• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.9-16
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• 2023

The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.

• Smart Structures and Systems
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• v.13 no.6
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• pp.1015-1039
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• 2014

The widening project on Freeway No.1 in Taiwan has a total length of roughly 14 kilometers, and includes three special bridges, namely a 216 m long-span bridge crossing the original freeway, an F-bent double decked bridge in a co-constructed section, and a steel and prestressed concrete composite bridge. This study employed in-situ monitoring in conjunction with numerical modeling to establish a real-time monitoring system for the three bridges. In order to determine the initial static and dynamic behavior of the real bridges, forced vibration experiments, in-situ static load experiments, and dynamic load experiments were first carried out on the newly-constructed bridges before they went into use. Structural models of the bridges were then established using the finite element method, and in-situ vehicle load weight, arrangement, and speed were taken into consideration when performing comparisons employing data obtained from experimental measurements. The results showed consistency between the analytical simulations and experimental data. After determining a bridge's initial state, the proposed in-situ monitoring system, which is employed in conjunction with the established finite element model, can be utilized to assess the safety of a bridge's members, providing useful reference information to bridge management agencies.

• Journal of the Korea Concrete Institute
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• v.26 no.2
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• pp.219-227
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• 2014

The purpose of this study is to investigate water purification properties of porous concrete by using effective microorganisms through the long-term continuous flow test. To solve the problems such as desorption of conventional microorganisms, in this study, tertiary treatment of the effective microorganisms identified by 16S rDNA sequence analysis was adopted per each step in the manufacturing process of porous concrete. And concentration for optimum continuous flow test and operation conditions through basic experiments according to retention time were investigated. Based on the experimental results, the porous concrete applying effective microorganisms showed no toxicity on the biological water quality and exhibited excellent removal efficiency than normal porous concrete. Therefore, contaminated water quality would be improved by treatment performance investigation of contaminants through long-term continuous flow test. If problems are complemented during the experiment process, it is expected to be able to reduce the non-point pollution sources flowing into river.

• Journal of the Korea Concrete Institute
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• v.17 no.6 s.90
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• pp.1025-1032
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• 2005

This paper presents experimental and analytical studies on long-term behavior of square CFT columns with diaphragm. In order to investigate the effect of the diaphragm on the long~term behavior, experiments for six specimens with two diaphragms and three different column length, and three-dimensional finite element analysis for each specimen have been performed. The finite element models considering the interface behavior between the steel tube and the inner concrete were verified from comparison of the test results with the analysis results. From the test and the analysis results, the following conclusions were obtained. The confinement effect created by the diaphragm does not depends on column length and influences only a part of the whole column that is from the end to the depth which is the same to the width of the column. The shortening of the column with diaphragm which covers more than a half of the cross sectional area of the inner concrete is the same as that of the column under a load applied on the steel tube and the entire section of the inner concrete.

• Journal of the Korean Geotechnical Society
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• v.22 no.8
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• pp.107-118
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• 2006

Among a variety of influencing components, time-variant seepage and long-term underground motion are important to understand the abnormal behavior of tunnels. Excessiveness of these two components could be the direct cause of severe damage on tunnels, however, it is not easy to quantify the effect of these on the behavior of tunnels. These parameters can be estimated by using inverse methods once the appropriate relationship between inputs and results is clarified. Various inverse methods or parameter estimation techniques such as artificial neural network and least square method can be used depending on the characteristics of given problems. Numerical analyses, experiments, or monitoring results are frequently used to prepare a set of inputs and results to establish the back analysis models. In this study, a back analysis method has been developed to estimate geotechnically hard-to-known parameters such as permeability of tunnel filter, underground water table, long-term rock mass load, size of damaged zone associated with seepage and long-term underground motion. The artificial neural network technique is adopted and the numerical models developed in the first part are used to prepare a set of data for learning process. Tunnel behavior, especially the displacements of the lining, has been exclusively investigated for the back analysis.

• Korean Journal of Social Welfare
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• v.41
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• pp.119-146
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• 2000

The goals and strategies of welfare-to-work (WTW) policies have been sources of contentious political debate. In the United States, despite 20 years of welfare reform, there remain important differences of opinion regarding how best to design and deliver WTW programs. The proliferation of state and local WTW experiments has led to the identification of two ideal-types of WTW programs: the Labor Force Attachment and Human Capital Development models. Most of the recent policy debate about WTW in America has focused on the relative merits and performance of LFA and HCD. While the Primary goal of the LFA model is for welfare recipients to achieve a rapid transition into work, the HCD model seeks to improve the long-term employability of welfare dependents through education and skill development. LFA policies tend to be strongly outcome-oriented and generally can yield quick results. Their "any job is a good job" philosophy has proved attractive to policy-makers who are anxious to see concrete results in a short-term period. In contrast, the HCD policies do not simply dump welfare dependents at the bottom of the labor market, but aim to secure relatively stable and well-paid jobs. However, these strengths are offset by several practical weaknesses including high unit costs and long-term investment in human capital. In recent years, LFA policies have been increasingly favored by both policy officials and politicians in the United States. The introduction of Temporaray Assistance to Needy Families of 1996 has been accelerating the trend. What is going to happen to welfare recipients? This simple shift to the LFA model, however, will only see an alarming increase of working poor in a near future.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.105-108
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• 2008

For the early seismic isolation design in Korea, foreign products of isolation bearings were used. But these days, the application of domestic products of isolation bearings is increasing. However various experimental studies can be found very seldom on the extreme and lonr term behaviors of isolation bearings. In this study, we considered the laminated rubber type isolation bearings that have many application cases in Korea and we evaluated their shear strength, long term characteristics such as aging and creep affecting shear strength of bearings in long term period. For the reality of experiments, fabricated isolation bearing specimens are designed for a real structure and shear loading was applied under design compressive loads. To evaluated aging effect, the specimens were exposed to high temperature environment for certain period and their shear properties were measured to compare with their original values. Also we measured creep amount of isolation bearings under constant compressive load for 1,000 hours and estimated creep amount after 60 years compatible with general life cycle of bridges.

• Korean Journal of Biological Psychiatry
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• v.4 no.2
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• pp.237-245
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• 1997

The study was designed to evaluate the significant roles of SSRI in rat of depression model. Chronic exposure to mild unpredictable stress has been found to depress the consumption of sweet 1% sucrose solutions in the Sprague-Dawley rats. We applied the variety of 11 types of stress regimens and identified depressive behaviours(developed by Willner) in 70 Sprague-Dawley rats. Rats in experiments were stratified into 6 groups, ie ; 3 kinds of SSRI(paroxetine, fluoxetine, sertraline), clomipramine, choline and saline control. Memory function was evaluated by passive avoidance learning and retention test. The authors determined how long memory retention would remain improved with 24 hour, 1 week, 2 weeks, 3 weeks, and 4 weeks at training-testing interval in depressive states of the Sprague-Dawley rats. The results were as follows ; 1) There were no significant differences between the 6 groups at the 24 hour training-testing interval. 2) The paroxetine treated group showed significant differences from the control group at the 1 week and 2 weeks training-testing interval. 3) The paroxetine and the fluoxetine treated groups showed singificant differences from the control group at 3 week training-testing interval. 4) The paroxetine and the choline treated groups showed significant differences from the control group at 4 week training-testing interval. In summary, paroxetine had an effect on long term memory processing from 1st week to 4th week. Also, fluoxetine(at 3rd week) and choline(at 4th week) had effect on long term memory processing. Sertraline, clomipramine were ineffective on memory processing during 4 weeks observation. Possible explanations why paroxetine had early effect on memory processing than the other selective serotonin reuptake inhibitors are rapid bioavailability, which is the characteristics of pharmacokinetics of paroxetine. In clinical situation, author carefully suggest that SSRI would be beneficial to improve the memory function caused by depressive neurochemical changes.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.2
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• pp.119-126
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• 2002

Recently, the flux community in Korea established a new regional flux network, so-called KoFlux, which will provide an infrastructure for collecting, synthesizing, and analysing long-term measurements of energy and mass exchange between the atmosphere and the various vegetated surfaces. KoFlux requires the collection of long time series of raw data, and a large amount of data are expected to accumulate due to continuous flux observations at each KoFlux sites. Therefore, we need a systematic and efficient tool to manage these raw data. As a part of this effort, a computer program far processing raw data measured from micrometeorological field experiments was developed for the flux community in Korea. In this paper, we introduce this program for processing raw data to estimate fluxes and other turbulent statistics and explain the micrometeolological processes coded in this data-processing program. Also, we show some examples on how to run the program and handle the outputs for the unique purpose of research interest.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.37-45
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• 2006

In this paper, we propose the development of MHLA(Modulatory Hippocampus Learning Algorithm) which remodel a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 3 steps system(DG, CA3, CAl) and improve speed of learning by addition of modulator to long-term memory learning. In hippocampal system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labelled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CAI region, convergence of connection weight which is used long-term memory is learned fast by neural networks which is applied modulator. To measure performance of MHLA, PCA(Principal Component Analysis) is applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by MHLA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.