• Smart Media Journal
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• v.6 no.4
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• pp.58-64
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• 2017

Modern society is characterized by rapid increase in world population, aging of the rural population, decrease of cultivation area due to industrialization. The food problem is becoming an important issue with the farmers and becomes rural. Recently, the researches about the field of the smart farm are actively carried out to increase the profit of the rural area. The existing smart farm researches mainly monitor the cultivation environment of the crops in the greenhouse, another way like in the case of poor quality t is being studied that the system to control cultivation environmental factors is automatically activated to keep the cultivation environment of crops in optimum conditions. The researches focus on the crops cultivated indoors, and there are not many studies applied to the cultivation environment of crops grown outside. In this paper, we propose a method to improve the harvestability of poor areas by monitoring the areas with bad harvests by using big data analysis, by precisely predicting the harvest timing of fruit trees growing in orchards. Factors besides for harvesting include fruit color information and fruit weight information We suggest that a harvest correlation factor data collected in real time. It is analyzed using the Apache Spark engine. The Apache Spark engine has excellent performance in real-time data analysis as well as high capacity batch data analysis. User device receiving service supports PC user and smartphone users. A sensing data receiving device purpose Arduino, because it requires only simple processing to receive a sensed data and transmit it to the server. It regulates a harvest time of fruit which produces a good quality fruit, it is needful to determine a poor harvest area or concentrate a bad area. In this paper, we also present an architectural model to determine the bad areas of fruit harvest using strong data analysis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.675-689
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• 2019

Development of smart construction materials with both self-strain and self-damage sensing capacities is still difficult because of little information about the self-damage sensing source. Herein, we investigate the effects of the matrix strength, fiber geometry, and fiber content on the electrical resistivity of steel-fiber-reinforced cement composites by multi-fiber pullout testing combined with electrical resistivity measurements. The results reveal that the electrical resistivity of steel-fiber-reinforced cement composites clearly decreased during fiber-matrix debonding. A higher fiber-matrix interfacial bonding generally leads to a higher reduction in the electrical resistivity of the composite during fiber debonding due to the change in high electrical resistivity phase at the fiber-matrix interface. Higher matrix strengths, brass-coated steel fibers, and deformed steel fibers generally produced higher interfacial bond strengths and, consequently, a greater reduction in electrical resistivity during fiber debonding.

• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.51-58
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• 2008

Wireless sensor networks are used by various application areas to implement smart data processing and ubiquitous system. In the recent research of parking management system based on wireless sensor networks, adaptive sensing and efficient data processing are not considered. The effectiveness of implementing these distributed computing devices affects the performance of the applications in parking management. This paper proposes an adaptive sensing using fuzzy wireless sensor for the ubiquitous networks of parking management system. The fuzzy inference system is encoded in the sensor for efficient car presence detection. Moreover, a rule base adaptive module is proposed which wirelessly transmit the new values to each sensor for adapting the environment of car park area. The result of experiments shows that the fuzzy wireless sensor provides more throughputs and less time delays compared to a normal method of data gathering by wireless sensors.

• Smart Structures and Systems
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• v.16 no.2
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• pp.281-294
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• 2015

Bolted joint connection is the most commonly used connection element in structures and devices. The loosening due to external dynamic loads cannot be observed and measured easily and may cause catastrophic loss especially in an extreme requirement and/or environment. In this paper, an innovative Real-time Cross-Correlation Method (RCCM) for monitoring of the bolted joint loosening was proposed. We apply time reversal process on stress wave propagation to obtain correlation signal. The correlation signal's peak amplitude represents the cross-correlation between the loosening state and the baseline working state; therefore, it can detect the state of loosening. Since the bolt states are uncorrelated with noise, the peak amplitude will not be affected by noise and disturbance while it increases SNR level and increases the measured signals' reliability. The correlation process is carried out online through physical wave propagation without any other post offline complicated analyses and calculations. We implemented the proposed RCCM on a single bolt/nut joint experimental device to quantitatively detect the loosening states successfully. After that we implemented the proposed method on a real large structure (reaction wall) with multiple bolted joint connections. Loosening indexes were built for both experiments to indicate the loosening states. Finally, we demonstrated the proposed method's great anti-noise and/or disturbance ability. In the instrumentation, we simply mounted Lead Zirconium Titanate (PZT) patches on the device/structure surface without any modifications of the bolted connection. The low-cost PZTs used as actuators and sensors for active sensing are easily extended to a sensing network for large scale bolted joint network monitoring.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.1
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• pp.63-71
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• 2012

Most of sensor networks are difficult to be debugged, verified, and upgraded once they are deployed in the fields, for they are usually deployed in real world and large scale. Therefore, before deploying the sensor networks, we should test and verify them sufficiently in realistic testbeds. However, since we need to control physical environments which interact with sensor networks, it takes much of time and cost to test and verify sensor networks at the level of resource-constrained sensor nodes in such environments. This paper proposes an efficient software framework for evaluating and verifying sensor networks in the view points of network and application operations (i.e., accuracy of sensing algorithms). Applying the proposed software framework to the development of a simulator for a smart parking application based on wireless sensor network, this paper verifies the feasibility of the proposed framework.

• Fashion & Textile Research Journal
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• v.18 no.6
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• pp.755-765
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• 2016

Recently, interest in monitoring health and sports is growing because of the emphasis on wellness, which is accelerating the development and commercialization of smart clothing for biosignal monitoring. In addition to exerciseeffect monitoring clothing that tracks heart rate and respiration, recently developed clothing makes it possible to monitor muscle balance using electromyogram (EMG). The electrode for EMG have to attached to an accurate location in order to obtain high-quality signals in surface EMG measurement. Therefore, this study develops monitoring clothing suitable for different types of human bodies and aims to extract suitable range of EMG according to movements in order to develop self-fitness monitoring clothing based on EMG measurement. This study identified and attached electrodes on six upper muscles and two lower muscles of ten males in their 20s. After selecting six main motions that create a load on muscles, the 8-ch wireless EMG system was used to measure amplitude value, noise, SNR and SNR (dB) in each part and statistical analysis was conducted using SPSS 20.0. As a result, the suitable range for EMG measurement to apply to clothing was identified as four parts in musculus pectoralis major; three parts in muscle rectus abdominis, two parts each in shoulder muscles, backbone erector, biceps brachii, triceps brachii, and musculus biceps femoris; and four part in quadriceps muscle of thigh. This was depicted diagrammatically on clothing, and the EMG-monitoring sensing locations were presented for development of self-fitness monitoring.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.3
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• pp.187-192
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• 2017

This paper presents an ongoing study to develop a novel pressure sensor by means of a Nano Carbon Piezoresistive Composite (NCPC). The sensor was fabricated using the 3D printing process. We designed a miniaturized cantilever-type sensor electrode to improve the pressure sensing performance and utilized a 3D printer to build a small-sized body. The sensor electrode was made of 2 wt％ MWCNT/epoxy piezoresistive nano-composite, and the sensor body was encapsulated with a pipe plug cap for easy installation to any pressure system. The piezoresistivity responses of the sensor were converted into stable voltage outputs by using a signal processing system, which is similar to a conventional foil strain gauge. We evaluated the pressure-sensing performances using a pressure calibrator in the lab environment. The 3D-printed cantilever electrode pressure sensor showed linear voltage outputs of up to 16,500 KPa, which is a 200％ improvement in the pressure sensing range when compared with the bulk-type electrode used in our previous work.

• Smart Structures and Systems
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• v.6 no.8
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• pp.939-951
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• 2010

A low-cost wireless sensor network (WSN) solution with highly expandable super and simple nodes was developed. The super node was designed as a sensing unit as well as a receiving terminal with low energy consumption. The simple node was designed to serve as a cheaper alternative for large-scale deployment. A 12-bit ADC inputs and DAC outputs were reserved for sensor boards to ease the sensing integration. Vibration and thermal field tests of the Chi-Lu Bridge were conducted to evaluate the WSN's performance. Integral acceleration, temperature and tilt sensing modules were constructed to simplify the task of long-term environmental monitoring on this bridge, while a star topology was used to avoid collisions and reduce power consumption. We showed that, given sufficient power and additional power amplifier, the WSN can successfully be active for more than 7 days and satisfy the half bridge 120-meter transmission requirement. The time and frequency responses of cables shocked by external force and temperature variations around cables in one day were recorded and analyzed. Finally, guidelines on power characterization of the WSN platform and selection of acceleration sensors for structural health monitoring applications were given.

• Polymer(Korea)
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• v.28 no.4
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• pp.285-290
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• 2004

Nondestructive damage sensitivity of carbon nanotube(CNT) and nanofiber (CNF)/epoxy composites with their adding contents was investigated using electro-micromechanical technique. Carbon black (CB) was used only for the comparison with CNT and CNF. The fracture of carbon fiber was detected by acoustic emission (AE), which was correlated to the change in electrical resistance, ΔR under double-matrix composites (DMC) test. Stress sensing on carbon nanocomposites was performed by electro-pullout test under uniform cyclic loading. At the same volume fraction, the damage sensitivity for fiber fracture, matrix deformation and stress sensing were highest for CNT/epoxy composite, whereas for CB/epoxy composite they were the lowest among three carbon nanomaterials (CNMs). Damage sensitivity was correlated with morphological observation of carbon nanocomposites. Homogeneous dispersion among CNMs could be keying parameters for better damage monitoring. In this study, damage sensing of carbon nanocomposites could be evaluated well nondestructively by the electrical resistance measurement with AE.

• Nano
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• v.13 no.11
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• pp.1850126.1-1850126.10
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• 2018

Flexible strain sensors, as the core member of the family of smart electronic devices, along with reasonable sensing range and sensitivity plus low cost, have rose a huge consumer market and also immense interests in fundamental studies and technological applications, especially in the field of biomimetic robots movement detection and human health condition monitoring. In this paper, we propose a new flexible strain sensor based on thick CVD graphene film and its low-cost fabrication strategy by using the commercial adhesive tape as flexible substrate. The tensile tests in a strain range of ~30% were implemented, and a gage factor of 30 was achieved under high strain condition. The optical microscopic observation with different strains showed the evolution of cracks in graphene film. Together with commonly used platelet overlap theory and percolation network theory for sensor resistance modeling, we established an overlap destructive resistance model to analyze the sensing mechanism of our devices, which fitted the experimental data very well. The finding of difference of fitting parameters in small and large strain ranges revealed the multiple stage feature of graphene crack evolution. The resistance fallback phenomenon due to the viscoelasticity of flexible substrate was analyzed. Our flexible strain sensor with low cost and simple fabrication process exhibits great potential for commercial applications.