Abstract: The problem under research is that of unpredictable modes occurring in two-stage centrifugal hydraulic pump as a result of hydraulic processes caused by vibrations of structural components. Numerical, analytical and experimental approaches are considered. A hypothesis was developed that the problem of unpredictable pressure decrease at the second stage of centrifugal pumps is caused by cavitation effects occurring upon vibration. The problem has been studied experimentally and theoretically as of today. The theoretical study was conducted numerically and analytically. Hydroelastic processes in dynamic “liquid – deformed structure” system were numerically modelled and analysed. Using ANSYS CFX program engineering analysis complex and computing capacity of a supercomputer the cavitation parameters were established to depend on vibration parameters. An influence domain of amplitudes and vibration frequencies on concentration of cavitation bubbles was formulated. The obtained numerical solution was verified using CFM program package developed in PNRPU. The package is based on a differential equation system in hyperbolic and elliptic partial derivatives. The system is solved by using one of finite-difference method options – the particle-in-cell method. The method defines the problem solution algorithm. The obtained numerical solution was verified analytically by model problem calculations with the use of known analytical solutions of in-pipe piston movement and cantilever rod end face impact. An infrastructure consisting of an experimental fast hydro-dynamic processes research installation and a supercomputer connected by a high-speed network, was created to verify the obtained numerical solutions. Physical experiments included measurement, record, processing and analysis of data for fast processes research by using National Instrument signals measurement system and Lab View software. The model chamber end face oscillated during physical experiments and, thus, loaded the hydraulic volume. The loading frequency varied from 0 to 5 kHz. The length of the operating chamber varied from 0.4 to 1.0 m. Additional loads weighed from 2 to 10 kg. The liquid column varied from 0.4 to 1 m high. Liquid pressure history was registered. The experiment showed dependence of forced system oscillation amplitude on loading frequency at various values: operating chamber geometrical dimensions, liquid column height and structure weight. Maximum pressure oscillation (in the basic variant) amplitudes were discovered at loading frequencies of approximately 1,5 kHz. These results match the analytical and numerical solutions in ANSYS and CFM.
Abstract: Cloud computing is the outcome of rapid growth of internet. Due to elastic nature of cloud computing and unpredictable behavior of user, load balancing is the major issue in cloud computing paradigm. An efficient load balancing technique can improve the performance in terms of efficient resource utilization and higher customer satisfaction. Load balancing can be implemented through task scheduling, resource allocation and task migration. Various parameters to analyze the performance of load balancing approach are response time, cost, data processing time and throughput. This paper demonstrates a two level load balancer approach by combining join idle queue and join shortest queue approach. Authors have used cloud analyst simulator to test proposed two level load balancer approach. The results are analyzed and compared with the existing algorithms and as observed, proposed work is one step ahead of existing techniques.
Abstract: The emergence of Cloud data centers has revolutionized
the IT industry. Private Clouds in specific provide Cloud services
for certain group of customers/businesses. In a real-time private
Cloud each task that is given to the system has a deadline that
desirably should not be violated. Scheduling tasks in a real-time
private CLoud determine the way available resources in the system
are shared among incoming tasks. The aim of the scheduling policy is
to optimize the system outcome which for a real-time private Cloud
can include: energy consumption, deadline violation, execution time
and the number of host switches. Different scheduling policies can be
used for scheduling. Each lead to a sub-optimal outcome in a certain
settings of the system. A Bayesian Scheduling strategy is proposed
for scheduling to further improve the system outcome. The Bayesian
strategy showed to outperform all selected policies. It also has the
flexibility in dealing with complex pattern of incoming task and has
the ability to adapt.
Abstract: The rapid generation of high volume and a broad variety of data from the application of new technologies pose challenges for the generation of business-intelligence. Most organizations and business owners need to extract data from multiple sources and apply analytical methods for the purposes of developing their business. Therefore, the recently decentralized data management environment is relying on a distributed computing paradigm. While data are stored in highly distributed systems, the implementation of distributed data-mining techniques is a challenge. The aim of this technique is to gather knowledge from every domain and all the datasets stemming from distributed resources. As agent technologies offer significant contributions for managing the complexity of distributed systems, we consider this for next-generation data-mining processes. To demonstrate agent-based business intelligence operations, we use agent-oriented modeling techniques to develop a new artifact for mining massive datasets.
Abstract: Large scale computing infrastructures have been widely
developed with the core objective of providing a suitable platform
for high-performance and high-throughput computing. These systems
are designed to support resource-intensive and complex applications,
which can be found in many scientific and industrial areas. Currently,
large scale data-intensive applications are hindered by the high
latencies that result from the access to vastly distributed data.
Recent works have suggested that improving data locality is key to
move towards exascale infrastructures efficiently, as solutions to this
problem aim to reduce the bandwidth consumed in data transfers, and
the overheads that arise from them. There are several techniques that
attempt to move computations closer to the data. In this survey we
analyse the different mechanisms that have been proposed to provide
data locality for large scale high-performance and high-throughput
systems. This survey intends to assist scientific computing community
in understanding the various technical aspects and strategies that
have been reported in recent literature regarding data locality. As a
result, we present an overview of locality-oriented techniques, which
are grouped in four main categories: application development, task
scheduling, in-memory computing and storage platforms. Finally, the
authors include a discussion on future research lines and synergies
among the former techniques.
Abstract: This paper presents a multiscale information measure of
Electroencephalogram (EEG) for analysis with a short data length.
A multiscale extension of permutation entropy (MPE) is capable of
fully reflecting the dynamical characteristics of EEG across different
temporal scales. However, MPE yields an imprecise estimation due
to coarse-grained procedure at large scales. We present an improved
MPE measure to estimate entropy more accurately with a short
time series. By computing entropies of all coarse-grained time series
and averaging those at each scale, it leads to the modified MPE
(MMPE) which provides an enhanced accuracy as compared to
MPE. Simulation and experimental studies confirmed that MMPE
has proved its capability over MPE in terms of accuracy.
Abstract: In this work we present a family of new convergent
type methods splitting high order no negative steps feature that
allows your application to irreversible problems. Performing affine
combinations consist of results obtained with Trotter Lie integrators
of different steps. Some examples where applied symplectic
compared with methods, in particular a pair of differential equations
semilinear. The number of basic integrations required is comparable
with integrators symplectic, but this technique allows the ability
to do the math in parallel thus reducing the times of which
exemplify exhibiting some implementations with simple schemes for
its modularity and scalability process.
Abstract: The overarching aim of this study is to develop a soft-computing system for the differential diagnosis of tropical diseases. These conditions are of concern to health bodies, physicians, and the community at large because of their mortality rates, and difficulties in early diagnosis due to the fact that they present with symptoms that overlap, and thus become ‘confusable’. We report on the first phase of our study, which focuses on the development of a fuzzy cognitive map model for early differential diagnosis of tropical diseases. We used malaria as a case disease to show the effectiveness of the FCM technology as an aid to the medical practitioner in the diagnosis of tropical diseases. Our model takes cognizance of manifested symptoms and other non-clinical factors that could contribute to symptoms manifestations. Our model showed 85% accuracy in diagnosis, as against the physicians’ initial hypothesis, which stood at 55% accuracy. It is expected that the next stage of our study will provide a multi-disease, multi-symptom model that also improves efficiency by utilizing a decision support filter that works on an algorithm, which mimics the physician’s diagnosis process.
Abstract: The learning process needs to be so pervasive to impart the quality in acquiring the knowledge about a subject by making use of the advancement in the field of information and communication systems. However, pervasive learning paradigms designed so far are system automation types and they lack in factual pervasive realm. Providing factual pervasive realm requires subtle ways of teaching and learning with system intelligence. Augmentation of intelligence with pervasive learning necessitates the most efficient way of representing knowledge for the system in order to give the right learning material to the learner. This paper presents a method of representing knowledge for Pervasive Toolroom Maintenance System (PTMS) in which a learner acquires sublime knowledge about the various kinds of tools kept in the toolroom and also helps for effective maintenance of the toolroom. First, we explicate the generic model of knowledge representation for PTMS. Second, we expound the knowledge representation for specific cases of toolkits in PTMS. We have also presented the conceptual view of knowledge representation using ontology for both generic and specific cases. Third, we have devised the relations for pervasive knowledge in PTMS. Finally, events are identified in PTMS which are then linked with pervasive data of toolkits based on relation formulated. The experimental environment and case studies show the accuracy and efficient knowledge representation of toolkits in PTMS.
Abstract: The estimation of gear tooth stiffness is important for finding the load distribution between the gear teeth when two consecutive sets of teeth are in contact. Based on dynamic model a C-program has been developed to compute mesh stiffness. By using this program position dependent mesh stiffness of spur gear tooth for various profile shifts have been computed for a fixed center distance and altering tooth-sum gearing (100 by ± 4%). It is found that the C-program using dynamic model is one of the rapid soft computing technique which helps in design of gears. The mesh tooth stiffness along the path of contact is studied for both 20° and 25° pressure angle gears at various profile shifts. Better tooth stiffness is noticed in case of negative alteration tooth-sum gears compared to standard and positive alteration tooth-sum gears. Also, in case of negative alteration tooth-sum gearing better mesh stiffness is noticed in 20° pressure angle when compared to 25°.
Abstract: Hazard management that can prevent fatal accidents and property losses is a fundamental process during the buildings’ construction stage. However, due to lack of safety supervision resources and operational pressures, the conduction of hazard management is poor and ineffective in China. In order to improve the quality of construction safety management, it is critical to explore the use of information technologies to ensure that the process of hazard management is efficient and effective. After exploring the existing problems of construction hazard management in China, this paper develops the griddization management model for construction hazard management. First, following the knowledge grid infrastructure, the griddization computing infrastructure for construction hazards management is designed which includes five layers: resource entity layer, information management layer, task management layer, knowledge transformation layer and application layer. This infrastructure will be as the technical support for realizing grid management. Second, this study divides the construction hazards into grids through city level, district level and construction site level according to grid principles. Last, a griddization management process including hazard identification, assessment and control is developed. Meanwhile, all stakeholders of construction safety management, such as owners, contractors, supervision organizations and government departments, should take the corresponding responsibilities in this process. Finally, a case study based on actual construction hazard identification, assessment and control is used to validate the effectiveness and efficiency of the proposed griddization management model. The advantage of this designed model is to realize information sharing and cooperative management between various safety management departments.
Abstract: Governments constantly seek to offer faster, more secure, efficient and effective services for their citizens. Recent changes and developments to communication services and technologies, mainly due the Internet, have led to immense improvements in the way governments of advanced countries carry out their interior operations Therefore, advances in e-government services have been broadly adopted and used in various developed countries, as well as being adapted to developing countries. The implementation of advances depends on the utilization of the most innovative structures of data techniques, mainly in web dependent applications, to enhance the main functions of governments. These functions, in turn, have spread to mobile and wireless techniques, generating a new advanced direction called m-government. This paper discusses a selection of available m-government applications and several business modules and frameworks in various fields. Practically, the m-government models, techniques and methods have become the improved version of e-government. M-government offers the potential for applications which will work better, providing citizens with services utilizing mobile communication and data models incorporating several government entities. Developing countries can benefit greatly from this innovation due to the fact that a large percentage of their population is young and can adapt to new technology and to the fact that mobile computing devices are more affordable. The use of models of mobile transactions encourages effective participation through the use of mobile portals by businesses, various organizations, and individual citizens. Although the application of m-government has great potential, it does have major limitations. The limitations include: the implementation of wireless networks and relative communications, the encouragement of mobile diffusion, the administration of complicated tasks concerning the protection of security (including the ability to offer privacy for information), and the management of the legal issues concerning mobile applications and the utilization of services.
Abstract: In this work, we propose an algorithm developed under Python language for the modeling of ordinary scalar Bessel beams and their discrete superpositions and subsequent calculation of optical forces exerted over dielectric spherical particles. The mathematical formalism, based on the generalized Lorenz-Mie theory, is implemented in Python for its large number of free mathematical (as SciPy and NumPy), data visualization (Matplotlib and PyJamas) and multiprocessing libraries. We also propose an approach, provided by a synchronized Software as Service (SaaS) in cloud computing, to develop a user interface embedded on a mobile application, thus providing users with the necessary means to easily introduce desired unknowns and parameters and see the graphical outcomes of the simulations right at their mobile devices. Initially proposed as a free Android-based application, such an App enables data post-processing in cloud-based architectures and visualization of results, figures and numerical tables.
Abstract: This study aims to investigate and explore the underlying causes of security concerns of customers emerged when WHSmith transformed its physical system to virtualized business model through NetSuite. NetSuite is essentially fully integrated software which helps transforming the physical system to virtualized business model. Modern organisations are moving away from traditional business models to cloud based models and consequently it is expected to have a better, secure and innovative environment for customers. The vital issue of the modern age race is the security when transforming virtualized through cloud based models and designers of interactive systems often misunderstand privacy and even often ignore it, thus causing concerns for users. The content analysis approach is being used to collect the qualitative data from 120 online bloggers including TRUSTPILOT. The results and finding provide useful new insights into the nature and form of security concerns of online users after they have used the WHSmith services offered online through their website. Findings have theoretical as well as practical implications for the successful adoption of cloud computing Business-to-Business model and similar systems.
Abstract: AmI proposes a new way of thinking about computers, which follows the ideas of the Ubiquitous Computing vision of Mark Weiser. In these, there is what is known as a Disappearing Computer Initiative, with users immersed in intelligent environments. Hence, technologies need to be adapted so that they are capable of replacing the traditional inputs to the system by embedding these in every-day artifacts. In this work, we present an approach, which uses Radiofrequency Identification (RFID) and Near Field Communication (NFC) technologies. In the latter, a new form of interaction appears by contact. We compare both technologies by analyzing their requirements and advantages. In addition, we propose using a combination of RFID and NFC.
Abstract: This research provides a technical account of
estimating Transition Probability using Time-homogeneous Markov
Jump Process applying by South African HIV/AIDS data from the
Statistics South Africa. It employs Maximum Likelihood Estimator
(MLE) model to explore the possible influence of Transition
Probability of mortality cases in which case the data was based on
actual Statistics South Africa. This was conducted via an integrated
demographic and epidemiological model of South African HIV/AIDS
epidemic. The model was fitted to age-specific HIV prevalence data
and recorded death data using MLE model. Though the previous
model results suggest HIV in South Africa has declined and AIDS
mortality rates have declined since 2002 – 2013, in contrast, our
results differ evidently with the generally accepted HIV models
(Spectrum/EPP and ASSA2008) in South Africa. However, there is
the need for supplementary research to be conducted to enhance the
demographic parameters in the model and as well apply it to each of
the nine (9) provinces of South Africa.
Abstract: Cloud computing is a business model which provides
an easier management of computing resources. Cloud users can
request virtual machine and install additional softwares and configure
them if needed. However, user can also request virtual appliance
which provides a better solution to deploy application in much faster
time, as it is ready-built image of operating system with necessary
softwares installed and configured. Large numbers of virtual
appliances are available in different image format. User can
download available appliances from public marketplace and start
using it. However, information published about the virtual appliance
differs from each providers leading to the difficulty in choosing
required virtual appliance as it is composed of specific OS with
standard software version. However, even if user choses the
appliance from respective providers, user doesn’t have any flexibility
to choose their own set of softwares with required OS and
application. In this paper, we propose a referenced architecture for
dynamically customizing virtual appliance and provision them in an
easier manner. We also add our experience in integrating our
proposed architecture with public marketplace and Mi-Cloud, a cloud
management software.
Abstract: Cloud computing can reduce the start-up expenses of implementing EHR (Electronic Health Records). However, many of the healthcare institutions are yet to implement cloud computing due to the associated privacy and security issues. In this paper, we analyze the challenges and opportunities of implementing cloud computing in healthcare. We also analyze data of over 5000 US hospitals that use Telemedicine applications. This analysis helps to understand the importance of smart phones over the desktop systems in different departments of the healthcare institutions. The wide usage of smartphones and cloud computing allows ubiquitous and affordable access to the health data by authorized persons, including patients and doctors. Cloud computing will prove to be beneficial to a majority of the departments in healthcare. Through this analysis, we attempt to understand the different healthcare departments that may benefit significantly from the implementation of cloud computing.
Abstract: Frequency transformation with Pascal matrix
equations is a method for transforming an electronic filter (analogue
or digital) into another filter. The technique is based on frequency
transformation in the s-domain, bilinear z-transform with pre-warping
frequency, inverse bilinear transformation and a very useful
application of the Pascal’s triangle that simplifies computing and
enables calculation by hand when transforming from one filter to
another. This paper will introduce two methods to transform a filter
into a digital filter: frequency transformation from the s-domain into
the z-domain; and frequency transformation in the z-domain. Further,
two Pascal matrix equations are derived: an analogue to digital filter
Pascal matrix equation and a digital to digital filter Pascal matrix
equation. These are used to design a desired digital filter from a given
filter.
Abstract: Ambient Computing or Ambient Intelligence (AmI) is
emerging area in computer science aiming to create intelligently
connected environments and Internet of Things. In this paper, we
propose communication middleware architecture for AmI. This
middleware architecture addresses problems of communication,
networking, and abstraction of applications, although there are other
aspects (e.g. HCI and Security) within general AmI framework.
Within this middleware architecture, any application developer might
address HCI and Security issues with extensibility features of this
platform.