• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1421-1429
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• 2017

Human activity recognition has been studied using various sensors and algorithms. Human activity recognition can be divided into sensor based and vision based on the method. In this paper, we proposed an activity recognition system using acceleration sensor and gyroscope sensor in smartphone among sensor based methods. We used Deep Belief Network (DBN), which is one of the most popular deep learning methods, to improve an accuracy of human activity recognition. DBN uses the entire input set as a common input. However, because of the characteristics of different time window depending on the type of human activity, the RBMs, which is a component of DBN, are configured hierarchically by combining them from different time windows. As a result of applying to real data, The proposed human activity recognition system showed stable precision.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.619-625
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• 2007

As low power, low cost wireless communication technology like Bluetooth, Zigbee, RFID has been put to practical use together with the wellbeing trend, the concern about ubiquitous health care has been greatly increased and u-Health is becoming one of the most important application in the sensor network field. Especially, development of the medical services to be able to cope with a state of emergency for solitary senior citizens and the aged in silver town is very meaningful itself and their needs are also expected to continuously increase with a rapid increase in an aging population. In this paper we demonstrate the feasibility of extracting accurate heart rate variability (HRV) measurements from photoelectric plethysmography(PPG) signals gathered by a ring type pulse oximeter sensor attached to the finger. For this, we made 2 types of ring sensor, that is reflective and pervious type, and developed the remote monitoring system which is able to collect HR data from ring sensor, analyze and cope with a state of emergency.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.50 no.7
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• pp.324-325
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• 2001

In this study, the characteristics of ultrasonic sensor fabricated with PZT-Polymer 1-3 type composites are investigated. The 3-D Underwater object recognition using the self-made ultrasonic sensor and SOFM neural network is presented. The ultrasonic sensor is satisfied with the required condition of commercial ultrasonic sensor in underwater. The 3-D underwater object recognition for the training data and the testing data are 100[100%], respectively. The experimental results have shown that the ultrasonic sensor fabricated with PZT-Polymer 1-3 type composites can be applied for sonar system.

• Structural Monitoring and Maintenance
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• v.3 no.4
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• pp.377-393
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• 2016

The present work develops an expert system for detecting and predicting the crude oil types and properties at normal temperature ${\theta}=25^{\circ}C$, by evaluating the dielectric properties of the fluid transfused inside glass fiber reinforced epoxy (GFRE) composite pipelines, by using electrical capacitance sensor (ECS) technique, then used the data measurements from ECS to predict the types of the other crude oil transfused inside the pipeline, by designing an efficient artificial neural network (ANN) architecture. The variation in the dielectric signatures are employed to design an electrical capacitance sensor (ECS) with high sensitivity to detect such problem. ECS consists of 12 electrodes mounted on the outer surface of the pipe. A finite element (FE) simulation model is developed to measure the capacitance values and node potential distribution of ECS electrodes by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Radial Basis neural network (RBNN), structure is applied, trained and tested to predict the finite element (FE) results of crude oil types transfused inside (GFRE) pipe under room temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an RBNN results, thus validating the accuracy and reliability of the proposed technique.

• Journal of Sensor Science and Technology
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• v.19 no.1
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• pp.17-24
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• 2010

This paper describes a real time reliable monitoring method and analysis system using wrist type oximeter for ubiquitous healthcare service based on IEEE 802.15.4 standard. Photoplethysmograph(PPG) is simple and cost effective technique to measure blood volume change. In order to obtain and monitor physiological body signals continuously, a small size and low power consumption wrist type oximeter is designed for the measurement of oxygen saturation of a patient unobtrusively. The measured data is transferred to a central PC or server computer by using wireless sensor nodes in wireless sensor network for storage and analysis purposes. LabVIEW server program is designed to monitor stress indicator from heart rate variability(HRV) and process the measured PPG to accelerated plethysmograph(APG) by appling second order derivatives in server PC. These experimental results demonstrate that APG can precisely describe the features of an individual's PPG and be used as estimation of vascular elasticity for blood circulation.

• Journal of the Korean Geotechnical Society
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• v.34 no.9
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• pp.19-32
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• 2018

In this study, the applicability and practicality of landslides monitoring by using wireless sensor network (WSN) was analysed. WSN system consists of a sensor node for collecting and transmitting data using IEEE 802.14e standard, a gateway for collecting data and transmitting the data to the monitoring server. In the topology of the sensor network, a highly flexible and reliable mesh type was adopted, and three testbeds were chosen in each location of Seoul metropolitan area. Soil moisture sensors, tensiometers, inclinometers, and a rain gauge were installed at each testbed and sensor node to monitor the landslide. For the estimation of the optimal network topology between sensor nodes, the susceptibility assessment of landslides, forest density and viewshed analysis of terrain were conducted. As a result, the network connection works quite well and measured value of the volumetric water content and matric suction simulates well the general trend of the soil water characteristic curve by the laboratory test. As such, it is noted that WSN system, which is the reliable technique, can be applied to the landslide monitoring.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.2
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• pp.176-184
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• 2011

As modern megalopolises become more complex and huge, convenience and safety of citizens are main components for a welfare state. In order to make safe society, telemetrics technology, which remotely measures the information of target system using electronic devices, is an essential component. In general, telemetrics technology consists of USN (ubiquitous sensor network) based on a wireless network, smart sensor, and SoC (system on chip). In the smart sensor technology, the following two problems should be overcome. Firstly, because it is very difficult for transducer manufacturers to develop smart sensors that support all the existing network protocols, the smart sensor must be independent of the type of networking protocols. Secondly, smart sensors should be modular so that a faulty sensor element can be replaced without replacing healthy communication element. To solve these problems, this paper investigates the feasibility of an IEEE 1451 based ZigBee smart sensor system. More specifically, a smart sensor for large network coverage has been developed using ZigBee for active telemetrics.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.5
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• pp.713-718
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• 2014

DC urban railway power system consists of DC power network and AC power network. The DC power network supplies electric power to railway vehicles and the AC power network supplies electric power to station electric equipment. Recently, because of power consumption reduction and peak load shaving, intelligent measurement of regenerative energy and renewable energy adapted on DC urban railway is required. For this reason, DC smart metering system for DC power network shall be developed. Therefore, in this paper, DC voltage sensor, current sensor, and DC smart meter were developed and evaluated by performance test. DC voltage sensor was developed for measuring standard voltage range of DC urban railway, and DC current sensor was developed as hall effect split core type in order to install in existing system. DC smart meter possesses function of general intelligent electric power meter, such as measuring electricity and wireless communication etc. And, DC voltage sensor showed average 0.17% of measuring error for 2,000V/50mA, and current sensor showed average 0.21% of measuring error for ${\pm}2,000V/{\pm}4V$ in performance test. Also DC smart meter showed maximum 0.92% of measuring error for output of voltage sensor and current sensor. In similar environment for real DC power network, measuring error rate was under 0.5%. In conclusion, accuracy of DC smart metering system was confirmed by performance test, and more detailed performance will be verified by further real operation DC urban railway line test.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.75-84
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• 2014

In this paper, we constructed a sensor network environment where various sensors are used. Then, we evaluated the performance when this environment adopted existing clustering algorithms that are designed for only single type sensors network. In our experiments, we considered two different types of the networks. In the first, all nodes are equipped with identical sensors. In the second, all nodes are equipped with three different types of sensors. We measured performance variations of several clustering schemes in accordance with sensor data accuracy, sensor node resource depletion timing, amount of available energy, node isolation ratio, and network lifetime. According to our performance analysis, we proved that existing clustering algorithms are partially inefficient to maintain the various-sensor network. Consequently we suggest that a new algorithm is required to take aim at the various sensor network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.4
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• pp.344-365
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• 2009

The performance of a tracking system is greatly increased if multiple types of sensors are combined to achieve the objective of the tracking instead of relying on single type of sensor. To conduct the multi-modal tracking, we have previously developed a multi-modal sensor-based tracking model where acoustic sensors mainly track the objects and visual sensors compensate the tracking errors [1]. In this paper, we find a network synchronization problem appearing in the developed tracking system. The problem is caused by the different location and traffic characteristics of multi-modal sensors and non-synchronized arrival of the captured sensor data at a processing server. To effectively deliver the sensor data, we propose a time-based packet aggregation algorithm where the acoustic sensor data are aggregated based on the sampling time and sent to the server. The delivered acoustic sensor data is then compensated by visual images to correct the tracking errors and such a compensation process improves the tracking accuracy in ideal case. However, in real situations, the tracking improvement from visual compensation can be severely degraded due to the aforementioned network synchronization problem, the impact of which is analyzed by simulations in this paper. To resolve the network synchronization problem, we differentiate the service level of sensor traffic based on Weight Round Robin (WRR) scheduling at the routers. The weighting factor allocated to each queue is calculated by a proposed Delay-based Weight Allocation (DWA) algorithm. From the simulations, we show the traffic differentiation model can mitigate the non-synchronization of sensor data. Finally, we analyze expected traffic behaviors of the tracking system in terms of acoustic sampling interval and visual image size.