, natural ventilation, energy efficiency, double-skin façade, stack ventilation, air conditioning.

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Authors: Ujjwala Bhand, Mridula Goel

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Global Competitiveness Index (GCI) prepared by World Economic Forum has become a benchmark in studying the competitiveness of countries and for understanding the factors that enable competitiveness. Innovation is a key pillar in competitiveness and has the unique property of enabling exponential economic growth. This paper attempts to analyze how the pillars comprising the Global Competitiveness Index affect innovation and whether GDP growth can directly affect innovation outcomes for a country. The key objective of the study is to identify areas on which governments of developing countries can focus policies and programs to improve their country’s innovativeness. We have compiled a panel data set for top innovating countries and large emerging economies called BRICS from 2007-08 to 2014-15 in order to find the significant factors that affect innovation. The results of the regression analysis suggest that government should make policies to improve labor market efficiency, establish sophisticated business networks, provide basic health and primary education to its people and strengthen the quality of higher education and training services in the economy. The achievements of smaller economies on innovation suggest that concerted efforts by governments can counter any size related disadvantage, and in fact can provide greater flexibility and speed in encouraging innovation.

Keywords: Innovation, Global Competitiveness Index, BRICS, economic growth.

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Authors: Mokhtar Kouki Inès Rekik

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This paper focuses on the assessment of the air pollution and morbidity relationship in Tunisia. Air pollution is measured by ozone air concentration and the morbidity is measured by the number of respiratory-related restricted activity days during the 2-week period prior to the interview. Socioeconomic data are also collected in order to adjust for any confounding covariates. Our sample is composed by 407 Tunisian respondents; 44.7% are women, the average age is 35.2, near 69% are living in a house built after 1980, and 27.8% have reported at least one day of respiratory-related restricted activity. The model consists on the regression of the number of respiratory-related restricted activity days on the air quality measure and the socioeconomic covariates. In order to correct for zero-inflation and heterogeneity, we estimate several models (Poisson, negative binomial, zero inflated Poisson, Poisson hurdle, negative binomial hurdle and finite mixture Poisson models). Bootstrapping and post-stratification techniques are used in order to correct for any sample bias. According to the Akaike information criteria, the hurdle negative binomial model has the greatest goodness of fit. The main result indicates that, after adjusting for socioeconomic data, the ozone concentration increases the probability of positive number of restricted activity days.

Keywords: Bootstrapping, hurdle negbin model, overdispersion, ozone concentration, respiratory-related restricted activity days.

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Authors: Diako Azizi, Ahmad Gholami, Vahid Abbasi

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High voltage generators are being subject to higher voltage rating and are being designed to operate in harsh conditions. Stator windings are the main component of generators in which Electrical, magnetically and thermal stresses remain major failures for insulation degradation accelerated aging. A large number of generators failed due to stator winding problems, mainly insulation deterioration. Insulation degradation assessment plays vital role in the asset life management. Mostly the stator failure is catastrophic causing significant damage to the plant. Other than generation loss, stator failure involves heavy repair or replacement cost. Electro thermal analysis is the main characteristic for improvement design of stator slot-s insulation. Dielectric parameters such as insulation thickness, spacing, material types, geometry of winding and slot are major design consideration. A very powerful method available to analyze electro thermal performance is Finite Element Method (FEM) which is used in this paper. The analysis of various stator coil and slot configurations are used to design the better dielectric system to reduce electrical and thermal stresses in order to increase the power of generator in the same volume of core. This paper describes the process used to perform classical design and improvement analysis of stator slot-s insulation.

Keywords: Electromagnetic field, field distribution, insulation, winding, finite element method

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Authors: W. Devapriya, C. Nelson Kennedy Babu, T. Srihari

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Automatic License plate recognition (ALPR) is a technology which recognizes the registration plate or number plate or License plate of a vehicle. In this paper, an Indian vehicle number plate is mined and the characters are predicted in efficient manner. ALPR involves four major technique i) Pre-processing ii) License Plate Location Identification iii) Individual Character Segmentation iv) Character Recognition. The opening phase, named pre-processing helps to remove noises and enhances the quality of the image using the conception of Morphological Operation and Image subtraction. The second phase, the most puzzling stage ascertain the location of license plate using the protocol Canny Edge detection, dilation and erosion. In the third phase, each characters characterized by Connected Component Approach (CCA) and in the ending phase, each segmented characters are conceptualized using cross correlation template matching- a scheme specifically appropriate for fixed format. Major application of ALPR is Tolling collection, Border Control, Parking, Stolen cars, Enforcement, Access Control, Traffic control. The database consists of 500 car images taken under dissimilar lighting condition is used. The efficiency of the system is 97%. Our future focus is Indian Vehicle License Plate Validation (Whether License plate of a vehicle is as per Road transport and highway standard).

Keywords: Automatic License plate recognition, Character recognition, Number plate Recognition, Template matching, morphological operation, canny edge detection.

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Authors: C. Snae Namahoot, M. Brueckner

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Thailand-s health system is challenged by the rising number of patients and decreasing ratio of medical practitioners/patients, especially in rural areas. This may tempt inexperienced GPs to rush through the process of anamnesis with the risk of incorrect diagnosis. Patients have to travel far to the hospital and wait for a long time presenting their case. Many patients try to cure themselves with traditional Thai medicine. Many countries are making use of the Internet for medical information gathering, distribution and storage. Telemedicine applications are a relatively new field of study in Thailand; the infrastructure of ICT had hampered widespread use of the Internet for using medical information. With recent improvements made health and technology professionals can work out novel applications and systems to help advance telemedicine for the benefit of the people. Here we explore the use of telemedicine for people with health problems in rural areas in Thailand and present a Telemedicine Diagnosis System for Rural Thailand (TEDIST) for diagnosing certain conditions that people with Internet access can use to establish contact with Community Health Centers, e.g. by mobile phone. The system uses a Web-based input method for individual patients- symptoms, which are taken by an expert system for the analysis of conditions and appropriate diseases. The analysis harnesses a knowledge base and a backward chaining component to find out, which health professionals should be presented with the case. Doctors have the opportunity to exchange emails or chat with the patients they are responsible for or other specialists. Patients- data are then stored in a Personal Health Record.

Keywords: Biomedical engineering, data acquisition, expert system, information management system, and information retrieval.

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Authors: Keith D. Nare, Mohau J. Phiri, James Carson, Chris D. Woolard, Shanganyane P. Hlangothi

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In an energy-intensive world, minimizing energy consumption is paramount to cost saving and reducing the carbon footprint. Improving mixture procedures utilizing warm mix additive Fischer-Tropsch (FT) wax in ethylene vinyl acetate (EVA) and modified bitumen highlights a greener and sustainable approach to modified bitumen. In this study, the impact of FT wax on optimized EVA/waste crumb rubber modified bitumen is assayed with a maximum loading of 2.5%. The rationale of the FT wax loading is to maintain the original maximum loading of EVA in the optimized mixture. The phase change abilities of FT wax enable EVA co-crystallization with the support of the elastomeric backbone of crumb rubber. Less than 1% loading of FT wax worked in the EVA/crumb rubber modified bitumen energy-sustainability nexus. Response surface methodology approach to the mixture design is implemented amongst the different loadings of FT wax, EVA for a consistent amount of crumb rubber and bitumen. Rheological parameters (complex shear modulus, phase angle and rutting parameter) were the factors used as performance indicators of the different optimized mixtures. The low temperature chemistry of the optimized mixtures is analyzed using elementary beam theory and the elastic-viscoelastic correspondence principle. Master curves and black space diagrams are developed and used to predict age-induced cracking of the different long term aged mixtures. Modified binder rheology reveals that the strain response is not linear and that there is substantial re-arrangement of polymer chains as stress is increased, this is based on the age state of the mixture and the FT wax and EVA loadings. Dominance of individual effects is evident over effects of synergy in co-interaction of EVA and FT wax. All-inclusive FT wax and EVA formulations were best optimized in mixture 4 with mixture 7 reflecting increase in ease of workability. Findings show that interaction chemistry of bitumen, crumb rubber EVA, and FT wax is first and second order in all cases involving individual contributions and co-interaction amongst the components of the mixture.

Keywords: Bitumen, crumb rubber, ethylene vinyl acetate, FT wax.

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Authors: Aynaz Lotfata

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Planning community has been long discussing emerging paradigms within the planning theory in the face of the changing conditions of the world order. The paradigm shift concept was introduced by Thomas Kuhn, in 1960, who claimed the necessity of shifting within scientific knowledge boundaries; and following him in 1970 Imre Loktas also gave priority to the emergence of multi-paradigm societies [24]. Multi-paradigm is changing our predetermined lifeworld through uncertainties. Those uncertainties are reflected in two sides, the first one is uncertainty as a concept of possibility and creativity in public sphere and the second one is uncertainty as a risk. Therefore, it is necessary to apply a resilience planning approach to be more dynamic in controlling uncertainties which have the potential to transfigure present time and space definitions. In this way, stability of system can be achieved. Uncertainty is not only an outcome of worldwide changes but also a place-specific issue, i.e. it changes from continent to continent, a country to country; a region to region. Therefore, applying strategic spatial planning with respect to resilience principle contributes to: control, grasp and internalize uncertainties through place-specific strategies. In today-s fast changing world, planning system should follow strategic spatial projects to control multi-paradigm societies with adaptability capacities. Here, we have selected two alternatives to demonstrate; these are; 1.Tehran (Iran) from the Middle East 2.Bath (United Kingdom) from Europe. The study elaborates uncertainties and particularities in their strategic spatial planning processes in a comparative manner. Through the comparison, the study aims at assessing place-specific priorities in strategic planning. The approach is to a two-way stream, where the case cities from the extreme end of the spectrum can learn from each other. The structure of this paper is to firstly compare semi-periphery (Tehran) and coreperiphery (Bath) cities, with the focus to reveal how they equip to face with uncertainties according to their geographical locations and local particularities. Secondly, the key message to address is “Each locality requires its own strategic planning approach to be resilient.--

Keywords: Adaptation, Relational Network, Socio-Spatial Strategic Resiliency, Uncertainty.

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Authors: Migbar Assefa

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The performance of a machine tool is eventually assessed by its ability to produce a component of the required geometry in minimum time and at small operating cost. It is customary to base the structural design of any machine tool primarily upon the requirements of static rigidity and minimum natural frequency of vibration. The operating properties of machines like cutting speed, feed and depth of cut as well as the size of the work piece also have to be kept in mind by a machine tool structural designer. This paper presents a novel approach to the design of machine tool column for static and dynamic rigidity requirement. Model evaluation is done effectively through use of General Finite Element Analysis software ANSYS. Studies on machine tool column are used to illustrate finite element based concept evaluation technique. This paper also presents results obtained from the computations of thin walled box type columns that are subjected to torsional and bending loads in case of static analysis and also results from modal analysis. The columns analyzed are square and rectangle based tapered open column, column with cover plate, horizontal partitions and with apertures. For the analysis purpose a total of 70 columns were analyzed for bending, torsional and modal analysis. In this study it is observed that the orientation and aspect ratio of apertures have no significant effect on the static and dynamic rigidity of the machine tool structure.

Keywords: Finite Element Modeling, Modal Analysis, Machine tool structure, Static Analysis.

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Authors: Vesna Bosilj Vukšić, Lucija Ivančić, Dalia Suša Vugec

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While struggling to succeed in today’s complex market environment and provide better customer experience and services, enterprises encompass digital transformation as a means for reaching competitiveness and foster value creation. A digital transformation process consists of information technology implementation projects, as well as organizational factors such as top management support, digital transformation strategy, and organizational changes. However, to the best of our knowledge, there is little evidence about digital transformation endeavors in organizations and how they perceive it – is it only about digital technologies adoption or a true organizational shift is needed? In order to address this issue and as the first step in our research project, a literature review is conducted. The analysis included case study papers from Scopus and Web of Science databases. The following attributes are considered for classification and analysis of papers: time component; country of case origin; case industry and; digital transformation concept comprehension, i.e. focus. Research showed that organizations – public, as well as private ones, are aware of change necessity and employ digital transformation projects. Also, the changes concerning digital transformation affect both manufacturing and service-based industries. Furthermore, we discovered that organizations understand that besides technologies implementation, organizational changes must also be adopted. However, with only 29 relevant papers identified, research positioned digital transformation as an unexplored and emerging phenomenon in information systems research. The scarcity of evidence-based papers calls for further examination of this topic on cases from practice.

Keywords: Digital strategy, digital technologies, digital transformation, literature review.

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Authors: Mehdi Modabberifar, Milad Roodi, Ehsan Souri

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In recent years, honeycomb fiber reinforced plastic (FRP) sandwich panels have been increasingly used in various industries. Low weight, low price and high mechanical strength are the benefits of these structures. However, their mechanical properties and behavior have not been fully explored. The objective of this study is to conduct a combined numerical-statistical investigation of honeycomb FRP sandwich beams subject to torsion load. In this paper, the effect of geometric parameters of sandwich panel on maximum shear strain in both face and core and angle of torsion in a honeycomb FRP sandwich structures in torsion is investigated. The effect of Parameters including core thickness, face skin thickness, cell shape, cell size, and cell thickness on mechanical behavior of the structure were numerically investigated. Main effects of factors were considered in this paper and regression equations were derived. Taguchi method was employed as experimental design and an optimum parameter combination for the maximum structure stiffness has been obtained. The results showed that cell size and face skin thickness have the most significant impacts on torsion angle, maximum shear strain in face and core.

Keywords: Finite element, honeycomb FRP sandwich panel, torsion, civil engineering.

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Authors: Diako Azizi, Ahmad Gholami, Vahid Abbasi

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High voltage generators are being subject to higher voltage rating and are being designed to operate in harsh conditions. Stator windings are the main component of generators in which Electrical, magnetical and thermal stresses remain major failures for insulation degradation accelerated aging. A large number of generators failed due to stator winding problems, mainly insulation deterioration. Insulation degradation assessment plays vital role in the asset life management. Mostly the stator failure is catastrophic causing significant damage to the plant. Other than generation loss, stator failure involves heavy repair or replacement cost. Electro thermal analysis is the main characteristic for improvement design of stator slot-s insulation. Dielectric parameters such as insulation thickness, spacing, material types, geometry of winding and slot are major design consideration. A very powerful method available to analyze electro thermal performance is Finite Element Method (FEM) which is used in this paper. The analysis of various stator coil and slot configurations are used to design the better dielectric system to reduce electrical and thermal stresses in order to increase the power of generator in the same volume of core. This paper describes the process used to perform classical design and improvement analysis of stator slot-s insulation.

Keywords: Electrical field, field distribution, insulation, winding, finite element method, electro thermal

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Authors: Julio C. Gómez-Mancilla, Luis M. Palacios-Pineda, Yunuén López-Grijalba

Abstract:

A new mechanism responsible for structural life consumption due to resonant fatigue in turbine blades, or vanes, is presented and explained. A rotating blade or vane in a gas turbine can change its contour due to erosion and/or material build up, in any of these instances, the surface pressure distribution occurring on the suction and pressure sides of blades-vanes can suffer substantial modification of their pressure and temperatures envelopes and flow characteristics. Meanwhile, the relative rotation between the blade and duct vane while the pressurized gas flows and the consequent wake crossings, will induce a fluctuating thrust force or lift that will excite the blade. An actual totally used up set of vane-blade components in a HP turbine power stage in a gas turbine is analyzed. The blade suffered some material erosion mostly at the trailing edge provoking a peculiar surface pressure envelope which evolved as the relative position between the vane and the blade passed in front of each other. Interestingly preliminary modal analysis for this eroded blade indicates several natural frequencies within the aeromechanic power spectrum, moreover, the highest frequency component is 94% of one natural frequency indicating near resonant condition. Independently of other simultaneously occurring fatigue cycles (such as thermal, centrifugal stresses).

Keywords: Aeromechanic induced vibration, potential flowinteraction, turbine unsteady flow, rotor/stator interaction, turbinevane-blade aerodynamic interaction, airfoil clocking

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Authors: Seongcheol Kwon, Jeongmin Kim, Kwang Ryel Ryu

Abstract:

We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.

Keywords: Situation-awareness, Smart home, IoT, Machine learning, Classifier.

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Authors: S. Sharma, U. Batra, S. Kapoor, A. Dua

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In this study, the effects and interactions of reaction time and capping agent assistance during sol-gel synthesis of magnesium substituted hydroxyapatite nanopowder (MgHA) on hydroxyapatite (HA) to β-tricalcium phosphate (β-TCP) ratio, Ca/P ratio and mean crystallite size was examined experimentally as well as through statistical analysis. MgHA nanopowders were synthesized by sol-gel technique at room temperature using aqueous solution of calcium nitrate tetrahydrate, magnesium nitrate hexahydrate and potassium dihydrogen phosphate as starting materials. The reaction time for sol-gel synthesis was varied between 15 to 60 minutes. Two process routes were followed with and without addition of triethanolamine (TEA) in the solutions. The elemental compositions of as-synthesized powders were determined using X-ray fluorescence (XRF) spectroscopy. The functional groups present in the assynthesized MgHA nanopowders were established through Fourier Transform Infrared Spectroscopy (FTIR). The amounts of phases present, Ca/P ratio and mean crystallite sizes of MgHA nanopowders were determined using X-ray diffraction (XRD). The HA content in biphasic mixture of HA and β-TCP and Ca/P ratio in as-synthesized MgHA nanopowders increased effectively with reaction time of sols (p<0.0001, two way ANOVA), however, these were independent of TEA addition (p>0.15, two way ANOVA). The MgHA nanopowders synthesized with TEA assistance exhibited 14 nm lower crystallite size (p<0.018, 2 sample t-test) compared to the powder synthesized without TEA assistance.

Keywords: Capping agent, hydroxyapatite, regression analysis, sol-gel, 2- sample t-test, two-way ANOVA.

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Authors: J. P. Dubois, O. M. Abdul-Latif

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Support Vector Machine (SVM) is a recent class of statistical classification and regression techniques playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM is applied to an infrared (IR) binary communication system with different types of channel models including Ricean multipath fading and partially developed scattering channel with additive white Gaussian noise (AWGN) at the receiver. The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these channel stochastic models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to classical binary signal maximum likelihood detection using a matched filter driven by On-Off keying (OOK) modulation. We found that the performance of SVM is superior to that of the traditional optimal detection schemes used in statistical communication, especially for very low signal-to-noise ratio (SNR) ranges. For large SNR, the performance of the SVM is similar to that of the classical detectors. The implication of these results is that SVM can prove very beneficial to IR communication systems that notoriously suffer from low SNR at the cost of increased computational complexity.

Keywords: Least square-support vector machine, on-off keying, matched filter, maximum likelihood detector, wireless infrared communication.

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Authors: Mohamed Ouzzane, Mahmoud Bady

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Due to energy and environment context, research is looking for the use of clean and energy efficient system in cooling industry. In this regard, the ejector represents one of the promising solutions. The thermal ejector is a passive component used for thermal compression in refrigeration and cooling systems, usually activated by heat either waste or solar. The present study introduces a theoretical analysis of the cooling system which uses a gas ejector thermal compression. A theoretical model is developed and applied for the design and simulation of the ejector, as well as the whole cooling system. Besides the conservation equations of mass, energy and momentum, the gas dynamic equations, state equations, isentropic relations as well as some appropriate assumptions are applied to simulate the flow and mixing in the ejector. This model coupled with the equations of the other components (condenser, evaporator, pump, and generator) is used to analyze profiles of pressure and velocity (Mach number), as well as evaluation of the cycle cooling capacity. A FORTRAN program is developed to carry out the investigation. Properties of refrigerant R134a are calculated using real gas equations. Among many parameters, it is thought that the generator pressure is the cornerstone in the cycle, and hence considered as the key parameter in this investigation. Results show that the generator pressure has a great effect on the ejector and on the whole cooling system. At high generator pressures, strong shock waves inside the ejector are created, which lead to significant condenser pressure at the ejector exit. Additionally, at higher generator pressures, the designed system can deliver cooling capacity for high condensing pressure (hot season).

Keywords: Air cooling system, refrigeration, thermal ejector, thermal compression.

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