Abstract: In this paper we consider the problem of distributed adaptive estimation in wireless sensor networks for two different observation noise conditions. In the first case, we assume that there are some sensors with high observation noise variance (noisy sensors) in the network. In the second case, different variance for observation noise is assumed among the sensors which is more close to real scenario. In both cases, an initial estimate of each sensor-s observation noise is obtained. For the first case, we show that when there are such sensors in the network, the performance of conventional distributed adaptive estimation algorithms such as incremental distributed least mean square (IDLMS) algorithm drastically decreases. In addition, detecting and ignoring these sensors leads to a better performance in a sense of estimation. In the next step, we propose a simple algorithm to detect theses noisy sensors and modify the IDLMS algorithm to deal with noisy sensors. For the second case, we propose a new algorithm in which the step-size parameter is adjusted for each sensor according to its observation noise variance. As the simulation results show, the proposed methods outperforms the IDLMS algorithm in the same condition.
Abstract: The Spiral development model has been used
successfully in many commercial systems and in a good number of
defense systems. This is due to the fact that cost-effective
incremental commitment of funds, via an analogy of the spiral model
to stud poker and also can be used to develop hardware or integrate
software, hardware, and systems. To support adaptive, semantic
collaboration between domain experts and knowledge engineers, a
new knowledge engineering process, called Spiral_OWL is proposed.
This model is based on the idea of iterative refinement, annotation
and structuring of knowledge base. The Spiral_OWL model is
generated base on spiral model and knowledge engineering
methodology. A central paradigm for Spiral_OWL model is the
concentration on risk-driven determination of knowledge engineering
process. The collaboration aspect comes into play during knowledge
acquisition and knowledge validation phase. Design rationales for the
Spiral_OWL model are to be easy-to-implement, well-organized, and
iterative development cycle as an expanding spiral.
Abstract: Negation is useful in the majority of the real world applications. However, its introduction leads to semantic and canonical problems. SEPN nets are well adapted extension of predicate nets for the definition and manipulation of stratified programs. This formalism is characterized by two main contributions. The first concerns the management of the whole class of stratified programs. The second contribution is related to usual operations optimization (maximal stratification, incremental updates ...). We propose, in this paper, useful algorithms for manipulating stratified programs using SEPN. These algorithms were implemented and validated with STRPRO tool.
Abstract: The recent drive for use of performance-based methodologies in design and assessment of structures in seismic areas has significantly increased the demand for the development of reliable nonlinear inelastic static pushover analysis tools. As a result, the adaptive pushover methods have been developed during the last decade, which unlike their conventional pushover counterparts, feature the ability to account for the effect that higher modes of vibration and progressive stiffness degradation might have on the distribution of seismic storey forces. Even in advanced pushover methods, little attention has been paid to the Unsymmetric structures. This study evaluates the seismic demands for three dimensional Unsymmetric-Plan buildings determined by the Displacement-based Adaptive Pushover (DAP) analysis, which has been introduced by Antoniou and Pinho [2004]. The capability of DAP procedure in capturing the torsional effects due to the irregularities of the structures, is investigated by comparing its estimates to the exact results, obtained from Incremental Dynamic Analysis (IDA). Also the capability of the procedure in prediction the seismic behaviour of the structure is discussed.
Abstract: Considering today-s increasing speed of change,
radical and innovative improvement - Kaikaku, is a necessity parallel
to continuous incremental improvement - Kaizen, especially for
SME-s in order to attain the competitive edge needed to be profitable.
During 2011, a qualitative single case study with the objective of
realizing a kaikaku in production has been conducted. The case study
was run as a one year project using a collaborative approach
including both researchers and company representatives. The case
study was conducted with the purpose of gaining further knowledge
about kaikaku realization as well as its implications. The empirical
results provide insights about the great productivity results achieved
by applying a specific kaikaku realization approach. However, it also
sheds light on the difficulty and contradiction of combining
innovation management and production system development.
Abstract: One of the most important parts of a cement factory is
the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral
movement of air and materials, together with chemical reactions take
place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only
in exceptional case; however, a large number of the involved parameter were crossed out and an approximation model was
presented instead. This issue caused many problems for designing a
cement rotary kiln controller. In this paper, we presented nonlinear predictor and simulator models for a real cement rotary kiln by using
nonlinear identification technique on the Locally Linear Neuro-
Fuzzy (LLNF) model. For the first time, a simulator model as well as
a predictor one with a precise fifteen minute prediction horizon for a
cement rotary kiln is presented. These models are trained by
LOLIMOT algorithm which is an incremental tree-structure
algorithm. At the end, the characteristics of these models are expressed. Furthermore, we presented the pros and cons of these
models. The data collected from White Saveh Cement Company is used for modeling.
Abstract: The quick training algorithms and accurate solution
procedure for incremental learning aim at improving the efficiency of
training of SVR, whereas there are some disadvantages for them, i.e.
the nonconvergence of the formers for changeable training set and
the inefficiency of the latter for a massive dataset. In order to handle
the problems, a new training algorithm for a changeable training
set, named Approximation Incremental Training Algorithm (AITA),
was proposed. This paper explored the reason of nonconvergence
theoretically and discussed the realization of AITA, and finally
demonstrated the benefits of AITA both on precision and efficiency.
Abstract: This paper presents a reliability-based approach to select appropriate wind turbine types for a wind farm considering site-specific wind speed patterns. An actual wind farm in the northern region of Iran with the wind speed registration of one year is studied in this paper. An analytic approach based on total probability theorem is utilized in this paper to model the probabilistic behavior of both turbines- availability and wind speed. Well-known probabilistic reliability indices such as loss of load expectation (LOLE), expected energy not supplied (EENS) and incremental peak load carrying capability (IPLCC) for wind power integration in the Roy Billinton Test System (RBTS) are examined. The most appropriate turbine type achieving the highest reliability level is chosen for the studied wind farm.
Abstract: This work presents a new phonetic transcription system based on a tree of hierarchical pronunciation rules expressed as context-specific grapheme-phoneme correspondences. The tree is automatically inferred from a phonetic dictionary by incrementally analyzing deeper context levels, eventually representing a minimum set of exhaustive rules that pronounce without errors all the words in the training dictionary and that can be applied to out-of-vocabulary words. The proposed approach improves upon existing rule-tree-based techniques in that it makes use of graphemes, rather than letters, as elementary orthographic units. A new linear algorithm for the segmentation of a word in graphemes is introduced to enable outof- vocabulary grapheme-based phonetic transcription. Exhaustive rule trees provide a canonical representation of the pronunciation rules of a language that can be used not only to pronounce out-of-vocabulary words, but also to analyze and compare the pronunciation rules inferred from different dictionaries. The proposed approach has been implemented in C and tested on Oxford British English and Basic English. Experimental results show that grapheme-based rule trees represent phonetically sound rules and provide better performance than letter-based rule trees.
Abstract: The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy architecture based on Extended Kalman filter. To test the performance and applicability of the proposed neuro-fuzzy model, simulation study of nonlinear complex dynamic system is carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction of financial time series. A benchmark case studie is used to demonstrate that the proposed model is a superior neuro-fuzzy modeling technique.
Abstract: Recent quasi-experimental evaluation of the Canadian Active Labour Market Policies (ALMP) by Human Resources and Skills Development Canada (HRSDC) has provided an opportunity to examine alternative methods to estimating the incremental effects of Employment Benefits and Support Measures (EBSMs) on program participants. The focus of this paper is to assess the efficiency and robustness of inverse probability weighting (IPW) relative to kernel matching (KM) in the estimation of program effects. To accomplish this objective, the authors compare pairs of 1,080 estimates, along with their associated standard errors, to assess which type of estimate is generally more efficient and robust. In the interest of practicality, the authorsalso document the computationaltime it took to produce the IPW and KM estimates, respectively.
Abstract: this article conducts a research about the relationship between cash dividend policy and enterprise value based on the data coming from the A-share listed companies over period 2005-2009. In conclusion, the enterprise value has a negative correlation with the incremental and the degressive cash dividend per share, and has a positive correlation with the stable cash dividend per share.
Abstract: In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the
kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental treestructure
algorithm. Then, by using this method, we obtained 3
distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes
prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes prediction horizon are presented.
At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used for in this study.
Abstract: In this paper, the requirement for Coke quality
prediction, its role in Blast furnaces, and the model output is
explained. By applying method of Artificial Neural Networking
(ANN) using back propagation (BP) algorithm, prediction model has
been developed to predict CSR. Important blast furnace functions
such as permeability, heat exchanging, melting, and reducing
capacity are mostly connected to coke quality. Coke quality is further
dependent upon coal characterization and coke making process
parameters. The ANN model developed is a useful tool for process
experts to adjust the control parameters in case of coke quality
deviations. The model also makes it possible to predict CSR for new
coal blends which are yet to be used in Coke Plant. Input data to the
model was structured into 3 modules, for tenure of past 2 years and
the incremental models thus developed assists in identifying the
group causing the deviation of CSR.
Abstract: An optimal solution for a large number of constraint
satisfaction problems can be found using the technique of
substitution and elimination of variables analogous to the technique
that is used to solve systems of equations. A decision function
f(A)=max(A2) is used to determine which variables to eliminate. The
algorithm can be expressed in six lines and is remarkable in both its
simplicity and its ability to find an optimal solution. However it is
inefficient in that it needs to square the updated A matrix after each
variable elimination. To overcome this inefficiency the algorithm is
analyzed and it is shown that the A matrix only needs to be squared
once at the first step of the algorithm and then incrementally updated
for subsequent steps, resulting in significant improvement and an
algorithm complexity of O(n3).
Abstract: This study aims at providing empirical evidence on a
comparison of two equity valuation models: (1) the dividend discount
model (DDM) and (2) the residual income model (RIM), in
estimating equity values of Thai firms during 1995-2004. Results
suggest that DDM and RIM underestimate equity values of Thai
firms and that RIM outperforms DDM in predicting cross-sectional
stock prices. Results on regression of cross-sectional stock prices on
the decomposed DDM and RIM equity values indicate that book
value of equity provides the greatest incremental explanatory power,
relative to other components in DDM and RIM terminal values,
suggesting that book value distortions resulting from accounting
procedures and choices are less severe than forecast and
measurement errors in discount rates and growth rates.
We also document that the incremental explanatory power of book
value of equity during 1998-2004, representing the information
environment under Thai Accounting Standards reformed after the
1997 economic crisis to conform to International Accounting
Standards, is significantly greater than that during 1995-1996,
representing the information environment under the pre-reformed
Thai Accounting Standards. This implies that the book value
distortions are less severe under the 1997 Reformed Thai Accounting
Standards than the pre-reformed Thai Accounting Standards.
Abstract: The paper presents an on-line recognition machine
(RM) for continuous/isolated, dynamic and static gestures that arise
in Flight Deck Officer (FDO) training. RM is based on generic pattern
recognition framework. Gestures are represented as templates using
summary statistics. The proposed recognition algorithm exploits temporal
and spatial characteristics of gestures via dynamic programming
and Markovian process. The algorithm predicts corresponding index
of incremental input data in the templates in an on-line mode.
Accumulated consistency in the sequence of prediction provides a
similarity measurement (Score) between input data and the templates.
The algorithm provides an intuitive mechanism for automatic detection
of start/end frames of continuous gestures. In the present paper,
we consider isolated gestures. The performance of RM is evaluated
using four datasets - artificial (W TTest), hand motion (Yang) and
FDO (tracker, vision-based ). RM achieves comparable results which
are in agreement with other on-line and off-line algorithms such as
hidden Markov model (HMM) and dynamic time warping (DTW).
The proposed algorithm has the additional advantage of providing
timely feedback for training purposes.
Abstract: As the enormous amount of on-line text grows on the
World-Wide Web, the development of methods for automatically
summarizing this text becomes more important. The primary goal of
this research is to create an efficient tool that is able to summarize
large documents automatically. We propose an Evolving
connectionist System that is adaptive, incremental learning and
knowledge representation system that evolves its structure and
functionality. In this paper, we propose a novel approach for Part of
Speech disambiguation using a recurrent neural network, a paradigm
capable of dealing with sequential data. We observed that
connectionist approach to text summarization has a natural way of
learning grammatical structures through experience. Experimental
results show that our approach achieves acceptable performance.
Abstract: Knowledge is indispensable but voluminous knowledge becomes a bottleneck for efficient processing. A great challenge for data mining activity is the generation of large number of potential rules as a result of mining process. In fact sometimes result size is comparable to the original data. Traditional data mining pruning activities such as support do not sufficiently reduce the huge rule space. Moreover, many practical applications are characterized by continual change of data and knowledge, thereby making knowledge voluminous with each change. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. Michalski & Winston proposed Censored Production Rules (CPRs), as an extension of production rules, that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: If P Then D Unless C, where C (Censor) is an exception to the rule. Such rules are employed in situations in which the conditional statement 'If P Then D' holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence, are tight or there is simply no information available as to whether it holds or not. Thus the 'If P Then D' part of the CPR expresses important information while the Unless C part acts only as a switch changes the polarity of D to ~D. In this paper a scheme based on Dempster-Shafer Theory (DST) interpretation of a CPR is suggested for discovering CPRs from the discovered flat PRs. The discovery of CPRs from flat rules would result in considerable reduction of the already discovered rules. The proposed scheme incrementally incorporates new knowledge and also reduces the size of knowledge base considerably with each episode. Examples are given to demonstrate the behaviour of the proposed scheme. The suggested cumulative learning scheme would be useful in mining data streams.
Abstract: Streaming Applications usually run in parallel or in
series that incrementally transform a stream of input data. It poses a
design challenge to break such an application into distinguishable
blocks and then to map them into independent hardware processing
elements. For this, there is required a generic controller that
automatically maps such a stream of data into independent processing
elements without any dependencies and manual considerations. In
this paper, Kahn Process Networks (KPN) for such streaming
applications is designed and developed that will be mapped on
MPSoC. This is designed in such a way that there is a generic Cbased
compiler that will take the mapping specifications as an input
from the user and then it will automate these design constraints and
automatically generate the synthesized RTL optimized code for
specified application.