Automation Of Method Research Articles

Electroacoustic modeling of speech intelligibility for a new road tunnel

This paper presents the process of electroacoustic design and modeling for tunnel public address (PA) system for the North East Link (NEL) road tunnel in Melbourne. The tunnel includes over 6km of twin-bore tunnels, with varying cross-sections along its length. The different tunnel spaces addressed in the design include mainline tunnel bores, interchanges, ramps, as well as pedestrian cross-passages and egress passages. This paper outlines how large data sets from CAD models were managed using a new automation method (Grasshopper 3D) throughout the design process to produce and maintain detailed electroacoustic modeling in Odeon software. In particular, this paper explores new and novel modeling methods adopted to address design constraints and to achieve a PA system capable of producing adequate speech intelligibility for emergency evacuation within very challenging acoustic environments.

Methods of automation and interpretation of the quality educational tests in distributed virtual learning systems

The subject matter of the article is the development of mathematical and algorithmic support for intellectual tools, which allows you to conduct continuous control of the knowledge of students (subjects of study) objectively and comprehensively. The goal of the work is to create methods for assessing the quality of educational tests and automating such processes. The following tasks were solved in the article: formation of a testing model in a distributed virtual learning environment and a validity assessment model based on the content of sets of test tasks. The following methods used are – algebra of finite predicates and operations, methods of mathematical statistics and methods of intellectual data analysis. The following results were obtained – the principles of intellectual analysis of the value of reliability coefficients, validity coefficients, discriminability coefficient, the difficulty index of the task of assessing the knowledge of subjects of training are formulated. Conclusions: the application of methods of formalization of test evaluation, analysis of software requirements, software development confirm the need to introduce quantitative methods of assessing students' knowledge into educational practice. The introduction of quantitative methods involves the correct setting of control goals, the selection of the measurement object and the selection of measurement tools. The use of pedagogical tests contributes to the effective implementation of all control functions and corresponds to its main principles for solving the problem of assessing the quality of tests, based on distributed virtual learning models and analysis methods. A test example of calculations was developed, with the help of which the efficiency of the proposed methods was researched.

IoT based home automation with energy management

Automation of home appliances has become the prime utility of embedded systems. This automation method is inclusive of a sensor-based automated system that requires no human/conventional interventions. This paper proposes the usage of voice commands to have control over the entire appliances, which is easy to handle by old age/disabled people. The major aspect of this paper is to introduce a new system for disabled and normal people. This method involves a Raspberry-Pi-3 control board, which has a WiFi module termed Raspberry-Pi-3. For sending the voice instruction to the Raspberry-Pi-3 a mobile application is used. The function of the application is to record the voice and convert the voice note into a command for Raspberry-Pi-3. This process is facilitated by WiFi communication. This system also uses IoT for measuring the power consumed by the active appliance over the current sensor and the Webserver can preview the power consumption.

Review of unmanned aerial vehicle obstacle avoidance planning based on artificial potential field

With the development and widely use of unmanned aerial vehicles (UAVs) in recent years, the development of efficient path planning methods for automation has become crucial. Obstacle avoidance and path planning are the key components of UAV path planning. This article provides an overview of obstacle avoidance and path planning techniques for UAVs based on the artificial potential field method (APF method). This article begins with the explaining the principles of artificial potential field on this basis discusses its advantages and limitations. The article then summarizes the improvement strategies proposed by previous researchers to address issues like local minimum values and unreachable targets, such as introducing a new repulsive potential energy function, combining APF with other planning methods, and utilizing flow functions. Furthermore, it presents examples of the application and the performance of usage of these techniques in both static and dynamic environments. Based on this, the prospects and developing trend of UAV obstacle avoidance methods based on artificial potential field are foresee, such as combined with DRL and deep learning.

Study on the Intent-based System Design Automation Method for Fault Detection and Fault Tolerance

ICT systems are becoming an important infrastructure indispensable for business operations in various industries. While the requirements are becoming more diverse and complex, stable service provision is required. As a result, the burden on operations is increasing, and there are growing expectations for automation technology to solve this problem. This study investigates techniques to significantly reduce the burden on operators by automating the construction and maintenance of systems that satisfy Intent, a concise description of users' requirements. However, even using the results of previous research, it has been difficult to automate operations with consideration for the stability of service provision. Therefore, this paper describes an automated method based on intent for detecting failures that occur in the target system and an automated design method for fault-tolerant systems. First, we describe the basic concept, present the results of thought experiments, and discuss its effectiveness.

Development of RNA/DNA automated extraction and purification device for infectious disease diagnosis

Genetic tests using RNA/DNA are the most accurate for diagnosing infectious diseases and assessing disease susceptibility, including COVID-19. However, manual specimen handling and the risk of secondary infections by medical staff highlight the need for automated equipment. Automation methods, such as bead purification, have limitations with high-viscosity specimens, while column purification requires complex equipment. This study aimed to develop an automated device using the column purification method for safe and reliable infectious disease diagnosis. We compared the yield and purification of three nucleic acid extraction methods (centrifugation, pressurization, and depressurization) and examined the adaptation of the extraction methods to automated device. Furthermore, we examined the feasibility of extracting SARS-CoV-2 RNA from COVID-19 patients and using qPCR analysis to determine whether the extraction method could be used as a clinical analyzer. Results varied with different columns and reagents, but pressurization method was selected for the automated device's RNA/DNA extraction. Using an automated device equipped with a pressurization method, RNA extracted from pharyngeal fluids from COVID-19 patients who had already been diagnosed with SARS-CoV-2 by qRT-PCR again tested positive. These findings demonstrate the device's effectiveness for nucleic acid extraction and virus-targeted diagnostics. Moreover, it holds potential for genetic testing in fields like food and environmental measurements. The automated device addresses specimen handling challenges and provides a reliable tool for infectious disease diagnosis.

Artificial intelligence in medicine: has the time come to hang up the stethoscope?

The question of whether the time has come to hang up the stethoscope is bound up in the promises of artificial intelligence (AI), promises that have so far proven difficult to deliver, perhaps because of the mismatch between the technical capability of AI and its use in real-world clinical settings. This perspective argues that it is time to move away from discussing the generalised promise of disembodied AI and focus on specifics. We need to focus on how the computational method underlying AI, i.e. machine learning (ML), is embedded into tools, how those tools contribute to clinical tasks and decisions and to what extent they can be relied on. Accordingly, we pose four questions that must be asked to make the discussion real and to understand how ML tools contribute to health care: (1) What does the ML algorithm do? (2) How is output of the ML algorithm used in clinical tools? (3) What does the ML tool contribute to clinical tasks or decisions? (4) Can clinicians act or rely on the ML tool? Two exemplar ML tools are examined to show how these questions can be used to better understand the role of ML in supporting clinical tasks and decisions. Ultimately, ML is just a fancy method of automation. We show that it is useful in automating specific and narrowly defined clinical tasks but likely incapable of automating the full gamut of decisions and tasks performed by clinicians.

Method of automating the process of object detection to increase the efficiency of deciphering aerial reconnaissance data

Problem factors that have a significant impact on the effectiveness of the process of deciphering air reconnaissance data obtained under the conditions of the use of unmanned aerial vehicles of the tactical level are analyzed, taking into account the experience of conducting combat operations on the territory of Ukraine. Requirements for operational efficiency and reliability of the process of recognition (detection) of aerial reconnaissance objects are being formed. Modern computer vision algorithms and deep machine learning technologies are studied from the point of view of integration into the process of deciphering aerial reconnaissance data. A method of automating the process of detecting aerial reconnaissance objects is being developed to increase the effectiveness of deciphering aerial photographs. The essence of the developed method is the integration of modern computer vision technologies, implemented on the basis of algorithms of artificial neural networks, in the process of deciphering aerial photographs. Analysis of the effectiveness of the developed method from the point of view of ensuring the required level of reliability shows that the use of the developed method allows obtaining the required values of the probability of recognition of aerial reconnaissance objects on aerial photographs. In turn, the analysis of quantitative evaluations of the efficiency of deciphering aerial reconnaissance data shows that the use of the developed method allows you to significantly reduce the time for deciphering an aerial photograph on average, up to 2 seconds per aerial photograph under the conditions of using rather low computing power. The features of the developed method: practical implementation of the specified automation method does not require high computing power; integration into the decryption process does not require significant personnel training and high technical requirements.

Machine learning accurately predicts food exchange list and the exchangeable portion.

Food Exchange Lists (FELs) are a user-friendly tool developed to help individuals aid healthy eating habits and follow a specific diet plan. Given the rapidly increasing number of new products or access to new foods, one of the biggest challenges for FELs is being outdated. Supervised machine learning algorithms could be a tool that facilitates this process and allows for updated FELs-the present study aimed to generate an algorithm to predict food classification and calculate the equivalent portion. Data mining techniques were used to generate the algorithm, which consists of processing and analyzing the information to find patterns, trends, or repetitive rules that explain the behavior of the data in a food database after performing this task. It was decided to approach the problem from a vector formulation (through 9 nutrient dimensions) that led to proposals for classifiers such as Spherical K-Means (SKM), and by developing this idea, it was possible to smooth the limits of the classifier with the help of a Multilayer Perceptron (MLP) which were compared with two other algorithms of machine learning, these being Random Forest and XGBoost. The algorithm proposed in this study could classify and calculate the equivalent portion of a single or a list of foods. The algorithm allows the categorization of more than one thousand foods with a confidence level of 97% at the first three places. Also, the algorithm indicates which foods exceed the limits established in sodium, sugar, and/or fat content and show their equivalents. Accurate and robust FELs could improve implementation and adherence to the recommended diet. Compared with manual categorization and calculation, machine learning approaches have several advantages. Machine learning reduces the time needed for manual food categorization and equivalent portion calculation of many food products. Since it is possible to access food composition databases of various populations, our algorithm could be adapted and applied in other databases, offering an even greater diversity of regional products and foods. In conclusion, machine learning is a promising method for automation in generating FELs. This study provides evidence of a large-scale, accurate real-time processing algorithm that can be useful for designing meal plans tailored to the foods consumed by the population. Our model allowed us not only to distinguish and classify foods within a group or subgroup but also to perform the calculation of an equivalent food. As a neural network, this model could be trained with other food bases and thus improve its predictive capacity. Although the performance of the SKM model was lower compared to other types of classifiers, our model allows selecting an equivalent food not from a group previously classified by machine learning but with a fully interpretable algorithm such as cosine similarity for comparing food.

Algorithm for automation of the method of testing statistical hypotheses according to the Pearson criterion by the means of MATLAB

The purpose of the work is to develop and improve the methods of computer modeling and digitalization of procedures for testing statistical hypotheses according to consent criteria for use in high-tech software and measuring complexes of automated control systems. In most cases that are important for practice, the analysis of the statistical properties of distributions requires multiple checks performed using numerical methods, which require the tools of powerful software and computing environments. Examples of such cases are the procedures for finding information subsets of features in regression and statistical analysis, as well as identifying parameter deviations in automated control systems. It is noted that the tasks of processing experimental data do not always fit into the framework of the theory of normal processes and have exclusively such hypotheses. To test such hypotheses, modern mathematical statistics has developed criteria based on specially developed probabilistic rules that distinguish between zero and alternative hypotheses. Moreover, if for the testable (zero) hypothesis the distribution of statistics is known exactly or approximately, then for the alternative it has a greater degree of uncertainty and it (the alternative) itself is not a complete negation of the null hypothesis. As the zero and alternative hypotheses move away from each other, the statistics of the consent criterion begins to take a larger absolute value than with the null hypothesis, which allows us to build a critical area on the basis of estimating the set of this statistics values. However, the use of analytical methods for statistical analysis to determine the discrepancy between empirical and hypothetical statistics in practice is associated with the use, as a rule, of complex computational calculation algorithms and the implementation of high requirements for the assessment accuracy, which they cannot provide. To solve this problem, it is proposed to use numerical methods of computer modeling and integration of probability density of distribution according to the Simpson algorithm using the tools of built-in functions of the Statistics Toolbox of the MATLAB environment. The proposed method for automation of statistical analysis of zero and alternative hypotheses is implemented on the example of applying the Pearson criterion to verify compliance with the normal distribution of two random data samples, one of which meets the normal distribution law. The obtained graphic and numerical results of computer modeling confirm the correspondence of one of the testable distributions to a given normal distribution according to the null hypothesis.

Online tangible robot programming: interactive automation method from teleoperation of manipulation task

Teleoperated robots are introduced to hospitals, restaurants and factories and are expected to learn the task from human-teleoperated data. The automation methods from teleoperation, as known as Programming by demonstration (PbD) or imitation learning, are recently proposed increasingly, but they are focused on simple task, and training phase and execution phase should be separated. In this paper, we propose online and interactive automation method from teleoperation, online tangible robot programming, for object manipulation with multiple procedures and physical contacts. We focus on target-objects' states, split the task into motion sequences based on the states' change, and program the order of motion sequences with tangible user interface. We also implement the user interruption for mode switching, and a user can switch between autonomous and teleoperation modes and correct the programmed motion orders while continuing the task execution. For the evaluation, we did the real-world experiments with dual-arm robot and demonstrate the usefulness and efficiency of online tangible robot programming by switching from autonomous mode to teleoperation mode and immediately programming and modifing the task procedures of the box rotating and piling tasks with two arms.

Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization.

Optical coherence tomography (OCT) is widely used to detect and classify retinal diseases. However, OCT-image-based manual detection by ophthalmologists is prone to errors and subjectivity. Thus, various automation methods have been proposed; however, improvements in detection accuracy are required. Particularly, automated techniques using deep learning on OCT images are being developed to detect various retinal disorders at an early stage. Here, we propose a deep learning-based automatic method for detecting and classifying retinal diseases using OCT images. The diseases include age-related macular degeneration, branch retinal vein occlusion, central retinal vein occlusion, central serous chorioretinopathy, and diabetic macular edema. The proposed method comprises four main steps: three pretrained models, DenseNet-201, InceptionV3, and ResNet-50, are first modified according to the nature of the dataset, after which the features are extracted via transfer learning. The extracted features are improved, and the best features are selected using ant colony optimization. Finally, the best features are passed to the k-nearest neighbors and support vector machine algorithms for final classification. The proposed method, evaluated using OCT retinal images collected from Soonchunhyang University Bucheon Hospital, demonstrates an accuracy of 99.1% with the incorporation of ACO. Without ACO, the accuracy achieved is 97.4%. Furthermore, the proposed method exhibits state-of-the-art performance and outperforms existing techniques in terms of accuracy.

The Effectiveness of the Green Design and Manufacturing Decision in Achieving the Outstanding Performance of the Industrial Companies: An Analytical Study in Al-Zawraa General Company

The objective of this research is to analyze the impact of product design decisions and green manufacturing on Al-Zawraa General Company's competitive advantage in Iraq. Data was collected through a questionnaire that measured key variables، such as product design، green manufacturing، and outstanding performance. Each major variable was further evaluated through three sub-dimensions. Out of the 75 employees invited to participate، 60 staff responded to the questionnaire. Quantitative and qualitative methods، such as arithmetic means، standard deviations، and confirmatory factor analysis، were employed to examine the effect of the key variables in implementing the green manufacturing system in the company. The study's findings suggest that the company's management should reconsider its approach to green technology by implementing modern automation methods that utilize environmentally friendly energy sources. This change would not only help to reduce industrial pollution rates and benefit the environment but also enhance the company's appeal to consumers and ensure its longevity and competitiveness in the local and global industrial markets. Furthermore، the study revealed a strong correlation between the key variables and the successful adoption of the green manufacturing system.

FanoutNet: A Neuralized PCB Fanout Automation Method Using Deep Reinforcement Learning

In modern electronic manufacturing processes, multi-layer Printed Circuit Board (PCB) routing requires connecting more than hundreds of nets with perplexing topology under complex routing constraints and highly limited resources, so that takes intense effort and time of human engineers. PCB fanout as a pre-design of PCB routing has been proved to be an ideal technique to reduce the complexity of PCB routing by pre-allocating resources and pre-routing. However, current PCB fanout design heavily relies on the experience of human engineers, and there is no existing solution for PCB fanout automation in industry, which limits the quality of PCB routing automation. To address the problem, we propose a neuralized PCB fanout method by deep reinforcement learning. To the best of our knowledge, we are the first in the literature to propose the automation method for PCB fanout. We combine with Convolution Neural Network (CNN) and attention-based network to train our fanout policy model and value model. The models learn representations of PCB layout and netlist to make decisions and evaluations in place of human engineers. We employ Proximal Policy Optimization (PPO) to update the parameters of the models. In addition, we apply our PCB fanout method to a PCB router to improve the quality of PCB routing. Extensive experimental results on real-world industrial PCB benchmarks demonstrate that our approach achieves 100% routability in all industrial cases and improves wire length by an average of 6.8%, which makes a significant improvement compared with the state-of-the-art methods.

A CASE STUDY OF THE DECISION-MAKING BEHIND THE AUTOMATION OF A COMPOSITES-BASED DESIGN PROCESS

AbstractAutomation and artificial intelligence (AI) are increasingly seen as appealing tools to perform design tasks traditionally accomplished by human designers. In today's digital economy, industries aim to adopt these tools to improve the efficiency of their complex design processes. But how does one decide what parts of their existing design process should be automated and which automation/AI tool to implement? With these questions in mind, we present a case study highlighting a company's decision-making process in converting its existing designer-dependent design process to one supported by automation. In this case study, we observed the company's decisions in selecting and rejecting certain automation and AI methods before finalizing a heuristics-based automation method that proved highly efficient compared to the company's traditional human-driven design program. In addition, we present three key discussion points observed in this case study: (1) the importance of implementing the designer's heuristics in the automation framework, (2) the importance of a uniform and modular design automation framework, and (3) the challenges of implementing AI methods.

ВПЛИВ ШТУЧНОГО ІНТЕЛЕКТУ НА ЗАЙНЯТІСТЬ У БІЗНЕСІ

The article is devoted to the study of the impact of artificial intelligence on employment in businesses. Recent advances in natural language processing and recognition, machine learning platforms and AI-optimized hardware, as well as free access to these technologies, have created an urgent need to implement them in business to ensure high results in financial and economic activities. The study presents the prerequisites that formed the basis for the formation and development of a new era of AI automation. The scientific achievements and conclusions of international companies regarding the role of artificial intelligence in the global economy are analyzed, and the trends in its use by organizations at different levels of management are revealed. The authors characterize the methods of automation of various types of work at an enterprise with the use of the technologies under study and the impact of the process on employment and wages. It is found that today the greatest impact is the recovery effect, based on which conclusions are drawn about the need for synergy of human and machine labor, rather than their separate functioning. The reasons for the attractiveness of AI automation for business are identified, and examples of their effective implementation are given. There are revealed advantages of the increasing use of technology in business: cost savings, increased revenue, increased productivity and competitiveness of companies, creativity and personalization, limiting the influence of the human factor on work results. The article highlights the threats of large-scale introduction of AI into business activities for employees, which include excessive control over them by employers, violation of the principles of information confidentiality, complexity and imperfection of the system, constant updating of requirements for advanced training, and the necessity to train employees to interact with intelligence. These threats can lead to employee dismissals, loss of motivation, and aggravation of social inequality and social justice issues, which will generally create a negative attitude toward the company. Based on the analysis of data sets on professions with a high risk of AI automation in various industries and the projected change in demand for them, the authors reveal a correlation between the level of threat and the complexity of tasks, the creativity involved, and the speed in decisionmaking and implementation. Finally, it is concluded that new approaches to regulating the impact of technology on the structure of employment should be applied due to its dynamic growth.

Expert Knowledge-Based RGT Solvers for Software Testing

Program testing is a way of assessing the quality of software and reducing the risk of software failure in operation [1]. Quality issues can cause as financial loss as well as harm to human lives (e.g., when the bug is in medical instruments, cars, etc.). So, it is very hard to underestimate the importance of testing. There are multiple testing techniques, which are split into 3 major categories. One of them includes experience-based techniques. Test cases and scenarios used in experience-based testing are derived from the tester’s knowledge and intuition, as well as their experience with similar applications and technologies. These techniques can be helpful in identifying tests that are not identified easily by other more systematic techniques. Depending on the tester’s approach and experience, experience-based techniques may achieve widely varying degrees of coverage and effectiveness [1]. We propose a method for automation of experience-based testing via a class of combinatorial problems (RGT class). A Solver is developed for the class. It acquires expert knowledge and elaborates effective strategies for RGT problems [2]. The proposed method generates test cases dynamically based on the response of the program. The adequacy of the method is being experimented for ”blender” open-source application, which has Python API allowing to experiment with testing and analyze test results.

Solar Street Lights Control Using PIR Sensors

Abstract: This project is based on the idea of maintaining maximum utilization and minimum loss of available energy. The plenty of solar energy available during the day time is stored in a solar cell and the stored energy is used to glow the street lights during the whole night. Also, the system provides a power saving mode of operation by adapting the method of automation. A dark sensor and a light sensor provide the automatic “ON”/” OFF” facility to the street lights, so that it will glow automatically when it is required (i.e., when the surrounding will be dark) and it will be turned “OFF” automatically if sufficient light is available in the surrounding. Again, the auto intensity control mechanism has been applied by the help of a microcontroller to control the light intensity of the luminaries as per the requirement. Hence the loss of energy due to unnecessary glow of the street lights can be avoided.

Automated design and usage of the Fault-Tolerant dynamic partial reconfiguration controller for FPGAs

This article presents a new design automation method for Fault-Tolerant (FT) systems implemented on dynamically reconfigurable Field Programmable Gate Arrays (FPGAs). The method aims at minimizing the human interactions needed to incorporate FT mechanisms into an existing system. It starts with a source code of an original unhardened circuit. It continues by automated manipulation of the source code, algorithmic strategic selection of suitable FT techniques, design space exploration of candidate FT implementations, and selection of the resulting implementation. The method also includes efficient evaluation of achieved FT parameters performed on the target HW. As a novel approach working on the level of HW description languages is employed, the code modification is separated, which differentiates our method from others. The case study utilizing this method targets the design of an experimental FT dynamic partial reconfiguration controller for an FPGA. This controller is helpful for the restoration of faulty components due to a single-event upset on an FPGA. We used the method to generate a set of Pareto-optimal controllers concerning the design’s Mean Time to Failure (MTTF) parameter, power consumption, and size. Then, the FT controller is connected to several benchmark circuits, and the reliability parameters are evaluated at the entire system level. Our results show that by replacing the standard reconfigurable controller with our automatically-designed FT controller for one specific benchmark, the design size increased by 20.1%, and MTTF increased by 11.7%. However, the efficiency is highly dependent on the target system size, MTTF, and circuit functionality. We also estimate that a complex system defined by half a million configuration bits would improve MTTF by more than 50%.

Decision Tree Method for Automation of Plant Sprinklers and Monitoring Based On Soil Moisture

Watering the plants is an important part of plant care, but due to our busy schedules, we frequently forget to do it. It can be caused by a number of variables, including the weather, in addition to human irresponsibility. The goal of this research is to develop a device that can automatically water plants and monitor plants based on soil moisture. Automatic sprinklers that use NodeMCU ESP8266 and Telegram bots employ the decision tree approach. The C 4.5 algorithm is used by the Decision Tree approach to choose the microcontroller's course of action. The value of various case studies utilized in this study is determined using algorithm C 4.5. The Arduino IDE, Fritzing, and Visual Studio Code were the tools used to design and produce this tool. Additionally, it configures Telegram bots using the Telegram universal bot library. The website setting for plant monitoring employs the native PHP language, but the configuration of the telegram bot uses the general telegraph bot library. The end result is an automatic plant sprinkler that operates based on the soil moisture value and activates the water pump when the soil moisture level drops below a threshold of 50%. When the soil moisture reading exceeds 75%, the water pump will then turn off. The outcomes of this investigation also comprise websites and Telegram bots for tracking soil moisture levels.