• 제목/요약/키워드: traditional experiments

검색결과 1,064건 처리시간 0.025초

Black box-assisted fine-grained hierarchical access control scheme for epidemiological survey data

  • Xueyan Liu;Ruirui Sun;Linpeng Li;Wenjing Li;Tao Liu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제17권9호
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    • pp.2550-2572
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    • 2023
  • Epidemiological survey is an important means for the prevention and control of infectious diseases. Due to the particularity of the epidemic survey, 1) epidemiological survey in epidemic prevention and control has a wide range of people involved, a large number of data collected, strong requirements for information disclosure and high timeliness of data processing; 2) the epidemiological survey data need to be disclosed at different institutions and the use of data has different permission requirements. As a result, it easily causes personal privacy disclosure. Therefore, traditional access control technologies are unsuitable for the privacy protection of epidemiological survey data. In view of these situations, we propose a black box-assisted fine-grained hierarchical access control scheme for epidemiological survey data. Firstly, a black box-assisted multi-attribute authority management mechanism without a trusted center is established to avoid authority deception. Meanwhile, the establishment of a master key-free system not only reduces the storage load but also prevents the risk of master key disclosure. Secondly, a sensitivity classification method is proposed according to the confidentiality degree of the institution to which the data belong and the importance of the data properties to set fine-grained access permission. Thirdly, a hierarchical authorization algorithm combined with data sensitivity and hierarchical attribute-based encryption (ABE) technology is proposed to achieve hierarchical access control of epidemiological survey data. Efficiency analysis and experiments show that the scheme meets the security requirements of privacy protection and key management in epidemiological survey.

생성형 AI는 인간 관리자를 대체할 수 있는가? 자동 생성된 관리자 응답이 고객에 미치는 영향 (Can Generative AI Replace Human Managers? The Effects of Auto-generated Manager Responses on Customers)

  • 박예은;안현철
    • 지식경영연구
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    • 제24권4호
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    • pp.153-176
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    • 2023
  • 최근 생성형 AI, 특히 ChatGPT와 같은 대화형 인공지능이 고객 서비스를 자동화하는 기술적 대안으로 주목받고 있다. 그러나 고객 서비스 자동화에 있어 현재의 생성형 AI 기술이 기존 인간 관리자를 효과적으로 대체할 수 있는지, 조건이나 환경에 따라 어떤 상황에서는 유리하고 다른 상황에서는 불리한지에 대한 연구는 아직 충분히 이루어지지 않은 상태이다. 이러한 배경에서 본 연구는 "고객 서비스 활동과 관련하여 생성형 AI가 인간 관리자를 대체할 수 있는가?"라는 질문에 답하기 위해, 음식 배달 플랫폼의 고객 온라인 리뷰에 대한 실험과 설문조사를 수행하였다. 또한 고객의 온라인 리뷰가 긍정적일 때와 부정적일 때에 따라 차이가 있는지 정교화 가능성 모델의 관점을 적용하여 가설을 도출하고 해당 가설이 지지되는지를 분석을 통해 확인하였다. 분석 결과, 긍정적인 리뷰에 대해서는 생성형 AI가 인간 관리자를 효과적으로 대체할 수 있지만, 부정적인 리뷰에 대해서는 완벽한 대체가 어려워 인간 관리자의 개입이 더 바람직한 것으로 확인되었다. 이러한 본 연구의 결과는 생성형 AI를 이용하여 고객 서비스 자동화하고자 하는 기업들에게 유의미한 실무적인 통찰을 제공해 줄 수 있을 것이다.

비색 MOF 가스센서 어레이 기반 고정밀 질환 VOCs 바이오마커 검출을 위한 머신비전 플랫폼 (Machine Vision Platform for High-Precision Detection of Disease VOC Biomarkers Using Colorimetric MOF-Based Gas Sensor Array)

  • 이준영;오승윤;김동민;김영웅;허정석;이대식
    • 센서학회지
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    • 제33권2호
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    • pp.112-116
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    • 2024
  • Gas-sensor technology for volatile organic compounds (VOC) biomarker detection offers significant advantages for noninvasive diagnostics, including rapid response time and low operational costs, exhibiting promising potential for disease diagnosis. Colorimetric gas sensors, which enable intuitive analysis of gas concentrations through changes in color, present additional benefits for the development of personal diagnostic kits. However, the traditional method of visually monitoring these sensors can limit quantitative analysis and consistency in detection threshold evaluation, potentially affecting diagnostic accuracy. To address this, we developed a machine vision platform based on metal-organic framework (MOF) for colorimetric gas sensor arrays, designed to accurately detect disease-related VOC biomarkers. This platform integrates a CMOS camera module, gas chamber, and colorimetric MOF sensor jig to quantitatively assess color changes. A specialized machine vision algorithm accurately identifies the color-change Region of Interest (ROI) from the captured images and monitors the color trends. Performance evaluation was conducted through experiments using a platform with four types of low-concentration standard gases. A limit-of-detection (LoD) at 100 ppb level was observed. This approach significantly enhances the potential for non-invasive and accurate disease diagnosis by detecting low-concentration VOC biomarkers and offers a novel diagnostic tool.

A Review of the Health Benefits of Kimchi Functional Compounds and Metabolites

  • Hyun Ju Kim;Min Sung Kwon;Hyelyeon Hwang;Ha-Sun Choi;WooJe Lee;Sang-Pil Choi;Haeun Jo;Sung Wook Hong
    • 한국미생물·생명공학회지
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    • 제51권4호
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    • pp.353-373
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    • 2023
  • Kimchi is a traditional Korean dish made with salted fermented vegetables and contains various nutrients and functional substances with potential health benefits. The fermentation process used to make kimchi creates chemical changes in the food, developing nutrients and functional substances that are more easily absorbed and enhanced by the body. Recent studies have shown that several lactic acid bacteria strains isolated from kimchi exhibit probiotic properties and have several health benefiting properties such as such as anticancer, anti-obesity, and anti-constipation; they also promote colon health and cholesterol reduction in in vitro and in vivo experiments, as well as in epidemiological cohort studies. Kimchi contains prebiotics, non-digestible fibers that nourish beneficial gut bacteria; therefore, its intake effectively provides both probiotics and prebiotics for improved gut health and a fortified gut-derived immune system. Furthermore, fermentation of kimchi produces a variety of metabolites that enhance its functionality. These metabolites include organic acids, enzymes, vitamins, bioactive compounds, bacteriocins, exopolysaccharides, and γ-aminobutyric acid. These diverse health-promoting metabolites are not readily obtainable from single food sources, positioning kimchi as a valuable dietary option for acquiring these essential components. In this review, the health functionalities of kimchi ingredients, lactic acid bacteria strains, and health-promoting metabolites from kimchi are discussed for their properties and roles in kimchi fermentation. In conclusion, consuming kimchi can be beneficial for health. We highlight the benefits of kimchi consumption and establish a rationale for including kimchi in a balanced, healthy diet.

모빌리티 전용 저장장치의 고온 고장 방지를 위한 온도 관리 시스템 설계 (A Design of Temperature Management System for Preventing High Temperature Failures on Mobility Dedicated Storage)

  • 이현섭
    • 사물인터넷융복합논문지
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    • 제10권2호
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    • pp.125-130
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    • 2024
  • 모빌리티 기술의 급격한 성장으로 산업 분야의 수요는 차량 내에 다양한 장비와 센서의 데이터를 안정적으로 처리할 수 있는 저장장치를 요구하고 있다. NAND 플래시 메모리는 외부에 강한 충격뿐만 아니라 저전력, 빠른 데이터 처리 속도의 장점이 있기 때문에 모빌리티 환경의 저장장치로 활용되고 있다. 그러나 플래시 메모리는 고온에 장기 노출될 경우 데이터 손상이 발생할 수 있는 특징이 있다. 따라서 태양 복사열 등 날씨나 외부 열원에 의한 고온 노출이 빈번한 모빌리티 환경에서는 온도를 관리하기 위한 전용 시스템이 필요하다. 본 논문은 모빌리티 환경에서 저장장치 온도 관리하기 위한 전용 온도 관리 시스템을 설계한다. 설계한 온도 관리 시스템은 전통적인 공기 냉각 방식과 수 냉각방식의 기술을 하이브리드로 적용하였다. 냉각 방식은 저장장치의 온도에 따라 적응형으로 동작하도록 설계하였으며, 온도 단계가 낮을 경우 동작하지 않도록 설계하여 에너지 효율을 높였다. 마지막으로 실험을 통해 각 냉각방식과 방열재질의 차이 따른 온도 차이를 분석하였고, 온도 관리 정책이 성능을 유지하는데 효과가 있음을 증명하였다.

Focal Loss와 앙상블 학습을 이용한 야생조류 소리 분류 기법 (Wild Bird Sound Classification Scheme using Focal Loss and Ensemble Learning)

  • 이재승;유제혁
    • 한국산업정보학회논문지
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    • 제29권2호
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    • pp.15-25
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    • 2024
  • 효과적인 동물 생태계 분석을 위해서는 동물 서식 현황을 자동으로 파악할 수 있는 동물 관제 기술이 중요하다. 특히 울음소리로 종을 판별하는 동물 소리 분류 기술은 영상을 통한 판별이 어려운 환경에서 큰 주목을 받고 있다. 기존 연구들은 단일 딥러닝 모델을 사용하여 동물 소리를 분류하였으나, 야외 환경에서 수집된 동물 소리는 많은 배경 잡음을 포함하여 단일 모델의 판별력을 악화시키며, 종에 따른 데이터 불균형으로 인해 모델의 편향된 학습을 야기한다. 이에, 본 논문에서는 클래스의 데이터 수를 고려하여 페널티를 부여하는 Focal Loss를 사용한 여러 분류 모델의 예측결과를 앙상블을 통해 결합하여 잡음이 많은 동물 소리를 효과적으로 분류할 수 있는 기법을 제안한다. 공개 데이터 셋을 사용한 실험에서, 제안된 기법은 단일 모델의 평균 성능에 비해 Recall 기준으로 최대 22.6%의 성능 개선을 달성하였다.

Abnormal State Detection using Memory-augmented Autoencoder technique in Frequency-Time Domain

  • Haoyi Zhong;Yongjiang Zhao;Chang Gyoon Lim
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제18권2호
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    • pp.348-369
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    • 2024
  • With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

Evaluation of the antinociceptive activities of natural propolis extract derived from stingless bee Trigona thoracica in mice

  • Nurul Alina Muhamad Suhaini;Mohd Faeiz Pauzi;Siti Norazlina Juhari;Noor Azlina Abu Bakar;Jee Youn Moon
    • The Korean Journal of Pain
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    • 제37권2호
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    • pp.141-150
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    • 2024
  • Background: Stingless bee propolis is a popular traditional folk medicine and has been employed since ancient times. This study aimed to evaluate the antinociceptive activities of the chemical constituents of aqueous propolis extract (APE) collected by Trigona thoracica in a nociceptive model in mice. Methods: The identification of chemical constituents of APE was performed using high-performance liquid chromatography (HPLC). Ninety-six male Swiss mice were administered APE (400 mg/kg, 1,000 mg/kg, and 2,000 mg/kg) before developing nociceptive pain models. Then, the antinociceptive properties of each APE dose were evaluated in acetic acid-induced abdominal constriction, hot plate test, and formalin-induced paw licking test. Administration of normal saline, acetylsalicylic acid (ASA, 100 mg/kg, orally), and morphine (5 mg/kg, intraperitoneally) were used for the experiments. Results: HPLC revealed that the APE from Trigona thoracica contained p-coumaric acid (R2 = 0.999) and caffeic acid (R2 = 0.998). Although all APE dosages showed inhibition of acetic acid-induced abdominal constriction, only 2,000 mg/kg was comparable to the result of ASA (68.7% vs. 73.3%, respectively). In the hot plate test, only 2,000 mg/kg of APE increased the latency time significantly compared to the control. In the formalin test, the durations of paw licking were significantly reduced at early and late phases in all APE groups with a decrease from 45.1% to 53.3%. Conclusions: APE from Trigona thoracica, containing p-coumaric acid and caffeic acid, exhibited antinociceptive effects, which supports its potential use in targeting the prevention or reversal of central and peripheral sensitization that may produce clinical pain conditions.

딥러닝 기반의 딥 클러스터링 방법에 대한 분석 (Analysis of deep learning-based deep clustering method)

  • 권현;이준
    • 융합보안논문지
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    • 제23권4호
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    • pp.61-70
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    • 2023
  • 클러스터링은 데이터의 정답값(실제값)이 없는 데이터를 기반으로 데이터의 특징벡터의 거리 기반 등으로 군집화를 하는 비지도학습 방법이다. 이 방법은 이미지, 텍스트, 음성 등 다양한 데이터에 대해서 라벨링이 없이 적용할 수 있다는 장점이 있다. 기존 클러스터링을 하기 위해 차원축소 기법을 적용하거나 특정 특징만을 추출하여 군집화하는 방법이 적용되었다. 하지만 딥러닝 기반 모델이 발전하면서 입력 데이터를 잠재 벡터로 표현하는 오토인코더, 생성 적대적 네트워크 등을 통해서 딥 클러스터링의 기술이 연구가 되고 있다. 본 연구에서, 딥러닝 기반의 딥 클러스터링 기법을 제안하였다. 이 방법에서 오토인코더를 이용하여 입력 데이터를 잠재 벡터로 변환하고 이 잠재 벡터를 클러스터 구조에 맞게 벡터 공간을 구성 및 k-평균 클러스터링을 하였다. 실험 환경으로 pytorch 머신러닝 라이브러리를 이용하여 데이터셋으로 MNIST와 Fashion-MNIST을 적용하였다. 모델로는 컨볼루션 신경망 기반인 오토인코더 모델을 사용하였다. 실험결과로 k가 10일 때, MNIST에 대해서 89.42% 정확도를 가졌으며 Fashion-MNIST에 대해서 56.64% 정확도를 가진다.

경옥고(瓊玉膏)의 열 스트레스에 의한 피부노화 억제 활성 (Resistance Activity of Kyung-Ok-Ko on Thermal Stress in C. elegans)

  • 정원석;조성영;조현우;이희운;정영일;김희택;유영법
    • 한방안이비인후피부과학회지
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    • 제37권1호
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    • pp.17-28
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    • 2024
  • Objectives : This study was conducted to reveal the scientific mechanism of the anti-skin aging activity of Kyung-Ok-Ko(KOK), which is highly useful as a Korean traditional medicine and functional food. Methods : The skin wrinkle and aging inhibitory activity of KOK was confirmed through in vitro experiments of human dermal fibroblast neonatal cell(HDFn) and in vivo of C. elegans, and hairless mouse(SKH-1). Results : The amount of the C-terminus of the collagen precursor in the HDFn cell culture medium treated with KOK using an enzymes-linked immunoassay kit. The group treated with KOK 200㎍/㎖ was a 28.3% increase of collagen precursor compared to the control group. KOK showed inhibitory activity of MMP-1 compared to the control group at a concentration of 200㎍/㎖. In addition, KOK 200㎍/㎖ showed significant inhibitory activity of thermal stress and an oxidative stress compared to the control group in C. elegans. Furthermore, KOK showed a concentration-dependent(100mg/kg and 500mg/kg) anti-wrinkle formation effect in UV-irradiated hairless mouse(SKH-1). Additionally, when KOK was administered to UV-irradiated hairless mice, an increase in procollagen -1 and -3 genes expression was observed, and mmp-1 and mmp-9 genes, which increase collagen decomposition, decreased with the administration of KOK. Conclusions : The skin aging inhibition mechanism of Kyung-Ok-Ko(KOK) is presumed to be achieved through suppressing thermal stress and oxidative stress, suppressing mmp-1 and mmp-9 genes, and increasing procollagen-1 and procollagen-3.