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This paper concentrates on addressing fractional inequalities for exponential type convex functions. By means of exponential-type convexity, we firstly establish Hermite-Hadamard (HH) type inequalities for fractional integrals with exponential kernels. Secondly, based on the discovered fractional identity by separating [a; b] to n equal subintervals, and the fact that the twice derivative in absolute value is exponential type convex, we present multipoint-based HH inequalities, which cover the trapezoid- and Bullen-type inequalities for n = 1 and 2, correspondingly. During the period, some numerical examples with graphs are provided to show the validity of the deduced inequalities. [ABSTRACT FROM AUTHOR]

Simpson inequalities for first-order differentiable convex functions and various fractional integrals have been studied extensively. However, Simpson type inequalities for twice-differentiable functions are researched slightly. Therefore, in the present paper, we endeavor to study fractional inequalities of Simpson type for twice-differentiable convex functions. To achieve this goal, we establish a new twicedifferentiable Simpson's identity by using tempered fractional integral operators. Based upon it, we prove several fractional Simpson type inequalities whose second derivatives in absolute value are convex. Finally, we give some examples to illustrate the correctness of the obtained results. [ABSTRACT FROM AUTHOR]

This paper established the idea of minimum evdominating matrix of semigraph and calculated its energy. The minimum ev-dominating energy EmeD(G) of a semigraph G is the sum of the absolute values of the eigenvalues of the minimum ev-dominating matrix. Here some results are also derived in connection with the energy of minimum evdominating matrix. Some lower bounds are also established. [ABSTRACT FROM AUTHOR]

The existing research on gesture classification has mainly focused on larger amplitudes. However, studying subtle movements is essential to achieving precise control of prosthetics or manipulators. This paper proposes a refined recognition method for finger and arm movements. Ten-finger and ten-arm movements were designed for subtle classification, respectively. Features extracted from the time domain, frequency domain, and time-frequency domain included Mean Absolute Value (MAV), Fast Fourier Transform (FFT), and Hilbert-Huang Transform (HHT). The study compares the effects of seven types of feature combinations and uses five classification algorithms to predict results. The combination of "FFT+HHT" yielded better results, and the linear discriminant analysis algorithm achieved better prediction accuracy. This research has significant implications for the precise control of robots or prosthetics and proper rehabilitation in later stages. [ABSTRACT FROM AUTHOR]

Ontology learning problem has raised much attention in semantic structure expression and information retrieval. As a powerful tool, ontology is evenly employed in various subjects, such as neuroscience, medical science, pharmacopedia, chemistry, education and other social science. Ontology similarity measuring plays a vital role in practical implementations since essential issues of ontology mapping are also similarity calculating. In ontology function learning, one learns a real valued score function that assigns scores to each ontology vertex which corresponds to a concept. Thus, the similarity between vertices is determined by means of the absolute value of difference between their corresponding scores. In this paper, we report the new optimization algorithms for obtaining ontology function in view of ontology sparse vector learning. The implementation of ontology algorithms is mainly based on iterative calculation in which we consider the whole matrix version of framework and ontology sparse vector are updated in each iterative. The data results obtained from four simulation experiments reveal that our newly proposed ontology approach has high efficiency and accuracy in biology and plant science with regard to ontology similarity measure, and humanoid robotics and education science with regard to ontology mapping. [ABSTRACT FROM AUTHOR]

In this research, an effective nine-accelerometer configuration is designed to form a gyroscope free strapdown inertial navigation system (GFSINS), and the absolute value method is adopted to calculate angular velocity. This paper proposes an UKF attitude estimation algorithm for the starsensor aided GFSINS integrated navigation system. The experimental results show that, UKF algorithm avoids the higher order truncation error in EKF, and improves the steady-state precision and convergence speed to some extent. Especially in complex models, UKF is free from computational complexity of the Jacobi matrix, thus highlighting the rapidity and improving the computation efficiency of the system. Consequently, this navigation methodology could be competent for some high precision and long-endurance navigation tasks. [ABSTRACT FROM AUTHOR]