• International Journal of Contents
• /
• v.12 no.2
• /
• pp.12-23
• /
• 2016

This paper presents a novel concept of Disaster Prevention Design (DPD) and its derived subjects and topics for the safety of folk villages in both Korea and Japan. Nowadays, design concepts are focused on 'human-oriented nature' as a whole and this tendency fits to be appropriate for disaster prevention against real dangers of a future society, which is expected to have far more complicated features. On the other hand, convergences have performed with other areas in the field of Information Communication Technology (ICT) so that we can easily find examples like 'the strategy of ICT-based convergence' of the Korean Government in 2014. Modern content designs including UI (user interface) and USN (ubiquitous sensor network) have been developed as one of the representative areas of ICT & UD (universal design) convergences. These days this novel concept of convergence is overcoming the existing limitations of the conventional design concept focused on product and/or service. First of all, from that point our deduced topic or subject would naturally be a monitoring system design of constructional structures in folk villages for safety. We offer an integrated model of maintenance and a management-monitoring scheme. Another important point of view in the research is a safety sign or sign system installed in folk villages or traditional towns and their standardization. We would draw up and submit a plan that aims to upgrade signs and sign systems applied to folk villages in Korea and Japan. According to our investigations, floods in Korea and earthquakes in Japan are the most harmful disasters of folk villages. Therefore, focusing on floods in the area of traditional towns in Korea would be natural. We present a water-level expectation model using deep learning simulation. We also apply this method to the area of 'Andong Hahoe' village which has been registered with the World Cultural Heritage of UNESCO. Folk village sites include 'Asan Oeam', 'Andong Hahoe' and 'Chonju Hanok' villages in Korea and 'Beppu Onsen' village in Japan. Traditional Streets and Markets and Safe Schools and Parks are also chosen as nearby test-beds for DPD based on ICT. Our final goal of the research is to propose and realize an integrated disaster prevention and/or safety system based on big data for both Korea and Japan.

• The KIPS Transactions:PartD
• /
• v.13D no.6 s.109
• /
• pp.775-788
• /
• 2006

Many disaster monitoring systems are constantly studied to prevent disasters such as environmental pollution, the breaking of a tunnel and a building, flooding, storm earthquake according to the progress of wireless telecommunication, the miniaturization of terminal devices, and the spread of sensor network. A disaster monitoring system can extract information of a remote place, process sensor data with rules to recognize disaster situation, and provide work for preventing disaster. However existing monitoring systems are not enough to predict and prevent disaster, because they can only process current sensor data through utilizing simple aggregation function and operators. In this paper, we design and implement a disaster prevention system to predict near future dangerous area through using outside sensor network and spatial Information. The provided prediction technique considers the change of spatial information over time with current sensor data, and indicates the place that could be dangerous in near future. The system can recognize which place would be dangerous and prepare the disaster prevention. Therefore, damage of disaster and cost of recovery would be reduced. The provided disaster prevention system and prediction technique could be applied to various disaster prevention systems and be utilized for preventing disaster and reducing damages.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.9
• /
• pp.4186-4192
• /
• 2012

Because wind turbines are larger and more off-shore construction due to economic and environmental factors, it is more difficult to access the wind turbine as well as the necessary parts and the maintenance costs are increasing. So, we need to minimize fault elements and to prevent a secondary accident at failure through monitoring to reduce maintenance costs and to increase reliability of operation. In this paper we have implemented ZigBee based wireless sensor nodes and network for wind turbine condition monitoring using temperature, humidity, voltage, current, wind direction, and wind speed sensors. ZigBee wireless sensor nodes signals are transmitted to a central monitoring system via routers. Also, the sensor signals are collected and processed using LabVIEW program to monitor the wind turbine conveniently. The administrators and users can monitor the condition of wind turbine at remote site in real time over TCP/IP.

• Nuclear Engineering and Technology
• /
• v.54 no.7
• /
• pp.2444-2452
• /
• 2022

The field-programmable gate array (FPGA) is gaining popularity in industrial automation such as nuclear power plant instrumentation and control (I&C) systems due to the benefits of having non-existence of operating system, minimum software errors, and minimum common reason failures. Separate functions can be processed individually and in parallel on the same integrated circuit using FPGAs in comparison to the conventional microprocessor-based systems used in any plant operations. The use of FPGAs offers the potential to minimize complexity and the accompanying difficulty of securing regulatory approval, as well as provide superior protection against obsolescence. Wireless sensor networks (WSNs) are a new technology for acquiring and processing plant data wirelessly in which sensor nodes are configured for real-time signal processing, data acquisition, and monitoring. ZigBee (IEEE 802.15.4) is an open worldwide standard for minimum power, low-cost machine-to-machine (M2M), and internet of things (IoT) enabled wireless network communication. It is always a challenge to follow the specific topology when different Zigbee nodes are placed in a large network such as a plant. The research article focuses on the hardware chip design of different topological structures supported by ZigBee that can be used for monitoring and controlling the different operations of the plant and evaluates the performance in Vitex-5 FPGA hardware. The research work presents a strategy for configuring FPGA with ZigBee sensor nodes when communicating in a large area such as an industrial plant for real-time monitoring.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.9B
• /
• pp.1322-1329
• /
• 2010

As body sensor network (BSN) research becomes mature, the need for managing power consumption of sensor nodes has become evident since most of the applications are designed for continuous monitoring. Real time Electrocardiograph (ECG) analysis on sensor nodes is proposed as an optimal choice for saving power consumption by reducing data transmission overhead. Smart sensor nodes with the ability to categorize lately detected ECG cycles communicate with base station only when ECG cycles are classified as abnormal. In this paper, ECG classification algorithms are described, which categorize detected ECG cycles as normal or abnormal, or even more specific cardiac diseases. Our Euclidean distance (ED) based classification method is validated to be most power efficient and very accurate in determining normal or abnormal ECG cycles. A close comparison of power efficiency and classification accuracy between our ED classification algorithm and generalized linear model (GLM) based classification algorithm is provided. Through experiments we show that, CPU cycle power consumption of ED based classification algorithm can be reduced by 31.21% and overall power consumption can be reduced by 13.63% at most when compared with GLM based method. The accuracy of detecting NSR, APC, PVC, SVT, VT, and VF using GLM based method range from 55% to 99% meanwhile, we show that the accuracy of detecting normal and abnormal ECG cycles using our ED based method is higher than 86%.

• Journal of information and communication convergence engineering
• /
• v.8 no.2
• /
• pp.219-222
• /
• 2010

An ubiquitous sensor network (USN) system to monitor the bio information and the emergency of the elderly in the silver town is presented. The USN system consists of the sensor node platforms based on MCU of Atmage128L and RF Chip of CC2420 satisfying IEEE 802.15.4, which includes the bios sensor module such as the electrocardiogram (ECG) sensor and the temperature sensor. Additionally, when an emergency of the elderly is occurred in the silver town, the routing algorithm suitable to find and inform the location of the elderly is proposed, and the proposed routing algorithm is applied to the USN. To collect and manage the ECG data at the PC connected to the sink node, LabView software is used. The bio information and the emergency of the elderly can also be monitored at the client PC by TCP/IP networks in the USN system.

• Journal of the Institute of Convergence Signal Processing
• /
• v.11 no.1
• /
• pp.53-62
• /
• 2010

Recently, various industrial embedded systems including vehicles controlled electronically are evolving to distributed multi-micro controller system. Accordingly, there is a need for standard CAN(Controller Area Network) protocol that ensures high stability and reliability of communication and is simple to construct object-oriented system with high control efficiency. CAN communication interface used general-purpose processor doesn't have many limitations in various application development because of fixed hardware architecture. This paper design and implement a CAN communication interface system based on FPGA. It is verified function and performance of system through monitoring communication with existing AT90CAN128 controller. Implemented CAN communication interface can be reused in development of application systems based on FPGA. And it provides low-cost, small-size and low-power design advantages.

• International Journal of Computer Science & Network Security
• /
• v.24 no.4
• /
• pp.147-154
• /
• 2024

The continuous increase in urban population due to migration of mases from rural areas to big cities has set urban water supply under serious stress. Urban water resources face scarcity of available water quantity, which ultimately effects the water supply. It is high time to address this challenging problem by taking appropriate measures for the improvement of water utility services linked with better understanding of demand side management (DSM), which leads to an effective state of water supply governance. We propose a dynamic framework for preventive DSM that results in optimization of water resource management. This paper uses Agent Based Modeling (ABM) with Digital Twin (DT) to model water consumption behavior of a population and consequently forecast water demand. DT creates a digital clone of the system using physical model, sensors, and data analytics to integrate multi-physical quantities. By doing so, the proposed model replicates the physical settings to perform the remote monitoring and controlling jobs on the digital format, whilst offering support in decision making to the relevant authorities.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.4
• /
• pp.244-254
• /
• 2009

This paper describes a remote system to monitor the circadian behavioral patterns of elders who live alone. The proposed system was designed and implemented to provide more conveniently and reliably the required functionalities of a remote monitoring system for elders based on the development of first phase prototype[2]. The developed system is composed of an in-house sensing system and a server system. The in-house sensing system is a set of wireless sensor nodes which have pyroelectric infrared (PIR) sensor to detect a motion of elder. Each sensing node sends its detection signal to a home gateway via wireless link. The home gateway stores the received signals into a remote database. The server system is composed of a database server and a web server, which provides web-based monitoring system to caregivers (friends, family and social workers) for more cost effective intelligent care service. The improved second phase system can provide 'automatic diagnosis', 'going out detection', and enhanced user interface functionalities. We have evaluated the first and second phase monitoring systems from real field experiments of 3/4 months continuous operation with installation of 9/15 elders' houses, respectively. The experimental results show the promising possibilities to estimate the behavioral patterns and the current status of elder even though the simplicity of sensing capability.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.5 no.5
• /
• pp.355-361
• /
• 2000

A recycling reactor system operated under sequential anoxic and oxic conditions for the treatment of swine wastewater has been developed, in which piggery slurry is fermentatively and aerobically treated and then part of the effluent is recycled to the pigsty. This system significantly removes offensive smells (at both the pigsty and the treatment plant), BOD and others, and may be cost effective for small-scale farms. The most dominant heterotrophic were, in order, Alcaligenes faecalis, Brevundimonas diminuta and Streptococcus sp., while lactic acid bacteria were dominantly observed in the anoxic tank. We propose a novel monitoring system for a recycling piggery slurry treatment system through the use of neural networks. In this study, we tried to model the treatment process for each tank in the system (influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) based upon the population densities of the heterotrophic and lactic acid bacteria. Principal component analysis(PCA) was first applied to identify a relationship between input and output. The input would be microbial densities and the treatment parameters, such as population densities of heterotrophic and lactic acid bacteria, suspended solids(SS), COD, NH$_4$(sup)+-N, ortho-phosphorus (o-P), and total-phosphorus (T-P). then multi-layer neural networks were employed to model the treatment process for each tank. PCA filtration of the input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of imput. Neural network independently trained for each treatment tank and their subsequent combined data analysis allowed a successful prediction of the treatment system for at least two days.