• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.72-81
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• 2011

In this paper, we propose adaptive convergence security policies and management technologies to improve security assurance in the home networking environment. Many security issues may arise in the home networking environment. Examples of such security issues include the user privacy, the service security, the integrated networking security, the middleware security and the device failure. All these security issues, however, should be fulfilled in phase due to many difficulties including deployment cost and technical complexity. For instance, fundamental security requirements such as authentication, access control and prevention of crime and disaster should be addressed first. Then, supplementary security policies and diverse security management technologies should be fulfilled. In this paper, we classify these requirements into three categories, a service authentication, a user authentication and a device authentication, and propose security policies and management technologies for each requirement. Since the home gateway is responsible for interconnection of many home devices and external network access, a variety of context information could be collected from such devices.

• Journal of Digital Convergence
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• v.16 no.8
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• pp.151-159
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• 2018

This study focused on appreciative inquiry(AI) of salespeople who have to respond to various types of emotions according to the desires of individual customers at service contact points and the effect of emotional labor on adaptive selling behavior and customer satisfaction. Dyadic questionnaires were administerd to 115 automobile salespeople and 2 customers who received service from each salesperson, and the collected data was analyzed by using structural equation modeling. The results showed that AI had positive influences on deep acting and surface acting. Only deep acting was found to have positive relationship with adaptive selling behavior, but not to surface acting. Adaptive selling behavior had a positive effect on customer satisfaction. This study will contribute to identifying the need for AI access for salespersons and for activating adaptive selling behavior through emotional labor related to AI practice.

• Nuclear Engineering and Technology
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• v.38 no.3
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• pp.285-292
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• 2006

Thermoresistant (TR) clones of radiation-induced fibrosarcoma (RIF) cells have been reported to show an adaptive response to 1cGy of low dose radiation, and HSP25 and inducible HSP70 are involved in this process. In this study, to further elucidate the mechanism by which HSP25 and inducible HSP70 regulate the adaptive response, HSP25 or inducible HSP70 overexpressed RIF cells were irradiated with 1cGy and the cell cycle was analyzed. HSP25 or inducible HSP70 overexpressed cells together with TR cells showed increased G1 phase after 1cGy irradiation, while RIF cells did not. $[^3H]-Thymidine$ and BrdU incorporation also indicated that both HSP25 and inducible HSP70 are involved in G1 arrest after 1cGy irradiation. Molecular analysis revealed upregulation of p27Cip/Kip protein in HSP25 and inducible HSP70 overexpressed cells, and cotransfection of p27Cip/Kip antisense abolished the induction of the adaptive response and 1cGy-mediated G1 arrest. The above results indicate that induction of an adaptive response by HSP25 and inducible HSP70 is mediated by upregulation of p27Cip/Kip protein, resulting in low dose radiation-induced G1 arrest.

• Journal of Environmental Health Sciences
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• v.40 no.5
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• pp.355-366
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• 2014

Objectives: This study seeks to evaluate the vulnerability assessment of the human health sector for $PM_{10}$, which is reflected in the regional characteristics and related disease mortality rates for $PM_{10}$ in Busan over the period of 2006-2010. Methods: According to the vulnerability concept suggested by the Intergovernmental Panel on Climate Change (IPCC), vulnerability to $PM_{10}$ is comprised of the categories of exposure, sensitivity, and adaptive capacity. The indexes of the exposure and sensitivity categories indicate positive effects, while the adaptive capacity index indicates a negative effect on vulnerability to $PM_{10}$. Variables of each category were standardized by the rescaling method, and each regional relative vulnerability was computed through the vulnerability index calculation formula. Results: The regions with a high exposure index are Jung-Gu (transportation region) and Saha-Gu (industrial region). Major factors determining the exposure index are the $PM_{10}$ concentration, days of $PM_{10}{\geq}50$, ${\mu}g/m^3$, and $PM_{10}$ emissions. The regions that show a high sensitivity index are urban and rural regions; these commonly have a high mortality rate for related disease and vulnerable populations. The regions that have a high adaptive capacity index are Jung-Gu, Gangseo-Gu, and Busanjin-Gu, all of which have a high level of economic/welfare/health care factors. The high-vulnerability synthesis of the exposure, sensitivity, and adaptive capacity indexes show that Dong-Gu and Seo-Gu have a risk for $PM_{10}$ potential effects and a low adaptive capacity. Conclusions: This study presents the vulnerability index to $PM_{10}$ through a relative comparison using quantitative evaluation to draw regional priorities. Therefore, it provides basic data to reflect environmental health influences in favor of an adaptive policy limiting damage to human health caused by vulnerability to $PM_{10}$.

• Journal of KIISE:Software and Applications
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• v.35 no.1
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• pp.33-40
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• 2008

In this paper, we propose a method to enhance adaptability in a dialogue system using the reinforcement learning that reduces response errors by trials and error-search similar to a human dialogue process. The adaptive dialogue strategy means that the dialogue system improves users' satisfaction and dialogue efficiency by loaming users' dialogue styles. To apply the reinforcement learning to the dialogue system, we use a main-dialogue span and sub-dialogue spans as the mathematic application units, and evaluate system usability by using features; success or failure, completion time, and error rate in sub-dialogue and the satisfaction in main-dialogue. In addition, we classify users' groups into beginners and experts to increase users' convenience in training steps. Then, we apply reinforcement learning policies according to users' groups. In the experiments, we evaluated the performance of the proposed method on the individual reinforcement learning policy and group's reinforcement learning policy.

• Journal of KIISE
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• v.42 no.7
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• pp.889-900
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• 2015

In order to solve problems happening in the practical environment of complicated system, the importance of the self-adaptive system has recently begun to emerge. However, since the differences between the model built at the time of system design and the practical environment can lead the system into unpredictable situations, the study into methods of dealing with it is also emerging as an important issue. In this paper, we propose a method for deciding on the adaptation time in an uncertain environment, and reflecting the real-time environment in the system's model. The proposed method calculates the Bayesian Surprise for the suitable adaptation time by comparing previous and current states, and then reflects the result following the performed policy in the design model to help in deciding the proper policy for the actual environment. The suggested method is applied to a navigation system to confirm its effectiveness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4268-4289
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• 2020

Advanced traffic signal timing method plays very important role in reducing road congestion and air pollution. Reinforcement learning is considered as superior approach to build traffic light timing scheme by many recent studies. It fulfills real adaptive control by the means of taking real-time traffic information as state, and adjusting traffic light scheme as action. However, existing works behave inefficient in complex intersections and they are lack of feasibility because most of them adopt traffic light scheme whose phase sequence is flexible. To address these issues, a novel adaptive traffic signal timing scheme is proposed. It's based on actor-critic reinforcement learning algorithm, and advanced techniques proximal policy optimization and generalized advantage estimation are integrated. In particular, a new kind of reward function and a simplified form of state representation are carefully defined, and they facilitate to improve the learning efficiency and reduce the computational complexity, respectively. Meanwhile, a fixed phase sequence signal scheme is derived, and constraint on the variations of successive phase durations is introduced, which enhances its feasibility and robustness in field applications. The proposed scheme is verified through field-data-based experiments in both medium and high traffic density scenarios. Simulation results exhibit remarkable improvement in traffic performance as well as the learning efficiency comparing with the existing reinforcement learning-based methods such as 3DQN and DDQN.

• Journal of Environmental Policy
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• v.9 no.2
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• pp.185-205
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• 2010

Climate change vulnerability assessment based on local conditions is a prerequisite for establishment of climate change adaptation policies. While some studies have developed a methodology for vulnerability assessment at the national level using statistical data, few attempts, whether domestic or overseas, have been made to develop methods for local vulnerability assessments that are easily applicable to a single city. Accordingly, the objective of this study was to develop a conceptual framework for climate change vulnerability, and then develop a general methodology for assessment at the regional level applied to a single coastal city, Mokpo, in Jeolla province, Korea. We followed the conceptual framework of climate change vulnerability proposed by the IPCC (1996) which consists of "climate exposure," "systemic sensitivity," and "systemic adaptive capacity." "Climate exposure" was designated as sea level rises of 1, 2, 3, 4, and 5 meter(s), allowing for a simple scenario for sea level rises. Should more complex forecasts of sea level rises be required later, the methodology developed herein can be easily scaled and transferred to other projects. Mokpo was chosen as a seaside city on the southwest coast of Korea, where all cities have experienced rising sea levels. Mokpo has experienced the largest sea level increases of all, and is a region where abnormal high tide events have become a significant threat; especially subsequent to the construction of an estuary dam and breakwaters. Sensitivity to sea level rises was measured by the percentage of flooded area for each administrative region within Mokpo evaluated via simulations using GIS techniques. Population density, particularly that of senior citizens, was also factored in. Adaptive capacity was considered from both the "hardware" and "software" aspects. "Hardware" adaptive capacity was incorporated by considering the presence (or lack thereof) of breakwaters and seawalls, as well as their height. "Software" adaptive capacity was measured using a survey method. The survey questionnaire included economic status, awareness of climate change impact and adaptation, governance, and policy, and was distributed to 75 governmental officials working for Mokpo. Vulnerability to sea level rises was assessed by subtracting adaptive capacity from the sensitivity index. Application of the methodology to Mokpo indicated vulnerability was high for seven out of 20 administrative districts. The results of our methodology provides significant policy implications for the development of climate change adaptation policy as follows: 1) regions with high priority for climate change adaptation measures can be selected through a correlation diagram between vulnerabilities and records of previous flood damage, and 2) after review of existing short, mid, and long-term plans or projects in high priority areas, appropriate adaptation measures can be taken as per this study. Future studies should focus on expanding analysis of climate change exposure from sea level rises to other adverse climate related events, including heat waves, torrential rain, and drought etc.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.28-33
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• 2006

The initial research of Task Computing in the ubiquitous computing (UbiComp) environment revealed the need for access control of services. Context-awareness of service requests in ubiquitous computing necessitates a well-designed model to enable effective and adaptive invocation. However, nowadays little work is being undertaken on service access control under the UbiComp environment, which makes the exposed service suffer from the problem of ill-use. One of the research focuses is how to handle the access to the resources over the network. Policy-Based Access Control is an access control method. It adopts a security policy to evaluate requests for resources but has a light-weight combination of the resources. Motivated by the problem above, we propose a universal model and an algorithm to enhance service access control in UbiComp. We detail the architecture of the model and present the access control implementation.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.39 no.2
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• pp.59-68
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• 2002

In this paper, we compared and analyzed two new models of cyclic packet dropping algorithm, Adaptive Cyclic Packet Dropping algorithm (ACPD), and Non-adaptive Cyclic Packet Dropping algorithm (NCPD) with RIO. The ACPD algorithm drops adaptively packets for the congestion control, as predicting traffic pattern between each cycle. Therefore the ACPD algorithm makes up for the drawback of RIO algorithm and minimizes the wastes of the bandwidth being capable of predicting in the NCPD algorithm. We modelled two cyclic packet drop algorithms and executed a simulation and analyzed the throughput and packet drop rate based on Sending Priority changing dynamically depending on network traffic. In this algorithm, applying the strict drop precedence policy, we get better performance on priority levels. The results show that two new algorithms may provide more efficient and stricter drop precedence policy as compared to RIO independent of traffic load. The ACPD algorithm can provide better performance on priority levels and keep stricter drop policy than other algorithms.