• 한국안전학회지
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• 제12권2호
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• pp.70-79
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• 1997

This paper presents a study on the fuzzy linguistic variable based approach for safety assessment of human body in ELF electromagnetic field considering power system states. To cope with the demand in modern industry, the power system becomes larger in scale, higher in voltage. The advent of high voltage system has increased the relative importance of field effects. The analysis of ELF electromagnetic field based on Quasi-Static Method is introduced while the power system is included to model the expected and/or unexpected uncertainty caused by the load fluctuation and parameter changes. In order to analyze the power system, Monte Carlo simulation method and contingency analysis method are adopted in normal state and alert state, respectively. In the safety assessment of human body, the approach based on fuzzy linguistic variable is employed to overcome the shortcomings resulting from a crisp set concept. The suggested scheme is applied to a sample system(modified IEEE 14 bus system) to validate the usefulness.

• 대한인간공학회지
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• 제30권6호
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• pp.705-713
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• 2011

Objective: The aim of this study is to compare results of emotion recognition by several algorithms which classify three different emotional states(happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. While three kinds of emotional stimuli were presented to participants, ANS responses(EDA, SKT, ECG, RESP, and PPG) as physiological signals were measured in twice first one for 60 seconds as the baseline and 60 to 90 seconds during emotional states. The obtained signals from the session of the baseline and of the emotional states were equally analyzed for 30 seconds. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotion classification was analyzed by Linear Discriminant Analysis(LDA, SPSS 15.0), Support Vector Machine (SVM), and Multilayer perceptron(MLP) using difference value which subtracts baseline from emotional state. Results: The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of ANS responses among three emotions by statistical analysis. The result of LDA showed that an accuracy of classification in three different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.

• ETRI Journal
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• 제44권3호
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• pp.426-437
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• 2022

To understand the multilateral characteristics of human behavior and physiological markers related to physical, emotional, and environmental states, extensive lifelog data collection in a real-world environment is essential. Here, we propose a data collection method using multimodal mobile sensing and present a long-term dataset from 22 subjects and 616 days of experimental sessions. The dataset contains over 10 000 hours of data, including physiological, data such as photoplethysmography, electrodermal activity, and skin temperature in addition to the multivariate behavioral data. Furthermore, it consists of 10 372 user labels with emotional states and 590 days of sleep quality data. To demonstrate feasibility, human activity recognition was applied on the sensor data using a convolutional neural network-based deep learning model with 92.78% recognition accuracy. From the activity recognition result, we extracted the daily behavior pattern and discovered five representative models by applying spectral clustering. This demonstrates that the dataset contributed toward understanding human behavior using multimodal data accumulated throughout daily lives under natural conditions.

• 대한의용생체공학회:의공학회지
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• 제33권1호
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• pp.15-24
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• 2012

Classification of human thought is an emerging research field that may allow us to understand human brain functions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introduce a new approach to classify various mental states from noninvasive electrophysiological recordings of human brain activity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signals recorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spatiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-related synchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current (input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices. Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For the verification of our approach, eight participants took part in the present study; their EEG data were recorded while they performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequently between trials in the same class than those in different classes, indicating that these spatiospectral pattern maps could be used to classify different mental states. The classification accuracy was evaluated for each participant from both the proposed approach and a conventional mental state classification method based on the inter-hemispheric spectral power asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (${\pm}9.64%$) was attained for the proposed method; whereas an average accuracy of 57% (${\pm}5.68%$) was attained for the conventional method (significance was assessed by the one-tail paired $t$-test, $p$ < 0.01), showing that the proposed simple classification approach might be one of the promising methods in discriminating various mental states.

• 한국환경독성학회:학술대회논문집
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• 한국환경독성학회 1998년도 추계 학술대회 발표 논문집
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• pp.84-84
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• 1998

• 한국영양학회:학술대회논문집
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• 한국영양학회 2004년도 춘계학술대회
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• pp.30-30
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• 2004

• 한국사회복지학회:학술대회논문집
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• 한국사회복지학회 2006년도 춘계학술대회
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• pp.33-53
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• 2006

• Bulletin of the Korean Chemical Society
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• 제19권6호
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• pp.608-611
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• 1998

• 한국산학기술학회논문지
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• 제10권4호
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• pp.722-727
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• 2009

인체동작상태를 구분하는 기술은 인체활동에 따라 변하는 생체신호의 측정 분석분야, 수면장애의 진단 치료 효과의 스크리닝 검사분야, 만성질환 환자의 운동 상태 진단 운동처방분야에 필요한 기술이다. Armband에 내장된 아날로그 디바이스사의 ADXL202AE을 이용하여 수직방향신호의 평균치(LAA), 수평방향신호의 평균치(TAA), 수직방향 신호의 가속도 변화량의 절대치의 평균치(L-MAD), 수평방향신호의 가속도 변화량의 절대치의 평균치(T-MAD)의 획득과 데이터 처리하여, 인체동작상태(눕기, 앉기, 걷기, 뛰기)를 구분하는 퍼지규칙 기반의 퍼지추론시스템을 구현하였다. 입력데이터(LAA, TAA, L-MAD, T-MAD)와 출력데이터(Lying, Sitting, Walking, Running)의 각 구역에서의 소속정도(menbership degree)와 퍼지규칙은 실험을 통해 얻은 수치 데이터를 사용하여 결정하였다. 눕기$\rightarrow$걷기$\rightarrow$뛰기$\rightarrow$눕기 순으로 생성한 모의실험용 데이터를 분석한 결과, 눕기, 앉기, 걷기, 뛰기의 동작상태 구분율은 각각 100%이었다.

• 식품과학과 산업
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• 제49권3호
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• pp.55-61
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• 2016

Even though the food safety system in the United States is one of the best in the world, many millions of people become sick and thousands die from foodborne illnesses caused by any of a number of microbial pathogens and other contaminants. Large recalls of United States Department of Agriculture (USDA) and the Food Drug and Administration (US FDA)-regulated food products due to findings of E. coli O157:H7, Listeria, Salmonella, and other problems occur each year. As the US FDA Food Safety Modernization Act (FSMA) passed in 2011, FSMA will require food processing, manufacturing, shipping, and other regulated entities to conduct an analysis of the most likely safety hazards and to design and implement risk-based controls to reduce or eliminate these hazards. FSMA also mandates increased scrutiny of food imports, which account for a growing share of U.S. food consumption; food import shipments will have to be accompanied by documentation showing that they can meet safety standards that are at least equivalent to those in the U.S. On September 17, 2015, the US FDA published final rules for Preventive Controls for Human and Animal Food and, continuing into 2016, the US FDA intends to finalize the remaining five rules it has proposed to implement FSMA. Among these rules, this article will review and discuss Preventive Controls for Human Food Rule and its components, and suggest how to comply with these FSMA rules as foreign human food and ingredients suppliers to the US.