• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.29-39
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• 2006

With the increasing popularity of the wireless network, the vulnerability issue on IEEE 802.1x Wireless Local Area Network (WLAN) are more serious than we expected. Security issues range from mis-configured wireless Access Point(AP) such as session hijacking to Denial of Service(DoS) attack. We propose a new system based on intrusion detection or prevention mechanism to protect the wireless network against these attacks. The proposed system has a security solution on AP that includes an intrusion detection and protection system(IDS/IPS) as an embedded module. In this paper, we suggest integrated wireless IDS/IPS module on AP with wireless traffic monitoring, analysis and packet filtering module against malicious wireless attacks. We also present that the system provides both enhanced security and performance such as on the university wireless campus network.

• Smart Structures and Systems
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• v.22 no.2
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• pp.175-183
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• 2018

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.675-687
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• 2010

Structural Health Monitoring (SHM) is the science and technology of monitoring and assessing the condition of aerospace, civil and mechanical infrastructures using a sensing system integrated into the structure. Impedance-based SHM measures impedance of a structure using a PZT (Lead Zirconate Titanate) patch. This paper presents a low-power wireless autonomous and active SHM node called Autonomous SHM Sensor 2 (ASN-2), which is based on the impedance method. In this study, we incorporated three methods to save power. First, entire data processing is performed on-board, which minimizes radio transmission time. Considering that the radio of a wireless sensor node consumes the highest power among all modules, reduction of the transmission time saves substantial power. Second, a rectangular pulse train is used to excite a PZT patch instead of a sinusoidal wave. This eliminates a digital-to-analog converter and reduces the memory space. Third, ASN-2 senses the phase of the response signal instead of the magnitude. Sensing the phase of the signal eliminates an analog-to-digital converter and Fast Fourier Transform operation, which not only saves power, but also enables us to use a low-end low-power processor. Our SHM sensor node ASN-2 is implemented using a TI MSP430 microcontroller evaluation board. A cluster of ASN-2 nodes forms a wireless network. Each node wakes up at a predetermined interval, such as once in four hours, performs an SHM operation, reports the result to the central node wirelessly, and returns to sleep. The power consumption of our ASN-2 is 0.15 mW during the inactive mode and 18 mW during the active mode. Each SHM operation takes about 13 seconds to consume 236 mJ. When our ASN-2 operates once in every four hours, it is estimated to run for about 2.5 years with two AAA-size batteries ignoring the internal battery leakage.

• Structural Engineering and Mechanics
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• v.32 no.6
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• pp.787-809
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• 2009

Recent interest in the use of wireless sensor networks for structural health monitoring (SHM) is mainly due to their low implementation costs and potential to measure the responses of a structure at unprecedented spatial resolution. Approaches capable of detecting damage using distributed processing must be developed in parallel with this technology to significantly reduce the power consumption and communication bandwidth requirements of the sensor platforms. In this investigation, a damage detection system based on a distributed processing approach is proposed and experimentally validated using a wireless sensor network deployed on two laboratory structures. In this distributed approach, on-board processing capabilities of the wireless sensor are exploited to significantly reduce the communication load and power consumption. The Damage Location Assurance Criterion (DLAC) is used for localizing damage. Processing of the raw data is conducted at the sensor level, and a reduced data set is transmitted to the base station for decision-making. The results indicate that this distributed implementation can be used to successfully detect and localize regions of damage in a structure. To further support the experimental results obtained, the capabilities of the proposed system were tested through a series of numerical simulations with an expanded set of damage scenarios.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.06a
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• pp.543-544
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• 2007

This paper introduce methods and analysis of a simple wireless sensor concept for detecting and locating faults as well as for load monitoring are presented. The concept is based on distributed wireless sensors that are attached to the incoming and outgoing power lines of secondary substations. A sensor measures only phase current characteristics of the wire it is attached to, is not synchronized to other sensors and does not need configuration of triggering levels. The main novelty of the concept is in detecting and locating faults by combining power distribution network characteristics on system level with low power sampling methods for individual sensors. This concept enables the sensor design to be simple, energy efficient and thus applicable in new installations and for retrofit purposes in both overhead and underground electrical distribution systems.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.189-196
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• 2011

The main issues of the researches are monitoring environment such as weather or temperature variation and natural accident, and sensor gateways which have mobile device, applications for mobile health care. In this paper, we propose the SFMS(Smart Factory Management System) that can effectively monitor and manage a green smart factory area based on M2M service and smart phone with android OS platform. The proposed system is performed based on the TinyOS-based IEEE 802.15.4 protocol stack. To validate system functionality, we built sensor network environments where were equipped with four application sensors such as Temp/Hum, PIR, door, and camera sensor. We also built and tested the SFMS system to provide a novel model for event detection systems with smart phone.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.3
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• pp.210-216
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• 2016

Malfunctioning of telecom systems can have serious implications on the safe navigation and operation of vessels and off-shore plants. Most safety-related accidents incur significant monetary damages and pollution due to complicated arrangements of the working environments and facilities. Therefore, an automated monitoring system that can collect data from configured telecom equipment connected to a network based on IP is required to ensure safe navigation and operation of such crucial institutions. This paper reports a list of such system requirements, system functions, and user-centered requirements based on the NORSOK T-100 (a standard of telecom management system). These findings were made through research with the newly designed and developed telecom management system (TMS). The TMS was tested by a testbed configured with CCTV, PA/GA, and other network equipment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3875-3891
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• 2021

In this study, a Velostat pressure sensor was manufactured to develop a plantar pressure measurement system and a C#-based application was developed to monitor and collect plantar pressure data in real time. In order to evaluate the characteristics of the proposed plantar pressure measurement system, the accuracy of plantar pressure index and personal classification was verified by comparing with MatScan, a commercial plantar pressure measurement system. As a result, the output characteristics according to the weight of the Velostat pressure sensor were evaluated and a trend line with the reliability of r² = 0.98 was detected. The Root Mean Square Error(RMSE) of the weighted area was 11.315 cm², the RMSE of the x coordinate of Center of Pressure(CoP_x) was 1.036 cm and the RMSE of the y coordinate of Center of Pressure(CoP_y) was 0.936 cm. Finally, inaccuracy of personal classification, the proposed system was 99.47% and MatScan was 96.86%. Based on the advantage of being simple to implement and capable of manufacturing at low cost, it is considered that it can be applied to various fields of measuring vital signs such as sitting posture and breathing in addition to the plantar pressure measurement system.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.05a
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• pp.319-324
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• 2005

The interface between host processor and the ShortStack Micro Server may be a Serial Communication Interface(SCI). The LonWorks control module with a high performance is developed, which is composed of the 8 bit PIC Microprocessor for host processor and the smart neuron chip for the ShoretStack Micro Server. This intelligent control board is verified as proceeding the various function tests from experimental system with an boost pump and inverter driving systems. It is also confirmed that the developed control module provides stably 0-10VDC linear signal to the input signal of inverter driving system for varying the induction motor speed. Thus, the experimental results show that the fabricating intelligent board carried out very well the various functions in the wide operating ranges of boost pump system. This developed control module expect to apply to industrial fields to require the comparatively exact control and monitoring such as multi-motor driving system with inverter, variable air volume system and the boost pump water supply systems.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.6
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• pp.597-603
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• 2014

The cargo of ships and offshore structures is the number of oil of combustibility and volatile, oil processing cargo. Furthermore heavy cargo of the vehicle or container box or bulk cargo are occupied the remainder of cargo. In addition, there is a possibility to move the location of the cargo and the vessel because it is received periodic / non-periodic a load of wave and ocean current. Therefore a shipboard hazard is much greater than onshore industry hazard. Monitoring and preparation for safety are necessary things because there is always risk of accidents arise from the impact of the freight and cargo of ships and offshore structures. In this study, we conducted a study with respect to the introduction of the wireless sensor network monitoring system to ensure the safety of the crew and workers on shipboard.