Auxiliary Library Research Articles

Influence of the Competencies of Lifting Crane Specialists on the Probability of Emergencies

Introduction. The operation of lifting cranes is an integral part of the production processes. For the trouble-free operation of these mechanisms, certain knowledge, skills and abilities are required, which should also be possessed by specialists performing organizational and supervisory functions at facilities where such cranes are involved. Here there is an important problem – the lack of a reasonable connection between the level of development of professional competencies and possible emergency situations, as well as various incidents during the operation of lifting cranes. The authors of this study are trying to solve it. Their goal in this regard is to assess the probability of an emergency during the operation of lifting cranes, depending on the level of professional competence of specialists, through the use of neural networks.Materials and Methods. The competencies of workers in the operation of lifting cranes (knowledge, skills and work responsibilities) provided for by the professional standard «Specialist in the operation of lifting structures» were used as initial data to train neural networks. Based on them, a list of possible incidents was compiled. For the purposes of training, the results of the certification of 200 conditional employees were generated. During the generation, the Monte Carlo method was used, and the data were output to Excel tables. Neural networks were trained in Python 3.10 in the PyCharm development environment. Open libraries Keras and TensorFlow, as well as auxiliary libraries for data representation and processing (Pandas, NumPy, Scikit-learn) were used for neural networks training.Results. As a result, a tool was obtained – a neural network in the form of executable program code, which makes it possible to assess the probability of emergencies during the operation of lifting cranes by analyzing the degree of proficiency of specialists in professional competencies. It is proposed to implement artificial intelligence technologies based on neural networks in order to assess the knowledge, skills and abilities of specialists of facilities operating lifting cranes, both during the certification of employees and in the course of work.Discussion and Conclusion. The main result of using neural networks to assess the knowledge of employees of facilities operating lifting cranes is the expected reduction in accidents, which can be ensured by timely identification of incompetent personnel at the stages of primary certification and, most importantly, during periodic tests of knowledge based on an impartial analysis and evaluation of data.

Embedded fuzzy logic for controlling pH and nutrition in hydroponic cultivation

The uptake of plant nutrients causes nutritional imbalances in hydroponic recirculating systems. Generally, the biological parameters of nutrition solution systems are regulated by pH and nutrients that affect plant health; thus, they must be monitored and managed. In order to solve this issue, a fuzzy logic-based automated control system for intelligent hydroponics was developed. This paper describes the implementation of fuzzy logic controllers into a hydroponic monitoring system. This method produces a controlled hydroponic system in hydroponics by first gathering hydroponic plant data in real time. The system is created using the following steps: system design, prototyping, testing, and assessment. The system’s input consists of TDS and pH sensors, which are used to detect the plant’s nutrient content and pH. The system’s output is a 4-channel relay coupled to 4 small pumps for raising the pH level (pH up), reducing the pH level (pH down), and delivering nutrition AB mix. The fuzzy inference engine employs a combination of the Min-Max and Mamdani techniques. Defuzzification was performed using the centroid technique. The embedded fuzzy logic library (eFLL) is an auxiliary library used by the Arduino IDE. The developed mechanism makes appropriate selections about the nutrition solution management system and pH level.

MONITORING DATA AGGREGATION OF DYNAMIC SYSTEMS USING INFORMATION TECHNOLOGIES

The subject matter of the article is models, methods and information technologies of monitoring data aggregation. The goal of the article is to determine the best deep learning model for reducing the dimensionality of dynamic systems monitoring data. The following tasks were solved: analysis of existing dimensionality reduction approaches, description of the general architecture of vanilla and variational autoencoders, development of their architecture, development of software for training and testing of autoencoders, conducting research on the performance quality of autoencoders for the problem of dimensionality reduction. The following models and methods were used: data processing and preparation, data dimensionality reduction. The software was developed using the Python language. Scikit-learn, Pandas, PyTorch, NumPy, argparse and others were used as auxiliary libraries. Obtained results: the work presents a classification of models and methods for dimensionality reduction, general reviews of vanilla and variational autoencoders, which include a description of the models, their properties, loss functions and their application to the problem of dimensionality reduction. Custom autoencoder architectures were also created, including visual representations of the autoencoder architecture and descriptions of each component. The software for training and testing autoencoders was developed, the dynamic system monitoring data set, and the steps for pre-training the data set were described. The metric for evaluating the quality of models is also described; the configuration of autoencoders and their training are considered. Conclusions: The vanilla autoencoder recovers the data much better than the variational one. Looking at the fact that the architectures of the autoencoders are the same, except for the peculiarities of the autoencoders, it can be noted that a vanilla autoencoder compresses data better by keeping more useful variables for later recovery from the bottleneck. Additionally, by training on different bottleneck sizes, you can determine the size at which the data is recovered best, which means that the most important variables are preserved. Looking at the results in general, the autoencoders work effectively for the dimensionality reduction task and the data recovery quality metric shows that they recover the data well with an error of 3–4 digits after 0. In conclusion, the vanilla autoencoder is the best deep learning model for aggregating monitoring data of dynamic systems.

Relational Action Bank with Semantic–Visual Attention for Few-Shot Action Recognition

Recently, few-shot learning has attracted significant attention in the field of video action recognition, owing to its data-efficient learning paradigm. Despite the encouraging progress, identifying ways to further improve the few-shot learning performance by exploring additional or auxiliary information for video action recognition remains an ongoing challenge. To address this problem, in this paper we make the first attempt to propose a relational action bank with semantic–visual attention for few-shot action recognition. Specifically, we introduce a relational action bank as the auxiliary library to assist the network in understanding the actions in novel classes. Meanwhile, the semantic–visual attention is devised to adaptively capture the connections to the foregone actions via both semantic correlation and visual similarity. We extensively evaluate our approach via two backbone models (ResNet-50 and C3D) on HMDB and Kinetics datasets, and demonstrate that the proposed model can obtain significantly better performance compared against state-of-the-art methods. Notably, our results demonstrate an average improvement of about 6.2% when compared to the second-best method on the Kinetics dataset.

Applying potential-based panel method for steady flow analysis across a wing with finite span

In the paper hereby, an incompressible irrotational steady flow across a submerged body with finite dimensions will be studied. For this purpose, it is necessary to solve Laplace’s differential equation about a potential function in order to obtain the conservative velocity vector field. A general solution to the problem utilizing the Green identity implies the double layer potential function at an arbitrary point not belonging to the boundary surface. The potential is expressed by source/sink and doublet singularities distributed over the body surface and a wake attached to the trailing edge. The wake ensures that the Kutta condition is fulfilled. The submerged body geometry is approximated further by quadrilateral panels in order to compute the surface integrals for each panel exactly. To form a linear non-homogenous algebraic system, it is essentially to compute each panel influence to a collocation point of interest. The obtained coefficient matrix is diagonally dominant. The system is solved iteratively by means of the Gauss-Seidel method. The goal is development of a non-proprietary source code in order to work out a solution to the stated problem. The developed source code is authentic. Auxiliary libraries have not been used. Validation case and numerical results are depicted and discussed in the paper.

RESEARCH AND ANALYSIS OF APPLICATION OF AUTOMATED TESTING IN WEB APPLICATIONS

The article discusses the issues of technology and methodology for automating the process of testing Web applications. Currently, developers and automation professionals are moving to popular developing development environments. In the process of developing and testing software in the team, not only developers and testers work, but also business professionals who define a set of changes when new versions of software products are released. When you update the product, an integral part of the release is the creation of automated testing. Means of providing automation is the use of an object-oriented approach to project implementation, a standard set of tools for providing processes for building and debugging programs, connecting auxiliary libraries for the programming language used (C++, C#, Java, etc.); checking the system versions, storing the automation project and the software product in one location.The main goal of this study is to identify and then analyze the weaknesses and strengths of methods for automating the testing of web applications, which are currently in the stage of rapid development. To carry out the research itself, information work was carried out (articles, reports, automation tools themselves) of the development stages of this topic. As a result, the authors made conclusions about further directions in the development of web application testing, identified the merits of existing solutions and identified areas that are not sufficiently developed.

Algorithm 965

Revisionist integral deferred correction methods are a family of parallel-in-time methods to solve systems of initial values problems. The approach is able to bootstrap lower-order time integrators to provide high-order approximations in approximately the same wall-clock time, hence providing a multiplicative increase in the number of compute cores utilized. Here we provide a library that automatically produces a parallel-in-time solution of a system of initial value problems given user-supplied code for the right-hand side of the system and a sequential code for a first-order timestep. The user-supplied timestep routine may be explicit or implicit and may make use of any auxiliary libraries that take care of the solution of any nonlinear algebraic systems that may arise or the numerical linear algebra required.

Effective fragment potential method in Q‐CHEM: A guide for users and developers

A detailed description of the implementation of the effective fragment potential (EFP) method in the Q-CHEM electronic structure package is presented. The Q-CHEM implementation interfaces EFP with standard quantum mechanical (QM) methods such as Hartree-Fock, density functional theory, perturbation theory, and coupled-cluster methods, as well as with methods for electronically excited and open-shell species, for example, configuration interaction, time-dependent density functional theory, and equation-of-motion coupled-cluster models. In addition to the QM/EFP functionality, a "fragment-only" feature is also available (when the system is described by effective fragments only). To aid further developments of the EFP methodology, a detailed description of the C++ classes and EFP module's workflow is presented. The EFP input structure and EFP job options are described. To assist setting up and performing EFP calculations, a collection of Perl service scripts is provided. The precomputed EFP parameters for standard fragments such as common solvents are stored in Q-CHEM's auxiliary library; they can be easily invoked, similar to specifying standard basis sets. The instructions for generating user-defined EFP parameters are given. Fragments positions can be specified by their center of mass coordinates and Euler angles. The interface with the IQMOL and WEBMO software is also described.

GLSV: Graphics library stereo vision for OpenGL

This work proposes the development of an auxiliary library for use with OpenGL, to facilitate the creation of graphic applications incorporating stereoscopic representation. This library, christened graphics library stereo vision (GLSV), is designed to remove all calculations involving knowledge of stereo vision theory from the task performed by the programmer without the latter having to change the way he/she has been working with the OpenGL library. The GLSV is distributed under the terms of the GNU Library General Public License agreement.