• Journal of KIISE:Databases
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• v.33 no.5
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• pp.495-512
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• 2006

Recently, researches on a future position prediction of moving objects have been progressed as the importance of the future position retrieval increases. New index structures are required to efficiently retrieve the consecutive positions of moving objects. Existing index structures significantly degrade the search performance of the moving objects because the search operation makes the unnecessary extension of the node in the index structure. To solve this problem, we propose a space partition based index structure considering the mobility of moving objects. To deal with the overflow of a node, our index structure first merges it and the sibling node. If it is impossible to merge them, our method splits the overflow node in which moving properties of objects are considered. Our index structure is always partitioned into overlap free subregions when a node is split. Our split strategy chooses the split position by considering the parameters such as velocities, the escape time of the objects, and the update time of a node. In the internal node, the split position Is determined from preventing the cascading split of the child node. We perform various experiments to show that our index structure outperforms the existing index structures in terms of retrieval performance. Our experimental results show that our proposed index structure achieves about $17%{\sim}264%$ performance gains on current position retrieval and about $107%{\sim}19l%$ on future position retrieval over the existing methods.

• Journal of KIISE:Databases
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• v.31 no.6
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• pp.624-640
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• 2004

Recently, with the rapid development of location-based techniques, index structures to efficiently manage moving objects have been required. In this paper, we propose a new spatio-temporal index structure that supports a future position retrieval and minimizes a update cost. The proposed index structure combines an assistant index structure that directly accesses current positions of moving objects with KDB-tree that is a space partitioning access method. The internal node in our proposed index structure keeps time parameters in order to support the future position retrieval and to minimize a update cost. Moreover, we propose new update and split methods to maximize the space utilization and the search performance. We perform various experiments to show that our proposed index structure outperforms the existing index structure.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.6
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• pp.156-163
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• 2008

Future Combat System(FCS), such as unmanned systems that reduce the danger faced by soldiers in the field, are likely to be studied and developed. Soldiers when finding and disposing of mines risk injury and death. Several methods of safe mine retrieval are investigated. In this paper, a mine removal method, which uses a remote controlled robot to get rid of mines using a 4 channel architecture teleoperation method is used. The robot, when in contact with soil and mines, is controlled by a remote control. The feasibility of using teleoperation controlled system to remove mines is demonstrated in this paper. The Matlab-Simulink was used as a tool to simulate mine removal with robots. The force and position of the robot{slave system of 4 channel architecture) and controller(master system of 4 channel architecture) are analyzed when users handle the controller with sinusoidal force.

• Journal of Internet Computing and Services
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• v.5 no.4
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• pp.77-94
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• 2004

Recently, the needs of index structure which manages moving objects efficiently have been increased because of the rapid development of location-based techniques. Existing index structures frequently need updates because moving objects change continuatively their positions. That caused entire performance loss of the index structures. In this paper, we propose a new index structure called the TPKDB-tree that is a spatio-temporal index structure based on KDB-tree. Our technique optimizes update costs and reduces a search time for moving objects and reduces unnecessary updates by expressing moving objects as linear functions. Thus, the TPKDB-tree efficiently supports the searches of future positions of moving objects by considering the changes of moving objects included in the node as time-parameter. To maximize space utilization, we propose the new update and split methods. Finally, we perform various experiments to show that our approach outperforms others.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.477-490
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• 2006

Existing index structures need very much update cost because they repeat delete and insert operations in order to update continuously moving objects. In this paper, we propose a new index structure which reduces the update cost of continuously moving objects. The proposed index structure consists of a space partitioning index structure that stores the location of the moving objects and an auxiliary index structure that directly accesses to their current positions. In order to increase the fanout of the node, it stores not the real partitioning area but kd-tree as the information about the child node of the node. In addition, we don't traverse a whole index structure, but access the leaf nodes directly and accomplish a bottom-up update strategy for efficiently updating the positions of moving objects. We show through the various experiments that our index structure outperforms the existing index structures in terms of insertion, update and retrieval.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.461-474
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• 2020

Korea launched Geostationary Environmental Monitoring Satellite (GEMS), a UV/visible spectrometer that measure pollution gases on 18 February 2020. Because satellite retrieval is an ill-posed inverse solving process, the validation with ground-based measurements or other satellite measurements is essential to obtain reliable products. For this purpose, satellite-based OMI and OMPS total column ozone (TCO), and ground-based Pandora TCO in Busan and Seoul were selected for future GEMS validation. First of all, the goal of this study is to validate the ground ozone data using characteristics that satellite data provide coherent ozone measurements on a global basis, although satellite data have a larger error than the ground-based measurements. In the cross validation between Pandora and OMI TCO, we have found abnormal deviation in ozone time series from Pandora #29 observed in Seoul. This shows that it is possible to perform inverse validation of ground data using satellite data. Then OMPS TCO was compared with verified Pandora TCO. Both data shows a correlation coefficient of 0.97, an RMSE of less than 2 DU and the OMPS-Pandora relative mean difference of >4%. The result also shows the OMPS-Pandora relative mean difference with SZA, TCO, cross-track position and season have insignificant dependence on those variables.In addition, we showed that appropriate thresholds depending on the spatial resolution of each satellite sensor are required to eliminate the impact of the cloud on Pandora TCO.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.131-150
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• 2015

Omission of noun phrases for obligatory cases is a common phenomenon in sentences of Korean and Japanese, which is not observed in English. When an argument of a predicate can be filled with a noun phrase co-referential with the title, the argument is more easily omitted in Encyclopedia texts. The omitted noun phrase is called a zero anaphor or zero pronoun. Encyclopedias like Wikipedia are major source for information extraction by intelligent application systems such as information retrieval and question answering systems. However, omission of noun phrases makes the quality of information extraction poor. This paper deals with the problem of developing a system that can restore omitted noun phrases in encyclopedia documents. The problem that our system deals with is almost similar to zero anaphora resolution which is one of the important problems in natural language processing. A noun phrase existing in the text that can be used for restoration is called an antecedent. An antecedent must be co-referential with the zero anaphor. While the candidates for the antecedent are only noun phrases in the same text in case of zero anaphora resolution, the title is also a candidate in our problem. In our system, the first stage is in charge of detecting the zero anaphor. In the second stage, antecedent search is carried out by considering the candidates. If antecedent search fails, an attempt made, in the third stage, to use the title as the antecedent. The main characteristic of our system is to make use of a structural SVM for finding the antecedent. The noun phrases in the text that appear before the position of zero anaphor comprise the search space. The main technique used in the methods proposed in previous research works is to perform binary classification for all the noun phrases in the search space. The noun phrase classified to be an antecedent with highest confidence is selected as the antecedent. However, we propose in this paper that antecedent search is viewed as the problem of assigning the antecedent indicator labels to a sequence of noun phrases. In other words, sequence labeling is employed in antecedent search in the text. We are the first to suggest this idea. To perform sequence labeling, we suggest to use a structural SVM which receives a sequence of noun phrases as input and returns the sequence of labels as output. An output label takes one of two values: one indicating that the corresponding noun phrase is the antecedent and the other indicating that it is not. The structural SVM we used is based on the modified Pegasos algorithm which exploits a subgradient descent methodology used for optimization problems. To train and test our system we selected a set of Wikipedia texts and constructed the annotated corpus in which gold-standard answers are provided such as zero anaphors and their possible antecedents. Training examples are prepared using the annotated corpus and used to train the SVMs and test the system. For zero anaphor detection, sentences are parsed by a syntactic analyzer and subject or object cases omitted are identified. Thus performance of our system is dependent on that of the syntactic analyzer, which is a limitation of our system. When an antecedent is not found in the text, our system tries to use the title to restore the zero anaphor. This is based on binary classification using the regular SVM. The experiment showed that our system's performance is F1 = 68.58%. This means that state-of-the-art system can be developed with our technique. It is expected that future work that enables the system to utilize semantic information can lead to a significant performance improvement.