• The Journal of the Acoustical Society of Korea
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• 제39권6호
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• pp.615-621
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• 2020

In this paper, we propose a multi-site based earthquake event classification method using graph convolution networks. In the traditional earthquake event classification methods using deep learning, they used single-site observation to estimate seismic event class. However, to achieve robust and accurate earthquake event classification on the seismic observation network, the method using the information from the multi-site observations is needed, instead of using only single-site data. Firstly, our proposed model employs convolution neural networks to extract informative embedding features from the single-site observation. Secondly, graph convolution networks are used to integrate the features from several stations. To evaluate our model, we explore the model structure and the number of stations for ablation study. Finally, our multi-site based model outperforms up to 10 % accuracy and event recall rate compared to single-site based model.

• Journal of the Korea Society for Simulation
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• 제21권4호
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• pp.35-46
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• 2012

As complexity of real-time systems is being increased ad hoc approaches to analysis of such systems would have limitations in completeness and coverability for states space search. Formal means using a model-based approach would solve such limitations. This paper proposes a model-based formal method for logical analysis, such as safety and liveness, of real-time systems at a discrete event system level. A discrete event model for real-time systems to be analyzed is specified by DEVS(Discrete Event Systems Specification) formalism, which specifies a discrete event system in hierarchical, modular manner. Analysis of such DEVS models is performed by Communicating DEVS (C-DEVS) formalism of a timed global state transition specification and an associated analysis algorithm. The C-DEVS formalism and an associated analysis algorithm guarantees that all possible states for a given system are visited in an analysis phase. A case study of a safety analysis for a rail road crossing system illustrates the effectiveness of the proposed method of the model-based approach.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제39권2호
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• pp.129-137
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• 2016

To identify the cause of the error and maintain the health of system, an administrator usually analyzes event log data since it contains useful information to infer the cause of the error. However, because today's systems are huge and complex, it is almost impossible for administrators to manually analyze event log files to identify the cause of an error. In particular, as OpenStack, which is being widely used as cloud management system, operates with various service modules being linked to multiple servers, it is hard to access each node and analyze event log messages for each service module in the case of an error. For this, in this paper, we propose a novel message-based log analysis method that enables the administrator to find the cause of an error quickly. Specifically, the proposed method 1) consolidates event log data generated from system level and application service level, 2) clusters the consolidated data based on messages, and 3) analyzes interrelations among message groups in order to promptly identify the cause of a system error. This study has great significance in the following three aspects. First, the root cause of the error can be identified by collecting event logs of both system level and application service level and analyzing interrelations among the logs. Second, administrators do not need to classify messages for training since unsupervised learning of event log messages is applied. Third, using Dynamic Time Warping, an algorithm for measuring similarity of dynamic patterns over time increases accuracy of analysis on patterns generated from distributed system in which time synchronization is not exactly consistent.

• Journal of KIISE
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• 제43권7호
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• pp.781-785
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• 2016

Learning from human behaviors in the real world is essential for human-aware intelligent systems such as smart assistants and autonomous robots. Most of research focuses on correlations between sensory patterns and a label for each activity. However, human activity is a combination of several event contexts and is a narrative story in and of itself. We propose a novel approach of human activity prediction based on event cognition. Egocentric multi-sensor data are collected from an individual's daily life by using a wearable device and smartphone. Event contexts about location, scene and activities are then recognized, and finally the users" daily activities are predicted from a decision rule based on the event contexts. The proposed method has been evaluated on a wearable sensor data collected from the real world over 2 weeks by 2 people. Experimental results showed improved recognition accuracies when using the proposed method comparing to results directly using sensory features.

• IEMEK Journal of Embedded Systems and Applications
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• 제14권2호
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• pp.87-96
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• 2019

Low power issue is one of the most critical problems in the Internet of Things (IoT), which are powered by battery. To solve this problem, various approaches have been presented so far. In this paper, we propose a method to reduce the power consumption by reducing the numbers of accesses into the flash memory consuming a large amount of power for on-chip software execution. Our approach is based on using cooperative logging structure to distribute the sampling overhead in single sensor node to adjacent nodes in case of rare-event applications. The proposed algorithm to identify event occurrence is newly introduced with negative feedback method by observing difference between past data and recent data coming from the sensor. When an event with need of flash access is determined, the proposed approach only allows access to write the sampled data in flash memory. The proposed event detection algorithm (EDA) result in 30% reduction of power consumption compared to the conventional flash write scheme for all cases of event. The sampled data from the sensor is first traced into the random access memory (RAM), and write access to the flash memory is delayed until the page buffer of the on-chip flash memory controller in the micro controller unit (MCU) is full of the numbers of the traced data, thereby reducing the frequency of accessing flash memory. This technique additionally reduces power consumption by 40% compared to flash-write all data. By sharing the sampling information via LoRa channel, the overhead in sampling data is distributed, to reduce the sampling load on each node, so that the 66% reduction of total power consumption is achieved in several IoT edge nodes by removing the sampling operation of duplicated data.

• Nuclear Engineering and Technology
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• 제49권2호
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• pp.395-403
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• 2017

An efficient evaluation method for the probability of a tornado missile strike without using the Monte Carlo method is proposed in this paper. A major part of the proposed probability evaluation is based on numerical results computed using an in-house code, Tornado-borne missile analysis code, which enables us to evaluate the liftoff and flight behaviors of unconstrained objects on the ground driven by a tornado. Using the Tornado-borne missile analysis code, we can obtain a stochastic correlation between local wind speed and flight distance of each object, and this stochastic correlation is used to evaluate the conditional strike probability, $Q_V(r)$, of a missile located at position r, where the local wind speed is V. In contrast, the annual exceedance probability of local wind speed, which can be computed using a tornado hazard analysis code, is used to derive the probability density function, p(V). Then, we finally obtain the annual probability of tornado missile strike on a structure with the convolutional integration of product of $Q_V(r)$ and p(V) over V. The evaluation method is applied to a simple problem to qualitatively confirm the validity, and to quantitatively verify the results for two extreme cases in which an object is located just in the vicinity of or far away from the structure.

• Journal of the Korean Institute of Telematics and Electronics C
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• 제34C권5호
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• pp.93-102
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• 1997

Conventional expert systems have difficulties in the inference on time-varing situations because they don't have the structure for processing time related informations and rule representation method to describe time explicitely. Some expert systems capable of temporal reasoning are not applicable to the domain in which state changes happen by unpredictble events that cannot be represented by periodic changes of data. In this paper, an event based temporal reasoning method is proposed. It is capable of processing te unpredictable events, representing the knowledge related to event and time, and infering by that knowledge as well as infering by periodically time-varing data. The NEO/temporal, an temporal inference engine, is implemented by applying the proposed temporal reasoning situation assessment and decision supporting system is implemented to show the benefits of the proposed temporal information processing model.

• Proceedings of the Korean Society of Computer Information Conference
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• 한국컴퓨터정보학회 2008년도 제38차 하계학술발표논문집 16권1호
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• pp.199-204
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• 2008

Visual surveillance is an active topic recently in Computer Vision. Event detection and recognition is one important and useful application of visual surveillance system. In this paper, we propose a new method to recognize the entering and exiting events based on the human's movement feature and the door's state. Without sensors, the proposed approach is based on novel and simple vision method as a combination of edge detection, motion history image and geometrical characteristic of the human shape. The proposed method includes several applications such as access control in visual surveillance and computer vision fields.

• KIEE International Transactions on Power Engineering
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• 제5A권3호
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• pp.280-285
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• 2005

This paper proposes a novel method for determining the kind and rating of power quality solutions. To determine the kind of solution, event cause and direction are utilized. According to the event cause and direction, an adequate type of solution is determined for effective compensation. To rate the required capacity of solution, the concept of lost energy is adopted. Lost voltage, lost power and lost energy are calculated and the rating of the solution is determined to compensate a specific event. The rating method that utilizes the result of stochastic diagnosis is also proposed. A power quality index such as CP95 is adopted for solution suggestion. The method developed in this paper is applied to the test system and proved to be useful for enhancing the power quality of the customer system. It can provide customers with information pertaining to what is a proper and cost-effective solution among various compensating devices.

• Journal of Global Scholars of Marketing Science
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• 제16권2호
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• pp.95-108
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• 2006

The major objective of this study is to test if an Olympic Games sponsorship program can influence investors' behavior: stock returns, stock volatility and transaction volumes. The paper deals with stock market reaction on Olympic sponsorship announcement for service organizations using event study method. Our research intention is to test 440 daily stock prices and transaction volumes, in order investigate the potent influence between the announcement of a grand sport sponsorship program and investors' behavior. For this study we examined the announcement data of three grand sponsors of Olympic Games of Athens 2004 (Alpha Bank. Delta and G.T.O) The main contribution of this study is to examine how stock investors' behavior is influenced by the sponsorship program of companies and to extend research scope of marketing field toward stock market. They authors suggest that organizations interested in influencing investors' behavior should invest in sponsorship activities at the sports' sector.