Search results for: High performance computing

Authors: Hiroshi Abe, Minoru Nakayama

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The frontal area in the brain is known to be involved in behavioral judgement. Because a Kanji character can be discriminated visually and linguistically from other characters, in Kanji character discrimination, we hypothesized that frontal event-related potential (ERP) waveforms reflect two discrimination processes in separate time periods: one based on visual analysis and the other based on lexcical access. To examine this hypothesis, we recorded ERPs while performing a Kanji lexical decision task. In this task, either a known Kanji character, an unknown Kanji character or a symbol was presented and the subject had to report if the presented character was a known Kanji character for the subject or not. The same response was required for unknown Kanji trials and symbol trials. As a preprocessing of signals, we examined the performance of a method using independent component analysis for artifact rejection and found it was effective. Therefore we used it. In the ERP results, there were two time periods in which the frontal ERP wavefoms were significantly different betweeen the unknown Kanji trials and the symbol trials: around 170ms and around 300ms after stimulus onset. This result supported our hypothesis. In addition, the result suggests that Kanji character lexical access may be fully completed by around 260ms after stimulus onset.

Keywords: Character discrimination, Event-related Potential, IndependentComponent Analysis, Kanji, Lexical access.

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Authors: S. Raja Balachandar, K.Kannan

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This paper presents a heuristic approach to solve the Generalized Assignment Problem (GAP) which is NP-hard. It is worth mentioning that many researches used to develop algorithms for identifying the redundant constraints and variables in linear programming model. Some of the algorithms are presented using intercept matrix of the constraints to identify redundant constraints and variables prior to the start of the solution process. Here a new heuristic approach based on the dominance property of the intercept matrix to find optimal or near optimal solution of the GAP is proposed. In this heuristic, redundant variables of the GAP are identified by applying the dominance property of the intercept matrix repeatedly. This heuristic approach is tested for 90 benchmark problems of sizes upto 4000, taken from OR-library and the results are compared with optimum solutions. Computational complexity is proved to be O(mn2) of solving GAP using this approach. The performance of our heuristic is compared with the best state-ofthe- art heuristic algorithms with respect to both the quality of the solutions. The encouraging results especially for relatively large size test problems indicate that this heuristic approach can successfully be used for finding good solutions for highly constrained NP-hard problems.

Keywords: Combinatorial Optimization Problem, Generalized Assignment Problem, Intercept Matrix, Heuristic, Computational Complexity, NP-Hard Problems.

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Authors: Arash Hassanpour Isfahani, Siavash Sadeghi

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Permanent magnet synchronous machines are known as a good candidate for hybrid electric vehicles due to their unique merits. However they have two major drawbacks i.e. high cost and small speed range. In this paper an optimal design of a permanent magnet machine is presented. A reduction of permanent magnet material for a constant torque and an extension in speed and torque ranges are chosen as the optimization aims. For this purpose the analytical model of the permanent magnet synchronous machine is derived and the appropriate design algorithm is devised. The genetic algorithm is then employed to optimize some machine specifications. Finally the finite element method is used to validate the designed machine.

Keywords: Design, Finite Element, Hybrid electric vehicle, Optimization, Permanent magnet synchronous machine.

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Authors: Arne Hitzmann, Philipp Wentscher, Alexander Gabel, Reinhard Gerndt

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Autonomous mobile robots can be found in a wide field of applications. Their types range from household robots over workshop robots to autonomous cars and many more. All of them undergo a number of testing steps during development, production and maintenance. This paper describes an approach to improve testing of robot behavior. It was inspired by the RoboCup @work competition that itself reflects a robotics benchmark for industrial robotics. There, scaled down versions of mobile industrial robots have to navigate through a workshop-like environment or operation area and have to perform tasks of manipulating and transporting work pieces. This paper will introduce an approach of automated vision-based testing of the behavior of the so called youBot robot, which is the most widely used robot platform in the RoboCup @work competition. The proposed system allows automated testing of multiple tries of the robot to perform a specific missions and it allows for the flexibility of the robot, e.g. selecting different paths between two tasks within a mission. The approach is based on a multi-camera setup using, off the shelf cameras and optical markers. It has been applied for test-driven development (TDD) and maintenance-like verification of the robot behavior and performance.

Keywords: Supervisory control, Testing, Markers, Mono Vision, Automation.

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Authors: M. A. Hassan, M. H. Sakinah, K. Kadirgama, D. Ramasamy, M. M. Noor, M. M. Rahman

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Tribological properties that include nanoparticles are an alternative to improve the tribological behaviour of lubricating oil, which has been investigated by many researchers for the past few decades. Various nanostructures can be used as additives for tribological improvement. However, this also depends on the characteristics of the nanoparticles. In this study, tribological investigation was performed to examine the effect of CuO nanoparticles on the tribological behaviour of Syntium 800 SL 10W−30. Three parameters used in the analysis using the wear tester (piston ring) were load, revolutions per minute (rpm), and concentration. The specifications of the nanoparticles, such as size, concentration, hardness, and shape, can affect the tribological behaviour of the lubricant. The friction and wear experiment was conducted using a tribo-tester and the Response Surface Methodology method was used to analyse any improvement of the performance. Therefore, two concentrations of 40 nm nanoparticles were used to conduct the experiments, namely, 0.005 wt % and 0.01 wt % and compared with base oil 0 wt % (control). A water bath sonicator was used to disperse the nanoparticles in base oil, while a tribo-tester was used to measure the coefficient of friction and wear rate. In addition, the thermal properties of the nanolubricant were also measured. The results have shown that the thermal conductivity of the nanolubricant was increased when compared with the base oil. Therefore, the results indicated that CuO nanoparticles had improved the tribological behaviour as well as the thermal properties of the nanolubricant oil.

Keywords: Concentration, improvement, tribological, Copper (II) oxide, nanolubricant.

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Authors: Deniz T. Sodiri, Venkat V S S Sastry

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The paper presents an on-line recognition machine (RM) for continuous/isolated, dynamic and static gestures that arise in Flight Deck Officer (FDO) training. RM is based on generic pattern recognition framework. Gestures are represented as templates using summary statistics. The proposed recognition algorithm exploits temporal and spatial characteristics of gestures via dynamic programming and Markovian process. The algorithm predicts corresponding index of incremental input data in the templates in an on-line mode. Accumulated consistency in the sequence of prediction provides a similarity measurement (Score) between input data and the templates. The algorithm provides an intuitive mechanism for automatic detection of start/end frames of continuous gestures. In the present paper, we consider isolated gestures. The performance of RM is evaluated using four datasets - artificial (W TTest), hand motion (Yang) and FDO (tracker, vision-based ). RM achieves comparable results which are in agreement with other on-line and off-line algorithms such as hidden Markov model (HMM) and dynamic time warping (DTW). The proposed algorithm has the additional advantage of providing timely feedback for training purposes.

Keywords: On-line Recognition Algorithm, IsolatedDynamic/Static Gesture Recognition, On-line Markovian/DynamicProgramming, Training in Virtual Environments.

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Authors: S. Bader, N. Essoukri Ben Amara

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With the technological development and rise of virtual worlds, these spaces are becoming more and more attractive for cybercriminals, hidden behind avatars and fictitious identities. Since access to these spaces is not restricted or controlled, some impostors take advantage of gaining unauthorized access and practicing cyber criminality. This paper proposes an identity management approach for securing access to virtual worlds. The major purpose of the suggested solution is to install a strong security mechanism to protect virtual identities represented by avatars. Thus, only legitimate users, through their corresponding avatars, are allowed to access the platform resources. Access is controlled by integrating an authentication process based on biometrics. In the request process for registration, a user fingerprint is enrolled and then encrypted into a watermark utilizing a cancelable and non-invertible algorithm for its protection. After a user personalizes their representative character, the biometric mark is embedded into the avatar through a watermarking procedure. The authenticity of the avatar identity is verified when it requests authorization for access. We have evaluated the proposed approach on a dataset of avatars from various virtual worlds, and we have registered promising performance results in terms of authentication accuracy, acceptation and rejection rates.

Keywords: Identity management, security, biometrics authentication and authorization, avatar, virtual world.

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Authors: Mia Wimala, Emma Akmalah, Ira Irawati, M. Rangga Sururi

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One way that has been aggressively implemented in creating a sustainable environment nowadays is through the implementation of green building concept. In order to ensure the success of its implementation, the support and initiation from educational institutions, especially higher education institutions are indispensable. This research was conducted to figure out the obstacles restraining the success of green campus implementation in Indonesia, as well as to propose strategies to overcome those obstacles. The data presented in this paper are mainly derived from interview and questionnaire distributed randomly to the staffs and students in 10 (ten) major institutions around Jakarta and West Java area. The data were further analyzed using ANOVA and SWOT analysis. According to 182 respondents, it is found that resistance to change, inadequate knowledge, information and understanding, no penalty for any environmental violation, lack of reward for green campus practices, lack of stringent regulations/laws, lack of management commitment, insufficient funds are the obstacles to the green campus movement in Indonesia. In addition, out of 6 criteria considered in UI GreenMetric World Ranking, education was the only criteria that had no significant difference between public and private universities in generating the green campus performance. The work concludes with recommendation of strategies to improve the implementation of green campus in the future.

Keywords: Green campus, obstacles, sustainable, higher education institutions.

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Authors: Nitai Biswas, Prasenjit Kapas, Arumay Jana, Asish Paul

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Introduction: Motivation is basic drive for all kinds of action. It has direct influence on academic achievement and sports performance that builds urge to incentive values of success. In other words, it can be defined as the need for success to attain excellence. Anxiety in pre competition especially in sports formulates positive inward settings in mind to overcome the challenge. There is a tendency to perceive competitive situations as some threatening issues and to respond them with feelings of apprehension and tension. Aim: Aim of the study was to compare the achievement motivation and competition anxiety among three different classes of students. Methods and Materials: To conduct the study the researcher has taken 131 male subjects from three different classes as Extra Department, Bachelor of Physical Education-I and Master of Physical EducationII, aged 19-28 years. Achievement motivation and sports competition anxiety were measured by the questionnaire. To analyze the data mean, standard deviation for each parameter as descriptive statistics and one way analysis of variance as inferential statistics were employed. Results: From the result of the study in achievement motivation (p ≥ 0.05) and competition anxiety (p ≥ 0.05) no significant differences were found among the said three groups. Conclusion: The study concluded that all three groups had almost the same state of achievement motivation and sports competition anxiety.

Keywords: Anxiety, sports psychology, sports competition anxiety, achievement motivation, academic hierarchy.

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Authors: Marina Velikova, Hennie Daniels, Ad Feelders

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In many data mining applications, it is a priori known that the target function should satisfy certain constraints imposed by, for example, economic theory or a human-decision maker. In this paper we consider partially monotone prediction problems, where the target variable depends monotonically on some of the input variables but not on all. We propose a novel method to construct prediction models, where monotone dependences with respect to some of the input variables are preserved by virtue of construction. Our method belongs to the class of mixture models. The basic idea is to convolute monotone neural networks with weight (kernel) functions to make predictions. By using simulation and real case studies, we demonstrate the application of our method. To obtain sound assessment for the performance of our approach, we use standard neural networks with weight decay and partially monotone linear models as benchmark methods for comparison. The results show that our approach outperforms partially monotone linear models in terms of accuracy. Furthermore, the incorporation of partial monotonicity constraints not only leads to models that are in accordance with the decision maker's expertise, but also reduces considerably the model variance in comparison to standard neural networks with weight decay.

Keywords: mixture models, monotone neural networks, partially monotone models, partially monotone problems.

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Authors: Bayar Shahab

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The fast development of technology that has advanced neuroscience and human interaction with computers has enabled solutions to various problems and issues of this new era. The Brain-Computer Interface (BCI) has opened the door to several new research areas and have been able to provide solutions to critical and vital issues such as supporting a paralyzed patient to interact with the outside world, controlling a robot arm, playing games in VR with the brain, driving a wheelchair. This review presents the state-of-the-art methods and improvements of canonical correlation analyses (CCA), an SSVEP-based BCI method. These are the methods used to extract EEG signal features or, to be said differently, the features of interest that we are looking for in the EEG analyses. Each of the methods from oldest to newest has been discussed while comparing their advantages and disadvantages. This would create a great context and help researchers understand the most state-of-the-art methods available in this field, their pros and cons, and their mathematical representations and usage. This work makes a vital contribution to the existing field of study. It differs from other similar recently published works by providing the following: (1) stating most of the main methods used in this field in a hierarchical way, (2) explaining the pros and cons of each method and their performance, (3) presenting the gaps that exist at the end of each method that can improve the understanding and open doors to new researches or improvements. 

Keywords: BCI, CCA, SSVEP, EEG

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Authors: E. Petrakis, V. Karmali, K. Komnitsas

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In this study grinding experiments were performed in a laboratory ball mill using Polish ferronickel slag in order to study the effect of the particle size on alkali activation and the properties of the produced alkali activated materials (AAMs). In this regard, the particle size distribution and the specific surface area of the grinding products in relation to grinding time were assessed. The experimental results show that products with high compressive strength, e.g. higher than 60 MPa, can be produced when the slag median size decreased from 39.9 μm to 11.9 μm. Also, finer fractions are characterized by higher reactivity and result in the production of AAMs with lower porosity and better mechanical properties.

Keywords: Alkali activated materials, compressive strength, particle size distribution, slag.

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Authors: Apoorva Vinod, Archana Mathur, Snehanshu Saha

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Though there exists a plethora of Activation Functions (AFs) used in single and multiple hidden layer Neural Networks (NN), their behavior always raised curiosity, whether used in combination or singly. The popular AFs – Sigmoid, ReLU, and Tanh – have performed prominently well for shallow and deep architectures. Most of the time, AFs are used singly in multi-layered NN, and, to the best of our knowledge, their performance is never studied and analyzed deeply when used in combination. In this manuscript, we experiment on multi-layered NN architecture (both on shallow and deep architectures; Convolutional NN and VGG16) and investigate how well the network responds to using two different AFs (Sigmoid-Tanh, Tanh-ReLU, ReLU-Sigmoid) used alternately against a traditional, single (Sigmoid-Sigmoid, Tanh-Tanh, ReLU-ReLU) combination. Our results show that on using two different AFs, the network achieves better accuracy, substantially lower loss, and faster convergence on 4 computer vision (CV) and 15 Non-CV (NCV) datasets. When using different AFs, not only was the accuracy greater by 6-7%, but we also accomplished convergence twice as fast. We present a case study to investigate the probability of networks suffering vanishing and exploding gradients when using two different AFs. Additionally, we theoretically showed that a composition of two or more AFs satisfies Universal Approximation Theorem (UAT).

Keywords: Activation Function, Universal Approximation function, Neural Networks, convergence.

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Authors: I. Djefour, M. Saidi, I. Tlemsani, S. Toubal

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Lignin is a molecule derived from wood and also generated as waste from the paper industry. With a view to its valorization and protection of the environment, we are interested in its use as a superplasticizer-type adjuvant in mortars and concretes to improve their mechanical strengths. The additives of the concrete have a very strong influence on the properties of the fresh and / or hardened concrete. This study examines the development and use of industrial waste and lignin extracted from a renewable natural source (wood) in cementitious materials. The use of these resources is known at present as a definite resurgence of interest in the development of building materials. Physicomechanical characteristics of mortars are determined by optimization quantity of the natural superplasticizer. The results show that the mechanical strengths of mortars based on natural adjuvant have improved by 20% (64 MPa) for a W/C ratio = 0.4, and the amount of natural adjuvant of dry extract needed is 40 times smaller than commercial adjuvant. This study has a scientific impact (improving the performance of the mortar with an increase in compactness and reduction of the quantity of water), ecological use of the lignin waste generated by the paper industry) and economic reduction of the cost price necessary to elaboration of self-compacting mortars and concretes).

Keywords: Biopolymer, lignin, industrial waste, mechanical resistances, self-compacting mortars.

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Authors: M. Zeydan, G. Toğa

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This study deals with a multi-criteria optimization problem which has been transformed into a single objective optimization problem using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Grey Relational Analyses (GRA) approach. Grey-RSM and Grey-ANN are hybrid techniques which can be used for solving multi-criteria optimization problem. There have been two main purposes of this research as follows. 1. To determine optimum and robust fiber dyeing process conditions by using RSM and ANN based on GRA, 2. To obtain the best suitable model by comparing models developed by different methodologies. The design variables for fiber dyeing process in textile are temperature, time, softener, anti-static, material quantity, pH, retarder, and dispergator. The quality characteristics to be evaluated are nominal color consistency of fiber, maximum strength of fiber, minimum color of dyeing solution. GRA-RSM with exact level value, GRA-RSM with interval level value and GRA-ANN models were compared based on GRA output value and MSE (Mean Square Error) performance measurement of outputs with each other. As a result, GRA-ANN with interval value model seems to be suitable reducing the variation of dyeing process for GRA output value of the model.

Keywords: Artificial Neural Network, Grey Relational Analysis, Optimization, Response Surface Methodology

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Authors: N. A. Zambri, A. Mohamed, H. Shareef, M. Z. C. Wanik

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This paper presents the modeling and simulation of a hybrid proton exchange membrane fuel cell (PEMFC) with an energy storage system for use in a stand-alone distributed generation (DG) system. The simulation model consists of fuel cell DG, lead-acid battery, maximum power point tracking and power conditioning unit which is modeled in the MATLAB/Simulink platform. Poor loadfollowing characteristics and slow response to rapid load changes are some of the weaknesses of PEMFC because of the gas processing reaction and the fuel cell dynamics. To address the load-tracking issues in PEMFC, a hybrid PEMFC and battery storage system is considered and modelled. The model utilizes PEMFC as the main energy source whereas the battery functions as energy storage to compensate for the limitations of PEMFC.Simulation results are given to show the overall system performance under light and heavyloading conditions.

Keywords: Hybrid, Lead–Acid Battery, Maximum Power Point Tracking, Proton Exchange Membrane Fuel Cell.

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Authors: Theodor D. Popescu

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The paper presents a method for multivariate time series forecasting using Independent Component Analysis (ICA), as a preprocessing tool. The idea of this approach is to do the forecasting in the space of independent components (sources), and then to transform back the results to the original time series space. The forecasting can be done separately and with a different method for each component, depending on its time structure. The paper gives also a review of the main algorithms for independent component analysis in the case of instantaneous mixture models, using second and high-order statistics. The method has been applied in simulation to an artificial multivariate time series with five components, generated from three sources and a mixing matrix, randomly generated.

Keywords: Independent Component Analysis, second order statistics, simulation, time series forecasting

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Authors: Asma Alzahrani, Elizabeth Stojanovski

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This study investigated factors that influence mathematics achievement based on a sample of ninth-grade students (N  =  21,444) from the High School Longitudinal Study of 2009 (HSLS09). Key aspects studied included efficacy in mathematics, interest and enjoyment of mathematics, identity with mathematics and future utility beliefs and how these influence mathematics achievement. The predictability of mathematics achievement based on these factors was assessed using correlation coefficients and multiple linear regression. Spearman rank correlations and multiple regression analyses indicated positive and statistically significant relationships between the explanatory variables: mathematics efficacy, identity with mathematics, interest in and future utility beliefs with the response variable, achievement in mathematics.

Keywords: Mathematics achievement, math efficacy, mathematics interest, identity.

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Authors: Wen-Yuh Jywe, Bor-Jeng Lin, Jing-Chung Shen, Jeng-Dao Lee, Hsueh-Liang Huang, Tung-Hsien Hsieh

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In this study, a simple 2-D measurement system based on optical design was developed to measure the motion errors of the linear guideway. Compared with the transitional methods about the linear guideway for measuring the motion errors, our proposed 2-D optical measurement system can simultaneously measure horizontal and vertical running straightness errors for the linear guideway.

The performance of the 2-D optical measurement system is verified by experimental results. The standard deviation of the 2-D optical measurement system is about 0.4μm in the measurement range of 100 mm. The maximum measuring speed of the proposed automatic measurement instrument is 1 m/sec.

Keywords: 2-D measurement, linear guideway, motion errors, running straightness.

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Authors: J. Umamaheswari, G. Radhamani

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This paper describes about the process of recognition and classification of brain images such as normal and abnormal based on PSO-SVM. Image Classification is becoming more important for medical diagnosis process. In medical area especially for diagnosis the abnormality of the patient is classified, which plays a great role for the doctors to diagnosis the patient according to the severeness of the diseases. In case of DICOM images it is very tough for optimal recognition and early detection of diseases. Our work focuses on recognition and classification of DICOM image based on collective approach of digital image processing. For optimal recognition and classification Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Support Vector Machine (SVM) are used. The collective approach by using PSO-SVM gives high approximation capability and much faster convergence.

Keywords: Recognition, classification, Relaxed Median Filter, Adaptive thresholding, clustering and Neural Networks

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Authors: Sandu L., Topală F., Porojan S.

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Cast metal inlays can be used on molars requiring a class II restoration instead amalgam and offer a durable alternative. Because it is known that class II inlays may increase the susceptibility to fracture, it is important to ensure optimal performance in selection of the adequate preparation design to reduce stresses in teeth structures and also in the restorations. The aim of the study was to investigate the influence of preparation design on stress distribution in molars with different class II preparations and in cast metal inlays. The first step of the study was to achieve 3D models in order to analyze teeth and cast metal class II inlays. The geometry of the intact tooth was obtained by 3D scanning using a manufactured device. With a NURBS modeling program the preparations and the appropriately inlays were designed. 3D models of first upper molars of the same shape and size were created. Inlay cavities designs were created using literature data. The geometrical model was exported and the mesh structure of the solid 3D model was created for structural simulations. Stresses were located around the occlusal contact areas. For the studied cases, the stress values were not significant influenced by the taper of the preparation. it was demonstrated stresses are higher in the cast metal restorations and therefore the strength of the teeth is not affected.

Keywords: cast metal inlays, class II restoration, molars, 3D models, structural simulations.

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Authors: Khaled M. Alhawiti

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This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition.

Keywords: Speech recognition, acoustic phonetic, artificial intelligence, Hidden Markov Models (HMM), statistical models of speech recognition, human machine performance.

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Authors: E.V. Butilă, F. Gîrbacia

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In order to give high expertise the computer aided design of mechanical systems involves specific activities focused on processing two type of information: knowledge and data. Expert rule based knowledge is generally processing qualitative information and involves searching for proper solutions and their combination into synthetic variant. Data processing is based on computational models and it is supposed to be inter-related with reasoning in the knowledge processing. In this paper an Intelligent Integrated System is proposed, for the objective of choosing the adequate material. The software is developed in Prolog – Flex software and takes into account various constraints that appear in the accurate operation of gears.

Keywords: Expert System, computer aided design, gear boxdesign, chose material, Prolog, Flex

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Authors: Ju-Chun Chien

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Rapid aging and depopulation could lead to serious problems, including workforce shortages and health expenditure costs. The current and predicted future LTC workforce shortages could be a real threat to Taiwan’s society. By means of comparison of data from 144 nursing aide trainees and 727 general public, the main purpose of the present study was to determine whether there were any notable differences between the two groups toward engaging in LTC services. Moreover, this study focused on recognizing the attributes of the general public who had the willingness to take LTC jobs but continue to ride the fence. A self-developed questionnaire was designed based on Ajzen’s Theory of Planned Behavior model. After conducting exploratory factor analysis (EFA) and reliability analysis, the questionnaire was a reliable and valid instrument for both nursing aide trainees and the general public. The main results were as follows: Firstly, nearly 70% of nursing aide trainees showed interest in LTC jobs. Most of them were middle-aged female (M = 46.85, SD = 9.31), had a high school diploma or lower, had unrelated work experience in healthcare, and were mostly unemployed. The most common reason for attending the LTC training program was to gain skills in a particular field. The second most common reason was to obtain the license. The third and fourth reasons were to be interested in caring for people and to increase income. The three major reasons that might push them to leave LTC jobs were physical exhaustion, payment is bad, and being looked down on. Secondly, the variables that best-predicted nursing aide trainees’ intention to engage in LTC services were having personal willingness, perceived behavior control, with high school diploma or lower, and supported from family and friends. Finally, only 11.80% of the general public reported having interest in LTC jobs (the disapproval rating was 50% for the general public). In comparison to nursing aide trainees who showed interest in LTC settings, 64.8% of the new workforce for LTC among the general public was male and had an associate degree, 54.8% had relevant healthcare experience, 67.1% was currently employed, and they were younger (M = 32.19, SD = 13.19) and unmarried (66.3%). Furthermore, the most commonly reason for the new workforce to engage in LTC jobs were to gain skills in a particular field. The second priority was to be interested in caring for people. The third and fourth most reasons were to give back to society and to increase income, respectively. The top five most commonly reasons for the new workforce to quitting LTC jobs were listed as follows: physical exhaustion, being looked down on, excessive working hours, payment is bad, and excessive job stress.

Keywords: Long-term care services, nursing aide trainees, Taiwanese people, theory of planned behavior.

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

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In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods.

Keywords: Human action recognition, Bayesian HMM, Dirichlet process mixture model, Gaussian-Wishart emission model, Variational Bayesian inference, Prior distribution and approximate posterior distribution, KTH dataset.

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Authors: Kaveh Shahi, Helmi Z. M. Shafri, Ebrahim Taherzadeh

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In remote sensing, shadow causes problems in many applications such as change detection and classification. It is caused by objects which are elevated, thus can directly affect the accuracy of information. For these reasons, it is very important to detect shadows particularly in urban high spatial resolution imagery which created a significant problem. This paper focuses on automatic shadow detection based on a new spectral index for multispectral imagery known as Shadow Detection Index (SDI). The new spectral index was tested on different areas of WorldView-2 images and the results demonstrated that the new spectral index has a massive potential to extract shadows with accuracy of 94% effectively and automatically. Furthermore, the new shadow detection index improved road extraction from 82% to 93%.

Keywords: Spectral index, shadow detection, remote sensing images, WorldView-2.

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Authors: Bhausaheb L. Pangarkar, M. G. Sane, Saroj B. Parjane, Rajendra M. Abhang, Mahendra Guddad

Abstract:

Vacuum membrane distillation (VMD) process can be used for water purification or the desalination of salt water. The process simply consists of a flat sheet hydrophobic micro porous PTFE membrane and diaphragm vacuum pump without a condenser for the water recovery or trap. The feed was used aqueous NaCl solution. The VMD experiments were performed to evaluate the heat and mass transfer coefficient of the boundary layer in a membrane module. The only operating parameters are feed inlet temperature, and feed flow rate were investigated. The permeate flux was strongly affected by the feed inlet temperature, feed flow rate, and boundary layer heat transfer coefficient. Since lowering the temperature polarization coefficient is essential enhance the process performance considerable and maximizing the heat transfer coefficient for maximizes the mass flux of distillate water. In this paper, the results of VMD experiments are used to measure the boundary layer heat transfer coefficient, and the experimental results are used to reevaluate the empirical constants in the Dittus- Boelter equation.

Keywords: Desalination, heat and mass transfer coefficient, temperature polarization, membrane distillation

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Authors: Christian Bräuer-Burchardt, Peter Kühmstedt, Gunther Notni

Abstract:

A new hybrid method to realise high-precision distortion determination for optical ultra-precision 3D measurement systems based on stereo cameras using active light projection is introduced. It consists of two phases: the basic distortion determination and the refinement. The refinement phase of the procedure uses a plane surface and projected fringe patterns as calibration tools to determine simultaneously the distortion of both cameras within an iterative procedure. The new technique may be performed in the state of the device “ready for measurement" which avoids errors by a later adjustment. A considerable reduction of distortion errors is achieved and leads to considerable improvements of the accuracy of 3D measurements, especially in the precise measurement of smooth surfaces.

Keywords: 3D Surface Measurement, Fringe Projection, Lens Distortion, Stereo.

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Authors: Nasrul Amri Mohd Amin, Martin Belusko, Frank Bruno

Abstract:

PCMs have always been viewed as a suitable candidate for off peak thermal storage, particularly for refrigeration systems, due to the high latent energy densities of these materials. However, due to the need to have them encapsulated within a container this density is reduced. Furthermore, PCMs have a low thermal conductivity which reduces the useful amount of energy which can be stored. To consider these factors, the true energy storage density of a PCM system was proposed and optimised for PCMs encapsulated in slabs. Using a validated numerical model of the system, a parametric study was undertaken to investigate the impact of the slab thickness, gap between slabs and the mass flow rate. The study showed that, when optimised, a PCM system can deliver a true energy storage density between 53% and 83% of the latent energy density of the PCM.

Keywords: Phase change material, refrigeration, sustainability, thermal energy storage.

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Authors: Roelien Goede

Abstract:

Data warehousing success is not high enough. User dissatisfaction and failure to adhere to time frames and budgets are too common. Most traditional information systems practices are rooted in hard systems thinking. Today, the great systems thinkers are forgotten by information systems developers. A data warehouse is still a system and it is worth investigating whether systems thinkers such as Churchman can enhance our practices today. This paper investigates data warehouse development practices from a systems thinking perspective. An empirical investigation is done in order to understand the everyday practices of data warehousing professionals from a systems perspective. The paper presents a model for the application of Churchman-s systems approach in data warehouse development.

Keywords: Data warehouse development, Information systemsdevelopment, Interpretive case study, Systems thinking

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