Automation Of Method Research Articles

Lightweight neural network for smart diagnosis of cholangiocarcinoma using histopathological images

Traditional Cholangiocarcinoma detection methodology, which involves manual interpretation of histopathological images obtained after biopsy, necessitates extraordinary domain expertise and a significant level of subjectivity, resulting in several deaths due to improper or delayed detection of this cancer that develops in the bile duct lining. Automation in the diagnosis of this dreadful disease is desperately needed to allow for more effective and faster identification of the disease with a better degree of accuracy and reliability, ultimately saving countless human lives. The primary goal of this study is to develop a machine-assisted method of automation for the accurate and rapid identification of Cholangiocarcinoma utilizing histopathology images with little preprocessing. This work proposes CholangioNet, a novel lightweight neural network for detecting Cholangiocarcinoma utilizing histological RGB images. The histological RGB image dataset considered in this research work was found to have limited number of images, hence data augmentation was performed to increase the number of images. The finally obtained dataset was then subjected to minimal preprocessing procedures. These preprocessed images were then fed into the proposed lightweight CholangioNet. The performance of this proposed architecture is then compared with the performance of some of the prominent existing architectures like, VGG16, VGG19, ResNet50 and ResNet101. The Accuracy, Loss, Precision, and Sensitivity metrics are used to assess the efficiency of the proposed system. At 200 epochs, the proposed architecture achieves maximum training accuracy, precision, and recall of 99.90%, 100%, and 100%, respectively. The suggested architecture's validation accuracy, precision, and recall are 98.40%, 100%, and 100%, respectively. When compared to the performance of other AI-based models, the proposed system produced better results making it a potential AI tool for real world application.

Development of a Text Processing Automation Method for Using Modern Korean-Chinese Mixed Text in General Education

This study aims to develop an automated text processing method for the use of modern Korean-Chinese mixed newspaper materials in liberal arts education. This article describes the process, its results, and additional tasks. In particular, the focus was placed on the batch processing of texts downloaded in large quantities. The problem with the existing computerized and serviced Korean-Chinese mixed texts is that most of the old Korean texts were computerized with PUA codes, which are not currently in Unicode standards. To process or analyze texts in computer language, it is necessary to convert these characters into standard code methods. Based on the standardized data brought in, the work of replacing the old form of words with the current Korean notation was carried out. Finally, in the text, phonological and Korean notation of Chinese characters are added in parentheses. Reviewing the results shows a significant improvement in readability. If you want to use modern Korean and Chinese newspaper materials for liberal arts education, it may be difficult unless a separate feed process is premised. When trying to use modern Korean-Chinese newspaper materials for liberal arts education, it can be difficult due to the problem of notations. Furthermore, this automated form of processing allows instructors to extract articles related to specific topics and to read articles with students that increase their readability. But as things stand, there are limits. For example, if a Chinese character has multiple consonants, there may be cases in which a Chinese character has a reading sound that does not fit the Chinese character word and rather interferes with the reading of it. These should be used for education purposes after correction. In addition, even if it is a Korean-Chinese mixed text and pronunciation is provided here, it is difficult for a student without knowledge of classical Chinese grammar to read that text if a lot of classical Chinese expressions are mixed in. This case ultimately becomes a problem that can be solved by the development of a classical Chinese morpheme analyzer. If progress is made on such things as the construction of the classic Chinese Corpus, it will be of great help in the development of history and liberal arts teaching tools.

Self-Checking Combinational Devices Synthesis Based on the Boolean Signal Correction Method Using Bose—Lin Codes

An organizing concurrent error-detection (CED) circuit new method for automation and computer technology combinational device is considered. The method based on the signal's Boolean correction method. The author proposed to use separable codes in the CED circuit synthesis when converting values from all operational outputs of the diagnosing object. The proposed approach allows to consider many possible transformations for each input combination. In addition, it will allow to choose the transformations to provide the conditions for building a completely self-checking device. In comparison with the methods previously discussed in the literature, the proposed approach makes it possible to synthesize a large number of control logic devices in the CED circuit and choose the most appropriate option. For example, with the technical implementation complexity lowest indicators. An algorithm for synthesizing the control logic block is given on the example of using a separable Bose — Lin code with module M = 4 by the signals Boolean correction method when organizing an CED circuit. The proposed method advantages and disadvantages for the CED circuit synthesis are described.

Possibility Assessment of Automatic Formation of Technological Processes in Small Batch Production

The work is devoted to urgent problem solution of lack of convenient tools with functional capabilities of the system of route technological process automated design, adapted to small-scale production. The estimation of potential possibility of automatic small-scale production formation by means of estimation of efficiency and expediency technological processes of automation of key stages of design of production technological preparation, such as equipment selection, selection of cutting modes, workpiece, cutting and measuring tools and tooling selection is given. The key stages of technological process development are considered and characterized, such as selection of technological bases, type of workpiece and equipment, formation of route technological process, calculation of cutting modes, selection of allowances and so on. The paper shows both the relevance and necessity of analyzing approaches to automation and their correct adaptation to production needs to achieve maximum efficiency and resource savings for small batch production, and the high relevance for small-batch production of the automatic formation of route technological processes. At the same time, the paper shows the lack of automated systems adapted to the specifics of small-batch production. The existing approaches to the formation of stages are analyzed, as well as the possible methods of automation of stages and the existing limitations of these methods are considered, the efficiency of the considered approaches is evaluated. The methods of automation of separate stages of technological preparation of production are analyzed and the efficiency of the considered approaches is evaluated. The original concept of solving the problem of automation for small-scale production by using a combination of a limited number of standard solutions, represented as a set of technological parameters or knowledge base, with individual solutions, adapted to the specifics of enterprises and industry, is proposed and the potential effectiveness of this approach is shown.

Handwritten Odia Digit Recognition using Learning Systems: A Comparison of Neural Networks and Support Vector Machine Models

The Odia language is one of the many regional languages spoken in India. It is the official language of Odisha, a State in eastern India. The Odia language carries a 1500-year-old history and worldwide is spoken by more than 50 million people. The Odia digits are complex due to the presence of many curves in each character. Handwritten scripts are even more complex due to free-style writing. However, the development of an innovative machine learning model is essential because Odia scripts consist of a huge number of historical documents of more than 1000 years old. A robust automation method will help in converting historical documents into digital form and will help to preserve the documents. This will solve a big problem in society. This work experiments with handwritten Odia numerals by implementing two different classifiers. The first one is the implementation of a Convolutional Neural Network (CNN) and the second experiment implements a Support Vector Machine (SVM). Finally, results from both experiments have been compared. The dataset has been generated through software by writing the digits on MS Paint. Both CNN and SVM models have been implemented through Python programming to recognize the inputs into a particular class. Both training and testing of the models have been done using this dataset. The accuracy from the CNN Model is obtained to be 94.999% which is ≈95% and for SVM, the model accuracy is 86%. Comparing both results, it is concluded that the CNN model is comparatively better than the SVM classifier in the case of the proposed work.

Computer aided design automation for triboelectric nanogenerators

For application of triboelectric nanogenerators (TENG), it is essential to design TENG with high energy conversion efficiency, and it is one of the crucial parts to obtain TENG designs with optimal output impedance to match with that of application systems. Current design processes are incapable of optimizing TENGs with complex structures effectively, which greatly prolongs the research and development process and hinders application of TENGs. Here, a universal computer aided design automation is proposed to generate the structural and material design of TENG for optimal power output under designated matching resistance. A semi-analytical method and a non-dominated sorting genetic algorithm (NSGA-II) are used for the design automation. For demonstration, design automation for freestanding rotational TENG (FR-TENG) is carried out. An acceleration factor of up to ∼900 is obtained with systematical simulation acceleration. A group of designs with optimal powers and matching resistances are obtained, from which different input impedances of power management units can be obtained for applications. For verification, a FR-TENG with an optimal average output power over 88 mW is designed and fabricated, the output impedance is intentionally chosen from pareto-optimal front to match the input impedance of the subsequent power management unit. This is used to develop a fully self-powered wireless humidity/temperature/ultraviolet environmental monitoring system. This system can transmit the sensing information every ∼ 7 s and display on a remote terminal, validating the effectiveness of the proposed design automation method.

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.