Abstract: A way of achieving nanodimentional structural elements in high carbon steel by special kind of heat treatment and cold plastic deformation is being explored. This leads to increasing interlamellar spacing of ferrite-carbide mixture. Decreasing the interlamellar spacing with cooling temperature increasing is determined. Experiments confirm such interlamellar spacing with which high carbon steel demonstrates the highest treatment and hardening capability. Total deformation degree effect on interlamellar spacing value in a ferrite-carbide mixture is obtained. Mechanical experiments results show that high carbon steel after heat treatment and repetitive cold plastic deformation possesses high tensile strength and yield strength keeping good percentage elongation.
Abstract: The sensitivity of orifice plate metering to disturbed
flow (either asymmetric or swirling) is a subject of great concern to
flow meter users and manufacturers. The distortions caused by pipe
fittings and pipe installations upstream of the orifice plate are major
sources of this type of non-standard flows. These distortions can alter
the accuracy of metering to an unacceptable degree. In this work, a
multi-scale object known as metal foam has been used to generate a
predetermined turbulent flow upstream of the orifice plate. The
experimental results showed that the combination of an orifice plate
and metal foam flow conditioner is broadly insensitive to upstream
disturbances. This metal foam demonstrated a good performance in
terms of removing swirl and producing a repeatable flow profile
within a short distance downstream of the device. The results of using
a combination of a metal foam flow conditioner and orifice plate for
non-standard flow conditions including swirling flow and asymmetric
flow show this package can preserve the accuracy of metering up to
the level required in the standards.
Abstract: We constructed a method of phase unwrapping for a typical wave-front by utilizing the maximizer of the posterior marginal (MPM) estimate corresponding to equilibrium statistical mechanics of the three-state Ising model on a square lattice on the basis of an analogy between statistical mechanics and Bayesian inference. We investigated the static properties of an MPM estimate from a phase diagram using Monte Carlo simulation for a typical wave-front with synthetic aperture radar (SAR) interferometry. The simulations clarified that the surface-consistency conditions were useful for extending the phase where the MPM estimate was successful in phase unwrapping with a high degree of accuracy and that introducing prior information into the MPM estimate also made it possible to extend the phase under the constraint of the surface-consistency conditions with a high degree of accuracy. We also found that the MPM estimate could be used to reconstruct the original wave-fronts more smoothly, if we appropriately tuned hyper-parameters corresponding to temperature to utilize fluctuations around the MAP solution. Also, from the viewpoint of statistical mechanics of the Q-Ising model, we found that the MPM estimate was regarded as a method for searching the ground state by utilizing thermal fluctuations under the constraint of the surface-consistency condition.
Abstract: Testable software has two inherent properties – observability and controllability. Observability facilitates observation of internal behavior of software to required degree of detail. Controllability allows creation of difficult-to-achieve states prior to execution of various tests. In this paper, we describe COTT, a Controllability and Observability Testing Tool, to create testable object-oriented software. COTT provides a framework that helps the user to instrument object-oriented software to build the required controllability and observability. During testing, the tool facilitates creation of difficult-to-achieve states required for testing of difficultto- test conditions and observation of internal details of execution at unit, integration and system levels. The execution observations are logged in a test log file, which are used for post analysis and to generate test coverage reports.
Abstract: The purpose of this study is to find natural gait of
biped robot such as human being by analyzing the COG (Center Of
Gravity) trajectory of human being's gait. It is discovered that human
beings gait naturally maintain the stability and use the minimum
energy. This paper intends to find the natural gait pattern of biped
robot using the minimum energy as well as maintaining the stability by
analyzing the human's gait pattern that is measured from gait image on
the sagittal plane and COG trajectory on the frontal plane. It is not
possible to apply the torques of human's articulation to those of biped
robot's because they have different degrees of freedom. Nonetheless,
human and 5-link biped robots are similar in kinematics. For this, we
generate gait pattern of the 5-link biped robot by using the GA
algorithm of adaptation gait pattern which utilize the human's ZMP
(Zero Moment Point) and torque of all articulation that are measured
from human's gait pattern. The algorithm proposed creates biped
robot's fluent gait pattern as that of human being's and to minimize
energy consumption because the gait pattern of the 5-link biped robot
model is modeled after consideration about the torque of human's each
articulation on the sagittal plane and ZMP trajectory on the frontal
plane. This paper demonstrate that the algorithm proposed is superior
by evaluating 2 kinds of the 5-link biped robot applied to each gait
patterns generated both in the general way using inverse kinematics
and in the special way in which by considering visuality and
efficiency.
Abstract: We board the problem of creating a seismic alert
system, based upon artificial neural networks, trained by using the
well-known back-propagation and genetic algorithms, in order to emit
the alarm for the population located into a specific city, about an
eminent earthquake greater than 4.5 Richter degrees, and avoiding
disasters and human loses. In lieu of using the propagation wave, we
employed the magnitude of the earthquake, to establish a correlation
between the recorded magnitudes from a controlled area and the city,
where we want to emit the alarm. To measure the accuracy of the
posed method, we use a database provided by CIRES, which contains
the records of 2500 quakes incoming from the State of Guerrero
and Mexico City. Particularly, we performed the proposed method to
generate an issue warning in Mexico City, employing the magnitudes
recorded in the State of Guerrero.
Abstract: This paper proposes a new parameter identification
method based on Linear Fractional Transformation (LFT). It is
assumed that the target linear system includes unknown parameters.
The parameter deviations are separated from a nominal system via
LFT, and identified by organizing I/O signals around the separated
deviations of the real system. The purpose of this paper is to apply LFT
to simultaneously identify the parameter deviations in systems with
fewer outputs than unknown parameters. As a fundamental example,
this method is implemented to one degree of freedom vibratory system.
Via LFT, all physical parameters were simultaneously identified in this
system. Then, numerical simulations were conducted for this system to
verify the results. This study shows that all the physical parameters of a
system with fewer outputs than unknown parameters can be effectively
identified simultaneously using LFT.
Abstract: Obtaining labeled data in supervised learning is often
difficult and expensive, and thus the trained learning algorithm tends
to be overfitting due to small number of training data. As a result,
some researchers have focused on using unlabeled data which may
not necessary to follow the same generative distribution as the labeled
data to construct a high-level feature for improving performance on
supervised learning tasks. In this paper, we investigate the impact of
the relationship between unlabeled and labeled data for classification
performance. Specifically, we will apply difference unlabeled data
which have different degrees of relation to the labeled data for
handwritten digit classification task based on MNIST dataset. Our
experimental results show that the higher the degree of relation
between unlabeled and labeled data, the better the classification
performance. Although the unlabeled data that is completely from
different generative distribution to the labeled data provides the lowest
classification performance, we still achieve high classification performance.
This leads to expanding the applicability of the supervised
learning algorithms using unsupervised learning.
Abstract: It is well known that a linear dynamic system including
a delay will exhibit limit cycle oscillations when a bang-bang sensor
is used in the feedback loop of a PID controller. A similar behaviour
occurs when a delayed feedback signal is used to train a neural
network. This paper develops a method of predicting this behaviour
by linearizing the system, which can be shown to behave in a manner
similar to an integral controller. Using this procedure, it is possible
to predict the characteristics of the neural network driven limit cycle
to varying degrees of accuracy, depending on the information known
about the system. An application is also presented: the intelligent
control of a spark ignition engine.
Abstract: In this paper the main objective is to analyze the
quality of service of the bus companies operating in the city of
Campos, located in the state of Rio de Janeiro, Brazil. This analysis,
based on the opinion of the bus customers, will help to determine
their degree of satisfaction with the service provided by the bus
companies. The result of this assessment shows that the bus
customers are displeased with the quality of service supplied by the
bus companies. Therefore, it is necessary to identify alternative
solutions to minimize the consequences of the main problems related
to customers- dissatisfaction identified in our evaluation and to help
the bus companies operating in Campos better fulfill their riders-
needs.
Abstract: This paper is concerned with a nonautonomous three species food chain model with Crowley–Martin type functional response and time delay. Using the Mawhin-s continuation theorem in theory of degree, sufficient conditions for existence of periodic solutions are obtained.
Abstract: The distribution, enrichment, accumulation, and potential ecological risk of copper (Cu) in the surface sediments of northern Kaohsiung Harbor, Taiwan were investigated. Sediment samples from 12 locations of northern Kaohsiung Harbor were collected and characterized for Cu, aluminum, water content, organic matter, total nitrogen, total phosphorous, total grease and grain size. Results showed that the Cu concentrations varied from 6.9–244 mg/kg with an average of 109±66 mg/kg. The spatial distribution of Cu reveals that the Cu concentration is relatively high in the river mouth region, and gradually diminishes toward the harbor entrance region. This indicates that upstream industrial and municipal wastewater discharges along the river bank are major sources of Cu pollution. Results from the enrichment factor and geo-accumulation index analyses imply that the sediments collected from the river mouth can be characterized between moderate and moderately severe degree enrichment and between none to medium and moderate accumulation of Cu, respectively. However, results of potential ecological risk index indicate that the sediment has low ecological potential risk.
Abstract: Transcription factors are a group of proteins that
helps for interpreting the genetic information in DNA.
Protein-protein interactions play a major role in the execution
of key biological functions of a cell. These interactions are
represented in the form of a graph with nodes and edges.
Studies have showed that some nodes have high degree of
connectivity and such nodes, known as hub nodes, are the
inevitable parts of the network. In the present paper a method
is proposed to identify hub transcription factor proteins using
sequence information. On a complete data set of transcription
factor proteins available from the APID database, the
proposed method showed an accuracy of 77%, sensitivity of
79% and specificity of 76%.
Abstract: This paper aims to explain the project carried out at
the University of Cordoba, specifically at the High Polytechnic
School in collaboration with two other organizations belonging to the
Andalusian Ministry of Innovation, Science and Business:
Andalusian Innovation and Development Agency (IDEA agency) [1]
and the Territorial Net of Entrepreneurship Support (in Spanish Red
Territorial de Apoyo al Emprendedor) [11].
The project is being developed in several stages of which only the
first one has already been completed. However, several important
preliminary results derive from it, based mainly in the description of
the nature of entrepreneurship in the field of university education and
its impact on student-s competency as recommended by the European
Higher Education Area. Some problems holding back the correct
future development will also be shown as derived from the specific
context of application of the project.
Abstract: Quality Function Deployment (QFD) is an expounded, multi-step planning method for delivering commodity, services, and processes to customers, both external and internal to an organization. It is a way to convert between the diverse customer languages expressing demands (Voice of the Customer), and the organization-s languages expressing results that sate those demands. The policy is to establish one or more matrices that inter-relate producer and consumer reciprocal expectations. Due to its visual presence is called the “House of Quality" (HOQ). In this paper, we assumed HOQ in multi attribute decision making (MADM) pattern and through a proposed MADM method, rank technical specifications. Thereafter compute satisfaction degree of customer requirements and for it, we apply vagueness and uncertainty conditions in decision making by fuzzy set theory. This approach would propound supervised neural network (perceptron) for MADM problem solving.
Abstract: Automatic face detection is a complex problem in
image processing. Many methods exist to solve this problem such as
template matching, Fisher Linear Discriminate, Neural Networks,
SVM, and MRC. Success has been achieved with each method to
varying degrees and complexities. In proposed algorithm we used
upright, frontal faces for single gray scale images with decent
resolution and under good lighting condition. In the field of face
recognition technique the single face is matched with single face
from the training dataset. The author proposed a neural network
based face detection algorithm from the photographs as well as if any
test data appears it check from the online scanned training dataset.
Experimental result shows that the algorithm detected up to 95%
accuracy for any image.
Abstract: The paper presents an approach for handling uncertain
information in deductive databases using multivalued logics. Uncertainty
means that database facts may be assigned logical values other
than the conventional ones - true and false. The logical values represent
various degrees of truth, which may be combined and propagated
by applying the database rules. A corresponding multivalued database
semantics is defined. We show that it extends successful conventional
semantics as the well-founded semantics, and has a polynomial time
data complexity.
Abstract: In association with path dependence, researchers often
talk of institutional “lock-in", thereby indicating that far-reaching
path deviation or path departure are to be regarded as exceptional
cases. This article submits the alleged general inclination for stability
of path-dependent processes to a critical review. The different
reasons for path dependence found in the literature indicate that
different continuity-ensuring mechanisms are at work when people
talk about path dependence (“increasing returns", complementarity,
sequences etc.). As these mechanisms are susceptible to fundamental
change in different ways and to different degrees, the path
dependence concept alone is of only limited explanatory value. It is
therefore indispensable to identify the underlying continuity-ensuring
mechanism as well if a statement-s empirical value is to go beyond
the trivial, always true “history matters".
Abstract: Direct search methods are evolutionary algorithms used to solve optimization problems. (DS) methods do not require any information about the gradient of the objective function at hand while searching for an optimum solution. One of such methods is Pattern Search (PS) algorithm. This paper presents a new approach based on a constrained pattern search algorithm to solve a security constrained power system economic dispatch problem (SCED). Operation of power systems demands a high degree of security to keep the system satisfactorily operating when subjected to disturbances, while and at the same time it is required to pay attention to the economic aspects. Pattern recognition technique is used first to assess dynamic security. Linear classifiers that determine the stability of electric power system are presented and added to other system stability and operational constraints. The problem is formulated as a constrained optimization problem in a way that insures a secure-economic system operation. Pattern search method is then applied to solve the constrained optimization formulation. In particular, the method is tested using one system. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that pattern search (PS) is very applicable for solving security constrained power system economic dispatch problem (SCED).
Abstract: In this paper I have developed a system for evaluating
the degree of fear emotion that the intelligent agent-based system
may feel when it encounters to a persecuting event. In this paper I
want to describe behaviors of emotional agents using human
behavior in terms of the way their emotional states evolve over time.
I have implemented a fuzzy inference system using Java
environment. As the inputs of this system, I have considered three
parameters related on human fear emotion. The system outputs can
be used in agent decision making process or choosing a person for
team working systems by combination the intensity of fear to other
emotion intensities.