• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.127-137
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• 2011

We have developed a wearable device that can convert sensor data into real-time step counts and activity levels. Sensor data on gait were acquired using a triaxial accelerometer. A test was performed according to a test protocol for different walking speeds, e.g., slow walking, walking, fast walking, slow running, running, and fast running. Each test was carried out for 36 min on a treadmill with the participant wearing a portable gas analyzer (K4B2), an Actical device, and the device developed in this study. The signal vector magnitude (SVM) was used to process the X, Y, and Z values output by the triaxial accelerometer into one representative value. In addition, for accurate step-count detection, we used three algorithms: an heuristic algorithm (HA), the adaptive threshold algorithm (ATA), and the adaptive locking period algorithm (ALPA). A regression equation estimating the energy expenditure (EE) was derived by using data from the accelerometer and information on the participants. The recognition rate of our algorithm was 97.34%, and the performance of the activity conversion algorithm was better than that of the Actical device by 1.61%.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.117-123
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• 2013

In the recent cloud storage environment, the amount of SSD (Solid-State Drive) replacing with the traditional hard disk drive is increasing. Management of SSD for its space efficiency has become important since SSD provides fast IO performance due to no mechanical movement whereas it has wearable characteristics and does not provide in place update. In order to manage space efficiency of SSD, data de-duplication technique is frequently used. However, this technique occurs much overhead because it consists of data chunking, hasing and hash matching operations. In this paper, we propose new data de-duplication method using PRAM cache. The proposed method uses hierarchical hash tables and LRU(Least Recently Used) for data replacement in PRAM. First hash table in DRAM is used to store hash values of data cached in the PRAM and second hash table in PRAM is used to store hash values of data in SSD storage. The method also enhance data reliability against power failure by maintaining backup of first hash table into PRAM. Experimental results show that average writing frequency and operation time of the proposed method are 44.2% and 38.8% less than those of existing data de-depulication method, respectively, when three workloads are used.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.269-272
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• 2015

this is the study of the human body antenna device which can detect the powder in a 360-degree on(under) the ground whether it is metal or nonmetal using superhigh frequency RF beam equipped with the body. and it is able to transmit the data of the detection of the powder, battle combats can share that among them. with its flexible roof radial antenna structure, it emits the superhigh frequency RF beam to the front and flank multiply, preprocesses through the powder preprocessing part. and with the non-linear regression model algorism engine part, reflecting the attenuation characteristics depend on the delayed time of degree of the signal power which is received to the superhigh frequency RF beam. so it is able to detect the signal of the most likely mine or powder based on the degree of the answer signal power according to the delayed time of the superhigh frequency RF beam. also, it can detect the powder whether it is metal or nonmetal, mine, dud, VBIED. it can increase the chance of detection about 90% more than existing mine detector.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.227-238
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• 2017

Cardiac arrest is a serious intensive emergency disease that causes death within less than several minutes by depriving the body and brain of blood supply. Survival rate of cardiac arrest patients outside of hospitals is especially low. This is because pedestrians usually do not perceive the patient as a sick person, also, even if they do so, they have no medical knowledge to properly react to such emergency. The purpose of this study is to propose a solution that uses widely spread smart phones to alert pedestrians of the cardiac arrest patient, prevents cardiac arrest, and provides first-aid measures. By applying the proposed solution, cardiac arrest can be prevented in advance, pedestrians can be alerted to keep the golden time(4 minutes), and first witness can quickly proceed with CPR, ultimately enhancing the survival rate of the cardiac arrest patient.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.397-407
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• 2015

In this paper, we designed 1 chip IC for 3-axis gyroscope and 3-axis accelerometer used for various IoT/M2M mobile devices such as smartphone, wearable device and etc. We especially focused on analysis of gyroscope noise and proposed new architecture for removing various noise generated by gyroscope MEMS and IC. Gyroscope, accelerometer and geo-magnetic sensors are usually used to detect user motion or to estimate moving distance, direction and relative position. It is very important element to designing a low noise IC because very small amount of noise may be accumulated and affect the estimated position or direction. We made a mathematical model of a gyroscope sensor, analyzed the frequency characteristics of MEMS and circuit, designed a low noise, compact and low power 1 chip 6-axis inertial sensor IC including 3-axis gyroscope and 3-axis accelerometer. As a result, designed IC has 0.01dps/${\sqrt{Hz}}$ of gyroscope sensor noise density.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.77-83
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• 2019

With the increase in population, the number of such senior citizens is increasing day by day. These senior citizens have a variety of care needs, but there are not enough health workers to look after them. Ambient Assisted Living (AAL) aims at ensuring the safety and health quality of the older adults and extending the number of years the senior citizens can live independently in an environment of their own preference. AAL provides a system comprising of smart devices, medical sensors, wireless networks, computer and software applications for healthcare monitoring. AAL can be used for various purposes like preventing, curing, and improving wellness and health conditions of older adults. While information security and privacy are critical to providing assurance that users of AAL systems are protected, few studies take into account this feature. In this paper, we propose a secure and lightweight authentication scheme for the AAL systems. The proposed authentication scheme not only supports several important security requirements needed by the AAL systems, but can also withstand various types of attacks. Also, the security analysis results are presented to show the proposed authentication scheme is more secure and efficient rather than existing authentication schemes.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.99-104
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• 2021

In this paper, we propose health risk management using feature extraction and cluster analysis considering time flow. The proposed method proceeds in three steps. The first is the pre-processing and feature extraction step. It collects user's lifelog using a wearable device, removes incomplete data, errors, noise, and contradictory data, and processes missing values. Then, for feature extraction, important variables are selected through principal component analysis, and data similar to the relationship between the data are classified through correlation coefficient and covariance. In order to analyze the features extracted from the lifelog, dynamic clustering is performed through the K-means algorithm in consideration of the passage of time. The new data is clustered through the similarity distance measurement method based on the increment of the sum of squared errors. Next is to extract information about the cluster by considering the passage of time. Therefore, using the health decision-making system through feature clusters, risks able to managed through factors such as physical characteristics, lifestyle habits, disease status, health care event occurrence risk, and predictability. The performance evaluation compares the proposed method using Precision, Recall, and F-measure with the fuzzy and kernel-based clustering. As a result of the evaluation, the proposed method is excellently evaluated. Therefore, through the proposed method, it is possible to accurately predict and appropriately manage the user's potential health risk by using the similarity with the patient.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.141-148
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• 2021

This paper presents a method to detect zebra-crossing using deep learning which combines SegNet and ResNet. For the blind, a safe crossing system is important to know exactly where the zebra-crossings are. Zebra-crossing detection by deep learning can be a good solution to this problem and robotic vision-based assistive technologies sprung up over the past few years, which focused on specific scene objects using monocular detectors. These traditional methods have achieved significant results with relatively long processing times, and enhanced the zebra-crossing perception to a large extent. However, running all detectors jointly incurs a long latency and becomes computationally prohibitive on wearable embedded systems. In this paper, we propose a model for fast and stable segmentation of zebra-crossing from captured images. The model is improved based on a combination of SegNet and ResNet and consists of three steps. First, the input image is subsampled to extract image features and the convolutional neural network of ResNet is modified to make it the new encoder. Second, through the SegNet original up-sampling network, the abstract features are restored to the original image size. Finally, the method classifies all pixels and calculates the accuracy of each pixel. The experimental results prove the efficiency of the modified semantic segmentation algorithm with a relatively high computing speed.

• Convergence Security Journal
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• v.22 no.2
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• pp.115-120
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• 2022

This study analyzed the recent trends in the sports environment to which big data and AI technologies, which are representative technologies of the 4th Industrial Revolution, and approached them from the perspective of convergence of big data and AI technologies in the sports field. And the results are as follows. First, it is being used for player and game data analysis and team strategy establishment and operation. Second, by combining big data collected using GPS, wearable equipment, and IoT with artificial intelligence technology, scientific physical training for each player is possible through user individual motion analysis, which helps to improve performance and efficiently manage injuries. Third, with the introduction of an AI-based judgment system, it is being used for judge judgment. Fourth, it is leading the change in marketing and game broadcasting services. The technology of the 4th Industrial Revolution is bringing innovative changes to all industries, and the sports field is also in the process. The combination of big data and AI is expected to play an important role as a key technology in the rapidly changing future in a sports environment where scientific analysis and training determine victory or defeat.

• Journal of Powder Materials
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• v.31 no.1
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• pp.16-22
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• 2024

Composite-based piezoelectric devices are extensively studied to develop sustainable power supply and self-powered devices owing to their excellent mechanical durability and output performance. In this study, we design a lead-free piezoelectric nanocomposite utilizing (Ba_0.85 Ca_0.15)(Ti_0.9Zr_0.1)O₃ (BCTZ) nanomaterials for realizing highly flexible energy harvesters. To improve the output performance of the devices, we incorporate porous BCTZ nanowires (NWs) into the nanoparticle (NP)-based piezoelectric nanocomposite. BCTZ NPs and NWs are synthesized through the solid-state reaction and sol-gel-based electrospinning, respectively; subsequently, they are dispersed inside a polyimide matrix. The output performance of the energy harvesters is measured using an optimized measurement system during repetitive mechanical deformation by varying the composition of the NPs and NWs. A nanocomposite-based energy harvester with 4:1 weight ratio generates the maximum open-circuit voltage and short-circuit current of 0.83 V and 0.28 ㎂, respectively. In this study, self-powered devices are constructed with enhanced output performance by using piezoelectric energy harvesting for application in flexible and wearable devices.