1,722,016 research outputs found
다자간 에너지 거래를 위한 바나듐-레독스 배터리 전해질 교환시 필요한 SOC결정 모델링에 대한 연구
No full textVRFB 는 충전가능한 flow 배터리로써, kg 당 25Wh 의 에너지 밀도를 얻을 수 있으며 충방전 효율이 75-80%이며 일만번 이상의 충전사이클을 견뎌낼 수 있다. [1]에 따르면 배터리를 담는 탱크의 부피를 늘리는데 큰 어려움이 없으며 이는 곧 배터리 용량을 증가시킴이 수월함을 의미한다. 이러한 액체상태의 배터리의 장점을 이용, 파이프를 연결하여 전해질 거래를 통한 에너지 거래에 대한 연구[2]가 선행되어 있다. 이 논문에서는 전해질 이온 농도를 파악, 서로간 합의한 에너지량에 도달하는 시점을 수학적 모델링을 통해 예측하여 필요 장비를 최소화 하고자 한다. VFRB 는 음극과 양극이 각각 음전해액과 양전해액 탱크 두기로 이루어지며 보통의 이차전지와 같이 산화환원 반응을 통해 충방전이 이루어진다. 양극 탱크는 2 가와 3 가 바나듐 이온을 담고 있으며 음극 탱크는 4 가와 5 가 바나듐 이온을 담고 있다. 충전 시 3 가 이온은 2 가로 변환되며 4 가의 이온은 5 가로 변환된다. State of Charge(SOC)는 양극의 경우 5 가 이온을 총 이온수로 나눈 수치이며 음극의 SOC 는 2 가의 이온을 총 이온수로 나눈 값이다. SOC 를 아는 것은 충전된 전력량을 아는 것과 동치이므로 5 가 혹은 2 가 이온에 주목할 필요가 있다
A Bio-inspired Algorithm Based Time Synchronization Strategy in Infrastructure Wireless Mesh Networks
No full textWireless mesh networks (WMNs) have severe synchronization problem due to propagation delay. However, using the concept of bio-inspired algorithm has recently considered as synchronization solution. This paper proposes a bio-inspired algorithm based time synchronization strategy in infrastructure WMNs, which adaptively steers the beacon interval per mesh router (MR) or station (STA) for the delayed beacon due to a traffic overrun
PROCEDURAL METADATA: STRUCTURED GUIDE FOR DATA INTEROPERABILITY IN SUPPORT OF WEB OF THINGS
No full textStrategic Gaming Analysis for Energy Trading in the Smart Grid : Matrix Algebraic Approach
No full textPower storage system is essential element in energy trading environment. In this paper we studied about energy trading system based on double auction between sellers and buyers. Multiple participants have their own strategy and each player responds for other players’. Therefore some equilibrium point under some specific utility function is needed. In view of these facts, some authors proved the existence of Nash equilibrium. We studied about more profound conditions for existence of Nash equilibrium and direct finding method for equilibrium points
DRL-Based Energy-Efficient Group Paging for Robust HTD Access Control in 5G Networks
No full textIn 5G networks, the Legacy Paging mechanism for device connections faces challenges due to limited resources and increasing demands from human-type communication (HTC) and massive machine-type communications (mMTCs). This results in higher connection setup time (CST) under high traffic. Group paging, developed for mMTC, allows multiple devices to respond to a single paging message, but increases energy consumption for HTC due to irregular data transmission patterns. To address this, we propose the deep reinforcement learning-based group configuration control (DRL-GCC) mechanism. DRL-GCC combines legacy and group paging methods to optimize paging for HTC by dynamically adjusting group configurations, using the proximal policy optimization algorithm to reduce energy consumption and satisfy the required CST. Simulation results demonstrate that DRL-GCC significantly reduces energy consumption in the random access channel (RACH) procedure and the overall energy usage of devices while ensuring compliance with the CST constraint compared to its alternative methods. Notably, DRL-GCC achieves up to a 43% reduction in energy used during the RACH procedure and a 9% decrease in total energy consumption compared to the existing benchmarks. These improvements imply DRL-GCC's ability to balance energy efficiency with operational performance, providing a robust solution for efficient HTC-focused service provisioning in 5G networks.
A study on the formation of thin films prepared by selenizing sputtered Cu-In precursors
No full text학위논문(석사) - 한국과학기술원 : 전자재료공학과, 1994.2, [ iii, 53 p. ]한국과학기술원 : 전자재료공학과
Sparse Signal Recovery via Tree Search Matching Pursuit
No full textRecently, greedy algorithm has received much attention as a cost-effective means to reconstruct the sparse signals from compressed measurements. Much of previous work has focused on the investigation of a single candidate to identify the support (index set of nonzero elements) of the sparse signals. Well-known drawback of the greedy approach is that the chosen candidate is often not the optimal solution due to the myopic decision in each iteration. In this paper, we propose a tree search based sparse signal recovery algorithm referred to as the tree search matching pursuit (TSMP). Two key ingredients of the proposed TSMP algorithm to control the computational complexity are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In numerical simulations of Internet of Things (IoT) environments, it is shown that TSMP outperforms conventional schemes by a large margin. © 2011 KICS.1
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