• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.5
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• pp.107-116
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• 2011

In this paper, a traffic adaptive sleep based medium access control (TAS-MAC) protocol for wireless sensor networks (WSNs) is proposed. The protocol aims for WSNs which consist of clustered sensor nodes and is based on TDMA-like schema. It is a typical schedule based mechanism which is adopted in previous protocols such as LEACH and Bit-Map Assisted MAC. The proposed MAC, however, considers unexpected long silent period in which sensor nodes have no data input and events do not happen in monitoring environment. With the simple traffic measurement, the TAS-MAC eliminates scheduling phases consuming energy in previous centralized approaches. A frame structure of the protocol includes three periods, investigation (I), transmission (T), and sleep-period (S). Through the I-period, TAS-MAC aggregates current traffic information from each end node and dynamically decide the length of sleep period to avoid energy waste in long silent period. In spite of the energy efficiency of this approach, the delay of data might increase. Thus, we propose an advanced version of TAS-MAC as well, each node in cluster sends one or more data packets to cluster head during the T-period of a frame. Through simulation, the performance in terms of energy consumption and transmission delay is evaluated. By comparing to BMA-MAC, the results indicate the proposed protocol is more energy efficient with tolerable expense in latency, especially in variable traffic situation.

• KIEAE Journal
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• v.7 no.5
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• pp.93-98
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• 2007

Effort has been performed for latest 20 years to improve resident's comfort and indoor environment in building. And interest and effort to improve indoor air environment among various indoor environment elements have continuously increased since 1990s, because it is examined scientifically that various contaminants generated indoor affect human body. Specially, indoor air contaminants generated from apartments are those exhausted from resident's indoor environment, closing materials and household. Indoor air environment in buildings is different according to pollution degree, existence availability of pollution source, ventilation amount, and meteorology. It is known that other contaminants more than about 900 kinds generate according to a kind of work or action in a room. Specially, nowadays buildings are well insulated and confidentiality-centered for environment protection and economical side. So indoor air contaminants are generated from indoor air pollution sauces of unprepared ventilation, human body carbon dioxide emissions, and various building materials. when these are accumulated in long term human body, it is harmful to resident's health, but awareness for this is very insufficient. Because bedroom is space that people inhabit for a long time by unconscious state and indoor environment occupies important part for resident's health and quality of life at sleep, the actual condition of air quality is investigated, improvement countermeasure is considered, and ventilation amount is analyzed. In this study, putting case that the most longest stayed time is sleeping time when people inhabit in the apartment, the air quality according to volume of bedroom space at sleep was measured and analyzed, and the data acquired will be the basis for improvement on this.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.8-16
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• 2019

Customized sleep care service needs to be provided differently for individuals since individual has different degree of sleep disorder. Because the brainwave data shows unique waveform characteristics for each person, this characteristic can be used to identify individuals. Personal identification provides an important role in enabling customized services. When you blink, you can obtain brain wave characteristics by measuring the area of the frontal lobe. Therefore, a real-time personal identification algorithm based on blinking EOG for customized sleep care service is proposed in this paper. For evaluation, 10 individuals were tested for personal identification accuracy. The results of the experiment confirmed that a maximum accuracy of 93% were taken. Algorithms can be developed by reflecting characteristics such as changes in the external environment in the future.

• Journal of Fashion Business
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• v.23 no.1
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• pp.14-24
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• 2019

Various types of functional bedding for inducing and maintaining sleep, are developed and launched with the importance of improving health through sleep emphasized currently. The purpose of this study is to examine development status and direction of functional bedding in the $4^{th}$ Industrial Revolution era, through systematic classification of elements of IoT-based smart bedding cases actively developed as functional bedding at home and abroad. Through previous research, literature and Internet data, characteristics and functional extension of smart bedding and the background of smart bed development was analyzed. And it was analyzed that smart bedding pursues recent functionalism and convergence of physical and digital concept such as IoT or AI, and also mental value to improve sleep quality. As bedroom where smart bedding place in has the private and limited characteristics and users are in sleep-conscious, that hard to ensure power and discomfort in carrying are moderated and the aesthetic elements are not very important, and that the smart bedding performance while sleeping were affected on developmental background. Based on CES case study and analysis on how smart beds are functionally expanded from conventional bedding, smart beds have gained information through digital sensing, and common properties that can be controlled anytime, anywhere, using a smart phone. Some set up the right environment and pose, while others stimulate nerves directly as active intervention. It is expected that smart bedding will be developed to cure user's body and mind, through active intervention when sleeping.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.98-101
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• 2019

In this paper, we propose the power-based pipelined-forwarding MAC protocol which can select relay nodes according to the residual power and energy harvesting rate in EH-WSN (energy-harvesting wireless sensor networks). The proposed MAC follows a pipelined-forwarding scheme in which nodes repeatedly sleep and wake up in an EH-WSN environment and data is continuously transmitted from a high-level node to a low-level node. The sleep interval is adaptively controlled so that nodes with low energy harvesting rate can be charged sufficiently, thereby minimizing the transmission delay and increasing the network lifetime. Simulation shows that the proposed MAC protocol improves the balance of residual power and network lifetime.

• Science of Emotion and Sensibility
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• v.14 no.3
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• pp.459-466
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• 2011

Sleep diseases such as snoring and sleep apnea are physically, mentally harmful and results serious health problems. Snoring, known as breathing noise, is caused by coupled oscillation of the airway when the air passes through the trachea, and sleep apnea is caused by upper airway blockage. In order to solve these problems, many attempts have been made to detect the snoring during sleep and alleviate it. In this study, a new sensing system and analysis algorithm were developed in order to detect snoring sounds correctly under various sleep environments. Two polyvinylidene fluoride (PVDF) vibration sensors were used inside the pillow. The first PVDF sensor detects vibration transmitted through skull caused by snoring. And the second PVDF sensor detects both snoring sounds and ambient noises. The signals of two sensors were acquired through the designed analog circuits, and analyzed for snoring detection. Ten volunteers were participated for the experiment under five different conditions. Data from two PVDF sensors were processed by the established analysis algorithm, and snoring sounds were compared to noises. The results indicated that the energy of snoring is 70% bigger than that of ambient noise, which proves effectiveness of sensing system and analysis algorithm. Further study would be continued for more wide clinical studies with various environment noises. Based on this study, development of anti-snore pillow and sleep monitoring system for comfort sleep could be developed.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.35-45
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• 2016

Melatonin hormone involved in human's circadian rhythm adjustment sensitively responds to light's specific short wavelength ratio. A shift worker's circadian rhythm disturbance and sleep disorder are caused by the existing lighting conditions, whose short wavelength ratio is fixed. The life pattern of a shift worker changes irregularly because of irregular working hours and the same lighting environment; thus, his/her concentration is reduced. For such a reason, negative effects ensue to the detriment of healthy everyday life, including a high risk of accidents or having unsound sleep after leaving work. A smartphone-based wavelength control LED lighting system that targets shift workers and that can easily measure and control lighting suitable for wake-sleep cycle, according to working hours and closing hours, is proposed in this paper. First, after the light characteristics of LED lighting that changes depending on light control ratio are measured through the color sensor installed on the smartphone and the externally-linked Mini-Spectrometer, they are stored in the database. Based on the stored optical characteristics data, the measurement module and light control module are implemented. Lighting is offered using a control ratio having the maximum rate of short wavelength in consideration of the target illuminance, classified according to work type by identifying working hours as time when waking is required for shift workers. After a shift work leaves work, the amount of lighting is varied, using a control ratio having a minimum short wavelength rate so that a shift worker can enter the sleep state naturally.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.941-946
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• 2006

It followed in system development and SMRT(Seoul Metropolitan Rapid Transit Co)System reached to an automatic train operation(ATO) and driverless operation(DLM) from the manual operation due to the train driver. The train like the general bus or the car vehicle was not serial riding in a car and the Parallel concept which the numerous passenger rides in a car simultaneously occur frequently the charge of the train driver unmanned bitterly from existing manual handling was a possibility of doing, train open door control(ODM) which bites also ATO, it handles it minimized. Like this ATO/DLM, the control system which bites being a Wayside to Train communication for immediacy, it is a system of the Vital concept the immediacy of the citizen Data evil the radio information transmission and the train of the interface which is accurate from unmanned operation and, will decipher, will accomplish it will guarantee. It respects the passenger accident prevention and an air question environment improvement from subway platform and phul leys the screen door of Platform(PSD) with the fire tube frost it refers and part it treats and to sleep it does, ODM which bites is accuracy and immediacy of altitude and when seeing from the viewpoint which demands the trust of altitude, ODM system the trust of car incest interface in the equipment construction which is safe and the comparative analysis back of the system analysis against the control which bites and case study and other subway system it leads from the research which it sees and signal - train in base grudge to sleep it contributes it does.

• ETRI Journal
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• v.44 no.3
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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.

• Journal of agricultural medicine and community health
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• v.38 no.4
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• pp.229-242
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• 2013

Objectives: The objective of this study was to identify the influence of shift work on sleep quality, daytime sleepiness, and perceived errors during nursing work among hospital nurses. Methods: The study subjects were 209 hospital nurses working in two general hospitals in South Korea. The data were collected through self-administered questionnaires in May 2013. Sleep quality was measured using Korea Pittsburgh Sleep Quality Index (PSQI-K), daytime sleepiness was measured using Epworth Sleepiness Scale (ESS), and perceived errors in past 6 months during nursing duty hours with the tool developed in this study. Results: The sleep quality was significantly worse among rotating shift nurses when compared to fixed day duty nurses. However, the odds ratio (OR) of 1.92 [95% confidence interval(CI): 0.58-6.37] adjusted for sociodemographic variables, health behaviors, and working environment in logistic regression was not significant. There was no significant difference in the daytime sleepiness between rotating shift nurses and fixed day duty nurses in both simple and multivariable analyses. There were significantly more perceived errors in rotating shift nurses than fixed day duty nurses. However, the OR of 1.26 (95% CI: 0.38-4.21) adjusted for sociodemographic variables, health behaviors, working environment, sleep quality, and daytime sleepiness in logistic regression was not significant. Depression (OR=3.31, 95% CI: 1.11-9.84) and daytime sleepiness(OR=1.18, 95% CI: 1.07-1.30) were significantly associated with perceived errors in logistic regression. Conclusion: This study suggested shift work among hospital nurses had no independent influence of sleep quality, daytime sleepiness, and perceived errors. However, daytime sleepiness had independent influence of perceived errors. More studies are warranted to achieve more reliable conclusion.