• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.209-213
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• 1997

This paper deals with sensing ability of $2^{nd}$ smart sensor that has a sensing ability of distinguish materials. We have developed new signal processing method that have distinguish different materials. We made the $2^{nd}$ smart sensor for experiment. The second type of smart sensor is HH type. We have developed a new signal processing method that can distinguish among different materials. The estimation method (RSAIIn dex) is developed for $2^{nd}$ smart sensor(HH smart sensor). Experiment and analysis are executed for estimation the new method. We estimated sensing ability of $2^{nd}$ smart sensor with RsA, method. Sensing Ability of the $2^{nd}$ smart sensor were evaluated relatively through a new RsAl method. According to frequency changing, influences of the $2^{nd}$ smart sensor are evaluated through a new recognition index RSAI. Applications of this method are for finding abnormal conditions of objects (automanufacturing), feeling of objects (medical product), robotics, safety diagnosis of structure, etc.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.360-374
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• 2016

Since WSNs (Wireless Sensor Networks) applied to their application areas such as smart home, smart factory, environment monitoring, etc., depend on sensor data, the sensor data is the most important among WSN components. The resources of each node consisting of WSN are extremely limited in energy, hardware and so on. Due to these limitation, communication failure probabilities become much higher and the communication failure causes data loss to occur. For this reason, this paper proposes 2MC (Maximum/Minimum Compression) that is a method to compress sensor data by selecting circular queue-based maximum/minimum sensor data values. Our proposed method reduces sensor data losses and value errors when they are recovered. Experimental results of 2MC method show the maximum/minimum 35% reduction efficiency in average sensor data accumulation error rate after the 3 times compression, comparing with CQP (Circular Queue Compression based on Period) after the compressed data recovering.

• Journal of Sensor Science and Technology
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• v.33 no.2
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• pp.63-69
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• 2024

A novel method for measuring hydrogen concentration is introduced, along with its working principle and a novel detection algorithm. This configuration requires no additional reference compartment for potentiometric electrochemical measurements; therefore, it is the most suitable for measuring dissolved hydrogen in the liquid phase. The sensor's electromotive force saturates at a certain point, depending on the hydrogen concentration during the heating process of the sensor operation. This dynamic temperature scanning method provides higher sensitivity than the constant temperature measurement method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.330-335
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• 2000

This paper deals with sensing ability of smart sensor that has a sensing ability of distinguish materials. We have developed new signal processing method that have distinguish different materials. We made the two type of smart sensors for experiment. The first type of smart sensor is H2 type. The second type of smart sensor is HH type. The smart sensor was developed for recognition of material. And then we developed estimation method of sensing ability of smart sensors. The first method(Sensing Ability Index) is developed for H2 smart sensor. The second method($R_{SAI}$ Index) is developed for HH smart sensor. We estimated sensing ability of smart sensor with new SAI and $R_{SAI}$ method. This paper describes our primary study for a new method of estimate sensing ability of smart sensor, which is need for precision work system. This is a study of dynamic characteristics of smart sensor according to frequency and displacement changing with new SAI and $R_{SAI}$ method. Experiment and analysis are executed for proper dynamic sensing condition. First, we developed advanced smart sensors. Second, we develop new SAI and $R_{SAI}$ methods that have a sensing ability of distinguish materials. Dynamic characteristics of smart sensor are evaluated through new SAI and $R_{SAI}$ method relatively. We can use the new SAI and $R_{SAI}$ method for finding materials. Applications of this method are finding abnormal condition of object(auto-manufacturing), feeling of object(medical product), robotics, safety diagnosis of structure, etc.

• Smart Structures and Systems
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• v.2 no.2
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• pp.189-208
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• 2006

Monitoring large-scale civil engineering structures such as offshore platforms and high-large buildings requires a large number of sensors of different types. Innovative sensor data information technologies are very extremely important for data transmission, storage and retrieval of large volume sensor data generated from large sensor networks. How to obtain the optimal sensor set and placement is more and more concerned by researchers in vibration-based SHM. In this paper, a method of determining the sensor location which aims to extract the dynamic parameter effectively is presented. The method selects the number and place of sensor being installed on or in structure by through the tolerance domain statistical inference algorithm combined with second order sensitivity technology. The method proposal first finds and determines the sub-set sensors from the theoretic measure point derived from analytical model by the statistical tolerance domain procedure under the principle of modal effective independence. The second step is to judge whether the sorted out measured point set has sensitive to the dynamic change of structure by utilizing second order characteristic value sensitivity analysis. A 76-high-building benchmark mode and an offshore platform structure sensor optimal selection are demonstrated and result shows that the method is available and feasible.

• Journal of Sensor Science and Technology
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• v.24 no.4
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• pp.258-263
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• 2015

In order to prevent drink-driving by detecting concentration of alcohol from driver's exhale breath, twenty chemical sensors fabricated. The one of purposes for sensor array which consists of those sensors is to discriminate between target gas(alcohol) and interference gases($CH_3CH_2OH$, CO, NOx, Toluene, and Xylene). Wilks's lambda was presented to achieve above purpose and optimal sensors were selected using the method. In this paper, step-wise sensor elimination based on Euclidean distance was investigated for selecting optimal sensors and compared with a result of Wilks's lambda method. The selectivity and sensitivity of sensor array were used for comparing performance of sensor array as a result of two methods. The data acquired from selected sensor were analyzed by pattern analysis methods, principal component analysis and Sammon's mapping to analyze cluster tendency in the low space (2D). The sensor array by stepwise sensor elimination method had a better sensitivity and selectivity compared to a result of Wilks's lambda method.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.2
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• pp.40-47
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• 2003

This paper presents a new evaporative system monitoring method using a virtual HC sensor for an automotive on-board diagnosis. A development was made at providing mathematical expressions from the lambda control information to estimate the HC mass flow purged into the intake manifold from the canister for implementing a virtual HC sensor. The change of the lambda averagevalue reflected the influence of the additional fuel from purging results the sensor estimation of the purged HC amount. Based on this virtual HC sensor, a new evaporative system monitoring method was proposed comparing the amount of purged HC amount with the amount of the HC gas evaporated from the fuel tank and absorbed into the canister. Finally, the method was validated with a simulation using the data logged from the retail car.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.2
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• pp.99-106
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• 2006

In this paper, the new method that estimates a sensing ability of smart sensor will be proposed. A study is estimation method that evaluates sensing ability about smart sensor respectively. According to acceleration(g) and displacement changing, we estimated sensing ability of smart sensor using SAI(Sensing Ability Index) method respectively. Smart sensors was made fer experiment. The types of smart sensor are two types(hard and soft smart sensor). Smart sensors developed for recognition of material. Experiment and analysis are executed for estimate the SAI method. In develop a smart sensor, the SAI method will be useful for finding optical design condition of smart sensor that can sense a material. And then dynamic characteristics of smart sensors(frequency changing, acceleration changing, critical point, etc.) are evaluated respectively through new method(SAI) that use the power spectrum density. Dynamic characteristic of sensor is evaluated with SAI method relatively. We can use the SAI for finding critical point of smart sensor, too.

• Journal of Sensor Science and Technology
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• v.26 no.2
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• pp.135-140
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• 2017

In this paper, a novel measurement method of radial artery pulse waveform using robotic applanation tonometry (RAT) was present to reduce the errors by the pressing direction of the vessel. The RAT consisted of an array of pressure sensors and 2-axis tilt sensor, which was attached to the universal joint with a linear spring and five-DOF robotic manipulator with a one-axis force sensor. Using the RAT mechanism, the pulse sensor could be manipulated to perpendicularly pressurize the radial artery. A pilot experimental result showed that the proposed mechanism could find the optimal pressurization angles of the pulse sensor within ${\pm}3^{\circ}$standard deviations. Coefficient values of variation of maximum pulse peaks extracted from the pulse waveforms were 4.692, 6.994, and 11.039 % for three channels with the highest magnitudes. It is expected that the proposed method can be helpful to develop more precise tonometry system measuring the pulse waveform on the radial artery.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1727-1731
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• 2004

We propose an ART2 neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, fault classifier by ART2 NN (adaptive resonance theory 2 neural network) with uneven vigilance parameters is used for fault isolation. The performances of the proposed fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.