Safety-oriented adaptive slow-closing design of check valves: An intelligent multi-objective optimization paradigm

Time optimal control (TOC) is a technique to provide fast transient recovery in dc-dc converters. Prior published approaches are incomplete in the formulation of TOC because they ignore voltage-deviation effects on inductor-current and load-current dynamics during a large-signal recovery. An accurate TOC algorithm based on capacitor current is presented. This method achieves minimum transient recovery time for load transients and tracking in a buck converter. The minimum recovery time is preserved even with a transient-detection delay. This control configuration ensures large-signal stability in a sense similar to that of sliding-mode control. The results are demonstrated in an experimental buck converter that uses a digital control algorithm.

Hydraulic oscillation is a fluctuating phenomenon of pressure and discharge in pipes, which can threaten the safety of hydropower, pumping and water conveyance systems. To analyse the associated problems, new methods with simple form and clear physics are needed. This paper presents a water-hammer reflection coefficient-based criterion for stability evaluation of free-vibration of hydraulic systems. The stability (or attenuation) condition for a single pipe system is that the modulus of the product of the reflection coefficients at the inlet and outlet should be smaller than 1. For a complex pipe system, the condition necessary for stability is that every single pipe system is stable. To apply this new criterion to stabilizing the reservoir-single pipe-constant power turbine system by adding a head loss valve in the pipe, a formula for the critical head loss is proposed and verified. This new method is theoretically consistent with conventional methods, but more convenient in application.

Aiming at the problems existing in the traditional teaching mode, this paper intelligently optimizes English teaching courses by using multidirectional mutation genetic algorithm and its optimization neural network method. Firstly, this paper gives the framework of intelligent English course optimization system based on multidirectional mutation genetic BP neural network and analyses the local optimization problems existing in the traditional BP algorithm. A BP neural network optimization algorithm based on multidirectional mutation genetic algorithm (MMGA-BP) is presented. Then, the multidirectional mutation genetic BPNN algorithm is applied to the intelligent optimization of English teaching courses. The simulation shows that the multidirectional mutation genetic BP neural network algorithm can solve the local optimization problem of traditional BP neural network. Finally, a control group and an experimental group are set up to verify the role of multidirectional mutation genetic algorithm and its optimization neural network in the intelligent optimization system of English teaching courses through the combination of summative and formative teaching evaluations. The data show that MMGA-BP algorithm can significantly improve the scores of academic students in English courses and has better teaching performance. The effect of vocabulary teaching under the guidance of MMGA-BP optimization theory is very significant, which plays a certain role in the intelligent curriculum optimization of the experimental class.

Diagnostic value of recovery time measured by body surface mapping in patients with congenital long QT syndrome

A high performance proportional-integral-derivative (PID) controller in a dc-dc converter requires a time optimal tuning rule. A suitable auto-tuning rule needs to perform large-signal minimum-time transient recovery, while maintaining a sufficient small-signal stability margin and closed-loop bandwidth. This paper applies a geometric approach to analytically formulate a time optimal PID controller tuning rule for a buck converter. It is shown that the proposed method achieves approximate minimum-time transient recovery in the large-signal sense. The controller gains during a small-signal transient can be shown to be representative of those obtained using a standard tuning rule. The proposed formulation closely follows the desired minimum-transient-time trajectory. This geometric representation ensures large-signal stability in a sense similar to that of sliding mode control. A buck converter prototype is tested, and the proposed scheme is implemented using the ALTERA FPGA Cyclone-II.

A hybrid buck-linear (HBL) technique in a load-preparation buck (LPB) converter for system-on-a-chip (Soc) is proposed in this paper. In case of the sudden load variation in Soc, the proposed converter with hybrid operation can effectively enhance the transient response with smaller dip voltage and faster transient recovery time. In addition, the high power conversion efficiency can be derived since an auxiliary power switch assures that hybrid operation is only activated in load transient period. Experimental results demonstrate that the improvements of transient dip voltage and recovery time are 53 % and 63 %, respectively, as well as 8 % in efficiency. The chip was fabricated by 0.25 μm CMOS process with a peak efficiency of 95 % for Soc applications.

The aim of the study was to determine the optimum time for administration of neostigmine during recovery from atracurium-induced neuromuscular blockade. The study comprised 103 patients anaesthetised with midazolam, fentanyl, thiopentone, halothane, and nitrous oxide. Relaxation was induced with atracurium 0.5 mg. kg-1 and maintained with supplements of 0.15 mg. kg-1. The ulnar nerve was stimulated with train-of-four (TOF) and double burst stimulation (DBS). Evoked MMG responses were recorded. Patients were randomized to spontaneous recovery (n = 20) or to assisted recovery by neostigmine (0.07 mg.kg-1) at varying intervals (6-50 min) from the last atracurium dose (n = 83). The reversal time (time from administration of neostigmine to TOF ratio 0.7) was always < 13 min, when T1 (first twitch in TOF) was detectable or when D1 (first twitch in DBS) was > 5%. Total assisted recovery time (time from last supplemental atracurium dose to TOF ratio 0.7) increased with increasing T1 and D1 twitch heights (P < 0.05). The curve fitted to the scattergram with total assisted recovery time vs time from last atracurium supplement to neostigmine administration decreased to reach a minimum after which it increased to approach the line of identity. The minimum of the curve (total assisted recovery time 30.7 min) was reached when neostigmine was given 18.6 min after last atracurium supplement. At this time the T1 and D1 twitch height averaged 4 and 8% respectively. If prolongation of the minimum total recovery time of 2.5% is accepted, neostigmine can be given at T1 and D1 twitch height values of 0 to 8% and 4 to 15%, respectively. The optimum time for neostigmine administration, taking both the reversal time and total recovery time into consideration, is when 0 < T1 < 8% or when 5 < D1 < 15%. Giving neostigmine at more profound degrees of blockade prolongs reversal time, while giving neostigmine later in the recovery phase prolongs total recovery time.

We Propose PSO-BP network traffic prediction algorithm which based on BP neural network and improved by the particle swarm optimization. Use PSO algorithm to optimize the initial weight and threshold values of BP network, and use history to train BP neural network and realize the simulation by MATLAB. The results show that, PSO-BP algorithm can improve network traffic prediction accuracy and speed up the convergence rate of BP network.

Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation

This work presents the experimental temperature dependence evaluation of the pseudo-resistor piecewise linear (PWL) macromodel, for temperature ranging from 300 to 333 K. The studied macromodel is based on the PWL methodology and it can be used for pseudo-resistors SPICE simulation, including both linear and non-linear operation regions. This study was carried out using a low-frequency bio-potential amplifier with narrow bandwidth for use in QRS complex (part of the electrocardiogram which is a combination of the Q wave, R wave and S wave) detection systems. The architecture based on pseudo-resistors provides DC offset cancellation, adequate frequency response and a significant reduction of the signal recovery time. Both the pseudo-resistor and the QRS complex detector performance were experimentally measured with the temperature variation, in order to include this dependency in the PWL model. The transient recovery time, as well as the QRS complex detector frequency response, was also widely evaluated, in the proposed temperature range, through experimental measurements. The temperature dependence was added to the model and implemented in the SPICE simulation. The model outputs showed strong adherence to experimental data, showing a disagreement less than 6% in the low cut-off frequency. The macromodel’s response to the recovery time also followed the same experimental results behavior, presenting a maximum error of 20% within human body temperature range. The obtained recovery time was less than a normal heartbeat period to the entire temperature range evaluated. All of the analyzed circuits were implemented using GF 8HP 0.13 μm BiCMOS technology from Global Foundries.

Impaired left ventricular function is associated with increased recovery time dispersion in patients with previous myocardial infarction

This article aims to explore the intelligent fuzzy optimization algorithm for data mining based on BP neural network. Although the database technology has been improved with the increase of the amount of data, facing the explosive growth of the amount of data, the previous database management methods have been unable to meet and analyze the hidden knowledge in this scale of data. Therefore, it is important to find better automated data processing methods to satisfy the classification and analysis of massive data. However, the current BP neural network is not yet perfect. This method has some problems, such as slow convergence speed. The problem is reflected in the problem of pattern recognition and insufficient generalization ability and stability. Based on the above description, the research content of this paper is an intelligent fuzzy optimization algorithm for data mining based on BP neural network. Considering that the training of the BP algorithm is based on the weight correction principle of error gradient descent, the genetic algorithm is good at global search, but it does not have accurate local searchability. Therefore, this paper uses the weight of the genetic algorithm. This paper improves BP neural network based on a genetic algorithm. The experimental simulation results of Iris show that the quantity of hidden nodes usually increases with the number of training samples. ACBP algorithm can construct a better network structure based on the number of training samples. And through the experimental comparison of the traditional BP neural network algorithm, it is concluded that the improved algorithm can allow data mining technology to mine relatively more ideal data from complex environments.

New shape optimization method for tree structures based on BP neural network

With the ever-growing amount of time-critical, compute-intensive, and private IoT applications, the need for High Availability (HA) Edge Clouds becomes indispensable. Realizing HA Edge Clouds is inherently challenging due to the geographically-dispersed hierarchy of the Distributed Cloud Infrastructure (DCI). For example, frequent isolation between the central Cloud and Edge Clouds due to networking instability necessitates some autonomous operations at the Edge Clouds. Furthermore, because Edge Clouds have fewer resources than central Clouds, configuring the Edge functions (i.e., control, compute, and storage) in HA clusters will undoubtedly reduce downtime. However, it will limit the Edge scalability. To that end, StarlingX is developing an HA-protected and scalable DCI virtualization platform based on the open-source ecosystem, focusing on low-touch management of Edge Clouds. StarlingX provides a fault management service that realizes DCI-wide alarming and logging capabilities, allowing for rapid response to virtualized infrastructure events. Recently, the IETF Network Working Group proposed that monitoring both the DCI and the Edge workloads (software containers) is critical for an Edge Computing Platform to maintain HA IoT application deployment. Indeed, the possibility of the infrastructure remaining stable and healthy while the workloads suffer a fatal failure simultaneously necessitates failover functionality that monitors both the infrastructure and the Edge workloads. In this paper, we first propose a dynamic failover functionality that centrally monitors Edge workloads to recover from deployment or Edge node failures, motivated by the IETF direction. Second, we experimentally optimize the failover functionality for monitoring a microservice-architected IoT application deployed on a StarlingX-based DCI testbed to collect temperature sensor readings from Raspberry Pis. Regardless of how quickly the Edge workload health checks are collected, the recorded failover measurements reveal that the recovery time will not drop below a predetermined level controlled by Edge resources and network speed. Furthermore, reducing the statistics collection timeout reduces the recovery time of an Edge node failure. When the timeout value is less than the minimum achievable recovery time, false-positive failures (FPFs) can occur. Third, to supplement the StarlingX fault management service, we provide a modular implementation of the proposed failover functionality. Finally, we present the first-ever introduction of the StarlingX platform’s software stack to promote its use in academic research.

Minimum infusion rates and recovery times from different durations of continuous infusion of fospropofol, a prodrug of propofol, in rabbits: a comparison with propofol emulsion