• 정보보호학회논문지
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• 제16권3호
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• pp.29-38
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• 2006

최근 무선통신기술의 발달은 센서 네트워크 관련연구를 촉진하였으며 다양한 형태의 센서 네트워크 통신방식에 적합한 방법들이 제안되고 있다. 센서 네트워크 연구방향은 제한된 자원에서 에너지효율을 극대화시키기 위한 방법과 그동안 주목받지 못했던 보안관련 연구들로 구분된다. 에너지효율을 높이기 위한 방법으로 노드간 데이터 통합과 통합을 수행하는 클러스터 헤드의 적절한 선택 알고리즘이 제안되었으며, 보안성 강화를 위해 센서에 적용 가능한 암호화 기법과 비밀 키를 관리하기 위한 방법들이 제안되고 있다. 그러나 다양한 형태의 통신방식이 존재하는 센서 네트워크에서 안전하면서도 동시에 에너지 효율성을 고려한 통합적 연구는 아직 초기단계에 있다. 본 논문에서는 자원효율적인 클러스터링 프로토콜과 다양한 통신방식에 적당한 키 관리 알고리즘을 결합하여 향후 민감한 데이터를 처리하는 센서네트워크 시스템에 적용할 수 있는 통합적 프로토콜을 제안한다.

• 한국정보기술학회논문지
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• 제17권10호
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• pp.57-63
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• 2019

항공기를 이용해서 영상레이더를 운영하는 경우 이상적인 경로와 항공기의 위치가 다르기 때문에 요동 보상이 필요하다. 항공기의 위치를 측정할 때 사용하는 항법 장치의 오차 때문에 보상 후에 잔여 오차가 존재한다. 자동초점 기능은 이런 잔여 요동과 시스템의 부정확성을 보정하기 위해서 제안되었다. 자동초점 기능으로 여러 가지 방법이 제안되었지만 PGA가 가장 널리 활용되고 있다. PGA는 적응적 반복 기법을 사용하고, 표적이나 위상에 대한 특정한 가정이 없다. 하지만, 적응적 반복 기법 특성상 연산 시간이 문제가 주요한 문제이기 때문에 본 논문은 PGA의 연산 시간을 줄이기 위해서 PGA 표적 선택 기법을 개선한다. 영상의 분산을 이용해서 높은 SNR을 가진 표적을 찾아서 연산 시간을 줄이고 수렴 속도를 높인다. 제안한 방법은 실제 영상레이더 데이터를 이용해서 성능을 검증한다.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.159-166
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• 2022

Artificial Intelligence has stimulated every aspect of today's life. Human thinking quality is trying to be involved through digital tools in all research areas of the modern era. The education industry is also leveraging artificial intelligence magical power. Uses of digital technologies in pedagogical paradigms are being observed from the last century. The widespread involvement of artificial intelligence starts reshaping the educational landscape. Adaptive learning is an emerging pedagogical technique that uses computer-based algorithms, tools, and technologies for the learning process. These intelligent practices help at each learning curve stage, from content development to student's exam evaluation. The quality of information technology students and professionals training has also improved drastically with the involvement of artificial intelligence systems. In this paper, we will investigate adopted digital methods in the education sector so far. We will focus on intelligent techniques adopted for information technology students and professionals. Our literature review works on our proposed framework that entails four categories. These categories are communication between teacher and student, improved content design for computing course, evaluation of student's performance and intelligent agent. Our research will present the role of artificial intelligence in reshaping the educational process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.929-946
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• 2022

Free viewpoint video (FVV) service is an immersive media service that allows a user to watch it from a desired location or viewpoint. It is composed of various forms according to the direction of the viewpoint of the provided video, and includes zoom in/out in the service. As consumers' demand for active watching is increasing, the importance of FVV services is expected to grow gradually. However, additional considerations are needed to seamlessly stream FVV service. FVV includes a plurality of videos, video changes may occur frequently due to movement of the viewpoint. Frequent occurrence of video switching or re-request another video can cause service delay and it also can lower user's quality of service (QoS). In this case, we assumed that if a video showing an object that the user wants to watch is selected and provided, it is highly likely to meet the needs of the viewer. In particular, it is important to provide an object-oriented FVV service when zooming in. When video zooming in in the usual way, it cannot be guaranteed to zoom in around the object. Zoom function does not consider about video viewing. It only considers the viewing screen size and it crop the video view as fixed screen location. To solve this problem, we propose a zoom in/out method of object-centered dynamic adaptive streaming of FVV in this paper. Through the method proposed in this paper, users can enjoy the optimal video service because they are provided with the desired object-based video.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.1892-1912
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• 2022

With the massive demand and growth of cloud computing, virtualization plays an important role in providing services to end-users efficiently. However, with the increase in services over Cloud Computing, it is becoming more challenging to manage and run multiple Virtual Machines (VMs) in Cloud Computing because of excessive power consumption. It is thus important to overcome these challenges by adopting an efficient technique to manage and monitor the status of VMs in a cloud environment. Reduction of power/energy consumption can be done by managing VMs more effectively in the datacenters of the cloud environment by switching between the active and inactive states of a VM. As a result, energy consumption reduces carbon emissions, leading to green cloud computing. The proposed Efficient Dynamic VM Scheduling approach minimizes Service Level Agreement (SLA) violations and manages VM migration by lowering the energy consumption effectively along with the balanced load. In the proposed work, VM Scheduling for Efficient Dynamically Migrated VM (VMS-EDMVM) approach first detects the over-utilized host using the Modified Weighted Linear Regression (MWLR) algorithm and along with the dynamic utilization model for an underutilized host. Maximum Power Reduction and Reduced Time (MPRRT) approach has been developed for the VM selection followed by a two-phase Best-Fit CPU, BW (BFCB) VM Scheduling mechanism which is simulated in CloudSim based on the adaptive utilization threshold base. The proposed work achieved a Power consumption of 108.45 kWh, and the total SLA violation was 0.1%. The VM migration count was reduced to 2,202 times, revealing better performance as compared to other methods mentioned in this paper.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제11권4호
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• pp.105-112
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• 2022

최근 환경의 변화에 적응적이고 특정 목적에 부합하는 오토스케일링 정책을 만들기 위해 강화학습 기반 오토스케일링을 사용하는 연구가 많이 이루어지고 있다. 하지만 실제 환경에서 강화학습 기반 수평적 파드 오토스케일러(HPA, Horizontal Pod Autoscaler)의 정책을 학습하기 위해서는 많은 비용과 시간이 요구되며, 서비스를 배포할 때마다 실제 환경에서 강화학습 기반 HPA 정책을 처음부터 다시 학습하는 것은 실용적이지 않다. 본 논문에서는 쿠버네티스에서 강화학습 기반 HPA를 구현하고, 강화학습 기반 HPA 정책에 대한 학습을 가속화하기 위해 대기행렬 모델 기반 시뮬레이션을 활용한 전이 학습 기법을 제안한다. 시뮬레이션을 활용한 사전 학습을 수행함으로써 실제 환경에서 시간과 자원을 소모하며 학습을 수행하지 않아도 시뮬레이션 경험을 통해 정책 학습이 이루어질 수 있도록 하였고, 전이 학습 기법을 사용함으로써 전이 학습 기법을 사용하지 않았을 때보다 약 42.6%의 비용을 절감할 수 있었다.

• Smart Structures and Systems
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• 제30권6호
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• pp.661-671
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• 2022

Stayed cables are the key components for transmitting loads in cable-stayed bridges. Therefore, it is very important to evaluate the cable force condition to ensure bridge safety. An online condition assessment and anomaly localization method is proposed for cables based on the spatiotemporal correlation of grouped cable forces. First, an anomaly sensitive feature index is obtained based on the distribution characteristics of grouped cable forces. Second, an adaptive anomaly detection method based on the k-nearest neighbor rule is used to perform dissimilarity measurements on the extracted feature index, and such a method can effectively remove the interference of environment factors and vehicle loads on online condition assessment of the grouped cable forces. Furthermore, an online anomaly isolation and localization method for stay cables is established, and the complete decomposition contributions method is used to decompose the feature matrix of the grouped cable forces and build an anomaly isolation index. Finally, case studies were carried out to validate the proposed method using an in-service cable-stayed bridge equipped with a structural health monitoring system. The results show that the proposed approach is sensitive to the abnormal distribution of grouped cable forces and is robust to the influence of interference factors. In addition, the proposed approach can also localize the cables with abnormal cable forces online, which can be successfully applied to the field monitoring of cables for cable-stayed bridges.

• 한국컴퓨터그래픽스학회논문지
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• 제28권3호
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• pp.113-123
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• 2022

광선 추적 기반의 렌더링은 래스터화 기반의 렌더링보다 훨씬 더 사실적인 이미지를 생성한다. 하지만 넓은 시야와 높은 디스플레이 갱신 속도를 요구하는 헤드 마운티드 디스플레이(HMD) 시스템을 대상으로 이를 구현할 때에는 여전히 많은 연산량으로 인하여 부담스럽다. 또한, HMD 화면에 고품질 이미지를 표시하기 위해서는 시각적으로 성가신 공간적/시간적 앨리어스를 줄이기 위해 픽셀당 충분한 수의 광선 샘플링을 수행해야 한다. 본 논문에서는 최근 Kim 등[1]이 제시한 선택적 포비티드 광선 추적법을 확장하여 주어진 HMD 시스템에서 고전적인 Whitted-스타일 광선 추적 수준의 렌더링 효과를 효율적으로 생성해주는 실시간 렌더링 기법을 제안한다. 특히, GPU의 광선 추적 하드웨어를 통한 가속과 시간 제한을 둔 렌더링 방법의 결합을 통하여 고속의 HMD 광선 추적에 적합한 사람의 시각 시스템에 매우 효율적인 적응적 광선 샘플링 방법을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4292-4307
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• 2021

For the research field of night infrared surveillance video, the target imaging in the video is easily affected by the light due to the characteristics of the active infrared camera and the classical ViBe algorithm has some problems for moving target extraction because of background misjudgment, noise interference, ghost shadow and so on. Therefore, an improved ViBe algorithm (I-ViBe) for moving target extraction in night infrared surveillance video is proposed in this paper. Firstly, the video frames are sampled and judged by the degree of light influence, and the video frame is divided into three situations: no light change, small light change, and severe light change. Secondly, the ViBe algorithm is extracted the moving target when there is no light change. The segmentation factor of the ViBe algorithm is adaptively changed to reduce the impact of the light on the ViBe algorithm when the light change is small. The moving target is extracted using the region growing algorithm improved by the image entropy in the differential image of the current frame and the background model when the illumination changes drastically. Based on the results of the simulation, the I-ViBe algorithm proposed has better robustness to the influence of illumination. When extracting moving targets at night the I-ViBe algorithm can make target extraction more accurate and provide more effective data for further night behavior recognition and target tracking.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.312-334
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• 2023

In large-scale computing, cloud computing plays an important role by sharing globally-distributed resources. The evolution of cloud has taken place in the development of data centers and numerous servers across the globe. But the cloud information centers incur huge operational costs, consume high electricity and emit tons of dioxides. It is possible for the cloud suppliers to leverage their resources and decrease the consumption of energy through various methods such as dynamic consolidation of Virtual Machines (VMs), by keeping idle nodes in sleep mode and mistreatment of live migration. But the performance may get affected in case of harsh consolidation of VMs. So, it is a desired trait to have associate degree energy-performance exchange without compromising the quality of service while at the same time reducing the power consumption. This research article details a number of novel algorithms that dynamically consolidate the VMs in cloud information centers. The primary objective of the study is to leverage the computing resources to its best and reduce the energy consumption way behind the Service Level Agreement (SLA)drawbacks relevant to CPU load, RAM capacity and information measure. The proposed VM consolidation Algorithm (PVMCA) is contained of four algorithms: over loaded host detection algorithm, VM selection algorithm, VM placement algorithm, and under loading host detection algorithm. PVMCA is dynamic because it uses dynamic thresholds instead of static thresholds values, which makes it suggestion for real, unpredictable workloads common in cloud data centers. Also, the Algorithms are adaptive because it inevitably adjusts its behavior based on the studies of historical data of host resource utilization for any application with diverse workload patterns. Finally, the proposed algorithm is online because the algorithms are achieved run time and make an action in response to each request. The proposed algorithms' efficiency was validated through different simulations of extensive nature. The output analysis depicts the projected algorithms scaled back the energy consumption up to some considerable level besides ensuring proper SLA. On the basis of the project algorithms, the energy consumption got reduced by 22% while there was an improvement observed in SLA up to 80% compared to other benchmark algorithms.