A study of radiation effects on various types of glasses, dielectric optical coatings, cemented optics and fiber was undertaken with a view to select them for extreme radiation environments. Samples were exposed to different radiation doses in the Pakistan Research Reactor-I (PARR-I) for neutron and Cobalt 60 source for gamma irradiation. Transmissions were measured before and after irradiation. The dielectric coatings were subjected to additional tests (adhesion, abrasion and humidity, etc.) as per MIL-M-13508C and MIL-C-675C. All 15 glasses studied showed varying amounts of transmission loss as expected, with negligible degradation for three types. Recovery of transmissions with time/ageing was also studied, with more or less complete recovery with temperature annealing. A faster bleaching of darkened/brown glasses was achieved by using UV lamps or UV laser. The dielectric coatings (HR, AR) and one of the two commercial optical cements showed excellent resistance to neutrons and gamma radiations, and could be good candidates for the fabrication and utilization of optical components in extreme radiation environments. The data allowed several Chinese glasses to be studied for the first time. 相似文献
A study was undertaken to examine the sensitivity of a wastewater population of coliphage, total coliforms and total flora present in raw sewage and secondary effluent after irradiating with similar doses delivered by a high-energy electron beam and y -radiation. The electron beam study was conducted on a large scale at the Virginia Key Wastewater Treatment Plant, Miami, Fla. The facility is equipped with a 1.5 MeV, 50 mA electron accelerator, with a wastewater flow rate of 8 ls−1. Concurrent y-radiation studies were conducted at laboratory scale using a 5000 Ci, 60Co y -source. Three logs reduction of all three test organisms were observed at an electron beam dose of 500 krads, while at least four logs reduction were observed at the same dose utilizing the y-source. 相似文献
Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.
Combining accurate neural networks (NN) in the ensemble with negative error correlation greatly improves the generalization ability. Mixture of experts (ME) is a popular combining method which employs special error function for the simultaneous training of NN experts to produce negatively correlated NN experts. Although ME can produce negatively correlated experts, it does not include a control parameter like negative correlation learning (NCL) method to adjust this parameter explicitly. In this study, an approach is proposed to introduce this advantage of NCL into the training algorithm of ME, i.e., mixture of negatively correlated experts (MNCE). In this proposed method, the capability of a control parameter for NCL is incorporated in the error function of ME, which enables its training algorithm to establish better balance in bias-variance-covariance trade-off and thus improves the generalization ability. The proposed hybrid ensemble method, MNCE, is compared with their constituent methods, ME and NCL, in solving several benchmark problems. The experimental results show that our proposed ensemble method significantly improves the performance over the original ensemble methods. 相似文献
Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queueing theory, and in applied probability at large. In this article, we analyse the performance of an importance sampling estimator for a rare event probability in a Jackson network. This article carries out strict deadlines to a two-node Jackson network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We have estimated the probability of network blocking for various sets of parameters, and also the probability of missing the deadline of customers for different loads and deadlines. We have finally shown that the probability of total population overflow may be affected by various deadline values, service rates and arrival rates. 相似文献
Recently, physical layer security commonly known as Radio Frequency (RF) fingerprinting has been proposed to provide an additional layer of security for wireless devices. A unique RF fingerprint can be used to establish the identity of a specific wireless device in order to prevent masquerading/impersonation attacks. In the literature, the performance of RF fingerprinting techniques is typically assessed using high-end (expensive) receiver hardware. However, in most practical situations receivers will not be high-end and will suffer from device specific impairments which affect the RF fingerprinting process. This paper evaluates the accuracy of RF fingerprinting employing low-end receivers. The vulnerability to an impersonation attack is assessed for a modulation-based RF fingerprinting system employing low-end commodity hardware (by legitimate and malicious users alike). Our results suggest that receiver impairment effectively decreases the success rate of impersonation attack on RF fingerprinting. In addition, the success rate of impersonation attack is receiver dependent. 相似文献
A series of NbOx/ZrO2 catalysts containing up to 2.67wt Nb (ca. 80 nominal surface coverage) was prepared by incipient wetness impregnation from niobium oxalate and oxalic acid solution. The structure of the catalysts was monitored by X-ray diffraction and Raman spectroscopy. The results indicated the presence of a surface Nb phase. No evidence for the formation of crystalline Nb2O5 species was found. The development of the acidity as a function of Nb loading was monitored by adsorption of a basic probe molecule followed by infrared spectroscopy. The results indicated the appearance of Brnsted acid sites for a threshold of Nb loading. The abundance of Brnsted acid sites correlated well with the isopropanol dehydration activity. The overall behavior was very similar to that reported earlier for the WOx/ZrO2 system. 相似文献