• Title/Summary/Keyword: Threshold model

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A Study on Flow Variation with Geometrical Characteristics of Fault Zones Using Three-dimensional Discrete Fracture Network (3차원 이산 균열망 모형을 이용한 단층지역의 기하학적 특성에 따른 흐름 변화에 관한 연구)

  • Jeong, Woo Chang
    • Proceedings of the Korea Water Resources Association Conference
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    • 2016.05a
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    • pp.326-326
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    • 2016
  • The fault can be defined, in a geological context, as a rupture plane showing a significant displacement generated in the case that the local tectonic stress exceeds a threshold of rupture along a particular plane in a rock mass. The hydrogeological properties of this fault can be varied with the spatial distribution and the connectivity of void spaces in a fault. When the formation of fault includes the process of the creation and the destruction of void spaces, a complex relation between the displacement along the fault and the variation of void spaces. In this study, the variation of flow with the geometrical characteristics of the fault is simulated and analyzed by using the three-dimensional discrete fracture network model. Three different geometrical characteristics of the faults are considered in this study: 1) simple hydraulic conductive plane, 2) damaged zone, and 3) relay structure of faults.

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Wind-excited stochastic vibration of long-span bridge considering wind field parameters during typhoon landfall

  • Ge, Yaojun;Zhao, Lin
    • Wind and Structures
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    • v.19 no.4
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    • pp.421-441
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    • 2014
  • With the assistance of typhoon field data at aerial elevation level observed by meteorological satellites and wind velocity and direction records nearby the ground gathered in Guangzhou Weather Station between 1985 and 2001, some key wind field parameters under typhoon climate in Guangzhou region were calibrated based on Monte-Carlo stochastic algorithm and Meng's typhoon numerical model. By using Peak Over Threshold method (POT) and Generalized Pareto Distribution (GPD), Wind field characteristics during typhoons for various return periods in several typical engineering fields were predicted, showing that some distribution rules in relation to gradient height of atmosphere boundary layer, power-law component of wind profile, gust factor and extreme wind velocity at 1-3s time interval are obviously different from corresponding items in Chinese wind load Codes. In order to evaluate the influence of typhoon field parameters on long-span flexible bridges, 1:100 reduced-scale wind field of type B terrain was reillustrated under typhoon and normal conditions utilizing passive turbulence generators in TJ-3 wind tunnel, and wind-induced performance tests of aero-elastic model of long-span Guangzhou Xinguang arch bridge were carried out as well. Furthermore, aerodynamic admittance function about lattice cross section in mid-span arch lib under the condition of higher turbulence intensity of typhoon field was identified via using high-frequency force-measured balance. Based on identified aerodynamic admittance expressions, Wind-induced stochastic vibration of Xinguang arch bridge under typhoon and normal climates was calculated and compared, considering structural geometrical non-linearity, stochastic wind attack angle effects, etc. Thus, the aerodynamic response characteristics under typhoon and normal conditions can be illustrated and checked, which are of satisfactory response results for different oncoming wind velocities with resemblance to those wind tunnel testing data under the two types of climate modes.

Sec-O-glucosylhamaudol mitigates inflammatory processes and autophagy via p38/JNK MAPK signaling in a rat neuropathic pain model

  • Oh, Seon Hee;Kim, Suk Whee;Kim, Dong Joon;Kim, Sang Hun;Lim, Kyung Joon;Lee, Kichang;Jung, Ki Tae
    • The Korean Journal of Pain
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    • v.34 no.4
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    • pp.405-416
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    • 2021
  • Background: This study investigated the effect of intrathecal Sec-O-glucosylhamaudol (SOG) on the p38/c-Jun N-terminal kinase (JNK) signaling pathways, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB)-related inflammatory responses, and autophagy in a spinal nerve ligation (SNL)-induced neuropathic pain model. Methods: The continuous administration of intrathecal SOG via an osmotic pump was performed on male Sprague-Dawley rats (n = 50) with SNL-induced neuropathic pain. Rats were randomized into four groups after the 7th day following SNL and treated for 2 weeks as follows (each n = 10): Group S, sham-operated; Group D, 70% dimethylsulfoxide; Group SOG96, SOG at 96 ㎍/day; and Group SOG192, SOG at 192 ㎍/day. The paw withdrawal threshold (PWT) test was performed to assess neuropathic pain. Western blotting of the spinal cord (L5) was performed to measure changes in the expression of signaling pathway components, cytokines, and autophagy. Additional studies with naloxone challenge (n = 10) and cells were carried out to evaluate the potential mechanisms underlying the effects of SOG. Results: Continuous intrathecal SOG administration increased the PWT with p38/JNK mitogen-activated protein kinase (MAPK) pathway and NF-κB signaling pathway inhibition, which induced a reduction in proinflammatory cytokines with the concomitant downregulation of autophagy. Conclusions: SOG alleviates mechanical allodynia, and its mechanism is thought to be related to the regulation of p38/JNK MAPK and NF-κB signaling pathways, associated with autophagy during neuroinflammatory processes after SNL.

'Pneumonia Weather': Short-term Effects of Meteorological Factors on Emergency Room Visits Due to Pneumonia in Seoul, Korea

  • Sohn, Sangho;Cho, Wonju;Kim, Jin A;Altaluoni, Alaa;Hong, Kwan;Chun, Byung Chul
    • Journal of Preventive Medicine and Public Health
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    • v.52 no.2
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    • pp.82-91
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    • 2019
  • Objectives: Many studies have explored the relationship between short-term weather and its health effects (including pneumonia) based on mortality, although both morbidity and mortality pose a substantial burden. In this study, the authors aimed to describe the influence of meteorological factors on the number of emergency room (ER) visits due to pneumonia in Seoul, Korea. Methods: Daily records of ER visits for pneumonia over a 6-year period (2009-2014) were collected from the National Emergency Department Information System. Corresponding meteorological data were obtained from the National Climate Data Service System. A generalized additive model was used to analyze the effects. The percent change in the relative risk of certain meteorological variables, including pneumonia temperature (defined as the change in average temperature from one day to the next), were estimated for specific age groups. Results: A total of 217 776 ER visits for pneumonia were identified. The additional risk associated with a $1^{\circ}C$ increase in pneumonia temperature above the threshold of $6^{\circ}C$ was 1.89 (95% confidence interval [CI], 1.37 to 2.61). Average temperature and diurnal temperature range, representing within-day temperature variance, showed protective effects of 0.07 (95% CI, 0.92 to 0.93) and 0.04 (95% CI, 0.94 to 0.98), respectively. However, in the elderly (65+ years), the effect of pneumonia temperature was inconclusive, and the directionality of the effects of average temperature and diurnal temperature range differed. Conclusions: The term 'pneumonia temperature' is valid. Pneumonia temperature was associated with an increased risk of ER visits for pneumonia, while warm average temperatures and large diurnal temperature ranges showed protective effects.

A Study on the Probabilistic Vulnerability Assessment of COTS O/S based I&C System (상용 OS기반 제어시스템 확률론적 취약점 평가 방안 연구)

  • Euom, Ieck-Chae
    • Journal of Convergence for Information Technology
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    • v.9 no.8
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    • pp.35-44
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    • 2019
  • The purpose of this study is to find out quantitative vulnerability assessment about COTS(Commercial Off The Shelf) O/S based I&C System. This paper analyzed vulnerability's lifecycle and it's impact. this paper is to develop a quantitative assessment of overall cyber security risks and vulnerabilities I&C System by studying the vulnerability analysis and prediction method. The probabilistic vulnerability assessment method proposed in this study suggests a modeling method that enables setting priority of patches, threshold setting of vulnerable size, and attack path in a commercial OS-based measurement control system that is difficult to patch an immediate vulnerability.

Classifying Indian Medicinal Leaf Species Using LCFN-BRNN Model

  • Kiruba, Raji I;Thyagharajan, K.K;Vignesh, T;Kalaiarasi, G
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.10
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    • pp.3708-3728
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    • 2021
  • Indian herbal plants are used in agriculture and in the food, cosmetics, and pharmaceutical industries. Laboratory-based tests are routinely used to identify and classify similar herb species by analyzing their internal cell structures. In this paper, we have applied computer vision techniques to do the same. The original leaf image was preprocessed using the Chan-Vese active contour segmentation algorithm to efface the background from the image by setting the contraction bias as (v) -1 and smoothing factor (µ) as 0.5, and bringing the initial contour close to the image boundary. Thereafter the segmented grayscale image was fed to a leaky capacitance fired neuron model (LCFN), which differentiates between similar herbs by combining different groups of pixels in the leaf image. The LFCN's decay constant (f), decay constant (g) and threshold (h) parameters were empirically assigned as 0.7, 0.6 and h=18 to generate the 1D feature vector. The LCFN time sequence identified the internal leaf structure at different iterations. Our proposed framework was tested against newly collected herbal species of natural images, geometrically variant images in terms of size, orientation and position. The 1D sequence and shape features of aloe, betel, Indian borage, bittergourd, grape, insulin herb, guava, mango, nilavembu, nithiyakalyani, sweet basil and pomegranate were fed into the 5-fold Bayesian regularization neural network (BRNN), K-nearest neighbors (KNN), support vector machine (SVM), and ensemble classifier to obtain the highest classification accuracy of 91.19%.

Robustness of Differentiable Neural Computer Using Limited Retention Vector-based Memory Deallocation in Language Model

  • Lee, Donghyun;Park, Hosung;Seo, Soonshin;Son, Hyunsoo;Kim, Gyujin;Kim, Ji-Hwan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.3
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    • pp.837-852
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    • 2021
  • Recurrent neural network (RNN) architectures have been used for language modeling (LM) tasks that require learning long-range word or character sequences. However, the RNN architecture is still suffered from unstable gradients on long-range sequences. To address the issue of long-range sequences, an attention mechanism has been used, showing state-of-the-art (SOTA) performance in all LM tasks. A differentiable neural computer (DNC) is a deep learning architecture using an attention mechanism. The DNC architecture is a neural network augmented with a content-addressable external memory. However, in the write operation, some information unrelated to the input word remains in memory. Moreover, DNCs have been found to perform poorly with low numbers of weight parameters. Therefore, we propose a robust memory deallocation method using a limited retention vector. The limited retention vector determines whether the network increases or decreases its usage of information in external memory according to a threshold. We experimentally evaluate the robustness of a DNC implementing the proposed approach according to the size of the controller and external memory on the enwik8 LM task. When we decreased the number of weight parameters by 32.47%, the proposed DNC showed a low bits-per-character (BPC) degradation of 4.30%, demonstrating the effectiveness of our approach in language modeling tasks.

Security Proof for a Leakage-Resilient Authenticated Key Establishment Protocol

  • Shin, Seong-Han;Kazukuni Kobara;Hideki Imai
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.14 no.4
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    • pp.75-90
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    • 2004
  • At Asiacrypt 2003, Shin et al., have proposed a new class for Authenticated Key Establishment (AKE) protocol named Leakage-Resilient AKE ${(LR-AKE)}^{[1]}$. The authenticity of LR-AKE is based on a user's password and his/her stored secrets in both client side and server side. In their LR-AKE protocol, no TRM(Tamper Resistant Modules) is required and leakage of the stored secrets from $.$my side does not reveal my critical information on the password. This property is useful when the following situation is considered :(1) Stored secrets may leak out ;(2) A user communicates with a lot of servers ;(3) A user remembers only one password. The other AKE protocols, such as SSL/TLS and SSH (based or PKI), Password-Authenticated Key Exchange (PAKE) and Threshold-PAKE (T-PAKE), do not satisfy that property under the above-mentioned situation since their stored secrets (or, verification data on password) in either the client or the servers contain enough information to succeed in retrieving the relatively short password with off-line exhaustive search. As of now, the LR-AKE protocol is the currently horn solution. In this paper, we prove its security of the LR-AKE protocol in the standard model. Our security analysis shows that the LR-AKE Protocol is provably secure under the assumptions that DDH (Decisional Diffie-Hellman) problem is hard and MACs are selectively unforgeable against partially chosen message attacks (which is a weaker notion than being existentially unforgeable against chosen message attacks).

A Study on Building Identification from the Three-dimensional Point Cloud by using Monte Carlo Integration Method (몬테카를로 적분을 통한 3차원 점군의 건물 식별기법 연구)

  • YI, Chaeyeon;AN, Seung-Man
    • Journal of the Korean Association of Geographic Information Studies
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    • v.23 no.4
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    • pp.16-41
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    • 2020
  • Geospatial input setting to represent the reality of spatial distribution or quantitative property within model has become a major interest in earth system simulation. Many studies showed the variation of grid resolution could lead to drastic changes of spatial model results because of insufficient surface property estimations. Hence, in this paper, the authors proposed Monte Carlo Integration (MCI) to apply spatial probability (SP) in a spatial-sampling framework using a three-dimensional point cloud (3DPC) to keep the optimized spatial distribution and area/volume property of buildings in urban area. Three different decision rule based building identification results were compared : SP threshold, cell size, and 3DPC density. Results shows the identified building area property tend to increase according to the spatial sampling grid area enlargement. Hence, areal building property manipulation in the sampling frameworks by using decision rules is strongly recommended to increase reliability of geospatial modeling and analysis results. Proposed method will support the modeling needs to keep quantitative building properties in both finer and coarser grids.

Distance Estimation Using Convolutional Neural Network in UWB Systems (UWB 시스템에서 합성곱 신경망을 이용한 거리 추정)

  • Nam, Gyeong-Mo;Jung, Tae-Yun;Jung, Sunghun;Jeong, Eui-Rim
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.10
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    • pp.1290-1297
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    • 2019
  • The paper proposes a distance estimation technique for ultra-wideband (UWB) systems using convolutional neural network (CNN). To estimate the distance from the transmitter and the receiver in the proposed method, 1 dimensional vector consisted of the magnitudes of the received samples is reshaped into a 2 dimensional matrix, and by using this matrix, the distance is estimated through the CNN regressor. The received signal for CNN training is generated by the UWB channel model in the IEEE 802.15.4a, and the CNN model is trained. Next, the received signal for CNN test is generated by filed experiments in indoor environments, and the distance estimation performance is verified. The proposed technique is also compared with the existing threshold based method. According to the results, the proposed CNN based technique is superior to the conventional method and specifically, the proposed method shows 0.6 m root mean square error (RMSE) at distance 10 m while the conventional technique shows much worse 1.6 m RMSE.