Abstract: Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drill-bits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drill-bit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drill-bit breakage using degradation signals.
Abstract: Avoidable unscheduled maintenance events and unnecessary
spare parts deliveries are mostly caused by an incorrect choice
of the underlying maintenance strategy. For a faster and more efficient
supply of spare parts for aircrafts of an airline we examine options for
improving the underlying logistics network integrated in an existing
aviation industry network. This paper presents a dynamic prediction
model as decision support for maintenance method selection considering
requirements of an entire flight network. The objective is
to guarantee a high supply of spare parts by an optimal interaction
of various network levels and thus to reduce unscheduled maintenance
events and minimize total costs. By using a prognostics-based
preventive maintenance strategy unscheduled component failures are
avoided for an increase in availability and reliability of the entire
system. The model is intended for use in an aviation company that
utilizes a structured planning process based on collected failures data
of components.
Abstract: Asthma is a condition that causing chronic health problems in children. In addition to basic therapy against disease, we must try to reduce the impact of chronic health problems and also optimize their medical aspect of growth and development. A boy with mild asthma attack frequent episode did not showed any improvement with medical treatment and his asthma control test was 11. From radiologic examination he got hyperaerated lung and billateral sinusitis maxillaris; skin test results were house dust, food and pet allergy; an overweight body; bad school grades; psychological and environmental problem. We followed and evaluated this boy in 6 months, treated holistically. Even we could not do much on environmental but no more psychological and school problems, his on a good bodyweight and his asthma control test was 22. A case of a child with mild asthma attack frequent episode was reported. Asthma clinical course show no significant improvement when other predisposing factor is not well-controlled and a child’s growth and development may be affected. Improving condition of the patient can be created with the help of loving and caring way of nurturing from the parents and supportive peer group. Therefore, continuous and consistent monitoring is required because prognosis of asthma is generally good when regularly and properly controlled.
Abstract: Improving the performance of the QCL through block diagram as well as mathematical models is the main scope of this paper. In order to enhance the performance of the underlined device, the mathematical model parameters are used in a reliable manner in such a way that the optimum behavior was achieved. These parameters play the central role in specifying the optical characteristics of the considered laser source. Moreover, it is important to have a large amount of radiated power, where increasing the amount of radiated power represents the main hopping process that can be predicted from the behavior of quantum laser devices. It was found that there is a good agreement between the calculated values from our mathematical model and those obtained with VisSim and experimental results. These demonstrate the strength of mplementation of both mathematical and block diagram models.
Abstract: Given a simple connected unweighted undirected graph G = (V (G), E(G)) with |V (G)| = n and |E(G)| = m, we present a new algorithm for the all-pairs shortest-path (APSP) problem. The running time of our algorithm is in O(n2 log n). This bound is an improvement over previous best known O(n2.376) time bound of Raimund Seidel (1995) for general graphs. The algorithm presented does not rely on fast matrix multiplication. Our algorithm with slight modifications, enables us to compute the APSP problem for unweighted directed graph in time O(n2 log n), improving a previous best known O(n2.575) time bound of Uri Zwick (2002).
Abstract: Revolutions Applications such as telecommunications, hands-free communications, recording, etc. which need at least one microphone, the signal is usually infected by noise and echo. The important application is the speech enhancement, which is done to remove suppressed noises and echoes taken by a microphone, beside preferred speech. Accordingly, the microphone signal has to be cleaned using digital signal processing DSP tools before it is played out, transmitted, or stored. Engineers have so far tried different approaches to improving the speech by get back the desired speech signal from the noisy observations. Especially Mobile communication, so in this paper will do reconstruction of the speech signal, observed in additive background noise, using the Kalman filter technique to estimate the parameters of the Autoregressive Process (AR) in the state space model and the output speech signal obtained by the MATLAB. The accurate estimation by Kalman filter on speech would enhance and reduce the noise then compare and discuss the results between actual values and estimated values which produce the reconstructed signals.
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: The purpose of this study is to suggest direction for
future study of the energy-IT industry that will be used for framework to increase IT utilization in the energy industry. Recently, Green IT is a becoming global issue because of global environmental pollution.
Also, IT roles in energy industry are becoming more important.
However, the related studies were IT industry oriented that is not sufficient to make plan for Green energy. Therefore, after analyzing
existing studies related to Green energy and Green IT,
re-categorization for Green energy-IT industry was suggested. Direction of framework is based on energy industry that enable to link between energy and IT. The results of this study suggest comprehensive insight to Green energy-IT industry. Thus it is able to
provide useful implications and guidelines to increase IT utilization in the energy industry.
Abstract: According to the Auckland climate, building passive
design more focus on improving winter indoor thermal and health
conditions. Based on field study data of indoor air temperature and
relative humidity close to ceiling and floor of an insulated Auckland
townhouse with and without a whole home mechanical ventilation
system, this study is to analysis variation of indoor microclimate data
of an Auckland townhouse using or not using the mechanical
ventilation system to evaluate winter indoor thermal and health
conditions for the future house design with a mechanical ventilation
system.
Abstract: Microarray data profiles gene expression on a whole
genome scale, therefore, it provides a good way to study associations
between gene expression and occurrence or progression of cancer.
More and more researchers realized that microarray data is helpful
to predict cancer sample. However, the high dimension of gene
expressions is much larger than the sample size, which makes this
task very difficult. Therefore, how to identify the significant genes
causing cancer becomes emergency and also a hot and hard research
topic. Many feature selection algorithms have been proposed in
the past focusing on improving cancer predictive accuracy at the
expense of ignoring the correlations between the features. In this
work, a novel framework (named by SGS) is presented for stable gene
selection and efficient cancer prediction . The proposed framework
first performs clustering algorithm to find the gene groups where
genes in each group have higher correlation coefficient, and then
selects the significant genes in each group with Bayesian Lasso and
important gene groups with group Lasso, and finally builds prediction
model based on the shrinkage gene space with efficient classification
algorithm (such as, SVM, 1NN, Regression and etc.). Experiment
results on real world data show that the proposed framework often
outperforms the existing feature selection and prediction methods,
say SAM, IG and Lasso-type prediction model.
Abstract: The paper presents an investigation in to the effect of neural network predictive control of UPFC on the transient stability performance of a multimachine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers, and an improved damping of the power oscillations as compared to the conventional PI controller.
Abstract: Laser beam forming is a novel technique developed for the joining of metallic components. In this study, an overview of the laser beam forming process, areas of application, the basic mechanisms of the laser beam forming process, some recent research
studies and the need to focus more research effort on improving the
laser-material interaction of laser beam forming of titanium and its
alloys are presented.
Abstract: Co-integration models the long-term, equilibrium relationship of two or more related financial variables. Even if cointegration is found, in the short run, there may be deviations from the long run equilibrium relationship. The aim of this work is to forecast these deviations using neural networks and create a trading strategy based on them. A case study is used: co-integration residuals from Australian Bank Bill futures are forecast and traded using various exogenous input variables combined with neural networks. The choice of the optimal exogenous input variables chosen for each neural network, undertaken in previous work [1], is validated by comparing the forecasts and corresponding profitability of each, using a trading strategy.
Abstract: In general, small-scale vegetables farmers experience
problems in improving the safety and quality of vegetables supplied
to high-class consumers in modern retailers. They also lack of
information to access market. The farmers group and/or cooperative
(FGC) should be able to assist its members by providing training in
handling and packing vegetables and enhancing marketing
capabilities to sell commodities to the modern retailers. This study
proposes an agri-food supply chain (ASC) model that involves the
corporate social responsibility (CSR) activities to cultivate the
capabilities of farmers to access market. Multi period ASC model is
formulated as Weighted Goal Programming (WGP) to analyze the
impacts of CSR programs to empower the FGCs in managing the
small-scale vegetables farmers. The results show that the proposed
model can be used to determine the priority of programs in order to
maximize the four goals to be achieved in the CSR programs.
Abstract: The introduction of sowing technologies into minimum- or no-tillage soil has a number of economical and environmental virtues, such as improving soil properties, decreasing soil erosion and degradation, and saving working time and fuel. However, the main disadvantage of these technologies is that plant residues on the soil surface reduce the quality of the planted crop seeds, thus requiring plant residues to be removed or cut. This paper presents a analysis of disc coulter parameters and an experimental investigation of cutting spring barley straw containing various amounts of moisture with different disc coulters (smooth and notched).
Abstract: In this paper we use exponential particle swarm
optimization (EPSO) to cluster data. Then we compare between
(EPSO) clustering algorithm which depends on exponential variation
for the inertia weight and particle swarm optimization (PSO)
clustering algorithm which depends on linear inertia weight. This
comparison is evaluated on five data sets. The experimental results
show that EPSO clustering algorithm increases the possibility to find
the optimal positions as it decrease the number of failure. Also show
that (EPSO) clustering algorithm has a smaller quantization error
than (PSO) clustering algorithm, i.e. (EPSO) clustering algorithm
more accurate than (PSO) clustering algorithm.
Abstract: The use of radar in Quantitative Precipitation Estimation (QPE) for radar-rainfall measurement is significantly beneficial. Radar has advantages in terms of high spatial and temporal condition in rainfall measurement and also forecasting. In Malaysia, radar application in QPE is still new and needs to be explored. This paper focuses on the Z/R derivation works of radarrainfall estimation based on rainfall classification. The works developed new Z/R relationships for Klang River Basin in Selangor area for three different general classes of rain events, namely low (10mm/hr, 30mm/hr) and also on more specific rain types during monsoon seasons. Looking at the high potential of Doppler radar in QPE, the newly formulated Z/R equations will be useful in improving the measurement of rainfall for any hydrological application, especially for flood forecasting.
Abstract: Large scale systems such as computational Grid is
a distributed computing infrastructure that can provide globally
available network resources. The evolution of information processing
systems in Data Grid is characterized by a strong decentralization of
data in several fields whose objective is to ensure the availability and
the reliability of the data in the reason to provide a fault tolerance
and scalability, which cannot be possible only with the use of the
techniques of replication. Unfortunately the use of these techniques
has a height cost, because it is necessary to maintain consistency
between the distributed data. Nevertheless, to agree to live with
certain imperfections can improve the performance of the system by
improving competition. In this paper, we propose a multi-layer protocol
combining the pessimistic and optimistic approaches conceived
for the data consistency maintenance in large scale systems. Our
approach is based on a hierarchical representation model with tree
layers, whose objective is with double vocation, because it initially
makes it possible to reduce response times compared to completely
pessimistic approach and it the second time to improve the quality
of service compared to an optimistic approach.
Abstract: In developing a text-to-speech system, it is well
known that the accuracy of information extracted from a text is
crucial to produce high quality synthesized speech. In this paper, a
new scheme for converting text into its equivalent phonetic spelling
is introduced and developed. This method is applicable to many
applications in text to speech converting systems and has many
advantages over other methods. The proposed method can also
complement the other methods with a purpose of improving their
performance. The proposed method is a probabilistic model and is
based on Smooth Ergodic Hidden Markov Model. This model can be
considered as an extension to HMM. The proposed method is applied
to Persian language and its accuracy in converting text to speech
phonetics is evaluated using simulations.
Abstract: This paper fist examines three set of bivariate cointegrations between any two of current accounts, stock markets, and currency exchange markets in ten Asian countries. Furthermore, we examined the effect of country characters on this bivariate cointegration. Our findings suggest that for three sets of cointegration test, each sample country at least exists one cointegration. India consistently exhibited a bi-directional causal relationship between any two of three indicators. Unlike Pan et al. (2007) and Phylaktis and Ravazzolo (2005), we found that such cointegration is influenced by three characteristics: capital control; flexibility in foreign exchange rates; and the ratio of trade to GDP. These characteristics are the result of liberalization in each Asian country. This implies that liberalization policies are effective on improving the cointegration between any two of financial markets and current account for ten Asian countries.