Aircraft Icing Severity Evaluation

Aircraft icing refers to the ice buildup on the surface of an aircraft flying in icing conditions. The ice accretion on the aircraft alters the original aerodynamic configuration and degrades the aerodynamic performances and may lead to unsafe flight conditions. Evaluating the flow structure, icing mechanism and consequences is of great importance to the development of an anti/deicing technique. Studies have shown computational fluid dynamics (CFD) and machine learning (ML) to be effective in predicting the ice shape and icing severity under different flight conditions. CFD solves a set of partial differential equations to obtain the air flow fields, water droplets trajectories and ice shape. ML is a branch of artificial intelligence and, based on the data, the self-improved computer algorithms can be effective in finding the nonlinear mapping relationship between the input flight conditions and the output aircraft icing severity features.

Toward an IoT based Software Defined Plumbing Network System with fault tolerance

in this paper we will see the application of computer science algorithms to the plumbing system. We propose a fault tolerant tap water system which is impossible without Internet of things and algorithms . We will show that the problem is a mutual exclusion group problem and we propose an adapted algorithm version from the literature as a solution . Coupling algorithms with the configurable plumbing network we believe that this will open new field of research on IoT we called it software defined plumbing Network where components that have been traditionally implemented in hardware (e.g. water mixers, spring faucets ,flow sensors, etc.) are instead implemented by means of software . This way we can solve other problem like instantaneous hot water,automatic cleaning of the water heater..etc since due to computer algorithms the systems can be easily smart, extensible and adaptive.

Robotizing bond portfolio selection on the Russian debt market on the basis of a modified strategy of riding the yield curve

The modern global debt market features historically low average interest rates, convergence of yields on bonds with different maturities, an increase of yield curve inversion emergence frequency and a large-scale trend to automate financial decision making. The researchers’ attention in these fields is mainly focused on designing models that describe the state of the debt market as whole or its individual instruments in particular, as well as on risk management methods. At the same time, the specialized literature offers very few works concerning the topic of computer algorithms for bond portfolio selection based on traditional or advanced investment strategies. The aim of the present research is to create a modification of the existing algorithm of riding the yield curve strategy application, employing, first, average bond yield over the holding period instead of traditional bond yield to maturity; second, a developed algorithm for calculating the market spread on bonds; and, third, alternative risk evaluation indicators (compensation coefficients), which allow us to measure objectively price risk, liquidity risk, transaction costs risk and a general risk. The modification and the development of the algorithm for calculating the market spread were carried out using the direct measurement of the result technique, which entails application of the strategy to the data on bond issues received through the Moscow Exchange API. The selection of financial instruments was conducted in all sectors of the Russian debt market: public bonds, sub-federal and municipal bonds, corporate bonds. The modified algorithm enabled us to get extra yield for each selected bond issue, thereby proving the high effectiveness of the technique compared to the traditional strategy. Software implementation of the algorithm can be integrated into any robotized or semi-robotized stock exchange trading application.

DEVELOPMENT OF MARKET THEORY: THE RELATIONSHIP BETWEEN COMPETITION RISK AND THE DIGITAL MARKET

The development of the Internet, big data, computer algorithms, artificial intelligence, selflearning robots and other areas of the digital economy contribute to improving life. At the same time, players with market power are emerging in the economy, based on the use of algorithms, big data, big analytics, the use of intellectual property rights, the widespread use of targeted marketing technologies on this basis, not only studying, but also forming consumer preferences. The consequences of digital transformation in the economy are significant, and symbolic changes in legislation and practice of its application cannot be dispensed with here. It is necessary to measure the risks and benefits of the digital economy for competition and public welfare. Risks need to be managed, and benefits need to be multiplied. It is necessary to evaluate the new situation in the markets based on the basic postulates, and also take into account that the dynamic nature of changes has become the main characteristic of the markets.

A Novel Approach to Damage Assessment in Structures Based on Artificial Neural Network Working Parallel With a Hybrid Stochastic Optimization

Artificial neural network (ANN) is the study of computer algorithms that can learn from experience to improve performance. ANN employs backpropagation (BP) algorithms using gradient descent (GD)-based learning methods to reduce the discrepancies between predicted and real targets. Even though these differences are considerably decreased after each iteration, the network may still face major risks of being entrapped in local minima if complex error surfaces contain too numerous the best local solutions. To overcome those drawbacks of ANN, numerous researchers have come up with solutions to local minimum prevention by choosing a beneficial starting position that relies on the global search capability of other algorithms. This strategy possibly assists the network in avoiding the first local minima. However, a network often has many local bests widely distributed. Hence, the solution of choosing good starting points may no further be beneficial because the particles are probably entrapped in other local optimal solutions throughout the process of looking for the global best. Therefore, in this work, a novel ANN working parallel with the stochastic search capacity of evolutionary algorithms, is proposed. Additionally, to increase the efficiency of the global search capacity, a hybrid of particle swarm optimization and genetic algorithm (PSOGA) is applied during the process of seeking the best solution, which effectively guarantees to assist the network of ANN in escaping from local minima. This strategy gains both benefits of GD techniques as well as the global search capacity of PSOGA that possibly solves the local minima issues thoroughly. The effectiveness of ANNPSOGA is assessed using both numerical models consisting of various damage cases (single and multiple damages) and a free-free steel beam with different damage levels calibrated in the laboratory. The results demonstrate that ANNPSOGA provides higher accuracy than traditional ANN, PSO, and other hybrid ANNs (even a higher level of noise is employed) and also considerably decreases calculational cost compared with PSO.

Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network

Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms using computer algorithms at initial stages could help radiologists and doctors. The architectural distortion star shapes ROIs detection, noise removal, and object location, affecting the classification performance, reducing accuracy. The computer vision-based technique automatically removes the noise and detects the location of objects from varying patterns. The current study investigated the gap to detect architectural distortion ROIs (region of interest) from mammograms using computer vision techniques. Proposed an automated computer-aided diagnostic system based on architectural distortion using computer vision and deep learning to predict breast cancer from digital mammograms. The proposed mammogram classification framework pertains to four steps such as image preprocessing, augmentation and image pixel-wise segmentation. Architectural distortion ROI`s detection, training deep learning, and machine learning networks to classify AD`s ROIs into malignant and benign classes. The proposed method has been evaluated on three databases, the PINUM, the CBIS-DDSM, and the DDSM mammogram images, using computer vision and depth-wise 2D V-net 64 convolutional neural networks and achieved 0.95, 0.97, and 0.98 accuracies, respectively. Experimental results reveal that our proposed method outperforms as compared with the ShuffelNet, MobileNet, SVM, K-NN, RF, and previous studies.

A Mechanical True Random Number Generator

Random number generation has become an indispensable part of information processing: it is essential for many numerical algorithms, security applications, and in securing fairness in everyday life. Random number generators (RNG) find application in many devices, ranging from dice and roulette wheels, via computer algorithms, lasers to quantum systems, which inevitably capitalize on their physical dynamics at respective spatio-temporal scales. Herein, to the best of our knowledge, we propose the first mathematically proven true RNG (TRNG) based on a mechanical system, particularly the triple linkage of Thurston and Weeks. By using certain parameters, its free motion has been proven to be an Anosov flow, from which we can show that it has an exponential mixing property and structural stability. We contend that this mechanical Anosov flow can be used as a TRNG, which requires that the random number should be unpredictable, irreproducible, robust against the inevitable noise seen in physical implementations, and the resulting distribution's controllability (an important consideration in practice). We investigate the proposed system's properties both theoretically and numerically based on the above four perspectives. Further, we confirm that the random bits numerically generated pass the standard statistical tests for random bits.

Principle of construction of cyberphysical system of control of work of the smart greenhouse

A cyberphysical system is a mechanism that is controlled or tracked by computer algorithms and is closely linked to the Internet and interaction with the physical world. The system describes a combination of three main components: the physical world, the software algorithm and the Internet. Based on these components, this article presents a method of building a control system for a smart greenhouse, describes the development environment with its functions and capabilities, provides a detailed description of launching and configuring the program with explanations of key points in the system. This system is aimed at improving and optimizing the process of growing vegetables. The system is easy to use. All software interacts with each other according to clearly defined protocols and therefore there are no system failures. One of the features of this system is the speed of the survey of sensors, which is relevant today. The system consists of a simple user interface that can be modified according to user requirements.

Structure-Behavior Coalescence Design to Improve the Modifiability of Home Care IoT System

Modifiability improvement is a key factor in the successful Home Care IoT System (HCIS) systems development. It includes disciplined system layering (DSL), well-defined components (WDC), published interface (PI), and well-defined behavior (WDB) which represent the four main factors that enhance the modifiability of HCIS. Structure-Behavior Coalescence (SBC) method uses three fundamental diagrams: a) framework diagram, b) component operation diagram, and c) interaction flow diagram to accomplish the design of HCIS. Through framework diagram, Structure-Behavior Coalescence design of HCIS demonstrates tremendous effects of disciplined system layering. Through component operation diagram, Structure-Behavior Coalescence design of HCIS demonstrates large effects of well-defined components and published interfaces. Through interaction flow diagram, Structure-Behavior Coalescence design of HCIS demonstrates tremendous effects of well-defined behaviors. Structural Equation Modeling (SEM) refers to a diverse set of unrelated computer algorithms and statistical methods, which are suitable for constructing networks for analysis. Applied SEM method can verify that Structure-Behavior Coalescence design is be able to enhance the Modifiability of HCIS.