Abstract: Construction of tunnels is connected with high
uncertainty in the field of costs, construction period, safety and
impact on surroundings. Risk management became therefore a
common part of tunnel projects, especially after a set of fatal
collapses occurred in 1990's. Such collapses are caused usually by
combination of factors that can be divided into three main groups, i.e.
unfavourable geological conditions, failures in the design and
planning or failures in the execution.
This paper suggests a procedure enabling quantification of the
excavation risk related to extraordinary accidents using FTA and
ETA tools. It will elaborate on a common process of risk analysis and
enable the transfer of information and experience between particular
tunnel construction projects. Further, it gives a guide for designers,
management and other participants, how to deal with risk of such
accidents and how to make qualified decisions based on a
probabilistic approach.
Abstract: Geographic Information System (GIS) is a computerbased
tool used extensively to solve various engineering problems
related to spatial data. In spite of growing popularity of GIS, its
complete potential to construction industry has not been realized. In
this paper, the summary of up-to-date work on spatial applications of
GIS technologies in construction industry is presented. GIS
technologies have the potential to solve space related problems of
construction industry involving complex visualization, integration of
information, route planning, E-commerce, cost estimation, etc. GISbased
methodology to handle time and space issues of construction
projects scheduling is developed and discussed in this paper.
Abstract: Connected dominating set (CDS) problem in unit disk
graph has signi£cant impact on an ef£cient design of routing protocols
in wireless sensor networks, where the searching space for a
route is reduced to nodes in the set. A set is dominating if all the
nodes in the system are either in the set or neighbors of nodes in the
set. In this paper, a simple and ef£cient heuristic method is proposed
for £nding a minimum connected dominating set (MCDS) in ad hoc
wireless networks based on the new parameter support of vertices.
With this parameter the proposed heuristic approach effectively
£nds the MCDS of a graph. Extensive computational experiments
show that the proposed approach outperforms the recently proposed
heuristics found in the literature for the MCD
Abstract: High voltage generators are being subject to higher
voltage rating and are being designed to operate in harsh conditions.
Stator windings are the main component of generators in which
Electrical, magnetically and thermal stresses remain major failures
for insulation degradation accelerated aging. A large number of
generators failed due to stator winding problems, mainly insulation
deterioration. Insulation degradation assessment plays vital role in the
asset life management. Mostly the stator failure is catastrophic
causing significant damage to the plant. Other than generation loss,
stator failure involves heavy repair or replacement cost. Electro
thermal analysis is the main characteristic for improvement design of
stator slot-s insulation. Dielectric parameters such as insulation
thickness, spacing, material types, geometry of winding and slot are
major design consideration. A very powerful method available to
analyze electro thermal performance is Finite Element Method
(FEM) which is used in this paper. The analysis of various stator coil
and slot configurations are used to design the better dielectric system
to reduce electrical and thermal stresses in order to increase the
power of generator in the same volume of core. This paper describes
the process used to perform classical design and improvement
analysis of stator slot-s insulation.
Abstract: In this paper we study the rheonomic mechanical systems from the point of view of Lagrange geometry, by means of its canonical semispray. We present an example of the constraint motion of a material point, in the rheonomic case.
Abstract: Named Entity Recognition (NER) aims to classify each word of a document into predefined target named entity classes and is now-a-days considered to be fundamental for many Natural Language Processing (NLP) tasks such as information retrieval, machine translation, information extraction, question answering systems and others. This paper reports about the development of a NER system for Bengali and Hindi using Support Vector Machine (SVM). Though this state of the art machine learning technique has been widely applied to NER in several well-studied languages, the use of this technique to Indian languages (ILs) is very new. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the four different named (NE) classes, such as Person name, Location name, Organization name and Miscellaneous name. We have used the annotated corpora of 122,467 tokens of Bengali and 502,974 tokens of Hindi tagged with the twelve different NE classes 1, defined as part of the IJCNLP-08 NER Shared Task for South and South East Asian Languages (SSEAL) 2. In addition, we have manually annotated 150K wordforms of the Bengali news corpus, developed from the web-archive of a leading Bengali newspaper. We have also developed an unsupervised algorithm in order to generate the lexical context patterns from a part of the unlabeled Bengali news corpus. Lexical patterns have been used as the features of SVM in order to improve the system performance. The NER system has been tested with the gold standard test sets of 35K, and 60K tokens for Bengali, and Hindi, respectively. Evaluation results have demonstrated the recall, precision, and f-score values of 88.61%, 80.12%, and 84.15%, respectively, for Bengali and 80.23%, 74.34%, and 77.17%, respectively, for Hindi. Results show the improvement in the f-score by 5.13% with the use of context patterns. Statistical analysis, ANOVA is also performed to compare the performance of the proposed NER system with that of the existing HMM based system for both the languages.
Abstract: Planning community has been long discussing emerging paradigms within the planning theory in the face of the changing conditions of the world order. The paradigm shift concept was introduced by Thomas Kuhn, in 1960, who claimed the necessity of shifting within scientific knowledge boundaries; and following him in 1970 Imre Loktas also gave priority to the emergence of multi-paradigm societies [24]. Multi-paradigm is changing our predetermined lifeworld through uncertainties. Those uncertainties are reflected in two sides, the first one is uncertainty as a concept of possibility and creativity in public sphere and the second one is uncertainty as a risk. Therefore, it is necessary to apply a resilience planning approach to be more dynamic in controlling uncertainties which have the potential to transfigure present time and space definitions. In this way, stability of system can be achieved. Uncertainty is not only an outcome of worldwide changes but also a place-specific issue, i.e. it changes from continent to continent, a country to country; a region to region. Therefore, applying strategic spatial planning with respect to resilience principle contributes to: control, grasp and internalize uncertainties through place-specific strategies. In today-s fast changing world, planning system should follow strategic spatial projects to control multi-paradigm societies with adaptability capacities. Here, we have selected two alternatives to demonstrate; these are; 1.Tehran (Iran) from the Middle East 2.Bath (United Kingdom) from Europe. The study elaborates uncertainties and particularities in their strategic spatial planning processes in a comparative manner. Through the comparison, the study aims at assessing place-specific priorities in strategic planning. The approach is to a two-way stream, where the case cities from the extreme end of the spectrum can learn from each other. The structure of this paper is to firstly compare semi-periphery (Tehran) and coreperiphery (Bath) cities, with the focus to reveal how they equip to face with uncertainties according to their geographical locations and local particularities. Secondly, the key message to address is “Each locality requires its own strategic planning approach to be resilient.--
Abstract: This paper proposes a copyright protection scheme for color images using secret sharing and wavelet transform. The scheme contains two phases: the share image generation phase and the watermark retrieval phase. In the generation phase, the proposed scheme first converts the image into the YCbCr color space and creates a special sampling plane from the color space. Next, the scheme extracts the features from the sampling plane using the discrete wavelet transform. Then, the scheme employs the features and the watermark to generate a principal share image. In the retrieval phase, an expanded watermark is first reconstructed using the features of the suspect image and the principal share image. Next, the scheme reduces the additional noise to obtain the recovered watermark, which is then verified against the original watermark to examine the copyright. The experimental results show that the proposed scheme can resist several attacks such as JPEG compression, blurring, sharpening, noise addition, and cropping. The accuracy rates are all higher than 97%.
Abstract: Col is a classic combinatorial game played on graphs
and to solve a general instance is a PSPACE-complete problem.
However, winning strategies can be found for some specific graph
instances. In this paper, the solution of Col on complete k-ary trees
is presented.
Abstract: Intelligent systems based on machine learning
techniques, such as classification, clustering, are gaining wide spread
popularity in real world applications. This paper presents work on
developing a software system for predicting crop yield, for example
oil-palm yield, from climate and plantation data. At the core of our
system is a method for unsupervised partitioning of data for finding
spatio-temporal patterns in climate data using kernel methods which
offer strength to deal with complex data. This work gets inspiration
from the notion that a non-linear data transformation into some high
dimensional feature space increases the possibility of linear
separability of the patterns in the transformed space. Therefore, it
simplifies exploration of the associated structure in the data. Kernel
methods implicitly perform a non-linear mapping of the input data
into a high dimensional feature space by replacing the inner products
with an appropriate positive definite function. In this paper we
present a robust weighted kernel k-means algorithm incorporating
spatial constraints for clustering the data. The proposed algorithm
can effectively handle noise, outliers and auto-correlation in the
spatial data, for effective and efficient data analysis by exploring
patterns and structures in the data, and thus can be used for
predicting oil-palm yield by analyzing various factors affecting the
yield.
Abstract: In this work, we derive two numerical schemes for
solving a class of nonlinear partial differential equations. The first
method is of second order accuracy in space and time directions, the
scheme is unconditionally stable using Von Neumann stability
analysis, the scheme produced a nonlinear block system where
Newton-s method is used to solve it. The second method is of fourth
order accuracy in space and second order in time. The method is
unconditionally stable and Newton's method is used to solve the
nonlinear block system obtained. The exact single soliton solution
and the conserved quantities are used to assess the accuracy and to
show the robustness of the schemes. The interaction of two solitary
waves for different parameters are also discussed.
Abstract: This article stands in the context of rural communities
in Brazil, where, like many others emerging countries, the
overwhelming increasing markets and the overcrowded cities are
leaving behind informal settlements based on obsolete agricultural
economies and techniques. The pilot project for the community of
Goiabeira reflects the attempt to imagine a development model that
privileges the actual improvement of living conditions, the education
and training, the social inclusion and participation of the dwellers of
rural communities. Through the inclusion of operative public space,
the aim is for them to become self-sustaining, encouraging the use of
local resources for appropriate architectural, ecological and energy
technologies and devices, that are efficient, affordable and foster
community participation, in the respect of the surrounding
environment.
Abstract: With the growth of electricity generation from gas
energy gas pipeline reliability can substantially impact the electric
generation. A physical disruption to pipeline or to a compressor
station can interrupt the flow of gas or reduce the pressure and lead
to loss of multiple gas-fired electric generators, which could
dramatically reduce the supplied power and threaten the power
system security. Gas pressure drops during peak loading time on
pipeline system, is a common problem in network with no enough
transportation capacity which limits gas transportation and causes
many problem for thermal domain power systems in supplying their
demand. For a feasible generation scheduling planning in networks
with no sufficient gas transportation capacity, it is required to
consider gas pipeline constraints in solving the optimization problem
and evaluate the impacts of gas consumption in power plants on gas
pipelines operating condition. This paper studies about operating of
gas fired power plants in critical conditions when the demand of gas
and electricity peak together. An integrated model of gas and electric
model is used to consider the gas pipeline constraints in the economic
dispatch problem of gas-fueled thermal generator units.
Abstract: A Data Warehouses is a repository of information
integrated from source data. Information stored in data warehouse is
the form of materialized in order to provide the better performance
for answering the queries. Deciding which appropriated views to be
materialized is one of important problem. In order to achieve this
requirement, the constructing search space close to optimal is a
necessary task. It will provide effective result for selecting view to be
materialized. In this paper we have proposed an approach to reoptimize
Multiple View Processing Plan (MVPP) by using global
common subexpressions. The merged queries which have query
processing cost not close to optimal would be rewritten. The
experiment shows that our approach can help to improve the total
query processing cost of MVPP and sum of query processing cost
and materialized view maintenance cost is reduced as well after views
are selected to be materialized.
Abstract: In this paper, an Interactive Compromise Approach
with Particle Swarm Optimization(ICA-PSO) is presented to solve the
Economic Emission Dispatch(EED) problem. The cost function and
emission function are modeled as the nonsmooth functions,
respectively. The bi-objective including both the minimization of cost
and emission is formulated in this paper. ICA-PSO is proposed to
solve EED problem for finding a better compromise solution. The
solution methodology can offer a global or near-global solution for
decision-making requirements. The effectiveness and efficiency of
ICA-PSO are demonstrated by a sample test system. Test results can
be shown that the proposed method provide a practical and flexible
framework for power dispatch.
Abstract: Stick models are widely used in studying the
behaviour of straight as well as skew bridges and viaducts subjected
to earthquakes while carrying out preliminary studies. The
application of such models to highly curved bridges continues to
pose challenging problems. A viaduct proposed in the foothills of the
Himalayas in Northern India is chosen for the study. It is having 8
simply supported spans @ 30 m c/c. It is doubly curved in horizontal
plane with 20 m radius. It is inclined in vertical plane as well. The
superstructure consists of a box section. Three models have been
used: a conventional stick model, an improved stick model and a 3D
finite element model. The improved stick model is employed by
making use of body constraints in order to study its capabilities. The
first 8 frequencies are about 9.71% away in the latter two models.
Later the difference increases to 80% in 50th mode. The viaduct was
subjected to all three components of the El Centro earthquake of May
1940. The numerical integration was carried out using the Hilber-
Hughes-Taylor method as implemented in SAP2000. Axial forces
and moments in the bridge piers as well as lateral displacements at
the bearing levels are compared for the three models. The maximum
difference in the axial forces and bending moments and
displacements vary by 25% between the improved and finite element
model. Whereas, the maximum difference in the axial forces,
moments, and displacements in various sections vary by 35%
between the improved stick model and equivalent straight stick
model. The difference for torsional moment was as high as 75%. It is
concluded that the stick model with body constraints to model the
bearings and expansion joints is not desirable in very sharp S curved
viaducts even for preliminary analysis. This model can be used only
to determine first 10 frequency and mode shapes but not for member
forces. A 3D finite element analysis must be carried out for
meaningful results.
Abstract: A self tuning PID control strategy using reinforcement
learning is proposed in this paper to deal with the control of wind
energy conversion systems (WECS). Actor-Critic learning is used to
tune PID parameters in an adaptive way by taking advantage of the
model-free and on-line learning properties of reinforcement learning
effectively. In order to reduce the demand of storage space and to
improve the learning efficiency, a single RBF neural network is used
to approximate the policy function of Actor and the value function of
Critic simultaneously. The inputs of RBF network are the system
error, as well as the first and the second-order differences of error.
The Actor can realize the mapping from the system state to PID
parameters, while the Critic evaluates the outputs of the Actor and
produces TD error. Based on TD error performance index and
gradient descent method, the updating rules of RBF kernel function
and network weights were given. Simulation results show that the
proposed controller is efficient for WECS and it is perfectly
adaptable and strongly robust, which is better than that of a
conventional PID controller.
Abstract: This paper examines two policy spaces–the ARC and TVA–and their spatialized politics. The research observes that the regional concept informs public policy and can contribute to the formation of stable policy initiatives. Using the subsystem framework to understand the political viability of policy regimes, the authors conclude policy geographies that appeal to traditional definitions of regions are more stable over time. In contrast, geographies that fail to reflect pre-existing representations of space are engaged in more competitive subsystem politics. The paper demonstrates that the spatial practices of policy regions and their directional politics influence the political viability of programs. The paper concludes that policy spaces should institutionalize pre-existing geographies–not manufacture new ones.
Abstract: Economic dispatch (ED) has been considered to be one of the key functions in electric power system operation which can help to build up effective generating management plans. The practical ED problem has non-smooth cost function with nonlinear constraints which make it difficult to be effectively solved. This paper presents a novel heuristic and efficient optimization approach based on the new Bat algorithm (BA) to solve the practical non-smooth economic dispatch problem. The proposed algorithm easily takes care of different constraints. In addition, two newly introduced modifications method is developed to improve the variety of the bat population when increasing the convergence speed simultaneously. The simulation results obtained by the proposed algorithms are compared with the results obtained using other recently develop methods available in the literature.
Abstract: This article addresses feature selection for breast
cancer diagnosis. The present process contains a wrapper approach
based on Genetic Algorithm (GA) and case-based reasoning (CBR).
GA is used for searching the problem space to find all of the possible
subsets of features and CBR is employed to estimate the evaluation
result of each subset. The results of experiment show that the
proposed model is comparable to the other models on Wisconsin
breast cancer (WDBC) dataset.