• Korean Journal of Construction Engineering and Management
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• v.16 no.6
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• pp.53-62
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• 2015

Time-Cost Trade-Off (TCTO) model is an important model in the construction project planning and control area. Two types of Existing TCTO model, continuous and discrete TCTO model, have been developed by researchers. However, Using only one type of model has a limitation to represent a realistic crash scenario of activities in the project. Thus, this paper presents a comprehensive TCTO model that combines a continuous and discrete model. Additional advanced features for non-linear relationship, incentive, and liquidated damage are included in the TCTO model. These features make the proposed model more applicable to the construction project. One CPM network with 6 activities is used to explain the proposed model. The model found an optimal schedule for the example to satisfy all the constraints. The results show that new model can represent more flexible crash scenario in TCTO model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8A
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• pp.787-793
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• 2008

3GPP evolved packet system (EPS) is an all-IP based system that supports various access networks such LTE, HSPA/HSPA+, and non-3GPP networks. Recently, the support of IP flows with packet level QoS profiles was added to the requirements of the EPS. This paper proposes an adaptive modulation and coding (AMC) scheme that supports the QoS of such IP flows in the 3G LTE access network of the EPS. Defining the retransmission as a critical factor for QoS, the proposed scheme applies different maximum packet error probability $P_{max}$ to each packet when selecting the AMC transmission mode. In determining $P_{max}$, the QoS constraints and NACK-to-ACK error as well as channel condition are considered, balancing two objectives: the satisfaction of the QoS and the maximization of spectral efficiency. The simulation results show that it is able to reduce both delay violation and status report by 10%, while improving the throughput 10% in comparison with an existing scheme.

• Journal of the Korea Society of Computer and Information
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• v.5 no.3
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• pp.29-32
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• 2000

Layered video coding schemes allow the video information to be transmitted in multiple video bitstreams to achieve scalability. they are attractive in theory for two reasons. First, they naturally allow for heterogeneity in networks and receivers in terms of client processing capability and network bandwidth. Second, they correspond to optimal utilization of available bandwidth when several video qualify levels are desired. In this paper we propose a scalable video codec architectures with motion estimation, which is suitable for real-time audio and video communication over packet networks. The coding algorithm is compatible with ITU-T recommendation H.263+ and includes various techniques to reduce complexity. Fast motion estimation is Performed at the H.263-compatible base layer and used at higher layers, and perceptual macroblock skipping is performed at all layers before motion estimation. Error propagation from packet loss is avoided by Periodically rebuilding a valid Predictor in Intra mode at each layer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3694-3722
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• 2022

In today's globalized world, there is no transparency in exchanging data and information between producers and consumers. However, these tasks experience many challenges, such as administrative barriers, confidential data leakage, and extensive time delays. To overcome these challenges, we propose a decentralized, secured, and verified smart chain framework using Ethereum Smart Contract which employs Inter Planetary File Systems (IPFS) and MongoDB as storage systems to automate the process and exchange information into blocks using the Tendermint algorithm. The proposed work promotes complete traceability of the product, ensures data integrity and transparency in addition to providing security to their personal information using the Lelantos mode of shipping. The Tendermint algorithm helps to speed up the process of validating and authenticating the transaction quickly. More so in this time of pandemic, it is easier to meet the needs of customers through the Ethermint Smart Chain, which increases customer satisfaction, thus boosting their confidence. Moreover, Smart contracts help to exploit more international transaction services and provide an instant block time finality of around 5 sec using Ethermint. The paper concludes with a description of product storage and distribution adopting the Ethermint technique. The proposed system was executed based on the Ethereum-Tendermint Smart chain. Experiments were conducted on variable block sizes and the number of transactions. The experimental results indicate that the proposed system seems to perform better than existing blockchain-based systems. Two configuration files were used, the first one was to describe the storage part, including its topology. The second one was a modified file to include the test rounds that Caliper should execute, including the running time and the workload content. Our findings indicate this is a promising technology for food supply chain storage and distribution.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.143-149
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• 2022

Since patent documents contain detailed results of research and development technologies, many studies on various patent analysis methods for effective technology analysis have been conducted. In particular, research on quantitative patent analysis by statistics and machine learning algorithms has been actively conducted recently. The most used patent data in quantitative patent analysis is technology keywords. Most of the existing methods for analyzing the keyword data were models based on the Gaussian probability distribution with random variable on real space from negative infinity to positive infinity. In this paper, we propose a model using gamma probability distribution to analyze the frequency data of patent keywords that can theoretically have values from zero to positive infinity. In addition, in order to determine the regression equation of the gamma-based regression model, two-mode network is constructed to visualize the technological association between keywords. Practical patent data is collected and analyzed for performance evaluation between the proposed method and the existing Gaussian-based analysis models.

• Journal of Korean Port Research
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• v.7 no.2
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• pp.37-60
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• 1993

Because of the rapid growing traffic volumes of cargo, especially between Seoul and Inchon, and lack of investment into transport infrastructure in the past, in Kyong-in area have suffered from the serious traffic congestion in the public-road and the express-way network, But the further expansion of the traffic volume in near future is difficult due to burden of the higher expansion of the traffic volume in near future is difficult due to burden of the higher construction cost. Although the traffic congestion on the Kyung-in railway, is not very serious comparing with the road sector, the shortage of capacity on some main lines becomes emerged as a problem as railway traffic has increased. Unlike these two modes, the water transport, which has been paid relatively less attention for commodity transport in Kyong-in area, has not any constaint in this respect. Han river has been used as a water transport route in Chosun Dynasty which is called Cho-wun. This paper therefore aims to propose the availability of Han river as the alternative water transportation mode, in order to decrease the congestion between Seoul-Inchon by considering the construction of Kyong-in artificial water channel in near future. In this paper, we investigate the availability of Nanji-do as the physical distribution depot connecting with the circulation express way in the national capital distribution depot connecting with the circulation express way in the national capital. We also estimate the traffic volume by using the push-barge carrier (300DWT) in the same channel through the simulation under some assumptions such as ship's turnaround time, speed, etc.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.99-106
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• 2004

This paper presents some comparisons between Cournot-Nash and Stackelberg game in bi-level program, composed of both upper level program and lower level one. The upper level can be formulated to optimize a specific objective function, while the lower formulated to express travelers' behavior patterns corresponding to the design parameter of upper level problem. This kind of hi-level program is to determine a design parameter, which leads the road network to an optimal state. Bi-level program includes traffic signal control, traffic information provision, congestion charge and new transportation mode introduction as well as road expansion. From the view point of game theory, many existing algorithms for bi-level program such as IOA (Iterative Optimization Assignment) or IEA (Iterative Estimation Assignment) belong to Cournot-Nash game. But sensitivity-based algorithms belongs to Stackelberg one because they consider the reaction of the lower level program. These two game models would be compared by using an example network and show some results that there is no superiority between the models in deterministic case, but in stochastic case Stackelberg approach is better than that of Cournot-Nash one as we expect.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.