• Title/Summary/Keyword: prompt

Search Result 1,496, Processing Time 0.02 seconds

Statistical Method and Deep Learning Model for Sea Surface Temperature Prediction (수온 데이터 예측 연구를 위한 통계적 방법과 딥러닝 모델 적용 연구)

  • Moon-Won Cho;Heung-Bae Choi;Myeong-Soo Han;Eun-Song Jung;Tae-Soon Kang
    • Journal of the Korean Society of Marine Environment & Safety
    • /
    • v.29 no.6
    • /
    • pp.543-551
    • /
    • 2023
  • As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

GPT-enabled SNS Sentence writing support system Based on Image Object and Meta Information (이미지 객체 및 메타정보 기반 GPT 활용 SNS 문장 작성 보조 시스템)

  • Dong-Hee Lee;Mikyeong Moon;Bong-Jun, Choi
    • Journal of the Institute of Convergence Signal Processing
    • /
    • v.24 no.3
    • /
    • pp.160-165
    • /
    • 2023
  • In this study, we propose an SNS sentence writing assistance system that utilizes YOLO and GPT to assist users in writing texts with images, such as SNS. We utilize the YOLO model to extract objects from images inserted during writing, and also extract meta-information such as GPS information and creation time information, and use them as prompt values for GPT. To use the YOLO model, we trained it on form image data, and the mAP score of the model is about 0.25 on average. GPT was trained on 1,000 blog text data with the topic of 'restaurant reviews', and the model trained in this study was used to generate sentences with two types of keywords extracted from the images. A survey was conducted to evaluate the practicality of the generated sentences, and a closed-ended survey was conducted to clearly analyze the survey results. There were three evaluation items for the questionnaire by providing the inserted image and keyword sentences. The results showed that the keywords in the images generated meaningful sentences. Through this study, we found that the accuracy of image-based sentence generation depends on the relationship between image keywords and GPT learning contents.

Segmentation Foundation Model-based Automated Yard Management Algorithm (의미론적 분할 기반 모델을 이용한 조선소 사외 적치장 객체 자동 관리 기술)

  • Mingyu Jeong;Jeonghyun Noh;Janghyun Kim;Seongheon Ha;Taeseon Kang;Byounghak Lee;Kiryong Kang;Junhyeon Kim;Jinsun Park
    • Smart Media Journal
    • /
    • v.13 no.2
    • /
    • pp.52-61
    • /
    • 2024
  • In the shipyard, aerial images are acquired at regular intervals using Unmanned Aerial Vehicles (UAVs) for the management of external storage yards. These images are then investigated by humans to manage the status of the storage yards. This method requires a significant amount of time and manpower especially for large areas. In this paper, we propose an automated management technology based on a semantic segmentation foundation model to address these challenges and accurately assess the status of external storage yards. In addition, as there is insufficient publicly available dataset for external storage yards, we collected a small-scale dataset for external storage yards objects and equipment. Using this dataset, we fine-tune an object detector and extract initial object candidates. They are utilized as prompts for the Segment Anything Model(SAM) to obtain precise semantic segmentation results. Furthermore, to facilitate continuous storage yards dataset collection, we propose a training data generation pipeline using SAM. Our proposed method has achieved 4.00%p higher performance compared to those of previous semantic segmentation methods on average. Specifically, our method has achieved 5.08% higher performance than that of SegFormer.

Automated Finite Element Analyses for Structural Integrated Systems (통합 구조 시스템의 유한요소해석 자동화)

  • Chongyul Yoon
    • Journal of the Computational Structural Engineering Institute of Korea
    • /
    • v.37 no.1
    • /
    • pp.49-56
    • /
    • 2024
  • An automated dynamic structural analysis module stands as a crucial element within a structural integrated mitigation system. This module must deliver prompt real-time responses to enable timely actions, such as evacuation or warnings, in response to the severity posed by the structural system. The finite element method, a widely adopted approximate structural analysis approach globally, owes its popularity in part to its user-friendly nature. However, the computational efficiency and accuracy of results depend on the user-provided finite element mesh, with the number of elements and their quality playing pivotal roles. This paper introduces a computationally efficient adaptive mesh generation scheme that optimally combines the h-method of node movement and the r-method of element division for mesh refinement. Adaptive mesh generation schemes automatically create finite element meshes, and in this case, representative strain values for a given mesh are employed for error estimates. When applied to dynamic problems analyzed in the time domain, meshes need to be modified at each time step, considering a few hundred or thousand steps. The algorithm's specifics are demonstrated through a standard cantilever beam example subjected to a concentrated load at the free end. Additionally, a portal frame example showcases the generation of various robust meshes. These examples illustrate the adaptive algorithm's capability to produce robust meshes, ensuring reasonable accuracy and efficient computing time. Moreover, the study highlights the potential for the scheme's effective application in complex structural dynamic problems, such as those subjected to seismic or erratic wind loads. It also emphasizes its suitability for general nonlinear analysis problems, establishing the versatility and reliability of the proposed adaptive mesh generation scheme.

Recent Understanding in Particular Matter-Mediated Aging and Age-Related Diseases (미세먼지에 의한 노화 및 노화 관련 질병에 대한 최근 연구 동향)

  • EunJin Bang;Yung Hyun Choi
    • Journal of Life Science
    • /
    • v.34 no.1
    • /
    • pp.68-77
    • /
    • 2024
  • Airborne particulate matter (PM) is an environmentally hazardous pollutant that originates from various sources. PM is comprised of solid particles and liquid droplets of diverse composition and size. Hazardous chemical compositions of PM include elemental and organic carbon, organic compounds, biological compounds and metals. Upon acute and chronic PM exposure, toxic contaminants enter and accumulate within physiological systems and prompt cell structure changes accompanied with intracellular endoplasmic reticulum stress, mitochondrial dysfunction, oxidative stress, inflammation, lipid accumulation, and cell cycle arrest. Ultimately, these cellular response leads to the development of key characteristics of aging. In addition, PM internalization enhances autophagy reflux and lysosomal dysfunction, which is involved in cell aging. Previous studies have emphasized a positive association between PM and increased mortality or decreased lifespan, although these are evidenced mostly by observational studies. Direct evidence of the link between PM and aging is still limited. This review evaluates the evidence from not only observational studies but also in vitro and in vivo evidence of PM on aging progression and age-related diseases development. This evidence is based on age-associated cellular changes including endoplasmic reticulum stress, mitochondrial dysfunction, oxidative stress, inflammation, adipose accumulation, autophagy, which strengthen the association between PM exposure and aging. Understanding the underlying cellular responses under PM may allow for the development of new therapeutic targets for PM-induced aging.

Analysis of Users' Sentiments and Needs for ChatGPT through Social Media on Reddit (Reddit 소셜미디어를 활용한 ChatGPT에 대한 사용자의 감정 및 요구 분석)

  • Hye-In Na;Byeong-Hee Lee
    • Journal of Internet Computing and Services
    • /
    • v.25 no.2
    • /
    • pp.79-92
    • /
    • 2024
  • ChatGPT, as a representative chatbot leveraging generative artificial intelligence technology, is used valuable not only in scientific and technological domains but also across diverse sectors such as society, economy, industry, and culture. This study conducts an explorative analysis of user sentiments and needs for ChatGPT by examining global social media discourse on Reddit. We collected 10,796 comments on Reddit from December 2022 to August 2023 and then employed keyword analysis, sentiment analysis, and need-mining-based topic modeling to derive insights. The analysis reveals several key findings. The most frequently mentioned term in ChatGPT-related comments is "time," indicative of users' emphasis on prompt responses, time efficiency, and enhanced productivity. Users express sentiments of trust and anticipation in ChatGPT, yet simultaneously articulate concerns and frustrations regarding its societal impact, including fears and anger. In addition, the topic modeling analysis identifies 14 topics, shedding light on potential user needs. Notably, users exhibit a keen interest in the educational applications of ChatGPT and its societal implications. Moreover, our investigation uncovers various user-driven topics related to ChatGPT, encompassing language models, jobs, information retrieval, healthcare applications, services, gaming, regulations, energy, and ethical concerns. In conclusion, this analysis provides insights into user perspectives, emphasizing the significance of understanding and addressing user needs. The identified application directions offer valuable guidance for enhancing existing products and services or planning the development of new service platforms.

Exploring Factors to Minimize Hallucination Phenomena in Generative AI - Focusing on Consumer Emotion and Experience Analysis - (생성형AI의 환각현상 최소화를 위한 요인 탐색 연구 - 소비자의 감성·경험 분석을 중심으로-)

  • Jinho Ahn;Wookwhan Jung
    • Journal of Service Research and Studies
    • /
    • v.14 no.1
    • /
    • pp.77-90
    • /
    • 2024
  • This research aims to investigate methods of leveraging generative artificial intelligence in service sectors where consumer sentiment and experience are paramount, focusing on minimizing hallucination phenomena during usage and developing strategic services tailored to consumer sentiment and experiences. To this end, the study examined both mechanical approaches and user-generated prompts, experimenting with factors such as business item definition, provision of persona characteristics, examples and context-specific imperative verbs, and the specification of output formats and tone concepts. The research explores how generative AI can contribute to enhancing the accuracy of personalized content and user satisfaction. Moreover, these approaches play a crucial role in addressing issues related to hallucination phenomena that may arise when applying generative AI in real services, contributing to consumer service innovation through generative AI. The findings demonstrate the significant role generative AI can play in richly interpreting consumer sentiment and experiences, broadening the potential for application across various industry sectors and suggesting new directions for consumer sentiment and experience strategies beyond technological advancements. However, as this research is based on the relatively novel field of generative AI technology, there are many areas where it falls short. Future studies need to explore the generalizability of research factors and the conditional effects in more diverse industrial settings. Additionally, with the rapid advancement of AI technology, continuous research into new forms of hallucination symptoms and the development of new strategies to address them will be necessary.

A Study on the Development of a Selection System for Preservation Formats of Image-Type Electronic Records (이미지 유형 전자기록물의 보존포맷 선정체계 구축방안 연구)

  • Song, ChaeEun;Yang, Dongmin
    • The Korean Journal of Archival Studies
    • /
    • no.79
    • /
    • pp.343-387
    • /
    • 2024
  • Electronic records, characterized by their inherent volatility and instability, necessitate sustainable preservation measures to ensure their long-term accessibility. The National Archives of Korea has instituted a selection system for preservation formats tailored predominantly for document-type electronic records. However, this system falls short in accommodating other record types such as audiovisual records. This study endeavors to broaden the applicability of the existing system, with a concentrated focus on image-type electronic records, and to formulate foundational guidelines for their long-term preservation. In South Korea, image-type electronic records rank as the second most prevalent category following document-type. The image-type electronic records are the most basic form of audio-visual records, and research on this lays the foundation for future discussions on other audio-visual records. Consequently, this research has led to the development of a selection system for preservation formats specifically for image-type electronic records. This system is designed to facilitate the prompt and efficient evaluation of preservation format suitability, even in the context of emerging image formats. The efficacy of this system was validated through its application to extant image formats, resulting in the selection of TIFF, JFIF, and PNG as the optimal preservation formats. The outcomes of this study offer valuable insights and practical reference points for future preservation format evaluations within the field of electronic record management.

Enhancing Empathic Reasoning of Large Language Models Based on Psychotherapy Models for AI-assisted Social Support (인공지능 기반 사회적 지지를 위한 대형언어모형의 공감적 추론 향상: 심리치료 모형을 중심으로)

  • Yoon Kyung Lee;Inju Lee;Minjung Shin;Seoyeon Bae;Sowon Hahn
    • Korean Journal of Cognitive Science
    • /
    • v.35 no.1
    • /
    • pp.23-48
    • /
    • 2024
  • Building human-aligned artificial intelligence (AI) for social support remains challenging despite the advancement of Large Language Models. We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce LLMs to reason about human emotional states. This method is inspired by various psychotherapy approaches-Cognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy (RT)-each leading to different patterns of interpreting clients' mental states. LLMs without CoE reasoning generated predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathic responses aligned with each psychotherapy model's different reasoning patterns. For empathic expression classification, the CBT-based CoE resulted in the most balanced classification of empathic expression labels and the text generation of empathic responses. However, regarding emotion reasoning, other approaches like DBT and PCT showed higher performance in emotion reaction classification. We further conducted qualitative analysis and alignment scoring of each prompt-generated output. The findings underscore the importance of understanding the emotional context and how it affects human-AI communication. Our research contributes to understanding how psychotherapy models can be incorporated into LLMs, facilitating the development of context-aware, safe, and empathically responsive AI.

Survivorship Analysis in Asymptomatic COVID-19+ Hip Fracture Patients: Is There an Increase in Mortality?

  • Mason D. Vialonga;Luke G. Menken;Alex Tang;John W. Yurek;Li Sun;John J. Feldman;Frank A. Liporace;Richard S. Yoon
    • Hip & pelvis
    • /
    • v.34 no.1
    • /
    • pp.25-34
    • /
    • 2022
  • Purpose: Mortality rates following hip fracture surgery have been well-studied. This study was conducted to examine mortality rates in asymptomatic patients presenting for treatment of acute hip fractures with concurrent positive COVID-19(+) tests compared to those with negative COVID-19(-) tests. Materials and Methods: A total of 149 consecutive patients undergoing hip fracture surgery during the COVID-19 pandemic at two academic medical centers were reviewed retrospectively. Patients were divided into two groups for comparative analysis: one group included asymptomatic patients with COVID-19+ tests versus COVID-19- tests. The primary outcome was mortality at 30-days and 90-days. Results: COVID-19+ patients had a higher mortality rate than COVID-19- patients at 30-days (26.7% vs 6.0%, P=0.005) and 90-days (41.7% vs 17.2%, P=0.046) and trended towards an increased length of hospital stay (10.1±6.2 vs 6.8±3.8 days, P=0.06). COVID-19+ patients had more pre-existing respiratory disease (46.7% vs 11.2%, P=0.0002). Results of a Cox regression analysis showed an increased risk of mortality at 30-days and 90-days from COVID-19+ status alone without an increased risk of death in patients with pre-existing chronic respiratory disease. Conclusion: Factors including time to surgery, age, preexisting comorbidities, and postoperative ambulatory status have been proven to affect mortality and complications in hip fracture patients; however, a positive COVID-19 test result adds another variable to this process. Implementation of protocols that will promote prompt orthogeriatric assessments, expedite patient transfer, limit operating room traffic, and optimize anesthesia time can preserve the standard of care in this unique patient population.