• Journal of Digital Convergence
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• v.19 no.6
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• pp.1-9
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• 2021

Artificial intelligence (AI) devices are rapidly emerging as a core platform of next-generation information and communication technology (ICT), this study investigated consumer usage behavior and user experience through AI devices that are widely applied to consumers' daily lives. To this end, data was collected from 600 consumers with experience in using AI devices were derived to recognize the attributes and behavior of AI devices. The analysis results are as follows. First, music listening was the most used among various attributes and it was found that simple functions such as providing weather information were usefully recognized. Second, the main devices used by AI device users were identified as AI speakers, smartphone, PC and laptops. Third, associative images of AI devices appeared in the order of fun, useful, novel, smart, innovative, and friendly. Therefore, practical implications are suggested to contribute to provision of user services using AI devices in the future by analyzing usage behaviors that reflect the characteristics of AI devices.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.597-599
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• 2022

When IoT device performs on-device AI, the device is required to use various AI models selectively according to target service and surrounding environment. Also, AI model can be updated by additional training such as federated learning or adapting the improved technique. Hence, for successful on-device AI, IoT device should acquire various AI models selectively or update previous AI model to new one. In this paper, we propose AI model repository to tackle this issue. The repository supports AI model registration, searching, management, and deployment along with dashboard for practical usage. We implemented it using Node.js and Vue.js to verify it works well.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1103-1110
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• 2022

Due to the rapid development of AI-related industries, countless edge devices are working in the real world. Since data generated within the smart space consisted of these devices is beyond imagination, it is becoming increasingly difficult for edge devices to process. To solve this issue, EdgeCPS has appeared. EdgeCPS is a technology to support harmonious execution of various application services including AI applications through interworking between edge devices and edge servers, and augmenting resources/functions. Therefore, we propose a knowledge-sharing graph-based componentized AI application support system applicable to the EdgeCPS platform. The graph is designed to effectively store information which are essential elements for creating AI applications. In order to easily change resource/function augmentation under the support of the EdgeCPS platform, AI applications are operated as components. The application support system is linked with the knowledge graph so that users can easily create and test applications, and visualizes the execution aspect of the application to users as a pipeline.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1037-1038
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• 2023

기술의 발전으로 인해 스마트 가전제품이 늘어나며 스마트 홈 기술이 주목을 받고 있다. 그러나 이러한 기술은 설정과정의 복잡성으로 사용자들이 쉽게 접근하기 어렵다. 특히 디지털 기기 사용에 익숙하지 않은 사용자들을 스마트 홈 기술로부터 소외시키는 결과를 낳고 있다. 본 논문에서는 사용자 친화적인 스마트 홈 시스템을 제안한다. 사용자의 시선 방향을 추적하여 디바이스를 선택하고 간단한 인터페이스의 컨트롤러로 디바이스를 손쉽게 조작할 수 있도록 한다. 또한, 생성형 인공지능과 RAG 를 결합하여 사용자가 가전제품과 자연스럽게 대화하며 정보를 얻을 수 있는 인터페이스를 제공한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.257-270
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• 2022

In this paper, we design a smart factory risk improvement system and risk improvement method using AI edge devices. The smart factory risk improvement system collects, analyzes, prevents, and promptly responds to the worker's work performance process in the smart factory using AI edge devices, and can reduce the risk that may occur during work with improving the defect rate when workers perfom jobs. In particular, based on worker image information, worker biometric information, equipment operation information, and quality information of manufactured products, it is possible to set an abnormal risk condition, and it is possible to improve the risk so that the work is efficient and for the accurate performance. In addition, all data collected from cameras and IoT sensors inside the smart factory are processed by the AI edge device instead of all data being sent to the cloud, and only necessary data can be transmitted to the cloud, so the processing speed is fast and it has the advantage that security problems are low. Additionally, the use of AI edge devices has the advantage of reducing of data communication costs and the costs of data transmission bandwidth acquisition due to decrease of the amount of data transmission to the cloud.

• Science of Emotion and Sensibility
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• v.22 no.3
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• pp.35-46
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• 2019

Artificial intelligence (AI) technology has been evolving to recognize and learn the languages, voice tones, and facial expressions of users so that they can respond to users' emotions in various contexts. Many AI-based services of particular importance in communications with users provide emotional interaction. However, research on nonverbal interaction as a means of expressing emotion in the AI system is still insufficient. We studied the effect of lighting on users' emotional interaction with an AI device, focusing on color and flickering motion. The AI device used in this study expresses emotions with six colors of light (red, yellow, green, blue, purple, and white) and with a three-level flickering effect (high, middle, and low velocity). We studied the responses of 50 men and women in their 20s and 30s to the emotions expressed by the light colors and flickering effects of the AI device. We found that each light color represented an emotion that was largely similar to the user's emotional image shown in a previous color-sensibility study. The rate of flickering of the lights produced changes in emotional arousal and balance. The change in arousal patterns produced similar intensities of all colors. On the other hand, changes in balance patterns were somewhat related to the emotional image in the previous color-sensibility study, but the colors were different. As AI systems and devices are becoming more diverse, our findings are expected to contribute to designing the users emotional with AI devices through lighting.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.1-8
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• 2024

On-device AI technology, which can operate AI models at the edge devices to support real-time processing and privacy enhancement, is attracting attention. As intelligent IoT is applied to various industries, services utilizing the on-device AI technology are increasing significantly. However, general deep learning models require a lot of computational resources for inference and learning. Therefore, various lightweighting methods such as quantization and pruning have been suggested to operate deep learning models in embedded edge devices. Among the lightweighting methods, we analyze how to lightweight and apply deep learning models to edge computing devices, focusing on pruning technology in this paper. In particular, we utilize dynamic and static pruning techniques to evaluate the inference speed, accuracy, and memory usage of a lightweight AI vision model. The content analyzed in this paper can be used for intelligent video control systems or video security systems in autonomous vehicles, where real-time processing are highly required. In addition, it is expected that the content can be used more effectively in various IoT services and industries.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.9-11
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• 2021

최근 임베디드 엣지 컴퓨팅 디바이스에서 AI와 같은 인공지은 연산을 수행하여 AI 추론 연산의 가속화 및 분산화가 많이 이루어지고 있다. 엣지 디바이스는 임베디드 프로세서를 기반으로 AI의 가속 연산을 위해서 내부에 딥러닝 가속기를 포함하여 가속화시키는 시스템 구성을 하고 있다. 딥러닝 가속기는 복잡한 Neural Network 연산을 위한 데이터 이동이 많으며 외부 메모리와 내부 딥러닝 가속기간의 효율적인 데이터 이동 및 버퍼링이 필요하다. 본 연구에서는 엣지 디바이스 딥러닝 가속기 내부의 버퍼 구조를 모델링하고, 버퍼의 크기에 따른 버퍼링 효과를 분석해 보았다. 딥러닝 가속기 버퍼 구조는 RISC-V 프로세서 기반 가상 플랫폼에 구현되었다. 이를 통해서 딥러닝 모델에 따른 딥러닝 가속기 버퍼의 사용성을 분석할 수 있다.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.397-402
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• 2024

Speech recognition and motion recognition technologies are applied and used in various smart devices, but they are composed of simple command recognition forms and are used as simple functions. Apart from simple functions for recognition data, professional command execution capabilities are required based on data learned in various fields. Research is being conducted on a system platform that provides optimal data to users using Generative AI, which is currently competing around the world, and can interact through voice recognition and motion recognition. The main technical processes designed for this study were designed using technologies such as voice and motion recognition functions, application of AI technology, and two-way communication. In this paper, two-way communication between a device and a user can be achieved by various input methods through voice recognition and motion recognition technology applied with AI technology.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.420-422
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• 2023

본 연구는 웨어러블 디바이스를 활용하여 장애인 재활운동 보조를 위한 AI 기반의 맞춤형 서비스 개발을 소개한다. 해당 서비스는 웨어러블 디바이스를 장착한 상태로 운동 중인 사용자의 심박수, 소모 칼로리, 운동 시간 등의 센서 데이터를 수집 및 관리한다. 사용자 생체 데이터는 클라이언트 서버 간 실시간 통신으로 관리되며, django rest framework 로 구축된 서버에 저장된다. 제안 시스템을 통해 수집된 데이터는 시계열 군집화 분석을 위해 k-means clustering 과 k-shape clustering 을 활용하여 체력 평가의 핵심 지표인 심박수를 분석하였다. 특히, 상대적으로 운동이 어려운 장애인 사용자를 위한 맞춤형 운동능력 분석 및 해석에 대한 정보 제공이 가능하다.