• Journal of Periodontal and Implant Science
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• v.23 no.1
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• pp.193-200
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• 1993

The purpose of this study is to investigate the relationship between occurrence and inducing factors of herpes labialis developed after periodontal therapy and to suggest prediction model of this lesion. A total of 100 patients were studied. A standard schedule was used for interviews of patients. It included demographic information, patient and familial history of recurrent aphthous ulcer and recurrent herpes labialis, history of systemic disease, religion, and emotional state. In case of female patients, the association of dysmenorrhea and onset of recurrent herpes labialis was also observed. After periodontal therapy, some details about therapy, such as the kind of therapy, location, spending time were recorded. At next appointment, the appearance and location of herpes labialis were examined. The frequency of herpes labialis after periodontal therapy was 8% and the location was predominantly mouth angle. The significant relationship was found between the onset of herpes labialis and the history of recurrent herpes labialis, surgical therapy rather than non-surgical therapy, and spending time. The prediction model of herpes labialis was not apparently established with the results of this study. In conclusion it is suggested that we should minimize traumatic manipulation and treatment time to prevent the onset of herpes labialis after periodontal therapy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.457-461
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• 2007

In many applications, received signal strength indicator is used for location tracking and sensor nodes localization. For location finding, the distances between sensor nodes can be estimated by converting received signal's power into distance using path loss prediction model. Many researches have done the analysis of power-distance relationship for radio channel characterization. In indoor environment, the general conclusion is the non-linear variation of RSSI values as distance varied linearly. This has been one of the difficulties for indoor localization. This paper presents works on indoor RSSI characterization based on statistical methods to find the overall trend of RSSI variation at different places and times within the same room From experiments, it has been shown that the variation of RSSI values can be determined by both spatial and temporal factors. This two factors are directly indicated by the two main parameters of path loss prediction model. The results show that all sensor nodes which are located at different places share the same characterization value for the temporal parameter whereas different values for the spatial parameters. Using this relationship, the characterization for location estimation can be more efficient and accurate.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.568-574
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• 2006

User adaptive services have been important features in many applications. To provide such services, various techniques with various kinds of data are being used. In this paper, we propose a method to analyze user's past moving paths and predict the goal position and the path to the goal by observing the user's current moving path. We develop a spatiotemporal similarity measure between paths. We choose a past path which is the most similar to the current path using the similarity. Based on the chosen path, user's spatiotemporal position is estimated. Through experiments we confirm this method is useful and effective.

• Journal of Communications and Networks
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• v.6 no.3
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• pp.245-257
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• 2004

Driven by the growing number of the mobile subscribers, efficient channel resource management plays a key role for provisioning multimedia service in the next generation personal communication systems. To reuse limited channel resources, diminishing the coverage areas of cells seems to be the ultimate solution. Thus, however, causes more handover events. To provide seamless connection environment for mobile terminals and applications, this article presents a novel handover scheme called the intelligent channel reservation (ICR) scheme, which exploits the location prediction technologies to accurately reserve channel resources for handover connections. Considering the fact that each mobile terminal has its individual mobility characteristic, the ICR scheme utilizes a channel reserving notification procedure (CRNP) to collect adequate parameters for predicting the future location of individual mobile terminals. These parameters will be utilized by the handover prediction function to estimate the expected handover blocking rate and the expected number of idle channels. Based on the handover prediction estimations, a cost function for calculating the damages from blocking the handover connections and idling channel resources, and a corresponding algorithm for minimizing the cost function are proposed. In addition, a guard channel decision maker (GCDM) determines the appropriate number of guard channels. The experimental results show that the ICR scheme does reduce the handover-blocking rate while keeping the number of idle channels small.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.295-302
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• 2022

The initial response to a marine accident can play a key role to minimize the accident. Therefore, various decision support systems have been developed using sensors, simulations, and active response equipment. In this study, we developed an algorithm to predict damage locations using ship motion data with bidirectional long short-term memory (BiLSTM), a type of recurrent neural network. To reflect the low frequency ship motion characteristics, 200 time-series data collected for 100 s were considered as input values. Heave, roll, and pitch were used as features for the prediction model. The F1-score of the BiLSTM model was 0.92; this was an improvement over the F1-score of 0.90 of a prior model. Furthermore, 53 of 75 locations of damage had an F1-score above 0.90. The model predicted the damage location with high accuracy, allowing for a quick initial response even if the ship did not have flood sensors. The model can be used as input data with high accuracy for a real-time progressive flooding simulator on board.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.389-391
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• 2021

Traffic regulations are expanding to prevent traffic accidents for people's safety, but traffic accidents are not decreasing. In this study, the probability of traffic accidents occurring at a specific time and place is estimated by analyzing various factors such as weather forecast data from the Meteorological Agency, day of the week, time of day, location data, and location information. This study combines objective data on the occurrence of numerous previous traffic accidents with various additional elements not considered in previous studies to derive a more improved traffic accident probability prediction model. The results of this study can be effectively used for various transportation-related services for the safety of people.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.5
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• pp.303-310
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• 2020

In this study, a method for selection of the location and number of monitoring sensors in a building structure using artificial neural networks is proposed. The acceleration-history values obtained from the installed accelerometers are defined as the input values, and the mass and stiffness values of each story in a building structure are defined as the output values. To select the installation location and number of accelerometers, several installation scenarios are assumed, artificial neural networks are obtained, and the prediction performance is compared. The installation location and number of sensors are selected based on the prediction accuracy obtained in this study. The proposed method is verified by applying it to 6- and 10-story structure examples.

• Journal of Communications and Networks
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• v.17 no.2
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• pp.126-132
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• 2015

In mobile computing research area, it is highly desirable to understand the characteristics of user movement so that the user friendly location aware services could be rendered effectively. Location based social networks (LBSNs) have flourished recently and are of great potential for movement behavior exploration and datadriven application design. While there have been some efforts on user check-in movement behavior in LBSNs, they lack comprehensive analysis of social influence on them. To this end, the social-spatial influence and social-temporal influence are analyzed synthetically in this paper based on the related information exposed in LBSNs. The check-in movement behaviors of users are found to be affected by their social friendships both from spatial and temporal dimensions. Furthermore, a probabilistic model of user mobile behavior is proposed, incorporating the comprehensive social influence model with extent personal preference model. The experimental results validate that our proposed model can improve prediction accuracy compared to the state-of-the-art social historical model considering temporal information (SHM+T), which mainly studies the temporal cyclic patterns and uses them to model user mobility, while being with affordable complexity.

• 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.

• Korean Journal of Head & Neck Oncology
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• v.22 no.1
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• pp.29-32
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• 2006

Background: The location of parotid gland tumors can influence the duration and the difficulty of the operation. If the information about tumor location was available preoperatively, it would allow accurate operative planning and counseling of patients in terms of the length of the operation and the potential morbidity. Methods: This study was based on a retrospective review of 100 patients with parotid gland tumors underwent parotidectomy from January 2000 to October 2005 at Yong-Dong Severance Hospital. Based on computed tomographic(CT) scan findings, 4 landmarks such as facial nerve(FN) line, Utrecht(U) line, Conn's are(CA), and retromandibular vein (RV) were drawn on the scans in same plane. The location of tumors were determined by the landmarks and confirmed by the operative findings. The accuracy of each landmarks was evaluated. To find out the accuracies according to tumor size, the tumors were divided into 2 groups; less than 2 cm and larger than 2 cm in diameter. Results: U line was the most accurate(94%), sensitive(89.3%) and specific(97.7%) in predicting tumor location of the parotid gland. However, in small tumors less than 2cm, FN line (p=0.022) and RV criteria (p=0.028) were more reliable in accuracy. Conclusion: CA, FN line, U line, and RV are all useful landmarks in preoperative prediction for the location of parotid gland tumors. However, U line was the most accurate, but we must consider that proper landmark should be used in prediction according to the size of tumor because the accuracy of landmark may change.