• Title/Summary/Keyword: artificial intelligence-based model

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Influence on overfitting and reliability due to change in training data

  • Kim, Sung-Hyeock;Oh, Sang-Jin;Yoon, Geun-Young;Jung, Yong-Gyu;Kang, Min-Soo
    • International Journal of Advanced Culture Technology
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    • v.5 no.2
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    • pp.82-89
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    • 2017
  • The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the GradientDescentOptimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.

The Communication Aspect of Film and Engineering (영화와 공학의 소통 방식)

  • Ham, Jong-ho
    • Journal of Engineering Education Research
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    • v.18 no.6
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    • pp.88-97
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    • 2015
  • This is paper aims to figure out the way movies and engineering have communicated and the right direction for better communication between them. Engineering no longer should be treated as playing a key role when it comes to film making. Engineering is essential in making films look real. It means that movies gain credibility from utilizing engineering, which all other types of arts also seek after. Films can be resonated better through mutual communication with engineering. The paper takes a close look at emotional aspects to figure out what the new direction of communication between movies and engineering is. People's lives shown in films and the material world that engineering represents are mingled to attain the emotional oneness between the two. This can be easily observed in Japanese movies where robots and humans have a close relationship or recent films whose theme is emotional exchange among humans and robots. This kind of contact leads us to explore newly found humans' position that was brought about in the wake of development in engineering and existential conditions that humans need to have accordingly. Artificial intelligence and neurology sectors that the engineering field is today earnestly working on are in line with it. Therefore, this article seeks to find out the meaning and value of communication between movies and engineering when establishing the fresh mankind model based on emotions and pursuit of diversity.

Detection of Incipient Faults in Induction Motors using FIS, ANN and ANFIS Techniques

  • Ballal, Makarand S.;Suryawanshi, Hiralal M.;Mishra, Mahesh K.
    • Journal of Power Electronics
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    • v.8 no.2
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    • pp.181-191
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    • 2008
  • The task performed by induction motors grows increasingly complex in modern industry and hence improvements are sought in the field of fault diagnosis. It is essential to diagnose faults at their very inception, as unscheduled machine down time can upset critical dead lines and cause heavy financial losses. Artificial intelligence (AI) techniques have proved their ability in detection of incipient faults in electrical machines. This paper presents an application of AI techniques for the detection of inter-turn insulation and bearing wear faults in single-phase induction motors. The single-phase induction motor is considered a proto type model to create inter-turn insulation and bearing wear faults. The experimental data for motor intake current, rotor speed, stator winding temperature, bearing temperature and noise of the motor under running condition was generated in the laboratory. The different types of fault detectors were developed based upon three different AI techniques. The input parameters for these detectors were varied from two to five sequentially. The comparisons were made and the best fault detector was determined.

Trends and Future of Digital Personal Assistant (디지털 개인비서 동향과 미래)

  • Kwon, O.W.;Lee, K.Y.;Lee, Y.H.;Roh, Y.H.;Cho, M.S.;Huang, J.X.;Lim, S.J.;Choi, S.K.;Kim, Y.K.
    • Electronics and Telecommunications Trends
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    • v.36 no.1
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    • pp.1-11
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    • 2021
  • In this study, we introduce trends in and the future of digital personal assistants. Recently, digital personal assistants have begun to handle many tasks like humans by communicating with users in human language on smart devices such as smart phones, smart speakers, and smart cars. Their capabilities range from simple voice commands and chitchat to complex tasks such as device control, reservation, ordering, and scheduling. The digital personal assistants of the future will certainly speak like a person, have a person-like personality, see, hear, and analyze situations like a person, and become more human. Dialogue processing technology that makes them more human-like has developed into an end-to-end learning model based on deep neural networks in recent years. In addition, language models pre-trained from a large corpus make dialogue processing more natural and better understood. Advances in artificial intelligence such as dialogue processing technology will enable digital personal assistants to serve with more familiar and better performance in various areas.

A Modular Based Approach on the Development of AI Math Curriculum Model (인공지능 수학교육과정의 모듈화 접근방법 연구)

  • Baik, Ran
    • Journal of Engineering Education Research
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    • v.24 no.3
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    • pp.50-57
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    • 2021
  • Although the mathematics education process in AI education is a very important issue, little cases are reported in developing effective methods on AI and mathematics education at the university level. The universities cover all fields of mathematics in their curriculums, but they lack in connecting and applying the math knowledge to AI in an efficient manner. Students are hardly interested in taking many math courses and it gets worse for the students in humanities, social sciences and arts. But university education is very slow in adapting to rapidly changing new technologies in the real world. AI is a technology that is changing the paradigm of the century, so every one should be familiar with this technology but it requires fundamental math knowledge. It is not fair for the students to study all math subjects and ride on the AI train. We recognize that three key elements, SW knowledge, mathematical knowledge, and domain knowledge, are required in applying AI technology to the real world problems. This study proposes a modular approach of studying mathematics knowledge while connecting the math to different domain problems using AI techniques. We also show a modular curriculum that is developed for using math for AI-driven autonomous driving.

Meta's Metaverse Platform Design in the Pre-launch and Ignition Life Stage

  • Song, Minzheong
    • International Journal of Internet, Broadcasting and Communication
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    • v.14 no.4
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    • pp.121-131
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    • 2022
  • We look at the initial stage of Meta (previous Facebook)'s new metaverse platform and investigate its platform design in pre-launch and ignition life stage. From the Rocket Model (RM)'s theoretical logic, the results reveal that Meta firstly focuses on investing in key content developers by acquiring virtual reality (VR), video, music content firms and offering production support platform of the augmented reality (AR) content, 'Spark AR' last three years (2019~2021) for attracting high-potential developers and users. In terms of three matching criteria, Meta develops an Artificial Intelligence (AI) powered translation software, partners with Microsoft (MS) for cloud computing and AI, and develops an AI platform for realistic avatar, MyoSuite. In 'connect' function, Meta curates the game concept submitted by game developers, welcomes other game and SNS based metaverse apps, and expands Horizon Worlds (HW) on VR devices to PCs and mobile devices. In 'transact' function, Meta offers 'HW Creator Funding' program for metaverse, launches the first commercialized Meta Avatar Store on Meta's conventional SNS and Messaging apps by inviting all fashion creators to design and sell clothing in this store. Mata also launches an initial test of non-fungible token (NFT) display on Instagram and expands it to Facebook in the US. Lastly, regarding optimization, especially in the face of recent data privacy issues that have adversely affected corporate key performance indicators (KPIs), Meta assures not to collect any new data and to make its privacy policy easier to understand and update its terms of service more user friendly.

Prediction of aerodynamics using VGG16 and U-Net (VGG16 과 U-Net 구조를 이용한 공력특성 예측)

  • Bo Ra, Kim;Seung Hun, Lee;Seung Hyun, Jang;Gwang Il, Hwang;Min, Yoon
    • Journal of the Korean Society of Visualization
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    • v.20 no.3
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    • pp.109-116
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    • 2022
  • The optimized design of airfoils is essential to increase the performance and efficiency of wind turbines. The aerodynamic characteristics of airfoils near the stall show large deviation from experiments and numerical simulations. Hence, it is needed to perform repetitive analysis of various shapes near the stall. To overcome this, the artificial intelligence is used and combined with numerical simulations. In this study, three types of airfoils are chosen, which are S809, S822 and SD7062 used in wind turbines. A convolutional neural network model is proposed in the combination of VGG16 and U-Net. Learning data are constructed by extracting pressure fields and aerodynamic characteristics through numerical analysis of 2D shape. Based on these data, the pressure field and lift coefficient of untrained airfoils are predicted. As a result, even in untrained airfoils, the pressure field is accurately predicted with an error of within 0.04%.

A comparative study on the performance of the parameter-based 3D human model generation techniques from a single image including multiple people (다중 인물 포함 단일 영상으로부터의 파라미터 기반 3차원 휴먼 모델 생성 기법 성능 비교 연구)

  • Gi-Mun Um;Jeong Hwan Kim;Wonjun Kim;Hee Kyung Lee;Seung-Jun Yang;Jeongil Seo
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2022.11a
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    • pp.157-160
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    • 2022
  • 본 논문에서는 다중 인물 포함 단일 영상으로부터 파라미터 기반 3차원 휴먼 모델 생성 기법 중 최근 발표된 SOTA 기법 4가지에 대해 대표적인 데이터 셋들에 대해 사전 학습 모델을 사용한 복원 성능 비교 실험을 수행하였다. 실험결과, CLIFF 기법과 PyMAF-x 기법이 PARE 기법이나 ROMP 기법에 비해 우수한 결과를 보였다.

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Generation of Super-Resolution Benchmark Dataset for Compact Advanced Satellite 500 Imagery and Proof of Concept Results

  • Yonghyun Kim;Jisang Park;Daesub Yoon
    • Korean Journal of Remote Sensing
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    • v.39 no.4
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    • pp.459-466
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    • 2023
  • In the last decade, artificial intelligence's dramatic advancement with the development of various deep learning techniques has significantly contributed to remote sensing fields and satellite image applications. Among many prominent areas, super-resolution research has seen substantial growth with the release of several benchmark datasets and the rise of generative adversarial network-based studies. However, most previously published remote sensing benchmark datasets represent spatial resolution within approximately 10 meters, imposing limitations when directly applying for super-resolution of small objects with cm unit spatial resolution. Furthermore, if the dataset lacks a global spatial distribution and is specialized in particular land covers, the consequent lack of feature diversity can directly impact the quantitative performance and prevent the formation of robust foundation models. To overcome these issues, this paper proposes a method to generate benchmark datasets by simulating the modulation transfer functions of the sensor. The proposed approach leverages the simulation method with a solid theoretical foundation, notably recognized in image fusion. Additionally, the generated benchmark dataset is applied to state-of-the-art super-resolution base models for quantitative and visual analysis and discusses the shortcomings of the existing datasets. Through these efforts, we anticipate that the proposed benchmark dataset will facilitate various super-resolution research shortly in Korea.

Strategy to coordinate actions through a plant parameter prediction model during startup operation of a nuclear power plant

  • Jae Min Kim;Junyong Bae;Seung Jun Lee
    • Nuclear Engineering and Technology
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    • v.55 no.3
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    • pp.839-849
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    • 2023
  • The development of automation technology to reduce human error by minimizing human intervention is accelerating with artificial intelligence and big data processing technology, even in the nuclear field. Among nuclear power plant operation modes, the startup and shutdown operations are still performed manually and thus have the potential for human error. As part of the development of an autonomous operation system for startup operation, this paper proposes an action coordinating strategy to obtain the optimal actions. The lower level of the system consists of operating blocks that are created by analyzing the operation tasks to achieve local goals through soft actor-critic algorithms. However, when multiple agents try to perform conflicting actions, a method is needed to coordinate them, and for this, an action coordination strategy was developed in this work as the upper level of the system. Three quantification methods were compared and evaluated based on the future plant state predicted by plant parameter prediction models using long short-term memory networks. Results confirmed that the optimal action to satisfy the limiting conditions for operation can be selected by coordinating the action sets. It is expected that this methodology can be generalized through future research.