• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.84-89
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• 2009

Personal mobile devices such as mobile phone, PMP and MP3 player have advanced incredibly. Such advance in mobile technology ignites the research related to the life-log to understand the daily life of an user. Since life-log collected by mobile sensors can aid memory of the user, many researches have been conducted. This paper suggests a methodology for user-context recognition and visualization based on the outdoor location by GPS as well as indoor location by wireless-lan. When the GPS sensor does not work well in an indoor location, wireless-lan plays a major role in recognizing the location of an user so that the recognition of user-context become more accurate. In this paper, we have also developed the method for visualization of the life-log based on map and blog interfaces. In the experiments, subjects have collected real data with mobile devices and we have evaluated the performance of the proposed visualization and context recognition method based on the data.

• Proceedings of the Korea Information Assurance Society Conference
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• 2004.05a
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• pp.103-108
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• 2004

A Home Location Register(HLR) database system manages each subscriber's location information, which continuously changes in a cellular network. For this purpose, the HLR database system provides table management, index management, and backup management facilities. In this thesis, I propose using a two-level index method for the mobile directory number(MDN) as a suitable method and a chained bucket hashing method for the electronic serial number(ESN). Both the MDN and the ESN are used as keys in the HLR database system. I also propose an efficient backup method that takes into account the characteristics of HLR database transactions. The retrieval speed and the memory usage of the two-level index method are better than those of the T-tree index method. The insertion and deletion overhead of the chained bucket hashing method is less than that of the modified linear hashing method. In the proposed backup method, I use two kinds of dirty flags in order to solvethe performance degradation problem caused by frequent registration-location operations. I also propose using additional attributes in the HLR database scheme for location information secrecy as a suitable security method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1005-1012
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• 2021

Recently, the use of various location-based services-based location information systems using maps on the web has been expanding, and there is a need for a monitoring system that can check power demand in real time as an alternative to energy saving. In this study, we developed a deep learning real-time virtual power demand prediction web system using open source-based mapping service to analyze and predict the characteristics of power demand data using deep learning. In particular, the proposed system uses the LSTM(Long Short-Term Memory) deep learning model to enable power demand and predictive analysis locally, and provides visualization of analyzed information. Future proposed systems will not only be utilized to identify and analyze the supply and demand and forecast status of energy by region, but also apply to other industrial energies.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.91-98
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• 2023

With the increasing flood damage by frequently localized heavy rains, flood prediction research are being conducted to prevent flooding damage in advance. In this paper, we present a machine-learning scheme for developing a flooding depth and location prediction model using real-time rainfall data. This scheme proposes a dataset configuration method using the data as input, which can robustly configure various rainfall distribution patterns and train the model with less memory. These data are composed of two: valid total data and valid local. The one data that has a significant effect on flooding predicted the flooding location well but tended to have different values for predicting specific rainfall patterns. The other data that means the flood area partially affects flooding refers to valid local data. The valid local data was well learned for the fixed point method, but the flooding location was not accurately indicated for the arbitrary point method. Through this study, it is expected that a lot of damage can be prevented by predicting the depth and location of flooding in a real-time manner.

• Korean Journal of Cognitive Science
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• v.26 no.1
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• pp.27-51
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• 2015

The present study tested the effect of a top-down influence on recalling the colors of Stroop words. Participants remembered the colors of 1, 2, 3 or 6 Stroop words. After 1 second of a memory delay, they were asked to recall the color of a cued Stroop word by selecting out its corresponding color on a color-wheel stimulus. The correct recall was defined when the participants chose a color that was within ${\pm}45^{\circ}$ from the exact location of Stroop word's color on the color-wheel. Otherwise, the recall was defined as incorrect. The analyses of the frequency distribution of the participants' responses in the error trials showed that the probability of choosing the color-name of the target Stroop word was higher than the probability of other five color-names on the color-wheel. Further analyses showed that increasing the number of Stroop words to manipulate memory load did not affect the probability of the Stroop interference. These results indicate that the top-down interference by Stroop manipulation may induce systematic distortion of the stored representation in visual working memory.

• Journal of Korea Spatial Information System Society
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• v.8 no.3
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• pp.1-14
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• 2006

Recently, to efficiently support the real-time requirements of LBS and Telematics services, interest in the spatial main memory DBMS is rising. In the spatial main memory DBMS, because all spatial data can be lost when the system failure happens, the recovery system is very important for the stability of the database. Especially, disk I/O in executing the log and the checkpoint becomes the bottleneck of letting down the total system performance. Therefore, it is urgently necessary to research about the recovery system to reduce disk I/O in the spatial main memory DBMS. In this paper, we study an efficient recovery system for the spatial main memory DBMS. First, the pre-commit log method is used for the decrement of disk I/O and the improvement of transaction concurrency. In addition, we propose the fuzzy-shadow checkpoint method for the recovery system of the spatial main memory DBMS. This method can solve the problem of duplicated disk I/O on the same page of the existing fuzzy-pingpong checkpoint method for the improvement of the whole system performance. Finally, we also report the experimental results confirming the benefit of the proposed recovery system.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.7
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• pp.349-358
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• 2005

False sharing is a result of co-location of unrelated data in the same unit of memory coherency, and is one source of unnecessary overhead being of no help to keep the memory coherency in multiprocessor systems. Moreover. the damage caused by false sharing becomes large in proportion to the granularity of memory coherency. To reduce false sharing in a page-based DSM system, it is necessary to allocate unrelated data objects that have different access patterns into the separate shared pages. In this paper we propose call-site tracing-based shared memory allocator. shortly CSTallocator. CSTallocator expects that the data objects requested from the different call-sites may have different access patterns in the future. So CSTailocator places each data object requested from the different call-sites into the separate shared pages, and consequently data objects that have the same call-site are likely to get together into the same shared pages. We use execution-driven simulation of real parallel applications to evaluate the effectiveness of our CSTallocator. Our observations show that by using CSTallocator a considerable amount of false sharing misses can be additionally reduced in comparison with the existing techniques.

• The Journal of the Korea Contents Association
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• v.5 no.2
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• pp.187-197
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• 2005

In this paper, we propose an extended TB-tree, called ETB-tree, which can improve the performance of an existing TB-tree proposed for indexing the trajectories of moving objects in Location-Based Service(LBS). The proposed ETB-tree directly accesses the preceding node by maintaining a direct table, called D-Table which contains the page number in disk and memory pointers pointing the leaf node with the first and last lines segment of moving objects. It can improve the insertion performance by quick searching the preceding node of a moving object and retrieval performance owing to accessing directly the corresponding trajectories In disk for the trajectory-based query. In addition, the ETB-tree provides consistency of a tree by reflecting a newly inserted line segment to the tree both in memory and disk. The experimental results show that the proposed indexing technique gains better performance than other traditional ones with respect to the insertion and retrieval of a trajectory query.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.237-248
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• 2009

Recently, based on dynamic location or mobility of moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for development of location based services. The performance of moving pattern mining depend on how analyze and process the huge set of spatio-temporal data. Some of traditional spatio-temporal pattern mining methods[1-6,8-11]have proposed to solve these problem, but they did not solve properly to reduce mining execution time and minimize required memory space. Therefore, in this paper, we propose new spatio-temporal pattern mining method which extract the sequential and periodic frequent moving patterns efficiently from the huge set of spatio-temporal moving data. The proposed method reduces mining execution time of $83%{\sim}93%$ rate on frequent moving patterns mining using the moving sequence tree which generated from historical data of moving objects based on hash tree. And also, for minimizing the required memory space, it generalize the detained historical data including spatio-temporal attributes into the real world scope of space and time using spatio-temporal concept hierarchy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.955-961
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• 2021

In modern warfare, the importance of electronic warfare, which carries out a mission that using radio wave to find out enemy information or to protect ally information, has increased. Radar jamming technique is one of the most representative techniques of EA(Electronic Attack), it disturbs and deceives enemy radar system in order to secure ally location information. Velocity deception jamming technique, which is one of the radar jamming techniques, generally operate against pulse-doppler radar which use doppler effect in order to track target's velocity and location. Velocity Deception Jamming Technique can be implemented using DRFM(Digital Radio Frequency Memory) that performs Frequency Modulation. In this paper, I describe implementation method of VGPO/VGPI(Velocity Gate Pull-Off/Pull-In) velocity deception jamming technique using phase-sampled DRFM, and verify the operation of VGPO/VGPI velocity deception jamming technique with board test under signal injection condition.