• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.8
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• pp.1347-1354
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• 2016

This paper presents optimal operational scheduling model and economic assessment of Li-ion battery energy storage systems installed in non-residential customers. The operation schedule of a BESS is determined to minimize electric bill, which is composed of demand and energy charges. Dynamic programming is introduced to solve the nonlinear optimization problem. Based on the optimal operation schedule result, the economics of a BESS are evaluated in the investor and the social perspective respectively. Calculated benefits in the investor or customer perspective are the savings of demand charge, energy charge, and related taxes. The social benefits include fuel cost savings of generating units, construction deferral effects of the generation capacity and T&D infra, and incremental CO2 emission cost impacts, etc. Case studies are applied to an large industrial customer that shows similarly repeated load patterns according to days of the week.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.4
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• pp.304-308
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• 2009

This paper presents the deadline driven CPU-Power management scheme for the Real-Time Embedded OS: named TMO-eCos. It used the scheduling scenarios generated by a task serialization technique for hard real- time TMO system. The serializer does a off-line analysis at design time with period, deadline and WCET of periodic tasks. Finally, TMO-eCos kernel controls the CPU speed to save the power consumption under the condition that periodic tasks do not violate deadlines. As a result, the system shows a reasonable amount of power saving. This paper presents all of these processes and test results.

• Journal of the Korea Computer Industry Society
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• v.6 no.3
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• pp.379-390
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• 2005

Intensive studies have been made in the area of VOD server Multimedia files in the VOD sever are characterized with the large volume of data, the requirements of synchronization and real-time playback of streams. The basic goal of the study is to find an efficient mechanism to allow maximum number of users under the limited resources such as Buffer size and disk bandwidth. we propose a efficient $pre\_fetching$ policy for multimedia services with dynamic monitoring and management of VOD sever resources. Simulation results show that the rate of buffer usage and service time of proposed scheme are about $28\%$ performance improved than that of traditional methods. This implies that our method can allow much more users for given resources.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.250-252
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• 2015

다양한 분야에서 활발히 연구되는 빅 데이터와 최근 이슈가 되고 있는 딥러닝(Deep-learning) 등은 컴퓨터공학 분야뿐만 아니라 다양한 분야와 접목하여 이에 대한 관심이 증가하고 있다. 대규모 클러스터를 통하여 빅데이터와 딥러닝 같은 계산 집약적인(computational-intensive) 작업을 빠르게 처리할 수 있다. 하지만 대규모 클러스터의 잦은 유휴상태는 클러스터의 활용률은 매우 낮아지게 한다. 본 논문에서는 작업 실행 시간 개선과 클러스터 활용 효율성을 향상시키는 HTCondor 클러스터를 위한 동적 자원 스케줄링 기법을 제안한다. 동적으로 자원 할당을 위해 가상머신으로 HTCondor 클러스터 환경을 구성하였으며, 가상머신의 관리를 위해 OpenStack을 사용하였다. OpenStack기반 HTCondor 클러스터 환경에서 HTCondor Python API와 OpenStack Python API를 사용하여 우리가 제안하는 동적 자원 스케줄링 기법을 구현하였으며, 실험을 통해 제안하는 기법의 성능 및 실현 가능성을 확인하였다.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.53-58
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• 2019

This paper proposes a class-based FQ-CoDel(Flow Queue-Control Delay) algorithm. A variety of application system services create bottlenecks in tactical communication network and the bottlenecks cause some problems such as traffic loss and delay. Therefore, more research on effective traffic processing is needed. The proposed class-based FQ-CoDel algorithm, suggests dynamic buffer management and scheduling, classifies specific packets in each queue according to service attribute and criticality and checks periodically latency of the packets in each queue. Also, it abandons the packets if some packets stay in queue above schedule time and manages the total amount of traffic stored in queue with certain level.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.833-852
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• 2019

In real time applications, due to their effective cost and small size, wireless networks play an important role in receiving particular data and transmitting it to a base station for analysis, a process that can be easily deployed. Due to various internal and external factors, networks can change dynamically, which impacts the localisation of nodes, delays, routing mechanisms, geographical coverage, cross-layer design, the quality of links, fault detection, and quality of service, among others. Conventional methods were programmed, for static networks which made it difficult for networks to respond dynamically. Here, machine learning strategies can be applied for dynamic networks effecting self-learning and developing tools to react quickly and efficiently, with less human intervention and reprogramming. In this paper, we present a wireless networks survey based on different machine learning algorithms and network lifetime parameters, and include the advantages and drawbacks of such a system. Furthermore, we present learning algorithms and techniques for congestion, synchronisation, energy harvesting, and for scheduling mobile sinks. Finally, we present a statistical evaluation of the survey, the motive for choosing specific techniques to deal with wireless network problems, and a brief discussion on the challenges inherent in this area of research.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.158-170
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• 2021

Virtualization technologies are being adopted and broadly utilized in many fields and at different levels. In cloud computing, achieving load balancing across large distributed virtual machines is considered a complex optimization problem with an essential importance in cloud computing systems and data centers as the overloading or underloading of tasks on VMs may cause multiple issues in the cloud system like longer execution time, machine failure, high power consumption, etc. Therefore, load balancing mechanism is an important aspect in cloud computing that assist in overcoming different performance issues. In this research, we propose a new approach that combines the advantages of different task allocation algorithms like Round robin algorithm, and Random allocation with different threshold techniques like the VM utilization and the number of allocation counts using least connection mechanism. We performed extensive simulations and experiments that augment different scheduling policies to overcome the resource utilization problem without compromising other performance measures like makespan and execution time of the tasks. The proposed system provided better results compared to the original round robin as it takes into consideration the dynamic state of the system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.67-74
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• 2020

One of the missions of active-duty units in charge of managing reserve forces is providing combat supplies to reserve forces to exercise their combat capabilities quickly when declaring a mobilization order. Rear area active-duty units must support large local reserve forces. On the other hand, military units have difficulties due to a lack of forces and the storage of large quantities of firearms and ammunition. For this reason, local reserve forces should move to an integrated storing place and receive their firearms and ammunition. The existing distribution plan (existing plan) is a first-in-first-out plan that can produce inconsistent distribution orders. The inconsistent distribution orders can increase the complete distribution time, which will affect the combat power of local reserve forces. Therefore, firearms and ammunition should be distributed quickly. Accordingly, this paper proposes the priority selection of firearms and ammunition for local reserve forces with a minimum complete distribution time using Dynamic Programming. To verify the proposed model, the existing plan was compared with the proposed model using real data, and the result showed that the proposed model outperformed the existing plan.

• Journal of IKEEE
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• v.5 no.1 s.8
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• pp.100-110
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• 2001

In this paper, we investigated several buffer management schemes for the design of shared-memory type ATM switches, which can enhance the utilization of switch resources and can support quality-of-service (QoS) functionalities. Our results show that dynamic threshold (DT) scheme demonstrate a moderate degree of robustness close to pushout(PO) scheme, which is known to be impractical in the perspective of hardware implementation, under various traffic conditions such as traffic loads, burstyness of incoming traffic, and load non-uniformity across output ports. Next, we considered buffer management strategies to support QoS functions, which utilize parameter values obtained via connection admission control (CAC) procedures to set tile threshold values. Through simulations, we showed that the buffer management schemes adopted behave well in the sense that they can protect regulated traffic from unregulated cell traffic in allocating buffer space. In particular, it was observed that dynamic partitioning is superior in terms of QoS support than virtual partitioning.