Construction of Doubly Periodic Solutions via the Poincare-Lindstedt Method in the case of Massless Phi^4 Theory

Doubly periodic (periodic both in time and in space) solutions for the Lagrange-Euler equation of the (1+1)-dimensional scalar Phi^4 theory are considered. The nonlinear term is assumed to be small, and the Poincare-Lindstedt method is used to find asymptotic solutions in the standing wave form. The principal resonance problem, which arises for zero mass, is solved if the leading-order term is taken in the form of a Jacobi elliptic function. It have been proved that the choice of elliptic cosine with fixed value of module k (k=0.451075598811) as the leading-order term puts the principal resonance to zero and allows us constructed (with accuracy to third order of small parameter) the asymptotic solution in the standing wave form. To obtain this leading-order term the computer algebra system REDUCE have been used. We have appended the REDUCE program to this paper.Comment: 16 pages, LaTeX 2.09. This paper have been published in the Electronic Proceedings of the Fourth International IMACS Conference on Applications of Computer Algebra (ACA'98) {Prague (Czech Republic)} at http://math.unm.edu/ACA/1998/sessions/dynamical/verno

High-tech component import substitution as an important factor in national security

The long-term nature of transformational crisis processes caused by accelerated scientific and technological progress, the coronavirus epidemic, and political conflicts increases significantly uncertainty and the need to organize domestic production of high-tech components. The research investigates how the shortage of semiconductors due to the epidemic and territorial issues has affected sectors that depend on semiconductors. The importance of autonomy for the successful and ongoing functioning of the high-tech industry is demonstrated by the example of the automotive industry. The article discusses aspects of the semiconductor industry's production organization, including the complex hierarchical structure that proves how increasing import substitution is crucial to improving Russia's competitiveness and national security and removing external pressure points on the country. A number of recommendations for the domestic semiconductor industry will help to boost the process of import substitution, develop science-intensive and high-tech industries in Russia, and increase competitiveness in the global marke

High-Tech industry budget assessment and planning model

In the conditions of a constant budget deficit allocated to the research and manufacturing complex, the problem of value for money take on particular significance. The solution to this problem largely depends on the scientific validity of planning budget expenditures for innovative development and, first of all, on the optimality of the plans being developed in the context of crises, pandemics or sanctions. In this connection, the article analyzes modern research and manufacturing complex development processes under fiscal stress. As a result, economic and mathematical tools based on the analytic hierarchy process will be scientifically substantiated and developed. This method is designed to build a model that allows to quantify and efficiently distribute funds allocated by the State. This is necessary for the formation of that part of the budget item, which refers to the innovative development of knowledge-based industry

An approach to tackle negative effects in project management

We introduce a model to support scenario analysis while managing costs of high-tech project. If the project results from joint efforts of firms with one per single stage the model suggests budget redistribution in case of funding shortage or exchange rate volatility. Given negative outer effects for the project the model requires the project to be completed and this constraint forces firms to diminish their profit or to make a loan if needed. The model proposed suggests human-machine (expert) interaction to build scenario for analysis but is simpler compared to method of successive concessions from computational point of view

Вдосконалення моделі розпізнавання об'єктів на аерофотознімках з використанням глибокої згорткової нейронної мережі

Detection and recognition of objects in images is the main problem to be solved by computer vision systems. As part of solving this problem, the model of object recognition in aerial photographs taken from unmanned aerial vehicles has been improved. A study of object recognition in aerial photographs using deep convolutional neural networks has been carried out. Analysis of possible implementations showed that the AlexNet 2012 model (Canada) trained on the ImageNet image set (China) is most suitable for this problem solution. This model was used as a basic one. The object recognition error for this model with the use of the ImageNet test set of images amounted to 15 %. To solve the problem of improving the effectiveness of object recognition in aerial photographs for 10 classes of images, the final fully connected layer was modified by rejection from 1,000 to 10 neurons and additional two-stage training of the resulting model. Additional training was carried out with a set of images prepared from aerial photographs at stage 1 and with a set of VisDrone 2021 (China) images at stage 2. Optimal training parameters were selected: speed (step) (0.0001), number of epochs (100). As a result, a new model under the proposed name of AlexVisDrone was obtained. The effectiveness of the proposed model was checked with a test set of 100 images for each class (the total number of classes was 10). Accuracy and sensitivity were chosen as the main indicators of the model effectiveness. As a result, an increase in recognition accuracy from 7 % (for images from aerial photographs) to 9 % (for the VisDrone 2021 set) was obtained which has indicated that the choice of neural network architecture and training parameters was correct. The use of the proposed model makes it possible to automate the process of object recognition in aerial photographs. In the future, it is advisable to use this model at ground stations of unmanned aerial vehicle complex control when processing aerial photographs taken from unmanned aerial vehicles, in robotic systems, in video surveillance complexes and when designing unmanned vehicle systemsОсновной задачей, которую решают системы компьютерного зрения, является обнаружение и распознавание объектов на изображении. В рамках решения данной задачи усовершенствована модель распознавания объектов на аэрофотоснимках, полученных с беспилотного летательного аппарата. Проведено исследование распознавания объектов на аэрофотоснимках глубокими сверточными нейронными сетями. Анализ возможных реализаций показал, что для выполнения данной задачи наиболее подходит модель AlexNet 2012 (Канада), обученная на наборе изображений ImageNet (Китай). Данная модель использована в качестве базовой. Ошибка распознавания объектов для данной модели на тестовом наборе изображений ImageNet составила 15 %. Для решения задачи повышения эффективности распознавания объектов на аэрофотоснимках по 10 классам выполнена модификация выходного полносвязного слоя путём его режекции с 1000 до 10 нейронов и двухэтапное дополнительное обучение полученной модели. На первом этапе дообучение проводилось набором изображений, подготовленным с аэрофотоснимков, на втором этапе – набором изображений VisDrone 2021 (Китай). Выбраны оптимальные параметры обучения: скорость (шаг) – 0,0001, число эпох – 100. В результате получено новую модель с предложенным названием AlexVisDrone. Проверка эффективности предложенной модели проводилась на тестовом наборе из 100 изображений по каждому классу, общее количестве классов – 10. В качестве основных показателей эффективности моделей выбраны точность и чувствительность. В результате получено повышение точности распознавания от 7 % (для изображений с аэрофотоснимков) до 9 % (для набора VisDrone 2021), что показывает правильность выбора архитектуры нейронной сети, параметров обучения. Использование предложенной модели позволяет автоматизировать процесс распознавания объектов на аэрофотоснимках. В дальнейшем данную модель целесообразно использовать на наземном пункте управления беспилотного авиационного комплекса; при обработке аэрофотоснимков, полученных с беспилотного летательного аппарата; в роботизированных системах; в комплексах видеонаблюдения; при создании систем беспилотного транспорта.Основним завданням, яке вирішують системи комп'ютерного зору, є виявлення і розпізнавання об'єктів на зображенні. В рамках вирішення даного завдання вдосконалена модель розпізнавання об'єктів на аерофотознімках, отриманих з безпілотного літального апарату. Проведено дослідження розпізнавання об’єктів на аерофотознімках глибокими згортковими нейронними мережами. Аналіз можливих реалізацій показав, що для виконання даного завдання найбільше підходить модель AlexNet 2012 (Канада), яка навчена набором зображень ImageNet (Китай). Дану модель використано за базову. Помилка розпізнавання об'єктів для даної моделі на тестовому наборі зображень ImageNet склала 15 %. Для вирішення завдання підвищення ефективності розпізнавання об’єктів на аерофотознімках за 10 класами проведено модифікацію вихідного повнозв'язного шару з 1000 до 10 нейронів та двоетапне додаткове навчання отриманої моделі. На першому етапі зазначене донавчання нової мережі здійснювалося набором зображень, підготовленим з аерофотознімків, а на другому етапі – набором зображень VisDrone 2021 (Китай). Обрані оптимальні параметри навчання: швидкість (крок) – 0,0001, число епох – 100. В результаті отримано нову модель с запропонованою назвою AlexVisDrone. Перевірка ефективності запропонованої моделі проводилася на тестовому наборі з 100 зображень за кожним класом, загальна кількість класів – 10. В якості основних показників ефективності нейронної мережі обрано точність і чутливість. В результаті отримано підвищення точності розпізнавання від 7 % (для зображень з аерофотознімків) до 9 % (для набора VisDrone 2021), що показує правильність вибору архітектури нейронної мережі, параметрів навчання. Використання запропонованої моделі дозволяє автоматизувати процес розпізнавання об’єктів на аерофотознімках. Доцільно використовувати дану модель: на наземному пункті управління безпілотного авіаційного комплексу, при обробці аерофотознімків, отриманих з безпілотного літального апарату; в роботизованих системах; в комплексах відеоспостереження; при створенні систем безпілотного транспорту

Facile Hydrolysis of Nickel(II) Complexes with N‑Heterocyclic Carbene Ligands

Metal complexes with N-heterocyclic carbene ligands (NHC) are ubiquitously used in catalysis, where the stability of the metal–ligand framework is a key issue. Our study shows that Ni-NHC complexes may undergo facile decomposition due to the presence of water in organic solvents (hydrolysis). The ability to hydrolyze Ni­(NHC)₂X₂ complexes decreases in the order of NHC = 1,2,4-triazolium > benzimidazolium ≈ imidazolium. Depending on the ligand and substituents, the half reaction time of the complex decomposition may change from several minutes to hours. The nature of the halogen is also an important factor, and the ability for decomposition of the studied complexes decreases in the order of Cl > Br > I. NMR and MS monitoring revealed that Ni-NHC complexes in the presence of water undergo hydrolysis with Ni–C_carbene bond cleavage, affording the corresponding <i>N</i>,<i>N</i>′-dialkylated azolium salts and nickel­(II) hydroxide. These findings are of great importance for designing efficient and recyclable catalytic systems, because trace water is a common contaminant in routine synthetic applications

Improvement of the Model of Object Recognition in Aero Photographs Using Deep Convolutional Neural Networks

Detection and recognition of objects in images is the main problem to be solved by computer vision systems. As part of solving this problem, the model of object recognition in aerial photographs taken from unmanned aerial vehicles has been improved. A study of object recognition in aerial photographs using deep convolutional neural networks has been carried out. Analysis of possible implementations showed that the AlexNet 2012 model (Canada) trained on the ImageNet image set (China) is most suitable for this problem solution. This model was used as a basic one. The object recognition error for this model with the use of the ImageNet test set of images amounted to 15 %. To solve the problem of improving the effectiveness of object recognition in aerial photographs for 10 classes of images, the final fully connected layer was modified by rejection from 1,000 to 10 neurons and additional two-stage training of the resulting model. Additional training was carried out with a set of images prepared from aerial photographs at stage 1 and with a set of VisDrone 2021 (China) images at stage 2. Optimal training parameters were selected: speed (step) (0.0001), number of epochs (100). As a result, a new model under the proposed name of AlexVisDrone was obtained. The effectiveness of the proposed model was checked with a test set of 100 images for each class (the total number of classes was 10). Accuracy and sensitivity were chosen as the main indicators of the model effectiveness. As a result, an increase in recognition accuracy from 7 % (for images from aerial photographs) to 9 % (for the VisDrone 2021 set) was obtained which has indicated that the choice of neural network architecture and training parameters was correct. The use of the proposed model makes it possible to automate the process of object recognition in aerial photographs. In the future, it is advisable to use this model at ground stations of unmanned aerial vehicle complex control when processing aerial photographs taken from unmanned aerial vehicles, in robotic systems, in video surveillance complexes and when designing unmanned vehicle system