bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of a Comprehensive Sequence Forecasting Framework Based on DAE-LSTM Algorithm
Authors: Zhao Yonghe , Ren Xiaochen , Zhang Xingyi
Published: January 2021

Sales forecasting is a multivariate time series forecasting problem. The main challenges of the forecasting task are the high-dimensional influence variables with noise and the complex time series relationships. In this paper, we propose a DAE-LSTM algorithm combined with denoising autoencoder(DAE) and long short-term memory(LSTM) to deal with this problem. The DAE is a non-linear dimensionality reduction method that enhances data robustness. The LSTM is a deep learning algorithm suitable for dealing with multivariate time series problem. Combining the advantages of the above two algorithms, DAE-LSTM is a new multivariate time series prediction algorithm, which transforms unsupervised DAE into supervised data feature extraction method, and adds feature selection function to LSTM algorithm. In addition, in order to build a comprehensive forecasting framework, the hyperparameters of the model are determined by Bayesian optimization method(BOM) and the parameters of the model are solved by adaptive moment estimation(ADAM) optimization algorithm. In the training process, due to the adaptability of the BOM and ADAM, the forecasting model has few parameters that need to be set artificially, only including dimensional scale and time steps determined by data. Finally, the proposed forecasting framework is applied to forecast the sales of online retailers on JD.COM. In comparison with other machine learning algorithms, including multiple linear regression(MLR), support vector regression(SVR), artificial neural network(ANN) and LSTM, the proposed DAE-LSTM achieves the best prediction accuracy.
An Optimal Design of High Output Power CMOS Class E Power Amplifier with Broadband Matching for RFID Applications
Authors: Zahid Muhammad Noaman , Jiang Jianliang , Lu Heng , Khan Shahrukh , Zhang Hengli
Published: January 2021

In the radio frequency frontend the power amplifiers are most essential block for reliable wireless communication. Power amplifiers are used for amplification and enhance the input signal to the desired power level at output. The class-E power amplifiers are used in many RF portable electronic devices that need low power consumption, high gain, and high efficiency. In this article, we present an optimum design of a complementary metal-oxide semiconductor (CMOS) class-E power amplifier (PA) that achieves high efficiency and high gain simultaneously for RF based electronic article surveillance (EAS) system. The simulated analysis and implementation of a class-E RF power amplifier with an appropriate output impedance matching network are presented. The proposed CMOS class-E PA has 41.7 dBm output power with 63% of power efficiency at frequency of 7.7 MHz to 8.7MHz.
Automation of detection of social network mental disorders – A review
Authors: Singh Anju , Singh Jaspreet
Published: January 2021

Recent years have seen a growing association between social media use and mental disorders. Experts opine that excessive use of social media sites has reached alarming levels, leading to Social Network Mental Disorders (SNMD). The Social Networking Sites (SNS) usage generates massive amount of complex data, which is difficult to analyze, find patterns within and make predictions manually, in turn making it difficult to detect SNMD in the SNS users. The paper summarizes the studies involved in the detection of mental disorders due to prolonged use of social networking sites and provides means of understanding an accurate predictive platform using data mining techniques to build machine-learning framework complementary to the conventional detection methods. We present an in-depth survey of the proposals that included the subtypes of SNMD in the SNS users – Cyber relationship addiction, Net compulsion, Information overload addiction, Cyber sex addiction and Computer addiction. The researchers bring forth models that integrate data mining techniques, Natural Language and computer vision programming tools, with social media and behaviour sciences, providing promising results. Data mining approaches to detect SNMD despite being challenging, are effective and can be used as an efficient tool. The challenges and issues related to automate the detection process is also analyzed in the paper. Thus we present that the automation of detection of SNMD in SNS users has potential to improve the existing health care systems.
RETRACTED: Analysis and identification of body features in Alzheimer’s patients for gait recognition and gait attractiveness
Authors: Abhilash  , Kaur Sukhkirandeep
Published: January 2021

Alzheimer’s infection is a degenerative mind malady and the most well-known reason for dementia [1, 2]. There has been a lot of past research on which substantial highlights are generally significant in gait investigation. The essential objective of the examination revealed here was to adopt a diagnostic strategy to addressing these inquiries, with regards to recognizing the highlights that are generally significant for gait acknowledgment and gait allure assessment. We dissected the relative movements from various body sections to a root marker of 30 guys by the fixed root strategy. The lower left arm, hips and lower legs were distinguished as significant highlights for gait acknowledgment. The lower legs were perceived as significant highlights for gait allure assessment. Three diverse extraction strategies were applied to accomplish gait marks. In extra, stride appeal was broke down by means of Principle component analysis and linear regression strategy.
Biomedical image classification using deep convolutional neural networks – overview
Authors: Bharath Simha Reddy M , Rana Pooja
Published: January 2021

Deep Learning is an advanced area of machine learning which gained much interest in the past decades. It has been widely used in a variety of applications and has proved to be an effective machine learning method for many complicated issues. Especially when it comes to the medical field, the classification of biomedical images is a complex task to identify and classify the images manually by the doctors. So, Deep Learning is a key to enhance the classification of biomedical images using various architectures. The biomedical picture classification aims to identify and classify biomedical characteristics efficiently, which have significant advantages to numerous study and development fields. In this paper, the framework focused on the different architectures that were used to classify the medical images along with their performances.
Fog Computing Serverless Architecture for Real Time Unpredictable Traffic
Authors: Kaur Navjeet , Mittal Ayush
Published: January 2021

Fog computing is a middle layer in the cloud-IoT model. The additional layer developed to offload the traffic from the cloud by sharing its workload. The offloading of tasks in Fog computing is done by deploying intelligent nodes near the edge of a network to process data locally. The smart nodes are also called Fog nodes, which act as a mini cloud within an area of the network. The Fog network comprises various Fog nodes and a Fog manager which handles their communication and allocates each incoming request to optimal capacity Fog node. The Fog manager at the Fog layer is busier in managing the network than providing the solution to different applications, which should be its primary responsibility. The vast responsibilities list of a Fog manager degrades its efficiency to provide solutions on time. This problem is targeting through this paper, and the proposed work provides a solution through a serverless approach at the Fog layer.
On Numerical Implementation of the Laplace Equation-Based Image Inpainting
Authors: Thanh Le Thi , Hoang Thanh Dang Ngoc , Hue Nguyen Minh , Thuy Linh Ha Nguyen , Surya Prasath V. B.
Published: January 2021

In the current study, we focus on the development of a numerical algorithm for the Laplace equation-based image inpainting problem. A software program to implement the proposed algorithm is also developed. In experiments, the proposed method recovered corrupted images effectively. It can also remove defects as well as unnecessary objects successfully. The program provides an effective, convenient, and easy solution to perform the image inpainting in practical tasks. It also provides a useful feature to assess image quality after processing with both full reference and blind image quality assessment.
A Statistical Analysis On Covid-2019 To Distinguish Between Myths And Facts With Data Visualization
Authors: Konar Karthik , Kabli Nirali
Published: January 2021

Covid-2019 pandemic which has become the world’s worst nightmare, with no cure in sight and a lot of misinformation available on the internet, one can have a very tough time in separating fact from fiction. From non-veg meats, garlic water and weird self test, one will find a lot of myths related to coronavirus infection. Therefore to separate myths from facts we conducted a survey to know what actually people think about the virus. The main objectives of our survey was:(1)To describe the level of knowledge of the participants regarding COVID-19.(2)To determine participants’ point of view related to the COVID-19 disaster.(3)Provide useful information to the government of India.(4)To distinguish between myths and facts related to COVID-19 Disaster.
Development of Association Rule Mining Model for Gender Classification
Authors: Tiwari Meena , Shanthi V. , Mishra Ashish
Published: January 2021

Fingers have unique and developmental properties for biometric authentication systems. This is because fingerprints have the following characteristics: workable, distinctive (clear), durable, accurate and reliable, and accept security and identity worldwide. Fingers are seen as legal proof of this evidence in courts around the world. SVM support machines, NN is a neural network, FCM is a fingerprint classification system used in various modes, such as the Fuzzy C method, which is widely used to adopt a model. Gender determination from fingerprints is an important step in reducing the list of offensive searches in anthropology, as few machine-based methods have been requested for gender acceptance and correction. Several researchers have carried out gender fingerprint analysis and obtained competitive results. This article explains the difference between the genders using communication methods and classification methods. It is recommended that competent studies be combined with different methods and methods with comparative indicators to predict results. This allows researchers to conduct a comprehensive study and further research on models for the management of gender-specific mines.
Detection of Pneumonia using ML & DL in Python
Authors: Sharma A , Negi M , Goyal A , Jain R , Nagrath P
Published: January 2021

Pneumonia is form of a respiratory infection that affects the lungs. In these acute respiratory diseases, human lungs which are made up of small sacs called alveoli which in air in normal and healthy people but in pneumonia these alveoli get filled with fluid or “pus” one of the major step of phenomena detection and treatment is getting the chest X-ray of the (CXR). Chest X-ray is a major tool in treating pneumonia, as well as many decisions taken by doctor are dependent on the chest X-ray. Our project is about detection of Pneumonia by chest X-ray using Convolutional Neural Network. This paper written by us is an efficient approach towards classifying chest X- rays into pneumonia and no pneumonia X-rays. We have taken this approach as the most used radiography method produces errors. So, we have used CNN and batch normalization from keras to develop this model, and calculated accuracy using confusion matrix. We were successful in doing so with the help of “Python” and “OpenCV”, both of which are freely available and are open source tools and can be used by anyone. Pneumonia day, states that by the year 2030, 11 million children who are under the age of 5 year.
Technology assisted farming: Implications of IoT and AI
Authors: Aggarwal N , Singh D
Published: January 2021

With the advancement of technologies, things became intelligent with the capabilities of self-communication between them. Internet of Things (IoT) connected daily household things to the Internet and make them able to make decisions like the human mind. Sensors collect the real atmospheric data and with the help of Artificial intelligence (AI) algorithms analysis of data takes place so that devices behave more smartly. The present article discusses how IoT revolutionized the agricultural community. According to the study, it is analyzed that 70% population is dependent on agriculture for their livelihood in India, but the status of agriculture is no more concealed from society. With the involvement of technology, it becomes easy to predict temperature, rainfall, humidity, the need for fertilizers, water requirements, etc. The introduction of modern agriculture techniques using IoT & AI is revolutionizing the traditional agriculture methodologies and are making farming a profitable venture also.
Efficient Mobile Sink Path Scheduling for Clustered based Wireless Sensor Network
Authors: Ghorpade Naveen , Vijaykarthik P.
Published: January 2021

WSN (Wireless Sensor Network) has found wide range of adoption in different sector and it is primary component of much real time application such as IoT, Big Data etc. Meanwhile modern application needs scalable real time data access and low latency since they are heterogeneous; however achieving the low latency for real time data causes the energy overhead in sensor device. Moreover, two technique i.e. clustering technique and Mobile sink helps in achieving the scalability and reducing the energy consumption in sensor device, further this causes the energy overhead in rendezvous node and CH (Cluster Head). In order to address this issue several optimization technique were presented for selection of rendezvous node and optimal cluster, the main drawbacks of these techniques are that they are applicable only for the homogeneous network. Hence, to overcome this issue, this research work presents technique EMSPS (Efficient Mobile Sink Path scheduling) technique for heterogeneous WSN, this technique is cluster based. Furthermore, EMSPS introduces the multiple-objective function such as network traffic for CH and rendezvous node selection, coverage time, radio selection strength, connection time and connectivity. EMSPS is evaluated considering the various parameter such as loss of connectivity, first node death, total node death and network lifetime, further comparison has been done with the state-of-art technique by considering the above parameters. Nevertheless, EMSPS provides the satisfactory trade-off between the energy and latency requirement.
Automatic Deep Neural Network Hyper-Parameter Optimization for Maize Disease Detection
Authors: Bansal Subodh , Kumar Anuj
Published: January 2021

Deep Convolutional Neural Networks (DCNNs) have proved to be very useful for image classification. These need to be optimized for the dataset in hand, for its optimal use. DCNNs have many attached hyper parameters. These hyper-parameters are fine-tuned for optimizing the DCNN model. The present research uses the Bayesian Optimization technique for fine-tuning the hyper-parameters for AlexNet DCNN model. The model is trained and tested on Maize (corn) disease sub-dataset of the Plant Village dataset. The trained DCNN model with optimized parameters achieved the accuracy of 96.05%.
The efficient consensus algorithm for land record management system
Authors: Yadav A S , Shikha S , Gupta S , Kushwaha D S
Published: January 2021

This research presents a scalable property/land registration framework. It is based on blockchain technology, where all registry offices come together on a single framework based on blockchain to improve the registration process. This paper mainly focuses on the problems in the land record and revenue sectors. In this paper, we explore the use of blockchain technology in current business scenarios. We discuss issues such as data security, privacy, and, more significantly, the absence of a shared forum amongst the involved organizations. This research proposes an efficient consensus mechanism to evaluate the performance of a blockchain-based implementation of a land revenue & recording automation system. The proposed Modified Round Robin Consensus Algorithm (MRRCA) reduced the 30% - 40% message overhead in committing a transaction to the network and thus adding the block to the blockchain.
Reconstruction of hyperspectral images from RGB images
Authors: Pushparaj Pratish , Dahiya Nishthavan , Dabas Mayank
Published: January 2021

This paper accounts for the problem of construction of hyper-spectral (hs) images from RGB-images, i.e recovery of the whole spectral details/signature from a three-channel RGB image. The dataset used in this paper consists of ‘clean’ images, that are images without noise. There are 450 clean images along with correlative 450 hyperspectral images and all the pages are in PNG (.png) format. We approached this problem using 3 models Convhs_5, Enhanced-ResNet and Dense-HSCNN (D-HSCNN). These models increase in complexity from Convhs_5 to Dense-HSCNN. In the evaluation phase, Convhs_5 achieved average results with not so much clarity, whereas the best model came out to be Enhanced-ResNet with satisfying results, however, due to insufficiency of computational power/resources, we were not able to get the expected results from the Dense-HSCNN model. Whilst having more number of images in the dataset and more computational power can reduce the loss in all of the models furthermore.
Holistic Approach in Deep Learning and its Applications
Authors: Yadav Sahil , Choudhary Karan , Singh Tanishq , Yadvanshi Dhruv , Goyal Monika
Published: January 2021

Deep learning has become a territory fundamental to scientists in the past barely any years. Convolutional Neural Organization is a significant learning approach that is thoroughly utilized for managing complex issues. It beats the prerequisites of standard Artificial Insight moves close. Critical learning, a technique with its establishment in fake neural systems, is making beginning late as a remarkable asset for computer based intelligence, promising to reshape the conceivable predetermination of man-made reasoning. Getting data and important encounters from unusual, high-dimensional and heterogeneous biomedical data remains a key test in changing clinical administrations. Moreover we will investigate the difficulties in enormous information assessment.
Image Sizing using Removal or Insertion of Seams based on Energy Function
Authors: Suhandas  , Santhosh Kumar G , Aravind K U
Published: January 2021

The nature of the image plays a significant role in any image sizing algorithm. The technique of improving or condensing an image while preserving its key image quality is challenging. The resolutions of various display devices vary from one another in order to fit the image according to the resolution image sizing plays a vital role. In order to size the image a operator is introduced which will remove or insert the seam from an images based on the resolution of the device used. A seam in an image is related to the pixel path from up to down or right to left, the seam vector to be deleted for image size is defined by the image energy attribute. The Seam removal or insertion operator can be applied to an image and aspect ratio can be adjusted accordingly by continuously removing or adding a seam in the desired region of image. The collection and order of seams decides the picture quality, as described by the energy function. By storing the order of seams in an image that can continuously change to fit a given size in real time, multi-sized images can be produced.
An Enhanced Adaptive Histogram Equalization Based Local Contrast Preserving Technique for HDR Images
Authors: Dar Khursheed Ahmad , Mittal Sumit
Published: January 2021

To improve the visualization of an image, the image enhancement techniques enhance the assured features of an image. In this work, the algorithm will enhance medical as well as normal or natural images which are captured in low light and day light conditions. An enhanced adaptive histogram equalization based local contrast preserving technique is developed with the help of image processing methods such as changing colour spaces, inverting images, dehazing, increasing saturation etc. The algorithm proposed here is intended to maintain the local image details while attaining the contrast enhancement. To express the performance of this algorithm, the image quality metrics calculated are peak signal to noise ratio and normalized absolute error. This metric parameters show that this model has better performance when compared to other existing methods.
Intelligent Traffic Light Control System Based On Traffic Environment Using Deep Learning
Authors: Sharma Moolchand , Bansal Ananya , Kashyap Vaibhav , Goyal Pramod , Sheikh Tariq Hussain
Published: January 2021

At present, the traffic control frameworks in India, need insight and go about as an open-loop control framework, with no input or detecting system. Present technologies use Inductive loops and sensors to detect the number of vehicles passing by. It is a very inefficient and expensive way to make traffic lights adaptive. Using a simple CCTV camera can improve the conditions. The visual tracking of objects is amongst the most critical areas of computer vision and deep learning. The objective of this work was to develop the traffic control framework by presenting a detecting system, which gives an input to the current system, with the goal that it can adjust the changing traffic density patterns and provides a vital sign to the controller in a continuous activity. Using this method, improvement of the traffic signal switching expands the street limit, saves time for voyaging, and prevents traffic congestion. The framework additionally goes for consolidating exceptional arrangements for clearing the path for emergency vehicles. In this paper, we will detect and track vehicles on a video stream and count those going through a defined line and to ultimately give an idea of what the real-time on-street situation is across the road network. Our real target is to advance the deferral in the travel of vehicles in odd hours of the day. It uses the YOLO (“You Only Look Once”) object detection technique to detect objects on each of the video frames And SORT (Simple Online and Real-time Tracking algorithm) to track those objects over different frames. Once the objects are detected and tracked over different frames, a simple mathematical calculation is applied to count the intersections between the vehicle’s previous and current frame positions with a defined line.

However, accuracy drops when vehicles are either close together or have large shadows, dark vehicles do not always meet the detection criteria, and night scenes are challenging to resolve as headlight beams can create large areas that meet threshold criteria. We are focusing on the Indian roads where the traffic conditions are harsh and abrupt. We have already tested it on a live feed from various traffic prone roads and have found satisfactory results. Other Adaptive Traffic Light Systems were not able to work on the Indian traffic conditions. Our model has the edge over the other by performing at par on Indian roads. We’ll increase or decrease the timer according to the conditions of the roads. This will tremendously improve the traffic conditions at a very low cost. Inductive loops are a feasible but expensive method. So, this system reduces costs and provides quick results.
Forecasting And Prediction Of Air Pollutants Concentrates Using Machine Learning Techniques: The Case Of India
Authors: Sharma Moolchand , Jain Samyak , Mittal Sidhant , Sheikh Tariq Hussain
Published: January 2021

Air quality index (AQI) is a number used by government agencies to communicate to the public how polluted the air currently. It is based on several factors like SO2, NO2, O3, RSPM/PM10, and PM2.5. Several methods were developed in the past by various researchers/environmental agencies for the determination of AQI. Still, there is no universally accepted method that exists, which is appropriate for all situations. We have developed a prediction model that is confined to standard classification or regression models. These prediction models have ignored the co-relation between sub-models in different time slots. The paper focusses on a refined model for inferring air pollutants based on historical and current meteorological datasets. Also, the model is designed to forecast AQI for the coming months, quarters or years where the emphasis is on how to improve its accuracy and performance. The algorithms are used on Air Pollution Geocodes Dataset (2016-2018), and results calculated for 196 cities of India on various classifiers. Accuracy of 94%-96% achieved from Linear Robust Regression, which increases to 97.92% after application of KNN and 97.91% after SVM and 97.47 after 5th epoch of ANN. Decision Tree Classifier has given the best accuracy of 99.7%, which increases by 0.02% on the application of the Random Forest Classifier. Forecasting achieved by Moving Average Smoothing using R-ARIMA, which offers daily values for the coming 45days or monthly data of AQI for the next year.
Detection of Cardiac problems by the Extraction of Multimodal functions and Machine Learning techniques
Authors: Kasturiwale Hemant , Kale Sujata N
Published: January 2021

The machine learning based model is designed for robustness on the basis of both ECG based HRV analysis and non-ECG based analysis. The goal is to evaluate the efficacy of different machine learning classification models. A statistical analysis is provided with repositories such as MIT/BIH Normal Sinus Rhythm (NSR) and MIT/BIH Atrial Fibrillation (AF) and Peripheral Pule Analyzer. The model was checked on all possible subject conditions, the form of ECG database and the non-ECG signal. The Best Feature was chosen from the various HRV Settings that will be used for classification. In our intra group selection analysis, traditional and well-known machine learning classification techniques, such as Random Forest, Support Vector Machine, K-Nearest Neighbours, Adaptive Boosting, Support Vector Machine. Robustness is driven primarily by precision, flexibility and specificity. The 5 percent higher accuracy band and lower band model are tested. The Random forest has produced better performance and has been tested for its robustness. Testing is carried out for more than 20 indices and more than 40,000 combinations generated and added to the model for study. The efficacy of these classifier-based Intra-Group selection models is tested by performing variety of dataset experiments obtained from standard sets as well as acquired data. Overall experimental findings and discussions will enable all researchers to assess the effect of the features on the model.
Deposition process of an epilam film during surface plastic deformation
Authors: Barsukov G V , Selemenev K F , Danilchenko S G , Petrukhin A V , Titov D I , Pankov G B
Published: January 2021

The article deals with the conditions of contact between the tool and the nominal surface during surface - plastic processing with balls due to centrifugal force. The dependences reflecting the impact of the oblique impact of the ball on the surface are given. It has been established that the deformation process can be significantly influenced by the epilamic coating of the working elements of the tool, which significantly changes the energies of the interaction of the contacting surfaces.
Investigation of the process of additive formation of fusible materials using a low-power solid-state laser
Authors: Kuts V V , Merkulov V S , Grechukhin A N , Privalov A S
Published: January 2021

The method was developed for the additive formation of fusible materials based on research. He suggests using a low-power solid-state (ytterbium) laser (maximum power 50 W) for this. A series of experiments were carried out and the elements were obtained for the shaping mode without complete melting while maintaining the shape of the workpiece, such as: power, focal length, travel speed. The results confirmed the possibility of using this type of equipment for the additive forming of products from fusible materials. They are the basis for further research on other materials, including with the additional use of a protective atmosphere.
Study of the parameters of the pure iron structure after surface plastic deformation treatment with a complex-profile tool
Authors: Blumenstein V Yu , Mitrofanova K S
Published: January 2021

The structure of pure iron after surface plastic deformation (SPD) by a multiradius roller (MR-roller) was studied using optical microscopy (OM) and atomic force microscopy (AFM). An optical microscope of reflected light is used to study the size and uniformity of grains and subgrains (Neophot-21). The fine structure of the sample surface was studied using an atomic force microscope (Solver PH47-PRO) contact method. The paper shows changes in the deformation relief of the material after processing with an MR-roller.
Study of non-linear synergy control laws on the experimental stand of pneumatic actuators
Authors: Obukhova E
Published: January 2021

The purpose of this work is to synthesize the laws for controlling the position of the piston of a pneumatic cylinder using the synergetic method of analytical design of aggregated controllers (ACAR), which takes into account the nonlinear dynamics of the system under consideration.

The procedure for the synthesis of synergetic control is based on the introduction of a sequence of invariant manifolds, proceeding from the technological problem of positioning the pneumatic cylinder rod at the required position, and the subsequent step-by-step dynamic decomposition of the original dynamic system.

As a result, the synthesized nonlinear two-channel regulator moves the pneumatic actuator rod to a predetermined position while simultaneously stabilizing the pressure in the drain chamber. Simulation of the system with a regulator showed that the control law obtained in analytical form moves the pneumatic actuator rod to a given position while simultaneously stabilizing the pressure in the drain chamber.

The paper deals with practical research that was carried out at the educational and experimental stand of pneumatic drives for vertical and horizontal movement of the Camozzi company.
Nonlinear dynamics of liquid crystal: ultrasonic light modulator
Authors: Denisova O A
Published: January 2021

The article discusses the influence on the physical properties of a planar-oriented cholesteric liquid crystal (chlesterylpelargonate) with a thickness of 10-15 microns, the propagation of elastic waves in the ultrasonic range (exposure frequency 600 kHz). Changing the pitch of the crystal spiral leads to changes in its optical properties, which is shown in the modulation spectrum of light reflection from the cholesteric layer in the selective scattering region. It was found that a positive peak is observed at a light wavelength of 436 nm, and a negative peak at 427 nm. A technique of experimental measurements is proposed, which uses the principles of modulation spectroscopy, which makes it possible to record with a high degree of accuracy even insignificant changes in optical spectra, which are not possible to register with conventional optical spectrometers. By analyzing the reconstructed reflection spectrum, it can be concluded how the swirling of a liquid crystal affects the processes that occur in it during the propagation of ultrasonic waves. The considered nonlinear processes are interesting for creating ultrasonic light modulators based on liquid crystals.
Theoretical studies of plain hinged linkage mechanisms movement in food processing robots-manipulators
Authors: Babenyshev S P , Bratsikhin A A , Mamay D S , Drofa E A , Prikhodchenko O V
Published: January 2021

Significance of the studied problem is conducted due to the lack of simple and accessible engineering methods that allow predicting kinematic characteristics and selecting parameters of the main elements of modern robotic manipulators that include mechanisms of parallel structure in their design. The solution of such tasks allows determining the ranges of operational indicators of the designed technological equipment in advance, as well as to develop criteria for their analysis. An algorithm for applying the Lagrange II equations to determine the equations of plain hinged linkage mechanisms movement with four degrees of freedom is proposed.
Antifriction properties of electrochemical coatings based on carbon materials
Authors: Drogan E G , Plekhanova E V , Burlakova V E , Varavka V N , Plekhanova T G
Published: January 2021

As a result of research by the detonation synthesis method, chemically stable nanodiamond particles with a size of up to 100 nm were obtained. Aqueous solutions of the obtained particles can be used as an electrolyte to obtain carbon coatings by the electrochemical method.
Computer Simulation of TiAlN Coatings and Its Analogues on Iron Surface
Authors: Migal Yu F , Kolesnikov V I , Shishiyanu D N
Published: January 2021

The strength of various variants of the TiAlN coating and its analogues on the iron surface is studied by using quantum chemistry methods. As a computer model, we consider a system of 5.5 atomic layers, 3 of which imitate the base (metallic iron), and 2.5 layers imitate its coating. The dependence of the coating strength on its chemical composition and the mutual arrangement of atoms of various elements is studied. It follows from the calculations that the strength of the optimal TiAlN coating is almost equal to the strength of the surface of pure iron. The proposed model was used also to study the role of the coating in protecting the iron surface from oxidation. It is shown that the considered coating variants practically do not undergo oxygen corrosion, which is one of the reasons for their high efficiency.
Pulsed laser deposition of Au nanoparticles on ZnO nanostructures
Authors: Nikolaev A L , Kamencev A S , Lyanguzov N V , Aizikovich S M
Published: January 2021

Au nanoparticles and ultrathin gold films were obtained on the surface of thin (100 nm) ZnO films on Si (001) by pulsed laser deposition (PLD) at high argon pressure by sputtering a pure gold target with a CL3100 pulsed excimer laser (λ = 248 nm) at room temperature. The dependence of the size and distribution of nanoparticles on the argon pressure in the vacuum chamber (PAr), the power density of laser radiation (j), and the number of laser pulses (N) was investigated. Also, to obtain nanostructures, axial and non-axial deposition was used, where the substrate was located perpendicular and parallel to the plasma torch, respectively. Stable modes of obtaining gold nanoparticles with high uniformity and average size from 4 to 10 nm were demonstrated. The modes of stable and reproducible deposition of ultrathin Au films (23-42 nm) and percolation structures (18-20 nm) were also demonstrated. Thus, PLD is a reliable and flexible tool for obtaining nanoparticles and ultrathin Au films the average size and thickness of which can be predictably controlled by varying the deposition parameters. This technique is well suited for coating with gold nanoparticles the surface of nanostructured materials based on chemically active substances that are particularly sensitive to surface cleanliness, which cannot be coated by standard methods.
Graphical interface software for the surgical table control system to monitor and adjust the parameters of surgical equipment
Authors: Filippenko V A , Epikhin A N , Korochentsev D A , Safaryan O A
Published: January 2021

The work is devoted to the development of software with a graphical interface for the surgical table control system, which allows you to monitor and adjust the parameters of surgical equipment. The object of the work is the processes between the computer and the microcontroller that control the surgical equipment. The subject is the software tool, which is used to ensure the operation of the surgical equipment control system. In the work there is an analysis of requirements and methods necessary for creation of safe medical software. After that, the algorithmic design takes place, which includes: selection of the design pattern, the structure of the codograms and the algorithm of the operation start.
Stimulated fluidity of Nd-Fe-B powder in magneto fluidized bed
Authors: Egorov I N , Egorov N Ya , Egorova S I
Published: January 2021

Experimental studies results of electromagnetic effect influence on stimulated fluidity of Nd-Fe-B powder are presented in the paper. Fine Nd-Fe-B powder doesn’t have natural fluidity. To make it flow through hopper nozzle, the powder was affected by constant and alternating gradient magnetic fields. Constant magnetic field induction lines were horizontal, and alternating field lines were vertical with gradient towards efflux direction. We studied influence of magnetic fields on powder fluidity speed to find optimal magnetic fields parameters that form steady efflux of Nd-Fe-B powder with an average particle size 4.6 μm through nozzles with diameters 1 and 2 mm.
Review of Methods for Solving Inverse Problems of Identification of the Magnetization of Permanent Magnets in Electrical Devices
Authors: Bakhvalov Y A , Balaban A L , Grechikhin V V , Denisov P A , Nekrasov S A
Published: January 2021

The article shows the need for identification of permanent magnets by studying the distribution of magnetization over their volume by solving inverse problems. The main approaches to solving inverse problems are considered: minimization of some functional and multiple solution of the direct problem of calculating the magnetic field; solution of an ill-posed problem and determination, using regularization methods, of a pseudosolution that is stable to small perturbations. An overview of the work performed aimed at improving the theoretical and practical approaches to solving these problems is given. New mathematical models and methods for calculating the magnetic field have been developed, which provide a decrease in the number of unknown parameters, an improvement in stability and a decrease in time consumption in the problems of identifying the magnetization of permanent magnets in electrical devices. The influence of the experimental data error on the solution result is estimated. Examples of solving test problems and experimental studies are given, which have shown the effectiveness of the proposed methods and approaches. The results obtained can also be used to solve direct and inverse problems of electrical engineering.
Intelligent Regularized Measurement Procedure Based on the Use of a Dynamic Model of the Combined Maximum Principle and the Theory of Regularization
Authors: Lazarenko S V , Kostoglotov A A , Khayrullin R Z , Kornev A S
Published: January 2021

A method for intellectualization of measurement procedures based on step-by-step optimization procedure is developed. The procedure reduces the stochastic estimation problem to a number of deterministic problems. The goal of the first stage of optimization is the synthesis of adaptive mathematical model of the measuring process based on the combined maximum principle. In contrast to the known methods this ensures the constructiveness of its use at the next stages of optimization. The goal of the subsequent stages of optimization is to solve the estimation problem based on the regularization method of A.N. Tikhonov. The equations for the iterative measurement procedure are obtained. The structure of their state transition vector functions differs from the known equations structure. This procedure belongs to the category of intelligent measurement procedures, since it makes a targeted choice of the parameter estimation closest to the true value the under the conditions of structural uncertainty of the model of the studied object and of parametric uncertainty of the observation model.
Software development for calculating the polluted by suspension and other impurities zones volumes on the basis of graphics accelerator
Authors: Chistyakov A E , Strazhko A V , Atayan A M , Protsenko S V
Published: January 2021

Software package construction for a distributed solution of the matter transfer problem in a reservoir is the aim of the work. The software package consists of several components that use different methods of interaction. The solution study posed by various methods and comparison of their performance is carried out. An algorithm for parallel solution on a graphics accelerator controlled by the CUDA system has been developed. A comparative analysis of the algorithms operation on CPU and GPU is carried out. The software implementation of the components included in the complex is described; the main classes and implemented methods are documented.
Synthesis of control of the aircraft angular velocities based on algorithm with a predictive model
Authors: Lazarenko S V , Trofimenko V N , Volkova A A , Kuchugura N O
Published: January 2021

An urgent issue is to obtain optimal control laws for nonlinear dynamic objects. The article presents a numerical algorithm to control stabilization of the angular velocities of spacecraft with various axial moments of inertia based on optimization with respect to the Krasovsky functional using the predictive model method. The simulation results are presented. The character of the curves of angular velocity dynamics and control moments obtained in the simulation corresponds to the curves of angular velocity dynamics and control moments obtained in the simulation of control using the analytical law for an axisymmetric spacecraft, which confirms the adequacy of the synthesized algorithm.
Detailing the model and objective function for the task of optimizing the power distribution process
Authors: Mokhov V A , Tkachev A N , Shaykhutdinov D V , Kostinskiy S S , Manatskov Y M
Published: January 2021

In this article, for the spectral expansion of periodic functions by means of a truncated segment of the Fourier series, the authors demonstrated options for their representation in the form of sets of coefficients and showed the relationship between the latter. On the basis of mathematical calculations, the authors have prepared an appropriate mechanism for organizing calculations, for which a number of linear matrix operations have been formed. The application of a new computing mechanism based on a discrete expansion of continuous periodic functions is demonstrated in detailing the problem of combinatorial optimization of the power distribution process, as well as the corresponding load model and target function.
Stability of terrestrial ecosystems under the influence of steroid glycoalkaloids
Authors: Seregin M , Dymnikova O , Bogdanova I
Published: January 2021

Soil-transmitted helminths are one of the most pressing problems of the urban area. Worm infestations are diseases with a high (more than 50%) mortality rate among both children and adults. Therefore, one of the most pressing safety problems of not only the technosphere, but also the homosphere, is the search for methods of disinfestation of the soil cover of urban areas with a minimum load on the biota and ecological risk tending to zero. As a solution to this problem, it is proposed to use secondary plant metabolites (steroid glycoalkaloids) as a means for disinfecting various environmental objects: wastewater and their sediments, soil, sand of playgrounds, etc.

The effect of various concentrations of potato juice (Solanum tuberosum) on the components of terrestrial ecosystems on the shores of water bodies: higher plants (oats (Avenum sativa)) and soil microflora (soil micromycetes Aspergillus niger, Aspergillus terrius, Alternaria alternaria, Fusarium moniliforme) was studied. Revealed moderate phytotoxicity for Avenum sativa, as well as a strong stimulatory effect for Fusarium moniliforme and Alternaria alternaria.
Techno-Economic Analysis of Increasing PV-Wind based DG Penetration in Sub-Transmission System
Authors: Mahajan Shreya , Verma Yajvender Pal
Published: January 2021

This paper deals with techno-economic impact of increasing photovoltaic and wind power distributed generation in sub-transmission system. This paper suggests a new approach to interpret a multi-objective problem of integrating different types of distributed generation using an improved elephant herding optimization method. The main aim of this paper is to minimize the power losses and elevate the voltage profile of the electrical system. The effectiveness of improved elephant herding optimization had been tested on IEEE 30 bus sub-transmission system and is compared with results obtained with Particle Swarm Optimization method. In this paper, three cases have been considered where different types of distributed generation are integrated into the system and the best results have been obtained for case 3 where both photovoltaic and wind power are penetrated in system network. The optimal solutions obtained are compared with particle swarm optimisation and base case. The comparison shows that the proposed optimization method is favourable. The economic analysis of the corresponding distributed generation has been presented in this paper.
Investigation of Adaptive Beam-forming Algorithms for Smart Antennas System
Authors: Kaur Simrandeep , Kumar Naresh , Dubey Subhash
Published: January 2021

Smart antennas are examined as a promising technology in wireless communication systems. In the development of smart antenna technology, Adaptive beam-forming is the most important aspect. Adaptive beam-forming uses various algorithms to identify the signals in the desired direction and separate the interference from the undesired/unwanted direction. In this paper, Proposed Adaptive Beam-forming Algorithm offers the better solution for reducing interference and improves the system capacity. The main objective of this paper is to analyze the different approaches used for designing the smart antennas to track the desired signal and put perfect null towards the interferer signal.
Applications of Ergonomic Studies in India: A Literature Review
Authors: S Shwetha
Published: January 2021

India, known for its diversity in resources and culture, is a land for a large variety of people resulting in huge variability in anthropometric dimensions. This necessitates human factors to be crucial in engendering usability in a product thereby making it beneficial. Due to the emerging complex work systems, knowledge on physical dimensions of humans with accuracy is influential. As a result, Production engineers are impelled to involve ergonomic factors into manufacturing a product. However, anthropometric data varies from region to region. Ergonomics accounts for all anthropometric differences in individuals. The paper tries to identify how well ergonomic aspects and anthropometric data have been applied in different areas. Proper ergonomic designs demands thorough knowledge on the effect of study of anthropometry, posture, repetitive motion, and workspace design on the user and also satisfies psychological desires of the user. The current work also addresses various literatures in identifying the importance of anthropometric and ergonomic studies in product design in India. Also, areas where Ergonomic knowledge should be considered is looked into. This literature can help in enlightening oneself about the areas of application of ergonomics and can be used for further investigation in researches related to anthropometry. It has been suggested that work study, statistical and optimization techniques can be used in combination with ergonomic applications to solve design issues.