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본 연구는 교통약자가 새로운 경로를 쉽게 찾을 수 있도록 경로추천 방법을 설계하고 구현하는 것이 목적이다. 이를 위해 교통약자가 이용하는 대중교통 수단과 이동편의시설, 통행시간, 대기시간, 환승 등의 이동불편요소를 이용하여 이동불편지수를 산정하였다. 산정된 불편지수를 이용하여 교통약자 유형별 최적경로를 추천하는 방법을 제시하였다. 서울 내 교통약자가 많이 거주하는 기점과 통행수요가 높은 종점을 추출하여 제시한 방법을 적용하였다. 적용 결과, 이동편의시설과 이동불편요소를 고려하여 교통약자 유형별로 이동 가능한 경로를 추천할 수 있음을 확인하였다. 본 연구의 결과는 향후 교통약자를 위한 경로정보 제공 서비스 구축에 활용할 수 있을 것으로 기대된다.

Virtual machines are used in various fields to increase the resource utilization. In a virtual machine monitor, I/O requests are the most frequent tasks for virtual machines. However, a virtual machine monitor cannot guarantee I/O responsiveness when multiple tasks are being performed on virtual machines. We propose a scheduling scheme that provides fast I/O responsiveness for virtual machines. The proposed scheme provides high resource utilization by dynamically allocating virtual resources to virtual machines according to the characteristics of their tasks. In experiments with virtual machines containing varying task characteristics, it is shown that the proposed scheme provides improved I/O responsiveness when compared to existing virtual machine schedulers.

This paper studies empirically business intelligence and social network service used to leverage the performance of collaborative decision making. A model is developed and tested empirically to find out meaningful relationships among those variables. This paper examines the current trend of social media within business intelligence, the importance of integrating these types of features within a BI solution, and why organizations should consider this type of functionality within their current decision making infrastructures. The result also shows that social media will increase in relation to use and expand within business related BI use, developing solutions that become easy to use is essential to successful collaborative decision making adoption.

This paper presents a method of building a security vulnerability information collection and management system in order to promptly identify which types of security vulnerability are included in IoT devices.

Credit scheduler, an original Xen virtual machine scheduler, uses a boost mechanism to improve responsiveness. However, the original boost mechanism is applied only to inactive virtual machines. Because of this, if virtual machine scheduling delay time increases, the I/O performance of an active virtual machine dramatically decreases. However, when the boost mechanism of a previous credit scheduler is applied to an active virtual machine, the allocation equality can become unbalanced. This research study proposes a boost mechanism that can be applied to an active virtual machine. Evaluation of the results showed that, when compared to the previous credit scheduler, the proposed method both did not upset the fairness of the CPU allocation between the virtual machines and improved the I/O performance.

Through recent developments in hardware, it has become possible to interpret user intent by integrating biological information recorded through various biometric devices. However, because multiple devices that acquire biological information have different OSs, data transmission protocols, and arrival times, it is not easy to collect and analyze biological information in real-time. Our proposed method solves the issue of different OS and data transmission protocols. To realize the same, the proposed synchronization framework implements a biometric information priority and algorithm. The implemented framework is able to analyze and process data in real-time in order to recognize user intention information.

This paper exposes the original Xen credit scheduler. In some case, the original credit scheduler results in low responsiveness. The credit scheduler is a proportional fair share central processing unit (CPU) scheduler and provides CPU fairness. However, in contrast with fine CPU fairness, the responsiveness is insufficient in the case of CPU intensive workloads. We modify the Xen credit scheduler to obtain an improved response time because VM scheduling significantly affects the responsiveness of virtual machines (VMs) in virtualization environments. The supposed scheduling boosts a VM with a conditional boost mechanism to maintain CPU fairness. The experimental results show that the supposed scheduling technique offers better responsiveness for a VM than the original credit scheduler.

We propose a multi-slope ADC with a Time to Digital Converter (TDC). In a single-slope ADC, when using a TDC that has resolution of n bits, the conversion time will be reduced by a factor of 2n. Applying the TDC that uses the multi-phase-clock signal reduced the circuit areas of TDC by half, achieved timing consistency, and realized robust meta-stability. We also apply multi-slope scheme for reducing the ADC-operation cycles. We designed and fabricated an 11-bit ADC, by using a TSMC 0.18-μm CMOS process. The ADC, at 100 kS/s, achieved SNDR of 59.6 dB (9.6 ENOB) and SFDR of 72.7 dB. Its INL and DNL were +1.32/-0.80LSB and +0.48/-0.37LSB, respectively. The entire ADC consumes 5.1μW from a 1-V supply.

This study proposes a method that measures the stability of networks by converting nodes with higher priorities into virtual articulation nodes. Further, although they're not articulation nodes, by considering critical nodes such as routers connected to external networks or servers that assume vital operations to networks, we are able to improve the monitoring of major nodes in networks and reallocate those nodes when designing networks in order to remove potential risk factors. We will elaborate on the method of creating virtual articulation nodes using virtual nodes and apply them to network topologies to perform a stability analysis through simulation.

In this research, we proposed two types of fault recovery schemes designed by combining the protection and the restoration methods in order to increase the survivability of GMPLS networks. The first method is a transformation of the 1+1 protection method where it consumes more resources than the 1+1 protection method but has a faster fault recovery speed. The second method is a combination of the 1+1 protection method and the restoration method and can enhance the performance after recoveries. Although the two methods proposed in this paper consume more resources than the ones before, they have outstanding recovery time and performance after recoveries.

The purpose of this study is to present the prediction analysis of traffic accident and future applications by calculating accident risks based on ITS (Intelligent Transport Systems) data via VDS (Vehicle Detecting System). Many researches have described various traffic accident predictions and reductions. However, until recently, empirical studies considering real traffic environments are still insufficient. Site selections in this study were made from accident related data and collected data from VDS to calculate traffic accident rates. Results from analysis showed occurrence of traffic accident by time and explore possibilities for its application in future traffic accident management.