• Smart Structures and Systems
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• v.25 no.4
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• pp.433-446
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• 2020

In this paper the effect of confining pressure and tunnel depth on the ground vertical settlement has been investigated using particle flow code (PFC2D). For this perpuse firstly calibration of PFC2D was performed using both of tensile test and triaxial test. Then a model with dimention of 100 m × 100 m was built. A circular tunnel with diameter of 20 m was drillled in the middle of the model. Also, a rectangular tunnel with wide of 10 m and length of 20 m was drilled in the model. The center of tunnel was situated 15 m, 20 m, 25 m, 30 m, 35 m, 40 m, 45 m, 50 m, 55 m and 60 m below the ground surface. these models are under confining pressure of 0.001 GPa, 0.005 GPa, 0.01 GPa, 0.03 GPa, 0.05 GPa and 0.07 GPa. The results show that the volume of colapce zone is constant by increasing the distance between ground surface and tunnel position. Also, the volume of colapce zone was increased by decreasing of confining pressure. The maximum of settlement occurs at the top of the tunnel roof. The maximum of settlement occurs when center of tunnel was situated 15 m below the ground surface. The settlement decreases by increasing the distance between tunnel center line and measuring circles in the ground surface. The minimum of settlement occurs when center of circular tunnel was situated 60 m below the surface ground. Its to be note that the settlement increase by decreasing the confining pressure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4754-4773
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• 2018

In sensor medium access control (SMAC) protocol, sensor nodes can only access the channel in the scheduling and listening period. However, this fixed working method may generate data latency and high conflict. To solve those problems, scheduling duty in the original SMAC protocol is divided into multiple small scheduling duties (micro duty MD). By applying different micro-dispersed contention channel, sensor nodes can reduce the collision probability of the data and thereby save energy. Based on the given micro-duty, this paper presents an adaptive duty cycle (DC) and back-off algorithm, aiming at detecting the fixed duty cycle in SMAC protocol. According to the given buffer queue length, sensor nodes dynamically change the duty cycle. In the context of low duty cycle and low flow, fair binary exponential back-off (F-BEB) algorithm is applied to reduce data latency. In the context of high duty cycle and high flow, capture avoidance binary exponential back-off (CA-BEB) algorithm is used to further reduce the conflict probability for saving energy consumption. Based on the above two contexts, we propose an improved SMAC protocol, micro duty adaptive SMAC protocol (MDA-SMAC). Comparing the performance between MDA-SMAC protocol and SMAC protocol on the NS-2 simulation platform, the results show that, MDA-SMAC protocol performs better in terms of energy consumption, latency and effective throughput than SMAC protocol, especially in the condition of more crowded network traffic and more sensor nodes.

• Structural Engineering and Mechanics
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• v.59 no.1
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• pp.37-57
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• 2016

Reservoir geomechanics can play an important role in hydrocarbon recovery mechanism. In $CO_2$-EOR process, reservoir geomechanics analysis is concerned with the simultaneous study of fluid flow and the mechanical response of the reservoir under $CO_2$ injection. Accurate prediction of geomechanical effects during $CO_2$ injection will assist in modeling the Carbon dioxide recovery process and making a better design of process and production equipment. This paper deals with the implementation of a program (FORTRAN 90 interface code), which was developed to couple conventional reservoir (ECLIPSE) and geomechanical (ABAQUS) simulators, using a partial coupling algorithm. A geomechanics reservoir partially coupled approach is presented that allows to iteratively take the impact of geomechanics into account in the fluid flow calculations and therefore performs a better prediction of the process. The proposed approach is illustrated on a realistic field case. The reservoir geomechanics coupled models show that in the case of lower maximum bottom hole injection pressure, the cumulative oil production is more than other scenarios. Moreover at the high injection pressures, the production rates will not change with the injection bottom hole pressure variations. Also the FEM analysis of the reservoir showed that at $CO_2$ injection pressure of 11000 Psi the plastic strain has been occurred in the some parts of the reservoir and the related stress path show a critical behavior.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.285-301
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• 2023

Due to the increasing demand and importance of non-face-to-face education, open online learning platforms are getting interests both domestically and internationally. These platforms exhibit different characteristics from online courses by universities and other educational institutions. In particular, students engaged in these platforms can receive more learner autonomy, and the development of tools to assist learning is required. From the past, researchers have attempted to utilize process mining to understand realistic study behaviors and derive learning patterns. However, it has a deficiency to employ it to the open online learning platforms. Moreover, existing research has primarily focused on the process model perspective, including process model discovery, but lacks a method for the process pattern and instance perspectives. In this study, we propose a method to identify learning patterns within an open online learning platform using process mining techniques. To achieve this, we suggest three different viewpoints, e.g., model-level, variant-level, and instance-level, to comprehend the learning patterns, and various techniques are employed, such as process discovery, conformance checking, autoencoder-based clustering, and predictive approaches. To validate this method, we collected a learning log of machine learning-related courses on a domestic open education platform. The results unveiled a spaghetti-like process model that can be differentiated into a standard learning pattern and three abnormal patterns. Furthermore, as a result of deriving a pattern classification model, our model achieved a high accuracy of 0.86 when predicting the pattern of instances based on the initial 30% of the entire flow. This study contributes to systematically analyze learners' patterns using process mining.

• Journal of Korea Water Resources Association
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• v.43 no.10
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• pp.921-932
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• 2010

In this study, the adjustment processes of the disturbed channels by sand or gravel mining were investigated by a two dimensional numerical model in the generalized coordinate system. As a numerical scheme, the CIP (cubic interpolated pseudoparticle method) method was used to calculate the advection term in the flow field and central difference method was used to the diffusion term in it. The pit of the channel was partially filled with sediment at the toe of the pit upstream. As time increased, the headcut erosion upstream in the pit was decreased due to the sediment inflow. The almost inflow sediment upstream was trapped into the pit and the sediment deposit wedge migrated downstream in the pit with the steep submerged angle of repose. The numerical model was reproduced well the evolution processes of the channel. The mining pit migrated with speed as the channel was steep, and the numerical results were in overall agreement with the experimental results.

• Computers and Concrete
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• v.19 no.5
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• pp.489-499
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• 2017

Self-Compacting Concrete (SCC) is a new technology capable to flow without segregation or any addition of energy which leads to efficient construction and cost savings. In this study, the effect of replacing the Ordinary Portland Cement (OPC) with Fly Ash (FA) on the strength, durability of the concrete was investigated experimentally, and carbon footprint and cost were also assessed. Four different replacement FA ratios (0%, 20%, 40% and 60%) were used to create four SCC mixes. Standard test methods were used to determine the workability, strength, and durability of the SCC mixes including resist chloride ion penetration, water permeability, water absorption, and initial surface absorption. The axial cube compressive strength tests were performed on the SCC mixes at 1, 7, 14, 28 and 35 days. Replacing the OPC with FA had a significant positive impact on chloride iron penetration resistance and water absorption but had a considerable negative impact on the compressive strength. The SCC mix with 60% FA had 36.7% and 15.8% enhancement in the resistance to chloride ion penetration and water absorption, respectively. Evaluation of the carbon footprint and the cost of each SCC mixes showed the $CO_2$ emissions mixes 1, 2, 3 and 4 were significantly reduced by increasing the FA content from 0% to 60%. Compared with the control mix, the cost of all mixes increased when the FA content increased, but no significant differences were seen between the estimated costs of all four mixes.

• Journal of Labour Economics
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• v.27 no.2
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• pp.29-66
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• 2004

In this paper, I investigate job creation and destruction in Korean mining and manufacturing between 1982 and 2000 using the raw data of Annual Mining and Manufacturing Survey. The rate of job creation and destruction of continuing plants averaged 9.75 and 10.33, respectively, which are higher than those of OECD countries, Chile, and Colombia. The created jobs showed weak persistence and the concentration of job reallocation is high, compared with other countries. Job reallocation accounts for major fraction of worker reallocation and the fraction has increased before 1997. Analysis of time series data of job flow revealed a general pattern of pro-cyclic job creation and counter-cyclic job destruction. However job reallocation in Korea is strongly acyclic whereas the rate is known to be counter-cyclical in the U.S.

• Journal of Korea Soil Environment Society
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• v.3 no.2
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• pp.79-88
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• 1998

The purpose of this study was to estimate the removal potential of landfill gases during landfill mining project. Air injection mode and landfill gas extraction mode were tested. A mode that air injected at one injection well and landfill gas extracted at another extraction well at the same time was also tested to compare. The flow rates of all modes were the same as 15$\textrm{km}^2$/min. Air injection mode was the most effective in removing $CH_4$. Air injection/extraction mode didn't improve the effectiveness of removing CH$_4$compared with air injection mode. Air injection mode were more advantageous than air injection/extraction mode in respect to energy consumption because that of air injection/extraction mode were doubled.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.691-700
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• 2009

An event means a flow which has a time attribute such as the a symptom of patients, an interval event has the time period between the start-time-point and the end-time-point. Although there are many studies for temporal data mining, they do not deal with discovering knowledge from interval event such as patient histories and purchase histories. In this paper, we suggest a method of temporal data mining that finds association rules of event causal relationships and predicts an occurrence of effect event based on discovered rules. Our method can predict the occurrence of an event by summarizing an interval event using the time attribute of an event and finding the causal relationship of event. As a result of simulation, this method can discover better knowledge than others by considering a lot of supports of an event and finding the significant rare relation on interval events which means an essential cause of an event, regardless of an occurrence support of an event in comparison with conventional data mining techniques.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.795-805
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• 2019

Recently, there is a growing interest in network anomaly detection technology to tackle unknown attacks. For this purpose, diverse studies using data mining, machine learning, and deep learning have been applied to detect network anomalies. In this paper, we evaluate the decision tree to see its feasibility for network anomaly detection on NSL-KDD data set, which is one of the most popular data mining techniques for classification. In order to handle the over-fitting problem of decision tree, we select 13 features from the original 41 features of the data set using chi-square test, and then model the decision tree using TensorFlow and Scik-Learn, yielding 84% and 70% of binary classification accuracies on the KDDTest+ and KDDTest-21 of NSL-KDD test data set. This result shows 3% and 6% improvements compared to the previous 81% and 64% of binary classification accuracies by decision tree technologies, respectively.