On-line Monitoring and Data Analysis of Environmental Vibration of High Concrete Dam

As we all know, our country has actually gradually developed into a country with frequent occurrence of global earthquakes. The global earthquakes in the southwest region are the most serious. The most severe earthquake occurred in parts of the southwest. In short, the seismic performance design of a high dam is reflected under the action of the high dam for a given amount of seismic motion and input load. At present, one of the main technical methods for studying high dam seismic engineering is to use model calculations and experiments on its structural dynamics. Moreover, the earliest dam prototype dynamic experiments in my country only occurred in the 1970s and 1980s, and few people in China conducted experiments on these dams. Although there were already a certain degree of practical results at that time, a batch of valuable materials was obtained. However, no matter from the perspective of safety or the actual effect on vibration, as well as the prototype dynamic measurement and test method of the above-mentioned main dam, it has not been well applied to the current expressway. Therefore, it is necessary to consider finding an alternative original kinetic test method. The purpose of this article is to in-depth study the data analysis and application of the online monitoring system of environmental vibration of reinforced concrete high dams in my country. A new online monitoring system for dam environmental vibration is carried out for simulation experiments. Experimental research shows that the noise frequency of the three detection points of environmental noise and vibration monitoring is basically controlled between 1.5~2.0 hz, while the noise frequency of the detection point of the symmetrical location is relatively close.

Interrelation between Spectral Online Monitoring and Postoperative T1-Weighted MRI in Interstitial Photodynamic Therapy of Malignant Gliomas

In a former study, interstitial photodynamic therapy (iPDT) was performed on patients suffering from newly diagnosed glioblastoma (n = 11; 8/3 male/female; median age: 68, range: 40–76). The procedure includes the application of 5-ALA to selectively metabolize protoporphyrin IX (PpIX) in tumor cells and illumination utilizing interstitially positioned optical cylindrical diffuser fibers (CDF) (2–10 CDFs, 2–3 cm diffusor length, 200 mW/cm, 635 nm, 60 min irradiation). Intraoperative spectral online monitoring (SOM) was employed to monitor treatment light transmission and PpIX fluorescence during iPDT. MRI was used for treatment planning and outcome assessment. Case-dependent observations included intraoperative reduction of treatment light transmission and local intrinsic T1 hyperintensity in non-contrast-enhanced T1-weighted MRI acquired within one day after iPDT. Intrinsic T1 hyperintensity was observed and found to be associated with the treatment volume, which indicates the presence of methemoglobin, possibly induced by iPDT. Based on SOM data, the optical absorption coefficient and its change during iPDT were estimated for the target tissue volumes interjacent between evaluable CDF-pairs at the treatment wavelength of 635 nm. By spatial comparison and statistical analysis, it was found that observed increases of the absorption coefficient during iPDT were larger in or near regions of intrinsic T1 hyperintensity (p = 0.003). In cases where PpIX-fluorescence was undetectable before iPDT, the increase in optical absorption and intrinsic T1 hyperintensity tended to be less. The observations are consistent with in vitro experiments and indicate PDT-induced deoxygenation of hemoglobin and methemoglobin formation. Further investigations are needed to provide more data on the time course of the observed changes, thus paving the way for optimized iPDT irradiation protocols.

Development of an IoT Architecture Based on a Deep Neural Network against Cyber Attacks for Automated Guided Vehicles

This paper introduces an integrated IoT architecture to handle the problem of cyber attacks based on a developed deep neural network (DNN) with a rectified linear unit in order to provide reliable and secure online monitoring for automated guided vehicles (AGVs). The developed IoT architecture based on a DNN introduces a new approach for the online monitoring of AGVs against cyber attacks with a cheap and easy implementation instead of the traditional cyber attack detection schemes in the literature. The proposed DNN is trained based on experimental AGV data that represent the real state of the AGV and different types of cyber attacks including a random attack, ramp attack, pulse attack, and sinusoidal attack that is injected by the attacker into the internet network. The proposed DNN is compared with different deep learning and machine learning algorithms such as a one dimension convolutional neural network (1D-CNN), a supported vector machine model (SVM), random forest, extreme gradient boosting (XGBoost), and a decision tree for greater validation. Furthermore, the proposed IoT architecture based on a DNN can provide an effective detection for the AGV status with an excellent accuracy of 96.77% that is significantly greater than the accuracy based on the traditional schemes. The AGV status based on the proposed IoT architecture with a DNN is visualized by an advanced IoT platform named CONTACT Elements for IoT. Different test scenarios with a practical setup of an AGV with IoT are carried out to emphasize the performance of the suggested IoT architecture based on a DNN. The results approve the usefulness of the proposed IoT to provide effective cybersecurity for data visualization and tracking of the AGV status that enhances decision-making and improves industrial productivity.