• Smart Structures and Systems
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• v.15 no.3
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• pp.699-715
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• 2015

A modal parameter based damage sensitive feature (DSF) is defined to mimic the relative change in any diagonal element of the stiffness matrix of a model of a structure. The damage assessment is performed in a statistical pattern recognition framework using empirical complementary cumulative distribution functions (ECCDFs) of the DSFs extracted from measured operational vibration response data. Methods are discussed to perform probabilistic structural health assessment with respect to the following questions: (a) "Is there a change in the current state of the structure compared to the baseline state?", (b) "Does the change indicate a localized stiffness reduction or increase?", with the latter representing a situation of retrofitting operations, and (c) "What is the severity of the change in a probabilistic sense?". To identify a range of normal structural variations due to environmental and operational conditions, lower and upper bound ECCDFs are used to define the baseline structural state. Such an approach attempts to decouple "non-damage" related variations from damage induced changes, and account for the unknown environmental/operational conditions of the current state. The damage assessment procedure is discussed using numerical simulations of ambient vibration testing of a bridge deck system, as well as shake table experimental data from a 4-story steel frame.

• Phonetics and Speech Sciences
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• v.7 no.3
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• pp.131-138
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• 2015

In this paper, we propose L1-norm regularization for state vector adaptation of subspace Gaussian mixture model (SGMM). When you design a speaker adaptation system with GMM-HMM acoustic model, MAP is the most typical technique to be considered. However, in MAP adaptation procedure, large number of parameters should be updated simultaneously. We can adopt sparse adaptation such as L1-norm regularization or sparse MAP to cope with that, but the performance of sparse adaptation is not good as MAP adaptation. However, SGMM does not suffer a lot from sparse adaptation as GMM-HMM because each Gaussian mean vector in SGMM is defined as a weighted sum of basis vectors, which is much robust to the fluctuation of parameters. Since there are only a few adaptation techniques appropriate for SGMM, our proposed method could be powerful especially when the number of adaptation data is limited. Experimental results show that error reduction rate of the proposed method is better than the result of MAP adaptation of SGMM, even with small adaptation data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2764-2782
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• 2021

Existing city-level boundary nodes identification methods need to locate all IP addresses on the path to differentiate which IP is the boundary node. However, these methods are susceptible to time-delay, the accuracy of location information and other factors, and the resource consumption of locating all IPes is tremendous. To improve the recognition rate and reduce the locating cost, this paper proposes an algorithm for city-level boundary node identification based on bidirectional approaching. Different from the existing methods based on time-delay information and location results, the proposed algorithm uses topological analysis to construct a set of candidate boundary nodes and then identifies the boundary nodes. The proposed algorithm can identify the boundary of the target city network without high-precision location information and dramatically reduces resource consumption compared with the traditional algorithm. Meanwhile, it can label some errors in the existing IP address database. Based on 45,182,326 measurement results from Zhengzhou, Chengdu and Hangzhou in China and New York, Los Angeles and Dallas in the United States, the experimental results show that: The algorithm can accurately identify the city boundary nodes using only 20.33% location resources, and more than 80.29% of the boundary nodes can be mined with a precision of more than 70.73%.

• The Journal of the Acoustical Society of Korea
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• v.18 no.6
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• pp.49-56
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• 1999

In many continuous speech recognition systems based on HMMs, decision tree-based state tying has been used for not only improving the robustness and accuracy of context dependent acoustic modeling but also synthesizing unseen models. To construct the phonetic decision tree, standard method performs one-level pruning using just single Gaussian triphone models. In this paper, two novel approaches, two-level decision tree and multi-mixture decision tree, are proposed to get better performance through more accurate acoustic modeling. Two-level decision tree performs two level pruning for the state tying and the mixture weight tying. Using the second level, the tied states can have different mixture weights based on the similarities in their phonetic contexts. In the second approach, phonetic decision tree continues to be updated with training sequence, mixture splitting and re-estimation. Multi-mixture Gaussian as well as single Gaussian models are used to construct the multi-mixture decision tree. Continuous speech recognition experiment using these approaches on BN-96 and WSJ5k data showed a reduction in word error rate comparing to the standard decision tree based system given similar number of tied states.

• Current Optics and Photonics
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• v.1 no.3
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• pp.196-202
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• 2017

Corona discharge is always a sign of failure processes of high-voltage electrical apparatus, including those utilized in electric railway systems. Solar-blind ultraviolet (UV) cameras are effective tools for corona inspection. In this work, we present an automatic railway corona-discharge detection system based on solar-blind ultraviolet detection. The UV camera, mounted on top of a train, inspects the electrical apparatus, including transmission lines and insulators, along the railway during fast cruising of the train. An algorithm based on the Hough transform is proposed for distinguishing the emitting objects (corona discharge) from the noise. The detection system can report the suspected corona discharge in real time during fast cruises. An experiment was carried out during a routine inspection of railway apparatus in Xinjiang Province, China. Several corona-discharge points were found along the railway. The false-alarm rate was controlled to less than one time per hour during this inspection.

• Molecules and Cells
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• v.38 no.5
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• pp.441-451
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• 2015

Recognition of cytosolic DNA initiates a series of innate immune responses by inducing IFN-I production and subsequent triggering JAK1-STAT1 signaling which plays critical roles in the pathogenesis of infection, inflammation and autoimmune diseases through promoting B cell activation and antibody responses. The stimulator of interferon genes protein (STING) has been demonstrated to be a critical hub of type I IFN induction in cytosolic DNA-sensing pathways. However, it still remains unknown whether cytosolic DNA can directly activate the JAK1-STAT1 signaling or not. And the role of STING is also unclear in this response. In the present study, we found that dsDNA directly triggered the JAK1-STAT1 signaling by inducing phosphorylation of the Lyn kinase. Moreover, this response is not dependent on type I IFN receptors. Interestingly, STING could inhibit dsDNA-triggered activation of JAK1-STAT1 signaling by inducing SHP-1 and SHP-2 phosphorylation. In addition, compared with normal B cells, the expression of STING was significantly lower and the phosphorylation level of JAK1 was significantly higher in B cells from MRL/lpr lupus-prone mice, highlighting the close association between STING low-expression and JAK1-STAT1 signaling activation in B cells in autoimmune diseases. Our data provide a molecular insight into the novel role of STING in dsDNA-mediated inflammatory disorders.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

• The Journal of Dong Guk Oriental Medicine
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• v.6 no.2
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• pp.167-190
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• 1998

Nowadays there were two tendencies of studies about prognostic factors in stroke. One way was to define prognostic factors according to the radiological features. And the other way was to define according to the mental state, recognition, perception, motors, language, urinary&bowel incontinence etc.. The former could be objectively investigated, while the latter was difficult. The purpose of this study was to determine which variables would be predictors of stroke and which factors would be affect predictions most. The subjects of this study were 32 patients who were admitted to the Dept. of Internal Medicine, Dongguk Univ. College of Oriental Medicine whthin 48 hours from attack, Medical records were reviewed FIM, CNS, NIH stroke scale. We compared each sub-items of FIM, CNS, NIH stroke scale about mental state, recognition, perception, motors, language, urinary&bowel incontinence with MBI score at 4 weeks from admission. Also, we analyzed the correlations of sub-items and groups which devided into 5 according to independence of MBI score. And we found out the most influent factors with multiple regression analysis. The major results were as follows; 1. In mean of MBI score at 4 weeks of each groups devided low, middle, high score at mental state, recognition, perception, motors, language, urinary&bowel incontinence items, there were statistical differences in all items. 2. The mental state and lim ataxia sub-items had no significant correlations with groups divided according to independence of MBI score. All the other items were significantly correlated. 3. The most influent factors was recognition. The second was sensory and the third was bowel incontinence. 4. The most influent scales was FIM, and the second was CNS, and NlH had no statistical significancy.

• Korean Journal of Social Welfare Studies
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• v.42 no.2
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• pp.205-233
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• 2011

This study aims to fathom the relationship between socio-economic security and social cohesion which are two sub-domains of Social Quality, on the institutional context of welfare state. In order to grasp the institutional context of welfare state, the study adopted welfare status theory and measured socio-economic resources of individuals via the status as welfare beneficiary and welfare taxpayer. The study postulates a theoretical model that the socio-economic security domain affects the social cohesion domain. In order to verity the theoretical hypothesis, this study utilized structural equations analysis(SEM) using social survey data conducted in year 2008. Recognition of social trust was included as the core index of social cohesion, and welfare statuses, socio-economic security and social trust were inserted in the sequence. Results revealed that the amount of resource in regards to welfare status of rights had significant influence on the socio-economic security, whereas it had no significant relations in regards to welfare status of duties. The recognition of socio-economic security derived from status of welfare rights and duties were positively associated with recognition of social trust. Also, the recognition of socio-eocnomic security turned out to have significant influence on social trust. Conclusively, among the two sub-domains of Social Quality, the study found that the socio-economic domain has influence on social cohesion domain. Such results suggest that in order to enhance the overall social cohesion in Korea, more delicate arrangement of welfare institutions are required.

• Korean Journal of Health Education and Promotion
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• v.23 no.4
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• pp.125-153
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• 2006

This study was intended to describe women's weight controlling by creating a hypothetic model on the weight adjustment behavior and by examining a cause and effect relationship, and to contribute to countermeasures for practicing their promotion of health and improving the quality of life through creating a predictable model. The subject of study was women who utilize the beauty shop located in Seoul, Busan and Daegu and the study period was 12 weeks from July 10 to September 30 in 2004. Gathered 1093 person's general specialty related with weight adjustment and analyzed covariance to prove the hypothesis using statistics compiled from authentic sources. Also proved coincidence of the hypothetical model. Exogenous variables of the hypothetical model are composed of recognition of her body shape, fatness level, age, stress, and self-respect. Endogenous variables are health- control mind, recognized health state, self-efficacy, intention, and behavior of weight adjustment. There were 5 measured variables for exogenous variable(x). There were 8 measured variable(y) for exogenous variable. And coincidence $x^2=297.38$, standard $x^2(x^2/df)=7.08$, GFI=0.962, AGFI=0.917, NFI=0.875, TLI=0.794, CFI=0.889, RMSEA=0.075. The result of hypothesis had an epoch-making record that 20 out of 27 hypothesis was proved positive way. Generally weight adjustment has been highly seen in housewives, the married and the old age. Health control mind seems to be high as fatness level, age, and self-respect are high and low stress. Recognized health state is high as age and self-respect are high and low stress. However, it is not much related with recognition of her body shape and fatness level. If age, self-respect, health control mind, recognized health state and self-efficacy are high intention of behavior is also high, but intention of behavior has no relation with recognition of her body shape, fatness level and stress. If fatness level, age, self-respect, health control mind, recognized health state and self-efficacy and intention of behavior are high, execution of weight adjustment will be high. However, recognized health state and stress has no influence for weight adjustment. To increase the coincidence of hypothesis and take a simple model I modified a model and then I got the coincidence $x^2=215.62$, standard $x^2(x^2/df)=6.34$, GFI=0.970, AGFI=0.931, NFI=0.902, TLI=0.901, CFI=0.915, RMSEA=0.070. This result is a bit better than original hypothetical model's so that this model might be more suitable. In this modification model, the factors of weight adjustment seems to be high according to this order self-efficacy, recognized health state, age, intention, health control mind, self-respect, fatness level and stress. With this result I suggest ; 1. Enforcement of IR that everybody can be controlled weight adjustment herself and continuous education, which is related with regular habit (food, exercise, restriction of a favorite food and behavior training etc.) is also needed. 2. Because self-efficacy is influenced to execution of weight adjustment specific program which can increase self-efficacy should have to develop and we need to utilize it to take care of herself. 3. To protect fatness and be active weight adjustment the peculiar program including the concept of self-respect, recognized health state, health control mind and intention must be developed and not only women but also all of people should be educated. 4. This hypothetical model is forecasting women's weight adjustment behavior and can be utilized for fundamental data to increase those people's health.