NONLINEAR PHENOMENA IN COMPLEX SYSTEMS. НЕЛИНЕЙНЫЕ ЯВЛЕНИЯ В СЛОЖНЫХ СИСТЕМАХ Vol. 22, no. 1, pp. 1 – 103, 2019
Contents
A Novel Non-Gaussian Feature Normalization Method and its Application in Content Based Image Retrieval Trung Hoang Xuan, Tuyet Dao Van, Huy Ngo Hoang, Sergey Ablameyko, Cuong Nguyen Quoc, and Quy Hoang Van . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
On P-noninvariant Wave Equation for a Spin 1/2 Particle with Anomalous Magnetic Moment V. V. Kisel, V. A. Pletyukhov, E. M. Ovsiyuk, and V. M. Red’kov . . . . . . . . . . .
18
Two-Dimensional Braiding of Two-Dimensional Majorana Fermions : Manifestation in Band Structure of Graphene Halina V. Grushevskaya and George Krylov . . . . . . . . . . . . . . . . . . . . . . . . 41 Optoacoustical Transducer Based on Plasmonic Nanoparticles V. I. Belotelov, A. N. Kalish, G. A. Knyazev, E. T. T. Nguen, O. G. Romanov, and A. L. Tolstik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
55
Squeezed States Engineering by Coherent Pulses Train Acting on Single Atom Laser V. P. Stefanov and S. Ya. Kilin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
64
Electroweak Interactions of Kaons E. Z. Avakyan and S. L. Avakyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
69
Formation of Chladni Patterns by Vibration Driven Random Walk of Particles Igor Grabec and Nikolaj Sok . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
75
i
A Schrödinger Potential Involving x2/3 and Centrifugal-Barrier terms Conditionally Integrable in Terms of the Confluent Hypergeometric Functions V. A. Manukyan, T. A. Ishkhanyan, and A. M. Ishkhanyan . . . . . . . . . . . . . . . 84 Local Dynamics of Cahn–Hilliard Equation S. A. Kashchenko and S. P. Plyshevskaya . . . . . . . . . . . . . . . . . . . . . . . . .
93
Gauge Boson Production in High Energy Electron–Photon Collisions as a Modern Tool of Searching Effects beyond the Standard Model I. A. Shershan and T. V. Shishkina . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
98
ii
Nonlinear Phenomena in Complex Systems, vol. 22, no. 1 (2019), pp. 1 - 17
A Novel Non-Gaussian Feature Normalization Method and its Application in Content Based Image Retrieval Trung Hoang Xuan∗ Hanoi University of Business and Technology, 29A Vinh Tuy Str, Hai Ba Trung Dist, Hanoi, VIETNAM
Tuyet Dao Van† Vietnam National Space Center of the Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Nghia Do, Cau Giay, Hanoi,Vietnam and Binh Dıхng University, 504 Binh Duong Ave., Thu Dau Mot, 820000 Binh Duong, VIETNAM
Huy Ngo Hoang‡ Electric Power University of the Vietnam Ministry of Industry and Trade, 235 Hoang Quoc Viet, Co Nhue, Tu Liem, 129823 Hanoi, VIETNAM
Sergey Ablameyko§ United Institute of Information Problems, of the National Academy of Sciences of Belarus, 6 Surganova Str, 220012 Minsk, BELARUS
Cuong Nguyen Quoc¶ Institute of Science and Technology of Industry 4.0 of Nguyen Tat Thanh University, 300A Nguyen Tat Thanh Str, Ho Chi Minh City, VIETNAM
Quy Hoang Van∗∗ Hong Duc University, Thanh Hoa City, VIETNAM (Received 3 January, 2019) In Content-Based Image Retrieval (CBIR) images are represented by multi low-level features that describe image color, texture, and shape of objects. The Efficient Manifold Ranking (EMR) algorithm is a semi-supervised learning algorithm on the low-level image features that has been used efficiently in CBIR. The combination of different image features to build the weighted EMR -graph usually uses normalized feature data for balancing the value of each feature. In this paper, we propose a novel normalization method for vector number data such as the low level image features where vector components are not consistent with the characteristics of the Gaussian distribution and its application for calculating the adjacent matrix of the weighted EMR-graph. Experiments show the effectiveness of the proposed algorithm for the EMR, the CBIR quality is really improved. Besides the testing normalization method for visual images, we also investigated the possibility to use the proposed method for medical image datasets. PACS numbers: 02.30.Gp, 02.40.Ky, 03.65Ge, 04.62.+v Keywords: content-based image retrieval, efficient manifold ranking, adjacent matrix, low-level features.
∗
E-mail: trungvnit@gmail.com E-mail: dvtuyet@vnsc.org.vn; Also at Belarusian State University, 4 Nezaleznasti Ave., Minsk, Belarus
‡
†
§
1
E-mail: huynh@epu.edu.vn E-mail: ablameyko@bsu.by; Also at Belarusian State