• Journal of Information Processing Systems
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• v.16 no.3
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• pp.612-628
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• 2020

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1373-1387
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• 2021

Forest fire poses a significant threat to the environment and society, affecting carbon cycle and surface energy balance, and resulting in socioeconomic losses. Widely used multi-spectral satellite image-based approaches for burned area detection have a problem in that they do not work under cloudy conditions. Therefore, in this study, Sentinel-1 Synthetic Aperture Radar (SAR) data from Europe Space Agency, which can be collected in all weather conditions, were used to identify forest fire damaged area based on a series of processes including Principal Component Analysis (PCA) and K-means clustering. Four forest fire cases, which occurred in Gangneung·Donghae and Goseong·Sokcho in Gangwon-do of South Korea and two areas in North Korea on April 4, 2019, were examined. The estimated burned areas were evaluated using fire reference data provided by the National Institute of Forest Science (NIFOS) for two forest fire cases in South Korea, and differenced normalized burn ratio (dNBR) for all four cases. The average accuracy using the NIFOS reference data was 86% for the Gangneung·Donghae and Goseong·Sokcho fires. Evaluation using dNBR showed an average accuracy of 84% for all four forest fire cases. It was also confirmed that the stronger the burned intensity, the higher detection the accuracy, and vice versa. Given the advantage of SAR remote sensing, the proposed statistical processing and K-means clustering-based approach can be used to quickly identify forest fire damaged area across the Korean Peninsula, where a cloud cover rate is high and small-scale forest fires frequently occur.

• Journal of Plant Biotechnology
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• v.38 no.1
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• pp.9-14
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• 2011

To determine whether pattern recognition based on metabolite fingerprinting for whole cell extracts can be used to discriminate cultivars metabolically, leaves of nine commercial Orostachys plants were subjected to Fourier transform infrared spectroscopy (FT-IR). FT-IR spectral data from leaves were analyzed by principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA). The dendrogram based on hierarchical clustering analysis of these PLS-DA data separated the nine Orostachys species into five major groups. The first group consisted of O. iwarenge 'Yimge', 'Jeju', 'Jeongsun' and O. margaritifolius 'Jinju' whereas in the second group, 'Sacheon' was clustered with 'Busan,' both of which belong to O. malacophylla species. However, 'Samchuk', belong to O. malacophylla was not clustered with the other O. malacophylla species. In addition, O. minuta and O. japonica were separated to the other Orostachys plants. Thus we suggested that the hierarchical dendrogram based on PLS-DA of FT-IR spectral data from leaves represented the most probable chemotaxonomical relationship between commercial Orostachys plants. Furthermore these metabolic discrimination systems could be applied for reestablishment of precise taxonomic classification of commercial Orostachys plants.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.13-18
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• 2016

In Long Term Evolution-Advanced (LTE-A), small cell enhancement(SCE) has been developed as a cost-effective way of supporting exponentially increasing demand of wireless data services and satisfying the user quality of service(QoS). However, there are many problems such as the transmission rate and transmission quality degradation due to the dense and irregular distribution of a large number of small cells. In this paper, we propose a clustering based interference management scheme in dense small cell network. We divide the small cells into different clusters according to the reference signal received power(RSRP) from user equipment(UE). Within a cluster, an almost blank subframe(ABS) is implemented to mitigate interference between the small cells. In addition, we apply the power control to reduce the interference between the clusters. Simulation results show that proposed scheme can improve Signal to Interference plus Noise Ratio(SINR), throughput, and spectral efficiency of small cell users. Eventually, proposed scheme can improve overall cell performance.

• The KIPS Transactions:PartC
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• v.13C no.2 s.105
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• pp.185-194
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• 2006

In this paper, we first propose a power-and-bandwidth efficient cooperative transmission protocol where a sensor node assists two others for their data transmission to a clusterhead in WSNs (Wireless Sensor Networks) using LEACH (Low-Energy Adaptive Clustering Hierarchy). Then we derive its closed-form BER expression which Is also a general BER one for the decode-and-forward protocol (DF) and Prove that the proposed protocol performs as same as the conventional DF but obtains higher spectral efficiency. A variety of numerical results reveal the cooperation can save the network power up to 11dB over direct transmission at BER of $10^{-3}$.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.1 s.106
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• pp.27-33
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• 2006

Fault diagnosis and condition monitoring for rotating machines are important for efficiency and accident prevention. The process of fault diagnosis is to extract the feature of signals and to classify each state. Conventionally, fault diagnosis has been developed by combining signal processing techniques for spectral analysis and pattern recognition, however these methods are not able to diagnose correctly for certain rotating machines and some faulty phenomena. In this paper, we add a minimum detection error algorithm to the previous method to reduce detection error rate. Vibration signals of the induction motor are measured and divided into subband signals. Each subband signal is processed to obtain the RMS, standard deviation and the statistic data for constructing the feature extraction vectors. We make a study of the fault diagnosis system that the feature extraction vectors are applied to K-means clustering algorithm and minimum detection error algorithm.

• Journal of Acupuncture Research
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• v.38 no.4
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• pp.300-311
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• 2021

Background: So far there is no confidence in the basics of acupoint/meridian phenomena, specifically in spatial and temporal electrical manifestations in the skin. Methods: Using the skin electrodynamic introscopy, the skin areas of 32 × 64 mm² were monitored for spectral electrical impedance landscape with spatial resolution of 1 mm, at 2 kHz and 1 MHz frequencies. The detailed baseline and 2D test-induced 2 kHz-impedance phase dynamics and the 4-parameter time plots of dozens of individual points in the St32-34 regions were examined in a healthy participant and a patient with mild gastritis. Non-thermal stimuli were used: (1) (for the sick subject), microwaves and ultraviolet radiation applied alternately from opposite directions of the meridian; and (2) (for the healthy one) microwaves to St17, and cathodic/anodic stimulation of the outermost St45, alternately. Results: In both cases, the following phenomena have been observed: emergence of in-phase and/or antiphase coherent structures, exceeding the acupoint conditional size of 1 cm; collective movement along the meridian; reversible with a reversed stimulus; counter-directional dynamics of both whole structures and adjacent points; local abnormalities in sensitivity and dynamics of the 1 MHz and 2 kHz parameters indicating existence of different waveguide paths. Conclusion: It is assumed that these findings necessitate reconsideration of some basic methodological issues regarding neurogenic/acupuncture points as spatial and temporal phenomena; this requires development of an appropriate approach for identifying the acuzones patterns. These findings may be used for developing new approaches to personalized/controlled therapy/treatment.

• 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.

• Journal of Plant Biotechnology
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• v.43 no.3
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• pp.383-390
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• 2016

To determine whether FT-IR spectral analysis based on multivariate analysis could be used to discriminate Chinese cabbage breeding line at metabolic level, whole cell extracts of nine different breeding lines (three paternal, three maternal and three $F_1$ lines) were subjected to Fourier transform infrared spectroscopy (FT-IR). FT-IR spectral data of Chinese cabbage plants were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA). The hierarchical dendrograms based on PLS-DA from two of three cross combinations showed that paternal, maternal, and their progeny $F_1$ lines samples were perfectly separated into three branches in breeding line dependent manner. However, a cross combination failed to fully discriminate them into three branches. Thus, hierarchical dendrograms based on PLS-DA of FT-IR spectral data of Chinese cabbage breeding lines could be used to represent the most probable chemotaxonomical relationship among maternal, paternal, and $F_1$ plants. Furthermore, these metabolic discrimination systems could be applied for rapid selection and classification of useful Chinese cabbage cultivars.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.111-124
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• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.