A hybrid artificial neural network - genetic algorithm for load shedding

This paper proposes the method of applying Artificial Neural Network (ANN) with Back Propagation (BP) algorithm in combination or hybrid with Genetic Algorithm (GA) to propose load shedding strategies in the power system. The Genetic Algorithm is used to support the training of Back Propagation Neural Networks (BPNN) to improve regression ability, minimize errors and reduce the training time. Besides, the Relief algorithm is used to reduce the number of input variables of the neural network. The minimum load shedding with consideration of the primary and secondary control is calculated to restore the frequency of the electrical system. The distribution of power load shedding at each load bus of the system based on the phase electrical distance between the outage generator and the load buses. The simulation results have been verified through using MATLAB and PowerWorld software systems. The results show that the Hybrid Gen-Bayesian algorithm (GA-Trainbr) has a remarkable superiority in accuracy as well as training time. The effectiveness of the proposed method is tested on the IEEE 37 bus 9 generators standard system diagram showing the effectiveness of the proposed method

Minimize the load reduction considering the activities control of the generators and phase distance

This study shows how to calculate the minimum load that needs to be reduced to restore the frequency to the specified threshold. To implement this problem, the actual operation of the electricity system in the event of a generator outage is considered. The main idea of this method is to use the power balance equation between the generation and the load with different frequency levels. In all cases of operating the electrical system before and after the generator outage, the reserve capacity of other generators is considered in each generator outage situation. The reduced load capacity is calculated based on the reciprocal phase angle sensitivity or phase distance. This makes the voltage phase angle and voltage value quality of recovery nodes better. The standard IEEE 9-generator 37-bus test scheme was simulated to show the result of the proposed technique

Load Shedding in Microgrid System with Combination of AHP Algorithm and Hybrid ANN-ACO Algorithm

This paper proposes a new load shedding method based on the application of intelligent algorithms, the process of calculating and load shedding is carried out in two stages. Stage-1 uses a backpropagation neural network to classify faults in the system, thereby determining whether or not to shed the load in that particular case. Stage-2 uses an artificial neural network combined with an ant colony algorithm (ANN-ACO) to determine a load shedding strategy. The AHP algorithm is applied to propose load shedding strategies based on ranking the importance of loads in the system. The proposed method in the article helps to solve the integrated problem of load shedding, classifying the fault to determine whether or not to shedding the load and proposing a correct strategy for shedding the load. The IEEE 25-bus 8-generator power system is used to simulate and test the effectiveness of the proposed method, the results show that the frequency of recovery is good in the allowable range

Assessing the efficacy and safety of magnesium sulfate for management of autonomic nervous system dysregulation in Vietnamese children with severe hand foot and mouth disease.

BACKGROUND: Brainstem encephalitis is a serious complication of hand foot and mouth disease (HFMD) in children. Autonomic nervous system (ANS) dysregulation and hypertension may occur, sometimes progressing to cardiopulmonary failure and death. Vietnamese national guidelines recommend use of milrinone if ANS dysregulation with Stage 2 hypertension develops. We wished to investigate whether magnesium sulfate (MgSO4) improved outcomes in children with HFMD if used earlier in the evolution of the ANS dysregulation (Stage 1 hypertension). METHODS: During a regional epidemic we conducted a randomized, double-blind, placebo-controlled trial of MgSO4 in children with HFMD, ANS dysregulation and Stage 1 hypertension, at the Hospital for Tropical Diseases in Ho Chi Minh city. Study participants received an infusion of MgSO4 or matched placebo for 72 h. We also reviewed data from non-trial HFMD patients in whom milrinone failed to control hypertension, some of whom received MgSO4 as second line therapy. The primary outcome for both analyses was a composite of disease progression within 72 h - addition of milrinone (trial participants only), need for ventilation, shock, or death. RESULTS: Between June 2014 and September 2016, 14 and 12 participants received MgSO4 or placebo respectively, before the trial was stopped due to futility. Among 45 non-trial cases with poorly controlled hypertension despite high-dose milrinone, 33 received MgSO4 while 12 did not. There were no statistically significant differences in the composite outcome between the MgSO4 and the placebo/control groups in either study (adjusted relative risk (95%CI) of [6/14 (43%) vs. 6/12 (50%)], 0.84 (0.37, 1.92), p = 0.682 in the trial and [1/33 (3%) vs. 2/12 (17%)], 0.16 (0.01, 1.79), p = 0.132 in the observational cohort). The incidence of adverse events was similar between the groups. Potentially toxic magnesium levels occurred very rarely with the infusion regime used. CONCLUSION: Although we could not demonstrate efficacy in these studies, there were no safety signals associated with use of 30-50 mg/kg/hr. MgSO4 in severe HFMD. Intermittent outbreaks of HFMD are likely to continue across the region, and an adequately powered trial is still needed to evaluate use of MgSO4 in controlling hypertension in severe HFMD, potentially involving a higher dose regimen. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01940250 (Registered 22 AUG 2013). Trial sponsor: University of Oxford

Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit

BackgroundInterpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) patients can be challenging, especially in low- and middle- income countries (LMICs) where there is limited training available. Despite recent advances in the use of Artificial Intelligence (AI) to automate many ultrasound imaging analysis tasks, no AI-enabled LUS solutions have been proven to be clinically useful in ICUs, and specifically in LMICs. Therefore, we developed an AI solution that assists LUS practitioners and assessed its usefulness in a low resource ICU.MethodsThis was a three-phase prospective study. In the first phase, the performance of four different clinical user groups in interpreting LUS clips was assessed. In the second phase, the performance of 57 non-expert clinicians with and without the aid of a bespoke AI tool for LUS interpretation was assessed in retrospective offline clips. In the third phase, we conducted a prospective study in the ICU where 14 clinicians were asked to carry out LUS examinations in 7 patients with and without our AI tool and we interviewed the clinicians regarding the usability of the AI tool.ResultsThe average accuracy of beginners' LUS interpretation was 68.7% [95% CI 66.8-70.7%] compared to 72.2% [95% CI 70.0-75.6%] in intermediate, and 73.4% [95% CI 62.2-87.8%] in advanced users. Experts had an average accuracy of 95.0% [95% CI 88.2-100.0%], which was significantly better than beginners, intermediate and advanced users (p < 0.001). When supported by our AI tool for interpreting retrospectively acquired clips, the non-expert clinicians improved their performance from an average of 68.9% [95% CI 65.6-73.9%] to 82.9% [95% CI 79.1-86.7%], (p < 0.001). In prospective real-time testing, non-expert clinicians improved their baseline performance from 68.1% [95% CI 57.9-78.2%] to 93.4% [95% CI 89.0-97.8%], (p < 0.001) when using our AI tool. The time-to-interpret clips improved from a median of 12.1 s (IQR 8.5-20.6) to 5.0 s (IQR 3.5-8.8), (p < 0.001) and clinicians' median confidence level improved from 3 out of 4 to 4 out of 4 when using our AI tool.ConclusionsAI-assisted LUS can help non-expert clinicians in an LMIC ICU improve their performance in interpreting LUS features more accurately, more quickly and more confidently