The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Seabed minerals in form of ferromanganese or polymetallic nodules (PMN) have been reported in the Pacific, Atlantic, and Indian oceans. The distribution of these PMN are influenced by several factors, such as the associated topographic undulations, surface chlorophyll levels, sediment type and thickness, and water depth. Traditionally, determination of the existence and the distribution of PMN involves...
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...
In the big data era, the information about the same object collected from multiple sources is inevitably conflicting. The task of identifying true information (i.e., the truths) among conflicting data is referred to as truth discovery, which incorporates the estimation of source reliability degrees into the aggregation of multi-source data. However, in many real-world applications, large-scale data...
Within the context of road estimation, the present paper addresses the problem of the fusion of several sources with different reliabilities. Thereby, reliability represents a higher-level uncertainty. This problem arises in automated driving and ADAS due to changing environmental conditions, e.g., road type or visibility of lane markings. Thus, we present an online sensor reliability assessment and...
Optical flow estimation remains challenging due to untextured areas, motion boundaries, occlusions, and more. Thus, the estimated flow is not equally reliable across the image. To that end, post-hoc confidence measures have been introduced to assess the per-pixel reliability of the flow. We overcome the artificial separation of optical flow and confidence estimation by introducing a method that jointly...
The work investigates the properties of the solutions derived from the minimum yield principle in problems of constructing optimal in continuous VaR-criterion (CC-VaR) portfolio for an investor with own partial market forecast and own risk preferences function. Fundamental theoretical results are adduced and illustrated by examples of two-sided exponential, equiprobability, and beta distributions...
The paper presents a variation of the systems' synthesis problem setting, where it is proposed to use the values of the entropy potentials of the output parameters as an optimization criterion. The use of such criteria makes it possible to improve the quality of the process dynamics assessment, what creates the prerequisites for the control efficiency improvement. With the reference to this specific...
This article provides an overview of modern methods and approaches to scientific and technological solutions' efficiency assessment. Much attention is paid to the problems of the criteria set formulation and the identification of their optimal structure for solving the stated problem. In the paper, based on practical results, the analysis of the effectiveness of scientific and technical solutions,...
Consistent reporting of Limit of Detection (LOD) and its inherent uncertainty is imperative as the number of sensors for challenging applications continues to rise. Here, we demonstrate a Bayesian approach to LOD estimation, which also provides estimates of LOD uncertainty. By using this method to compare seemingly similar sensors, we demonstrate that LOD uncertainty is at least as important as LOD...
The article considers methods of processing uncertainties in solving dynamic planning problems. Various types of uncertainties are considered, such as stochastic uncertainties, uncertainties in the parameters and structure of models, the uncertainty of the amplitude type and the probabilistic type. Methods for processing data for reducing uncertainties are proposed.
High-intensity focused ultrasound (HIFU) fields can be characterized by specific hydrophones that are able to withstand the large acoustical pressures used in medical applications. Such hydrophones may show a non-flat frequency response due to their being strengthened by protection layers against cavitation damage. Therefore, hydrophone calibration data should be used in deconvolution in order to...
Distributed energy resources (DER) systems introduce uncertainties in the electrical grid that cannot be addressed by classical deterministic methods. Power system analytic tools, such as Load Flow (LF), should be revisited to address such uncertainties. Probabilistic Load Flow (PLF) provides a solution to this problem by handling these uncertainties as random variables. Among the existing sampling...
This paper considers the problem of decentralized, cooperative, and dynamic self-localization in wireless sensor networks. In particular, we are interested in a restrictive but very realistic scenario where few anchors are deployed and each anchor whose location is priori known may only communicate with very few agents (e.g. just one agent) whose location is unknown and to-be-estimated. The lack of...
This paper provides an overview of the (sub) mm wave testing capabilities at the European Space Agency and contributes the efforts of the community in the estimation of measurement uncertainty for antenna testing in the THz domain.
We present a method to model and classify trajectory data that come from surveillance videos. Observations of the locations of moving entities are used to estimate their expected velocity in the scene. Such estimation is performed by a Gaussian process regression that enables to approximate probabilistically the expected velocity of entities given some observed evidence in the scene. Subsequently,...
Cloud service recommendation has become an important technique that helps users decide whether a service satisfies their requirements or not. However, the few existing recommendation systems are not suitable for real world environments and only deal with services hosted in a single cloud, which is simply unrealistic. In addition, a same service may be hosted on more than one cloud and, hence, may...
In this paper a new direct nonparametric estimation of the period and the shape of a periodic component in short duration signals is proposed and evaluated. Classical Fourier Transform (FT) methods lack precision and resolution when the duration of the signal is very short and the signal is noisy. The proposed method is based on the direct description of the problem as a linear inverse problem and...
In this work, we propose a method for finding an optimal, non-uniform, sampling scheme for a general class of signals in which the signal measurements may be non-linear functions of the parameters to be estimated. Formulated as a convex optimization problem reminiscent of the sensor selection problem, the method determines an optimal sampling scheme given a suitable estimation bound on the parameters...
Estimating the direction of arrival (DOA) in sensor arrays is a crucial task in array signal processing systems. This task becomes more difficult when the sensors have gain/phase uncertainty. We have addressed this issue by modeling the problem as a combination of two sparse components, the DOA vector and the gain/phase uncertainty vector. Therefore, a sparse decomposition technique is suggested to...
This paper investigates the event-triggered consensus for linear continuous-time uncertain multi-agent systems. The parameter uncertainty is assumed to be time-varying but norm-bounded. An event-triggered consensus protocol is proposed based on the predictive method to make the multi-agent system achieve consensus without continuous communication among agents. A necessary and sufficient condition...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.