An analytical expression for the power coefficient of an ideal horizontal-axis wind turbine

The paper shows that an analytical evaluation of the power coefficient C_p for an ideal horizontal-axis wind turbine can be made via an expression giving the direct relationship of C_p and the axial induction factor a. The results obtained agree closely with those obtained numerically from the usual integral expression involving several variables.     

Multiperson interaction recognition in images: A body keypoint based feature image analysis

Most interaction recognition approaches have been limited to single‐person action classification in videos. However, for still images where motion information is not available, the task becomes more complex. Aiming to this point, we propose an approach for multiperson human interaction recognition in images with keypoint‐based feature image analysis. Proposed method is a three‐stage framework. In the first stage, we propose feature‐based neural network (FCNN) for action recognition trained with feature images. Feature images are body features, that is, effective distances between a set of body part pairs and angular relation between body part triplets, rearranged in 2D gray‐scale image to learn effective representation of complex actions. In the later stage, we propose a voting‐based method for direction encoding to anticipate probable motion in steady images. Finally, our multiperson interaction recognition algorithm identifies which human pairs are interacting with each other using an interaction parameter. We evaluate our approach on two real‐world data sets, that is, UT‐interaction and SBU kinect interaction. The empirical experiments show that results are better than the state‐of‐the‐art methods with recognition accuracy of 95.83% on UT‐I set 1, 92.5% on UT‐I set 2, and 94.28% on SBU clean data set.     

Correction to: EDVWDD: Event‑Driven Virtual Wheel‑based Data Dissemination for Mobile Sink‑Enabled Wireless Sensor Networks

The Journal of Supercomputing - A correction to this paper has been published: https://doi.org/10.1007/s11227-021-03790-9     

High-utility and diverse itemset mining

High-utility Itemset Mining (HUIM) finds patterns from a transaction database with their utility no less than a user-defined threshold. The utility of an itemset is defined as the sum of the utilities of its items. The utility notion enables a data analyst to associate a profit score with each item and thereof to a pattern. We extend the notion of high-utility with diversity to define a new pattern type called High-utility and Diverse pattern (HUD). The notion of diversity of a pattern captures the extent of the different categories covered by the selected items in the pattern. An application of diverse-pattern lies in the recommendation task where a system can recommend to a customer a set of items from a new class based on her previously bought items. Our notion of diversity is easy to compute and also captures the basic essence of a previously proposed diversity notion. The existing algorithm to compute frequent-diverse patterns is 2-phase, i.e., in the first phase, frequent patterns are computed, out of which diverse patterns are filtered out in the second phase. We, in this paper, give an integrated algorithm that efficiently computes high-utility and diverse patterns in a single phase. Our experimental study shows that our proposed algorithm is very efficient as compared to a 2-phase algorithm that extracts high-utility itemsets in the first phase and filters out the diverse itemsets in the second phase.

     

An efficient neural-network model for real-time fault detection in industrial machine

Neural Computing and Applications - Induction machines have extensive demand in industries as they are used for large-scale production and, therefore, vulnerable to both electrical and mechanical...     

On the network effect in Web 2.0 applications

In recent years, the proliferation of the world wide web has lead to an increase in a number of applications such as search, social networks and auctions, whose success depends critically upon the number of users of that service. Two examples of such applications are internet auctions and social networks. One of the characteristics of online auctions is that a successful implementation requires a high volume of buyers and sellers at its website. Consequently, auction sites which have a high volume of traffic have an advantage over those in which the volume is limited. This results in even greater polarization of buyers and sellers towards a particular site. The same is true for social networks in which greater use of a given social network increases the use from other participants on the network. This is often referred to as the “network effect” in a variety of interaction-centric applications in networks. While this effect has qualitatively been known to increase the value of the overall network, its effect has never been modeled or studied rigorously. In this paper, we construct a Markov model to analyze the network effect in the case of two important classes of web applications. These correspond to auctions and social networks. We show that the network effect is very powerful and can result in a situation in which an auction or a social networking site can quickly overwhelm its competing sites. Thus, the results of this paper show the tremendous power of the network effect for Web 2.0 applications.     

Ionic liquid mediated nano-composite polymer gel electrolyte for rechargeable battery application

ABSTRACT

Nano-composite polymer gel electrolytes (NPGEs) based on polymer poly(vinylidene fluoride-co-hexafluoropropylene) PVdF-HFP, ionic liquid, 1-butyl-3- methylimidazolium bis(trifluoromethanesulfonyl)imide BMIMTFSI, Li-salt along with the addition of SiO₂ nanoparticles have been synthesized and characterized by various techniques. Prepared NPGEs show high room temperature ionic conductivity (~10^?3 S/cm) and have a wide electrochemical window (ECW) (~3.3–3.5 V). The galvanostatic charge/discharge profile was studied by sandwiching best performing NPGEs between a LiFePO₄ cathode and lithium metal anode. The specific discharge capacity of the cell (Li/NPGE/LiFePO₄) room temperature at 0.1C rate is found to be 138 mAh/g.     

Breakthrough and Sulfate Conversion Analysis during Removal of Sulfur Dioxide by Calcium Oxide Sorbents

Sorption of sulfur dioxide (SO₂) was carried out on calcium‐based sorbents under dynamic conditions in a fixed bed. The experimental conditions were reaction temperature (700 to 1000°C), SO₂ concentration (1000‐10 000 ppm), sorbent particles size (1 to 2 mm) and the types of sorbents (hydroxide or carbonate). The sorption process was found to be effective at low concentration levels (less than 10 000 ppm) as the breakthrough time significantly decreased with increase in concentration. The maximum removal of SO₂ was observed at a reaction temperature of 950°C. The hydroxide‐based sorbents of relatively smaller particle size were found to exhibit superior sorption performance in terms of longer breakthrough time and higher sulfate conversion. A mathematical model developed, assuming a porous structure of the sorbent materials, attributed the low sulfation conversion during SO₂ sorption due to a pore diffusion mechanism.