• Journal of Integrative Natural Science
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• v.9 no.3
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• pp.199-205
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• 2016

In this paper, we established ZIGBEE home network and combined smart-grid and u-Healthcare system. We assisted for amount of electricity management of household by interlocking home devices of wireless sensor, PLC modem, DCU and realized smart grid and u-Healthcare at the same time by verifying body heat, pulse, blood pressure change and proceeded living body signal by using SVM algorithm and variety of ZIGBEE network channel and enabled it to check real-time through IHD which is developed by user interface. In addition, we minimized the rate of energy consumption of each sensor node when living body signal is processed and realized Query Processor which is able to optimize accuracy and speed of query. We were able to check the result that is accuracy of classification 0.848 which is less accounting for average 17.9% of storage more than the real input data by using Mjoin, multiple query process and SVM algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.9
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• pp.905-911
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• 2006

Smart homes are expected to offer various intelligent services by recognizing the residents' life pattern, health, and feeling. One of the key issues for realizing the smart home is how to detect the locations of residents. Currently, the research effort is focused on two approaches: terminal-based and non-terminal-based method. The terminal-based method employs a type of device that should be carried by the resident while the non-terminal-based method has no such device. This paper presents a novel non-terminal-based approach using an array of pyroelectric infrared sensors (PIRs) that can detect residents. The feasibility of the system is evaluated experimentally on a test bed.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.281-283
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• 2009

This paper presents the smart fan which is operated by a small conventional motor and an ultrasonic sensor. The smart fan generates cool wind with regulated speed of wing by the distance between the user and the fan. In this research, an 8-bit microcontroller (ATmega128) and an ultrasonic sensor (NT-TS601) are utilized for the system control and sensing information. In order to obtain the speed information from the encoderless DC motor, a stroboscope is used, which provides the voltage variation by the motor speed. The proposed smart fan makes the user feel cool, convenient and safe at a low cost.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.221-228
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• 2021

Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.72-78
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• 2022

The aim of this research is to improve the effectiveness of digital media advertising because current advertisements -in digital signage - indiscriminately appeals to the general public rather than to a specific target. In order to deliver efficient and customized advertisement information, an IoT human body detection sensor mounted on digital signage detected human faces and then classified them firstly by gender. The digital signage here is a smart digital signage that can analyze facial signals, discriminate them based on patterns, and apply the extracted data by displaying the corresponding information to the user. In addition, by identifying the customer's location approaching the smart digital signage and displaying the optimized content information for the customer's location through an algorithm, the digital signage can dramatize the advertisement Thus, this is a study meant forimproving information efficiency while reducing noise and driving power waste generated from unnecessary digital information reproduction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.65-71
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• 2009

As a core area of the new computing paradigm, the Ubiquitous Sensor Network Technology utilizes a wireless sensor networking which can be applied to the Context Information Monitoring System. When the technology is used in a poor user-environment for monitoring purposes, it can cost-effectively gather the context data on real-time basis, analyze the information gathered, effectively response to the user situation, and execute orders to create environmental factors desired by the user. This study structures a system able to monitor information in regards to a user-environment based on wireless-node sensor technology coupled with the Ubiquitous Sensor Network Technology. In this paper, the protocol in which it manages the wireless sensor network as the zone based by using the management protocol standardized at the smart home with a profile is proposed. The proposed system requires a minimal collection of data without continuous monitoring. Monitoring periodically, it can sense the user-environment more efficiently than the existing monitoring technologies based on the wire-communication technology.

• Smart Media Journal
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• v.12 no.10
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• pp.55-62
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• 2023

Innovation and change are occurring rapidly in the agriculture and livestock industry, and new technologies such as smart bams are being introduced, and data that can be used to control equipment is being collected by utilizing various sensors. However, there are various challenges in the operation of bams, and virtual sensor technology is needed to solve these challenges. In this paper, we define various data items and sensor data types used in livestock farms, study cases that utilize virtual sensors in other fields, and implement and design a virtual sensor system for the final smart livestock farm. MBE and EVRMSE were used to evaluate the finalized system and analyze performance indicators. As a result of collecting and managing data using virtual sensors, there was no obvious difference in data values from physical sensors, showing satisfactory results. By utilizing the virtual sensor system in smart livestock farms, innovation and efficiency improvement can be expected in various areas such as livestock operation and livestock health status monitoring. This paper proposes an innovative method of data collection and management by utilizing virtual sensor technology in the field of smart livestock, and has obtained important results in verifying its performance. As a future research task, we would like to explore the connection of digital livestock using virtual sensors.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.136-141
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• 2007

This paper introduces an artificial neuron which is a nano composite continuous sensor. The continuous nano sensor is fabricated as a thin and narrow polymer film sensor that is made of carbon nanotubes composites with a PMMA or a silicone matrix. The sensor can be embedded onto a structure like a neuron in a human body and it can detect deteriorations of the structure. The electrochemical impedance and dynamic strain response of the neuron change due to deterioration of the structure where the sensor is located. A network of the long nano sensor can form a structural neural system to provide large area coverage and an assurance of the operational health of a structure without the need for actuators and complex wave propagation analyses that are used with other methods. The artificial neuron is expected to effectively detect damage in large complex structures including composite helicopter blades and composite aircraft and vehicles.

• Journal of Advanced Navigation Technology
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• v.18 no.6
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• pp.648-652
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• 2014

The core of existing IT industry has been multimedia contents based on internet network. But nowadays, ubiquitous technology which combines sensors and network technology is emerging and is called USN (ubiquitous sensor network). Health care industry is one of suitable areas for USN application. In this paper, sensor network for baby care is proposed. The proposed baby care system consists of bluetooth network to transfer information which comes from various sensors like falling detecting, crying and fever detecting sensor to keep infant's status safe from external-potential threats. The effectiveness of the implemented system is showed through demonstration.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1322-1329
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• 2010

As body sensor network (BSN) research becomes mature, the need for managing power consumption of sensor nodes has become evident since most of the applications are designed for continuous monitoring. Real time Electrocardiograph (ECG) analysis on sensor nodes is proposed as an optimal choice for saving power consumption by reducing data transmission overhead. Smart sensor nodes with the ability to categorize lately detected ECG cycles communicate with base station only when ECG cycles are classified as abnormal. In this paper, ECG classification algorithms are described, which categorize detected ECG cycles as normal or abnormal, or even more specific cardiac diseases. Our Euclidean distance (ED) based classification method is validated to be most power efficient and very accurate in determining normal or abnormal ECG cycles. A close comparison of power efficiency and classification accuracy between our ED classification algorithm and generalized linear model (GLM) based classification algorithm is provided. Through experiments we show that, CPU cycle power consumption of ED based classification algorithm can be reduced by 31.21% and overall power consumption can be reduced by 13.63% at most when compared with GLM based method. The accuracy of detecting NSR, APC, PVC, SVT, VT, and VF using GLM based method range from 55% to 99% meanwhile, we show that the accuracy of detecting normal and abnormal ECG cycles using our ED based method is higher than 86%.