• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.650-652
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• 2013

As VANET(Vehicular Ad-hoc NETwork) improves road safety, efficiency, and comfort, and provides a value-added service such as commerce information or internet access. it is the most important technology in ITS(Intelligent Transportation System). But, In VANETs, better communication efficiency can be achieved by sacrificing security and vice versa. VANETs cannot get started without either of them. Therefore, to solve these problems simultaneously, this paper proposes MAC(Message Authentication Code) based SDAP(Secure Data Aggregation Protocol) which removes redundant data or abnormal data between vehicles and verifies the integrity of message. The MAC based SDAP not only improves the efficiency of data delivery but also enhances the security by detecting malicious attacks such as propagation jamming attack, forgery attack, and disguised attack.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.10
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• pp.129-137
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• 2003

As the Internet is quickly evolving from best-effort networks to a very critical communications infrastructure that requires higher quality Internet services and the delivery of such communications services become competitive, large-scale NSPs or ISPs have to concern much more on the performance and efficient resource usages of their networks. This situation naturally leads the providers to seek a possible solution from traffic engineering (TE) methodologies. In this paper, we propose a TE server solution for a large-scale MPLS-based IP autonomous system, which addresses these TE requirements such as the measurement, characterization, modeling and control of Internet traffic.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.1
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• pp.128-136
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• 2013

This study presents the development of Gateway Control Module using SAE J1939 protocol for the commercial vehicles. Presently, the load rate of CAN bus is increased by the single network composition and addition of new ECUs for development of intelligent vehicles. Because the embedded system of the integrated network control function has the errors of the CAN bus caused by the increase of ECU, it is needed for development of commercial vehicles. Also, this study presents the development of smart functions that can diagnosis CAN bus errors, fault diagnosis of ECU and basic function that arbitrates CAN bus between ECUs of commercial vehicle. GCM was designed for 4channel separation about Gateway function as solution of load rate decrease and smart functions. HILS(Hardware in the loop simulation)system that can achieve simulation about CAN Messages of all systems on vehicle was applied to evaluate performance and verification of all functions and performance. The load rate on CAN bus was decreased at using functions what was delivery, block and process of GCM. Through this, it was enabled to organize systematic architecture for gateway.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.

• Korean Chemical Engineering Research
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• v.47 no.4
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• pp.453-458
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• 2009

Recently, new methods to synthesize and process polymers without toxic organic solvents are needed in order to solve environmental problems. The use of supercritical carbon dioxide as a solvent for the polymer synthesis is attractive since it is non-toxic, non-flammable, naturally abundant, and the product may be easily separated from the solvent. In this study, we developed the method using super critical $CO_2$ to prepare P(MAA-co-EGMA) hydrogel nanoparticles as an intelligent drug delivery carrier. The effects of concentrations of PtBuMA-PEO as a dispersion stabilizer and AIBN as an initiator on the particle synthesis were investigated. When PtBuMA-PEO concentration increased, the particle size decreased. However, there was no significant difference in the particle size according to the AIBN concentration. There was a drastic change of the equilibrium weight swelling ratio of P(MAA-co-EGMA) hydrogel nanoparticles at a pH of around 5, which is the $pK_a$ of PMAA. At a pH below 5, the hydrogels were in a relatively collapsed state but at a pH higher than 5, the hydrogels swelled to a high degree. In release experiments using Rh-B as a model solute, the P(MAA-co-EGMA) hydrogel nanoparticles showed a pH-sensitive release behavior. At low pH(pH 4.0) a small amount of Rh-B was released while at high pH(pH 6.0) a relatively large amount of Rh-B was released from the hydrogels.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.4
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• pp.409-417
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• 2003

In recent years, better quality in power electric services is being required with the development of information industries and the improvement of living standards. Also, the small scaled dispersed storage and generation (DSG) systems are being interconnected with the distribution systems and customers by the influence of the recent issues such as deregulation and global environmental problems in power system. Under these circumstances, it is very important to maintain the customer voltages within allowable limits for the distribution system which is located at the most sensitive part in the power system. To overcome these problems, this paper shows the basic concepts of FRIENDS which is considered as one of the power delivery system in the near future and also presents an evaluation method on the impacts of customer voltages by operation models of FRIENDS. The FRIENDS can change the system configuration in a flexible manner by using the static switches and offer the different power qualities in power services through the power quality control centers which play the most important role in FRIENDS. Numerical examples are shown in order to indicate the efficiency of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.825-840
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• 2015

Adaptive bitrate (ABR) streaming technology has become an important and prevalent feature in many multimedia delivery systems, with content providers such as Netflix and Amazon using ABR streaming to increase bandwidth efficiency and provide the maximum user experience when channel conditions are not ideal. Where such systems could see improvement is in the delivery of live video with a closed loop cognitive control of video encoding. In this paper, we present streaming camera system which provides spatially and temporally adaptive video streams, learning the user's preferences in order to make intelligent scaling decisions. The system employs a hardware based H.264/AVC encoder for video compression. The encoding parameters can be configured by the user or by the cognitive system on behalf of the user when the bandwidth changes. A cognitive video client developed in this study learns the user's preferences(i.e. video size over frame rate) over time and intelligently adapts encoding parameters when the channel conditions change. It has been demonstrated that the cognitive decision system developed has the ability to control video bandwidth by altering the spatial and temporal resolution, as well as the ability to make scaling decisions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.5
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• pp.38-48
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• 2010

Adverse weather (e.g. strong winds, snow and ice) will probably appear as a more serious and frequent threat to road traffic than in clear climate. Another consequence of climate change with a natural disastrous on road traffic is respond to traffic accident more the large and high-rise bridge zone, tunnel zone, inclined plane zone and de-icing zone than any other zone, which in turn calls for continuous adaption of monitoring procedures. Accident mitigating measures against this accident category may consist of intense winter maintenance, the use of road weather information systems for data collection and early warnings, road surveillance and traffic control. While hazard from reduced road friction due to snow and ice may be eliminated by snow removal and de-icing measures, the effect of strong winds on road traffic are not easily avoided. The purpose of the study described here, was to design of amber information the relationship between traffic safety, weather, user information on road weather and driving conditions in local-scale Geographic. The most applications are the optimization of the amber information definition, improvements to road surveillance, road weather monitoring and improved accuracy of user information delivery. Also, statistics on wind gust, surface condition, vehicle category and other relevant parameters for wind induced accidents provide basis for traffic control, early warning policies and driver education for improved road safety at bad weather-exposed locations.

• Women's Health Nursing
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• v.26 no.1
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• pp.5-9
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• 2020

Artificial intelligence (AI), which includes machine learning and deep learning has been introduced to nursing care in recent years. The present study reviews the following topics: the concepts of AI, machine learning, and deep learning; examples of AI-based nursing research; the necessity of education on AI in nursing schools; and the areas of nursing care where AI is useful. AI refers to an intelligent system consisting not of a human, but a machine. Machine learning refers to computers' ability to learn without being explicitly programmed. Deep learning is a subset of machine learning that uses artificial neural networks consisting of multiple hidden layers. It is suggested that the educational curriculum should include big data, the concept of AI, algorithms and models of machine learning, the model of deep learning, and coding practice. The standard curriculum should be organized by the nursing society. An example of an area of nursing care where AI is useful is prenatal nursing interventions based on pregnant women's nursing records and AI-based prediction of the risk of delivery according to pregnant women's age. Nurses should be able to cope with the rapidly developing environment of nursing care influenced by AI and should understand how to apply AI in their field. It is time for Korean nurses to take steps to become familiar with AI in their research, education, and practice.