• Atmosphere
• /
• v.29 no.5
• /
• pp.627-637
• /
• 2019

Severe and long-lasting heat waves over Korea and many regions in the Northern Hemisphere (NH) during the 2016 summer, have been attributed to global warming and atmospheric teleconnection coupled with tropical convective activities. Yet, what controls subseasonsal time scale of heat wave has not been well addressed. Here we show a critical role of two dominant boreal summer intraseasonal oscillation (BSISO) modes, denominated as BSISO1 and BSISO2, on modulating temporal structure of heat waves in the midst of similar climate background. The 2016 summer was characterized by La Nina development following decay of strong 2015/2016 El Nino. The NH circumglobal teleconnection pattern (CGT) and associated high temperature anomalies and heat waves were largely driven by convective activity over northwest India and Pakistan during summer associated with La Nina development. However, the heat wave event in Korea from late July to late August was accompanied by the phase 7~8 of 30~60-day BSISO1 characterized by convective activity over the South China Sea and Western North Pacific and anticyclonic circulation (AC) anomaly over East Asia. Although the 2010 summer had very similar climate anomalies as the 2016 summer with La Nina development and CGT, short-lasting but frequent heat waves were occurred during August associated with the phase 1~2 of 10~30-day BSISO2 characterized by convective activity over the Philippine and South China Sea and AC anomaly over East Asia. This study has an implication on importance of BSISO for better understanding mechanism and temporal structure of heat waves in Korea.

• Journal of KIISE
• /
• v.42 no.8
• /
• pp.1022-1030
• /
• 2015

The context-awareness of mobile applications yields several issues for testing, since mobile applications should be able to be tested in any environment and under any contextual input. In previous studies of testing for Android applications as an event-driven system, many researchers have focused on using generated test cases considering only Graphical User Interface (GUI) events. However, it is difficult to find failures that could be detected when considering the changes in the context in which applications run. It is even more important to consider various contexts since the mobile applications adapt and use the new features and sensors of mobile devices. In this paper, we provide a method of systematically generating various executing contexts from permissions. By referring to the lists of permissions, the resources used by the applications for running Android applications can be easily inferred. To evaluate the efficiency of our testing method, we applied the method on two open source projects and showed that it contributes to improve the statement code coverage.

• Journal of Korea Water Resources Association
• /
• v.54 no.3
• /
• pp.157-166
• /
• 2021

Forecasting dam inflow based on high reliability is required for efficient dam operation. In this study, deep learning technique, which is one of the data-driven methods and has been used in many fields of research, was manipulated to predict the dam inflow. The Long Short-Term Memory deep learning with Sequence-to-Sequence model (LSTM-s2s), which provides high performance in predicting time-series data, was applied for forecasting inflow of Soyang River dam. Various statistical metrics or evaluation indicators, including correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and error in peak value (PE), were used to evaluate the predictive performance of the model. The result of this study presented that the LSTM-s2s model showed high accuracy in the prediction of dam inflow and also provided good performance for runoff event based runoff prediction. It was found that the deep learning based approach could be used for efficient dam operation for water resource management during wet and dry seasons.

• Journal of Korea Society of Industrial Information Systems
• /
• v.16 no.2
• /
• pp.85-97
• /
• 2011

Wireless sensor networks have emerged as one of the key enabling technologies for ubiquitous computing since wireless intelligent sensor nodes connected by short range communication media serve as a smart intermediary between physical objects and people in ubiquitous computing environment. We recognize the wireless sensor network as a massively distributed and deeply embedded system. Such systems require concurrent and asynchronous event handling as a distributed system and resource-consciousness as an embedded system. Since the operating environment and architecture of wireless sensor networks, with the seemingly conflicting requirements, poses unique design challenges and constraints to developers, we propose a very new operating system for sensor nodes based on finite state machine. In this paper, we clarify the design goals reflected from the characteristics of sensor networks, and then present the heart of the design and implementation of a compact and efficient state-driven operating system, SenOS. We describe how SenOS can operate in an extremely resource constrained sensor node while providing the required reactivity and dynamic reconfigurability with low update cost. We also compare our experimental results after executing some benchmark programs on SenOS with those on TinyOS.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.34 no.5
• /
• pp.301-308
• /
• 2021

It is important to develop a digital SOC (Social Overhead Capital) maintenance system for preemptive maintenance in response to the rapid aging of social infrastructures. Abnormal signals induced from structures can be detected quickly and optimal decisions can be made promptly using IoT sensors deployed on the structures. In this study, a digital SOC monitoring system incorporating a multimetric IoT sensor was developed for long-term monitoring, for use in cloud-computing server for automated and powerful data analysis, and for establishing databases to perform : (1) multimetric sensing, (2) long-term operation, and (3) LTE-based direct communication. The developed sensor had three axes of acceleration, and five axes of strain sensing channels for multimetric sensing, and had an event-driven power management system that activated the sensors only when vibration exceeded a predetermined limit, or the timer was triggered. The power management system could reduce power consumption, and an additional solar panel charging could enable long-term operation. Data from the sensors were transmitted to the server in real-time via low-power LTE-CAT M1 communication, which does not require an additional gateway device. Furthermore, the cloud server was developed to receive multi-variable data from the sensor, and perform a displacement fusion algorithm to obtain reference-free structural displacement for ambient structural assessment. The proposed digital SOC system was experimentally validated on a steel railroad and concrete girder bridge.

• Journal of KIISE:Computer Systems and Theory
• /
• v.31 no.12
• /
• pp.704-715
• /
• 2004

Recently, a single or multi processor system uses the hierarchical memory structure to reduce the time gap between processor clock rate and memory access time. A cache memory system includes especially two or three levels of caches to reduce this time gap. Moreover, one of the most important things In the hierarchical memory system is the hit rate in level 1 cache, because level 1 cache interfaces directly with the processor. Therefore, the high hit rate in level 1 cache is critical for system performance. A victim cache, another high level cache, is also important to assist level 1 cache by reducing the conflict miss in high level cache. In this paper, we propose the advanced high level cache management scheme based on the processor reuse information. This technique is a kind of cache replacement policy which uses the frequency of processor's memory accesses and makes the higher frequency address of the cache location reside longer in cache than the lower one. With this scheme, we simulate our policy using Augmint, the event-driven simulator, and analyze the simulation results. The simulation results show that the modified processor reuse information scheme(LIVMR) outperforms the level 1 with the simple victim cache(LIV), 6.7% in maximum and 0.5% in average, and performance benefits become larger as the number of processors increases.

• Sleep Medicine and Psychophysiology
• /
• v.8 no.1
• /
• pp.45-51
• /
• 2001

Objectives: The experience of traffic accident is a kind of the psychosocial stressors to person. The traffic accident-related patients may show the psychophysiologic hyperarousal. So we examined the differences of psychophysiologic response between patients with and without the memory of experienceing a traffic accident. Methods: Twenty-four traffic accident-related patients were divided into two groups according to ther memory of a traffic accident. In psychological assessment, levels of anxiety and depression were evaluated by State-Trait Anxiety Inventory, Beck's Depression Inventory, and Hamilton Rating Scales For Anxiety and Depression. Heart rate, electrodermal response (EDR), and electromyographic activity (EMG) were measured by biofeedback system, and systolic and diastolic blood pressure by automated vital sign monitor during baseline, task, and rest periods. We utilized script-driven imagery technique as a stressful task. The patients listened to the script describing their own traffic accident experience and were instructed to imagine the event during the task period. Statistically analytic data were obtained from the differences of psychological and psychophysiologic data between two groups. Results: The memory group did not show significantly higher EDR than the none memory group, but showed higher tendency during baseline, imagery, and rest periods. The memory group showed significantly lower EMG than the none memory group during rest period. However, there were no differences in other psychophysiologic reponses between the two groups. Conclusion: Our results showed that the memory group had higher tendency in autonomic arousal level such as electrodermal response than the none memory group. We suggest that physicians need to minimize repetitive imagery of traffic accident (reexperience), and decrease the autonomic hyperarousal in the treatment of traffic accident-related patients.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.38 no.5
• /
• pp.671-683
• /
• 2018

The flood damage caused by heavy rains in urban watershed is increasing, and, as evidenced by many previous studies, urban flooding usually exceeds the water capacity of drainage networks. The flood on the area which considerably urbanized and densely populated cause serious social and economic damage. To solve this problem, deterministic and probabilistic studies have been conducted for the prediction flooding in urban areas. However, it is insufficient to obtain lead times and to derive the prediction results for the flood volume in a short period of time. In this study, IDNN, TDNN and NARX were compared for real-time flood prediction based on urban runoff analysis to present the optimal real-time urban flood prediction technique. As a result of the flood prediction with rainfall event of 2010 and 2011 in Gangnam area, the Nash efficiency coefficient of the input delay artificial neural network, the time delay neural network and nonlinear autoregressive network with exogenous inputs are 0.86, 0.92, 0.99 and 0.53, 0.41, 0.98 respectively. Comparing with the result of the error analysis on the predicted result, it is revealed that the use of nonlinear autoregressive network with exogenous inputs must be appropriate for the establishment of urban flood response system in the future.

• Journal of Broadcast Engineering
• /
• v.12 no.6
• /
• pp.630-640
• /
• 2007

This paper presents the design of service model, transport channel, and service signaling for the Terrestrial DMB Automatic Emergency Alert Service (AEAS) Standard. The paper begins with the analysis of technical backgrounds related to the design topics. Next, the raper presents the design of service model for the AEAS. Since, unlike the regular T-DMB services, the AEAS is event-driven and common to all services, some problems have been identified to design a service model conforming to the T-DMB standard. So, the paper proposes a new concept, called the common service, and the AEAS is modeled using the common service. Next, in order to decide the transport channel for the alert information, the paper proposes to divide the alert information into the message which contains code/text-based essential information, and the supplemental multimedia information. Then, the paper tries to find the most efficient transport channels. Emergency Warning Service (EWS) which uses FIG 5/2 is selected for the delivery of the message. The paper proposes no constraints on delivery of supplemental information except that it shall use the MSC. Finally, it proposes the service signaling for the common service and transport channel. Due to the problems of conventional signaling using the MCI, it proposes a new signaling method. The paper will contribute as a guideline to the development for emergency alert service standards fur other broadcasting media.

• Journal of KIISE:Information Networking
• /
• v.36 no.5
• /
• pp.413-424
• /
• 2009

Wireless sensor network is based on an event driven system. Sensor nodes collect the events in surrounding environment and the sensing data are relayed into a sink node. In particular, when events are detected, the data sensing periods are likely to be shorter to get the more correct information. However, this operation causes the traffic congestion on the sensor nodes located in a routing path. Since the traffic congestion generates the data queue overflows in sensor nodes, the important information about events could be missed. In addition, since the battery energy of sensor nodes exhausts quickly for treating the traffic congestion, the entire lifetime of wireless sensor networks would be abbreviated. In this paper, a new congestion control method is proposed on the basis of genetic algorithm. To apply genetic algorithm, the data traffic rate of each sensor node is utilized as a chromosome structure. The fitness function of genetic algorithm is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets, the proposed method selects the optimal data forwarding sensor nodes for relieving the traffic congestion. In experiments, when compared with other methods to handle the traffic congestion, the proposed method shows the efficient data transmissions due to much less queue overflows and supports the fair data transmission between all sensor nodes as possible. This result not only enhances the reliability of data transmission but also distributes the energy consumptions across the network. It contributes directly to the extension of total lifetime of wireless sensor networks.