• 한국전자통신학회논문지
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• 제17권1호
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• pp.119-124
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• 2022

성공적인 수중 위치측정을 위해서는 다양한 수중 로봇에 탑재 가능한 대규모 병렬 컴퓨팅 환경이 필요하다. 이에, 본 논문에서는 수중 위치측정을 위한 인공지능 컴퓨팅 플랫폼 설계 방법을 제안한다. 제안한 플랫폼은 총 4개의 하드웨어 모듈로 구성된다. Transponder 및 hydrophone 모듈은 음파를 송수신하며 FPGA 모듈은 송수신한 음파 신호를 빠르게 병렬로 전처리한다. Jetson 모듈은 인공지능 기반 알고리즘 처리한다. 해당 플랫폼은 실제 수중 환경에서 거리에 따라 수중 위치측정을 위한 음파 송수신 실험을 수행하였으며 이를 통해 설계한 플랫폼을 검증할 수 있었다.

• ETRI Journal
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• 제46권1호
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• pp.106-117
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• 2024

Large language models (LLMs) have revolutionized various applications in natural language processing and exhibited proficiency in generating programming code. We propose a framework for evaluating the code generation ability of LLMs and introduce a new metric, pass-ratio@n, which captures the granularity of accuracy according to the pass rate of test cases. The framework is intended to be fully automatic to handle the repetitive work involved in generating prompts, conducting inferences, and executing the generated codes. A preliminary evaluation focusing on the prompt detail, problem publication date, and difficulty level demonstrates the successful integration of our framework with the LeetCode coding platform and highlights the applicability of the pass-ratio@n metric.

• 전자통신동향분석
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• 제33권6호
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• pp.107-117
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• 2018

In recent years, with the development of autonomous driving technology, high-performance artificial intelligence computing hardware platforms have been developed that can process multi-sensor data, object recognition, and vehicle control for autonomous vehicles. Most of these hardware platforms have been developed overseas, such as NVIDIA's DRIVE PX, Audi's zFAS, Intel GO, Mobile Eye's EyeQ, and BAIDU's Apollo Pilot. In Korea, however, ETRI's artificial intelligence computing platform has been developed. In this paper, we discuss the specifications, structure, performance, and development status centering on hardware platforms that support autonomous driving rather than the overall contents of autonomous driving technology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권9호
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• pp.2334-2347
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• 2023

Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

• 전자통신동향분석
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• 제33권5호
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• pp.121-134
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• 2018

The Von Neumann based architecture of the modern computer has dominated the computing industry for the past 50 years, sparking the digital revolution and propelling us into today's information age. Recent research focus and market trends have shown significant effort toward the advancement and application of artificial intelligence technologies. Although artificial intelligence has been studied for decades since the Turing machine was first introduced, the field has recently emerged into the spotlight thanks to remarkable milestones such as AlexNet-CNN and Alpha-Go, whose neural-network based deep learning methods have achieved a ground-breaking performance superior to existing recognition, classification, and decision algorithms. Unprecedented results in a wide variety of applications (drones, autonomous driving, robots, stock markets, computer vision, voice, and so on) have signaled the beginning of a golden age for artificial intelligence after 40 years of relative dormancy. Algorithmic research continues to progress at a breath-taking pace as evidenced by the rate of new neural networks being announced. However, traditional Von Neumann based architectures have proven to be inadequate in terms of computation power, and inherently inefficient in their processing of vastly parallel computations, which is a characteristic of deep neural networks. Consequently, global conglomerates such as Intel, Huawei, and Google, as well as large domestic corporations and fabless companies are developing dedicated semiconductor chips customized for artificial intelligence computations. The AI Processor Research Laboratory at ETRI is focusing on the research and development of super low-power AI processor chips. In this article, we present the current trends in computation platform, parallel processing, AI processor, and super-threaded AI processor research being conducted at ETRI.

• 스마트미디어저널
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• 제12권2호
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• pp.42-55
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• 2023

본 논문에서는 블록체인(Blockchain, BC), 인공지능 (Arificial Interlligence, AI), 양자 컴퓨팅 (Quantum Computing, QC) 및 사물 인터넷 (Internet of Things, IoT)의 결합이 의료, 물류 및 금융을 비롯한 다양한 산업 및 분야를 변화시킬 수 있는 가능성을 다룹니다. 이에 대해 기술들이 통합되는 경향과 발전, 그리고 이러한 기술들과 함께하는 잠재적 이점과 도전 과제를 분석합니다. 본 논문에서는 BC, AI, QC 및 IoT를 통합하기 위한 개념적 프레임워크를 제시하고, 제시한 프레임워크의 주요 특징과 도전 과제에 대해서 논의합니다. 또한 이러한 기술들을 통합하는 최신 연구와 개발, 그리고 이러한 기술들과 함께하는 주요 도전 과제와 기회를 살펴봅니다. 본 논문의 분석은 기술들을 통합하는 잠재적 이점을 강조하며, 보안성, 개인 정보 보호 및 효율성의 증대에 초점을 맞추어 이러한 기술들의 미래에 대한 인사이트를 제공합니다.

• 사물인터넷융복합논문지
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• 제10권1호
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• pp.69-75
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• 2024

본 연구에서는 산업용 사물인터넷(IIoT)의 맥락에서 포그 컴퓨팅(Fog Computing, FC)를 위해 특별히 고안된 혁신적인 인지 사물인터넷(Cognitive IoT) 프레임워크를 제안한다. 본 논문에서는 인지 IoT 플랫폼의 복잡한 설계 및 기능적 아키텍처에 초점을 맞추고, 이 아키텍처는 서비스 제공, 인지 의사결정, 분산 모니터링 및 제어와 같은 핵심 구성 요소를 원활하게 통합하는 것을 제안한다. 이 플랫폼의 중요한 측면은 기계 학습(ML) 및 인공 지능(AI)을 통합하는 것으로, 다양한 산업 애플리케이션에서 운영의 유연성과 상호 운용성을 향상시켜 실시간 기계 상태 모니터링에 중점을 둔 예측 유지보수-서비스(Predictive Maintenance-as-a-Service, PdM-as-a-Service) 모델을 통해 제시된다. 이 모델은 실시간 데이터 분석을 활용하여 유지보수 및 관리 작업을 수행함으로써 전통적인 유지보수 접근법을 뛰어넘고, 실증적 결과는 포그 컴퓨팅 환경 내에서 플랫폼의 효과성을 입증하며, 산업용 IoT 애플리케이션 분야에서의 변혁적 잠재력을 보여 IIoT 플랫폼 개발에 기여 하는 연구이다.

• Journal of Information Processing Systems
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• 제14권1호
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• pp.129-139
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• 2018

The crowdsourcing software development (CSD) is growing rapidly in the open call format in a competitive environment. In CSD, tasks are posted on a web-based CSD platform for CSD workers to compete for the task and win rewards. Task searching and assigning are very important aspects of the CSD environment because tasks posted on different platforms are in hundreds. To search and evaluate a thousand submissions on the platform are very difficult and time-consuming process for both the developer and platform. However, there are many other problems that are affecting CSD quality and reliability of CSD workers to assign the task which include the required knowledge, large participation, time complexity and incentive motivations. In order to attract the right person for the right task, the execution of action plans will help the CSD platform as well the CSD worker for the best matching with their tasks. This study formalized the task assignment method by utilizing different situations in a CSD competition-based environment in artificial intelligence (AI) planning. The results from this study suggested that assigning the task has many challenges whenever there are undefined conditions, especially in a competitive environment. Our main focus is to evaluate the AI automated planning to provide the best possible solution to matching the CSD worker with their personality type.

• 한국멀티미디어학회논문지
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• 제24권1호
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• pp.75-94
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• 2021

The 4th industrial revolution needs a fusion of artificial intelligence, robotics, the Internet of Things (IoT), edge computing, and other technologies. For the fusion of technologies, cloud computing technology can provide flexible and high-performance computing resources so that cloud computing can be the foundation technology of new emerging services. The emerging services become a global-scale, and require much higher performance, availability, and reliability. Public cloud providers already provide global-scale services. However, their services, costs, performance, and policies are different. Enterprises/ developers to come out with a new inter-operable service are experiencing vendor lock-in problems. Therefore, multi-cloud technology that federatively resolves the limitations of single cloud providers is required. We propose a software platform, denoted as Cloud-Barista. Cloud-Barista is a multi-cloud service common platform for federating multiple clouds. It makes multiple cloud services as a single service. We explain the functional architecture of the proposed platform that consists of several frameworks, and then discuss the main design and implementation issues of each framework. To verify the feasibility of our proposal, we show a demonstration which is to create 18 virtual machines on several cloud providers, combine them as a single resource, and manage it.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2292-2313
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• 2023

With improving computing performance, various digital platforms are being developed to enable easily utilization of high-performance computing environments. EDISON 1.0 is an online simulation platform widely used in computational science and engineering education. As the research paradigm changes, the demand for developing the EDISON 1.0 platform centered on simulation into the EDISON 2.0 platform centered on data and artificial intelligence is growing. Herein, a data framework, a core module for data-centric research on EDISON 2.0 digital platform, is proposed. The proposed data framework provides the following three functions. First, it provides a data repository suitable for the data lifecycle to increase research reproducibility. Second, it provides a new data model that can integrate, manage, search, and utilize heterogeneous data to support a data-driven interdisciplinary convergence research environment. Finally, it provides an exploratory data analysis (EDA) service and data enrichment using an AI model, both developed to strengthen data reliability and maximize the efficiency and effectiveness of research endeavors. Using the EDISON 2.0 data framework, researchers can conduct interdisciplinary convergence research using heterogeneous data and easily perform data pre-processing through the web-based UI. Further, it presents the opportunity to leverage the derived data obtained through AI technology to gain insights and create new research topics.