Abstract: This study presents a cost-effective approach for rapid
fabricating modeling platforms utilized in fused deposition modeling
system. A small-batch production of modeling platforms about 20
pieces can be obtained economically through silicone rubber mold
using vacuum casting without applying the plastic injection molding.
The air venting systems is crucial for fabricating modeling platform
using vacuum casting. Modeling platforms fabricated can be used for
building rapid prototyping model after sandblasting. This study offers
industrial value because it has both time-effectiveness and
cost-effectiveness.
Abstract: In this paper, we study the rainfall using a time series
for weather stations in Nakhon Ratchasima province in Thailand by
various statistical methods to enable us to analyse the behaviour of
rainfall in the study areas. Time-series analysis is an important tool in
modelling and forecasting rainfall. The ARIMA and Holt-Winter
models were built on the basis of exponential smoothing. All the
models proved to be adequate. Therefore it is possible to give
information that can help decision makers establish strategies for the
proper planning of agriculture, drainage systems and other water
resource applications in Nakhon Ratchasima province. We obtained
the best performance from forecasting with the ARIMA
Model(1,0,1)(1,0,1)12.
Abstract: We introduced an all-optical multicasting
characteristics with wavelength conversion based on a novel
all-optical triode using negative feedback semiconductor optical
amplifier. This study was demonstrated with a transfer speed of 10
Gb/s to a non-return zero 231-1 pseudorandom bit sequence system.
This multi-wavelength converter device can simultaneously provide
three channels of output signal with the support of non-inverted and
inverted conversion. We studied that an all-optical multicasting and
wavelength conversion accomplishing cross gain modulation is
effective in a semiconductor optical amplifier which is effective to
provide an inverted conversion thus negative feedback. The
relationship of received power of back to back signal and output
signals with wavelength 1535 nm, 1540 nm, 1545 nm, 1550 nm, and
1555 nm with bit error rate was investigated. It was reported that the
output signal wavelengths were successfully converted and modulated
with a power penalty of less than 8.7 dB, which the highest is 8.6 dB
while the lowest is 4.4 dB. It was proved that all-optical multicasting
and wavelength conversion using an optical triode with a negative
feedback by three channels at the same time at a speed of 10 Gb/s is a
promising device for the new wavelength conversion technology.
Abstract: Fuzzy systems have been successfully used for
exchange rate forecasting. However, fuzzy system is very confusing
and complex to be designed by an expert, as there is a large set of
parameters (fuzzy knowledge base) that must be selected, it is not a
simple task to select the appropriate fuzzy knowledge base for an
exchange rate forecasting. The researchers often look the effect of
fuzzy knowledge base on the performances of fuzzy system
forecasting. This paper proposes a genetic fuzzy predictor to forecast
the future value of daily US Dollar/Euro exchange rate time’s series.
A range of methodologies based on a set of fuzzy predictor’s which
allow the forecasting of the same time series, but with a different
fuzzy partition. Each fuzzy predictor is built from two stages, where
each stage is performed by a real genetic algorithm.
Abstract: Load Forecasting plays a key role in making today's
and future's Smart Energy Grids sustainable and reliable. Accurate
power consumption prediction allows utilities to organize in advance
their resources or to execute Demand Response strategies more
effectively, which enables several features such as higher
sustainability, better quality of service, and affordable electricity
tariffs. It is easy yet effective to apply Load Forecasting at larger
geographic scale, i.e. Smart Micro Grids, wherein the lower available
grid flexibility makes accurate prediction more critical in Demand
Response applications. This paper analyses the application of
short-term load forecasting in a concrete scenario, proposed within the
EU-funded GreenCom project, which collect load data from single
loads and households belonging to a Smart Micro Grid. Three
short-term load forecasting techniques, i.e. linear regression, artificial
neural networks, and radial basis function network, are considered,
compared, and evaluated through absolute forecast errors and training
time. The influence of weather conditions in Load Forecasting is also
evaluated. A new definition of Gain is introduced in this paper, which
innovatively serves as an indicator of short-term prediction
capabilities of time spam consistency. Two models, 24- and
1-hour-ahead forecasting, are built to comprehensively compare these
three techniques.
Abstract: In the present study, M2 high speed steels were
fabricated by using electro-slag rapid remelting process. Carbide
structure was analysed and the fracture toughness and hardness were
also measured after austenitization treatment at 1190 and 1210oC
followed by tempering treatment at 535oC for billets with various
diameters from 16 to 60 mm. Electro-slag rapid remelting (ESRR)
process is an advanced ESR process combined by continuous casting
and successfully employed in this study to fabricate a sound M2 high
speed ingot. Three other kinds of commercial M2 high speed steels,
produced by traditional method, were also analysed for comparison.
Distribution and structure of eutectic carbides of the ESRR billet were
found to be comparable to those of commercial alloy and so was the
fracture toughness.
Abstract: Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. Interval Type 2 Fuzzy Logic System (IT2 FLS), with additional degrees of freedom, gives an excellent tool for handling uncertainties and it improved the prediction accuracy. The training data used in this study covers the period from January 1, 2012 to February 1, 2012 for winter season and the period from July 1, 2012 to August 1, 2012 for summer season. The actual load forecasting period starts from January 22, till 28, 2012 for winter model and from July 22 till 28, 2012 for summer model. The real data for Iraqi power system which belongs to the Ministry of Electricity.
Abstract: Flanges are widely used for connecting valves, pipes and other industrial devices such as gearboxes. Method of producing a flange has a considerable impact on the manner of their involvement with the industrial engines and gearboxes. By Using die casting instead of sand casting and machining for manufacturing flanges, production speed and dimensional accuracy of the parts increases. Also, in die casting, obtained dimensions are close to final dimensions and hence the need for machining flanges after die casting process decreases which makes a significant savings in raw materials and improves the mechanical properties of flanges. In this paper, a typical die of an industrial helical gearbox flange (size ISO 50) was designed and die casting process for producing this type of flange was simulated using ProCAST software. The results of simulation were used for optimizing die design. Finally, using the results of the analysis, optimized die was built.
Abstract: A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method.
Abstract: This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity.
The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.
Abstract: Technical analysis comprised by various technical indicators is a holistic way of representing price movement of stocks in the market. Various forms of indicators have evolved from the primitive ones in the past decades. There have been many attempts to introduce volume as a major determinant to determine strong patterns in market forecasting. The law of demand defines the relationship between the volume and price. Most of the traders are familiar with the volume game. Including the time dimension to the law of demand provides a different visualization to the theory. While attempting the same, it was found that there are different thresholds in the market for different companies. These thresholds have a significant influence on the price. This article is an attempt in determining the thresholds for companies using the three dimensional graphs for optimizing the portfolios. It also emphasizes on the magnitude of importance of volumes as a key factor for determining of predicting strong price movements, bullish and bearish markets. It uses a comprehensive data set of major companies which form a major chunk of the Indian automotive sector and are thus used as an illustration.
Abstract: In the frame of this work, we present an optical multicasting approach based on optical code-words. Our approach associates, in the edge node, an optical code-word to a group multicast address. In the core node, a set of tunable decoders are used to send a traffic data to multiple destinations based on the received code-word. The use of code-words, which correspond to the combination of an input port and a set of output ports, allows the implementation of an optical switching matrix. At the reception of a burst, it will be delayed in an optical memory. And, the received optical code-word is split to a set of tunable optical decoders. When it matches a configured code-word, the delayed burst is switched to a set of output ports.
Abstract: SiC reinforced Aluminum samples were produced by stir casting of liquid AA1200 aluminum alloy at 600-650ºC casting temperature. 83µm SiC particles were rinsed in 10g/l, 20g/l and 30g/l molar concentration of Sncl2 through cleaning times of 0, 60, 120, and 180 minutes. Some cast samples were tested for mechanical properties and some were subjected to heat treatment before testing. The SnCl2 rinsed SiC reinforced aluminum exhibited higher yield strength, hardness, stiffness and elongation which increases with cleaning concentration and time up to 120 minutes, compared to composite with untreated SiC. However, the impact energy resistance decreases with cleaning concentration and time. The improved properties were attributed to good wettability and mechanical adhesion at the fiber-matrix interface. Quenching and annealing the composite samples further improve the tensile/yield strengths, elongation, stiffness, hardness similar to those of the as-cast samples.
Abstract: In the frame of this work, we present an optical multicasting approach based on optical code-words. Our approach associates, in the edge node, an optical code-word to a group multicast address. In the core node, a set of tunable decoders are used to send a traffic data to multiple destinations based on the received code-word. The use of code-words, which correspond to the combination of an input port and a set of output ports, allows the implementation of an optical switching matrix. At the reception of a burst, it will be delayed in an optical memory. And, the received optical code-word is split to a set of tunable optical decoders. When it matches a configured code-word, the delayed burst is switched to a set of output ports.
Abstract: This paper deals with a stabilization problem for
multi-agent systems, when all agents in a multi-agent system receive
the same broadcasting control signal and the controller can measure
not each agent output but the sum of all agent outputs. It is
analytically shown that when the sum of all agent outputs is bounded
with a certain broadcasting controller for a given reference, each agent
output is separately bounded: stabilization of the sum of agent outputs
always results in the stability of every agent output. A numerical
example is presented to illustrate our theoretic findings in this paper.
Abstract: Tensile specimens of nonflammable AZ91D Mg alloy were fabricated in this study via cold chamber die-casting process. Dimensions of tensile specimens were 25mm in length, 4mm in width, and 0.8 or 3.0mm in thickness. Microstructure observation was conducted before and after tensile tests at room temperature. In the die casting process, various injection distances from 150 to 260mm were employed to obtain optimum process conditions. Distribution of Al12Mg17 phase was the key factor to determine the mechanical properties of die-cast Mg alloy. Specimens with 3mm of thickness showed superior mechanical properties to those with 0.8mm of thickness. Closed networking of Al12Mg17 phase along grain boundary was found to be detrimental to mechanical properties of die-cast Mg alloy.
Abstract: Despite the availability of natural disaster related time series data for last 110 years, there is no forecasting tool available to humanitarian relief organizations to determine forecasts for emergency logistics planning. This study develops a forecasting tool based on identifying probability of disaster for each country in the world by using decision tree modeling. Further, the determination of aggregate forecasts leads to efficient pre-disaster planning. Based on the research findings, the relief agencies can optimize the various resources allocation in emergency logistics planning.
Abstract: Due to a lot of limited conditions, a large box type floating structure is inevitably constructed by connecting many pontoons. When a floating structure is made with concrete, concrete shear key with saw-teeth shape is often used to carry shear force. Match casting for the shear key and precise construction on a sea are very important for making separated two pontoons as one body but those are not easy work and may increase construction time and cost. To solve this problem, one-way shear key is studied in this paper for a connected part where there is some difference between upward and downward shear force. It has only one inclined plane and can resist shear force in one direction. Big shear force is resisted by concrete which forms an inclined plane and small shear force is resisted by steel bar. This system can reduce manufacturing cost of individual pontoon and construction time and cost for constructing a floating structure on a sea. In this paper, the feasibility study about one-way shear key system is performed by comparing with design example.
Abstract: This study investigates the reliability of management earnings forecasts with reference to these two ingredients: verifiability and neutrality. Specifically, we examine the biasedness (or accuracy) of management earnings forecasts and company specific characteristics that can be associated with accuracy. Based on sample of 102 IPO prospectuses published for admission on NYSE Euronext Paris from 2002 to 2010, we found that these forecasts are on average optimistic and two of the five test variables, earnings variability and financial leverage are significant in explaining ex post bias. Acknowledging the possibility that the bias is the result of the managers’ forecasting behavior, we then examine whether managers decide to under-predict, over-predict or forecast accurately for self-serving purposes. Explicitly, we examine the role of financial distress, operating performance, ownership by insiders and the economy state in influencing managers’ forecasting preferences. We find that managers of distressed firms seem to over-predict future earnings. We also find that when managers are given more stock options, they tend to under-predict future earnings. Finally, we conclude that the management earnings forecasts are affected by an intentional bias due to managers’ forecasting preferences.
Abstract: Hydrological modelling plays a crucial role in the planning and management of water resources, most especially in water stressed regions where the need to effectively manage the available water resources is of critical importance. However, due to the complex, nonlinear and dynamic behaviour of hydro-climatic interactions, achieving reliable modelling of water resource systems and accurate projection of hydrological parameters are extremely challenging. Although a significant number of modelling techniques (process-based and data-driven) have been developed and adopted in that regard, the field of hydrological modelling is still considered as one that has sluggishly progressed over the past decades. This is majorly as a result of the identification of some degree of uncertainty in the methodologies and results of techniques adopted. In recent times, evolutionary computation (EC) techniques have been developed and introduced in response to the search for efficient and reliable means of providing accurate solutions to hydrological related problems. This paper presents a comprehensive review of the underlying principles, methodological needs and applications of a promising evolutionary computation modelling technique – genetic programming (GP). It examines the specific characteristics of the technique which makes it suitable to solving hydrological modelling problems. It discusses the opportunities inherent in the application of GP in water related-studies such as rainfall estimation, rainfall-runoff modelling, streamflow forecasting, sediment transport modelling, water quality modelling and groundwater modelling among others. Furthermore, the means by which such opportunities could be harnessed in the near future are discussed. In all, a case for total embracement of GP and its variants in hydrological modelling studies is made so as to put in place strategies that would translate into achieving meaningful progress as it relates to modelling of water resource systems, and also positively influence decision-making by relevant stakeholders.