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With the economy developing, effective financial distress prediction methods of artificial intelligence have got more and more attention of the academia. Concept drift in a data flow is another hot research topic. This paper firstly introduces several kinds of existing batch weighted methods for financial distress prediction modeling, and analyzes their shortages. To find a solution to deal with them,...
Speech coders based on the linear prediction model are widely in use today. This paper describes the algorithms of low bit-rate vocoders, viz. Code-Excited Linear Prediction (CELP) and Mixed Excitation Linear Prediction (MELP) and their performance for Indian dialects. A Linux platform has been used for execution of the vocoders. Mean Opinion Score testing has been performed with speech samples of...
The DC arc hazard is a great concern to industry. Quantitative arc-hazard assessments are performed on DC systems to determine a nearby worker's potential incident-energy exposure during an arcing event. Four viable DC assessment methods are reviewed in this paper. The most widely used model for predicting DC incident energy is based on Lee's theoretical arc model; the electrical arc power is determined...
An update algorithm of least squares support vector machine (LSSVM) is proposed to tackle the time-varying characteristics of the real industrial process. The process variations are concluded to two categories, and accordingly the samples adding and samples replacement are proposed to update the initial LSSVM model incrementally. Then the LSSVM model with proposed updating measures is applied in the...
Predicting the future speed of the ego-vehicle is a necessary component of many Intelligent Transportation Systems (ITS) applications, in particular for safety and energy management systems. In the last four decades many parametric speed prediction models have been proposed, the most advanced ones being developed for use in traffic simulators. More recently non-parametric approaches have been applied...
Small-scale Unmanned Aerial Vehicles (UAVs) are gaining the attention of researchers around the world, due to their versatility and performance in both indoor and outdoor applications. The whole project aims to specify and implement a symbiotic simulation platform to assist the design, development and testing of UAVs that use a quad-rotor configuration. Additionally, in this paper we firstly discuss...
Abstract-Recently, concept drift has become an important issue while analyzing non-stationary distribution data in data mining. For example, data streams carry a characteristic that data vary by time, and there is probably concept drift in this type of data. Concept drifts can be categorized into sudden and gradual concept drifts in brief. Most of research only can solve one type of concept drift...
The 2.4m×2.4m wind tunnel is a system with the properties of strong nonlinear, multiple variables, serious coupling, large lagging, time-varying, etc. The complexity of all these phenomena makes the development of suitable dynamic Mach number models based on the aerodynamics laws very difficult. As an alternative, the Ensemble Neural Networks (ENN) model based on the feature subsets is proposed to...
This paper presents a novel alternative “safe” test method for line and load regulation specifications of hysteretic controlled switching power converters. These specifications have not been tested in production before because the conventional measurement techniques lead to huge voltage spikes during switching transients. Those voltage spikes are caused by energy accumulation from parasitic inductances...
As smartphones work their way into mission-critical applications, there is a need to gain knowledge of access network speeds and their variability at different locations. This information is vital to ensuring the efficient transmission of time-sensitive data and can mean the difference between life and death for some patients [1]. However, the decision to adopt smartphone applications (apps) that...
In this paper, an adaptive technique based modeling of the optimal bidding strategies for competitive electricity market is proposed. Here, Artificial Bees Colony (ABC) is an optimization tool, which is used in two phases, the employee bee and the onlooker bee to optimize the bidding parameters. From the optimized parameters the exact solution is predicted by the Cuckoo Search (CS) algorithm, which...
Much software lacks test oracles, which limits automated testing. Metamorphic testing is one proposed method for automating the testing process for programs without test oracles. Unfortunately, finding appropriate metamorphic relations for use in metamorphic testing remains a labor intensive task, which is generally performed by a domain expert or a programmer. We are investigating novel approaches...
Network Management System (NMS) plays an important role in networks to maintain the best performance of a network. It employs variety of tools, applications, and devices in order to support network administrators to monitor and maintain the stability of a network. Fault management is part where the NMS dealing with problems and failures, such as congestion, in the network. Generally, most NMSs use...
Grid-Connected Photovoltaic (GCPV) system is a type of photovoltaic (PV) systems which has been widely used as a renewable-based electricity generation. Nevertheless, the intermittency and fluctuation in weather conditions have caused inconsistent and varying output performance of a GCPV system. This paper presents a Multi-Layer Feedforward Neural Network (MLFNN) model for predicting the AC power...
Workload prediction in cloud system is an important task and it helps in efficient resource allocation by minimizing cost and thus maximizing the profit. In this paper we analyze a large scale production workload trace (version 2) which is recently made publicly available by Google. The main objective of our research is to design and compare different forecasting models. We develop models through...
This paper discusses new implementations of the predictive alternate test strategy that exploit model redundancy in order to improve test confidence. The key idea is to build during the training phase, not only one regression model for each specification as in the classical implementation, but several regression models. This redundancy is then used during the testing phase to identify suspect predictions...
A lattice time series model may be used to estimate the inter-area electromechanical modes of a power system from measured synchrophasor data. The accuracy of these estimates is sensitive to the order of the model. This paper describes a methodology for real-time, order-recursive whiteness testing of the prediction errors. This hypothesis testing methodology may be used in conjunction with the lattice...
Cognitive learning has been applied in various fields for the purpose of discussing human behavior. In this study, we employ learning functions in software reliability growth models (SRGMs) and consider the conscious/unconscious behavior and multiple learning effects in the models simultaneously to discuss the influence of learning effects on work performance in software debugging projects and also...
Our experiences with industrial software development projects have often revealed that requirements change even after their formal approvals. Although the requirements are never stable, proactive identification of potentially changeable or deferrable requirements, and estimation of their impacts early in a project can be useful in minimizing the risks and cost overruns. In practice, the decisions...
Along with the development of electronic and intelligence in the world's stock market advances, the accumulation of the stock data grows larger over time. It is of great concern on the ways to find the hidden rules of information in the mass of data. Given the background above, this paper explores the methods of data mining by using the combination of Decision tree algorithm and Clustering algorithm...
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