Search results for: Rapid Compression Machine.

Authors: Mehdi Ghayoumi

Abstract:

We have applied new accelerated algorithm for linear discriminate analysis (LDA) in face recognition with support vector machine. The new algorithm has the advantage of optimal selection of the step size. The gradient descent method and new algorithm has been implemented in software and evaluated on the Yale face database B. The eigenfaces of these approaches have been used to training a KNN. Recognition rate with new algorithm is compared with gradient.

Keywords: lda, adaptive, svm, face recognition.

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Authors: Saurabh Sharma, Yong Sun, Bruce Vernham

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Accurate prediction of NOx emission is a continuous challenge in the field of diesel engine-out emission modeling. Performing experiments for each conditions and scenario cost significant amount of money and man hours, therefore model-based development strategy has been implemented in order to solve that issue. NOx formation is highly dependent on the burn gas temperature and the O₂ concentration inside the cylinder. The current empirical models are developed by calibrating the parameters representing the engine operating conditions with respect to the measured NOx. This makes the prediction of purely empirical models limited to the region where it has been calibrated. An alternative solution to that is presented in this paper, which focus on the utilization of in-cylinder combustion parameters to form a predictive semi-empirical NOx model. The result of this work is shown by developing a fast and predictive NOx model by using the physical parameters and empirical correlation. The model is developed based on the steady state data collected at entire operating region of the engine and the predictive combustion model, which is developed in Gamma Technology (GT)-Power by using Direct Injected (DI)-Pulse combustion object. In this approach, temperature in both burned and unburnt zone is considered during the combustion period i.e. from Intake Valve Closing (IVC) to Exhaust Valve Opening (EVO). Also, the oxygen concentration consumed in burnt zone and trapped fuel mass is also considered while developing the reported model.  Several statistical methods are used to construct the model, including individual machine learning methods and ensemble machine learning methods. A detailed validation of the model on multiple diesel engines is reported in this work. Substantial numbers of cases are tested for different engine configurations over a large span of speed and load points. Different sweeps of operating conditions such as Exhaust Gas Recirculation (EGR), injection timing and Variable Valve Timing (VVT) are also considered for the validation. Model shows a very good predictability and robustness at both sea level and altitude condition with different ambient conditions. The various advantages such as high accuracy and robustness at different operating conditions, low computational time and lower number of data points requires for the calibration establishes the platform where the model-based approach can be used for the engine calibration and development process. Moreover, the focus of this work is towards establishing a framework for the future model development for other various targets such as soot, Combustion Noise Level (CNL), NO₂/NOx ratio etc.

Keywords: Diesel engine, machine learning, NOx emission, semi-empirical.

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Authors: Yuri Bizzoni, Marianne Reboul

Abstract:

Our program compares French and Italian translations of Homer’s Odyssey, from the XVIth to the XXth century. We focus on the third point, showing how distributional semantics systems can be used both to improve alignment between different French translations as well as between the Greek text and a French translation. Although we focus on French examples, the techniques we display are completely language independent.

Keywords: Translation studies, machine translation, computational linguistics, distributional semantics.

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Authors: Parit Kanjanavirojkul, Kiatwarakorn Keeratishananond, Prapun Suksompong

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We study the performance of compressed beamforming weights feedback technique in generalized triangular decomposition (GTD) based MIMO system. GTD is a beamforming technique that enjoys QoS flexibility. The technique, however, will perform at its optimum only when the full knowledge of channel state information (CSI) is available at the transmitter. This would be impossible in the real system, where there are channel estimation error and limited feedback. We suggest a way to implement the quantized beamforming weights feedback, which can significantly reduce the feedback data, on GTD-based MIMO system and investigate the performance of the system. Interestingly, we found that compressed beamforming weights feedback does not degrade the BER performance of the system at low input power, while the channel estimation error and quantization do. For comparison, GTD is more sensitive to compression and quantization, while SVD is more sensitive to the channel estimation error. We also explore the performance of GTDbased MU-MIMO system, and find that the BER performance starts to degrade largely at around -20 dB channel estimation error.

Keywords: MIMO, MU-MIMO, GTD, Imperfect CSI.

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Authors: Fei Wang, Kaimin Shih

Abstract:

The persistent nature of perfluorochemicals (PFCs) has attracted global concern in recent years. Perfluorooctanesulfonate (PFOS) and perfluorooctanoate (PFOA) are the most commonly found PFC compounds, and thus their fate and transport play key roles in PFC distribution in the natural environment. The kinetic behavior of PFOS or PFOA on boehmite consists of a fast adsorption process followed by a slow adsorption process which may be attributed to the slow transport of PFOS or PFOA into the boehmite pore surface. The adsorption isotherms estimated the maximum adsorption capacities of PFOS and PFOA on boehmite as 0.877 μg/m2 and 0.633 μg/m2, with the difference primarily due to their different functional groups. The increase of solution pH led to a moderate decrease of PFOS and PFOA adsorption, owing to the increase of ligand exchange reactions and the decrease of electrostatic interactions. The presence of NaCl in solution demonstrated negative effects for PFOS and PFOA adsorption on boehmite surfaces, with potential mechanisms being electrical double layer compression, competitive adsorption of chloride.

Keywords: PFOS, PFOA, adsorption, electrostatic interaction, ligand exchange

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Authors: Sherin M. Youssef, Ahmed Abouelfarag, Noha M. Ghatwary

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An adaptive Fuzzy Inference Perceptual model has been proposed for watermarking of digital images. The model depends on the human visual characteristics of image sub-regions in the frequency multi-resolution wavelet domain. In the proposed model, a multi-variable fuzzy based architecture has been designed to produce a perceptual membership degree for both candidate embedding sub-regions and strength watermark embedding factor. Different sizes of benchmark images with different sizes of watermarks have been applied on the model. Several experimental attacks have been applied such as JPEG compression, noises and rotation, to ensure the robustness of the scheme. In addition, the model has been compared with different watermarking schemes. The proposed model showed its robustness to attacks and at the same time achieved a high level of imperceptibility.

Keywords: Watermarking, The human visual system (HVS), Fuzzy Inference System (FIS), Local Binary Pattern (LBP), Discrete Wavelet Transform (DWT).

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Authors: Siyanda S. Biyela, Willie A. Cronje

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This paper presents a desktop study of comparing two different wave energy to electricity technologies (WECs) using a techno-economic approach. This techno-economic approach forms basis of a framework for rapid comparison of current and future technologies. The approach also seeks to assist in investment and strategic decision making expediting future deployment of wave energy harvesting in South Africa.

Keywords: Cost of energy, tool, wave energy converter, WEC-Sim.

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Authors: Hireni R. Mankodi, D. J. Chudasama

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The textile preforms play a key role in providing the mechanical properties and gives the idea about selection parameter of preforms to improve the quality and performance of laminates. The main objectives of this work are to study the effect of non-crimp fabric preform structure in final properties of laminates. It has been observed that the multi-axial preform give better mechanical properties of laminates as compared to woven and biaxial fabrics. This study investigated the effect of different non-crimp glass preform structure on tensile strength, bending and compression properties of glass laminates. The different woven, bi-axial and multi-axial fabrics with similar GSM used to manufacture the laminates using polyester resin. The structural and mechanical properties of preform and laminates were studied using standard methods. It has been observed that the glass fabric geometry, including type of weaves, warps and filling density and number of layer plays significant role in deciding mechanical properties of laminates.

Keywords: Preform, non-crimp, laminates, bi-axial, multiaxial.

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Authors: H. Benjamin Fredrick David, R. Balasubramanian, A. Anbarasa Pandian

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Image retrieval is the most interesting technique which is being used today in our digital world. CBIR, commonly expanded as Content Based Image Retrieval is an image processing technique which identifies the relevant images and retrieves them based on the patterns that are extracted from the digital images. In this paper, two research works have been presented using CBIR. The first work provides an automated and interactive approach to the analysis of CBIR techniques. CBIR works on the principle of supervised machine learning which involves feature selection followed by training and testing phase applied on a classifier in order to perform prediction. By using feature extraction, the image transforms such as Contourlet, Ridgelet and Shearlet could be utilized to retrieve the texture features from the images. The features extracted are used to train and build a classifier using the classification algorithms such as Naïve Bayes, K-Nearest Neighbour and Multi-class Support Vector Machine. Further the testing phase involves prediction which predicts the new input image using the trained classifier and label them from one of the four classes namely 1- Normal brain, 2- Benign tumour, 3- Malignant tumour and 4- Severe tumour. The second research work includes developing a tool which is used for tumour stage identification using the best feature extraction and classifier identified from the first work. Finally, the tool will be used to predict tumour stage and provide suggestions based on the stage of tumour identified by the system. This paper presents these two approaches which is a contribution to the medical field for giving better retrieval performance and for tumour stages identification.

Keywords: Brain tumour detection, content based image retrieval, classification of tumours, image retrieval.

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Authors: Young-Seok Choi

Abstract:

This paper proposes a complementary combination scheme of affine projection algorithm (APA) filters with different order of input regressors. A convex combination provides an interesting way to keep the advantage of APA having different order of input regressors. Consequently, a novel APA which has the rapid convergence and the reduced steady-state error is derived. Experimental results show the good properties of the proposed algorithm.

Keywords: Adaptive filter, affine projection algorithm, convex combination, input order.

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Authors: Faiza Belbachir, Thibault Schienhinski

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The massive development of online social networks allows users to post and share their opinions on various topics. With this huge volume of opinion, it is interesting to extract and interpret these information for different domains, e.g., product and service benchmarking, politic, system of recommendation. This is why opinion detection is one of the most important research tasks. It consists on differentiating between opinion data and factual data. The difficulty of this task is to determine an approach which returns opinionated document. Generally, there are two approaches used for opinion detection i.e. Lexical based approaches and Machine Learning based approaches. In Lexical based approaches, a dictionary of sentimental words is used, words are associated with weights. The opinion score of document is derived by the occurrence of words from this dictionary. In Machine learning approaches, usually a classifier is trained using a set of annotated document containing sentiment, and features such as n-grams of words, part-of-speech tags, and logical forms. Majority of these works are based on documents text to determine opinion score but dont take into account if these texts are really correct. Thus, it is interesting to exploit other information to improve opinion detection. In our work, we will develop a new way to consider the opinion score. We introduce the notion of trust score. We determine opinionated documents but also if these opinions are really trustable information in relation with topics. For that we use lexical SentiWordNet to calculate opinion and trust scores, we compute different features about users like (numbers of their comments, numbers of their useful comments, Average useful review). After that, we combine opinion score and trust score to obtain a final score. We applied our method to detect trust opinions in TRIPADVISOR collection. Our experimental results report that the combination between opinion score and trust score improves opinion detection.

Keywords: Tripadvisor, Opinion detection, SentiWordNet, trust score.

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Authors: Barbara Covarrubias Venegas, Sabine Groblschegg, Bernhard Klaus, Julia Domnanovich

Abstract:

Research Objectives: The roles and activities of Human Resource Management (HRM) have changed a lot in the past years. Driven by a changing environment and therefore new business requirements, the scope of human resource (HR) activities has widened. The extent to which these activities should focus on strategic issues to support the long term success of a company has been discussed in science for many years. As many economies of Central and Eastern Europe (CEE) experienced a phase of transition after the socialist era and are now recovering from the 2008 global crisis it is needed to examine the current state of HR positioning. Furthermore a trend in HR work developing from rather administrative units to being strategic partners of management can be noticed. This leads to the question of better understanding the underlying competencies which are necessary to support organisations. This topic was addressed by the international study “HR Competencies in international comparison”. The quantitative survey was conducted by the Institute for Human Resources & Organisation of FHWien University of Applied Science of WKW (A) in cooperation with partner universities in the countries Bosnia- Herzegovina, Croatia, Serbia and Slovenia. Methodology: Using the questionnaire developed by Dave Ulrich we tested whether the HR Competency model can be used for Austria, Bosnia and Herzegovina, Croatia, Serbia and Slovenia. After performing confirmatory and exploratory factor analysis for the whole data set containing all five countries we could clearly distinguish between four competencies. In a further step our analysis focused on median and average comparisons between the HR competency dimensions. Conclusion: Our literature review, in alignment with other studies, shows a relatively rapid pace of development of HR Roles and HR Competencies in BCSS in the past decades. Comparing data from BCSS and Austria we still can notice that regards strategic orientation there is a lack in BCSS countries, thus competencies are not as developed as in Austria. This leads us to the tentative conclusion that HR has undergone a rapid change but is still in a State of Transition from being a rather administrative unit to performing the role of a strategic partner.

Keywords: Comparative study, HR competencies, HRM, HR Roles.

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Authors: Shivali D. Kulkarni, Ameya K. Naik, Nitin S. Nagori

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In this paper we present simulation results for the application of a bandwidth efficient algorithm (mapping algorithm) to an image transmission system. This system considers three different real valued transforms to generate energy compact coefficients. First results are presented for gray scale and color image transmission in the absence of noise. It is seen that the system performs its best when discrete cosine transform is used. Also the performance of the system is dominated more by the size of the transform block rather than the number of coefficients transmitted or the number of bits used to represent each coefficient. Similar results are obtained in the presence of additive white Gaussian noise. The varying values of the bit error rate have very little or no impact on the performance of the algorithm. Optimum results are obtained for the system considering 8x8 transform block and by transmitting 15 coefficients from each block using 8 bits.

Keywords: Additive white Gaussian noise channel, mapping algorithm, peak signal to noise ratio, transform encoding.

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Authors: Farhad F. Yusifov

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With the explosive growth of data available on the Internet, personalization of this information space become a necessity. At present time with the rapid increasing popularity of the WWW, Websites are playing a crucial role to convey knowledge and information to the end users. Discovering hidden and meaningful information about Web users usage patterns is critical to determine effective marketing strategies to optimize the Web server usage for accommodating future growth. The task of mining useful information becomes more challenging when the Web traffic volume is enormous and keeps on growing. In this paper, we propose a intelligent model to discover and analyze useful knowledge from the available Web log data.

Keywords: Clustering, Self organizing map, Web log files, Web traffic.

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Authors: Xu Zhao

Abstract:

Text categorization techniques are widely used to many Information Retrieval (IR) applications. In this paper, we proposed a simple but efficient method that can automatically find the relationship between any pair of terms and documents, also an indexing matrix is established for text categorization. We call this method Indexing Matrix Categorization Machine (IMCM). Several experiments are conducted to show the efficiency and robust of our algorithm.

Keywords: Text categorization, Sub-space learning, Latent Semantic Space

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

Abstract:

Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: Affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, Signal Detection Theory, student engagement.

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Authors: Raquel Vianna Duarte Cardoso, Eduarda Lobato Faria, José Jorge Boueri

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Rapid urbanization has transformed the global landscape, presenting significant challenges to public health. This article delves into the impact of urbanization on the spread of infectious diseases in cities and identifies crucial strategies to enhance urban community resilience. Massive urbanization over recent decades has created conducive environments for the rapid spread of diseases due to population density, mobility, and unequal living conditions. Urbanization has been observed to increase exposure to pathogens and foster conditions conducive to disease outbreaks, including seasonal flu, vector-borne diseases, and respiratory infections. In order to tackle these issues, a range of cross-disciplinary approaches are suggested. These encompass the enhancement of urban healthcare infrastructure, emphasizing the need for robust investments in hospitals, clinics, and healthcare systems to keep pace with the burgeoning healthcare requirements in urban environments. Moreover, the establishment of disease monitoring and surveillance mechanisms is indispensable, as it allows for the timely detection of outbreaks, enabling swift responses. Additionally, community engagement and education play a pivotal role in advocating for personal hygiene, vaccination, and preventive measures, thus playing a pivotal role in diminishing disease transmission. Lastly, the promotion of sustainable urban planning, which includes the creation of cities with green spaces, access to clean water, and proper sanitation, can significantly mitigate the risks associated with waterborne and vector-borne diseases. The article is based on the analysis of scientific literature, and it offers a comprehensive insight into the complexities of the relationship between urbanization and health. It places a strong emphasis on the urgent need for integrated approaches to improve urban resilience in the face of health challenges.

Keywords: Infectious diseases dissemination, public health, urbanization impacts, urban resilience.

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Authors: Kaman M. O., Celik N.

Abstract:

In this study, stress distributions on dental implants made of functionally graded biomaterials (FGBM) are investigated numerically. The implant body is considered to be subjected to axial compression loads. Numerical problem is assumed to be 2D, and ANSYS commercial software is used for the analysis. The cross section of the implant thread varies as varying the height (H) and the width (t) of the thread. According to thread dimensions of implant and material properties of FGBM, equivalent stress distribution on the implant is determined and presented with contour plots along with the maximum equivalent stress values. As a result, with increasing material gradient parameter (n), the equivalent stress decreases, but the minimum stress distribution increases. Maximum stress values decrease with decreasing implant radius (r). Maximum von Mises stresses increases with decreasing H when t is constant. On the other hand, the stress values are not affected by variation of t in the case of H = constant.

Keywords: Functionally graded biomaterials, dental implant finite element method.

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Authors: Goh Yongli, Ooi Shih Yin, Pang Ying Han

Abstract:

Unintentional falls are rife throughout the ages and have been the common factor of serious or critical injuries especially for the elderly society. Fortunately, owing to the recent rapid advancement in technology, fall detection system is made possible, enabling detection of falling events for the elderly, monitoring the patient and consequently provides emergency support in the event of falling. This paper presents a review of 3 main categories of fall detection techniques, ranging from year 2005 to year 2010. This paper will be focusing on discussing the techniques alongside with summary and conclusion for them.

Keywords: State of the art, fall detection, wearable devices, ambient analyser, motion detection.

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Authors: Afzel Noore

Abstract:

A new digital watermarking technique for images that are sensitive to blocking artifacts is presented. Experimental results show that the proposed MDCT based approach produces highly imperceptible watermarked images and is robust to attacks such as compression, noise, filtering and geometric transformations. The proposed MDCT watermarking technique is applied to fingerprints for ensuring security. The face image and demographic text data of an individual are used as multiple watermarks. An AFIS system was used to quantitatively evaluate the matching performance of the MDCT-based watermarked fingerprint. The high fingerprint matching scores show that the MDCT approach is resilient to blocking artifacts. The quality of the extracted face and extracted text images was computed using two human visual system metrics and the results show that the image quality was high.

Keywords: Digital watermarking, data hiding, modified discretecosine transformation (MDCT).

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Authors: Mohamed Omar

Abstract:

Steel bracing members are widely used in steel  structures to reduce lateral displacement and dissipate energy during  earthquake motions. Concentric steel bracing provide an excellent  approach for strengthening and stiffening steel buildings. Using these  braces the designer can hardly adjust the stiffness together with  ductility as needed because of buckling of braces in compression. In  this study the use of SMA bracing and steel bracing (Mega) utilized  in steel frames are investigated. The effectiveness of these two  systems in rehabilitating a mid-rise eight-storey steel frames were  examined using time-history nonlinear analysis utilizing seismostruct  software. Results show that both systems improve the strength and  stiffness of the original structure but due to excellent behavior of  SMA in nonlinear phase and under compressive forces this system  shows much better performance than the rehabilitation system of  Mega bracing.

 

Keywords: Finite element analysis, seismic response, shapes memory alloy, steel frame, mega bracing.

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Authors: C. De Lazzari, E. Del Prete, I. Genuini, F. Fedele

Abstract:

Cardiovascular human simulator can be a useful tool in understanding complex physiopathological process in cardiocirculatory system. It can also be a useful tool in order to investigate the effects of different drugs on hemodynamic parameters. The aim of this work is to test the potentiality of our cardiovascular numerical simulator CARDIOSIM© in reproducing flow/pressure coronary waveforms in presence of two different drugs: Amlodipine (AMLO) and Adenosine (ADO). In particular a time-varying intramyocardial compression, assumed to be proportional to the left ventricular pressure, was related to the venous coronary compliances in order to study its effects on the coronary blood flow and the flow/pressure loop. Considering that coronary circulation dynamics is strongly interrelated with the mechanics of the left ventricular contraction, relaxation, and filling, the numerical model allowed to analyze the effects induced by the left ventricular pressure on the coronary flow.

Keywords: Cardiovascular system, Coronary blood flow, Hemodynamic, Numerical simulation.

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Authors: M. Siddhpura, R. Paurobally

Abstract:

This paper investigates the occurrence of regenerative chatter vibrations in facing and turning processes. Orthogonal turning (facing) and normal turning experiments are carried out under stable as well as in the presence of controlled chatter vibrations. The effects of chatter vibrations on various sensor signals are captured and analyzed using frequency domain methods, which successfully detected the chatter vibrations close to the dominant mode of the machine tool system.

Keywords: Chatter vibrations, facing, turning.

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Authors: Jafar Abbaszadeh Chekan, Kaveh Merat, Hassan Zohoor

Abstract:

In this study the regional stability of a rotor system which is supported on rolling bearings with radial clearance is studied. The rotor is assumed to be rigid. Due to radial clearance of bearings and dynamic configuration of system, each rolling elements of bearings has the possibility to be in contact with both of the races (under compression) or lose its contact. As a result, this change in dynamic of the system makes it to be known as switching system which is a type of Hybrid systems. In this investigation by adopting Multiple Lyapunov Function theorem and using Hamiltonian function as a candidate Lyapunov function, the stability of the system is studied. The purpose of this study is to inspect the regional stability of rotor-roller bearing and rotor-ball bearing systems.

Keywords: Stability analysis, Rotor-rolling bearing systems, Switching systems, Multiple Lyapunov Function Method

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Authors: S. Manjunatha, M. S. Dharmaprakash

Abstract:

Well-defined 2D Eu⁺³ co-doped ZrO₂: Gd⁺³ nanoparticles were successfully synthesized by microwave assisted solution combustion technique for luminescent applications. The present investigation reports the rapid and effective method for the synthesis of the Eu⁺³ co-doped ZrO₂:Gd⁺³ nanoparticles and study of the luminescence behavior of Eu⁺³ ion in ZrO₂:Gd⁺³ nanostructures. The optical properties of the prepared nanostructures were investigated by using UV-visible spectroscopy and photoluminescence spectra. The phase formation and the morphology of the nanoplatelets were studied by XRD, FESEM and HRTEM. The average grain size was found to be 45-50 nm. The presence of Gd³⁺ ion increases the crystallinity of the material and hence acts as a good nucleating agent. The ZrO₂:Gd³⁺ co-doped with Eu⁺³ nanoplatelets gives an emission at 607 nm, a strong red emission under the excitation wavelength of 255 nm.

Keywords: Nanoparticles, XRD, TEM, photoluminescence.

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Authors: Li Yongfeng, Liu Yixing, Li Jian, Li Jun

Abstract:

A novel biomass composite inspired from wood porous structure was manufactured by impregnating vinyl monomer into wood cellular structure under vacuum conditions, and initiating the monomer for in situ polymerization through a thermal treatment. The vacuum condition was studied, and the mechanical properties of the composite were also tested. SEM observation shows that polymer generated in the wood porous structure, and strongly interacted with wood matrix; and the polymer content increased with vacuum value increasing. FTIR indicates that polymer grafted onto wood matrix, resulting chemical complex between them. The rate of monomer loading increased with increasing vacuum value and time, accordance with rate of polymer loading. The compression strength and modulus of elasticity linearly increased with the increasing rate of polymer loading. Results indicate that the novel biomass composite possesses good mechanical properties capable of applying in the fields of construction, traffic and so forth.

Keywords: Biomass composite, manufacture, vinyl monomer, wood cellular structure.

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Authors: Sehijpal Singh Khangura, Jai Inder Preet Singh, Vikas Dhawan

Abstract:

Global warming, growing awareness of the environment, waste management issues, dwindling fossil resources, and rising oil prices resulted to increase the research in the materials that are friendly to our health and environment. Due to these reasons, green products are increasingly being promoted for sustainable development. In this work, fully biodegradable green composites have been developed using jute fibers as reinforcement and poly lactic acid as matrix material by film stacking technique. The effect of curing temperature during development of composites ranging from 160 °C, 170 °C, 180 °C and 190 °C was investigated for various mechanical properties. Results obtained from various tests indicate that impact strength decreases with an increase in curing temperature, but tensile and flexural strength increases till 180 °C, thereafter both the properties decrease. This study gives an optimum curing temperature for the development of jute/PLA composites.

Keywords: Natural fibers, polymer matrix composites, jute, compression molding, biodegradation.

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Authors: Shigeyuki Haruyama, Hiroyuki Tanaka, Dai-Heng Chen, Aidil Khaidir Bin Muhamad

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In this paper, the deformation modes of a compact impact absorption member subjected to axial compression are investigated using finite element method and experiments. A multiple combination compact impact absorption member, referred to as a 'compress-expand member', is proposed to substitute the conventional thin-walled circular tube. This study found that the proposed compact impact absorption member has stable load increase characteristics and a wider range of high load efficiency (Pave/Pmax) than the thin-walled circular tube. Moreover, the proposed compact impact absorption member can absorb larger loads in a smaller radius than the thin-walled cylindrical tube, as it can maintain its stable deformation in increased wall thicknesses.

Keywords: axial collapse, compact impact absorption member, finite element method, thin-walled cylindrical tube.

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Authors: Kumiko Inagaki

Abstract:

Japanese society is experiencing an aging population and declining birth rate along with the popularization of higher education, spread of economic globalization, rapid progress in technical innovation, changes in employment conditions, and emergence of a knowledge-based society. Against this background, interest in career education at Japanese universities has increased in recent years. This paper describes how the government has implemented career education policies in Japan, and introduces the cases of two universities that have successfully linked career education to university education in Japan.

Keywords: Career Education, Employability, Higher Education, Japanese University, University Education.

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Authors: Raouf Mobasheri, Zhijun Peng

Abstract:

A CFD simulation has applied to explore the effects of combustion chamber geometry on engine performance and pollutant emissions in a HSDI diesel engine. Three ITs (Injection Timing) at 2.65 CA BTDC, 0.65 CA BTDC and 1.35 CA ATDC, all with 30 crank angle pilot separations has firstly considered to identify the optimum IT for achieving the minimum amount of pollutant emissions. In order to investigate the effect of combustion chamber, thirteen different piston bowl configurations have been designed and analyzed. For all the studied cases, compression ratio, squish bowl volume and the amount of injected fuel were kept constant to assure that variation in the engine performance were only caused by geometric parameters. The results showed that by changing the geometric parameters on piston bowl, the amount of emission pollutants can be decreased while the other performance parameters of engine remain constant.

Keywords: HSDI Diesel Engine, Combustion Chamber Geometry, Pilot Injection, Injection Timing.

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