• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.321-328
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• 2014

In this paper, we present a MapReduce-based mobility pattern mining system which can predict efficiently the next place of mobile users. It learns the mobility pattern model of each user, represented by Hidden Markov Models(HMM), from a large-scale trajectory dataset, and then predicts the next place for the user to visit by applying the learned models to the current trajectory. Our system consists of two parts: the back-end part, in which the mobility pattern models are learned for individual users, and the front-end part, where the next place for a certain user to visit is predicted based on the mobility pattern models. While the back-end part comprises of three distinct MapReduce modules for POI extraction, trajectory transformation, and mobility pattern model learning, the front-end part has two different modules for candidate route generation and next place prediction. Map and reduce functions of each module in our system were designed to utilize the underlying Hadoop infrastructure enough to maximize the parallel processing. We performed experiments to evaluate the performance of the proposed system by using a large-scale open benchmark dataset, GeoLife, and then could make sure of high performance of our system as results of the experiments.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.73-84
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• 2024

Owing to the high strength and abrasive characteristics of cobble-boulders, cutters are easily worn and damaged during shield tunneling, making construction inefficient. In the present work, the stress on the ripper and scraper on the cutterhead was analyzed by the PFC3D-FLAC3D coupling model of shield tunneling to get insight into the performance of the cutterhead for cutting underground cobble and boulders. The numerical calculation results revealed that the increase in trajectory radius leads to a rising stress on the cutters, and the stress on the front cutting surface is greater than that on the back of the cutters. Moreover, the correlation between cutter wear and stress is revealed based on field measurement data. The distribution of the cutter stress is consistent with the cutter wear and breakage characteristics in actual construction, in which more extensive cutter stress is exhibited, extreme cutter wear appears, and more cutter breakage occurs. Finally, the relationship between the cutterhead opening area's layout and cutter wear distribution was investigated, indicating that the cutter wear extent is the most severe in the region where the radial opening ratio dropped sharply.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.1-15
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• 2017

Recently terrorism targets unspecified masses and causes massive destruction, which is so-called Super Terrorism. Many countries have tried hard to protect their citizens with various preparation and safety net. With inexpensive and advanced technologies of sensors, the surveillance systems have been paid attention, but few studies associated with the classification of the pedestrians' trajectories and the difference among themselves have attempted. Therefore, we collected individual trajectories at Samseoung Station using an analytical solution (system) of pedestrian trajectory by LiDAR sensor. Based on the collected trajectory data, a comprehensive framework of classifying the types of pedestrians' trajectories has been developed with data normalization and "trajectory association rule-based algorithm." As a result, trajectories with low similarity within the very same cluster is possibly detected.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.217-225
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• 2019

The Latent Growth Modeling(LGM) is known as the typical analysis method of longitudinal data and it could be classified into unconditional model and conditional model. It is common to assume that the growth trajectory of unconditional model of LGM is linear. In the case of quasi-linear, the methodology for improving the model fitness using Sequential Pattern of Association Rule Mining is suggested. To do this, we divide longitudinal data into quintiles and extract periodic changes of the longitudinal data in each quintiles and make sequential pattern based on this periodic changes. To evaluate the effectiveness, the LGM module in SPSS AMOS was used and the dataset of the Youth Panel from 2001 to 2006 of Korea Employment Information Service. Our methodology was able to increase the fitness of the model compared to the simple linear growth trajectory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1236-1251
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• 2013

Sharing geo-tagged photos has been a hot social activity in the daily life because these photos not only contain geo information but also indicate people's hobbies, intention and mobility patterns. However, the present raw geo-tagged photo routes cannot provide information as enough as complete GPS trajectories due to the defects hidden in them. This paper mainly aims at analyzing the large amounts of geo-tagged photos and proposing a novel travel route restoring method. In our approach we first propose an Interest Measure Ratio to rank the hot spots based on density-based spatial clustering arithmetic. Then we apply the Hidden Semi-Markov model and Mean Value method to demonstrate migration discipline in the hot spots and restore the significant region sequence into complete GPS trajectory. At the end of the paper, a novel experiment method is designed to demonstrate that the approach is feasible in restoring route, and there is a good performance.

• Coupled systems mechanics
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• v.2 no.1
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• pp.23-51
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• 2013

An analytical algorithm for the estimation of the resistance forces exerted on the dipper of a cable shovel and the specific energy consumed in the cutting-loading process is presented. Forces due to payload and to cutting of geomaterials under given initial conditions, cutting trajectory of the bucket, bucket's design, and geomaterial properties are analytically computed. The excavation process has been modeled by means of a kinematical shovel model, as well as of dynamic payload and cutting resistance models. For the calculation of the cutting forces, a logsandwich passive failure mechanism of the geomaterial is considered, as has been found by considering that a slip surface propagates like a mixed mode crack. Subsequently, the Upper-Bound theorem of Limit Analysis Theory is applied for the approximate calculation of the maximum reacting forces exerted on the dipper of the cable shovel. This algorithm has been implemented into an Excel$^{TM}$ spreadsheet to facilitate user-friendly, "transparent" calculations and built-in data analysis techniques. Its use is demonstrated with a realistic application of a medium-sized shovel. It was found, among others, that the specific energy of cutting exhibits a size effect, such that it decreases as the (-1)-power of the cutting depth for the considered example application.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.181-184
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• 1997

Practical applications of learning systems usually involve complex domains exhibiting nonlinear behavior and dilution by noise. Consequently, an intelligent system must be able to adapt to nonlinear processes as well as probabilistic phenomena. An important class of application for a knowledge based systems in prediction: forecasting the future trajectory of a process as well as the consequences of any decision made by e system. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes in the form of chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a Henon process in the presence of various patterns of noise.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.473-482
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• 2019

There have already been studies analyzing the trajectories of objects from data streams of moving objects. Among those studies, there are also studies to discover groups of objects that move together, called companion groups. Most studies to discover companion groups use existing clustering techniques to find groups of objects close to each other. However, these clustering-based methods are often difficult to find the right companion groups because the number of clusters is unpredictable in advance or the shape or size of clusters is hard to control. In this study, we propose a new method that discovers companion groups based on the distance specified by the user. The proposed method does not apply the existing clustering techniques but periodically determines the groups of objects close to each other, by using a technique that efficiently finds the groups of objects that exist within the user-specified distance. Furthermore, unlike the existing methods that return only companion groups and their trajectories, the proposed method also returns their appearance and disappearance time. Through various experiments, we show that the proposed method can detect companion groups correctly and very efficiently.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.2
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• pp.179-189
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• 2006

Ambient gas phase mercury concentrations including elemental mercury ($Hg^0$) were measured at the Potsdam, Stockton, and Sterling sites in NY from 2000 to 2003. Also, concentrations of ambient reactive gaseous mercury (RGM; $Hg^{2+}$) were measured at the Potsdam site during one year. The contribution of RGM($4.2{\pm}6.4pg/m^3$) was about $0.2{\sim}3%$ of the total gas phase mercury concentration measured (TGM: $1.84{\pm}1.24,\;1.83{\pm}0.32,\;3.02{\pm}2.14ng/m^3$ in Potsdam. Stockton, and Sterling, respectively) at the receptor sites. Potential Source Contribution Function (PSCF), a hybrid receptor modeling incorporating backward trajectories was performed to identify source areas of TGM. Using PSCF, southern New York, North Carolina, and eastern Massachusetts were identified as important source areas in the United States, while the copper smelters and waste incinerators located in eastern Quebec and Ontario were determined to be significant sources in Canada. The Atlantic Ocean was suggested to be a possible mercury source. PSCF incorporating back-dispersion and deposition was applied for RGM , as well as PSCF based on 2-days back-trajectories. Two different approaches yielded considerably different results, primarily due to the consideration of dispersion rather than deposition. Using back-trajectory based PSCF, eastern Ohio, southern New York, and southern Pennsylvania where large coal -fired power plants area located were identified as the large sources in US. Metallurgical industry located in eastern Quebec was resolved as well. From the result of back-dispersion and deposition based PSCF, Pennsylvania, mining facilities around Lake Superior, Toronto, Boston, MA, Quebec, and coal power plants in NY were identified to be the significant source areas for Potsdam site.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.11
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• pp.1332-1340
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• 2009

The front end wheel loader is widely used for the loading of materials in mining and construction fields. It has repetitive digging, loading and dumping procedures. The bucket is subjected to large resistance force from the soil during scooping. We considered the soil reaction force characteristics from scooping procedure, the protection by overload and automatic scooping mode algorithm. The main topic of this paper is the analysis of the soil reaction force characteristics. The analysis of soil mechanics is carried out and the developed soil model is verified by experimental results from the simplified experimental equipment. A simplified model of the soil shape and bucket trajectory is used to determine the scooping direction based on an estimation of the resistance force applied on the bucket during the scooping motion. In the future, this model will be used for the generation of an appropriate path for the wheel loader automation.