特殊天气污水溶解氧浓度的自适应广义预测控制

污水处理过程具有非线性、时变、大滞后的特点,尤其在雨天暴雨等特殊天气下,污水的入水波动会对控制器造成严重干扰。文中提出一种基于模糊神经网络模型的自适应广义预测控制算法,实现对污水处理过程中溶解氧浓度的实时控制。该算法利用反馈线性化思想实现自适应广义预测控制器的设计,在证明其李雅普诺夫稳定的同时,得到修正系统的受控自回归积分滑动平均模型参数自适应规则,动态调整模型参数使系统跟踪误差达到最小。仿真实验结果表明,该算法能够稳定、快速地控制溶解氧浓度,具有较强的抗干扰能力和鲁棒性,该控制算法特别适合用于污水处理过程的特殊天气（如雨天和暴雨天）中。     

污水活性污泥处理过程的溶解氧增益调度控制

考察污水活性污泥处理过程溶解氧控制效果的重要性能指标是溶解氧浓度的波动情况和能耗。针对溶解氧的非线性传递模型,本文提出了以溶解氧设定值作为调度变量的溶解氧增益调度控制方法。即在溶解氧设定值进行泰勒级数展开得到线性模型,采用参考模型方法设计控制器,再根据不同的调度值(溶解氧设定值),调度线性时不变控制器。仿真结果表明,本文提出的增益调度控制方法无论在能耗方面还是在控制精度方面都要明显优越于常规的开关控制和PID控制。     

基于光纤的溶解氧浓度荧光测定法

针对溶解氧浓度微量探测的现实需求,提出了一种基于荧光猝灭原理、利用多孔光纤实现的溶解氧浓度测定新方法。该方法将钌联吡啶[Ru（dpp）₃]Cl₂掺杂的凝胶薄膜修饰在多孔光纤的内壁上,制备了一种溶解氧测定探头并对其测试性能进行表征。光纤贯穿整个长度的孔洞结构既可以作为敏感膜的载体,也可以作为待测物流过的通道和反应场所。与传统测试方法相比,该测试探头的多孔道结构显著提高了比表面积,指示剂可以与溶解氧直接反应,提高了探头的敏感性并且具有微量探测的潜力。实验结果表明,在0~20 mg/L的浓度范围内,Stern-Volmer曲线近似线性,响应敏感度I₀/I为3.6,响应时间为200 ms。该测试方法在溶解氧微量探测领域具有重要用途。     

基于光纤的溶解氧浓度荧光测定法

针对溶解氧浓度微量探测的现实需求,提出了一种基于荧光猝灭原理、利用多孔光纤实现的溶解氧浓度测定新方法。该方法将钌联吡啶[Ru(dpp)3]Cl2掺杂的凝胶薄膜修饰在多孔光纤的内壁上,制备了一种溶解氧测定探头并对其测试性能进行表征。光纤贯穿整个长度的孔洞结构既可以作为敏感膜的载体,也可以作为待测物流过的通道和反应场所。与传统测试方法相比,该测试探头的多孔道结构显著提高了比表面积,指示剂可以与溶解氧直接反应,提高了探头的敏感性并且具有微量探测的潜力。实验结果表明,在0~20mg/L的浓度范围内,Stern-Volmer曲线近似线性,响应敏感度I0/I为3.6,响应时间为200 ms。该测试方法在溶解氧微量探测领域具有重要用途。     

混沌神经网络的控制

提出了一种针对混沌神经网络的钉扎控制方法.利用此方法对混沌神经网络进行控制,使网络的输出稳定在网络的一个存储的模式上.实现了混沌神经网络的信息搜索,并对影响混沌神经网络控制效果的几个因素进行了讨论. 关键词： 混沌控制 混沌神经网络 钉扎控制方法     

混沌神经网络的动态阈值控制

提出了混沌神经网络的动态阈值控制方法, 将大脑脑电波的主要成分, 正弦信号作为控制变量实现对混沌神经网络内部状态的阈值动态改变, 从而达到了控制混沌神经网络混沌的目的. 利用该方法可以将混沌神经网络的输出稳定在一个与网络初始模式相关的存储模式和其反相模式上, 从而使混沌神经网络在模式识别、信息搜索等信息处理功能得以实现. 该控制方法不需要事先指定阈值, 是一种自适应方法, 符合实际人脑的思维运动. 关键词： 混沌控制 混沌神经网络 动态阈值控制 信息处理     

混沌系统的遗传神经网络控制

提出遗传神经网络控制混沌新方法.将小扰动技术和周期控制技术结合起来，用遗传算法训练神经网络，使之成为混沌控制器.对Henon映射和Logistic映射的仿真结果说明控制器能产生小扰动控制序列信号，将混沌振荡转变成规则运动状态.该方法无需了解动态系统数学模型，具有一定抗噪声干扰能力，可将它推广应用到其他混沌系统的控制中. 关键词： 遗传算法 神经网络 混沌 周期控制     

溶解氧对鲁米诺电化学发光的增强效应研究

本文对溶解氧增强鲁米诺的电化学发光(ECL)行为进行了系统的研究,发现电学参数等因素很大程度地影响溶解氧增强ECL的产生、发光强度及稳定性。通过对电极的预处理,在0.15 mol·L-1的KNO3-中性硼酸缓冲溶液中,-0.4-0.9 V(vs.Ag)的矩形脉冲激励下,溶解氧显著增强鲁米诺的ECL且具有良好的稳定性,线性范围从0-16 mg·L-1,相对标准偏差为0.4％。讨论了溶解氧增强鲁米诺ECL的机理及表面活性剂的影响。     

非线性系统混沌运动的神经网络控制

设计前馈反传神经网络控制非线性系统混沌运动的新方法.根据扰动参数模型输入输出数据,按照非线性学习算法训练网络产生系统稳定所需的小扰动控制信号,去镇定混沌运动,使嵌入在混沌吸引子中的不稳定周期轨道回到稳定不动点上.Hnon映射数值仿真结果表明,这种方法控制非线性混沌系统响应速度快、控制精度高 关键词： 混沌控制 神经网络 吸引子 非线性     

水体中溶解氧的静态注射化学发光法测定

建立了静态注射化学发光碘与鲁米诺体系测定水体中的溶解氧,线性方程为y=4.32×10^6＋1.29×10^7x,相关系数0.9951,与滴定分析方法相比无显著性差异,方法简单、快速,适于环境监测部门的质量监控。     

Scavenging dissolved oxygen via acoustic droplet vaporization

Acoustic droplet vaporization (ADV) of perfluorocarbon emulsions has been explored for diagnostic and therapeutic applications. Previous studies have demonstrated that vaporization of a liquid droplet results in a gas microbubble with a diameter 5–6 times larger than the initial droplet diameter. The expansion factor can increase to a factor of 10 in gassy fluids as a result of air diffusing from the surrounding fluid into the microbubble. This study investigates the potential of this process to serve as an ultrasound-mediated gas scavenging technology. Perfluoropentane droplets diluted in phosphate-buffered saline (PBS) were insonified by a 2 MHz transducer at peak rarefactional pressures lower than and greater than the ADV pressure amplitude threshold in an in vitro flow phantom. The change in dissolved oxygen (DO) of the PBS before and after ADV was measured. A numerical model of gas scavenging, based on conservation of mass and equal partial pressures of gases at equilibrium, was developed. At insonation pressures exceeding the ADV threshold, the DO of air-saturated PBS decreased with increasing insonation pressures, dropping as low as 25% of air saturation within 20 s. The decrease in DO of the PBS during ADV was dependent on the volumetric size distribution of the droplets and the fraction of droplets transitioned during ultrasound exposure. Numerically predicted changes in DO from the model agreed with the experimentally measured DO, indicating that concentration gradients can explain this phenomenon. Using computationally modified droplet size distributions that would be suitable for in vivo applications, the DO of the PBS was found to decrease with increasing concentrations. This study demonstrates that ADV can significantly decrease the DO in an aqueous fluid, which may have direct therapeutic applications and should be considered for ADV-based diagnostic or therapeutic applications.     

CSR工程3维测量控制网

 主要介绍了兰州重离子加速器冷却储存环CSR控制网的建立及测量分析方法,详细说明了激光跟踪仪转站原理和计算机算法。根据一般大地测量采取的建网原则,首先用全站仪建立了初级控制网,在此基础上加密形成了由168个网点构成的主环控制网;然后运用激光跟踪仪及其配套软件测量了主环控制网,其网点的点位精度可达到0.08 mm。应用2005年3月控制网的测量结果对主环磁铁进行了调节,结果显示四极铁横向就位精度达到0.15 mm, 偏转角精度达到0.3 mrad。     

Dissolved oxygen concentration field measurement in micro-scale water flows using PtOEP/PS film sensor

A planar optode system based on an oxygen quenchable luminophore platinum (II) octaethyporphrin (PtOEP) bound with thin polystyrene (PS) film and UV light-emitting diodes (UV-LEDs) was developed to measure the dissolved oxygen (DO) concentration field in micro-scale water flows. An intensity-based method adopting a pixel-to-pixel in situ calibration technique was used to visualize DO concentration fields around an impinging micro-nozzle. The achievable spatial resolution of the acquired concentration map could be as high as 2.94 μm. A micro-round water jet having 100% of DO was obliquely impinged on to a PtOEP/PS film coated plate placed in a 0% of DO water container. Velocity fields were obtained by computational fluid dynamics (CFD) analysis and it is demonstrated that the high DO concentration region was coincided with the impingement area. The DO concentration gradient due to DO diffusion was affected by the Reynolds number.     

基于模糊聚类算法的工控网络控制故障检测方法研究

针对传统大型工控网络控制故障检测过程中,没有考虑故障延时特性,从而导致的故障信号检测准确率下降,检测效率降低。为此,提出一种基于模糊算法的大型工控网络控制故障检测方法。引入模糊算法,对大型工控网络控制中的故障信号延迟进行模糊化建模,通过随机时延切换设计故障观测参数和故障观测参数的残差对大型工控网络控制系统进行故障检测,克服信号延迟弊端。实验结果表明,利用本文方法进行大型工控网络控制系统故障检测,能够有效提高故障的准确率,效果令人满意。     

Neural network modeling of emotion

This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models.Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.     

Neural network applied to reconstruction of complex objects based on fringe projection

The neural network has been introduced into the reconstruction of the complex object based on fringe projection. In this method, the neural network with powerful property of approximation is used to get the continuous approximate function of a discrete fringe pattern captured by an image frame grabber. The depth-related phase of the measured object modulated into the fringe pattern can be demodulated by dealing with the approximate function. Compared with the Fourier transform profilometry (FTP), in the network method, one deformed fringe pattern is needed to reconstruct the tested object, and a high spatial resolution is maintained for no filtering process. Therefore, this method performs better than FTP in the measurement of the complex object. Moreover, the network method is capable of demodulating more depth-related phase even in the case that the local shadow exists in the fringe pattern. Computer simulations and experiments validate the feasibility of this method.     

Variation of dissolved organic nitrogen concentration during the ultrasonic pretreatment to Microcystis aeruginosa

Algae cells were the main sources of dissolved organic nitrogen (DON) in raw water with plenty of algae, and ultrasonic pretreatment was one of the algae-controlling methods through the damage of algae cells. However, the variation of DON concentration during the ultrasonic treatment process was not confirmed. Variation of DON concentration during the processes of low frequency ultrasound treatment of Microcystis aeruginosa was investigated. In addition, the effect of sonication on the metabolite concentration, algae cellar activity and the subsequent coagulation performance were discussed. The results showed that after a long duration of ultrasonic (60 s), nearly 90% of the algal cells were damaged and the maximum concentration of DON attained more than 3 mg/L. In order to control the leakage extent of DON, the sonication time should be less than 30 s with power intensity of more than 1.0 W/cm³. In the mean time, ultrasonic treatment could inhibit the reactivation and the proliferation of algal, keep the algae cell wall integrity and enhance coagulation effectively under the same condition. However, ultrasound frequency had little effect on DON at the frequency range used in this study (20–150 kHz).     

Predicting the Classification of Home Oxygen Therapy for Post-COVID-19 Rehabilitation Patients Using a Neural Network

Objective: We evaluated the accuracy of a neural network to classify and predict the possibility of home oxygen therapy at the time of discharge from hospital based on patient information post-coronavirus disease (COVID-19) at admission. Methods: Patients who survived acute treatment with COVID-19 and were admitted to the Amagasaki Medical Co-operative Hospital during August 2020–December 2021 were included. However, only rehabilitation patients (n = 88) who were discharged after a rehabilitation period of at least 2 weeks and not via home or institution were included. The neural network model implemented in R for Windows (4.1.2) was trained using data on patient age, gender, and number of days between a positive polymerase chain reaction test and hospitalization, length of hospital stay, oxygen flow rate required at hospitalization, and ability to perform activities of daily living. The number of training trials was 100. We used the area under the curve (AUC), accuracy, sensitivity, and specificity as evaluation indicators for the classification model. Results: The model of states at rest had as AUC of 0.82, sensitivity of 75.0%, specificity of 88.9%, and model accuracy of 86.4%. The model of states on exertion had an ACU of 0.82, sensitivity of 83.3%, specificity of 81.3%, and model accuracy of 81.8%. Conclusion: The accuracy of this study’s neural network model is comparable to that of previous studies recommended by Japanese Guidelines for the Physical Therapy and is expected to be used in clinical practice. In future, it could be used as a more accurate clinical support tool by increasing the sample size and applying cross-validation.