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The increase in renewable energy generators introduced into the electricity grid is putting pressure on its stability and management as predictions of renewable energy sources cannot be accurate or fully controlled. This, with the additional pressure of fluctuations in demand, presents a problem more complex than the current methods of controlling electricity distribution were designed for. A global...
Disassembly is a systematic method for separating a product into its constituent parts. Based on the uncertain feature of disassembly process, combined with the defined probability transmission rules of different constrain nodes, the optimal disassembly sequence is determined by the probabilistic planning method. Simultaneously, a simple example is presented to test the proposed probabilistic planning...
Pattern matching over event streams is well developed. However, with the increasing demand of measurement accuracy, confidence of more complex events sourced from original, continuously arriving events generated from sensor kind electronic devices is becoming more and more been concerned. Actually, some applications such as RFID-based supply chain management and monitoring in health care require data...
An annotated survey of approaches to design optimization based on possibility theory and evidence theory is presented and prominent characteristics are described in this paper, especially addressing epistemic uncertainty for large-scale and complex systems when statistical data is scarce or incomplete. We first analyze the uncertainties encountered in design and the limitations of probabilistic approach...
The study of multi-objective optimization has matured to a level where uncertainty is considered when comparing and evaluating solutions for any given problem. This paper reviews the current techniques that have been proposed to include uncertainty within a multi-objective framework. Probabilistic as well as fuzzy methods are reviewed. A new method to identify sample representative solutions from...
Mechanical components design is subjected to uncertainties in material and geometrical properties, loads and other variables. For reliability optimization design with uncertain parameters, based on the non-probabilistic reliability theory, robust design and optimal design method, the uncertain parameters of mechanical components are expressed by non-probabilistic interval variables, and a non-probabilistic...
A probabilistic support vector machine (PSVM) is proposed for classification of data with uncertainties. Performance of the traditional SVM algorithm is very sensitive to uncertainties. The noises in input space will cause uncertainties of the mapping in feature space. The traditional SVM algorithm may not be effective when uncertainty is large. A new probabilistic optimization is proposed to determine...
Data uncertainty is common in real-world applications due to various causes, including imprecise measurement, network latency, outdated sources and sampling errors. These kinds of uncertainty have to be handled cautiously, or else the mining results could be unreliable or even wrong. In this paper, we propose a new rule-based classification and prediction algorithm called uRule for classifying uncertain...
We consider the downlink of a cellular system in which the base station is equipped with multiple antennas and each user has a single antenna. We study the design of linear precoders with probabilistically-constrained quality of service (QoS) requirements for each user, in scenarios with uncertain channel state information (CSI) at the transmitter. Our goal is to design the precoder so as to minimize...
We propose a probabilistic extension of Allen's interval algebra for managing uncertain temporal relations. Although previous work on various uncertain forms of quantitative and qualitative temporal networks have been proposed in the literature, little has been addressed to the most obvious type of uncertainty, namely the probabilistic one. More precisely, our model adapts the probabilistic constraint...
When optimising receiver operating characteristic (ROC) curves there is an inherent degree of uncertainty associated with the operating point evaluation of a model parameterisation x. This is due to the finite amount of training data used to evaluate the true and false positive rates of x. The uncertainty associated with any particular x can be reduced, but only at the computation cost of evaluating...
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