Abstract: This paper presents an advance in monitoring and
process control of surface roughness in CNC machine for the turning
and milling processes. An integration of the in-process monitoring
and process control of the surface roughness is proposed and
developed during the machining process by using the cutting force
ratio. The previously developed surface roughness models for turning
and milling processes of the author are adopted to predict the inprocess
surface roughness, which consist of the cutting speed, the
feed rate, the tool nose radius, the depth of cut, the rake angle, and
the cutting force ratio. The cutting force ratios obtained from the
turning and the milling are utilized to estimate the in-process surface
roughness. The dynamometers are installed on the tool turret of CNC
turning machine and the table of 5-axis machining center to monitor
the cutting forces. The in-process control of the surface roughness
has been developed and proposed to control the predicted surface
roughness. It has been proved by the cutting tests that the proposed
integration system of the in-process monitoring and the process
control can be used to check the surface roughness during the cutting
by utilizing the cutting force ratio.
Abstract: Chronic conditions carry with them strong emotions
and often lead to charged relationships between patients and their
health providers and, by extension, patients and health researchers.
Persons are both autonomous and relational and a purely cognitive
model of autonomy neglects the social and relational basis of chronic
illness. Ensuring genuine informed consent in research requires a
thorough understanding of how participants perceive a study and
their reasons for participation. Surveys may not capture the
complexities of reasoning that underlies study participation.
Contradictory reasons for participation, for instance an initial claim
of altruism as rationale and a subsequent claim of personal benefit
(therapeutic misconception), affect the quality of informed consent.
Individuals apply principles through the filter of personal values and
lived experience. Authentic autonomy, and hence authentic consent
to research, occurs within the context of patients- unique life
narratives and illness experiences.
Abstract: In Korea, the technology of a load fo nuclear power plant has been being developed.
automatic controller which is able to control temperature and axial power distribution was developed. identification algorithm and a model predictive contact former transforms the nuclear reactor status into
numerically. And the latter uses them and ge
manipulated values such as two kinds of control ro
this automatic controller, the performance of a coperation was evaluated. As a result, the automatic generated model parameters of a nuclear react to nuclear reactor average temperature and axial power the desired targets during a daily load follow.
Abstract: Recent years have seen a growing trend towards the
integration of multiple information sources to support large-scale
prediction of protein-protein interaction (PPI) networks in model
organisms. Despite advances in computational approaches, the
combination of multiple “omic" datasets representing the same type
of data, e.g. different gene expression datasets, has not been
rigorously studied. Furthermore, there is a need to further investigate
the inference capability of powerful approaches, such as fullyconnected
Bayesian networks, in the context of the prediction of PPI
networks. This paper addresses these limitations by proposing a
Bayesian approach to integrate multiple datasets, some of which
encode the same type of “omic" data to support the identification of
PPI networks. The case study reported involved the combination of
three gene expression datasets relevant to human heart failure (HF).
In comparison with two traditional methods, Naive Bayesian and
maximum likelihood ratio approaches, the proposed technique can
accurately identify known PPI and can be applied to infer potentially
novel interactions.
Abstract: Based on a non-linear single track model which
describes the dynamics of vehicle, an optimal path planning strategy
is developed. Real time optimization is used to generate reference
control values to allow leading the vehicle alongside a calculated lane
which is optimal for different objectives such as energy consumption,
run time, safety or comfort characteristics. Strict mathematic
formulation of the autonomous driving allows taking decision on
undefined situation such as lane change or obstacle avoidance. Based
on position of the vehicle, lane situation and obstacle position, the
optimization problem is reformulated in real-time to avoid the
obstacle and any car crash.
Abstract: The present study concentrates on solving the along wind oscillation problem of a tall square building from first principles and across wind oscillation problem of the same from empirical relations obtained by experiments. The criterion for human comfort at the worst condition at the top floor of the building is being considered and a limiting value of height of a building for a given cross section is predicted. Numerical integrations are carried out as and when required. The results show severeness of across wind oscillations in comparison to along wind oscillation. The comfort criterion is combined with across wind oscillation results to determine the maximum allowable height of a building for a given square cross-section.
Abstract: Most Decision Support Systems (DSS) for waste
management (WM) constructed are not widely marketed and lack
practical applications. This is due to the number of variables and
complexity of the mathematical models which include the
assumptions and constraints required in decision making. The
approach made by many researchers in DSS modelling is to isolate a
few key factors that have a significant influence to the DSS. This
segmented approach does not provide a thorough understanding of
the complex relationships of the many elements involved. The
various elements in constructing the DSS must be integrated and
optimized in order to produce a viable model that is marketable and
has practical application. The DSS model used in assisting decision
makers should be integrated with GIS, able to give robust prediction
despite the inherent uncertainties of waste generation and the plethora
of waste characteristics, and gives optimal allocation of waste stream
for recycling, incineration, landfill and composting.
Abstract: The correct design of the regulators structure requires complete prediction of the ultimate dimensions of the scour hole profile formed downstream the solid apron. The study of scour downstream regulator is studied either on solid aprons by means of velocity distribution or on movable bed by studying the topography of the scour hole formed in the downstream. In this paper, a new technique was developed to study the scour hole downstream regulators on movable beds. The study was divided into two categories; the first is to find out the sum of the lengths of rigid apron behind the gates in addition to the length of scour hole formed downstream, while the second is to find the minimum length of rigid apron behind the gates to prevent erosion downstream it. The study covers free and submerged hydraulic jump conditions in both symmetrical and asymmetrical under-gated regulations. From the comparison between the studied categories, we found that the minimum length of rigid apron to prevent scour (Ls) is greater than the sum of the lengths of rigid apron and that of scour hole formed behind it (L+Xs). On the other hand, the scour hole dimensions in case of submerged hydraulic jump is always greater than free one, also the scour hole dimensions in asymmetrical operation is greater than symmetrical one.
Abstract: Mathematical programming has been applied to various
problems. For many actual problems, the assumption that the parameters
involved are deterministic known data is often unjustified. In
such cases, these data contain uncertainty and are thus represented
as random variables, since they represent information about the
future. Decision-making under uncertainty involves potential risk.
Stochastic programming is a commonly used method for optimization
under uncertainty. A stochastic programming problem with recourse
is referred to as a two-stage stochastic problem. In this study, we
consider a stochastic programming problem with simple integer
recourse in which the value of the recourse variable is restricted to a
multiple of a nonnegative integer. The algorithm of a dynamic slope
scaling procedure for solving this problem is developed by using a
property of the expected recourse function. Numerical experiments
demonstrate that the proposed algorithm is quite efficient. The
stochastic programming model defined in this paper is quite useful
for a variety of design and operational problems.
Abstract: Grid computing provides an effective infrastructure for massive computation among flexible and dynamic collection of individual system for resource discovery. The major challenge for grid computing is to prevent breaches and secure the data from trespassers. To overcome such conflicts a semantic approach can be designed which will filter the access requests of peers by checking the resource description specifying the data and the metadata as factual statements. Between every node in the grid a semantic firewall as a middleware will be present The intruder will be required to present an application specifying there needs to the firewall and hence accordingly the system will grant or deny the application request.
Abstract: While many studies have conducted the achievement
gap between groups of students in school districts, few studies have
utilized resilience research to investigate achievement gaps within
classrooms. This paper aims to summarize and discuss some recent
studies Waxman, Padr├│n, and their colleagues conducted, in which
they examined learning environment differences between resilient
and nonresilient students in reading and mathematics classrooms.
The classes consist of predominantly Hispanic elementary school
students from low-income families. These studies all incorporated
learning environment questionnaires and systematic observation
methods. Significant differences were found between resilient and
nonresilient students on their classroom learning environments and
classroom behaviors. The observation results indicate that the amount
and quality of teacher and student academic interaction are two of the
most influential variables that promote student outcomes. This paper
concludes by suggesting the following teacher practices to promote
resiliency in schools: (a) using feedback from classroom observation
and learning environment measures, (b) employing explicit teaching
practices; and (c) understanding students on a social and personal
level.
Abstract: Many exist studies always use Markov decision
processes (MDPs) in modeling optimal route choice in
stochastic, time-varying networks. However, taking many
variable traffic data and transforming them into optimal route
decision is a computational challenge by employing MDPs in
real transportation networks. In this paper we model finite
horizon MDPs using directed hypergraphs. It is shown that the
problem of route choice in stochastic, time-varying networks
can be formulated as a minimum cost hyperpath problem, and
it also can be solved in linear time. We finally demonstrate the
significant computational advantages of the introduced
methods.
Abstract: This paper deals with the application of a well-known neural network technique, multilayer back-propagation (BP) neural network, in financial data mining. A modified neural network forecasting model is presented, and an intelligent mining system is developed. The system can forecast the buying and selling signs according to the prediction of future trends to stock market, and provide decision-making for stock investors. The simulation result of seven years to Shanghai Composite Index shows that the return achieved by this mining system is about three times as large as that achieved by the buy and hold strategy, so it is advantageous to apply neural networks to forecast financial time series, the different investors could benefit from it.
Abstract: Physical education (PE) is still neglected in schools
despite its academic, social, psychological, and health benefits.
Based on the assumption that Information and Communication
Technologies (ICTs) can contribute to the development of PE in
schools, this study aims to design a model of the factors affecting the
adoption of ICTs for PE in schools. The proposed model is based on
a sound theoretical framework. It was designed following a literature
review of technology adoption theories and of ICT adoption factors
for physical education. The technology adoption model that fitted to
the best all ICT adoption factors was then chosen as the basis for the
proposed model. It was found that the Unified Theory of Acceptance
and Use of Technology (UTAUT) is the most adequate theoretical
framework for the modeling of ICT adoption factors for physical
education.
Abstract: In this paper, we present a new algorithm for clustering data in large datasets using image processing approaches. First the dataset is mapped into a binary image plane. The synthesized image is then processed utilizing efficient image processing techniques to cluster the data in the dataset. Henceforth, the algorithm avoids exhaustive search to identify clusters. The algorithm considers only a small set of the data that contains critical boundary information sufficient to identify contained clusters. Compared to available data clustering techniques, the proposed algorithm produces similar quality results and outperforms them in execution time and storage requirements.
Abstract: In networks, mainly small and medium-sized businesses benefit from the knowledge, experiences and solutions offered by experts from industry and science or from the exchange with practitioners. Associations which focus, among other things, on networking, information and knowledge transfer and which are interested in supporting such cooperations are especially well suited to provide such networks and the appropriate web platforms. Using METORA as an example – a project developed and run by the Federal Association for Information Economy, Telecommunications and New Media e.V. (BITKOM) for the Federal Ministry of Economics and Technology (BMWi) – This paper will discuss how associations and other network organizations can achieve this task and what conditions they have to consider.
Abstract: Long term rainfall analysis and prediction is a
challenging task especially in the modern world where the impact of
global warming is creating complications in environmental issues.
These factors which are data intensive require high performance
computational modeling for accurate prediction. This research paper
describes a prototype which is designed and developed on grid
environment using a number of coupled software infrastructural
building blocks. This grid enabled system provides the demanding
computational power, efficiency, resources, user-friendly interface,
secured job submission and high throughput. The results obtained
using sequential execution and grid enabled execution shows that
computational performance has enhanced among 36% to 75%, for
decade of climate parameters. Large variation in performance can be
attributed to varying degree of computational resources available for
job execution.
Grid Computing enables the dynamic runtime selection, sharing
and aggregation of distributed and autonomous resources which plays
an important role not only in business, but also in scientific
implications and social surroundings. This research paper attempts to
explore the grid enabled computing capabilities on weather indices
from HOAPS data for climate impact modeling and change
detection.
Abstract: this paper presents a novel neural network controller
with composite adaptation low to improve the trajectory tracking
problems of biped robots comparing with classical controller. The
biped model has 5_link and 6 degrees of freedom and actuated by
Plated Pneumatic Artificial Muscle, which have a very high power to
weight ratio and it has large stoke compared to similar actuators. The
proposed controller employ a stable neural network in to approximate
unknown nonlinear functions in the robot dynamics, thereby
overcoming some limitation of conventional controllers such as PD
or adaptive controllers and guarantee good performance. This NN
controller significantly improve the accuracy requirements by
retraining the basic PD/PID loop, but adding an inner adaptive loop
that allows the controller to learn unknown parameters such as
friction coefficient, therefore improving tracking accuracy.
Simulation results plus graphical simulation in virtual reality show
that NN controller tracking performance is considerably better than
PD controller tracking performance.
Abstract: This purpose of this paper is to develop and validate a
model to accurately predict the cell temperature of a PV module that
adapts to various mounting configurations, mounting locations, and
climates while only requiring readily available data from the module
manufacturer. Results from this model are also compared to results
from published cell temperature models. The models were used to
predict real-time performance from a PV water pumping systems in
the desert of Medenine, south of Tunisia using 60-min intervals of
measured performance data during one complete year. Statistical
analysis of the predicted results and measured data highlight possible
sources of errors and the limitations and/or adequacy of existing
models, to describe the temperature and efficiency of PV-cells and
consequently, the accuracy of performance of PV water pumping
systems prediction models.
Abstract: Results of Chilean wine classification based on the
information provided by an electronic nose are reported in this paper.
The classification scheme consists of two parts; in the first stage,
Principal Component Analysis is used as feature extraction method to
reduce the dimensionality of the original information. Then, Radial
Basis Functions Neural Networks is used as pattern recognition
technique to perform the classification. The objective of this study is
to classify different Cabernet Sauvignon, Merlot and Carménère wine
samples from different years, valleys and vineyards of Chile.