• Journal of KIISE:Software and Applications
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• v.34 no.10
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• pp.910-917
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• 2007

Of the possible interactions between human and robot, touch is an important means of providing human beings with emotional relief. However, most previous studies have focused on interactions based on voice and images. In this paper. a method of recognizing human touching behaviors is proposed for developing a robot that can naturally interact with humans through touch. In this method, the recognition process is divided into pre-process and recognition Phases. In the Pre-Process Phase, recognizable characteristics are calculated from the data generated by the touch detector which was fabricated using force sensors. The force sensor used an FSR (force sensing register). The recognition phase classifies human touching behaviors using a multi-layer perceptron which is a neural network model. Experimental data was generated by six men employing three types of human touching behaviors including 'hitting', 'stroking' and 'tickling'. As the experimental result of a recognizer being generated for each user and being evaluated as cross-validation, the average recognition rate was 82.9% while the result of a single recognizer for all users showed a 74.5% average recognition rate.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.19-25
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• 2021

This study proposes a digital twin implementation method through simulation so that the manufacturing process can be optimized in a manual manufacturing site. The scope of the proposal is a knowledge management mechanism that collects manual motion with a sensor and optimizes the manufacturing process with repetitive experimental data for motion recognition. In order to achieve the research purpose, a simulation of the distribution site was conducted, and a plan to create an optimized digital twin was prepared by repeatedly experiencing the work simulation based on the basic knowledge expressed by the worker's experience. As a result of the experiment, it was found that it is possible to continuously improve the manufacturing process by transmitting the result of configuring the optimized resources to the physical system by generating the characteristics of the work space configuration and working step within a faster time with the simulation that creates the digital twin.

• Smart Structures and Systems
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• v.20 no.1
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• pp.35-42
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• 2017

Recently, compressive sensing based data loss recovery techniques have become popular for Structural Health Monitoring (SHM) applications. These techniques involve an encoding process which is onerous to sensor node because of random sensing matrices used in compressive sensing. In this paper, we are presenting a model where the sampled raw acceleration data is directly transmitted to base station/receiver without performing any type of encoding at transmitter. The received incomplete acceleration data after data losses can be reconstructed faithfully using compressive sensing based reconstruction techniques. An in-depth simulated analysis is presented on how random losses and continuous losses affects the reconstruction of acceleration signals (obtained from a real bridge). Along with performance analysis for different simulated data losses (from 10 to 50%), advantages of performing interleaving before transmission are also presented.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.11-13
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• 2007

There has been much interest in stream query processing. Various index techniques and advanced join techniques have been proposed to efficiently process data stream queries. Previous proposals support rapid and advanced response to the data stream queries. However, the amount of data stream is increasing and the data stream query processing needs more speedup than before. In this paper, we proposed novel query processing techniques for large number of incoming documents stream. We proposed Dissection Technique for efficient query processing in the data stream environment. We focused on the dissection technique in join query processing. Our technique shows efficient operation performance comparing with the other proposal in the data stream. Proposed technique is applied to the sensor network system and XML database.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.22-28
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• 2021

Multivariate processes, such as chemical and mechanical process, power plants are operated in a state where several facilities are complexly connected, the fault of a particular system can also have fatal consequences for the entire process. In addition, since process data is measured in an unstable environment, outlier is likely to be include in the data. Therefore, monitoring technology is essential, which can remove outlier from measured data and detect failures in advance. In this paper, data obtained from dynamic and multivariate process models was used to detect fault in various type of processes. The dynamic process is a simulation of a process with autoregressive property, and the multivariate process is a model that describes a situation when a specific sensor fault. Mahalanobis distance was used to remove outlier contained in the data generated by dynamic process model and multivariate process model, and fault detection was performed using ICA. For comparison, we compared performance with and a conventional single ICA method. The proposed fault detection method improves performance by 0.84%p for bias data and 6.82%p for drift data in the dynamic process. In the case of the multivariate process, the performance was improves by 3.78%p, therefore, the proposed method showed better fault detection performance.

• Proceedings of the Korean Society of Crop Science Conference
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• 2006.04a
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• pp.40-54
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• 2006

It's not easy to detect the high quality and functional compounds for control quality of food materials. The electronic nose was an instrument, which comprised of an array of electronic chemical sensors with partial specificity and an appropriate pattern recognition system, capable of recognizing simple or complex odors. It can conduct fast analysis and provide simple and straightforward results and is best suited for quality control and process monitoring in the field of functional foods. Numbers of applications of an electronic nose in the functional food industry include discrimination of habitats for medicinal food materials, monitoring storage process, lipid oxidation, and quality control of food and/or processing with principal component analysis, neural network analysis and the electronic nose based on GC-SAW sensor. The electronic nose would be possibly useful for a wide variety of quality control in the functional food and plant cultivation when correlating traditional analytical instrumental data with sensory evaluation results or electronic nose data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.117-126
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• 2019

The data generated in the IoT environment is very diverse. Especially, the development of the fourth industrial revolution has made it possible to increase the number of fixed and unstructured data generated in manufacturing facilities such as Smart Factory. With Big Data related solutions, it is possible to collect, store, process, analyze and visualize various large volumes of data quickly and accurately. Therefore, in this paper, we will directly generate data using Raspberry Pi used in IoT environment, and analyze using various Big Data solutions. Collected by using an Sqoop solution collected and stored in the database to the HDFS, and the process is to process the data by using the solutions available Hive parallel processing is associated with Hadoop. Finally, the analysis and visualization of the processed data via the R programming will be used universally to end verification.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.6
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• pp.547-553
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• 2020

As aging infrastructures increase along with time, the efficient maintenance becomes more significant and accurate responses from the sensors are pre-requisite. Among various responses, strain is commonly used to detect damage such as crack and fatigue. Optical fiber sensor is one of the promising sensing techniques to measure strains with high-durability, immunity for electrical noise, long transmission distance. Fiber Bragg Grating (FBG) is a point sensor to measure the strain based on reflected signals from the grating, while Brillouin Optic Correlation Domain Analysis (BOCDA) is a distributed sensor to measure the strain along with the optical fiber based on scattering signals. Although the FBG provides the signal with high accuracy and reproducibility, the number of sensing points is limited. On the other hand, the BOCDA can measure a quasi-continuous strain along with the optical fiber. However, the measured signals from BOCDA have low accuracy and reproducibility. This paper proposed a multi-fidelity data-fusion method based on Gaussian Process Regression to improve the fidelity of the strain distribution by fusing the advantages of both systems. The proposed method was evaluated by laboratory test. The result shows that the proposed method is promising to improve the fidelity of the strain.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.335-342
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• 2014

According to the recent trends in the growing elderly population, the chronically ill have increased. Thus the importance of the health care issues for them has emerged. In this paper, we want to implement a chronic disease management system using smart mobile devices. Proposed chronic disease management system is consisted of the biometric sensor, smart mobile devices, the patient management server, patient management DB, and patient symptoms analysis agent. The biometric sensor detects a biological information. Smart mobile devices receive the patient information from the sensor and transmit the information to the patient management server. The patient management server, patient management DB, and patient symptoms agent analysis agent analyze to process data delivered through a wireless communication network. Bio-signals includes modules of ECG, blood pressure, blood sugar and PPG. We are able to determine the current health status by monitoring measured biometric data through chronically ill health management system. We will focus on the individual service to be appropriate for a patient group in a mobile environment.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.122-132
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• 2010

The analysis of ocean environment offers us essential information for ocean exploration. But ocean environment has a lot of environmental variables such as the movements of nodes by an ocean current, corrosion by salt water, attenuation of radio wave, occurrences of multi-path and difficulty of sensor nodes' deployment. It is accordingly difficult and complex to gather and process the environmental information through ocean data communication due to these constraints of ocean environment unlike the terrestrial wireless networks. To overcome these problems, we organized ocean communication network for monitoring underwater environment by real experiment in Gyeongpoho similar to ocean environment. Therefore, this paper aims at overcoming major obstacles in ocean environment, effectively deploying sensor nodes for ocean environment monitoring and defining an efficient structure suitable for communication environment by the implementation of ocean environment monitoring system in Gyeongpoho.