Abstract: It is an important problem to compute the geodesics on
a surface in many fields. To find the geodesics in practice, however,
the traditional discrete algorithms or numerical approaches can only
find a list of discrete points. The first author proposed in 2010 a new,
elegant and accurate method, the geodesic-like method, for
approximating geodesics on a regular surface. This paper will present
by use of this method a computation of the Bezier geodesic-like curves
on spheres.
Abstract: Real options theory suggests that managerial flexibility embedded within irreversible investments can account for a significant value in project valuation. Although the argument has become the dominant focus of capital investment theory over decades, yet recent survey literature in capital budgeting indicates that corporate practitioners still do not explicitly apply real options in investment decisions. In this paper, we explore how real options decision criteria can be transformed into equivalent capital budgeting criteria under the consideration of uncertainty, assuming that underlying stochastic process follows a geometric Brownian motion (GBM), a mixed diffusion-jump (MX), or a mean-reverting process (MR). These equivalent valuation techniques can be readily decomposed into conventional investment rules and “option impacts", the latter of which describe the impacts on optimal investment rules with the option value considered. Based on numerical analysis and Monte Carlo simulation, three major findings are derived. First, it is shown that real options could be successfully integrated into the mindset of conventional capital budgeting. Second, the inclusion of option impacts tends to delay investment. It is indicated that the delay effect is the most significant under a GBM process and the least significant under a MR process. Third, it is optimal to adopt the new capital budgeting criteria in investment decision-making and adopting a suboptimal investment rule without considering real options could lead to a substantial loss in value.
Abstract: Eye localization is necessary for face recognition and
related application areas. Most of eye localization algorithms reported
so far still need to be improved about precision and computational
time for successful applications. In this paper, we propose an eye
location method based on multi-scale Gabor feature vectors, which is
more robust with respect to initial points. The eye localization based
on Gabor feature vectors first needs to constructs an Eye Model Bunch
for each eye (left or right eye) which consists of n Gabor jets and
average eye coordinates of each eyes obtained from n model face
images, and then tries to localize eyes in an incoming face image by
utilizing the fact that the true eye coordinates is most likely to be very
close to the position where the Gabor jet will have the best Gabor jet
similarity matching with a Gabor jet in the Eye Model Bunch. Similar
ideas have been already proposed in such as EBGM (Elastic Bunch
Graph Matching). However, the method used in EBGM is known to be
not robust with respect to initial values and may need extensive search
range for achieving the required performance, but extensive search
ranges will cause much more computational burden. In this paper, we
propose a multi-scale approach with a little increased computational
burden where one first tries to localize eyes based on Gabor feature
vectors in a coarse face image obtained from down sampling of the
original face image, and then localize eyes based on Gabor feature
vectors in the original resolution face image by using the eye
coordinates localized in the coarse scaled image as initial points.
Several experiments and comparisons with other eye localization
methods reported in the other papers show the efficiency of our
proposed method.
Abstract: Radio frequency identification (RFID) applications have grown rapidly in many industries, especially in indoor location identification. The advantage of using received signal strength indicator (RSSI) values as an indoor location measurement method is a cost-effective approach without installing extra hardware. Because the accuracy of many positioning schemes using RSSI values is limited by interference factors and the environment, thus it is challenging to use RFID location techniques based on integrating positioning algorithm design. This study proposes the location estimation approach and analyzes a scheme relying on RSSI values to minimize location errors. In addition, this paper examines different factors that affect location accuracy by integrating the backpropagation neural network (BPN) with the LANDMARC algorithm in a training phase and an online phase. First, the training phase computes coordinates obtained from the LANDMARC algorithm, which uses RSSI values and the real coordinates of reference tags as training data for constructing an appropriate BPN architecture and training length. Second, in the online phase, the LANDMARC algorithm calculates the coordinates of tracking tags, which are then used as BPN inputs to obtain location estimates. The results show that the proposed scheme can estimate locations more accurately compared to LANDMARC without extra devices.
Abstract: World population growth drives food demand, promotes intensification of agriculture, development of new production technologies and varieties more suitable for regional nature conditions. Climate change can affect the length of growing period, biomass and carbon accumulation in winter wheat. The increasing mean air temperature resulting from climate change can reduce the length of growth period of cereals, and without adequate adjustments in growing technologies or varieties, can reduce biomass and carbon accumulation. Deeper understanding and effective measures for monitoring and management of cereal growth process are needed for adaptation to changing climate and technological conditions.
Abstract: It is suggested to evaluate environmental performance
of energy sector using Data Envelopment Analysis with nondiscretionary
factors (DEA-ND) with relative indicators as inputs and
outputs. The latter allows for comparison of the objects essentially
different in size. Inclusion of non-discretionary factors serves
separation of the indicators that are beyond the control of the objects.
A virtual perfect object comprised of maximal outputs and minimal
inputs was added to the group of actual ones. In this setting, explicit
solution of the DEA-ND problem was obtained. Energy sector of the
United States was analyzed using suggested approach for the period
of 1980 – 2006 with expected values of economic indicators for 2030
used for forming the perfect object. It was obtained that
environmental performance has been increasing steadily for the
period from 7.7% through 50.0% but still remains well below the
prospected level
Abstract: Quantitative characterization of nonlinear directional
couplings between stochastic oscillators from data is considered. We
suggest coupling characteristics readily interpreted from a physical
viewpoint and their estimators. An expression for a statistical
significance level is derived analytically that allows reliable coupling
detection from a relatively short time series. Performance of the
technique is demonstrated in numerical experiments.
Abstract: This paper presents a new stable robust adaptive controller and observer design for a class of nonlinear systems that contain i. Coupling of unmeasured states and unknown parameters ii. Unknown dead zone at the system actuator. The system is firstly cast into a modified form in which the observer and parameter estimation become feasible. Then a stable robust adaptive controller, state observer, parameter update laws are derived that would provide global adaptive system stability and desirable performance. To validate the approach, simulation was performed to a single-link mechanical system with a dynamic friction model and unknown dead zone exists at the system actuation. Then a comparison is presented with the results when there is no dead zone at the system actuation.
Abstract: The purpose of the current study is to gain insight into the relative role of professional self-image (PSI) for service providers among leader-member exchange (LMX), career success. Lack of studies demonstrated that PSI of service providers affect on their CS. So, it is necessary to, according to service providers- perspective, explore the relationship among LMX and CS in hospitality industry. The result of the current study can suggest strategic directions for hospitality practitioners in terms of constructing LMX relationship, so as to make service providers realize and build their PSI, and to promote their CS. Implications of these findings for hospitality implementations as well as future research directions are subsequently discussed.
Abstract: Soil erosion is the most serious problem faced at
global and local level. So planning of soil conservation measures has
become prominent agenda in the view of water basin managers. To
plan for the soil conservation measures, the information on soil
erosion is essential. Universal Soil Loss Equation (USLE), Revised
Universal Soil Loss Equation 1 (RUSLE1or RUSLE) and Modified
Universal Soil Loss Equation (MUSLE), RUSLE 1.06, RUSLE1.06c,
RUSLE2 are most widely used conventional erosion estimation
methods. The essential drawbacks of USLE, RUSLE1 equations are
that they are based on average annual values of its parameters and so
their applicability to small temporal scale is questionable. Also these
equations do not estimate runoff generated soil erosion. So
applicability of these equations to estimate runoff generated soil
erosion is questionable. Data used in formation of USLE, RUSLE1
equations was plot data so its applicability at greater spatial scale
needs some scale correction factors to be induced. On the other hand
MUSLE is unsuitable for predicting sediment yield of small and large
events. Although the new revised forms of USLE like RUSLE 1.06,
RUSLE1.06c and RUSLE2 were land use independent and they have
almost cleared all the drawbacks in earlier versions like USLE and
RUSLE1, they are based on the regional data of specific area and
their applicability to other areas having different climate, soil, land
use is questionable. These conventional equations are applicable for
sheet and rill erosion and unable to predict gully erosion and spatial
pattern of rills. So the research was focused on development of nonconventional
(other than conventional) methods of soil erosion
estimation. When these non-conventional methods are combined with
GIS and RS, gives spatial distribution of soil erosion. In the present
paper the review of literature on non- conventional methods of soil
erosion estimation supported by GIS and RS is presented.
Abstract: In this paper, a Neural Network based predictive
DTC algorithm is proposed .This approach is used as an
alternative to classical approaches .An appropriate riate Feed -
forward network is chosen and based on its value of
derivative electromagnetic torque ; optimal stator voltage
vector is determined to be applied to the induction motor (by
inverter). Moreover, an appropriate torque and flux observer
is proposed.
Abstract: It is estimated that the total cost of abnormal
conditions to US process industries is around $20 billion dollars in
annual losses. The hydrotreatment (HDT) of diesel fuel in petroleum
refineries is a conversion process that leads to high profitable
economical returns. However, this is a difficult process to control
because it is operated continuously, with high hydrogen pressures
and it is also subject to disturbances in feed properties and catalyst
performance. So, the automatic detection of fault and diagnosis plays
an important role in this context. In this work, a hybrid approach
based on neural networks together with a pos-processing
classification algorithm is used to detect faults in a simulated HDT
unit. Nine classes (8 faults and the normal operation) were correctly
classified using the proposed approach in a maximum time of 5
minutes, based on on-line data process measurements.
Abstract: In this paper, we propose a high capacity image hiding
technology based on pixel prediction and the difference of modified histogram. This approach is used the pixel prediction and the
difference of modified histogram to calculate the best embedding point.
This approach can improve the predictive accuracy and increase the pixel difference to advance the hiding capacity. We also use the
histogram modification to prevent the overflow and underflow. Experimental results demonstrate that our proposed method within the
same average hiding capacity can still keep high quality of image and low distortion
Abstract: In the globalization context and competitiveness, the role of a university is further enhanced. University is no longer confined to traditional roles. Universities need to interact with others in order to be relevant and progressive. Symbiosis relationships between the university and industry are very significant because the relationship between those two can foster economic development of a nation. In a world of fast changing technology and competition, it is necessary for the university to collaborate with industry to combine efforts fostering the diffusion of knowledge, increasing research and development, patenting innovation and commercializing products. It has become increasingly accepted that the necessity of close university-industry interactions as a mean of national economic prosperity. Therefore, this paper is aim to examine the level of linkages in university-industry interactions to which promotes the regional economic growth and development. This paper will explore the formation of linkages between the Higher Education Institution (University Technology MARA) and industries located in the Klang Valley region of Malaysia. It will present the university-industry linkages with emphasis on the type of linkages existed, the benefits of having such linkages to promote regional economic development and finally the constraints that might impede the linkages and potentials to enhance the linkages towards economic growth and development.
Abstract: In this paper, a new reversible watermarking method is presented that reduces the size of a stereoscopic image sequence while keeping its content visible. The proposed technique embeds the residuals of the right frames to the corresponding frames of the left sequence, halving the total capacity. The residual frames may result in after a disparity compensated procedure between the two video streams or by a joint motion and disparity compensation. The residuals are usually lossy compressed before embedding because of the limited embedding capacity of the left frames. The watermarked frames are visible at a high quality and at any instant the stereoscopic video may be recovered by an inverse process. In fact, the left frames may be exactly recovered whereas the right ones are slightly distorted as the residuals are not embedded intact. The employed embedding method reorders the left frame into an array of consecutive pixel pairs and embeds a number of bits according to their intensity difference. In this way, it hides a number of bits in intensity smooth areas and most of the data in textured areas where resulting distortions are less visible. The experimental evaluation demonstrates that the proposed scheme is quite effective.
Abstract: The efficient operation of any biological treatment
process requires pre-treatment of incompatible pollutants such as
acids, bases, oil, toxic substances, etc. which hamper the treatment
of other major components which are otherwise degradable. The
pre-treatment of alkaline waste-waters, generated from various
industries like textile, paper & pulp, potato-processing industries,
etc., having a pH of 10 or higher, is essential. The pre-treatment,
i.e., neutralization of such alkaline waste-waters can be achieved by
chemical as well as biological means. However, the biological pretreatment
offers better package over the chemical means by being
safe and economical. The biological pre-treatment can be
accomplished by using a blend of microorganisms able to withstand
such harsh alkaline conditions. In the present study, for the proper
pre-treatment of alkaline waste-waters, a package of alkalophilic
bacteria is formulated to neutralise the alkaline pH of the industrial
waste-waters. The developed microbial package is cost-effective as
well as environmental friendly.
Abstract: The paper shows how the perceptions of five organizational virtuousness dimensions (optimism, trust, compassion, integrity, and forgiveness) explain organizational citizenship behaviors (altruism, sportsmanship, courtesy, conscientiousness, and civic virtue). A sample comprising 216 individuals from 14 industrial organizations was collected. Individuals reported their perceptions of organizational virtuousness, their organizational citizenship behaviors (OCB) being reported by their supervisors. The main findings are the following: (a) the perceptions of trust predict altruism; (b) the perceptions of integrity predict civic virtue.
Abstract: The reliability of distributed systems and computer
networks have been modeled by a probabilistic network or a graph G.
Computing the residual connectedness reliability (RCR), denoted by
R(G), under the node fault model is very useful, but is an NP-hard
problem. Since it may need exponential time of the network size to
compute the exact value of R(G), it is important to calculate its tight
approximate value, especially its lower bound, at a moderate
calculation time. In this paper, we propose an efficient algorithm for
reliability lower bound of distributed systems with unreliable nodes.
We also applied our algorithm to several typical classes of networks
to evaluate the lower bounds and show the effectiveness of our
algorithm.
Abstract: One of the most important problems to solve is eye
location for a driver fatigue monitoring system. This paper presents an
efficient method to achieve fast and accurate eye location in grey level
images obtained in the real-word driving conditions. The structure of
eye region is used as a robust cue to find possible eye pairs. Candidates
of eye pair at different scales are selected by finding regions which
roughly match with the binary eye pair template. To obtain real one,
all the eye pair candidates are then verified by using support vector
machines. Finally, eyes are precisely located by using binary vertical
projection and eye classifier in eye pair images. The proposed method
is robust to deal with illumination changes, moderate rotations, glasses
wearing and different eye states. Experimental results demonstrate its
effectiveness.
Abstract: We describe a work with an evolutionary computing
algorithm for non photo–realistic rendering of a target image. The
renderings are produced by genetic programming. We have used two
different types of strokes: “empty triangle" and “filled triangle" in
color level. We compare both empty and filled triangular strokes to
find which one generates more aesthetic pleasing images. We found
the filled triangular strokes have better fitness and generate more
aesthetic images than empty triangular strokes.