• Title/Summary/Keyword: Machine intelligence

Search Result 1,124, Processing Time 0.034 seconds

Development of Elementary Machine Learning Education Program to Solve Daily Life Problems Using Sound Data (소리 데이터를 기반으로 일상생활 문제를 해결하는 초등 머신러닝 교육 프로그램 개발)

  • Moon, Woojong;Ko, Seunghwan;Lee, Junho;Kim, Jonghoon
    • Journal of The Korean Association of Information Education
    • /
    • v.25 no.5
    • /
    • pp.705-712
    • /
    • 2021
  • This study aims to develop artificial intelligence education programs that can be easily applied in elementary schools according to the trend of the times called artificial intelligence. The training program designed the purpose and direction based on the analysis results of the needs of 70 elementary school teachers according to the steps of the ADDIE model. According to the survey, elementary school students developed a machine learning education program to set sound data as the theme of the most accessible in their daily lives and to learn the principles of artificial intelligence in solving problems using sound data in real life. These days, when the need for artificial intelligence education emerges, elementary machine learning education programs that solve daily life problems based on sound data developed in this study will lay the foundation for elementary artificial intelligence education.

A Methodology for Bankruptcy Prediction in Imbalanced Datasets using eXplainable AI (데이터 불균형을 고려한 설명 가능한 인공지능 기반 기업부도예측 방법론 연구)

  • Heo, Sun-Woo;Baek, Dong Hyun
    • Journal of Korean Society of Industrial and Systems Engineering
    • /
    • v.45 no.2
    • /
    • pp.65-76
    • /
    • 2022
  • Recently, not only traditional statistical techniques but also machine learning algorithms have been used to make more accurate bankruptcy predictions. But the insolvency rate of companies dealing with financial institutions is very low, resulting in a data imbalance problem. In particular, since data imbalance negatively affects the performance of artificial intelligence models, it is necessary to first perform the data imbalance process. In additional, as artificial intelligence algorithms are advanced for precise decision-making, regulatory pressure related to securing transparency of Artificial Intelligence models is gradually increasing, such as mandating the installation of explanation functions for Artificial Intelligence models. Therefore, this study aims to present guidelines for eXplainable Artificial Intelligence-based corporate bankruptcy prediction methodology applying SMOTE techniques and LIME algorithms to solve a data imbalance problem and model transparency problem in predicting corporate bankruptcy. The implications of this study are as follows. First, it was confirmed that SMOTE can effectively solve the data imbalance issue, a problem that can be easily overlooked in predicting corporate bankruptcy. Second, through the LIME algorithm, the basis for predicting bankruptcy of the machine learning model was visualized, and derive improvement priorities of financial variables that increase the possibility of bankruptcy of companies. Third, the scope of application of the algorithm in future research was expanded by confirming the possibility of using SMOTE and LIME through case application.

Development of Artificial Intelligence Instructional Program using Python and Robots (파이썬과 로봇을 활용한 인공지능(AI) 교육 프로그램 개발)

  • Yoo, Inhwan;Jeon, Jaecheon
    • 한국정보교육학회:학술대회논문집
    • /
    • 2021.08a
    • /
    • pp.369-376
    • /
    • 2021
  • With the development of artificial intelligence (AI) technology, discussions on the use of artificial intelligence are actively taking place in many fields, and various policies for nurturing artificial intelligence talents are being promoted in the field of education. In this study, we propose a robot programming framework using artificial intelligence technology, and based on this, we use Python, which is used frequently in the machine learning field, and an educational robot that is highly utilized in the field of education to provide artificial intelligence. (AI) education program was proposed. The level of autonomous driving (levels 0-5) suggested by the International Society of Automotive Engineers (SAE) is simplified to four levels, and based on this, the camera attached to the robot recognizes and detects lines (objects). The goal was to make a line detector that can move by itself. The developed program is not a standardized form of solving a given problem by simply using a specific programming language, but has the experience of defining complex and unstructured problems in life autonomously and solving them based on artificial intelligence (AI) technology. It is meaningful.

  • PDF

A TabNet - Based System for Water Quality Prediction in Aquaculture

  • Nguyen, Trong–Nghia;Kim, Soo Hyung;Do, Nhu-Tai;Hong, Thai-Thi Ngoc;Yang, Hyung Jeong;Lee, Guee Sang
    • Smart Media Journal
    • /
    • v.11 no.2
    • /
    • pp.39-52
    • /
    • 2022
  • In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.

Artificial intelligence (AI) based analysis for global warming mitigations of non-carbon emitted nuclear energy productions

  • Tae Ho Woo
    • Nuclear Engineering and Technology
    • /
    • v.55 no.11
    • /
    • pp.4282-4286
    • /
    • 2023
  • Nuclear energy is estimated by the machine learning method as the mathematical quantifications where neural networking is the major algorithm of the data propagations from input to output. As the aspect of nuclear energy, the other energy sources of the traditional carbon emission-characterized oil and coal are compared. The artificial intelligence (AI) oriented algorithm like the intelligence of a robot is applied to the modeling in which the mimicking of biological neurons is utilized in the mathematical calculations. There are graphs for nuclear priority weighted by climate factor and for carbon dioxide mitigation weighted by climate factor in which the carbon dioxide quantities are divided by the weighting that produces some results. Nuclear Priority and CO2 Mitigation values give the dimensionless values that are the comparative quantities with the normalization in 2010. The values are 1.0 in 2010 of the graphs which are changed to 24.318 and 0.0657 in 2040, respectively. So, the carbon dioxide emissions could be reduced in this study.

Experimental study of assembly of the carbon nanotube tip for SPM (SPM 용 카본 나노튜브 팁 조립의 실험적 연구)

  • Park J.K.;Kim J.E.;Han C.S.;Park Y.G.;Hwang K.H.
    • Proceedings of the Korean Society of Precision Engineering Conference
    • /
    • 2005.06a
    • /
    • pp.1228-1231
    • /
    • 2005
  • This paper reports about the development of scanning probe microscopy (SPM) tip with multi-walled carbon nanotube (MWNT). For making a carbon nanotube (CNT) modified tips, AC electric field which causes the dielectrophoresis was used for alignment and deposition of CNTs to the metal coated SPM tip. By dropping the MWNT solution and applying an electric field between an SPM tip and an electrode, MWNTs which were dispersed into a diluted solution were directly assembled onto the apex of the SPM tips due to the attraction by the dielectrophoretic force. In this paper, we investigate experimental conditions about the alignment of the CNT to tip axis according to the change of the angle between a tip and an electrode. Experimental results are presented, and then fabricated CNT tips are showed and measurement results for 15nm gold particles are compared with that of the conventional silicon tip.

  • PDF

A Study on Prediction of Business Status Based on Machine Learning

  • Kim, Ki-Pyeong;Song, Seo-Won
    • Korean Journal of Artificial Intelligence
    • /
    • v.6 no.2
    • /
    • pp.23-27
    • /
    • 2018
  • Korea has a high proportion of self-employment. Many of them start the food business since it does not require high-techs and it is possible to start the business relatively easily compared to many others in business categories. However, the closure rate of the business is also high due to excessive competition and market saturation. Cafés and restaurants are examples of food business where the business analysis is highly important. However, for most of the people who want to start their own business, it is difficult to conduct systematic business analysis such as trade area analysis or to find information for business analysis. Therefore, in this paper, we predicted business status with simple information using Microsoft Azure Machine Learning Studio program. Experimental results showed higher performance than the number of attributes, and it is expected that this artificial intelligence model will be helpful to those who are self-employed because it can easily predict the business status. The results showed that the overall accuracy was over 60 % and the performance was high compared to the number of attributes. If this model is used, those who prepare for self-employment who are not experts in the business analysis will be able to predict the business status of stores in Seoul with simple attributes.

Design of a Hybrid Serial-Parallel Robot for Multi-Tasking Machining Processes (ICCAS 2005)

  • Kyung, Jin-Ho;Han, Hyung-Suk;Ha, Young-Ho;Chung, Gwang-Jo
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 2005.06a
    • /
    • pp.621-625
    • /
    • 2005
  • This paper presents a new hybrid serial-parallel robot(HSPR), which has six degrees of freedom driven by ball screw linear actuators and motored joints. This hybrid robot design presents a compromise between high rigidity of fully parallel manipulators and extended workspace of serial manipulators. The hybrid robot has a large, singularity-free workspace and high stiffness. Therefore, the presented kinematic structure of the hybrid robot is particularly suitable for multi-tasking machining processes such as milling, drilling, deburring and grinding. In addition to the machining processes, the hybrid robot can be used for welding, fixturing, material handling and so on. The study on design of the hybrid robot is performed. A kinematic analysis and mechanism description of the hybrid robot with six-controlled degree of freedom is presented. In the virtual design works by DADS, workspace and force analysis are discussed. A numerical model is treated to demonstrate our analysis and to determine the range of permissible extension of the struts. Also, we determine some important design parameters for the hybrid robot.

  • PDF

Runout Control of a Magnetically Suspended High Speed Spindle Using Adaptive Feedforward Method

  • Ro Seung-Kook;Kyung Jin-Ho;Park Jong-Kwon
    • International Journal of Precision Engineering and Manufacturing
    • /
    • v.6 no.2
    • /
    • pp.19-25
    • /
    • 2005
  • In this paper, the feedforward control with least mean square (LMS) adaptive algorithm is proposed and examined to reduce rotating error by runout of an active magnetic bearing system. Using eddy-current type gap sensors for control, the electrical runout caused by non-uniform material properties of sensor target produces rotational error amplified in feedback control loop, so this runout should be eliminated to increase rotating accuracy. The adaptive feedforward controller is designed and examined its tracking performances and stability numerically with established frequency response function. The designed feedforward controller was applied to a grinding spindle system which is manufactured with a 5.5 kW internal motor and 5-axis active magnetic bearing system including 5 eddy current gap sensors which have approximately 15∼30㎛ of electrical runout. According to the experimental results, the error signal in radial bearings is reduced to less than 5 ,Urn when it is rotating up to 50,000 rpm due to applying the feedforward control for first order harmonic frequency, and corresponding vibration of the spindle is also removed.

Nano-precision Polishing of CVD SiC Using MCF (Magnetic Compound Fluid) Slurry

  • Wu, Yongbo;Wang, Youliang;Fujimoto, Masakazu;Nomura, Mitsuyoshi
    • Journal of the Korean Society of Manufacturing Technology Engineers
    • /
    • v.23 no.6
    • /
    • pp.547-554
    • /
    • 2014
  • CVD SiC is a perfect material used for molds/dies in hot press molding of glass lens. In its fabrication process, nano-precision polishing is essential finally. For this purpose, a novel polishing method using MCF (Magnetic Compound Fluid) slurry is proposed. In this method, MCF slurry is supplied into a given gap between the workpiece and a MCF slurry carrier, and constrained within the polishing zone by magnetic forces from permanent magnet. In this paper, after an experimental rig used to actually realize the proposed method has been constructed, the fundamental polishing characteristics of CVD SiC such as the effects of process parameters including MCF slurry composition on work-surface roughness were experimentally investigated. As a result, nano-precision surface finish of CVD SiC was successfully attained with MCF slurry and the optimum process parameters for obtaining the smoothest work-surface were determined.