• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.6
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• pp.672-677
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• 2010

Recently, the efficient management system for large-scale sensor data has been required due to the increasing deployment of large-scale sensor networks. In this paper, we propose a cloud-based sensor data management system with low cast, high scalability, and efficiency. Sensor data in sensor networks are transmitted to the cloud through a cloud-gateway. At this point, outlier detection and event processing is performed. Transmitted sensor data are stored in the Hadoop HBase, distributed column-oriented database, and processed in parallel by query processing module designed as the MapReduce model. The proposed system can be work with the application of a variety of platforms, because processed results are provided through REST-based web service.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.6
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• pp.295-311
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• 2019

The smart product market is growing year by year and is being used in many areas. There are various ways of interacting with smart products and users by inputting voice recognition, touch and finger movements. It is most important to detect an accurate hand region as a whole step to recognize hand movement. In this paper, we propose a method to detect accurate hand region in real time in various environments. A conventional method of detecting a hand region includes a method using depth information of a multi-sensor camera, a method of detecting a hand through machine learning, and a method of detecting a hand region using a color model. Among these methods, a method using a multi-sensor camera or a method using a machine learning requires a large amount of calculation and a high-performance PC is essential. Many computations are not suitable for embedded systems, and high-end PCs increase or decrease the price of smart products. The algorithm proposed in this paper detects the hand region using the color model, corrects the problems of the existing hand detection algorithm, and detects the accurate hand region based on various experimental environments.

• Clinical Endoscopy
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• v.57 no.1
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• pp.73-81
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• 2024

Background/Aims: Upper gastrointestinal bleeding (UGIB) is a life-threatening condition that necessitates early identification and intervention and is associated with substantial morbidity, mortality, and socioeconomic burden. However, several diagnostic challenges remain regarding risk stratification and the optimal timing of endoscopy. The PillSense System is a noninvasive device developed to detect blood in patients with UGIB in real time. This study aimed to assess the safety and performance characteristics of PillSense using a simulated bleeding model. Methods: A preclinical study was performed using an in vivo porcine model (14 animals). Fourteen PillSense capsules were endoscopically placed in the stomach and blood was injected into the stomach to simulate bleeding. The safety and sensitivity of blood detection and pill excretion were also investigated. Results: All the sensors successfully detected the presence or absence of blood. The minimum threshold was 9% blood concentration, with additional detection of increasing concentrations of up to 22.5% blood. All the sensors passed naturally through the gastrointestinal tract. Conclusions: This study demonstrated the ability of the PillSense System sensor to detect UGIB across a wide range of blood concentrations. This ingestible device detects UGIB in real time and has the potential to be an effective tool to supplement the current standard of care. These favorable results will be further investigated in future clinical studies.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.4
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• pp.254-260
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• 2000

This paper deals with the development of fault tolerant control for a nonlinear boiler system with noise and disturbance. The MCMBPC(Multivariable Constrained Model Based Predictive Control) is adopted for the control of the specific boiler turbin model. The fault detection and diagnosis are accomplished with the Kalman filter and two bias estimators. Once a fault is detected, two Bias estimators are driven to estimate the fault and to discriminate Process fault and sensor fault. In this paper, a fault tolerant control scheme combining MCMBPC with a fault compensation method based on the bias estimator is proposed. The proposed scheme has been applied to the nonlinear boiler system and shown a satisfactory performance through some simulations.

• Structural Monitoring and Maintenance
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• v.4 no.4
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• pp.365-379
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• 2017

This paper presents a comparison of the black-box and the physics based derived gray-box models for subspace identification for structures subjected to support-excitation. The study compares the damage detection capabilities of both these methods for linear time invariant (LTI) systems as well as linear time-varying (LTV) systems by extending the gray-box model for time-varying systems using short-time windows. The numerically simulated IASC-ASCE Phase-I benchmark building has been used to compare the two methods for different damage scenarios. The efficacy of the two methods for the identification of stiffness parameters has been studied in the presence of different levels of sensor noise to simulate on-field conditions. The proposed extension of the gray-box model for LTV systems has been shown to outperform the black-box model in capturing the variation in stiffness parameters for the benchmark building.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.35-38
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• 2008

This paper develops an aero engine LPV mathematical model to exactly describe aero engine dynamic process characteristics, eliminate the effect of modeling error. Design FDF with eigenstructure assignment. The simulation results of turbofan engine control system sensor fault show that this method has good performance in focusing discrimination in fault signal with modeling eror, enhancing the robustness to unknown input, detecting accuracy is high and satisfiying real-time requirement.

• Journal of IKEEE
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• v.24 no.2
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• pp.475-485
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• 2020

Recently, a crowdsensing system that provides a new sensing service with real-time sensing data provided from a user's device including a sensor without installing a separate sensor has attracted attention. In the crowdsensing system, meaningless data may be provided due to a user's operation error or communication problem, or false data may be provided to obtain compensation. Therefore, the detection and removal of the abnormal data determines the quality of the crowdsensing service. The proposed methods in the past to detect these anomalies are not efficient for the fast-changing environment of crowdsensing. This paper proposes an anomaly data detection method by extracting the characteristics of continuously and rapidly changing sensing data environment by using machine learning technology and modeling it with an appropriate algorithm. We show the performance and feasibility of the proposed system using deep learning binary classification model of supervised learning and autoencoder model of unsupervised learning.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.19-25
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• 2024

This paper proposes an artificial intelligence-based personal information area object detection optimization method in an embedded system to de-identify personal information in video data. As an object detection optimization method, first, in order to increase the detection rate for personal information areas when detecting objects, a gyro sensor is used to collect the shooting angle of the image data when acquiring the image, and the image data is converted into a horizontal image through the collected shooting angle. Based on this, each learning model was created according to changes in the size of the image resolution of the learning data and changes in the learning method of the learning engine, and the effectiveness of the optimal learning model was selected and evaluated through an experimental method. As a de-identification method, a shuffling-based masking method was used, and double-key-based encryption of the masking information was used to prevent restoration by others. In order to reuse the original image, the original image could be restored through a security key. Through this, we were able to secure security for high personal information areas and improve usability through original image restoration. The research results of this paper are expected to contribute to industrial use of data without personal information leakage and to reducing the cost of personal information protection in industrial fields using video through de-identification of personal information areas included in video data.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06d
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• pp.360-365
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• 2010

In this paper, we present a distributed algorithm for detecting dynamic event regions in wireless sensor network with the consideration on energy saving. Our model is that the sensing field is monitored by a large number of randomly distributed sensors with low-power battery and limited functionality, and that the event region is dynamic with motion or changing the shape. At any time that the event happens, we need some sensors awake to detect it and to wake up its k-hop neighbors to detect further events. Scheduling for the network to save the total power-cost or to maximize the monitoring time has been studied extensively. Our scheme is that some predetermined sensors, called critical sensors are awake all the time and when the event is detected by a critical sensor the sensor broadcasts to the neighbors to check their sensing area. Then the neighbors check their area and decide whether they wake up or remain in sleeping mode with certain criteria. Our algorithm uses only 2 bit of information in communication between sensors, thus the total communication cost is low, and the speed of detecting all event region is high. We adapt two kinds of measure for the wake-up decision. With suitable threshold values, our algorithm can be applied for many applications and for the trade-off between energy saving and the efficiency of event detection.

• KIEAE Journal
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• v.9 no.5
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• pp.97-104
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• 2009

In the process of urban development, the increase in the number and the complexity of urban facilities gives rise to a variety of problems, such as increase in construction and maintenance cost. In particular, taking into account the fact that an emergency situation in an urban facility can cause substantial loss of property as well as casualties, it becomes important to intelligently perceive states of facilities and properly execute countermeasures through real-time monitoring. In recent years, practitioners and researchers have made efforts to improve current passive and manpower-dependent facility management systems to be more active and intelligent, by applying diverse ubiquitous computing technologies for the u-City project. In this study, after discussing major drawbacks of the conventional facilities management, the concept and the model of a sensor-based integrated intelligent management system for urban facilities are proposed. The proposed model, by analyzing and processing real-time sensor data from urban facilities, not only supports the management of individual facilities, but also enables the detection of complex facility-related events and the process of their countermeasures. This active and intelligent management of urban facilities is expected to overcome the limitation of the conventional facilities management, and provide more suitable facility management services for the u-City development.