• ETRI Journal
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• v.44 no.3
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• pp.426-437
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• 2022

To understand the multilateral characteristics of human behavior and physiological markers related to physical, emotional, and environmental states, extensive lifelog data collection in a real-world environment is essential. Here, we propose a data collection method using multimodal mobile sensing and present a long-term dataset from 22 subjects and 616 days of experimental sessions. The dataset contains over 10 000 hours of data, including physiological, data such as photoplethysmography, electrodermal activity, and skin temperature in addition to the multivariate behavioral data. Furthermore, it consists of 10 372 user labels with emotional states and 590 days of sleep quality data. To demonstrate feasibility, human activity recognition was applied on the sensor data using a convolutional neural network-based deep learning model with 92.78% recognition accuracy. From the activity recognition result, we extracted the daily behavior pattern and discovered five representative models by applying spectral clustering. This demonstrates that the dataset contributed toward understanding human behavior using multimodal data accumulated throughout daily lives under natural conditions.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.710-713
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• 2008

센서 네트워크는 습도, 온도, 조도 등의 다양한 정보를 수집할 수 있는 센서들을 특정한 지역이나 광범위한 지역에 분포하여 특정 이벤트를 탐지하거나 계속적으로 환경을 관찰하여 수집된 정보를 효율적으로 Base Station으로 전송하는 일종의 애드 혹 네트워크이다. 본 논문은 센서 네트워크의 라우팅 프로토콜 중 PEGASIS와 동심원 형태의 클러스터링 방법에 대해 취약점을 알아보고, 이를 해결하기 위한 방법으로 클러스터 헤드 선출을 위한 두 가지 기준을 정하고, 퍼지 이론을 기반으로 적절한 선택 값을 도출하여 효율적인 클러스터 헤드를 선출하는 방법을 제안한다. 이 방법은 각 센서 노드들의 남아있는 에너지를 고려할 수 있으며, 각 레벨에서 클러스터 헤드들은 가깝게 위치하게 되어 Multi-hop으로 데이터 전송 시 기존의 방법들보다 전송 거리를 줄일 수 있는 장점을 가지고 있다.

• Journal of Food Hygiene and Safety
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• v.35 no.5
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• pp.430-437
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• 2020

This is an approach study on the sensory properties (taste and odor) of 15 types of Korean conventional soups and stews using electronic nose and tongue. The relative sensor intensity for the taste components of the samples using electronic tongue was demonstrated. By SRS (sourness) sensor, sogogi-baechuguk (beef and cabbage soup) had the highest rate of 9.0. The STS (saltiness) sensor showed the highest score of 8.2 for ojingeoguk (squid soup). For the UMS (umami) sensor, which identifies savoriness, the sogogi-baechuguk was the highest at 10.1. The SWS (sweetness) sensors showed relatively little difference, with sigeumchi-doenjangguk (spinach and bean paste soup) at the highest of 7.3. According to the BRS sensor, which tests for bitterness, the siraegi-doenjangguk (dried radish green and bean paste soup) was the highest at 7.8. By principal component analysis (PCA), we observed variances of 56.21% in principal component 1 (PC1) and 25.23% in PC2. For each flavor component, we observed -0.95 and -0.20 for factor loading of PC1 and PC2 for SRS sensors, 0.96 and 0.14 for STS sensors, and -0.94 and 0.22 for PC1 and PC2 for UMS sensors, and PC1 and 0.22 for PC1 and PC2 loading for SWS sensors. The similarity between the samples identified by clustering analysis was largely identified by 4 clusters. A total of 25 kinds of volatile compounds in 15 samples were identified, and the ones showing the highest relative content in all samples were identified as ethanol and 2-methylthiophhene. The main ingredient analysis confirmed variances of 28.54% in PC1 and 20.80% in PC2 as a result of the pattern for volatile compounds in 15 samples. In the cluster analysis, it was found to be largely classified into 3 clusters. The data in this study can be used for a sensory property database of conventional Korean soups and stews using electronic sensors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.131-147
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• 2020

RFID (Radio Frequency Identification) identifies a specific object by radio signals. As the tag provides a unique ID for the purpose of identification, RFID technology effectively solves the ambiguity and occlusion problem that challenges the laser or camera-based approach. This paper proposes an approach to track a moving object based on the integration of RFID and laser ranging information using a particle filter. To be precise, we split laser scan points into different clusters which contain the potential moving objects and calculate the radial velocity of each cluster. The velocity information is compared with the radial velocity estimated from RFID phase difference. In order to achieve the positioning of the moving object, we select a number of K best matching clusters to update the weights of the particle filter. To further improve the positioning accuracy, we incorporate RFID signal strength information into the particle filter using a pre-trained sensor model. The proposed approach is tested on a SCITOS service robot under different types of tags and various human velocities. The results show that fusion of signal strength and laser ranging information has significantly increased the positioning accuracy when compared to radial velocity matching-based or signal strength-based approaches. The proposed approach provides a solution for human machine interaction and object tracking, which has potential applications in many fields for example supermarkets, libraries, shopping malls, and exhibitions.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.7
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• pp.17-24
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• 2012

As the resources and power on Ad-hoc networks are limited, new data aggregation techniques are required for energy efficiency. The current research on data aggregation techniques is actively in progress, but existing studies don't consider the density of nodes. If nodes are densely placed in a particular area, the information which the sensor nodes placed on those areas detect can be judged as very strong association. But, the energy spent transmitting this information is a waste of energy. In this paper the densely-concentrated node area is designated as Hotspot_Zone in the multi-hop clustering environment using the AMC and a key node is selected in the area. If the request message of data aggregation is transmitted, the key node among the neighboring nodes sends its environmental information to a manager to avoid duplicate sensing information. Therefore, the life of networks can be prolonged due to this.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1960-1969
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• 2017

There have been ongoing researches to identify and analyze the patterns of electric power IoT data inside sensor nodes to supplement the stable supply of power and the efficiency of energy consumption. This study set out to propose an analysis process for electric power IoT data with the K-means algorithm, which is an unsupervised learning technique rather than a supervised one. There are a couple of problems with the old K-means algorithm, and one of them is the selection of cluster number K in a heuristic or random method. That approach is proper for the age of standardized data. The investigator proposed an analysis process of selecting an automated cluster number K through principal component analysis and the space division of normal distribution and incorporated it into electric power IoT data. The performance evaluation results show that it recorded a higher level of performance than the old algorithm in the cluster classification and analysis of pitches and rolls included in the communication bodies of utility poles.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.5B
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• pp.801-808
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• 2010

LEACH, one of hierarchical based routing protocols, was proposed for energy efficiency which is the most important requirement of Wireless Sensor Network(WSN). LEACH protocol is composed of a cluster of certain large number of clusters, which have a cluster head and member nodes. Member nodes send sensing data to their cluster heads, and the cluster heads aggregate the sensing data and transmit it to BS. The challenges of LEACH protocol are that cluster heads are not evenly distributed, and energy consumption to transmit aggregated data from Cluster heads directly to BS is excessive. This study, to improve LEACH protocol, suggests LECEEP that transmit data to contiguity cluster head that is the nearest and not far away BS forming chain between cluster head, and then the nearest cluster head from BS transmit aggregated data finally to BS. According to simulation, LECEEP consumes less energy and retains more number of survival node than LEACH protocol.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1797-1801
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• 2005

SLAM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important issues in mobile robot research. Until now expensive sensors like a laser sensor have been used for the mobile robot's localization. Currently, as the RFID reader devices like antennas and RFID tags become increasingly smaller and cheaper, the proliferation of RFID technology is advancing rapidly. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used to identify the mobile robot's location on the smart floor. We discuss a number of challenges related to this approach, such as RFID tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, because the reader just can senses whether a RFID tag is in its sensing area, the localization error occurs as much as the sensing area of the RFID reader. And, until now, there is no study to estimate the pose of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. We use the Markov localization algorithm to reduce the location(X,Y) error and the Kalman Filter algorithm to estimate the pose(q) of a mobile robot. We applied these algorithms in our experiment with our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors like odometers and RFID tags for the mobile robot's localization on the smart floor.

• Journal of Digital Contents Society
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• v.19 no.8
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• pp.1463-1469
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• 2018

The development of multimedia devices with built-in cameras such as smart devices and various studies using video-related multimedia data such as images and video obtained from the devices have been actively conducted. These studies deal with image data. An image can be defined as a set of color information obtained from a digital sensor called a pixel. Images contain various cognitive information such as color, lighting, objects and so on. In order to extract or process such information, it is necessary to clearly understand the composition of colors. In this paper, we introduce 3-dimensional information visualization method which can effectively express the results of image processing together with color distribution. This study visualizes the characteristics of image related multimedia data as well as the characteristics of various analytical data derived from it, so that researchers can transmit the image information more clearly and effectively.