• Applied Biological Chemistry
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• v.49 no.3
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• pp.254-258
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• 2006

Vitellogenin has been known as a potent biomarker protein for the estrogenic activity in fish exposed to endocrine disruptors. In this study, a piezoelectric immunosensor making use of an anticarp vitellogenin antibody and an AT-cut quartz crystal microbalance as the biological component and transducer was prepared, followed by its application to the analysis of carp vitellogenin as follows. Antibody immobilization was conducted by chemisorption of a thiolated antibody with a heterobifunctional thiolation cross-linker, sulfosuccinimidyl 6-[3-(2-pyridyldithio)propionamido]hexanoate. The reaction buffer for the immunosensor system was optimized as 0.1 M sodium phosphate (pH 7.4). Concentration-dependent sensor responses were obtained in the vitellogenin concentrations ranging from 0.4864 to 486.4000 nM, with a linear correlation between vitellogenin concentration and frequency shift in double-logarithmic scale. The limit of detection of the immunosensor for carp vitellogenin was presumed as 0.4864 nM.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.534-537
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• 2006

Currently, The fast development which radio communication technique is creating new services from the industry sector which is various from convenient characteristic and the ease characteristic side. Specially, rise and development of the radio communication technique which is various from environment monitor ring field makes to be improved about sharp curtailment of establishment and logistics relation expense, and collection period of the data which occurs real-time from site, reliability and delivery characteristic. But continuous application and the success about the environment monitor ring field of radio communication technique will be able to trust And It is important to provide time information which is appropriate real-time. So this paper plans the sensor network based on environment measurement watch which applies a radio communication technique.

• Journal of the Earthquake Engineering Society of Korea
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• v.7 no.4
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• pp.81-87
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• 2003

In this paper, the feasibility of using Shannon's sampling theorem to reconstruct exact mode shapes of a structural system from a limited number of sensor points and localizing damage in that structure with reconstructed mode shapes is investigated. Shannon's sampling theorem for the time domain is reviewed. The theorem is then extended to the spatial domain. To verify the usefulness of extended theorem, mode shapes of a simple beam are reconstructed from a limited amount of data and the reconstructed mode shapes are compared to the exact mode shapes. On the basis of the results, a simple rule is proposed for the optimal placement of accelerometers in modal parameter extraction experiments. Practicality of the proposed rule and the extended Shannon's theorem is demonstrated by detecting damage in laboratory beam structure with two-span via applying to mode shapes of pre and post damage states.

• Aerospace Engineering and Technology
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• v.7 no.1
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• pp.115-120
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• 2008

The thermistors and AD590 are widely used for temperature measurement in space application. The resistance of thermistor will vary according to the temperature variation therefore the external voltage or current stimulus signal have to be provided to measure resistance variation. Recently high resolution electro optic camera system of satellite requires tight thermal control of the camera structure to minimize the thermal structural distortion which can affects the image quality. In order to achieve $1^{\circ}$(deg C) thermal control requirement, the accuracy of temperature measurement have to be higher than $0.3^{\circ}$(deg C). In this paper, the accuracy of temperature measurement using thermistors is estimated and analyzed.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.949-958
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• 2022

Modern people have many kinds of clothes for individuals, and not just for storing clothes, but also for managing the condition of the closet, and users of smart closets created smart closets that provide daily convenience and optimal closet conditions, suggesting the possibility of developing smart furniture for various environments. In this developed system, smart closet is controlled using app inventor and touch LCD through bluetooth wireless communication, based on Arduino MEGA and user's clothes is recommended depending on the weather. In addition, this smart closet is designed with real-time weather status checking and easy ventilation function. It was implemented through the Arduino and app inventor program so that the weather can be printed on the LCD screen and the user's suitable clothes can be recommended to the application.

• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.433-440
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• 2022

Recently, owing to global warming, average summer temperatures are increasing and the number of hot days is increasing is increasing, which leads to an increase in heat stroke. In particular, outdoor workers directly exposed to the heat are at higher risk of heat stroke; therefore, preventing heat-related illnesses and managing safety have become important. Although various wearable devices have been developed to prevent heat stroke for outdoor workers, applying various sensors to the safety helmets that workers must wear is an excellent alternative. In this study, we developed a smart helmet that measures various vital signs of the wearer such as body temperature, heart rate, and sweat rate; external environmental signals such as temperature and humidity; and movement signals of the wearer such as roll and pitch angles. The smart helmet can acquire the various data by connecting with a smartphone application. Environmental data can check the status of heat wave advisory, and the individual vital signs can monitor the health of workers. In addition, we developed an algorithm that classifies the risk of heat-related illness as normal and abnormal by inputting a set of vital signs of the wearer using a support vector machine technique, which is a machine learning technique that allows for rapid binary classification with high reliability. Furthermore, the classified results suggest that the safety manager can supervise the prevention of heat stroke by receiving feedback from the control system.

• PNF and Movement
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• v.20 no.3
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• pp.451-459
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• 2022

Purpose: Temporomandibular disorder (TMD) is a common musculoskeletal problem that causes pain in and disability of masticatory muscles, the temporo-mandibular joint (TMJ), and related structures. The purpose of this study was to compare pressure pain thresholds (PPTs) of masticatory muscles, cervical ranges of motion (ROM), and pelvic mobility during gait of subjects with or without TMD. Methods: In this study, pain thresholds and changes in the mobility of the cervical vertebrae and pelvis were measured in 25 patients with TMD and 25 healthy controls. Using a pressure algometer, the pressure pain thresholds (PPTs) of the masseter and temporalis muscles were measured in both groups. A gyroscope sensor with a mobile application was used to determine cervical ROM in the frontal and sagittal planes. A 3D-motion analysis system was used to evaluate pelvic mobility in the sagittal, frontal, and transverse planes during gait. Results: The TMD group showed significantly decreased PPTs of masseter and temporalis muscles compared with the control group (p < 0.05). Cervical ROM in flexion, extension, and lateral bending were significantly decreased in the TMD group compared with the control group (p < 0.05). In addition, antero-posterior pelvic tilt was significantly decreased in the TMD group (p < 0.05). Conclusion: The results of the current study suggest that there are close anatomical and functional relationships between TMD and muscle chains related to the cervical spine and pelvis. Therefore, more comprehensive body posture assessments, especially of painful areas, should be undertaken when studying TMD patients.

• Journal of Advanced Technology Convergence
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• v.1 no.1
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• pp.1-6
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• 2022

Since 2015, the government has been making efforts to distribute Korean smart farms. However, the supply is limited to large-scale facility vegetable farms due to the limitations of technology and current cultivation research data. In addition, the efficiency and reliability compared to the introduction cost are low due to the simple application of IT technology that does not consider the crop growth and cultivation environment. Therefore, in this paper, data analysis services was performed based on public and external data. To this end, a data-based target smart farm system was designed that is suitable for the situation of farms growing in facilities. To this end, a farm risk information notification service was developed. In addition, light environment maps were provided for proper fertilization. Finally, a disease prediction model for each cultivation crop was designed using temperature and humidity information of facility farms. Through this, it was possible to implement a smart farm data service by linking and utilizing existing smart farm sensor data. In addition, economic efficiency and data reliability can be secured for data utilization.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.343-353
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• 2022

An attempt to derive the surface temperature from the Korea Multi-purpose Satellite (KOMPSAT)-3A mid-wave infrared (MWIR) data acquired over the southern California on Nov. 14, 2015 has been made using the MODerate resolution atmospheric TRANsmission (MODTRAN) radiative transfer model. Since after the successful launch on March 25, 2015, the KOMPSAT-3A spacecraft and its two payload instruments - the high-resolution multispectral optical sensor and the scanner infrared imaging system (SIIS) - continue to operate properly. SIIS uses the MWIR spectral band of 3.3-5.2 ㎛ for data acquisition. As input data for the realistic simulation of the KOMPSAT-3A SIIS imaging conditions in the MODTRAN model, we used the National Centers for Environmental Prediction (NCEP) atmospheric profiles, the KOMPSAT-3Asensor response function, the solar and line-of-sight geometry, and the University of Wisconsin emissivity database. The land cover type of the study area includes water,sand, and agricultural (vegetated) land located in the southern California. Results of surface temperature showed the reasonable geographical pattern over water, sand, and agricultural land. It is however worthwhile to note that the surface temperature pattern does not resemble the top-of-atmosphere (TOA) radiance counterpart. This is because MWIR TOA radiances consist of both shortwave (0.2-5 ㎛) and longwave (5-50 ㎛) components and the surface temperature depends solely upon the surface emitted radiance of longwave components. We found in our case that the shortwave surface reflection primarily causes the difference of geographical pattern between surface temperature and TOA radiance. Validation of the surface temperature for this study is practically difficult to perform due to the lack of ground truth data. We therefore made simple comparisons with two datasets over Salton Sea: National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) field data and Salton Sea data. The current estimate differs with these datasets by 2.2 K and 1.4 K, respectively, though it seems not possible to quantify factors causing such differences.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.93-102
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• 2023

Data processing through the cloud causes many problems, such as latency and increased communication costs in the communication process. Therefore, many researchers study edge computing in the IoT, and autonomous driving is a representative application. In indoor self-driving, unlike outdoor, GPS and traffic information cannot be used, so the surrounding environment must be recognized using sensors. An efficient autonomous driving system is required because it is a mobile environment with resource constraints. This paper proposes a machine-learning method using neural networks for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the distance data measured by the LiDAR sensor. We designed six learning models to evaluate according to the number of input data of the proposed neural networks. In addition, we made an autonomous vehicle based on Raspberry Pi for driving and learning and an indoor driving track produced for collecting data and evaluation. Finally, we compared six neural network models in terms of accuracy, response time, and battery consumption, and the effect of the number of input data on performance was confirmed.