This study investigates the impact of renewable energy penetration on system stability and validates the performance of the (Proportional-Integral-Derivative) PID-(reinforcement learning) RL control technique. Three s...
详细信息
This study investigates the impact of renewable energy penetration on system stability and validates the performance of the (Proportional-Integral-Derivative) PID-(reinforcement learning) RL control technique. Three scenarios were examined: no photovoltaic (PV), 25% PV, and 50% PV, to evaluate the impact of PV penetration on system stability. The results demonstrate that while the absence of renewable energy yields a more stable frequency response, a higher PV penetration (50%) enhances stability in tie-line active power flow between interconnected systems. This shows that an increased PV penetration improves frequency balance and active power flow stability. Additionally, the study evaluates three control scenarios: no control input, PID-(Particle Swarm Optimization) PSO, and PID-RL, to validate the performance of the PID-RL control technique. The findings show that the EV system with PID-RL outperforms the other scenarios in terms of frequency response, tie-line active power response, and frequency difference response. The PID-RL controller significantly enhances the damping of the dominant oscillation mode and restores the stability within the first 4 s-after the disturbance in first second. This leads to an improved stability compared to the EV system with PID-PSO (within 21 s) and without any control input (oscillating more than 30 s). Overall, this research provides the improvement in terms of frequency response, tie-line active power response, and frequency difference response with high renewable energy penetration levels and the research validates the effectiveness of the PID-RL control technique in stabilizing the EV system. These findings can contribute to the development of strategies for integrating renewable energy sources and optimizing control systems, ensuring a more stable and sustainable power grid.
Recent advancements in steganography analysis based on deep neural networks have led to the development of steganography schemes that incorporate deep network technology like adversarial example, GAN, and reinforce le...
详细信息
Recent advancements in steganography analysis based on deep neural networks have led to the development of steganography schemes that incorporate deep network technology like adversarial example, GAN, and reinforce learning. However, most deep network-based steganography schemes are unable to ensure error-free extraction of secret information because of the design similar to information reconstruction. To address this issue, this work proposes a novel audio steganography cover enhancement framework that leverages two networks-a policy network and an environment network-to enhance the undetectability and imperceptibility of the steganographic audio. The proposed schemes utilize the reinforcement algorithm to optimize steganography cover modification for improving undetectability and imperceptibility. And our method guarantees 100% extracting accuracy by only enhancing on domains that do not affect information extraction. The experimental results demonstrate that our method significantly enhances the missing detection rate of the target audio steganography analysis network over 90% when resisting a steganalysis network, and meanwhile, our method has strong imperceptibility which can hardly be distinguished by human ears.
作者:
Xing, WeiZhao, XudongHainan Univ
Sch Informat & Commun Engn Haikou 570228 Hainan Peoples R China Dalian Univ Technol
Minist Educ Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China
In this paper, a dynamic zero-sum game is formulated to describe the power decision-making process of the sensor/relay and the DoS attacker in cyber-physical systems. The sensor and the relay cooperate with each other...
详细信息
In this paper, a dynamic zero-sum game is formulated to describe the power decision-making process of the sensor/relay and the DoS attacker in cyber-physical systems. The sensor and the relay cooperate with each other to transmit the state estimation to the remote estimator, when the attacker, on the contrary, aims to disturb the wireless communication channels strategically for deterioration of the system performance but can do this taking into account its limited energy. Different from conventional battery-powered nodes, the sensor and the relay can harvest energy from the external environment and store it in their batteries for data transmission. We model the external environment state as a Markov chain to overcome the randomness of the harvested energy. In addition, to tackle the computation complexity of the Nash equilibrium (NE), we restrict our attention to a special case, i.e., the DoS attacker can only launch interference on one of the two communication channels over an infinite time horizon, and provide the corresponding NE strategy of the game using the Markov decision process and the multi-agent reinforcement learning algorithm. Finally, simulation examples are given to illustrate the theoretical findings of the paper.
Owing to the advancements in cloud computing our lives are significantly altering the means of utilizing data by the data-intensive business and research. The coming era of ubiquitous computing is greatly supported by...
详细信息
Owing to the advancements in cloud computing our lives are significantly altering the means of utilizing data by the data-intensive business and research. The coming era of ubiquitous computing is greatly supported by the evolution of cloud computing with networked multicore GPU processors to avail consistent data utilization. In such a domain, computing, data stockpiling and correspondence turns into a utility. In this paper, a new algorithm is devised that aids in ranking the tasks based on generated cluster indices. The proposed algorithm offers a new strategy for simultaneous task partitioning, it's ranking and load assignment, thus improving the computational performance of the given workload.
Modern network applications demand low-latency traffic engineering in the presence of network failure, while preserving the quality of service constraints like delay and capacity. Fast Re-Route (FRR) mechanisms are wi...
详细信息
Modern network applications demand low-latency traffic engineering in the presence of network failure, while preserving the quality of service constraints like delay and capacity. Fast Re-Route (FRR) mechanisms are widely used for traffic re-routing purposes in failure scenarios. Control plane FRR typically computes the backup forwarding rules to detour the traffic in the data plane when the failure occurs. This mechanism could be computed in the data plane with the emergence of programmable data planes. In this paper, we propose a system (called TEL) that contains two FRR mechanisms, namely, TEL-C and TEL-D. The first one computes backup forwarding rules in the control plane, satisfying max-min fair allocation. The second mechanism provides FRR in the data plane. Both algorithms require minimal memory on programmable data planes and are well-suited with modern line rate match-action forwarding architectures (e.g., PISA). We implement both mechanisms on P4 programmable software switches (e.g., BMv2 and Tofino) and measure their performance on various topologies. The obtained results from a datacenter topology show that our FRR mechanism can improve the flow completion time up to 4.6x-7.3x (i.e., small flows) and 3.1x-12x (i.e., large flows) compared to recirculation-based mechanisms, such as F10, respectively.
This paper considers the optimal transmission power scheduling for accurate remote state estimation in cyber-physical systems (CPSs). The plant is modeled as a discrete-time stochastic linear system with sensor measur...
详细信息
This paper considers the optimal transmission power scheduling for accurate remote state estimation in cyber-physical systems (CPSs). The plant is modeled as a discrete-time stochastic linear system with sensor measurements transmitted to the remote estimator over an intelligent relay. A dynamic cooperative team is formulated in which the sensor and the relay sharing the same cost function are equipped with corresponding energy harvesters, and need to decide on the transmission power levels to minimize the average error covariance at the remote estimator. Since the nodes make simultaneous decisions to achieve a common goal, the sensor and the relay do not have access to the transmission power that will be selected by another node in advance, and hence they cannot build a value estimation of the cost function directly at each time point, which poses a challenge to determine the optimal transmission policies of both sides. To overcome this challenge, we provide a local-action value function and prove that both nodes select the transmission power with respect to the local-action value function without any loss of performance. In addition, a Markov decision process (MDP) and a multi-agent reinforcement learning algorithm are presented to obtain the optimal transmission power schedule over an infinite time horizon. Finally, simulation examples are provided to corroborate and illustrate the theoretical results.(c) 2023 Elsevier Ltd. All rights reserved.
This paper addresses the problem of video summarization through an automatic selection of a single representative keyframe. The proposed solution is based on the mutual reinforcement paradigm, where a keyframe is sele...
详细信息
ISBN:
(纸本)9781479909551
This paper addresses the problem of video summarization through an automatic selection of a single representative keyframe. The proposed solution is based on the mutual reinforcement paradigm, where a keyframe is selected thanks to its highest and most frequent similarity to the rest of considered frames. Two variations of the algorithm are explored: a first one where only frames within the same video are used (intraclip mode) and a second one where the decision also depends on the previously selected keyframes of related videos (interclip mode). These two algorithms were evaluated by a set of professional documentalists from a broadcaster's archive, and results concluded that the proposed techniques outperform the semi-manual solution adopted so far in the company.
This computer era leads human to interact with computers and networks but there is no such solution to get rid of security problems. Securities threats misleads internet, we are sometimes losing our hope and reliabili...
详细信息
ISBN:
(纸本)9781538644911
This computer era leads human to interact with computers and networks but there is no such solution to get rid of security problems. Securities threats misleads internet, we are sometimes losing our hope and reliability with many server based access. Even though many more crypto algorithms are coming for integrity and authentic data in computer access still there is a non reliable threat penetrates inconsistent vulnerabilities in networks. These vulnerable sites are taking control over the user's computer and doing harmful actions without user's privileges. Though Firewalls and protocols may support our browsers via setting certain rules, still our system couldn't support for data reliability and confidentiality. Since these problems are based on network access, lets we consider TCP/IP parameters as a dataset for analysis. By doing preprocess of TCP/IP packets we can build sovereign model on data set and clump cluster. Further the data set gets classified into regular traffic pattern and anonymous pattern using KNN classification algorithm. Based on obtained pattern for normal and threats data sets, security devices and system will set rules and guidelines to learn by it to take needed stroke. This paper analysis the computer to learn security actions from the given data sets which already exist in the previous happens.
Orchestration and management of cloud computing entities necessitate measuring and analysis of real-time monitored performance metrics. However, decision making in current management platforms are addressed separately...
详细信息
ISBN:
(纸本)9781538672358
Orchestration and management of cloud computing entities necessitate measuring and analysis of real-time monitored performance metrics. However, decision making in current management platforms are addressed separately in different cloud stack layers. These isolated active management decisions may degrade the total performance of the cloud system. Since, cloud computing platforms lack an integrated analytics and management capability, in this paper, we propose an integrated platform to detect and predict situations where corrective actions are required. First, a Dynamic Bayesian Network (DBN) is trained and updated by collected data to calculate the causal dependencies among various entities in different cloud service layers. The correlation values are then fed into a Long ShortTerm Memory (LSTM) neural network to predict the future states. States that violate the Service Level Agreement(SLA) of cloud services are learned with training data, and if the forecasted states threaten the SLA of cloud services, associated events are generated to trigger management actions. Next, management actions are assigned a different set of events using a reinforcement learning approach. A set of experiments based on collected data from a real cloud service environment is conducted to validate the proposed approach. Experimental results indicate that the proposed method outperforms the current management solutions and improves web request response time by up to 7% and decreases SLA violation by 79% in the context of web application auto scaling.
This paper presents a new method for optimizing continuous complex functions based on a learning automaton. This method can be considered as active learning permitting to select on-line the most significant data sampl...
详细信息
This paper presents a new method for optimizing continuous complex functions based on a learning automaton. This method can be considered as active learning permitting to select on-line the most significant data samples in order to quickly converge to a quasi global optimum of the functions to be optimized with a fewer number of tests or calculations. Like other stochastic optimization algorithms, it aims at finding a compromise between exploitation and exploration, i.e. converging to the nearest local optima and exploring the function behavior in order to discover global optimal regions. During the optimization procedure, this method enhances local search in interesting regions or intervals and reduces the whole searching space by removing useless regions or intervals. (C) 2004 Elsevier Inc. All rights reserved.
暂无评论