Machine Learning Concepts Research Articles

Assessment of the three-dimensional flow field in the reactor pressure vessel in Hualong One nuclear power plants

This study uses computational fluid dynamics (CFD) to investigate the three-dimensional flow field under normal operating conditions in the reactor pressure vessel (RPV) in the Hualong One nuclear power plants (NPPs). With a particular focus on the flowrate distribution at the core inlet, the numerical framework is validated against the integral hydraulic experiment in a 1:4-scaled RPV of CNP1000, the prototype of the Hualong One reactor. The simulation results of the normalized flowrate at the core inlet agree reasonably well with the measured data. Based on the experimental data, several methods of calibrating the CFD turbulence model coefficients are suggested by introducing the concepts of data assimilation and machine learning. The flow field in a realistic RPV for Hualong One is predicted using the validated numerical framework, showing that the flowrate distribution at the core inlet is nearly homogeneous and that the turbulent intensity is acceptably low for each fuel assembly. It can provide essential information for the reactor core thermal–hydraulic design and the fuel assembly mechanical assessment.

Machine learning assisted dual port metasurface loaded MIMO antenna with linearly polarized to circularly polarized conversion features for n257 band of 5G mm‐wave applications

SummaryIn this communication, a dual port printed multi‐input and multi‐output (MIMO) antenna with high isolation of dimension 12 × 8.5 × 0.8 mm3 is designed, analyzed, and investigated for 28‐GHz 5G mm‐wave applications. The metasurface layer is suspended over the MIMO antenna to convert the linearly polarized (LP) wave to a circularly polarized (CP) wave. The unit cell of the metasurface consists of a square‐shaped conducting strip with one side of diagonal points interconnected. The conducting strip and rectangular gap between the strips etched on the substrate are capable of generating the π/2 phase shifted electric field components (Ex and Ey) resulting in polarization conversion from LP to CP. The machine learning concept is used to characterize the dimensional configuration of the antenna to achieve the optimum throughput. The MIMO diversity performance parameters and antenna essential results are validated by the measured counterparts.

인공지능 기반 심리상담에 활용되는 딥러닝 기법 고찰

Objectives This study aims to review and summarize deep learning techniques that have been developed to date and to discuss how they can be applied in practice to artificial intelligence-based psychology counseling robots.
 Methods To examine the latest deep learning techniques utilized in artificial intelligence psychological counseling, an extensive literature search was conducted for research papers and relevant books from both domestic and in-ternational sources in the fields of emotion analysis, facial expression generation, and text generation.. For do-mestic literature, databases such as RISS and KISS were utilized, and for international literature, research paper search sites including ERIC (ProQuest), JSTOR, and Google Scholar were employed.
 Results When applying artificial intelligence in psychological counseling, the prominent technology of interest in-volves recognizing emotions from the interlocutor's facial expressions and generating similar expressions, or rec-ognizing emotions from sentences spoken by the interlocutor and generating responsive sentences. The study in-troduced the concepts of machine learning and deep learning and explained the workings of deep learning techni-ques, such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), LSTM (Long Short Term Memory), Transformer, BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pretrained Transformer), and GAN (Generative Adversarial Network). These techniques are founded on the princi-ples derived from human brain functioning. Particularly, emotion recognition programs through image analysis have predominantly relied on CNN, while research in the realm of language has advanced with the utilization of GPT-2, LSTM, and similar methods.
 Conclusions Among the existing deep learning techniques, the recognition of emotions from the interlocutor's fa-cial expressions and the generation of similar expressions, or the recognition of emotions from sentences spoken by the interlocutor and the generation of responsive sentences, are the technologies predominantly under scrutiny for their application in artificial intelligence-based psychological counseling. Nevertheless, rule-based chatbots continue to dominate the landscape. This paper explored the merits and demerits of AI-based counseling em-ploying such deep learning techniques. Looking ahead, there is a need for the advancement of technology that en-ables natural interactions, coupled with the essential verification of its effectiveness.

Analysis of Multimodal Data in Project Management: Prospects for Using Machine Learning

The modern project environment is characterized by high complexity, uncertainty, speed and depth of changes that affect the project during its life cycle. However, the project’s change management processes do not take into account the need to implement analytical procedures for dynamic processing of multimodal data arrays. The purpose of the study is to determine the content of analytical procedures for project management and substantiate the use of machine learning technologies for their effective implementation. The methodological basis was project management methods, theory of change, concepts of artificial intelligence and machine learning, as well as analytical approaches. Methods of descriptive modeling of the project management process and expert assessments of the prospects for using machine learning technologies were also used in the work. The information base was made up of scientific materials on the topic under consideration, as well as expert assessments. The results of the study allowed us to conclude that for the analysis of multimodal data, natural language processing and intellectual decision support technologies are most in demand, which can serve as the basis for new technological solutions in the field of project management.

Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions.

Emerging concepts from scientific deep machine learning such as physics-informed neural networks (PINNs) enable a data-driven approach for the study of complex kinetic problems. We present an extended framework that combines the advantages of PINNs with the detailed consideration of experimental parameter variations for the simulation and prediction of chemical reaction kinetics. The approach is based on truncated Taylor series expansions for the underlying fundamental equations, whereby the external variations can be interpreted as perturbations of the kinetic parameters. Accordingly, our method allows for an efficient consideration of experimental parameter settings and their influence on the concentration profiles and reaction kinetics. A particular advantage of our approach, in addition to the consideration of univariate and multivariate parameter variations, is the robust model-based exploration of the parameter space to determine optimal reaction conditions in combination with advanced reaction insights. The benefits of this concept are demonstrated for higher-order chemical reactions including catalytic and oscillatory systems in combination with small amounts of training data. All predicted values show a high level of accuracy, demonstrating the broad applicability and flexibility of our approach.

Entropy removal of medical diagnostics

Shannon entropy is a core concept in machine learning and information theory, particularly in decision tree modeling. To date, no studies have extensively and quantitatively applied Shannon entropy in a systematic way to quantify the entropy of clinical situations using diagnostic variables (true and false positives and negatives, respectively). Decision tree representations of medical decision-making tools can be generated using diagnostic variables found in literature and entropy removal can be calculated for these tools. This concept of clinical entropy removal has significant potential for further use to bring forth healthcare innovation, such as quantifying the impact of clinical guidelines and value of care and applications to Emergency Medicine scenarios where diagnostic accuracy in a limited time window is paramount. This analysis was done for 623 diagnostic tools and provided unique insights into their utility. For studies that provided detailed data on medical decision-making algorithms, bootstrapped datasets were generated from source data to perform comprehensive machine learning analysis on these algorithms and their constituent steps, which revealed a novel and thorough evaluation of medical diagnostic algorithms.

The role of lifelong machine learning in bridging the gap between human and machine learning: A scientometric analysis

AbstractDue to advancements in data collection, storage, and processing techniques, machine learning has become a thriving and dominant paradigm. However, one of its main shortcomings is that the classical machine learning paradigm acts in isolation without utilizing the knowledge gained through learning from related tasks in the past. To circumvent this, the concept of Lifelong Machine Learning (LML) has been proposed, with the goal of mimicking how humans learn and acquire cognition. Human learning research has revealed that the brain connects previously learned information while learning new information from a single or small number of examples. Similarly, an LML system continually learns by storing and applying acquired information. Starting with an analysis of how the human brain learns, this paper shows that the LML framework shares a functional structure with the brain when it comes to solving new problems using previously learned information. It also provides a description of the LML framework, emphasizing its similarities to human brain learning. It also provides citation graph generation and scientometric analysis algorithms for the LML literatures, including information about the datasets and evaluation metrics that have been used in the empirical evaluation of LML systems. Finally, it presents outstanding issues and possible future research directions in the field of LML.This article is categorized under: Technologies > Machine Learning

The Current Status and Future Prospects of Machine Learning in the Chinese Stock Market

This article explores the current status and prospects of machine learning techniques in the stock market of china. It begins by providing an overview of the relevant concepts of machine learning and analyzing its strengths and weaknesses in the stock market context. The article also introduces the current application of machine learning techniques in the gobal stock market. In the methodology section, several machine learning models that have performed well in the stock market are listed. The article then summarizes and analyzes the specific applications of machine learning and its algorithms in the stock market of China, highlighting the advantages and limitations due to the unique characteristics of the market.Finally, the article concludes by summarizing the aforementioned content and providing an outlook on the future development direction of machine learning technology in the stock market of chinese. While the performance of machine learning technology in the stock market has its pros and cons, it is undeniable that it holds an important position in the future development of the financial market, especially within the wave of innovation and progress in the financial industry.

E-Airs: Interactive Ergonomic Monitoring System for Work Posture

Work posture and/or position of work is a crucial problem in work system. Unnatural posture will lead to discomfort when doing the task such as pain at part of body such that it will cause accident in the end. This accident will affect level of work performance of the worker so as the work productivity fails achieve a target determined. The failure mode, of course, results in a big loss for a company. This state should be avoided by use of the natural posture by worker in completing a job. However, many workers do not still understand what kind of a natural and unnatural work posture directly. So, this condition has high risk for being occurred the discomfort. Thus, it is important to develop a tool for helping them to identify a certain posture of work. Objective of this study is to design a monitoring system devices to capture and to determine an natural posture of the worker based on ergonomic principles. Rapid Upper Limb Assessment method is applied to determine a risk of work posture. Machine Learning concept is implemented to support in designing a system by also applying python programming language. Result of this study shows that monitoring system devices developed is usable in informing an unnatural posture of worker in completing a job. Thus, this result will beneficial for worker to improve work posture to be natural position so as discomfort can be prevented.

Causal inference and machine learning in endocrine epidemiology.

With the rapid development of computer science, there is an increasing demand for the use of causal inference methods and machine learning in the research of endocrine disorders and their long-term health outcomes. However, studies on the effective and appropriate applications of these approaches in real-world data and clinical settings are still limited. This review will illustrate the use of causal inference and machine learning in epidemiological research within the field of endocrinology and metabolism. It will examine each concept of causal inference and machine learning through application examples of endocrine disorders. Subsequently, the paper will discuss the integration of machine learning within the causal inference framework, including (i) the estimation of treatment effects or the causal relationship between exposure and outcomes, and (ii) the evaluation of heterogeneity in such treatment effects (or exposure-outcome causal relationship) based on individuals' characteristics. Accurately assessing causal relationships and their heterogeneity across different individuals is crucial not only for determining effective interventions, but also for the appropriate allocation of medical resources and reducing healthcare disparities. By illustrating some application examples in endocrinology, this review aims to enhance readers' understanding and application of causal inference and machine learning in future epidemiological studies focusing on endocrine disorders.

Experience of Data Analysis application for proceeding research data in solving the problem of target age group establishment

The article presents the experience of artificial intelligence application in research process. The article contains general information about basic concepts of machine learning (clustering and visualization), as well as considers more detaily an experience of clinical testing. The effectiveness of applying Data Analysis methods and means as one of the research stages is demonstrated on the example of a case on processing medical information using algorithms of machine learning: solving the problem on diagnostic value of the proposed indicator (FTF) for determining target age groups. Implementation of such approach of digital transformation improves the operational effectiveness of researches, as well as quality and availability of final technological products being developed - software for solving expert problems.

Machine learning-based optimization of storage ring injection efficiency

Since the DELTA accelerator facility does not use a White circuit-driven fast topping-up mode, each software-driven injection ramp cycle takes about 7 seconds. Depending on the injection efficiency, 150 to 200 ramp cycles are required to reach the maximum beam current of 130 mA in the storage ring. Thus, for fast post-injection, a high electron transfer rate is crucial. During the injection process, a large number of parameters (e. g., magnet settings, timings of pulsed elements) have to be adjusted manually. The injection efficiency depends mainly on the settings of the booster extraction elements, the T2 transfer line magnets, and the storage ring injection components. In order to automate the injection procedure and to improve the electron transfer efficiency, the application of innovative machine learning concepts was studied.

Application of kubeflow as a universal approach for the development and implementation of artificial intelligence systems

An overview of the concept of machine learning and processes (Machine Learning and Operation, MLOps), which is a set of techniques for implementation and automatic continuous integration, as well as delivery to the production environment and model training, is made. The concept of MLOps was considered in terms of Kubeflow tools - a cloud-native open-source system running on the Kubernetes platform. The possibility of using modern MLOps solutions to improve the development processes of machine learning information systems has been studied. The results of the operation of the model in the Kubeflow arsenal have been checked using such improvement factors as speed of development, implementation of changes, reduction of time to search for problems, recovery after global interruptions, and decrease of the number of errors in the model. For practical analysis, a publicly available model was deployed in a Kubeflow cluster using the Seldon Core Serving application manifest. The conducted research showed that Kubeflow consists of a set of various open-source components that have a high level of integration with each other through the Kubernetes platform. At the same time, Kubeflow uses the Kubernetes pattern of operators for machine-learning objects extremely effectively.

Electric Signals in Machine Learning Using AppInventor and Arduino

Recent developments in Artificial Intelligence (AI) have introduced machine learning and its applications into everyday life. As technology becomes increasingly integrated into the educational system, researchers are focused on developing tools that allow students to interact with machine learning in a way that sparks their curiosity and teaches them essential concepts. Our instructional proposal, titled “Electric Signals in Machine Learning Using App Inventor,” focuses on applying learning, transfer, and classification models of audio spectrograms to teach students in the first year of high Secondary school (A’ Lyceum) fundamental concepts of machine learning. This is accomplished using MIT App Inventor and Arduino’s visual programming environments. Students will use the website “Personal Audio Classifier” to train an audio model and App Inventor to connect computer science and machine learning. In addition, with the aid of the Arduino microcontroller, students will engage in visualising Morse code signals and investigating Physical Computing, allowing them to create digital solutions that connect to the real world.

Photonic reservoir computing for energy efficient and versatile machine learning application

Time-multiplexed reservoir computing is a machine learning concept which can be realised in photonic hardware systems using only one physical node. The concept can be used for various problems, ranging from classification problems to time-series prediction tasks, while being fast and energy efficient. Here, a theoretical analysis of a reservoir computer realised via delay-coupled semiconductor lasers is presented and the role of the internal system time-scales and the bifurcation structure is discussed. It is further shown that optimal performance can be reached by tailoring the coupling delays to the specific memory requirements of the given task.

Artificial Intelligence Techniques in Medical Imaging: A Systematic Review

This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts.

A decision support system for supplier quality evaluation based on MCDM-aggregation and machine learning

Evaluating suppliers’ quality performance is one of critical tasks of quality management since it is directly related to quality assurance, improvement and development, especially for manufacturing companies. However, the supplier quality evaluation remains challenging due to the involvement of a large amount of qualitative and quantitative data, multi-dimensional attributes, as well as a large number of items and suppliers, etc. In addition, while various multiple criteria decision making methods (MCDMs) exist and can be used for supplier quality evaluation, individual choice and application of the methods often result in inconsistent or conflicting evaluation results. To overcome the challenges, in this study, we develop a decision support system for supplier quality evaluation to deal with the practical complexity and to take advantage of the availability of the big industrial data. The system is presented as a conceptual framework including three modules: the decision matrix and criteria initialization module, the MCDM method selection and implementation module, and the aggregation module. The sequential conduction of each module formulates and solves the multiple supplier and item quality evaluation problem. The implementation of the system can be applied to dynamically monitor and evaluate supplier and item quality performance. Moreover, as the core intelligent part of the decision support system, in the aggregation module, we propose an aggregated MCDM model based on the machine learning concepts. It aims at providing more reliable evaluations across multiple suppliers and items. The aggregation model also contributes to the MCDM literature as an independent method which is not restricted to the purpose of supplier quality evaluation. The methods shed lights on how to construct and solve MCDM problems in the big-data era. Finally, the application of the decision support system is illustrated by a case of a large automotive company. The case shows that our decision support system provide more robust and reliable evaluation results than the traditional individual multiple criteria decision making method.

Leveraging a combination of machine learning and formal concept analysis to locate the implementation of features in software variants

Leveraging a combination of machine learning and formal concept analysis to locate the implementation of features in software variants

Machine learning inspired workflow to revise field development plan under uncertainty

We present an efficient and risk-informed closed-loop field development (CLFD) workflow for recurrently revising the field development plan (FDP) using the accrued information. To make the process practical, we integrated multiple concepts of machine learning, an intelligent selection process to discard the worst FDP options and a growing set of representative reservoir models. These concepts were combined and used with a cluster-based learning and evolution optimizer to efficiently explore the search space of decision variables. Unlike previous studies, we also added the execution time of the CLFD workflow and worked with more realistic timelines to confirm the utility of a CLFD workflow. To appreciate the importance of data assimilation and new well-logs in a CLFD workflow, we carried out researches at rigorous conditions without a reduction in uncertainty attributes. The proposed CLFD workflow was implemented on a benchmark analogous to a giant field with extensively time-consuming simulation models. The results underscore that an ensemble with as few as 100 scenarios was sufficient to gauge the geological uncertainty, despite working with a giant field with highly heterogeneous characteristics. It is demonstrated that the CLFD workflow can improve the efficiency by over 85% compared to the previously validated workflow. Finally, we present some acute insights and problems related to data assimilation for the practical application of a CLFD workflow.

Prediction on Tencent Stock Using the Machine Learning Methods

With the development of computer and AI, machine learning has become a significantly popular topic to study about. One application of machine learning is analyzing and predicting stock. This paper first reviews the concepts of machine learning and the reason for using machine learning to predict stocks, then analyzes and predicts the stock of Tencent which is a Chinese corporation mainly making social media and games by using machines learning. Incorporating with methods of Linear Regression and Muti-Layer Perceptron (MLP) regression on basic data and data after log difference, this study demonstrates the accuracy on each method based on Mean-Squared-Error (MSE) and accuracy for predicting the trend of up or down. It discusses the feasibility and effectiveness for each method to predict Tencent stock data and indeed on other stocks, focusing on the accuracy and the graph shown along with other papers discussing machine learning algorithms. The conclusion of this paper can hopefully be used in later prediction on Tencent and other corporations stocks.