• Title/Summary/Keyword: Correlation Network

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Determination of Road Image Quality Using Fuzzy-Neural Network (퍼지신경망을 이용한 도로 영상의 양불량 판정)

  • 이운근;백광렬;이준웅
    • Journal of Institute of Control, Robotics and Systems
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    • v.8 no.6
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    • pp.468-476
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    • 2002
  • The confidence of information from image processing depends on the original image quality. Enhancing the confidence by an algorithm has an essential limitation. Especially, road images are exposed to lots of noisy sources, which makes image processing difficult. We, in this paper, propose a FNN (fuzzy-neural network) capable oi deciding the quality of a road image prior to extracting lane-related information. According to the decision by the FNN, road images are classified into good or bad to extract lane-related information. A CDF (cumulative distribution function), a function of edge histogram, is utilized to construct input parameters of the FNN, it is based on the fact that the shape of the CDF and the image quality has large correlation. Input pattern vector to the FNN consists of ten parameters in which nine parameters are from the CDF and the other one is from intensity distribution of raw image. Correlation analysis shows that each parameter represents the image quality well. According to the experimental results, the proposed FNN system was quite successful. We carried out simulations with real images taken by various lighting and weather conditions and achieved about 99% successful decision-making.

Precoder Distribution and Adaptive Codebook in Wideband Precoding

  • Long, Hang;Kim, Kyeong Jin;Xiang, Wei;Wang, Jing;Liu, Yuanan;Wang, Wenbo
    • ETRI Journal
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    • v.34 no.5
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    • pp.655-665
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    • 2012
  • Based on wideband precoding (WBP) in the multiple-input multiple-output orthogonal frequency division multiplexing system, an adaptive nonuniform codebook is presented in this paper. The relationship between the precoder distribution and spatial correlation is analyzed at first. A closed-form expression based on overlapped isosceles triangles is proposed as an approximation of the precoder distribution. Then, the adaptive codebook design is derived with the approximate distribution to minimize quantization errors. The capacity and bit error rate performance demonstrate that the adaptive codebook with WBP outperforms the conventional fixed uniform codebook.

A Framework for Wide-area Monitoring of Tree-related High Impedance Faults in Medium-voltage Networks

  • Bahador, Nooshin;Matinfar, Hamid Reza;Namdari, Farhad
    • Journal of Electrical Engineering and Technology
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    • v.13 no.1
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    • pp.1-10
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    • 2018
  • Wide-area monitoring of tree-related high impedance fault (THIF) efficiently contributes to increase reliability of large-scaled network, since the failure to early location of them may results in critical lines tripping and consequently large blackouts. In the first place, this wide-area monitoring of THIF requires managing the placement of sensors across large power grid network according to THIF detection objective. For this purpose, current paper presents a framework in which sensors are distributed according to a predetermined risk map. The proposed risk map determines the possibility of THIF occurrence on every branch in a power network, based on electrical conductivity of trees and their positions to power lines which extracted from spectral data. The obtained possibility value can be considered as a weight coefficient assigned to each branch in sensor placement problem. The next step after sensors deployment is to on-line monitor based on moving data window. In this on-line process, the received data window is evaluated for obtaining a correlation between low frequency and high frequency components of signal. If obtained correlation follows a specified pattern, received signal is considered as a THIF. Thereafter, if several faulted section candidates are found by deployed sensors, the most likely location is chosen from the list of candidates based on predetermined THIF risk map.

Audio and Video Bimodal Emotion Recognition in Social Networks Based on Improved AlexNet Network and Attention Mechanism

  • Liu, Min;Tang, Jun
    • Journal of Information Processing Systems
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    • v.17 no.4
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    • pp.754-771
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    • 2021
  • In the task of continuous dimension emotion recognition, the parts that highlight the emotional expression are not the same in each mode, and the influences of different modes on the emotional state is also different. Therefore, this paper studies the fusion of the two most important modes in emotional recognition (voice and visual expression), and proposes a two-mode dual-modal emotion recognition method combined with the attention mechanism of the improved AlexNet network. After a simple preprocessing of the audio signal and the video signal, respectively, the first step is to use the prior knowledge to realize the extraction of audio characteristics. Then, facial expression features are extracted by the improved AlexNet network. Finally, the multimodal attention mechanism is used to fuse facial expression features and audio features, and the improved loss function is used to optimize the modal missing problem, so as to improve the robustness of the model and the performance of emotion recognition. The experimental results show that the concordance coefficient of the proposed model in the two dimensions of arousal and valence (concordance correlation coefficient) were 0.729 and 0.718, respectively, which are superior to several comparative algorithms.

Mediating Effects of Interpersonal Problems in the Relationship between Social Network Service Use Tendency and Depression among University Students (대학생의 소셜 네트워크 서비스(Social Network Service) 중독경향성과 우울의 관계에서 대인관계 문제의 매개효과)

  • Park, Min-Jeong;Chung, Mi Young
    • Research in Community and Public Health Nursing
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    • v.30 no.1
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    • pp.38-46
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    • 2019
  • Purpose: The purpose of this study isto identify the mediating effects of interpersonal problems in the relationship between Social Network Service (SNS) use tendency and depression among university students. Methods: Data were collected from April 28 to May 7, 2018 and the participants included 222 university students, who responded to the question regarding SNS use tendency, interpersonal problems and depression. The data were analyzed by descriptive statistics, t-test, ANOVA, Pearson's correlation coefficients, and multiple regression using the SPSS/WIN 23.0 program. Results: A positive correlation is found between depression and SNS use tendency (r=.24, p<.001), and among interpersonal problems (r=.62, p<.001), SNS use tendency and interpersonal problems (r=.34, p<.001). Interpersonal problems have a full mediating effect on the relationship between SNS use tendency and depression (Sobel test: 5.24, p<.001). Conclusion: These results suggest that it is important to manage interpersonal problems to prevent depression caused by SNS use tendency.

Application of the machine learning technique for the development of a condensation heat transfer model for a passive containment cooling system

  • Lee, Dong Hyun;Yoo, Jee Min;Kim, Hui Yung;Hong, Dong Jin;Yun, Byong Jo;Jeong, Jae Jun
    • Nuclear Engineering and Technology
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    • v.54 no.6
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    • pp.2297-2310
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    • 2022
  • A condensation heat transfer model is essential to accurately predict the performance of the passive containment cooling system (PCCS) during an accident in an advanced light water reactor. However, most of existing models tend to predict condensation heat transfer very well for a specific range of thermal-hydraulic conditions. In this study, a new correlation for condensation heat transfer coefficient (HTC) is presented using machine learning technique. To secure sufficient training data, a large number of pseudo data were produced by using ten existing condensation models. Then, a neural network model was developed, consisting of a fully connected layer and a convolutional neural network (CNN) algorithm, DenseNet. Based on the hold-out cross-validation, the neural network was trained and validated against the pseudo data. Thereafter, it was evaluated using the experimental data, which were not used for training. The machine learning model predicted better results than the existing models. It was also confirmed through a parametric study that the machine learning model presents continuous and physical HTCs for various thermal-hydraulic conditions. By reflecting the effects of individual variables obtained from the parametric analysis, a new correlation was proposed. It yielded better results for almost all experimental conditions than the ten existing models.

Collaborative filtering by graph convolution network in location-based recommendation system

  • Tin T. Tran;Vaclav Snasel;Thuan Q. Nguyen
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.18 no.7
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    • pp.1868-1887
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    • 2024
  • Recommendation systems research is a subfield of information retrieval, as these systems recommend appropriate items to users during their visits. Appropriate recommendation results will help users save time searching while increasing productivity at work, travel, or shopping. The problem becomes more difficult when the items are geographical locations on the ground, as they are associated with a wealth of contextual information, such as geographical location, opening time, and sequence of related locations. Furthermore, on social networking platforms that allow users to check in or express interest when visiting a specific location, their friends receive this signal by spreading the word on that online social network. Consideration should be given to relationship data extracted from online social networking platforms, as well as their impact on the geolocation recommendation process. In this study, we compare the similarity of geographic locations based on their distance on the ground and their correlation with users who have checked in at those locations. When calculating feature embeddings for users and locations, social relationships are also considered as attention signals. The similarity value between location and correlation between users will be exploited in the overall architecture of the recommendation model, which will employ graph convolution networks to generate recommendations with high precision and recall. The proposed model is implemented and executed on popular datasets, then compared to baseline models to assess its overall effectiveness.

Coexistence of OSCR-Based IR-UWB System with IEEE 802.11a WLAN

  • Wu, Weiwei;Huang, Han;Yin, Huarin;Wang, Weidong;Wang, Dong-Jin
    • ETRI Journal
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    • v.28 no.1
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    • pp.91-94
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    • 2006
  • Impulse radio (IR) is a competitive candidate for ultra-wideband (UWB) systems. In this letter, we evaluated the coexistence of an IR-UWB system based on an orthogonal sinusoidal correlation receiver (OSCR) with an IEEE 802.11a WLAN through a detailed simulation. The coexistence performance of the two systems is characterized in terms of the receiver's bit-error rates. Then, some approaches to interference mitigation are discussed.

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The influence of employees' social networks on organization's communication and innovativeness (조직원의 사회적 네트워크가 의사소통 및 혁신능력에 미치는 영향)

  • Jin, Dongcheol;Hong, Ah Jeong
    • Knowledge Management Research
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    • v.13 no.2
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    • pp.1-18
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    • 2012
  • This article describes how attributes of social network contribute to enhance innovativeness of corporation which is considered to be one of the competitive factors in this global market. The study especially focused on employees' formal and informal communication that is expected to play a mediating role between the factors. 211 employees were randomly selected to participate in an online survey. The result has shown that the static correlation exists between social network, communication, and innovativeness. Closeness of social network was the only influencing factor on communication and innovativeness, and had a partial mediated effect between social network and innovativeness. Based on the suggested contribution for HRD intervention, various communication channels should be developed and supported in order to enhance innovation among social networks in organizations.

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Application of Neural Network Model to the Real-time Forecasting of Water Quality (실시간 수질 예측을 위한 신경망 모형의 적용)

  • Cho, Yong-Jin;Yeon, In-Sung;Lee, Jae-Kwan
    • Journal of Korean Society on Water Environment
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    • v.20 no.4
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    • pp.321-326
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    • 2004
  • The objective of this study is to test the applicability of neural network models to forecast water quality at Naesa and Pyongchang river. Water quality data devided into rainy day and non-rainy day to find characteristics of them. The mean and maximum data of rainy day show higher than those of non-rainy day. And discharge correlate with TOC at Pyongchang river. Neural network model is trained to the correlation of discharge with water quality. As a result, it is convinced that the proposed neural network model can apply to the analysis of real time water quality monitoring.