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Total Cost of Ownership (TCO) is a comprehensive tool for cost estimation, provisioning, and decision making in a data center. The goal of this paper is to introduce an accurate yet simple model of TCO for data centers. TCO-estimation helps to clear the cost trade-offs, highlights the most impactful parameters on TCO in a datacenter which helps us to focus on research and development efforts to optimize...
The term structure of interest rate is an important foundation for studies such as asset pricing and risk management. In this paper, the sensitivity of parameters in the NSS model of the yield curve is studied with the help of genetic algorithm, and the impact of the relevant parameter setting on the fitting precision of the model is also discussed. Based on the positive analysis of Chinese Treasury...
In the electronic system manufacturing process, the board-level functional test is recognized as the most significant step to prevent defective products from entering the market. In recent years, machine learning and data mining have proven to be efficient techniques in determining root cause from the problematic functional test result, especially when the integrated circuits (IC) are becoming increasingly...
System condition is an important characteristic in the stage of operation and maintenance during a life cycle. The system condition in respective time periods usually correlates to system time deterioration. Since the degradation may lead to both soft and hard failure, reliability characteristics might be needed to describe each type of such failure. We concentrate on selected oil characteristics...
The accurate estimation of helicopter component loads is an important goal for ensuring safe operation as well as for life cycle management and life extension efforts. In this research, the use of computational intelligence, neural network, and machine learning techniques is explored to estimate helicopter component loads and their fatigue life, in particular the main rotor yoke load of the CH-146...
Spatio-temporal data is intrinsically high dimensional, so unsupervised modeling is only feasible if we can exploit structure in the process. When the dynamics are local in both space and time, this structure can be exploited by splitting the global field into many lower-dimensional “light cones”. We review light cone decompositions for predictive state reconstruction, introducing three simple light...
The so-called asymptotic theory of Ljung (1985) gives an error description of identified black-box model in the frequency domain. Based on the theory the asymptotic method of identification has been developed and applied successively in industrial MPC control and PID tuning. In this work, new results that extend the asymptotic theory and method will be presented: (1) an asymptotically globally convergent...
In this paper two Soft Sensors, for the estimation of pollutants in the output flow of a Sour Water Stripping plant, are described. The plant operates in a large refinery in Italy. The Soft Sensors have been implemented by non linear data-driven approaches, by using neural networks. In order to face the issue of different sampling intervals of process and quality variables, a deep learning approach...
This paper extends the idea of Universum learning to regression problems. We propose new Universum-SVM formulation for regression problems that incorporates a priori knowledge in the form of additional data samples. These additional data samples, or Universum samples, belong to the same application domain as the training samples, but they follow a different distribution. Several empirical comparisons...
Nearly all existing estimations of the central subspace in regression take the frequentist approach. However, when the predictors fall naturally into a number of groups, these frequentist methods treat all predictors indiscriminately and can result in loss of the group-specific relation between the response and the predictors. In this article, we propose a Bayesian solution for dimension reduction...
We consider the estimation of the state transition matrix in vector autoregressive models when the time sequence data is limited but nonsequence steady-state data is abundant. To leverage both sources of data, we formulate the problem as the least-squares minimization regularized by a Lyapunov penalty. Explicit cardinality or rank constraints are imposed to reduce the complexity of the model. The...
We develop an abstract approximation and convergence framework for the estimation of random parameters in infinite dimensional dynamical systems governed by regularly dissipative operators in a Gelfand triple setting. Our results are motivated by a problem involving the development of a data analysis system for a transdermal alcohol biosensor. Our approach combines some recent results for random abstract...
In this paper, we develop an autocovariance-based method for estimating plant-model mismatch in unconstrained model predictive control systems using discrete-time, linear time-invariant state space models. We rely on knowledge of the process noise model, together with other reasonable assumptions, to derive an explicit expression for the autocovariance matrix of the closed-loop outputs. Then, we prove...
The demand for indoor localization services has led to the development of techniques that create a Fingerprint Map (FM) of sensor signals (e.g., magnetic, Wi-Fi, bluetooth) at designated positions in an indoor space and then use FM as a reference for subsequent localization tasks. With such an approach, it is crucial to assess the quality of the FM before deployment, in a manner disregarding data...
Estimation of precipitation is necessary for optimum utilization of water resources and their appropriate management. The economy of India being heavily dependent on agriculture becomes vulnerable due to lack of adequate irrigation facilities. In this paper, a multiple linear regression model has been developed to reckon annual precipitation over Cuttack district, Odisha, India. The model forecasts...
Information filtering model for personalized recommendation has dramatically promote the development of recommendation technology. In various kinds of information filtering models, mass diffusion model has rise fruitful researches. Although traditional work assumes in heterogeneous bipartite network consideration of initial resource from the collected object can much enhance the recommendation accuracy,...
Automatic and remote reading systems of energy meters are spreading more each day. However, electricity meter data sometimes bear missing elements and outliers, due to communication faults, device faults, or energy fraud. We set up mathematical models in order to be able to interpolate missing data and detect fraud. In this work, two models are developed and compared in terms of performance, using...
We present a deterministic mathematical model that describes the transmission dynamics of intramammary infections (IMI) caused by Corynebacterium spp. (Corynebacterium species) in lactating dairy herds. Longitudinal, quantitative, dynamic models are likely to be valuable for predicting infections outbreak risk, quantify the effectiveness of response tactics and performing response planning. Previous...
In software engineering, project scheduling is an essential factor that determines success of projects. Success is influenced by various project scheduling estimates, such as accurate estimates of project's duration and budget. These estimates highly depend on uncertainties related to commonly occurring unpredictable events during a project's duration. Furthermore, budget and duration estimates depend...
Traditional data stream classification techniques assume that the stream of data is generated from a single non-stationary process. On the contrary, a recently introduced problem setting, referred to as Multistream Classification involves two independent non-stationary data generating processes. One of them is the source stream that continuously generates labeled data instances. The other one is the...
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