Thermal Analysis and Optimization of the Phase Diagram of the Cu-Ag Sulfide System

Thermal stabilities of selected ternary phases of industrial interest in the Ag-Cu-S system have been studied by the calorimetric and electromotive force techniques. The ternary compounds Ag1.2Cu0.8S (mineral mackinstryite) and AgCuS (mineral stromeyerite) were equilibrated through high-temperature reaction of the pure Cu2S and Ag2S in an inert atmosphere. The synthesized single solid sample constituting the two ternary phases was ground into fine powders and lightly pressed into pellets before calorimetric measurements. An electrochemical cell incorporating the two equilibrated phase and additional CuS as a cathode material was employed. The measurement results obtained with both techniques were analyzed and thermodynamic properties in the system have been determined and compared with the available literature values. Enthalpy of fusion data of the Ag-richer solid solution (Ag,Cu)2S have also been determined directly from the experimental data for the first time. The thermodynamic quantities determined in this work can be used to calculate thermal energy of processes involving the Ag-Cu-S-ternary phases. By applying the obtained results and the critically evaluated literature data, we have developed a thermodynamic database. The self-developed database was combined with the latest pure substances database of the FactSage software package to model the phase diagram of the Ag2S-Cu2S system.

Multidomain Fusion Data Privacy Security Framework

With the collaborative collection of the Internet of Things (IoT) in multidomain, the collected data contains richer background knowledge. However, this puts forward new requirements for the security of data publishing. Furthermore, traditional statistical methods ignore the attributes sensitivity and the relationship between attributes, which makes multimodal statistics among attributes in multidomain fusion data set based on sensitivity difficult. To solve the above problems, this paper proposes a multidomain fusion data privacy security framework. First, based on attributes recognition, classification, and grading model, determine the attributes sensitivity and relationship between attributes to realize the multimode data statistics. Second, combine them with the different modal histograms to build multimodal histograms. Finally, we propose a privacy protection model to ensure the security of data publishing. The experimental analysis shows that the framework can not only build multimodal histograms of different microdomain attribute sets but also effectively reduce frequency query error.

A fusion data security protection scheme for sensitive E-documents in the open network environment

E-documents are carriers of sensitive data, and their security in the open network environment has always been a common problem with the field of data security. Based on the use of encryption schemes to construct secure access control, this paper proposes a fusion data security protection scheme. This scheme realizes the safe storage of data and keys by designing a hybrid symmetric encryption algorithm, a data security deletion algorithm, and a key separation storage method. The scheme also uses file filter driver technology to design a user operation state monitoring method to realize real-time monitoring of user access behavior. In addition, this paper designs and implements a prototype system. Through the verification and analysis of its usability and security, it is proved that the solution can meet the data security protection requirements of sensitive E-documents in the open network environment.

Near real-time streaming analysis of big fusion data

Experiments on fusion plasmas produce high-dimensional data time series with ever increasing magnitude and velocity, but turn-around times for analysis of this data have not kept up. For example, many data analysis tasks are often performed in a manual, ad-hoc manner some time after an experiment. In this article we introduce the DELTA framework that facilitates near real-time streaming analysis of big and fast fusion data. By streaming measurement data from fusion experiments to a high-performance compute center, DELTA allows computationally expensive data analysis tasks to be performed in between plasma pulses. This article describe the modular and expandable software architecture of DELTA and present performance benchmarks of individual components as well as of an example workflows. Focusing on a streaming analysis workflow where Electron cyclotron emission imaging (ECEi) data measured at KSTAR on NERSC's supercomputer we routinely observe data transfer rates of about 4 Gigabit per second. At NERSC, a demanding turbulence analysis workflow effectively utilizes multiple nodes and graphical processing units and executes in under 5 minutes. We further discuss how DELTA uses modern database systems and container orchestration services to provide web-based real-time data visualization. For the case of ECEi data we demonstrate how data visualizations can be augmented with outputs from machine learning models. By providing session leaders and physics operators results of higher order data analysis using live visualizations may make more informed decisions on how to configure the machine for the next shot.

A Novel Method for Estimating Spatial Distribution of Forest Above-Ground Biomass Based on Multispectral Fusion Data and Ensemble Learning Algorithm

Optical remote sensing technology has been widely used in forest resources inventory. Due to the influence of satellite orbits, sensor parameters, sensor errors, and atmospheric effects, there are great differences in vegetation spectral information captured by different satellite sensor images. Spectral fusion technology can couple the advantages of different multispectral sensor images to produce new multispectral data with high spatial and spectral resolution, it has great potential for improving the spectral sensitivity of forest vegetation and alleviating the spectral saturation. However, how to quickly and effectively select the multi-spectral fusion data suitable for forest above-ground biomass (AGB) estimation is a very critical issue. This study proposes a scheme (RF-S) to comprehensively evaluate multispectral fused images and develop the appropriate model for forest AGB estimation, on the basis of random forest (RF) and the stacking ensemble algorithm. First, four classic fusion methods are used to fuse the preprocessed GaoFen-2 (GF-2) multispectral image with Sentinel-2 image to generate 12 fused Sentinel-like images. Secondly, we apply a comprehensive evaluation method to quickly select the optimal fused image for the follow-up research. Subsequently, two feature combination optimization methods are used to select feature variables from the three feature sets. Finally, the stacking ensemble algorithm based on model dynamic integration and hyperparameter automatic optimization, as well as some classic machine learners, are used to construct the forest AGB estimation model. The results show that the fused image NND_B3 (based on nearest neighbor diffusion pan sharpening method and Band3_Red) selected by the evaluation method proposed in this study has the best performance in AGB estimation. Using the stacking ensemble method and NND_B3 image, we get the highest estimation accuracy, with the adjusted R2 and relative root mean square error (RMSEr) of 0.6306 and 15.53%, respectively. The AGB estimation RMSEr of NND_B3 is 19.95% and 24.90% lower than those of GF-2 and Sentinel-2, respectively. We also found that the multi-window texture factor has better performance in the area with low AGB, and it can suppress the overestimation significantly. The AGB spatial distribution estimated using the NND_B3 image matches the field observations well, indicating that the multispectral fusion image combined with the Stacking algorithm can increase the accuracy and saturation of the AGB estimates.

Detection of Changes of Ancient Buildings from Terrestrial Laser Scanning and Hyperspectral Imaging

Ancient buildings have various geometric and material changes caused by the historical and natural factors, and their comprehensive detection has also been a more important challenge. This way, in this paper, a flexible, scientific approach from terrestrial laser scanning and hyperspectral imaging is provided for this issue. It is possible to flexibly and accurately detect some potential crisis, which cannot be found in some surface phenomena of historical buildings. Furthermore, one of the main characteristic of this method is that the time and place of the two data acquisition need not be limited, but they can be accurately fused. Another one of the main features is that the fusion data can synthetically detect geometric and material changes of historical buildings. This method was applied to the case study of the Beijing Tianningsi Tower, an extremely dazzling pearl of the Chinese Buddhist pagoda, on which the signs of deformation and restoration were found in the tower shape and in the tower-body sculpture. It was possible to assess the typical physical, chemical, and biological changes of historical buildings, to provide scientific basis for comprehensive research. The results demonstrate that this method is feasible and applicable for detecting changes of ancient buildings and is applied to similar research using more analytical methods for multisource data.

Fusion Method and Application of Several Source Vibration Fault Signal Spatio-Temporal Multi-Correlation

Combined with other signal processing methods, related algorithms are widely used in the diagnosis and identification of rotor faults. In order to solve the problem that the vibration signal of a single sensor is too single, a new multi-source vibration signal fusion method is proposed. This method explores the correlation between vibration sensors at different locations by using multiple cross-correlations of spatial locations. First, wavelet noise reduction and linear normalization are used to process the original data. Then, the signal energy correlation function between the sensors is established, and the adaptive weight is obtained. Finally, the data fusion result is obtained. Taking rotor bearing and gear failures at different speeds as an example, the data of three vibration sensors at different positions are fused using the spatio-temporal multiple correlation fusion method (STMF). Through the intelligent fault diagnosis method stacked auto encoder (SAE), compared with single sensor data, average weighted fusion data and neural network fusion data, STMF method can reach a diagnosis accuracy of more than 94% at 700 rpm, 900 rpm and 1100 rpm. It is concluded that the result of the STMF method is more effective and superior.