• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.335-343
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• 2019

Healthy living is an essential element of human happiness. Quality eating provides the basis for life, and the health of livestock, which provides meat and dairy products, has a direct impact on human health. In the case of calves, diarrhea is the cause of all diseases.In this paper, we use a sensor to measure calf 's biometric data to diagnose calf diarrhea. The collected biometric data is subjected to a preprocessing process for use as meaningful information. We measure calf birth history and calf biometrics. The ontology is constructed by inputting environmental information of housing and biochemistry, immunity, and measurement information of human body for disease management. We will build a knowledge base for predicting calf diarrhea by predicting calf diarrhea through logical reasoning. Predict diarrhea with the knowledge base on the name of the disease, cause, timing and symptoms of livestock diseases. These knowledge bases can be expressed as domain ontologies for parent ontology and prediction, and as a result, treatment and prevention methods can be suggested.

• Journal of Animal Science and Technology
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• v.65 no.4
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• pp.759-766
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• 2023

Visual estrus observation can only be confirmed at a rate of 50%-60%, which is lower than that obtained using a biosensor. Thus, the use of biosensors provides more opportunities for artificial insemination because it is easier to confirm estrus than by visual observation. This study determines the accuracy of estrus prediction using a ruminoreticular biosensor by analyzing ruminoreticular temperature during the estrus cycle and measuring changes in body activity. One hundred and twenty-five Hanwoo cows (64 with a ruminal biosensor in the test group and 61 without biosensors in the control group) were studied. Ruminoreticular temperatures and body activities were measured every 10 min. The first service of artificial insemination used gonadotropin-releasing hormone (GnRH)-based fixed-time artificial insemination protocol in the control and test groups. The test group received artificial insemination based on the estrus prediction made by the biosensor, and the control group received artificial insemination according to visual estrus observation. Before artificial insemination, the ruminoreticular temperature was maintained at an average of 38.95 ± 0.05℃ for 13 h (-21 to -9 h), 0.73℃ higher than the average temperature observed at -48 h (38.22 ± 0.06℃). The body activity, measured using an indwelling 3-axis accelerometer, averaged 1502.57 ± 27.35 for approximately 21 h from -4 to -24 h before artificial insemination, showing 203 indexes higher body activity than -48 hours (1299 ± 9.72). Therefore, using an information and communication techonology (ICT)-based biosensor is highly effective because it can reduce the reproductive cost of a farm by accurately detecting estrus and increasing the rate of estrus confirmation in cattle.

• International Journal of Computer Science & Network Security
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• v.24 no.10
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• pp.1-16
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• 2024

Due to its complexity and high diagnosis and treatment costs, heart attack (HA) is the top cause of death globally. Heart failure's widespread effect and high morbidity and death rates make accurate and fast prognosis and diagnosis crucial. Due to the complexity of medical data, early and accurate prediction of HA is difficult. Healthcare providers must evaluate data quickly and accurately to intervene. This novel hybrid approach predicts HA using Long Short-Term Memory (LSTM) networks, Deep belief networks (DBNs) with attention mechanism, and robust data mining to fill this essential gap. HA is predicted using Kaggle, PhysioNet, and UCI datasets. Wearable sensor data, ECG signals, and demographic and clinical data provide a solid analytical base. To maintain consistency, ECG signals are normalized and segmented after thorough cleaning to remove missing values and noise. Feature extraction employs complex approaches like Principal Component Analysis (PCA) and Autoencoders to pick time-domain (MNN, SDNN, RMSSD, PNN50) and frequency-domain (PSD at VLF, LF, HF bands) characteristics. The hybrid model architecture uses LSTM networks for sequence learning and DBNs for feature representation and selection to create a robust and comprehensive prediction model. Accuracy, precision, recall, F1-score, and ROC-AUC are measured after cross-entropy loss and SGD optimization. The LSTM-DBN model outperforms predictive methods in accuracy, sensitivity, and specificity. The findings show that several data sources and powerful algorithms can improve heart attack predictions. The proposed architecture performed well on many datasets, with an accuracy rate of 96.00%, sensitivity of 98%, AUC of 0.98, and F1-score of 0.97. High performance proves this system's dependability. Moreover, the proposed approach is outperformed compared to state-of-the-art systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.191-197
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• 2012

In this paper we propose the FQSM(Fairness and QoS Supporting MAC) protocol that supports fairness and Quality of Service(QoS). The received or measured data traffics will be assigned a priority level according to its transmission urgency in the FQSM. And the load prediction algorithm is used to support the fairness between different priority traffics. For this, the buffer length values of the nodes are continuously monitored for a some period. Based on the buffer length variations for this period, the order of transmission is determined. FQSM also adapts cross-layer concept to rearrange the data transmission order in each sensor node's buffer, saves energy consumption by allowing few nodes in data transmission, and prolongs the network lifetime.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.3
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• pp.62-73
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• 2012

IEEE 802.15.4 standard which has low-speed, low-power, low-cost can be efficiently used in wireless sensor network environment. Among various topologies used in IEEE 802.15.4 standard, a cluster-tree topology which has many nodes in it, transmit delay, energy consumption and data loss due to traffic concentration around the sink node. In this paper, we propose the MRS-DCA algorithm that minimizes conflicts between packets for efficient data transmission, and dynamically allocates the active period for efficient use of limited energy. The MRS-DCA algorithm allocates RP(Reservation Period) to the active period of IEEE 802.15.4 and guarantees reliable data transmission by allocating RP and CAP dynamically which is based on prediction using EWMA. The comparison result shows that the MRS-DCA algorithm reduces power consumption by reducing active period, and increasing transmission rate by avoiding collision.

• Fire Science and Engineering
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• v.31 no.1
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• pp.58-62
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• 2017

There is a growing need for seismic resistance design. But it is controversial that standards of sway brace device in non-structural elements for buildings like pump waterway is vary widely. Therefore, in this study to get a valid range of sway brace device in seismic resistance design, using load test of sway brace device. As a result, load of safe range from 0 to 18.5 kN and under 29.4 kN, no structural fault of sway brace device. And using internet of things get a data of seismic resistance design from sensor node like accelerometer, GPS, tilt sensor and temperature sensor through steps of sampling and prediction. These results will be acceptable for monitoring system for seismic resistance in non-structural elements.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.2
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• pp.23-30
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• 2010

The research presents moisture content measuring and calibration method of road pavement, especially asphalt concrete pavement for performance evaluation or remaining life prediction using Time Domain Reflectometry(TDR) sensor, CS616 made by campbell INC. Before calibration test of CS616, accomplished a sensor verification tests. Verification test items were covering depth and interference effect of two CS616 sensors, temperature effects between $5^{\circ}C\sim25^{\circ}C$ and compaction ratio effects. Covering depth and interference effects between two CS616 sensors were just small and the effects of temperature and compaction ratio effected a Volumetric Moisture Contents at $\pm6%$ under disregard appeared with the fact that was possible. Also, obtained the calibration equation of the subgrade and subbase course, $R^2$ showed above of all 0.9.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1779-1785
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• 2018

In this paper, we propose an error correction method to improve the accuracy of human activity recognition using sensor event data obtained by smart devices such as wearable and smartphone. In the context awareness through the smart device, errors inevitably occur in sensing the necessary context information due to the characteristics of the device, which degrades the prediction performance. In order to solve this problem, we apply Kalman filter's error correction algorithm to compensate the signal values obtained from 3-axis acceleration sensor of smart device. As a result, it was possible to effectively eliminate the error generated in the process of the data which is detected and reported by the 3-axis acceleration sensor constituting the time series data through the Kalman filter. It is expected that this research will improve the performance of the real-time context-aware system to be developed in the future.

• Atmosphere
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• v.27 no.1
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• pp.93-104
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• 2017

The effect of Advanced Microwave Sounding Unit-A (AMSU-A) observations on the short-range forecast in East Asia (EA) was investigated for the Northern Hemispheric (NH) summer and winter months, using the Forecast Sensitivity to Observations (FSO) method. For both periods, the contribution of radiosonde (TEMP) to the EA forecast was largest, followed by AIRCRAFT, AMSU-A, Infrared Atmospheric Sounding Interferometer (IASI), and the atmospheric motion vector of Communication, Ocean and Meteorological Satellite (COMS) or Multi-functional Transport Satellite (MTSAT). The contribution of AMSU-A sensor was largely originated from the NOAA 19, NOAA 18, and MetOp-A (NOAA 19 and 18) satellites in the NH summer (winter). The contribution of AMSU-A sensor on the MetOp-A (NOAA 18 and 19) satellites was large at 00 and 12 UTC (06 and 18 UTC) analysis times, which was associated with the scanning track of four satellites. The MetOp-A provided the radiance data over the Korea Peninsula in the morning (08:00~11:30 LST), which was important to the morning forecast. In the NH summer, the channel 5 observations on MetOp-A, NOAA 18, 19 along the seaside (along the ridge of the subtropical high) increased (decreased) the forecast error slightly (largely). In the NH winter, the channel 8 observations on NOAA 18 (NOAA 15 and MetOp-A) over the Eastern China (Tibetan Plateau) decreased (increased) the forecast error. The FSO provides useful information on the effect of each AMSU-A sensor on the EA forecasts, which leads guidance to better use of AMSU-A observations for EA regional numerical weather prediction.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.8 s.145
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• pp.1168-1175
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• 2005

Clothing pressure has close relation with clothing comfort and depends on the pattern and properties of textile fabrics. Choosing a suitable clothing pressure is an essential factor for designing functional clothing such as the foundation for reshaping of a body contour or medical items for bum patient, and etc. However, it is hard to measure pressure values at the curved surface of a human body correctly. Recently, an air pack type pressure sensor, which has relatively excellent performance has been used to measure clothing pressure, however, it is still inconvenient to apply because it is a contact- type sensor. Therefore, in this paper, we suggest an indirect method that can measure clothing pressure without touching the subject by improving the equation of Kirk and Ibrahim (1966). However, confusions have been occurred when someone use the equation since the definition of parameters are somewhat vague. Furthermore, the estimated clothing pressure obtained by the previous method are quite different from the real values because this method does not consider the 3D effect of a human body and property changes of a transformed fabric. In this paper, the direction of principal stress and the radius of curvature in the principal direction were searched in the 3D image of the deformed girdle to get more accurate clothing pressure. The estimated clothing pressure was verified by comparing the result of the air pack type pressure sensor. It was found that the accuracy of the pressure estimation was improved by considering the 3D curvature of human body and the directional characteristics of textile fabrics.