91 research outputs found

    Cumulative subject index volumes 48–51

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    Fast optimization of multithreshold entropy linear classifier

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    Multithreshold Entropy Linear Classifier (MELC) is a density based model which searches for a linear projection maximizing the Cauchy-Schwarz Divergence of dataset kernel density estimation. Despite its good empirical results, one of its drawbacks is the optimization speed. In this paper we analyze how one can speed it up through solving an approximate problem. We analyze two methods, both similar to the approximate solutions of the Kernel Density Estimation querying and provide adaptive schemes for selecting a crucial parameters based on user-specified acceptable error. Furthermore we show how one can exploit well known conjugate gradients and L-BFGS optimizers despite the fact that the original optimization problem should be solved on the sphere. All above methods and modifications are tested on 10 real life datasets from UCI repository to confirm their practical usability

    An intelligent gradient detector for monitoring of passenger flows

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    An intelligent gradient detector for monitoring of passenger flows

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    In this paper, we study the problem of monitoring of passenger flows. In particular, we consider an intelligent gradient algorithm to solve the problem

    Linear-Time Recognition of Double-Threshold Graphs

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    A graph G=(V, E) is a double-threshold graph if there exist a vertex-weight function w:V→ℝ and two real numbers lb, ub ∈ ℝ such that uv ∈ E if and only if lb ≤ w(u)+w(v) ≤ ub. In the literature, those graphs are studied also as the pairwise compatibility graphs that have stars as their underlying trees. We give a new characterization of double-threshold graphs that relates them to bipartite permutation graphs. Using the new characterization, we present a linear-time algorithm for recognizing double-threshold graphs. Prior to our work, the fastest known algorithm by Xiao and Nagamochi [Algorithmica 2020] ran in O(n³ m) time, where n and m are the numbers of vertices and edges, respectively

    Dynamics of methane ebullition from a peat monolith revealed from a dynamic flux chamber system

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    Methane (CH4) ebullition in northern peatlands is poorly quantified in part due to its high spatiotemporal variability. In this study, a dynamic flux chamber (DFC) system was used to continuously measure CH4 fluxes from a monolith of near‐surface Sphagnum peat at the laboratory scale to understand the complex behavior of CH4 ebullition. Coincident transmission ground penetrating radar measurements of gas content were also acquired at three depths within the monolith. A graphical method was developed to separate diffusion, steady ebullition, and episodic ebullition fluxes from the total CH4 flux recorded and to identify the timing and CH4 content of individual ebullition events. The results show that the application of the DFC had minimal disturbance on air‐peat CH4 exchange and estimated ebullition fluxes were not sensitive to the uncertainties associated with the graphical model. Steady and episodic ebullition fluxes were estimated to be averagely 36 ± 24% and 38 ± 24% of the total fluxes over the study period, respectively. The coupling between episodic CH4 ebullition and gas content within the three layers supports the existence of a threshold gas content regulating CH4 ebullition. However, the threshold at which active ebullition commenced varied between peat layers with a larger threshold (0.14 m3 m−3) observed in the deeper layers, suggesting that the peat physical structure controls gas bubble dynamics in peat. Temperature variation (23°C to 27°C) was likely only responsible for small episodic ebullition events from the upper peat layer, while large ebullition events from the deeper layers were most likely triggered by drops in atmospheric pressure

    Monitoring cantilever beam with a vision-based algorithm and smartphone

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    The study presented in this manuscript deals with a non-contact structural health monitoring approach based on the use of smartphone and computer vision algorithm to estimate the vibrating characteristics of a cantilever slender beam. We hypothesize that the vibration of the beam can be captured using a smartphone in slow-motion modality and the natural frequency of the beam can be extracted using a computer vision algorithm. The results show an excellent agreement between the conventional contact method and the non-contact novel approach proposed here

    Processing and Transmission of Information

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    Contains research objectives and reports on two research projects.National Aeronautics and Space Administration under Grant NsG-334Joint Services Electronics Programs (U. S. Army, U. S. Navy, and U. S. Air Force) under Contract DA 36-039-AMC-03200(E)National Science Foundation (Grant GP-2495)National Aeronautics and Space Administration (Grant NsG-496
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