• Title/Summary/Keyword: AI 모델

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Privacy Preserving Techniques for Deep Learning in Multi-Party System (멀티 파티 시스템에서 딥러닝을 위한 프라이버시 보존 기술)

  • Hye-Kyeong Ko
    • The Journal of the Convergence on Culture Technology
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    • v.9 no.3
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    • pp.647-654
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    • 2023
  • Deep Learning is a useful method for classifying and recognizing complex data such as images and text, and the accuracy of the deep learning method is the basis for making artificial intelligence-based services on the Internet useful. However, the vast amount of user da vita used for training in deep learning has led to privacy violation problems, and it is worried that companies that have collected personal and sensitive data of users, such as photographs and voices, own the data indefinitely. Users cannot delete their data and cannot limit the purpose of use. For example, data owners such as medical institutions that want to apply deep learning technology to patients' medical records cannot share patient data because of privacy and confidentiality issues, making it difficult to benefit from deep learning technology. In this paper, we have designed a privacy preservation technique-applied deep learning technique that allows multiple workers to use a neural network model jointly, without sharing input datasets, in multi-party system. We proposed a method that can selectively share small subsets using an optimization algorithm based on modified stochastic gradient descent, confirming that it could facilitate training with increased learning accuracy while protecting private information.

A Study on the AI-based Fish Classification and Weight Estimation System (인공지능 기반 어류 분류 및 무게 추정 시스템에 관한 연구)

  • Go, Jun-Hyeok;Oh, dong-Hyub;Lee, Ji-won;Im, Tae-ho
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.10a
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    • pp.229-232
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    • 2022
  • Recently, production of offshore fisheries in Korea has been decreasing. Since production of offshore fisheries in 2016 fell below 1 million tons for the first time in 44 years, it has not recovered and has been decreasing. In order to cope with such a decrease in fishery resources, the TAC (total allowable catch) system is implemented internationally for fisheries resource management. Since 1999, South Korea has introduced the TAC system to perform resource management. In this paper, we propose an artificial intelligence-based fish classification and weight estimation system that can be used to investigate fishery resources of land observers essential for the implementation of the TAC system. The system consists of an app and a cloud server that automatically measures the body size and height of fish and takes photos using a terminal equipped with a lidar sensor. In the cloud server, fish classification is performed using a CNN-based efficientnet model and the weight of fish is predicted using automatically measured body length and body height information. Using this system, it is possible to improve the existing method in which the land observer manually writes after measuring the tape measure and weight in the stomach market.

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Study on the Performance Improvement of Marine Engine Generator Exciter Control using Neural Network Controller (신경망 회로 제어기를 이용한 선박 엔진 발전기의 여자기 제어 성능 개선에 관한 연구)

  • HeeMoon Kim;JongSu Kim;SeongWan Kim;HyeonMin Jeon
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.29 no.6
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    • pp.659-665
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    • 2023
  • The exciter of a ship generator adjusts the magnetic flux through excitation current control to maintain the output terminal voltage constant. The voltage controller inside the exciter typically uses a proportional integral control method. however, the response characteristics determined by the gain and time constant produce unwanted output owing to an inappropriate setting value that can reduce the quality and stability of power within the ship. In this study, a neural network circuit is learned using stable input/output data that can be obtained through the AC4A type exciter model provided by IEEE, and the simulation is performed by replacing the existing proportional integral control type voltage controller with the learned neural network circuit controller. Consequently, overshooting was improved by up to 9.63% compared with that of the previous model, and excellence in stable response characteristics was confirmed.

Breakdown of Boundaries Between Assistive Devices and Wearbles: An Evolutionary Case Study of Starkey Hearing Aid (장애보조기구와 스마트 웨어러블의 경계 붕괴: 스타키 보청기 사례 연구)

  • Yujin Pyo;Jungwoo Lee
    • Information Systems Review
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    • v.24 no.3
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    • pp.23-41
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    • 2022
  • This case research investigates on how hearing aids, which is one of disability aids, is becoming a smart device, focusing on the case of Starkey Hearing Technologies(Starkey Inc.). Starkey, founded in 1967, has been a leader in innovating forms and functions of hearing aids, and has recently introduced the world's first hearing aid implemented with AI and biological sensors. In this study, history of disability aids, hearing aids(especially Starkey Inc.'s), smart wearable devices and smart earphones are compared. It has been found that recently, there has been a breakdown of boundaries between hearing aids and smart wearable devices in terms of their functions, since entertainment and life assistant functions are added to hearing aids. Based on this trend, the development model of disability aids and smart wearable devices are derived, and according social changes are discussed.

AI Chatbot-Based Daily Journaling System for Eliciting Positive Emotions (긍정적 감정 유발을 위한 AI챗봇기반 일기 작성 시스템)

  • Jun-Hyeon Kim;Mi-Kyeong Moon
    • The Journal of the Korea institute of electronic communication sciences
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    • v.19 no.1
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    • pp.105-112
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    • 2024
  • In contemporary society, the expression of emotions and self-reflection are considered pivotal factors with a positive impact on stress management and mental well-being, thereby highlighting the significance of journaling. However, traditional journaling methods have posed challenges for many individuals due to constraints in terms of time and space. Recent rapid advancements in chatbot and emotion analysis technologies have garnered significant attention as essential tools to address these issues. This paper introduces an artificial intelligence chatbot that integrates the GPT-3 model and emotion analysis technology, detailing the development process of a system that automatically generates journals based on users' chat data. Through this system, users can engage in journaling more conveniently and efficiently, fostering a deeper understanding of their emotions and promoting positive emotional experiences.

A Study on the Surface Damage Detection Method of the Main Tower of a Special Bridge Using Drones and A.I. (드론과 A.I.를 이용한 특수교 주탑부 표면 손상 탐지 방법 연구)

  • Sungjin Lee;Bongchul Joo;Jungho Kim;Taehee Lee
    • Journal of Korean Society of Disaster and Security
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    • v.16 no.4
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    • pp.129-136
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    • 2023
  • A special offshore bridge with a high pylon has special structural features.Special offshore bridges have inspection blind spots that are difficult to visually inspect. To solve this problem, safety inspection methods using drones are being studied. In this study, image data of the pylon of a special offshore bridge was acquired using a drone. In addition, an artificial intelligence algorithm was developed to detect damage to the pylon surface. The AI algorithm utilized a deep learning network with different structures. The algorithm applied the stacking ensemble learning method to build a model that formed the ensemble and collect the results.

A Study on Trend Using Time Series Data (시계열 데이터 활용에 관한 동향 연구)

  • Shin-Hyeong Choi
    • Advanced Industrial SCIence
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    • v.3 no.1
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    • pp.17-22
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    • 2024
  • History, which began with the emergence of mankind, has a means of recording. Today, we can check the past through data. Generated data may only be generated and stored at a certain moment, but it is not only continuously generated over a certain time interval from the past to the present, but also occurs in the future, so making predictions using it is an important task. In order to find out trends in the use of time series data among numerous data, this paper analyzes the concept of time series data, analyzes Recurrent Neural Network and Long-Short Term Memory, which are mainly used for time series data analysis in the machine learning field, and analyzes the use of these models. Through case studies, it was confirmed that it is being used in various fields such as medical diagnosis, stock price analysis, and climate prediction, and is showing high predictive results. Based on this, we will explore ways to utilize it in the future.

A study on the acoustic performance of an absorptive silencer applying the optimal arrangement of absorbing materials (흡음재 최적 배치를 적용한 흡음형 소음기의 음향성능 연구)

  • Dongheon Kang;Haesang Yang;Woojae Seong
    • The Journal of the Acoustical Society of Korea
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    • v.43 no.3
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    • pp.261-269
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    • 2024
  • In this paper, the acoustic performance of an absorptive silencer was enhanced by optimizing an arrangement of multi-layered absorbing materials. The acoustic performance of the silencer was evaluated through transmission loss, and finite element method-based numerical analysis program was employed to calculate the transmission loss. Polyurethane, a porous elastic material frequently used in absorptive silencers, was employed as the absorbing material. The Biot-Allard model was applied, assuming that air is filled inside the polyurethane. By setting the frequency range of interest up to the 2 kHz and the acoustic performance affecting properties of the absorbing materials were investigated when it was composed as a single layer. And the acoustic performance of the silencers with the single and multi-layered absorbing materials was compared with each other based on polyurethane material properties. Subsequently, the arrangement of the absorbing materials was optimized by applying the Nelder-Mead method. The results demonstrated that the average transmission loss improved compared to the single-layered absorptive silencer.

A Study on the Operational Planning Assist System for Ground Forces (지상군 작전계획 수립 보조 시스템 설계 연구)

  • Ikhyun Kim;Sunju Lee
    • Journal of The Korean Institute of Defense Technology
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    • v.5 no.1
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    • pp.7-18
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    • 2023
  • The military leader makes an operation plan to accomplish combat missions. The current doctrine for an operation planning requires the use of simple and clear procedures and methods that can be carried out with human effort under adverse conditions in the field. The work in the process of an operation planning can be said to be a series of decision-making, and the criteria for decision-making generally apply mission variables. However, detailed standards are not fixed as doctrine, but are creatively established and applied. However, for AI-based decision-making, it is necessary to formalize the criteria and the format used. This paper first aims to standardize various criteria and forms to present a method that can be used in a semi-automated assist system, and to seek a plan to artificialize it. To this end, mathematical models and decision-making methods established in the field of operations research were applied to improve efficiency.

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Impact of Supply Chain Digital Transformation on Corporate Performance (공급망 디지털 전환이 기업 성과에 미치는 영향)

  • Kyung-Ihl Kim;Seong-Hyo Lee
    • Advanced Industrial SCIence
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    • v.3 no.3
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    • pp.1-7
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    • 2024
  • The purpose of this study is to investigate how supply chain digital transformation affects corporate performance by building supply chain agility and innovation capabilities based on the resource-based view (RBV) theory. The model was verified using structural equation modeling based on a data set of 271 domestic companies, and mediation and moderation analyzes were performed to test the research hypotheses. The study found a positive correlation between supply chain digital transformation and corporate performance that is fully mediated by both supply chain agility and innovation capability, with the potential for the interaction between supply chain agility and innovation capability to have adverse consequences for corporate performance. This study is expected to advance our understanding on the antecedents of corporate performance by integrating supply chain digital transformation and the mediating mechanisms of supply chain agility and innovation capabilities that serve as a conduit between supply chain digital transformation and RBV-based corporate performance.