Численные методы и анализ данных. Рубрика в журнале - Компьютерная оптика
Multigrammatical modelling of neural networks
Статья научная
This paper is dedicated to the proposed techniques of modelling artificial neural networks (NNs) by application of the multigrammatical framework. Multigrammatical representations of feed-forward and recurrent NNs are described. Application of multiset metagrammars to modelling deep learning of NNs of the aforementioned classes is considered. Possible developments of the announced approach are discussed.
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Network community partition based on intelligent clustering algorithm
Статья научная
The division of network community is an important part of network research. Based on the clustering algorithm, this study analyzed the partition method of network community. Firstly, the classic Louvain clustering algorithm was introduced, and then it was improved based on the node similarity to get better partition results. Finally, experiments were carried out on the random network and the real network. The results showed that the improved clustering algorithm was faster than GN and KL algorithms, the community had larger modularity, and the purity was closer to 1. The experimental results show the effectiveness of the proposed method and make some contributions to the reliable community division.
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Recognition of biosignals with nonlinear properties by approximate entropy parameters
Статья научная
More and more attention is being paid to the development of methods for the objective analysis of biosignals for computer medical systems. The search for new non-standard methods is aimed at improving the reliability of diagnostics and expanding the areas of their practical application. In this paper, methods for recognizing biomedical signals by the degree of severity of their nonlinear components are considered. An approach based on the use of approximate entropy closely related to Kolmogorov entropy ( K -entropy) is used. Its parameters can be used to detect dynamic irregularities associated with nonlinear properties of signals. The algorithm for calculating this characteristic is considered in detail. Based on model experiments, its main properties are analyzed. It is shown that the entropy of a finite sequence, calculated in accordance with a multistep procedure, can give an erroneous estimate of the degree of regularity of the signal. A procedure for correcting the approximate entropy is proposed, which expands the area of analysis of this function for estimating nonlinearity. It has been established that the transition to adjusted entropy makes it possible to increase the reliability of the detection of chaotic components. A set of entropy parameters is proposed for constructing recognition procedures. Examples of solving the problems of detecting atrial fibrillation by the parameters of the nonlinearity of the rhythmogram, as well as assessing the depth of anesthesia by the electroencephalogram (EEG) are given. Experiments conducted on real recordings of electrocardiogram (ECG) and EEG signals have shown the high efficiency of the proposed algorithms. The proposed methods and algorithms can be used in the development of systems for monitoring ECG of cardiological patients, as well as monitoring the depth of anesthesia by EEG during surgical operations.
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Renewed empirical formulas of Weibull distribution parameters estimates
Статья научная
The empirical formulas proposed in the literature for estimating the parameters of a two-parameter Weibull distribution, obtained using the equations of the moment method, are considered. It is noted that the formulas used to estimate the shape parameter take the form of various types of dependences on the coefficient of variation of the distribution. By modeling the empirical formulas selected for analysis, a comparative analysis of their errors relative to accurate numerical solutions of the moment method equations was carried out. A renewed empirical formula for the shape parameter is proposed. An approach to estimating the scale parameter is proposed, in which the empirical formula of the latter is reduced to the product of the standard deviation of the distribution by a power function of the coefficient of variation with an exponent equal to – 1.027. The results of applying the updated empirical formulas to numerical data obtained by modeling a random sample from the Weibull distribution are presented. It is shown that the accuracy of the proposed empirical formulas is quite high.
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Research on robot motion control and trajectory tracking based on agricultural seeding
Статья научная
With the development of science and technology, agricultural production has been gradually industrialized, and the use of robots instead of humans for seeding is one of the agricultural industrializations. This paper studied the seeding path planning and path tracking algorithms of the seeding robot, carried out experiments, and compared the improved proportion, integral, differential (PID) algorithm with the traditional PID control algorithm. The results demonstrated that both the improved and non-improved control algorithms played a good role in tracking on the straight path, but the improved control algorithm had a better tracking effect on the turning path; the displacement deviation and angle deviation of the tracking trajectory of the improved PID algorithm were reduced faster and more stable than the traditional PID algorithm; the tracking trajectory was shorter and the operation time of the robot was less under the improved PID algorithm than the traditional one.
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Security detection of network intrusion: application of cluster analysis method
Статья научная
In order to resist network malicious attacks, this paper briefly introduced the network intrusion detection model and K-means clustering analysis algorithm, improved them, and made a simulation analysis on two clustering analysis algorithms on MATLAB software. The results showed that the improved K-means algorithm could achieve central convergence faster in training, and the mean square deviation of clustering center was smaller than the traditional one in convergence. In the detection of normal and abnormal data, the improved K-means algorithm had higher accuracy and lower false alarm rate and missing report rate. In summary, the improved K-means algorithm can be applied to network intrusion detection.
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Study on the planning of rural land spatial utilization by improved particle swarm optimization
Статья научная
The planning of rural land space utilization is a very important problem. In this paper, the objective function of rural land use planning was analyzed firstly, and then the improved particle swarm optimization (IPSO) algorithm was obtained by improving the inertia weight for solution. The results showed that the land space use in the study area was more reasonable after the planning based on the IPSO algorithm, the forest land and construction land increased, the area of grassland, cultivated land and water area reduced appropriately, the aggregation degree of all types of land improved, and the space distribution was more planned, which was more conducive to production activities. The analysis results verify the effectiveness of the IPSO method in land space use planning, which can improve the efficiency and benefit of land space use, and it can be popularized in practical application.
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The basic assembly of skeletal models in the fall detection problem
Статья научная
The paper considers the appliance of the featureless approach to the human activity recognition problem, which exclude the direct anthropomorphic and visual characteristics of human figure from further analysis and thus increase the privacy of the monitoring system. A generalized pairwise comparison function of two human skeletal models, invariant to the sensor type, is used to project the object of interest to the secondary feature space, formed by the basic assembly of skeletons. A sequence of such projections in time forms an activity map, which allows an application of deep learning methods based on convolution neural networks for activity recognition. The proper ordering of skeletal models in a basic assembly plays an important role in secondary space design. The study of ordering of the basic assembly by the shortest unclosed path algorithm and correspondent activity maps for video streams from the TST Fall Detection v2 database are presented.
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The optimization of automated goods dynamic allocation and warehousing model
Статья научная
In the development of modern logistics, the role of automated cargo warehousing is gradually reflected, which is essential for the automatic distribution of goods. This paper briefly introduced the automatic location allocation model and the particle swarm optimization (PSO) algorithm used to optimize the model. At the same time, it introduced the concept of genetic operator and multi-group co-evolution to improve the algorithm, and then the simulation analysis of standard PSO and improved PSO was performed on MATLAB software. The results showed that the improved PSO iterated fewer times and get better solution sets; compared with the manual allocation scheme, the improved PSO calculation reduced more warehousing time, lowered more center of gravity height, and improved shelf stability. In summary, the improved PSO algorithm can effectively optimize the automated goods dynamic allocation and warehousing model.
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Towards monitored tomographic reconstruction: algorithm-dependence and convergence
Статья научная
The monitored tomographic reconstruction (MTR) with optimized photon flux technique is a pioneering method for X-ray computed tomography (XCT) that reduces the time for data acquisition and the radiation dose. The capturing of the projections in the MTR technique is guided by a scanning protocol built on similar experiments to reach the predetermined quality of the reconstruction. This method allows achieving a similar average reconstruction quality as in ordinary tomography while using lower mean numbers of projections. In this paper, we, for the first time, systematically study the MTR technique under several conditions: reconstruction algorithm (FBP, SIRT, SIRT-TV, and others), type of tomography setup (micro-XCT and nano-XCT), and objects with different morphology. It was shown that a mean dose reduction for reconstruction with a given quality only slightlyvaries with choice of reconstruction algorithm, and reach up to 12.5 % in case of micro-XCT and 8.5 % for nano-XCT. The obtained results allow to conclude that the monitored tomographic reconstruction approach can be universally combined with an algorithm of choice to perform a controlled trade-off between radiation dose and image quality. Validation of the protocol on independent common ground truth demonstrated a good convergence of all reconstruction algorithms within the MTR protocol.
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Статья научная
The three-dimensional perception applications have been growing since Light Detection and Ranging devices have become more affordable. On those applications, the navigation and collision avoidance systems stand out for their importance in autonomous vehicles, which are drawing an appreciable amount of attention these days. The on-road object classification task on three-dimensional information is a solid base for an autonomous vehicle perception system, where the analysis of the captured information has some factors that make this task challenging. On these applications, objects are represented only on one side, its shapes are highly variable and occlusions are commonly presented. But the highest challenge comes with the low resolution, which leads to a significant performance dropping on classification methods. While most of the classification architectures tend to get bigger to obtain deeper features, we explore the opposite side contributing to the implementation of low-cost mobile platforms that could use low-resolution detection and ranging devices. In this paper, we propose an approach for on-road objects classification on extremely low-resolution conditions. It uses directly three-dimensional point clouds as sequences on a transformer-convolutional architecture that could be useful on embedded devices. Our proposal shows an accuracy that reaches the 89.74 % tested on objects represented with only 16 points extracted from the Waymo, Lyft’s level 5 and Kitti datasets. It reaches a real time implementation (22 Hz) in a single core processor of 2.3 Ghz.
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Статья научная
In this paper, we examine the applicability limits of different methods of compensation of the individual properties of self-emitting displays with significant non-uniformity of chromaticity and maximum brightness. The aim of the compensation is to minimize the perceived image non-uniformity. Compensation of the displayed image non-uniformity is based on minimizing the perceived distance between the target (ideally displayed) and the simulated image displayed by the calibrated screen. The S-CIELAB model of the human visual system properties is used to estimate the perceived distance between two images. In this work, we compare the efficiency of the channel-wise and linear (with channel mixing) compensation models depending on the models of variation in the characteristics of display elements (subpixels). It was found that even for a display with uniform chromatic subpixels characteristics, the linear model with channel mixing is superior in terms of compensation accuracy.
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Veiling glare removal: synthetic dataset generation, metrics and neural network architecture
Статья научная
In photography, the presence of a bright light source often reduces the quality and readability of the resulting image. Light rays reflect and bounce off camera elements, sensor or diaphragm causing unwanted artifacts. These artifacts are generally known as “lens flare” and may have different influences on the photo: reduce contrast of the image (veiling glare), add circular or circular-like effects (ghosting flare), appear as bright rays spreading from light source (starburst pattern), or cause aberrations. All these effects are generally undesirable, as they reduce legibility and aesthetics of the image. In this paper we address the problem of removing or reducing the effect of veiling glare on the image. There are no available large-scale datasets for this problem and no established metrics, so we start by (i) proposing a simple and fast algorithm of generating synthetic veiling glare images necessary for training and (ii) studying metrics used in related image enhancement tasks (dehazing and underwater image enhancement). We select three such no-reference metrics (UCIQE, UIQM and CCF) and show that their improvement indicates better veil removal. Finally, we experiment on neural network architectures and propose a two-branched architecture and a training procedure utilizing structural similarity measure.
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Статья научная
Предложены абстрактная модель искусственной иммунной сети на базе комитета классификаторов и два алгоритма ее обучения (с учителем и с подкреплением) для задач классификации, которые характеризуются малыми объемами и низкой репрезентативностью обучающих выборок. Оценка эффективности модели и алгоритмов выполнена на примере задачи аутентификации по клавиатурному почерку с использованием 3 баз данных биометрических образов. Разработанная искусственная иммунная сеть обладает эмерджентностью, памятью, двойной пластичностью, устойчивостью обучения. Эксперименты показали, что искусственная иммунная сеть дает меньший или сопоставимый процент ошибок по сравнению с некоторыми архитектурами нейронных сетей при гораздо меньшем объеме обучающей выборки.
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Автоматизированный метод вычисления DST-индекса на основе вейвлет-модели вариаций геомагнитного поля
Статья научная
Предложен метод вычисления индекса геомагнитной активности Dst , основанный на вейвлет-модели вариаций геомагнитного поля. Метод позволяет в автоматическом режиме получать значения Dst -индекса с 1-минутным разрешением. Апробация метода выполнена на данных приэкваториальных станций [1]. В работе описан алгоритм выполнения расчетов и приведены результаты оценок. Выполнено сравнение результатов расчета с классическим подходом и с методом, используемым в Мировом центре данных Киото. Показано, что предлагаемый метод позволяет в оперативном режиме получать значения Dst -индекса с допустимой погрешностью.
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Автоматическое определение количества минимальных единиц языка по артикуляции
Статья научная
Представленная работа посвящена автоматическому анализу паравербального компонента общения человека. В статье описаны системы, определяющие количество минимальных языковых единиц (слогов и фонем) в устной речи по видеоданным. Такие системы могут быть использованы в оценке темпа артикулирования говорящего, что может применяться в доклинической диагностике некоторых патологических состояний или определении эмоционального статуса. Для проведения исследования была модифицирована существующая база данных слов английского языка и получена разметка, содержащая информацию о количестве слогов и фонем в каждом слове. В ходе исследования адаптирована система распознавания слов для решения поставленной задачи, а также разработана новая архитектура нейронной сети для определения количества слогов и фонем в слове. Оценка эффективности разработанных систем производилась как на наборах заранее известных системам слов, так и на новых для них словах. В результате работы получена система, определяющая количество минимальных единиц языка в произнесённом слове, предоставляющая возможность последующей оценки темпа артикулирования информанта.
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Алгоритм выделения интенсивных аномальных изменений во временном ходе параметров ионосферы
Статья научная
В работе представлена модифицированная многокомпонентная модель временного ряда параметров ионосферы. Модель описывает регулярные вариации и аномальные изменения разномасштабной структуры, характеризующие возникновение ионосферных неоднородностей. Идентификация компонент модели основана на совместном применении вейвлет-преобразования и моделей авторегрессии проинтегрированного скользящего среднего. На основе предложенной модели разработан алгоритм анализа ионосферных параметров, позволяющий в оперативном режиме выделять интенсивные ионосферные аномалии, характеризующие возникновение сильных ионосферных бурь. Представлены результаты апробации алгоритма, выполненные на примере обработки и анализа часовых и 15-минутных данных критической частоты ионосферы (foF2) в периоды магнитных бурь, произошедших в 2015-2017 гг. Выполненные оценки показали эффективность алгоритма и возможность его применения в задачах прогноза космической погоды.
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Статья научная
В работе представлены новые алгоритмы генерации, встраивания и извлечения стойкого цифрового водяного знака в гиперспектральные изображения дистанционного зондирования Земли. Предлагаемый алгоритм генерации ЦВЗ предполагает генерацию двумерного шумоподобного изображения (шаблона встраивания), кодирующего ЦВЗ, на основе пароля (секретного ключа) пользователя. Предложенные алгоритмы обладают рядом преимуществ по сравнению с существующими аналогами. В частности, предложенный алгоритм генерации шаблонов встраивания на основе пароля обеспечивает высокую устойчивость встроенного цифрового водяного знака к атакам прямого перебора ключа (сложность атаки подбора ключа составляет 10 14 попыток извлечения по сравнению с 10 4 - 10 5 попытками для существующих аналогов).
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Статья научная
Предложен экономичный алгоритм коррекции влияния поглощения света в атмосферных газах на коэффициент яркости солнечного света, отраженного системой атмосфера - земная поверхность. Алгоритм не требует предварительного задания оптических параметров аэрозоля, предположений о подстилающей поверхности и общем содержании газов. В алгоритме коррекция выполняется домножением коэффициента яркости на корректирующий множитель, полученный из анализа спектральной зависимости коэффициента яркости; алгоритм применим только к гиперспектральным данным. Приведены результаты тестирования алгоритма на модельных задачах.
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Алгоритм реконструкции трёхмерной структуры кристалла по двумерным проекциям
Статья научная
В статье рассматривается задача трёхмерной реконструкции кристаллической решётки, являющаяся важным этапом рентген-структурного анализа вещества. От качества реконструкции напрямую зависит точность параметрической и структурной идентификации кристалла. Предлагаемый алгоритм реконструкции трёхмерной кристаллической решётки основан на минимизации расстояний от узла до прямой, спроецированной на заданную плоскость. В качестве исходных данных используются три набора двумерных координат узлов решётки, полученные по трём двумерным проекциям. Также произведено аналитическое вычисление ошибки реконструкции, позволяющее оценить точность проведённой реконструкции. Результаты, полученные в ходе вычислительного эксперимента, подтвердили высокое качество предложенного алгоритма реконструкции и его устойчивость к возможным искажениям исходных координат узлов. Кроме того, выявлена проблема разделимости моноклинных, ромбических и тетрагональных решёток, точность идентификации которых составила 34 %, 53 % и 10 % соответственно.
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