Abstract: Bond Graph as a unified multidisciplinary tool is widely
used not only for dynamic modelling but also for Fault Detection and
Isolation because of its structural and causal proprieties. A binary
Fault Signature Matrix is systematically generated but to make the
final binary decision is not always feasible because of the problems
revealed by such method. The purpose of this paper is introducing a
methodology for the improvement of the classical binary method of
decision-making, so that the unknown and identical failure signatures
can be treated to improve the robustness. This approach consists of
associating the evaluated residuals and the components reliability data
to build a Hybrid Bayesian Network. This network is used in two
distinct inference procedures: one for the continuous part and the
other for the discrete part. The continuous nodes of the network are
the prior probabilities of the components failures, which are used by
the inference procedure on the discrete part to compute the posterior
probabilities of the failures. The developed methodology is applied
to a real steam generator pilot process.
Abstract: Data Warehouses (DWs) are repositories which contain the unified history of an enterprise for decision support. The data must be Extracted from information sources, Transformed and integrated to be Loaded (ETL) into the DW, using ETL tools. These tools focus on data movement, where the models are only used as a means to this aim. Under a conceptual viewpoint, the authors want to innovate the ETL process in two ways: 1) to make clear compatibility between models in a declarative fashion, using correspondence assertions and 2) to identify the instances of different sources that represent the same entity in the real-world. This paper presents the overview of the proposed framework to model the ETL process, which is based on the use of a reference model and perspective schemata. This approach provides the designer with a better understanding of the semantic associated with the ETL process.
Abstract: Quantitative methods of economic decision-making as
the methodological base of the so called operational research
represent an important set of tools for managing complex economic
systems,both at the microeconomic level and on the macroeconomic
scale. Mathematical models of controlled and controlling processes
allow, by means of artificial experiments, obtaining information
foroptimalor optimum approaching managerial decision-making.The
quantitative methods of economic decision-making usually include a
methodology known as structural analysis -an analysisof
interdisciplinary production-consumption relations.
Abstract: In this paper three different approaches for person
verification and identification, i.e. by means of fingerprints, face and
voice recognition, are studied. Face recognition uses parts-based
representation methods and a manifold learning approach. The
assessment criterion is recognition accuracy. The techniques under
investigation are: a) Local Non-negative Matrix Factorization
(LNMF); b) Independent Components Analysis (ICA); c) NMF with
sparse constraints (NMFsc); d) Locality Preserving Projections
(Laplacianfaces). Fingerprint detection was approached by classical
minutiae (small graphical patterns) matching through image
segmentation by using a structural approach and a neural network as
decision block. As to voice / speaker recognition, melodic cepstral
and delta delta mel cepstral analysis were used as main methods, in
order to construct a supervised speaker-dependent voice recognition
system. The final decision (e.g. “accept-reject" for a verification
task) is taken by using a majority voting technique applied to the
three biometrics. The preliminary results, obtained for medium
databases of fingerprints, faces and voice recordings, indicate the
feasibility of our study and an overall recognition precision (about
92%) permitting the utilization of our system for a future complex
biometric card.
Abstract: Markov games are a generalization of Markov
decision process to a multi-agent setting. Two-player zero-sum
Markov game framework offers an effective platform for designing
robust controllers. This paper presents two novel controller design
algorithms that use ideas from game-theory literature to produce
reliable controllers that are able to maintain performance in presence
of noise and parameter variations. A more widely used approach for
controller design is the H∞ optimal control, which suffers from high
computational demand and at times, may be infeasible. Our approach
generates an optimal control policy for the agent (controller) via a
simple Linear Program enabling the controller to learn about the
unknown environment. The controller is facing an unknown
environment, and in our formulation this environment corresponds to
the behavior rules of the noise modeled as the opponent. Proposed
controller architectures attempt to improve controller reliability by a
gradual mixing of algorithmic approaches drawn from the game
theory literature and the Minimax-Q Markov game solution
approach, in a reinforcement-learning framework. We test the
proposed algorithms on a simulated Inverted Pendulum Swing-up
task and compare its performance against standard Q learning.
Abstract: This paper investigates the relationship between state and business in the context of structural and institutional transformations in Indonesia following the collapse of the New Order regime in 1998. Since 1998, Indonesia has embarked on a shift from an authoritarian to democratic polity and from a centralised to a decentralised system of governance, transforming the country into the third largest democracy and one of the most decentralised states in the world. This paper examines whether the transformation of the Indonesian state has altered the pattern of state and business relations with focus on clientism and corruption as the key dependent variable, and probes how/to what extent this has changed as a result of the transformation and the ensuring shifts in business and state relations. Based on interviews with key government and business actors as well as prominent scholars in Indonesia, it is found that since the demise of the New Order, business associations in Indonesia have become more independent of state control and more influential in public decision-making whereas the government has become more responsive of business concerns and more committed to combat corruption and clientism. However, these changes have not necessarily rendered business people completely leave individualclientelistic relationship with the government, and simply pursue wider sectoral and business-wide collectivism as an alternative way of channelling their aspirations, which is expected to help reduce corruption and clientism in Indonesia. This paper concludes that democratisation and a more open politics may have helped reduce corruption and clientism in Indonesia through changes in government. However, it is still difficult to imply that such political transformation has fostered business collective action and a broader, more encompassing pattern of business lobbying and activism, which is expected to help reduce corruption and clientism.
Abstract: The major challenge faced by wireless sensor networks is security. Because of dynamic and collaborative nature of sensor networks the connected sensor devices makes the network unusable. To solve this issue, a trust model is required to find malicious, selfish and compromised insiders by evaluating trust worthiness sensors from the network. It supports the decision making processes in wireless sensor networks such as pre key-distribution, cluster head selection, data aggregation, routing and self reconfiguration of sensor nodes. This paper discussed the kinds of trust model, trust metrics used to address attacks by monitoring certain behavior of network. It describes the major design issues and their countermeasures of building trust model. It also discusses existing trust models used in various decision making process of wireless sensor networks.
Abstract: this article proposed a methodology for computer
numerical control (CNC) machine scoring. The case study company
is a manufacturer of hard disk drive parts in Thailand. In this
company, sample of parts manufactured from CNC machine are
usually taken randomly for quality inspection. These inspection data
were used to make a decision to shut down the machine if it has
tendency to produce parts that are out of specification. Large amount
of data are produced in this process and data mining could be very
useful technique in analyzing them. In this research, data mining
techniques were used to construct a machine scoring model called
'machine priority assessment model (MPAM)'. This model helps to
ensure that the machine with higher risk of producing defective parts
be inspected before those with lower risk. If the defective prone
machine is identified sooner, defective part and rework could be
reduced hence improving the overall productivity. The results
showed that the proposed method can be successfully implemented
and approximately 351,000 baht of opportunity cost could have
saved in the case study company.
Abstract: We introduce a logic-based framework for database
updating under constraints. In our framework, the constraints are
represented as an instantiated extended logic program. When performing
an update, database consistency may be violated. We provide
an approach of maintaining database consistency, and study the
conditions under which the maintenance process is deterministic. We
show that the complexity of the computations and decision problems
presented in our framework is in each case polynomial time.
Abstract: Detection of human emotions has many potential applications. One of application is to quantify attentiveness audience in order evaluate acoustic quality in concern hall. The subjective audio preference that based on from audience is used. To obtain fairness evaluation of acoustic quality, the research proposed system for multimodal emotion detection; one modality based on brain signals that measured using electroencephalogram (EEG) and the second modality is sequences of facial images. In the experiment, an audio signal was customized which consist of normal and disorder sounds. Furthermore, an audio signal was played in order to stimulate positive/negative emotion feedback of volunteers. EEG signal from temporal lobes, i.e. T3 and T4 was used to measured brain response and sequence of facial image was used to monitoring facial expression during volunteer hearing audio signal. On EEG signal, feature was extracted from change information in brain wave, particularly in alpha and beta wave. Feature of facial expression was extracted based on analysis of motion images. We implement an advance optical flow method to detect the most active facial muscle form normal to other emotion expression that represented in vector flow maps. The reduce problem on detection of emotion state, vector flow maps are transformed into compass mapping that represents major directions and velocities of facial movement. The results showed that the power of beta wave is increasing when disorder sound stimulation was given, however for each volunteer was giving different emotion feedback. Based on features derived from facial face images, an optical flow compass mapping was promising to use as additional information to make decision about emotion feedback.
Abstract: Decision support based upon risk analysis into
comparison of the electricity generation from different renewable
energy technologies can provide information about their effects on
the environment and society. The aim of this paper is to develop the
assessment framework regarding risks to health and environment,
and the society-s benefits of the electric power plant generation from
different renewable sources. The multicriteria framework to
multiattribute risk analysis technique and the decision analysis
interview technique are applied in order to support the decisionmaking
process for the implementing renewable energy projects to
the Bangkok case study. Having analyses the local conditions and
appropriate technologies, five renewable power plants are postulated
as options. As this work demonstrates, the analysis can provide a tool
to aid decision-makers for achieving targets related to promote
sustainable energy system.
Abstract: Ireland developed a National Strategy 2030 that
argued for the creation of a new form of higher education institution,
a Technological University. The research reported here reviews the
first stage of this partnership development. The study found that
national policy can create system capacity and change, but that
individual partners may have more to gain or lose in collaborating.
When presented as a zero-sum activity, fear among partners is high.
The level of knowledge and networking within the higher education
system possessed by each partner contributed to decisions to
participate or not in a joint proposal for collaboration. Greater
success resulted when there were gains for all partners. This research
concludes that policy mandates can provide motivation to
collaborate, but that the partnership needs to be built more on shared
values versus coercion by mandates.
Abstract: In the past decade, artificial neural networks (ANNs)
have been regarded as an instrument for problem-solving and
decision-making; indeed, they have already done with a substantial
efficiency and effectiveness improvement in industries and businesses.
In this paper, the Back-Propagation neural Networks (BPNs) will be
modulated to demonstrate the performance of the collaborative
forecasting (CF) function of a Collaborative Planning, Forecasting and
Replenishment (CPFR®) system. CPFR functions the balance between
the sufficient product supply and the necessary customer demand in a
Supply and Demand Chain (SDC). Several classical standard BPN will
be grouped, collaborated and exploited for the easy implementation of
the proposed modular ANN framework based on the topology of a
SDC. Each individual BPN is applied as a modular tool to perform the
task of forecasting SKUs (Stock-Keeping Units) levels that are
managed and supervised at a POS (point of sale), a wholesaler, and a
manufacturer in an SDC. The proposed modular BPN-based CF
system will be exemplified and experimentally verified using lots of
datasets of the simulated SDC. The experimental results showed that a
complex CF problem can be divided into a group of simpler
sub-problems based on the single independent trading partners
distributed over SDC, and its SKU forecasting accuracy was satisfied
when the system forecasted values compared to the original simulated
SDC data. The primary task of implementing an autonomous CF
involves the study of supervised ANN learning methodology which
aims at making “knowledgeable" decision for the best SKU sales plan
and stocks management.
Abstract: Nagaland, the 16th state of India in order of
statehood, is situated between 25° 6' and 27° 4' latitude north and
between 93º 20' E and 95º 15' E longitude of equator in the North
Eastern part of the India. Endowed with varied topography, soil and
agro climatic conditions it is known for its potentiality to grow all
most all kinds of horticultural crops. Pineapple being grown since
long organically by default is one of the most promising crops of the
state with emphasis being laid for commercialization by the
government of Nagaland. In light of commercialization, globalization
and scope of setting small-scale industries, a research study was
undertaken to examine the socio-economic and personal
characteristics, entrepreneurial characteristics and attitude of the
pineapple growers towards improved package of practices of
pineapple cultivation. The study was conducted in Medziphema
block of Dimapur district of the Nagaland state of India following ex
post facto research design. Ninety pineapple growers were selected
from four different villages of Medziphema block based on
proportionate random selection procedure. Findings of the study
revealed that majority of the respondents had medium level of
entrepreneurial characteristics in terms of knowledge level, risk
orientation, self confidence, management orientation, farm decision
making ability and leadership ability and most of them had
favourable attitude towards improved package of practices of
pineapple cultivation. The variables age, education, farm size, risk
orientation, management orientation and sources of information
utilized were found important to influence the attitude of the
respondents. The study revealed that favourable attitude and
entrepreneurial characteristics of the pineapple cultivators might be
harnessed for increased production of pineapple in the state thereby
bringing socio economic upliftment of the marginal and small-scale
farmers.
Abstract: As in today's semiconductor industries test costs can make up to 50 percent of the total production costs, an efficient test error detection becomes more and more important. In this paper, we present a new machine learning approach to test error detection that should provide a faster recognition of test system faults as well as an improved test error recall. The key idea is to learn a classifier ensemble, detecting typical test error patterns in wafer test results immediately after finishing these tests. Since test error detection has not yet been discussed in the machine learning community, we define central problem-relevant terms and provide an analysis of important domain properties. Finally, we present comparative studies reflecting the failure detection performance of three individual classifiers and three ensemble methods based upon them. As base classifiers we chose a decision tree learner, a support vector machine and a Bayesian network, while the compared ensemble methods were simple and weighted majority vote as well as stacking. For the evaluation, we used cross validation and a specially designed practical simulation. By implementing our approach in a semiconductor test department for the observation of two products, we proofed its practical applicability.
Abstract: This paper deals with stakeholders’ decisions within energy neutral urban redevelopment processes. The decisions of these stakeholders during the process will make or break energy neutral ambitions. An extensive form of game theory model gave insight in the behavioral differences of stakeholders regarding energy neutral ambitions and the effects of the changing legislation. The results show that new legislation regarding spatial planning slightly influences the behavior of stakeholders. An active behavior of the municipality will still result in the best outcome. Nevertheless, the municipality becomes more powerful when acting passively and can make the use of planning tools to provide governance towards energy neutral urban redevelopment. Moreover, organizational support, recognizing the necessity for energy neutrality, keeping focused and collaboration among stakeholders are crucial elements to achieve the objective of an energy neutral urban (re)development.
Abstract: Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved.
Abstract: Although backpropagation ANNs generally predict
better than decision trees do for pattern classification problems, they
are often regarded as black boxes, i.e., their predictions cannot be
explained as those of decision trees. In many applications, it is
desirable to extract knowledge from trained ANNs for the users to
gain a better understanding of how the networks solve the problems.
A new rule extraction algorithm, called rule extraction from artificial
neural networks (REANN) is proposed and implemented to extract
symbolic rules from ANNs. A standard three-layer feedforward ANN
is the basis of the algorithm. A four-phase training algorithm is
proposed for backpropagation learning. Explicitness of the extracted
rules is supported by comparing them to the symbolic rules generated
by other methods. Extracted rules are comparable with other methods
in terms of number of rules, average number of conditions for a rule,
and predictive accuracy. Extensive experimental studies on several
benchmarks classification problems, such as breast cancer, iris,
diabetes, and season classification problems, demonstrate the
effectiveness of the proposed approach with good generalization
ability.
Abstract: One of the long standing challenging aspect in mobile robotics is the ability to navigate autonomously, avoiding modeled and unmodeled obstacles especially in crowded and unpredictably changing environment. A successful way of structuring the navigation task in order to deal with the problem is within behavior based navigation approaches. In this study, Issues of individual behavior design and action coordination of the behaviors will be addressed using fuzzy logic. A layered approach is employed in this work in which a supervision layer based on the context makes a decision as to which behavior(s) to process (activate) rather than processing all behavior(s) and then blending the appropriate ones, as a result time and computational resources are saved.
Abstract: Ensemble learning algorithms such as AdaBoost and
Bagging have been in active research and shown improvements in
classification results for several benchmarking data sets with mainly
decision trees as their base classifiers. In this paper we experiment to
apply these Meta learning techniques with classifiers such as random
forests, neural networks and support vector machines. The data sets
are from MAGIC, a Cherenkov telescope experiment. The task is to
classify gamma signals from overwhelmingly hadron and muon
signals representing a rare class classification problem. We compare
the individual classifiers with their ensemble counterparts and
discuss the results. WEKA a wonderful tool for machine learning has
been used for making the experiments.