• 한국식품위생안전성학회지
• /
• 제38권4호
• /
• pp.202-210
• /
• 2023

현재까지 한국 벌화분 잔류농약 함량 조사는 벌이 농약에 노출된 정도를 파악하기 위한 목적으로 분석되어 왔고, 식품의 관점에서 벌화분의 잔류농약 함량 연구는 보고된 바 없었다. 본 연구는 화분가공품으로서 식용으로 판매되는 벌화분 제품의 잔류농약 함량 모니터링을 통해 한국에서 유통되는 벌화분에서 잔류농약이 얼마나 검출되는지를 파악하였다. 조사 결과 다양한 농약 성분들이 벌화분에 잔류되어 있었고, 그 중에서 Chlorfenvinphos, Chlorpyrifos 같이 식용작물에서 사용금지된 농약들이 포함되어 있었다. 벌의 활동범위는 식용작물에 국한되어 있지 않기 때문에, 식용작물에만 제한적 농약사용 등의 인위적 관리만으로는 식용 벌화분의 유해물질로부터 안전성을 확보하는 것이 불가능하다는 것을 파악할 수 있었다. 따라서, 식용 벌화분의 잔류농약 안전성에 대한 연구 및 기준 설정이 필요할 것으로 보인다. 또한, 벌화분 원료와 완제품의 성상적 차이가 미미하고 주로 완제품 형태로 유통된다는 것을 고려하였을 때, 원료에만 기준을 두어 잔류농약 검사를 하는 것이 아니라 벌화분의 완제품에서도 잔류농약 검사가 필요할 것으로 보인다.

• International Journal of Computer Science & Network Security
• /
• 제23권1호
• /
• pp.112-119
• /
• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.

• 한국콘텐츠학회논문지
• /
• 제16권8호
• /
• pp.1-9
• /
• 2016

소셜미디어의 정치사회적인 활용도가 높아짐에 따라 소셜빅데이터 기반 온라인 동향분석 및 모니터링 기술에 대한 수요 역시 급증하고 있다. 본 논문에서는 이러한 요구에 부합, 특히 여론 형성의 악영향을 끼치는 부정적 이슈 탐지를 위해 사회적으로 파장이 큰 이슈 중 공공여론이 부정적으로 형성될 이슈를 '리스크'로 정의하고 세부 유형을 분류한다. 리스크 유형 정의를 위해 뉴스 문서집합을 대상으로 전수조사를 실시하였으며, 이슈 분야 즉 도메인별 특성을 파악하여 세부 유형을 정의한다. 또한 뉴스와 같은 공적미디어를 통해 정의된 리스크 유형이 개인화된 소셜 미디어에 나타난 리스크 유형과 어떤 차이가 있는지를 알아보기 위해 교차분석을 수행한다. 조사 결과에 따라 6개의 도메인별로 58개의 세부 유형을 정의하고 기계학습 방법을 통해 자동 분류 학습 모델을 구축한다. 실험 결과를 통해 소셜 미디어에 나타난 사회적 이슈 리스크를 자동으로 탐지, 분류가 가능함을 보인다.

• Smart Structures and Systems
• /
• 제16권2호
• /
• pp.281-294
• /
• 2015

Bolted joint connection is the most commonly used connection element in structures and devices. The loosening due to external dynamic loads cannot be observed and measured easily and may cause catastrophic loss especially in an extreme requirement and/or environment. In this paper, an innovative Real-time Cross-Correlation Method (RCCM) for monitoring of the bolted joint loosening was proposed. We apply time reversal process on stress wave propagation to obtain correlation signal. The correlation signal's peak amplitude represents the cross-correlation between the loosening state and the baseline working state; therefore, it can detect the state of loosening. Since the bolt states are uncorrelated with noise, the peak amplitude will not be affected by noise and disturbance while it increases SNR level and increases the measured signals' reliability. The correlation process is carried out online through physical wave propagation without any other post offline complicated analyses and calculations. We implemented the proposed RCCM on a single bolt/nut joint experimental device to quantitatively detect the loosening states successfully. After that we implemented the proposed method on a real large structure (reaction wall) with multiple bolted joint connections. Loosening indexes were built for both experiments to indicate the loosening states. Finally, we demonstrated the proposed method's great anti-noise and/or disturbance ability. In the instrumentation, we simply mounted Lead Zirconium Titanate (PZT) patches on the device/structure surface without any modifications of the bolted connection. The low-cost PZTs used as actuators and sensors for active sensing are easily extended to a sensing network for large scale bolted joint network monitoring.

• Smart Structures and Systems
• /
• 제15권4호
• /
• pp.1121-1137
• /
• 2015

This study presents probabilistic-based damage identification technique for highlighting damage in metallic structures. This technique utilizes distributed piezoelectric transducers to generate and monitor the ultrasonic Lamb wave with narrowband frequency. Diagnostic signals were used to define the scatter signals of different paths. The energy of scatter signals till different times were calculated by taking root mean square of the scatter signals. For each pair of parallel paths an error function based on the energy of scatter signals is introduced. The resultant error function then is used to estimate the probability of the presence of damage in the monitoring area. The presented method with an autofocusing feature is applied to aluminum plates for method verification. The results identified using both simulation and experimental Lamb wave signals at different central frequencies agreed well with the actual situations, demonstrating the potential of the presented algorithm for identification of damage in metallic structures. An obvious merit of the presented technique is that in addition to damages located inside the region between transducers; those who are outside this region can also be monitored without any interpretation of signals. This novelty qualifies this method for online structural health monitoring.

• 한국환경농학회:학술대회논문집
• /
• 한국환경농학회 2009년도 정기총회 및 국제심포지엄
• /
• pp.119-126
• /
• 2009

In recent years, research at the Environmental Microbial and Food Safety Laboratory (EMFSL), Agricultural Research Service (ARS) has focused on the development of novel image-based sensing technologies to address agro-food safety concerns, and transformation of these novel technologies into practical instrumentation for industrial implementations. The line-scan-based hyperspectral imaging techniques have often served as a research tool to develop rapid multispectral methods based on only a few spectral bands for rapid online applications. We developed a newer line-scan hyperspectral imaging platform for high-speed inspection on high-throughput processing lines, capable of simultaneous multiple inspection algorithms for different agro-food safety problems such as poultry carcass inspection for wholesomeness and apple inspection for fecal contamination and defect detection. In addition, portable imaging devices were developed for in situ identification of contamination sites and for use by agrofood producer and processor operations for cleaning and sanitation inspection of food processing surfaces. The aim of this presentation is to illustrate recent advances in the above agro.food safety sensing technologies.

• 동의생리병리학회지
• /
• 제29권5호
• /
• pp.434-442
• /
• 2015

The purpose of this review was to survey the Korean Medicine related papers about women with primary dysmenorrhea in order to develop the clinical protocol of the diagnostic medical device. We searched the literature from 2000 through April 2015 using 5 online databases including Oriental Medicine Advanced Searching Integrated Sysptem (OASIS), Research Information Sharing Service (RISS), DataBase Periodical Information Academic (DBpia) and Korean Medical Database (KMBase). We selected papers to meet the following inclusion criteria: the papers involved dysmenorrhea (excluding secondary dysmenorrhea), published papers (excluding textbook, educational materials, conferences, etc.) and the papers matched search keywords or scope, but excluded papers to meet the following exclusion criteria: the duplicative papers, get out of the keywords and scope and not in english or korean language. Finally we found 17 papers and classified the papers according to the three search purposes which were diagnostic tools for evaluating the menstrual pain, dysmenorrhea' pattern identification and menstrual phase. Out of the 16 studies, 4 studies were focused on the diagnostic tools including Visual Analogue Scale (VAS), Measurement of Menstrual Pain (MMP) and etc. Other 5 studies were aimed at menstrual phase, and the other 7 studies were studied for pattern identification. The VAS has been widely used in research and in clinical practice for the detection of the menstrual pain. Treatments for patients with primary dysmenorrhea can be prescribed in consideration of their patterns of sasang constitution or body constitution as following: Qi stagnation-Blood deficiency, cold dampness, Qi deficiency-blood deficiency and liver-kidney deficiency etc. This results of research will be used as a useful material during plan a clinical study of primary dysmenorrhea and acquisition of good clinical data.

• 한국지능시스템학회논문지
• /
• 제23권5호
• /
• pp.412-417
• /
• 2013

태양광 발전 시스템의 해석적 모델링은 시스템의 동특성을 예측하거나 고장검출 및 진단 등과 같은 고급 공학 기술에 중요하게 적용할 수 있어 최근 많은 각광을 받고 있다. 본 논문은 대용량 학습 데이터를 갖는 태양광 발전 시스템에 대한 확률론적 모델링을 제시한다. 우선 태양광 일사량과 온도 입력 변수에 대한 태양광 시스템의 출력 전력과의 입출력 함수관계를 정의한다. 이 함수관계를 바탕으로 세 확률변수(일사량, 온도, 전력)에 대하여 조건부 확률 식으로 표현한다. 조건부 확률 분포 추정은 대용량 데이터 시스템에 적합한, 전체 표본 데이터 수 대비 관련 변수의 경우의 수에 대한 비율로 나타내었다. 추정한 확률분포를 통해 평균값 이론을 적용하여 시스템의 출력을 추정하게 된다. 본 논문에서 제안한 모델링 기법은 두 태양광 발전 단지의 사례 연구를 통해 성능을 검증하였다.

• 융합정보논문지
• /
• 제10권11호
• /
• pp.195-201
• /
• 2020

빠르게 발전하고 있는 정보화 시대에 맞춰 교육환경에서도 많은 발전과 영향이 있다. 이에 대표적으로 이러닝(E-Learning)이 있다. 그러나 이러닝은 직접적인 교류와 참여율이 낮아 집중을 유지하기가 어렵고, 교수자 또한 학습자의 집중 여부를 파악하는데 한계가 있다. 본 논문은 이러닝을 사용하는 학습자의 집중도를 사용자 눈 개폐와 정수리 인식을 통하여 집중도 측정할 수 있는 시스템을 개발하였다. 본 시스템은 눈과 정수리를 인식하여 집중도를 측정하고 지표화하여 교수자에게 제공한다. 눈과 정수리를 인식한 경우 이벤트가 발생하고 사용자의 반응 결과에 따라 집중도가 지표화된다. 시스템 검증을위해 실험집단과 통제집단으로 실험하였고 집중도 지표가 90% 이상의 정확도를 보였다.

• Journal of Power Electronics
• /
• 제11권5호
• /
• pp.759-766
• /
• 2011

This paper focuses on monitoring and predicting the short circuit faults of the rotor windings of large turbo-generator systems. For the purpose of increasing efficiency and decreasing maintenance cost, a method that combines the HHT (Hilbert Huang Transform) with a wavelet has been studied. This method is based on analyzing a classical Albright detecting coil. Due to the Empirical Mode Decomposition (EMD) and the Intrinsic Mode Functions (IMF) of the HHT the exact location of a short circuit of rotor windings may be given. However, a part of the useful information is eliminated by the unreasonable decomposing scale of the wavelet. Based on the thermodynamics modeling method, this study was illustrated with a 50MW turbo-generator system that is installed in Northern China. The analysis results, which have very good agreement with those of a previous study, show that the method of combining the HHT with a wavelet is an effective way to analyze and predict the short circuit faults of the rotor windings of large generators, such as supercritical turbo-generator systems and wind turbo-generator systems. This work can offer a useful reference for analyzing smart grids by improving the power quality of a distribution network that is supplied by a turbo-generator system.