Abstract: The tradition competitive newsvendor game assumes decision makers are rational. However, there are behavioral biases when people make decisions, such as loss aversion, mental accounting and overconfidence. Overestimation of a subject’s own performance is one type of overconfidence. The objective of this research is to analyze the impact of the overestimated demand in the newsvendor competitive game with two players. This study builds a competitive newsvendor game model where newsvendors have private information of their demands, which is overestimated. At the same time, demands of each newsvendor forecasted by a third party institution are available. This research shows that the overestimation leads to demand steal effect, which reduces the competitor’s order quantity. However, the overall supply of the product increases due to overestimation. This study illustrates the boundary condition for the overestimated newsvendor to have the equilibrium order drop due to the demand steal effect from the other newsvendor. A newsvendor who has higher critical fractile will see its equilibrium order decrease with the drop of estimation level from the other newsvendor.
Abstract: Compliant foil gas lubricated bearings are used for the
support of light loads in the order of few kilograms at high speeds, in
the order of 50,000 RPM. The stiffness of the foil bearings depends
both on the stiffness of the compliant foil and on the lubricating
gas film. The stiffness of the bearings plays a crucial role in the
stable operation of the supported rotor over a range of speeds. This
paper describes a numerical approach to estimate the stiffness of the
bearings using pseudo spectral scheme. Methodology to obtain the
stiffness of the foil bearing as a function of weight of the shaft is
given and the results are presented.
Abstract: In this paper, a brief review of the corrosion mechanism in buried pipe and modes of failure is provided together with the available corrosion models. Moreover, the sensitivity analysis is performed to understand the influence of corrosion model parameters on the remaining life estimation. Further, the probabilistic analysis is performed to propagate the uncertainty in the corrosion model on the estimation of the renaming life of the pipe. Finally, the comparison among the corrosion models on the basis of the remaining life estimation will be provided to improve the renewal plan.
Abstract: In this paper, propose method that can user’s position
that based on database is built from single camera. Previous
positioning calculate distance by arrival-time of signal like GPS
(Global Positioning System), RF(Radio Frequency). However, these
previous method have weakness because these have large error range
according to signal interference. Method for solution estimate position
by camera sensor. But, signal camera is difficult to obtain relative
position data and stereo camera is difficult to provide real-time
position data because of a lot of image data, too. First of all, in this
research we build image database at space that able to provide
positioning service with single camera. Next, we judge similarity
through image matching of database image and transmission image
from user. Finally, we decide position of user through position of most
similar database image. For verification of propose method, we
experiment at real-environment like indoor and outdoor. Propose
method is wide positioning range and this method can verify not only
position of user but also direction.
Abstract: This paper describes a method for AWGN (Additive White Gaussian Noise) variance estimation in noisy stochastic signals, referred to as Multiplicative-Noising Variance Estimation (MNVE). The aim was to develop an estimation algorithm with minimal number of assumptions on the original signal structure. The provided MATLAB simulation and results analysis of the method applied on speech signals showed more accuracy than standardized AR (autoregressive) modeling noise estimation technique. In addition, great performance was observed on very low signal-to-noise ratios, which in general represents the worst case scenario for signal denoising methods. High execution time appears to be the only disadvantage of MNVE. After close examination of all the observed features of the proposed algorithm, it was concluded it is worth of exploring and that with some further adjustments and improvements can be enviably powerful.
Abstract: Strategic investment decisions are characterized by
high innovation potential and long-term effects on the
competitiveness of enterprises. Due to the uncertainty and risks
involved in this complex decision making process, the need arises for
well-structured support activities. A method that considers cost and
the long-term added value is the cost-benefit effectiveness estimation.
One of those methods is the “profitability estimation focused on
benefits – PEFB”-method developed at the Institute of Management
Cybernetics at RWTH Aachen University. The method copes with
the challenges associated with strategic investment decisions by
integrating long-term non-monetary aspects whilst also mapping the
chronological sequence of an investment within the organization’s
target system. Thus, this method is characterized as a holistic
approach for the evaluation of costs and benefits of an investment.
This participation-oriented method was applied to business
environments in many workshops. The results of the workshops are a
library of more than 96 cost aspects, as well as 122 benefit aspects.
These aspects are preprocessed and comparatively analyzed with
regards to their alignment to a series of risk levels. For the first time,
an accumulation and a distribution of cost and benefit aspects
regarding their impact and probability of occurrence are given. The
results give evidence that the PEFB-method combines precise
measures of financial accounting with the incorporation of benefits.
Finally, the results constitute the basics for using information
technology and data science for decision support when applying
within the PEFB-method.
Abstract: Routing in adhoc networks is a challenge as nodes are
mobile, and links are constantly created and broken. Present ondemand
adhoc routing algorithms initiate route discovery after a path
breaks, incurring significant cost to detect disconnection and
establish a new route. Specifically, when a path is about to be broken,
the source is warned of the likelihood of a disconnection. The source
then initiates path discovery early, avoiding disconnection totally. A
path is considered about to break when link availability decreases.
This study modifies Adhoc On-demand Multipath Distance Vector
routing (AOMDV) so that route handoff occurs through link
availability estimation.
Abstract: In some applications, such as image recognition or
compression, segmentation refers to the process of partitioning a
digital image into multiple segments. Image segmentation is typically
used to locate objects and boundaries (lines, curves, etc.) in images.
Image segmentation is to classify or cluster an image into several
parts (regions) according to the feature of image, for example, the
pixel value or the frequency response. More precisely, image
segmentation is the process of assigning a label to every pixel in an
image such that pixels with the same label share certain visual
characteristics. The result of image segmentation is a set of segments
that collectively cover the entire image, or a set of contours extracted
from the image. Several image segmentation algorithms were
proposed to segment an image before recognition or compression. Up
to now, many image segmentation algorithms exist and be
extensively applied in science and daily life. According to their
segmentation method, we can approximately categorize them into
region-based segmentation, data clustering, and edge-base
segmentation. In this paper, we give a study of several popular image
segmentation algorithms that are available.
Abstract: In statistics parameter theory, usually the
parameter estimations have two kinds, one is the least-square
estimation (LSE), and the other is the best linear unbiased
estimation (BLUE). Due to the determining theorem of
minimum variance unbiased estimator (MVUE), the parameter
estimation of BLUE in linear model is most ideal. But since
the calculations are complicated or the covariance is not
given, people are hardly to get the solution. Therefore, people
prefer to use LSE rather than BLUE. And this substitution
will take some losses. To quantize the losses, many scholars
have presented many kinds of different relative efficiencies in
different views. For the linear weighted regression model, this
paper discusses the relative efficiencies of LSE of β to BLUE
of β. It also defines two new relative efficiencies and gives
their lower bounds.
Abstract: This paper proposes a rotational invariant texture
feature based on the roughness property of the image for psoriasis
image analysis. In this work, we have applied this feature for image
classification and segmentation. The fuzzy concept is employed to
overcome the imprecision of roughness. Since the psoriasis lesion is
modeled by a rough surface, the feature is extended for calculating
the Psoriasis Area Severity Index value. For classification and
segmentation, the Nearest Neighbor algorithm is applied. We have
obtained promising results for identifying affected lesions by using
the roughness index and severity level estimation.
Abstract: Human motion capture has become one of the major
area of interest in the field of computer vision. Some of the major
application areas that have been rapidly evolving include the
advanced human interfaces, virtual reality and security/surveillance
systems. This study provides a brief overview of the techniques and
applications used for the markerless human motion capture, which
deals with analyzing the human motion in the form of mathematical
formulations. The major contribution of this research is that it
classifies the computer vision based techniques of human motion
capture based on the taxonomy, and then breaks its down into four
systematically different categories of tracking, initialization, pose
estimation and recognition. The detailed descriptions and the
relationships descriptions are given for the techniques of tracking and
pose estimation. The subcategories of each process are further
described. Various hypotheses have been used by the researchers in
this domain are surveyed and the evolution of these techniques have
been explained. It has been concluded in the survey that most
researchers have focused on using the mathematical body models for
the markerless motion capture.
Abstract: The wear measuring and wear modelling are
fundamental issues in the industrial field, mainly correlated to the
economy and safety. Therefore, there is a need to study the wear
measurements and wear estimation. Pin-on-disc test is the most
common test which is used to study the wear behaviour. In this paper,
the pin-on-disc (AEROTECH UNIDEX 11) is used for the
investigation of the effects of normal load and hardness of material on
the wear under dry and sliding conditions. In the pin-on-disc rig, two
specimens were used; one, a pin is made of steel with a tip, positioned
perpendicular to the disc, where the disc is made of aluminium. The
pin wear and disc wear were measured by using the following
instruments: The Talysurf instrument, a digital microscope, and the
alicona instrument. The Talysurf profilometer was used to measure
the pin/disc wear scar depth, digital microscope was used to measure
the diameter and width of wear scar, and the alicona was used to
measure the pin wear and disc wear. After that, the Archard model,
American Society for Testing and Materials model (ASTM), and
neural network model were used for pin/disc wear modelling.
Simulation results were implemented by using the Matlab program.
This paper focuses on how the alicona can be used for wear
measurements and how the neural network can be used for wear
estimation.
Abstract: Neurons in the nervous system communicate with
each other by producing electrical signals called spikes. To
investigate the physiological function of nervous system it is essential
to study the activity of neurons by detecting and sorting spikes in the
recorded signal. In this paper a method is proposed for considering
the spike sorting problem which is based on the nonlinear modeling
of spikes using exponential autoregressive model. The genetic
algorithm is utilized for model parameter estimation. In this regard
some selected model coefficients are used as features for sorting
purposes. For optimal selection of model coefficients, self-organizing
feature map is used. The results show that modeling of spikes with
nonlinear autoregressive model outperforms its linear counterpart.
Also the extracted features based on the coefficients of exponential
autoregressive model are better than wavelet based extracted features
and get more compact and well-separated clusters. In the case of
spikes different in small-scale structures where principal component
analysis fails to get separated clouds in the feature space, the
proposed method can obtain well-separated cluster which removes
the necessity of applying complex classifiers.
Abstract: Construction cost estimation is one of the most
important aspects of construction project design. For generations, the
process of cost estimating has been manual, time-consuming and
error-prone. This has partly led to most cost estimates to be unclear
and riddled with inaccuracies that at times lead to over- or underestimation
of construction cost. The development of standard set of
measurement rules that are understandable by all those involved in a
construction project, have not totally solved the challenges. Emerging
Building Information Modelling (BIM) technologies can exploit
standard measurement methods to automate cost estimation process
and improve accuracies. This requires standard measurement
methods to be structured in ontological and machine readable format;
so that BIM software packages can easily read them. Most standard
measurement methods are still text-based in textbooks and require
manual editing into tables or Spreadsheet during cost estimation. The
aim of this study is to explore the development of an ontology based
on New Rules of Measurement (NRM) commonly used in the UK for
cost estimation. The methodology adopted is Methontology, one of
the most widely used ontology engineering methodologies. The
challenges in this exploratory study are also reported and
recommendations for future studies proposed.
Abstract: Performance of different filtering approaches depends
on modeling of dynamical system and algorithm structure. For
modeling and smoothing the data the evaluation of posterior
distribution in different filtering approach should be chosen carefully.
In this paper different filtering approaches like filter KALMAN,
EKF, UKF, EKS and smoother RTS is simulated in some trajectory
tracking of path and accuracy and limitation of these approaches are
explained. Then probability of model with different filters is
compered and finally the effect of the noise variance to estimation is
described with simulations results.
Abstract: We present a solution to the Maxmin u/E parameters
estimation problem of possibility distributions in m-dimensional
case. Our method is based on geometrical approach, where minimal
area enclosing ellipsoid is constructed around the sample. Also we
demonstrate that one can improve results of well-known algorithms
in fuzzy model identification task using Maxmin u/E parameters
estimation.
Abstract: Locating Radio Controlled (RC) devices using their
unintended emissions has a great interest considering security
concerns. Weak nature of these emissions requires near field
localization approach since it is hard to detect these signals in far
field region of array. Instead of only angle estimation, near field
localization also requires range estimation of the source which makes
this method more complicated than far field models. Challenges of
locating such devices in a near field region and real time environment
are analyzed in this paper. An ESPRIT like near field localization
scheme is utilized for both angle and range estimation. 1-D search
with symmetric subarrays is provided. Two 7 element uniform linear
antenna arrays (ULA) are employed for locating RC source.
Experiment results of location estimation for one unintended emitting
walkie-talkie for different positions are given.
Abstract: This study is purposed to develop an efficient fault
detection method for Global Navigation Satellite Systems (GNSS)
applications based on adaptive noise covariance estimation. Due to the
dependence on radio frequency signals, GNSS measurements are
dominated by systematic errors in receiver’s operating environment.
In the proposed method, the pseudorange and carrier-phase
measurement noise covariances are obtained at time propagations and
measurement updates in process of Carrier-Smoothed Code (CSC)
filtering, respectively. The test statistics for fault detection are
generated by the estimated measurement noise covariances. To
evaluate the fault detection capability, intentional faults were added to
the filed-collected measurements. The experiment result shows that
the proposed method is efficient in detecting unhealthy measurements
and improves GNSS positioning accuracy against fault occurrences.
Abstract: The current study explored the effect of economic
development, financial development and institutional quality on
environmental destruction in upper-middle income countries during
the time period of 1999-2011. The dependent variable is logarithm of
carbon dioxide emissions that can be considered as an index for
destruction or quality of the environment given to its effects on the
environment. Financial development and institutional development
variables as well as some control variables were considered. In order
to study cross-sectional correlation among the countries under study,
Pesaran and Friz test was used. Since the results of both tests show
cross-sectional correlation in the countries under study, seemingly
unrelated regression method was utilized for model estimation. The
results disclosed that Kuznets’ environmental curve hypothesis is
confirmed in upper-middle income countries and also, financial
development and institutional quality have a significant effect on
environmental quality. The results of this study can be considered by
policy makers in countries with different income groups to have
access to a growth accompanied by improved environmental quality.
Abstract: In this paper two approaches to joint signal detection,
time of arrival (ToA) and angle of arrival (AoA) estimation in
multi-element antenna array are investigated. Two scenarios were
considered: first one, when the waveform of the useful signal
is known a priori and, second one, when the waveform of the
desired signal is unknown. For first scenario, the antenna array
signal processing based on multi-element matched filtering (MF)
with the following non-coherent detection scheme and maximum
likelihood (ML) parameter estimation blocks is exploited. For second
scenario, the signal processing based on the antenna array elements
covariance matrix estimation with the following eigenvector analysis
and ML parameter estimation blocks is applied. The performance
characteristics of both signal processing schemes are thoroughly
investigated and compared for different useful signals and noise
parameters.