Abstract: A seismic isolation pad produced by utilizing the scrap
tire rubber which contains interleaved steel reinforcing cords has been
proposed. The steel cords are expected to function similar to the steel
plates used in conventional laminated rubber bearings. The scrap tire
rubber pad (STRP) isolator is intended to be used in low rise
residential buildings of highly seismic areas of the developing
countries. Experimental investigation was conducted on unbonded
STRP isolators, and test results provided useful information including
stiffness, damping values and an eventual instability of the isolation
unit. Finite element analysis (FE analysis) of STRP isolator was
carried out on properly bonded samples. These types of isolators
provide positive incremental force resisting capacity up to shear strain
level of 155%. This paper briefly discusses the force deformation
behavior of bonded STRP isolators including stability of the isolation
unit.
Abstract: Incremental forming is a complex forming process with
continuously local cumulative deformation taking place during its
process, and springback that forming quality affected by would occur.
The springback evaluation method based on forming error
compensation also was proposed, which it can be defined as the
difference between theory and the actual amount of compensation
along the measured direction. According to forming error
compensation evaluation method, experiments was designed and
implemented. And from the results that obtained it can be show, the
magnitude of springback average (δE) of formed parts was very small,
and the forming precision could be significantly improved by adopting
compensation method. Based on double tensile stress state in the main
deformation area, a hypothesis that there is little springback be arisen
by bending behavior on the formed parts that was proposed.
Abstract: Considering the theory of attribute grammars, we use
logical formulas instead of traditional functional semantic rules.
Following the decoration of a derivation tree, a suitable algorithm
should maintain the consistency of the formulas together with the
evaluation of the attributes. This may be a Prolog-like resolution, but
this paper examines a somewhat different strategy, based on
production specialization, local consistency and propagation: given a
derivation tree, it is interactively decorated, i.e. incrementally
checked and evaluated. The non-directed dependencies are
dynamically directed during attribute evaluation.
Abstract: The current speech interfaces in many military
applications may be adequate for native speakers. However,
the recognition rate drops quite a lot for non-native speakers
(people with foreign accents). This is mainly because the nonnative
speakers have large temporal and intra-phoneme
variations when they pronounce the same words. This
problem is also complicated by the presence of large
environmental noise such as tank noise, helicopter noise, etc.
In this paper, we proposed a novel continuous acoustic feature
adaptation algorithm for on-line accent and environmental
adaptation. Implemented by incremental singular value
decomposition (SVD), the algorithm captures local acoustic
variation and runs in real-time. This feature-based adaptation
method is then integrated with conventional model-based
maximum likelihood linear regression (MLLR) algorithm.
Extensive experiments have been performed on the NATO
non-native speech corpus with baseline acoustic model trained
on native American English. The proposed feature-based
adaptation algorithm improved the average recognition
accuracy by 15%, while the MLLR model based adaptation
achieved 11% improvement. The corresponding word error
rate (WER) reduction was 25.8% and 2.73%, as compared to
that without adaptation. The combined adaptation achieved
overall recognition accuracy improvement of 29.5%, and
WER reduction of 31.8%, as compared to that without
adaptation.
Abstract: Transient Stability is an important issue in power systems planning, operation and extension. The objective of transient stability analysis problem is not satisfied with mere transient instability detection or evaluation and it is most important to complement it by defining fast and efficient control measures in order to ensure system security. This paper presents a new Fuzzy Support Vector Machines (FSVM) to investigate the stability status of power systems and a modified generation rescheduling scheme to bring back the identified unstable cases to a more economical and stable operating point. FSVM improves the traditional SVM (Support Vector Machines) by adding fuzzy membership to each training sample to indicate the degree of membership of this sample to different classes. The preventive control based on economic generator rescheduling avoids the instability of the power systems with minimum change in operating cost under disturbed conditions. Numerical results on the New England 39 bus test system show the effectiveness of the proposed method.
Abstract: The choice of finite element to use in order to predict
nonlinear static or dynamic response of complex structures becomes
an important factor. Then, the main goal of this research work is to
focus a study on the effect of the in-plane rotational degrees of
freedom in linear and geometrically non linear static and dynamic
analysis of thin shell structures by flat shell finite elements. In this
purpose: First, simple triangular and quadrilateral flat shell finite
elements are implemented in an incremental formulation based on the
updated lagrangian corotational description for geometrically
nonlinear analysis. The triangular element is a combination of DKT
and CST elements, while the quadrilateral is a combination of DKQ
and the bilinear quadrilateral membrane element. In both elements,
the sixth degree of freedom is handled via introducing fictitious
stiffness. Secondly, in the same code, the sixth degrees of freedom in
these elements is handled differently where the in-plane rotational
d.o.f is considered as an effective d.o.f in the in-plane filed
interpolation. Our goal is to compare resulting shell elements. Third,
the analysis is enlarged to dynamic linear analysis by direct
integration using Newmark-s implicit method. Finally, the linear
dynamic analysis is extended to geometrically nonlinear dynamic
analysis where Newmark-s method is used to integrate equations of
motion and the Newton-Raphson method is employed for iterating
within each time step increment until equilibrium is achieved. The
obtained results demonstrate the effectiveness and robustness of the
interpolation of the in-plane rotational d.o.f. and present deficiencies
of using fictitious stiffness in dynamic linear and nonlinear analysis.
Abstract: Recent years, adaptive pushover methods have been
developed for seismic analysis of structures. Herein, the accuracy of
the displacement-based adaptive pushover (DAP) method, which is
introduced by Antoniou and Pinho [2004], is evaluated for Irregular
buildings. The results are compared to the force-based procedure.
Both concrete and steel frame structures, asymmetric in plan and
elevation are analyzed and also torsional effects are taking into the
account. These analyses are performed using both near fault and far
fault records. In order to verify the results, the Incremental Dynamic
Analysis (IDA) is performed.
Abstract: A multi-board run-time reconfigurable (MRTR)
system for evolvable hardware (EHW) is introduced with the aim to
implement on hardware the bidirectional incremental evolution (BIE)
method. The main features of this digital intrinsic EHW solution rely
on the multi-board approach, the variable chromosome length
management and the partial configuration of the reconfigurable
circuit. These three features provide a high scalability to the solution.
The design has been written in VHDL with the concern of not being
platform dependant in order to keep a flexibility factor as high as
possible. This solution helps tackling the problem of evolving
complex task on digital configurable support.
Abstract: The Major Depressive Disorder has been a burden of
medical expense in Taiwan as well as the situation around the world.
Major Depressive Disorder can be defined into different categories by
previous human activities. According to machine learning, we can
classify emotion in correct textual language in advance. It can help
medical diagnosis to recognize the variance in Major Depressive
Disorder automatically. Association language incremental is the
characteristic and relationship that can discovery words in sentence.
There is an overlapping-category problem for classification. In this
paper, we would like to improve the performance in classification in
principle of no overlapping-category problems. We present an
approach that to discovery words in sentence and it can find in high
frequency in the same time and can-t overlap in each category, called
Association Language Features by its Category (ALFC).
Experimental results show that ALFC distinguish well in Major
Depressive Disorder and have better performance. We also compare
the approach with baseline and mutual information that use single
words alone or correlation measure.
Abstract: The increasing interest on processing data created by
sensor networks has evolved into approaches to implement sensor
networks as databases. The aggregation operator, which calculates a
value from a large group of data such as computing averages or sums,
etc. is an essential function that needs to be provided when
implementing such sensor network databases. This work proposes to
add the DURING clause into TinySQL to calculate values during a
specific long period and suggests a way to implement the aggregation
service in sensor networks by applying materialized view and
incremental view maintenance techniques that is used in data
warehouses. In sensor networks, data values are passed from child
nodes to parent nodes and an aggregation value is computed at the root
node. As such root nodes need to be memory efficient and low
powered, it becomes a problem to recompute aggregate values from all
past and current data. Therefore, applying incremental view
maintenance techniques can reduce the memory consumption and
support fast computation of aggregate values.
Abstract: Fast delay estimation methods, as opposed to
simulation techniques, are needed for incremental performance
driven layout synthesis. On-chip inductive effects are becoming
predominant in deep submicron interconnects due to increasing clock
speed and circuit complexity. Inductance causes noise in signal
waveforms, which can adversely affect the performance of the circuit
and signal integrity. Several approaches have been put forward which
consider the inductance for on-chip interconnect modelling. But for
even much higher frequency, of the order of few GHz, the shunt
dielectric lossy component has become comparable to that of other
electrical parameters for high speed VLSI design. In order to cope up
with this effect, on-chip interconnect has to be modelled as
distributed RLCG line. Elmore delay based methods, although
efficient, cannot accurately estimate the delay for RLCG interconnect
line. In this paper, an accurate analytical delay model has been
derived, based on first and second moments of RLCG
interconnection lines. The proposed model considers both the effect
of inductance and conductance matrices. We have performed the
simulation in 0.18μm technology node and an error of as low as less
as 5% has been achieved with the proposed model when compared to
SPICE. The importance of the conductance matrices in interconnect
modelling has also been discussed and it is shown that if G is
neglected for interconnect line modelling, then it will result an delay
error of as high as 6% when compared to SPICE.