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The method of compressive sensing is applied to moving source data created by simulation to estimate the mode wavenumbers, mode depth functions and mode amplitudes without any environmental acoustic information, such as sound speed profile or bottom properties. The method needs in principle only data covering a short range span and is thus applicable to a range-dependent environment to estimate the...
Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection across millions of metrics. Classical time series models are often used in conjunction...
Model-based software estimation uses algorithms and past project data to make predictions for new projects. This paper presents a comparative assessment of four modeling approaches, including the original COCOMO, COCOMO calibration, k-Nearest Neighbors, and a combination of COCOMO calibration and k-Nearest Neighbors. Our results indicate that using kNN to select the nearest projects and calibrating...
Randomized experiments have been critical tools of decision making for decades. However, subjects can show significant heterogeneity in response to treatments in many important applications. Therefore it is not enough to simply know which treatment is optimal for the entire population. What we need is a model that correctly customize treatment assignment base on subject characteristics. The problem...
This paper proposes a Model predictive control (MPC) for linear induction motor drive. Model Predictive Control (MPC) is a strong method for controlling drives of the linear induction motor. In comparison with the Direct Force Control (DFC), MPC is more precise and more effective in the selection of the voltage vector. The results show that this control method has a better performance in comparison...
Aiming at the integrated navigation system with model errors, the model errors is assumed as a process noise for Gaussian white noise to process in Kalman filtering, thus causing larger state estimation error of nonlinear filtering system and even divergent. This paper presents a nonlinear model predictive particle filtering method considers the model error of real-time estimation, and then corrects...
Total number of failures of a software system can help practitioners to have a better understanding of the software quality. In this paper, we propose a model to predict the total number of software failures in a software system by analyzing the failure data from testing using models based on Zipf's law together with the information on code coverage. Failure data and code coverage are combined in...
Objective markers obtained from acoustic analysis of speech are of great importance for clinical evaluation of voice disorders because they are non-invasive and provide a severity index of the disorder which allows clinicians to monitor the progress of patients and documents quantitatively the degree of perceived hoarseness. The object of the present study is to introduce a fractional order long-term...
Flapping Wings MAV (FWMAV) is the result of UAV technology development with the mechanism of flapping wings and with the dimensions of the object as small as possible. Modeling of FWMAV dynamics is needed to predict future conditions based on information from current conditions and designed control actions. Modeling of the FWMAV system is done by system identification, based on measurable data from...
It is shown that the excessive inhalation of PM (Particulate Matter) 2.5 will seriously affect the health of human. Many countries have deployed various detectors for air pollution in order to report concentration of PM2.5 to show how much seriousness of air pollution is. But, what more important is how much PM 2.5 has been inhaled by people anytime and anywhere. Therefore, in this paper, we propose...
To estimate the detection efficiency of a ground based lightning location system, typically the ground based locations are compared to those seen by satellite. This works well because the detection efficiency of the satellite is relatively uniform with space, and reasonably good. However, this technique does not work for the 2015 or 2016 calendar years because the LIS satellite used is no longer in...
To predict the vibration displacement distributed on a vibrating surface, an r order Non-equidistant Grey Model (r-NGM) is proposed in this paper. This model is built by accumulating the initial discrete non-equidistant vibration displacement set with the r order Accumulated Generating Operation (r-AGO). The r-NGM is applied to a vibrating surface of a shaker to displacement prediction. The experimental...
With the increase of state dimension, the calculation of UKF algorithm increases rapidly, and UKF is more sensitive to model error, and it is not suitable for the system model with noise as non-Gaussian distribution. Aiming at this problem, this paper proposes a robust model predictive Unscented Kalman filter based on the study of robust estimation, model predictive filtering and UKF. The algorithm...
In Infrastructure as a Service clouds, customers lease virtual resources (e.g., CPU, memory, network) offered by cloud providers, paying for the allocated capacity of resources, regardless of their effective use. In this context, it is in the interest of the customers to reserve resources with sufficient capacity so that their applications achieve good performance while, at the same time, minimizing...
In this paper, anomaly symptom detection using ensemble prediction based on newly developed weighting method is presented for time series. This weighting method is characterized that weights are determined in proportion to the indices defined by the prediction errors in a certain time period in the past. Next, alarm prediction based on this prediction method is proposed. Prediction accuracy is considered...
The article deals with the constructing of the decision-making system using the modern software which supports the automation of reporting and management. For this purpose we will implement the procedure of quality estimation and risk-contributing factors forecasting. The system can be used for the formation of the development strategy of the high-technology enterprises.
The interpretability of prediction mechanisms with respect to the underlying prediction problem is often unclear. While several studies have focused on developing prediction models with meaningful parameters, the causal relationships between the predictors and the actual prediction have not been considered. Here, we connect the underlying causal structure of a data generation process and the causal...
The paper proposed prediction model to study dengue occurrence in Malaysia, focusing on a region of Petaling district, in the state of Selangor. A number of different linear regression models were compared using model orders of lag time, and best model is selected using Akaike Information Criterion (AIC) value. First, dengue estimation models were built for Petaling district using weather variables...
Interest in risk measurement for high-frequency data has increased since the volume of high-frequency trading stepped up over the two last decades. This paper proposes a multimodal extension of the Exponential Power Distribution (EPD), called the Multimodal Asymmetric Exponential Power Distribution (MAEPD). We derive moments and we propose a convenient stochastic representation of the MAEPD. We establish...
Enterprise Resource Planning (ERP) systems are large scale integrated systems covering most of the business processes of an enterprise. ERP projects differ from software projects with customization, modification, integration and data conversion phases. Most of the time effort and time estimations are performed in an ad-hoc fashion in ERP projects and as a result they frequently suffer from time and...
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