Capacity Control Research Articles

Attack Detection and Prediction Using Machine Learning

Data plane and control plane are divided by Software Defined Networking (SDN). A centralized controller oversees and manages the entire network. With SDN, the network may be programmed and flow regulations can be created dynamically. Numerous benefits including adaptability, programmability, and centralized management are offered by this decoupling. However, SDN also creates new vulnerabilities as a result of desired data plane and control plane connectivity. Attacks on switch buffer overflows and control plane saturation are two examples of threats that exploit such flaws. The controller is vulnerable to Distributed Denial of Service (DDoS) attacks, which induce resource exhaustion and impair the controller's capacity to provide services. By flooding the control plane with TCP SYN packets from the data plane (i.e., switches), several attacks can be launched. SVM is the most popular and often used classifier, both for classification and regression, thanks to its high accuracy and low false positive rate. For DDoS detection, the SVM classifier is examined and contrasted with other classifiers. In order to identify anomalies, such as malicious traffic, and report them, Snort, an intrusion detection system, examines the traffic and packets. The entropy approach is used to assess the flow data's randomness. An IP address for the intended recipient and a few TCP flag attributes make up the entropy information. We implement it as an additional module in the Floodlight Controller and assess its viability and efficacy. We thoroughly evaluate how we have implemented things via Mininet, substantial emulation.

Abstract 274: Defective Branched Chain Amino Acid Catabolism Impairs Exercise Endurance

The branched chain amino acids (BCAA), leucine, isoleucine and valine, are essential for mammalians, and they play a positive role in exercise capacity, muscle development, and a lean body phenotype. BCAA supplementation is commonly paired with exercise in order to promote muscle growth, increase resistance to fatigue and reduce muscle soreness. On the other hand, elevated serum BCAA is strongly and positively correlated with the development of insulin resistance, coronary heart disease, and type II diabetes, and is predictive of patient response to therapeutics and intervention outcomes. We have previously shown that defective BCAA catabolism in mice impaired glucose metabolism in the heart and increased susceptibility to stress-induced cardiac damage. In this study we sought to determine the effects of elevated BCAA levels on skeletal muscle performance and response to exercise training using mouse models with systemically elevated BCAA levels. Supplementation of BCAA (1.5mg/g bodyweight/day, ratio of Leu:Ile:Val = 1.5:0.8:1) was administered to mice with impairment of BCAA catabolism due to the deletion of mitochondrial-localized protein phosphatase 2C (KO), a key enzyme in activating BCAA catabolism, and their littermate controls (CON). Mice were subjected to one week of daily exercise training via forced treadmill running and an exercise capacity test was performed at the beginning and end of training. Baseline maximum running time was decreased in the KO compared to CON (mean 73.4 and 82.5 min, respectively). One week of training resulted in increased exercise capacity in CON with an attenuated increase in KO mice (mean 136 and 112 min, respectively). BCAA supplementation did not further improve exercise capacity in CON (mean 131 min) and abrogated the response to training in KO (mean 70.1 min). Reduced exercise capacity positively correlated with elevated serum BCAA levels. Additionally, KO supplemented with BCAAs demonstrated elevated serum succinate, alanine and glutamate levels, which are metabolic markers of physiological stress. We conclude that short term supplementation of BCAA has no benefit for exercise capacity and accumulation of BCAAs has a negative effect on endurance exercise capacity.

Analysis of controllable capacity for electric vehicle battery swapping stations

Battery swapping is an effective way for the energy fast recharge of electric vehicles (EVs). The batteries stored in the battery swapping stations (BSSs) might be an emerging form of energy storage that can be used in the future power systems. Unlike traditional battery storage, the application of BSSs storage needs to consider the behaviour of EV users and the charging process in the BSSs. The calculation of controllable capacity (CC) plays a key role in the application of BSSs storage. This study proposes a Monte Carlo stochastic simulation method (MCSS) to estimate the CC of BSSs. The queuing problems in EV batteries swapping are simulated by the proposed MCSS method. The CC then can be obtained from the queues analysis. Simulation results demonstrate the availability of the proposed method.

Supplementary automatic generation control using controllable energy storage in electric vehicle battery swapping stations

One of the significant impacts of the growing penetration of intermittent renewable energy sources is upon the frequency response of power system. Compared with the dispersive electric vehicle energy storage, electric vehicle battery swapping station (BSS), as an emerging form of storage, can provide a more reliable supplementary regulation service for frequency control. This study has proposed a new supplementary automatic generation control (AGC) strategy using controllable energy storage in BSSs, referred to as station-to-grid (S2G). A Monte–Carlo stochastic simulation method is utilised to estimate the equivalent controllable capacity (CC) of BSSs, and then the lumped S2G equivalent model subject to SOC limits and CC constrains is presented. A filter-based AGC coordinated strategy is used to allocate the regulation power between generators and BSSs. The proposed AGC strategy is validated in a two-area interconnected power system with significant load and wind power fluctuations. Comparison analysis demonstrates the availability of the proposed models and control methods.

Educating novice practitioners to detect elder financial abuse: a randomised controlled trial

BackgroundHealth and social care professionals are well positioned to identify and intervene in cases of elder financial abuse. An evidence-based educational intervention was developed to advance practitioners’ decision-making in this domain. The objective was to test the effectiveness of a decision-training educational intervention on novices’ ability to detect elder financial abuse. The research was funded by an E.S.R.C. grant reference RES-189-25-0334.MethodsA parallel-group, randomised controlled trial was conducted using a judgement analysis approach. Each participant used the World Wide Web to judge case sets at pre-test and post-test. The intervention group was provided with training after pre-test testing, whereas the control group were purely given instructions to continue with the task. 154 pre-registration health and social care practitioners were randomly allocated to intervention (n78) or control (n76). The intervention comprised of written and graphical descriptions of an expert consensus standard explaining how case information should be used to identify elder financial abuse. Participants’ ratings of certainty of abuse occurring (detection) were correlated with the experts’ ratings of the same cases at both stages of testing.ResultsAt pre-test, no differences were found between control and intervention on rating capacity. Comparison of mean scores for the control and intervention group at pre-test compared to immediate post-test, showed a statistically significant result. The intervention was shown to have had a positive moderate effect; at immediate post-test, the intervention group’s ratings had become more similar to those of the experts, whereas the control’s capacity did not improve. The results of this study indicate that the decision-training intervention had a positive effect on detection ability.ConclusionsThis freely available, web-based decision-training aid is an effective evidence-based educational resource. Health and social care professionals can use the resource to enhance their ability to detect elder financial abuse. It has been embedded in a web resource at http://www.elderfinancialabuse.co.uk.

DUAL VERSION OF NEURAL ADAPTIVE CONTROLLER FOR STOCHASTIC NONLINEAR SYSTEMS WITH FUNCTIONAL UNCERTAINTY

A dual adaptive control scheme for general class of nonlinear stochastic discrete control systems is proposed. It enhances the controller's capacity for adaptation, stabilization, management of uncertainty and learning in more complex and uncertain plants. The control law in the proposed dual adaptive control scheme is derived by taking into consideration model uncertainty. This is achieved by extending techniques from the stochastic adaptive control, known to be computationally expensive, to the functional adaptive case leading to a novel dual control laws for neural control schemes. The proposed scheme avoids the pre-control neural network training phase by taking into consideration model uncertainty and its effect on tracking, in the on-line control phase. Its implementation requires finding a suitable method for estimating model uncertainty. Both simulation and experiment are given in the paper to demonstrate the efficiency of the proposed method.