• ETRI Journal
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• v.45 no.6
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• pp.929-938
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• 2023

The detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model-based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data-driven methods perform slightly worse than model-based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning-based method by improving a state-of-the-art data-driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error-propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model-based methods in perfect CSI environments and the best performance in imperfect CSI environments.

• Journal of the Korea Society of Computer and Information
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• v.23 no.8
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• pp.9-16
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• 2018

In this paper, a kidnapping detection scheme in which human pose estimation is used to classify accurately between kidnapping cases and normal ones is proposed. To estimate human poses from input video, human's 10 joint information is extracted by OpenPose library. In addition to the features which are used in the previous study to represent the size change rates and the regularities of human activities, the human pose estimation features which are computed from the location of detected human's joints are used as the features to distinguish kidnapping situations from the normal accompanying ones. A frame-based kidnapping detection scheme is generated according to the selection of J48 decision tree model from the comparison of several representative classification models. When a video has more frames of kidnapping situation than the threshold ratio after two people meet in the video, the proposed scheme detects and notifies the occurrence of kidnapping event. To check the feasibility of the proposed scheme, the detection accuracy of our newly proposed scheme is compared with that of the previous scheme. According to the experiment results, the proposed scheme could detect kidnapping situations more 4.73% correctly than the previous scheme.

• Journal of Multimedia Information System
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• v.1 no.1
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• pp.55-67
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• 2014

Image topic and emotion analysis is an important component of online image retrieval, which nowadays has become very popular in the widely growing social media community. However, due to the gaps between images and texts, there is very limited work in literature to detect one image's Topics and Emotions in a unified framework, although topics and emotions are two levels of semantics that often work together to comprehensively describe one image. In this work, a unified model, Joint Topic/Emotion Multi-Modal Hierarchical Latent Dirichlet Allocation (JTE-MMHLDA) model, which extends previous LDA, mmLDA, and JST model to capture topic and emotion information at the same time from heterogeneous data, is proposed. Specifically, a two level graphical structured model is built to realize sharing topics and emotions among the whole document collection. The experimental results on a Flickr dataset indicate that the proposed model efficiently discovers images' topics and emotions, and significantly outperform the text-only system by 4.4%, vision-only system by 18.1% in topic detection, and outperforms the text-only system by 7.1%, vision-only system by 39.7% in emotion detection.

• Smart Structures and Systems
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• v.26 no.1
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• Journal of the Korea Computer Industry Society
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• v.6 no.2
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• pp.357-364
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• 2005

The prefabricated type used generally in Korea to join cable runs on new installations or to repair broken Cable runs on existing installations, because installation is very simple and save time. This type is a permanent, shielded and submersible cable joint for direct burial or vault application. It confirms to the requirements of IEEE std. 404-1993 by factory testing, but many problems of insulated cable systems are caused by internal defects of the joint part which have to be mounted ensile. Faults arise from impurities or voids. A suitable solution for a monitoring of cable joints during the after-laying test and service is partial discharge detection. Specimen obtained 1mm thickness from the insulation of real power cable and cable joint. <중략>The partial discharges are measured to determine their time dependence for 60 minutes and the influence of applied electrical stress under 30kV. $\Phi-q-n$ properties were measured using detection impedance, high pass filter and computerized data acquisition system. Statistic Value like maximum charge, repetition rate, average charge, etc. are calculated. It is possible to quantitative analysis of $\Phi-q-n$ properties from this statistic value and pattern analysis.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.8 no.4
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• pp.259-265
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• 2014

The aims of this study are to investigate the walking intention detection of a rollator based on Infraed (IR) sensor measuring knee joint anterior displacement and leg muscle activities. We used Active Walker attached IR sensor to measure the knee joint anterior displacement and EMG signal of leg muscles(rectus femoris, biceps femoris, tibialis anterior, gastrocnemius) were taken by Delsys bagnli-8ch. Subjects were eight healthy males(age $23.7{\pm}0.5years$, height $175.4{\pm}2.3cm$, weight $70.6{\pm}5.6kg$) and they were involved in experiments which had been proceeded 30 minutes a week, during 3 weeks. This system indicates that the knee joint anterior displacement had the distinction increases according to the gait slope and velocity. We showed the increase of the femoral muscle activities along the anterior tilt and the increase of the crural muscle activities along the posterior tilt.

• Journal of Acupuncture Research
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• v.31 no.4
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• pp.45-55
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• 2014

Objectives : The main aim of this study was to assess the comparative efficiency of two preparation types of Hwangryunhaedok decoction(HRHD-D) using distilled and mixed extraction by measuring the index components and indicators of antioxidant and anti-inflammatory effects. Methods : The antioxidant activity was assessed by comparing distilled and mixed extractions of HRHD-D using an ELISA reader. The anti-inflammatory effect was determined by measuring NO amounts in RAW 264.7 cells. The contents were analyzed with high performance liquid chromatography-diode array detector(HPLC-DAD). Results : The electron donating ability of mixed and distilled extractions obtained with 500 ppm DPPH(1,1-diphenyl-2-picrylhydrazyl assay) solution were 57.8 % and 4.2 %, respectively. The total phenolic content of mixed extraction was 6.9 times that of distilled extraction and total flavonoid content was 51.5 times higher. The anti-inflammatory effect was assessed by NO measurement, and was found to increase significantly dependent on concentration in all mixed extract concentrations(25, 50, 100, 200, $400{\mu}g/mL$), but the difference in distilled extraction by concentration was only significant at 200 and $400{\mu}g/mL$. The HPLC analysis results of mixed extract of HRHD-D showed detection of all four main active constituents of HRHD-D. However, they were not detected in the distilled extract of HRHD-D. Conclusions : Mixed extraction with distillation added to decoction of HRHD-D showed better efficacy in antioxidant and anti-inflammatory effects, and ingredient quantities compared to distilled extraction. Further stability and clinical efficacy studies for standardization of mixed extractions are required.

• Journal of Advanced Navigation Technology
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• v.6 no.2
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• pp.138-150
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• 2002

This paper addresses the problem of combining adaptive polarization processing and space-time processing for further performance improvement of radar target detection in clutter and Jammer environments. Since the most straightforward cascade combinations have quite limited performance improvement potentials, we focus on the development of adaptive processing in the joint polarization-space-time domain. Unlike a direct extension of some existing space-time processing algorithms to the joint domain, the processing algorithm developed in this paper does not need a potentially costly polarization filter bank to cover the unknown target polarization parameter. The performance of the new algorithm is derived and evaluated in terms of the probability of detection and the probability of false alarm, and it is compared with other algorithms that do not utilize the polarization information or assume that the target polarization is known.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.491-500
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• 2021

In this study, we propose a method for detecting fall behavior using MS Kinect v2 RGBD Camera-based Human-Skeleton Keypoints and a 2-Stacked Bi-LSTM model. In previous studies, skeletal information was extracted from RGB images using a deep learning model such as OpenPose, and then recognition was performed using a recurrent neural network model such as LSTM and GRU. The proposed method receives skeletal information directly from the camera, extracts 2 time-series features of acceleration and distance, and then recognizes the fall behavior using the 2-Stacked Bi-LSTM model. The central joint was obtained for the major skeletons such as the shoulder, spine, and pelvis, and the movement acceleration and distance from the floor were proposed as features of the central joint. The extracted features were compared with models such as Stacked LSTM and Bi-LSTM, and improved detection performance compared to existing studies such as GRU and LSTM was demonstrated through experiments.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.57-65
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• 2023

A vibration response-based detection system was used to investigate the adhesive areas of single-lap joints using a nonlinear transformation approach for deep learning. In industry or engineering fields, it is difficult to know the condition of an invisible part within a structure that cannot easily be disassembled and the conditions of adhesive areas of adhesively bonded structures. To address these issues, a detection method was devised that uses nonlinear transformation to determine the adhesive areas of various single-lap-jointed specimens from the vibration response of the reference specimen. In this study, a frequency response function with nonlinear transformation was employed to identify the vibration characteristics, and a virtual spectrogram was used for classification in convolutional neural network based deep learning. Moreover, a vibration experiment, an analytical solution, and a finite-element analysis were performed to verify the developed method with aluminum, carbon fiber composite, and ultra-high-molecular-weight polyethylene specimens.