• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.8
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• pp.682-685
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• 2011

An engine oil sensor based on multiwall carbon nanotubes was fabricated with screen printing method. Since carbon nanotubes are generally intertwined, dispersion of the carbon nanotubes in the binding agent (ethyl cellulose, a-terpineol, frit) is a key factor for large yield of engine oil sensor. By conventional dispersion method, a hand-mill method, the maximum yield was 80% at most. However, we used the hand ultrasonic, in order to increase the yield of the sensors. As a results, our engine oil sensor fabricated by the screen printing method shows excellent yield rate of 97%, when we dispersed a paste by the hand ultrasonic method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10B
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• pp.894-903
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• 2008

The position of sensors generally affects coverage, communication costs, and resource management of surveillance sensor networks. Thus we are required to place a sensor in the best location. However, it is difficult to consider that terrain and climate factors influencing sensors when sensor nodes are deployed in the real world, such as a mountain area or a downtown area. We therefore require a sensor deployment method for detecting effectively targets of interest in terms of surveillance area coverage in such environment. Thus in this paper, we analyze various environmental factors related to sensor deployment, and quantify these factors to use when we deploy sensors. By considering these quantified factors, we propose a practical and effective method for deploying sensors in terms of sensing coverage. We also demonstrate the propriety of the proposed method through implementing a sensor deployment management system according to the method.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.163-166
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• 2000

This paper describes the principle of humidity sensor using high frequency resistance method for Hopper-Scale that is used in RPC(Rice processing Complex) which is spreaded out in domestic, and we also understand the principle and specificity of controller and humidity sensor. After artworking the humidity sensor and controller circuit, we measure the humidity of the designed system. In this progress, we suggested substitute parts which are easy to get in domestic and also we could propose correct method of humidity detection.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.6
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• pp.323-334
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• 2014

In this paper, we proposed indoor positioning system with improved accuracy. The proposed indoor location measurement system is based pedestrian location measurement method that use the embedded sensor of smartphone. So, we do not need wireless external resources, such as GPS or WiFi signals. The conventional methods measure indoor location by generating a movement route of pedestrian by step and direction recognition. In this paper, to correct the direction sensor error, we use the common feature of the normal indoor floor map that the indoor path is lattice-structured. And we quantize moving directions depending on the direction of indoor path. In addition, we propose moving direction measuring method using geomagnetic sensor and gyro sensor to improve the accuracy. Also, the proposed step detection method uses angle and accelerometer sensors. The proposed step detection method is not affected by the posture of the smartphone. Direction errors caused by direction sensor error is corrected due to proposed moving direction measuring method. The proposed location error correction method corrects location error caused by step detection error without the need for external wireless signal resources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1618-1637
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• 2018

By means of compressive sensing (CS) technique, this paper considers the collection of sensor data with spatiotemporal correlations in wireless sensor networks (WSNs). In energy-constrained WSNs, one-dimensional CS methods need a lot of data transmissions since they are less applicable in fully exploiting the spatiotemporal correlations, while the Kronecker CS (KCS) methods suffer performance degradations when the signal dimension increases. In this paper, an appropriate sensing matrix as well as an efficient sensing method is proposed to further reduce the data transmissions without the loss of the recovery performance. Different matrices for the temporal signal of each sensor node are separately designed. The corresponding energy-efficient data gathering method is presented, which only transmitting a subset of sensor readings to recover data of the entire WSN. Theoretical analysis indicates that the sensing structure could have the relatively small mutual coherence according to the selection of matrix. Compared with the existing spatiotemporal CS (CS-ST) method, the simulation results show that the proposed efficient measurement method could reduce data transmissions by about 25% with the similar recovery performance. In addition, compared with the conventional KCS method, for 95% successful recovery, the proposed sensing structure could improve the recovery performance by about 20%.

• International journal of advanced smart convergence
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• v.6 no.1
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• pp.50-56
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• 2017

Wireless sensor nodes have limited energy, so it is important to optimize energy consumption to preserve network lifetime. Various protocols have been proposed for this purpose. LEACH protocol and SEP are the representative protocols. These protocols become less effective as the Sensor Field becomes wider. To improve this, MR-SEP and L-SEP were proposed. These protocols increase the energy efficiency by dividing the Sensor Field into layers and reducing the transmission distance. However, when dividing a layer, there are cases where it is divided inefficiently, and a node within a certain range from a Base Station has a better transmission efficiency than a direct transmission method using a cluster method. In this paper, we propose a Single-hop layer for L-SEP to improve inefficient layer division and near node transmission efficiency. When the larger the Sensor Field, the better the performance of the proposed method by up to 87%. The larger the sensor field, the more efficient the proposed method is over the conventional method. That is, the proposed method is suitable for the wide Sensor Field.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.3
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• pp.73-81
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• 2006

This paper describes our primary study for a new method of recognizing materials, which is need for precision work system. This is a study of dynamic characteristics of smart sensors, new method$(R_{SAI})$ has the sensing ability of distinguishing materials. Experiment and analysis are executed for finding the proper dynamic sensing condition. First, we developed advanced smart sensor. We made smart sensors for experiment. The type of smart sensor is HH type. The smart sensor was developed for recognition of material. Second, we develop new estimation methods that have a sensing ability of distinguish materials. Dynamic characteristics of sensor are evaluated through new recognition index$(R_{SAI})$ that ratio of sensing ability index. Distinguish of object is executed with $R_{SAI}$ method relatively. We can use the $R_{SAI}$ 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.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.633-642
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• 2016

It is important for advanced active safety systems and autonomous driving cars to get the accurate estimates of the nearby vehicles in order to increase their safety and performance. This paper proposes a sensor fusion method for radar and vision sensors to accurately estimate the state of the preceding vehicles. In particular, we performed a study on compensating for the lateral state error on automotive radar sensors by using a vision sensor. The proposed method is based on the Interactive Multiple Model(IMM) algorithm, which stochastically integrates the multiple Kalman Filters with the multiple models depending on lateral-compensation mode and radar-single sensor mode. In addition, a Probabilistic Data Association Filter(PDAF) is utilized as a data association method to improve the reliability of the estimates under a cluttered radar environment. A two-step correction method is used in the Kalman filter, which efficiently associates both the radar and vision measurements into single state estimates. Finally, the proposed method is validated through off-line simulations using measurements obtained from a field test in an actual road environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.870-872
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• 2007

Sensor networks have been widely applied for data collection. Due to the energy limitation of the sensor nodes and the most energy consuming data transmission, we should allocate as much work as possible to the sensors, such as data compression and aggregation, to reduce data transmission and save energy. Querying extreme values is a general query type in wireless sensor networks. In this paper, we propose a novel querying method called Two-Level Filter (TLF) for querying extreme values in wireless sensor networks. We first divide the whole sensor network into domains using the Distributed Data Aggregation Model (DDAM). The sensor nodes report their data to the cluster heads using push method. The advantages of two-level filter lie in two aspects. When querying extreme values, the number of pull operations has the lower boundary. And the query results are less affected by the topology changes of the wireless sensor network. Through this method, the sensors preprocess the data to share the burden of the base station and it combines push and pull to be more energy efficient.

• Journal of the Korean Society of Industry Convergence
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• v.10 no.2
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• pp.105-113
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• 2007

Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of application. Sensor position is used for its data to be meaningful and for energy efficient data routing algorithm especially geographic routing. The previous works for sensor localization utilize global positioning system(GPS) or estimate unknown-location nodes position with help of some small reference nodes which know their position previously. However, the traditional localization techniques are not well suited in the senor network for the cost of sensors is too high. In this paper, we propose the sensor localization method with a mobile robot, which knows its position, moves through the sensing field along pre-scheduled path and gives position information to the unknown-location nodes through wireless channel to estimate their position. We suggest using the sensor position estimation method and an efficient mobility path model. To validate our method, we carried out a computer simulation, and observed that our technique achieved sensor localization more accurately and efficiently than the conventional one.