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Process monitoring has a central role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. In this paper, a statistical approach that exploit the advantages of multiscale PLS models (MSPLS) and those of a generalized likelihood ratio (GLR) test to better detect anomalies is proposed. Specifically, to consider the multivariate and multi-scale...
Data-trained predictive models see widespread use, but for the most part they are used as black boxes which output a prediction or score. It is therefore hard to acquire a deeper understanding of model behavior, and in particular how different features influence the model prediction. This is important when interpreting the behavior of complex models, or asserting that certain problematic attributes...
Light-weight Self-Compacting Concrete (LWSCC) might be the answer to the increasing construction requirements of slenderer and more heavily reinforced structural elements. However there are limited studies to prove its ability in real construction projects. In conjunction with the traditional methods, artificial intelligent based modeling methods have been applied to simulate the non-linear and complex...
The convergence of public data and statistical modeling has created opportunities for public safety officials to prioritize the deployment of scarce resources on the basis of predicted crime patterns. Current crime prediction methods are trained using observed crime and information describing various criminogenic factors. Researchers have favored global models (e.g., of entire cities) due to a lack...
The alternate test paradigm has been proposed as a low-cost replacement to expensive and time consuming conventional specification tests of analog/radio-frequency (RF) integrated circuits. Feasibility of alternate tests may be compromised if the pertinent models that are used for the prediction of a circuit's performance are of poor accuracy. To construct accurate models across the whole design space,...
Proposed paper describes prepared corpora and the process of training and testing of statistical models of the human-human dialogue interaction. Such dialogue models can be used to model and manage human-human and human-machine dialogue interactions. Two databases of dialogues in Slovak language were prepared to provide data for training and testing. Statistical dialogue models were trained in the...
Recent years have seen a proliferation of complex Advanced Driver Assistance Systems (ADAS), in particular, for use in autonomous cars. These systems consist of sensors and cameras as well as image processing and decision support software components. They are meant to help drivers by providing proper warnings or by preventing dangerous situations. In this paper, we focus on the problem of design time...
“Transfer learning”: is the process of translating quality predictors learned in one data set to another. Transfer learning has been the subject of much recent research. In practice, that research means changing models all the time as transfer learners continually exchange new models to the current project. This paper offers a very simple “bellwether” transfer learner. Given N data sets, we find which...
Seasional Autoregressive Integrated Moving Average (SARIMA) model was built to predict the number of tourists arrived via the Kualanamu International Airport in Medan, Indonesia. The data size was 72 periods, taken from January 2008 to December 2013 for determining models, and 23 periods from January 2014 until November 2015 for testing the model accuracy. Steps of SARIMA involve describing the data...
Even when acoustic tokens vary substantially, they nevertheless can usually be recognized accurately. Two opposing models have been proposed to account for how the speech recognition mechanism works to achieve the perceptual consistency. The abstract model holds that there is a unitary cognitive representation for each phonological category. The speech signal, after having variations filtered out...
Utilizing a language model in a brain-computer-interface-based (BCI-based) speller has been proven helpful in improving the performance of the system. Since it is important to evaluate the effect of the language model on the system, it is necessary to choose the words in a way that they can represent different levels of difficulty based on the language model. In this paper, we will give a brief introduction...
Software defect prediction (SDP) is a most dynamic research area in software engineering. SDP is a process used to predict the deformities in the software. To identifying the defects before the arrival of item or aimed the software improvement, to make software dependable, defect prediction model is utilized. It is always desirable to predict the defects at early stages of life cycle. Hence to predict...
In this work, we develop an artificial neural network model to predict the potential of solar power in Libya. We use multilayered, feed-forward, back-propagation neural networks for the mean monthly solar radiation using the data of 25 cities spread over Libya for the period of 6 years (2010–2015). Meteorological and geographical data (longitude, latitude, and altitude, month, mean sunshine duration,...
This preliminary study investigates feasibility of a running speed based heart rate (HR) prediction. It is basically motivated from the assumption that there is a significant relationship between HR and the running speed. In order to verify the assumption, HR and running speed data from 217 subjects of varying aerobic capabilities were simultaneously collected during an incremental treadmill exercise...
Generally, overflowing and unexpected amount of water level from normal conditions, especially in areas that are usually dry it is called flood. Kelantan River was synonym with flood especially during the months of November to February because of the northeast monsoon season. Nonlinear autoregressive with exogenous input (NARX) is well-known as one of the technique that has the ability to predict...
Flood is defined as an overflow of large amount of water beyond its normal limits. Therefore, it has become threat to people's life and can cause damages to properties. However, in Malaysia, the only existing flood warning system are the alarming system which only notify residents nearby flood location to evacuate only when flood occur. Thus, flood water level prediction is very much needed in order...
Endurance testing of the chassis, components and vehicle axles is a safety issue and therefore an essential part of the vehicle development process. In the automotive industry, durability experiments of vehicle axles are carried out in laboratories using multi-channel durability test rigs. The accurate load data replication of field measured data is important to obtain convincing durability results...
The fraudulent financial statement of a company is becoming serious over the last few years, so, finding a valid forecasting fraudulent financial statement model is an urgent work for academic research and financial practice. The UTilities Additives DIScriminants (UTADIS) classification method is an effective approach to classify some data into different groups. Therefore, based on UTADIS method,...
With much attention paid to global warming and related environment issues, long-term temperature trend forecasting work becomes more attractive than ever before. Multiple sine functions decomposition (MSFD) method is an effective forecasting method, which has been applied to long-term temperature trend forecasting. Generally, the original mode of MFSD method is to decompose a historical temperature...
RNN Encoder-Decoder and attentional mechanism have lately been used to improve neural machine translation (NMT) on bilingual parallel corpus. In this paper, we propose tri-lingual NMT. Based on the Encoder-Decoder and attentional mechanism, we translate source language to target language, meanwhile translate another parallel source language to target language. We provides two approaches called splicing-model...
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