• Smart Structures and Systems
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• v.25 no.5
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• pp.593-604
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• 2020

Temperature may have more significant influences on structural responses than operational loads or structural damage. Therefore, a comprehensive understanding of temperature distributions has great significance for proper design and maintenance of bridges. In this study, the temperature distribution of the steel box girder is systematically investigated based on the structural health monitoring system (SHMS) of the Sutong Cable-stayed Bridge. Specifically, the characteristics of the temperature and temperature difference between different measurement points are studied based on field temperature measurements. Accordingly, the probability density distributions of the temperature and temperature difference are calculated statistically, which are further described by the general formulas. The results indicate that: (1) the temperature and temperature difference exhibit distinct seasonal characteristics and strong periodicity, and the temperature and temperature difference among different measurement points are strongly correlated, respectively; (2) the probability density of the temperature difference distribution presents strong non-Gaussian characteristics; (3) the probability density function of temperature can be described by the weighted sum of four Normal distributions. Meanwhile, the temperature difference can be described by the weighted sum of Weibull distribution and Normal distribution.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.197-202
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• 2014

It is used the important method that Oriental doctor determines patient's disease status observing patient's state of tongue in Oriental medicine clinic. In this paper, it developed the how to use the pulse diagnosis and tongue diagnosis based on s mart based electronic acupuncture. It will do objective judgment without wrong diagnosis. In this paper, we developed the algorithm that it automatically determines patient health condition and smart electronic acupuncture kit using fuzzy logic and fuzzy reasoning system were completed. In this paper, Simulation results proved that acupuncture is effective than the traditional method of using electronic intelligence.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.2
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• pp.529-546
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• 2012

To address the need for autonomous control of remote and distributed mobile systems, Machine-to-Machine (M2M) communications are rapidly gaining attention from both academia and industry. M2M communications have recently been deployed in smart grid, home networking, health care, and vehicular networking environments. This paper focuses on M2M communications in the vehicular networking context and investigates areas where M2M principles can improve vehicular networking. Since connected vehicles are essentially a network of machines that are communicating, preferably autonomously, vehicular networks can benefit a lot from M2M communications support. The M2M paradigm enhances vehicular networking by supporting large-scale deployment of devices, cross-platform networking, autonomous monitoring and control, visualization of the system and measurements, and security. We also present some of the challenges that still need to be addressed to fully enable M2M support in the vehicular networking environment. Of these, component standardization and data security management are considered to be the most significant challenges.

• The Journal of Korean Physical Therapy
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• v.30 no.2
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• pp.54-57
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• 2018

Purpose: Most studies have reported pain in the head-neck and upper-limbs according to smartphone usage, which is related to the proprioception sense in the head and neck, but there have been few studies. Therefore, the aim of this study was identify the adverse effects of the proprioceptive sense in the head-neck according to smartphone usage. Methods: Twenty-seven young adults (male: 9, female: 18) were enrolled in this study. The proprioceptive sense was measured through the joint reposition sense error and neural positon error in the head-neck during smartphone usage for 0, 5, and 20 minutes. The Noraxon MyoMotion system was used to record the joint position angle and neutral positon in the head-neck. One-way repeated ANOVA was used to identify the differences between the three smartphone use durations and the least-squares difference was used as a post hoc test. The data were analyzed using SPSS 18.0 software. Results: The joint reposition sense error and neural positon error in the head-neck were significantly different among the 0, 5, and 20 minutes of smartphone usage (p<0.05). In the post hoc test, the joint reposition sense error and neural positon error showed a significant difference between smartphone use for 0 minute and 5 minute, and between smartphone use for 0 minute and 20 minutes. Conclusion: This study suggests that smartphone use within 5 minutes can have adverse effects on the proprioceptive sense. Therefore, it is necessary to consider the appropriate use time and break time when using smart phones.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.678-681
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• 2010

최근 건설 기술의 비약적인 발전으로, 국내외적으로 장대교량의 건설이 증가하고 있다. 이러한 장대교량들은 지속적인 안전성 관리가 필요하며 현재 국내 다수의 장대교량들에는 다양한 센서로 이루어진 구조건전성 모니터링 시스템이 설치, 운용되고 있다. 장대교량과 같은 대형 구조물의 물리적 상태를 정확하게 파악하기 위해서는 다수의 센서배치가 필요로 하지만, 이는 센서 자체의 비용뿐만 아니라 센서와 데이터 서버를 연결하는 케이블의 설치비용으로 인하여 상당히 제약을 받을 수 밖에 없다. 실례로 홍콩의 Tsing Ma 대교의 경우 350개의 센서를 설치하는데 약 8 백만불이 넘는 금액이 들었으며(Farrar 등, 2004), Celeby(2002)의 보고에 의하면 각 센서의 채널 당 케이블의 설치비용으로 약 5000불의 비용이 소모되고 있다. 본 연구에서는 이러한 센서와 이러한 불편함을 극복하기 위하여 자체 연산기능을 가지고 있는 스마트 무선 센서를 개발하고, 이를 해남과 진도를 잇는 연육교인 제 2 진도대교에 적용, 사장교의 구조 건전성을 저비용 및 높은 편의성으로 모니터링 하기 위한 테스트베드를 구축하였다.

• Smart Structures and Systems
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• v.14 no.2
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• pp.105-128
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• 2014

The stochastic damage locating vector (SDLV) method has been studied extensively in recent years because of its potential to determine the location of damage in structures without the need for measuring the input excitation. The SDLV method has been shown to be a particularly useful tool for damage localization in steel truss bridges through numerical simulation and experimental validation. However, several issues still need clarification. For example, two methods have been suggested for determining the observation matrix C identified for the structural system; yet little guidance has been provided regarding the conditions under which the respective formulations should be used. Additionally, the specific layout of the sensors to achieve effective performance with the SDLV method and the associated relationship to the specific type of truss structure have yet to be explored. Moreover, how the location of truss members influences the damage localization results should be studied. In this paper, these three issues are first investigated through numerical simulation and subsequently the main results are validated experimentally. The results of this paper provide guidance on the effective use of the SDLV method.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.291-308
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• 2013

A novel concept of transmissibility based on a wavelet transform for structural damage detection is presented in this paper. The main objective of the research was the development of a method for detecting slight damage at the incipient stage. As a vibration-based approach, the concept of transmissibility has attracted considerable interest because of its advantages and effectiveness in damage detection. However, like other vibration-based methods, transmissibility-based approaches suffer from insensitivity to slight local damage because of the regularity of the traditional Fourier transform. Therefore, the powerful signal processing techniques must be found to solve this problem. Wavelet transform that is able to capture subtle information in measured signals has received extensive attention in the field of damage detection in recent decades. In this paper, we first propose a novel transmissibility concept based on the wavelet transform. Outlier analysis was adopted to construct a damage detection algorithm with wavelet-based transmissibility. The feasibility of the proposed method was numerically investigated with a typical six-degrees-of-freedom spring-mass system, and comparative investigations were performed with a conventional transmissibility approach. The results demonstrate that the proposed transmissibility is more sensitive than conventional transmissibility, and the former is a promising tool for structural damage detection at the incipient stage.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.13-23
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• 2017

In order to protect personal information and important information (confidential information, sales information, user information, etc.) in the internal network, companies and organizations apply encryption to the Server-To-Server or Server-To-Client communication section, And are experiencing difficulties due to the increasing number of known attacks and intelligent security attacks. In order to apply the existing S / W encryption technology, it is necessary to modify the application. In the financial sector, "Comprehensive Measures to Prevent the Recurrence of Personal Information Leakage in the Domestic Financial Sector" has been issued, and standard guidelines for financial computing security have been laid out, and it is required to expand the whole area of encryption to the internal network. In addition, even in environments such as U-Health and Smart Grid, which are based on the Internet of Things (IoT) environment, which is increasingly used, security requirements for each collection gateway and secure transmission of the transmitted and received data The requirements of the secure channel for the use of the standard are specified in the standard. Therefore, in this paper, we propose a secure encryption algorithm through mutual authentication and grouping for each node through H / W based Network Control Server (NCS) applicable to internal system and IoT environment provided by enterprises and organizations. We propose a protocol design that can set the channel.

• Smart Structures and Systems
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• v.25 no.3
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• pp.265-277
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• 2020

Recently, the concept of "drive-by" bridge monitoring system using indirect measurements from a passing vehicle to extract key parameters of a bridge has been rapidly developed. As one of the most key parameters of a bridge, the natural frequency has been successfully extracted theoretically and in practice using indirect measurements. The frequency of bridge is generally calculated applying Fast Fourier Transform (FFT) directly. However, it has been demonstrated that with the increase in vehicle velocity, the estimated frequency resolution of FFT will be very low causing a great extracted error. Moreover, because of the low frequency resolution, it is hard to detect the frequency drop caused by any damages or degradation of the bridge structural integrity. This paper will introduce a new technique of bridge frequency extraction based on Hilbert Transform (HT) that is not restricted to frequency resolution and can, therefore, improve identification accuracy. In this paper, deriving from the vehicle response, the closed-form solution associated with bridge frequency removing the effect of vehicle velocity is discussed in the analytical study. Then a numerical Vehicle-Bridge Interaction (VBI) model with a quarter car model is adopted to demonstrate the proposed approach. Finally, factors that affect the proposed approach are studied, including vehicle velocity, signal noise, and road roughness profile.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.