Wireless Passive Temperature Sensor Realized on Multilayer HTCC Tapes for Harsh Environment

A wireless passive temperature sensor is designed on the basis of a resonant circuit, fabricated on multilayer high temperature cofired ceramic (HTCC) tapes, and measured with an antenna in the wireless coupling way. Alumina ceramic used as the substrate of the sensor is fabricated by lamination and sintering techniques, and the passive resonant circuit composed of a planar spiral inductor and a parallel plate capacitor is printed and formed on the substrate by screen-printing and postfiring processes. Since the permittivity of the ceramic becomes higher as temperature rises, the resonant frequency of the sensor decreases due to the increasing capacitance of the circuit. Measurements on the input impedance versus the resonant frequency of the sensor are achieved based on the principle, and discussions are made according to the exacted relative permittivity of the ceramic and quality factor (Q) of the sensor within the temperature range from 19°C (room temperature) to 900°C. The results show that the sensor demonstrates good high-temperature characteristics and wide temperature range. The average sensitivity of the sensor with good repeatability and reliability is up to 5.22 KHz/°C. It can be applied to detect high temperature in harsh environment

Spatial Distribution Prediction of Oil and Gas Based on Bayesian Network with Case Study

Effectively predicting the spatial distribution of oil and gas contributes to delineating promising target areas for further exploration. Determining the location of hydrocarbon is a complex and uncertain decision problem. This paper proposes a method for predicting the spatial distribution of oil and gas resource based on Bayesian network. In this method, qualitative dependency relationship between the hydrocarbon occurrence and key geologic factors is obtained using Bayesian network structure learning by integrating the available geoscience information and the current exploration results and then using Bayesian network topology structure to predict the probability of hydrocarbon occurrence in the undiscovered area; finally, the probability map of hydrocarbon-bearing is formed by interpolation method. The proposed method and workflow are further illustrated using an example from the Carboniferous Huanglong Formation (C2hl) in the eastern part of the Sichuan Basin in China. The prediction results show that the coincidence rate between the results of 248 known exploration wells and the predicted results reaches 89.5%, and it has been found that the gas fields are basically located in the high value area of the hydrocarbon-bearing probability map. The application results show that the Bayesian network method can effectively predict the spatial distribution of oil and gas resources, thereby reducing exploration risks, optimizing exploration targets, and improving exploration benefits

A Ranking Recommendation Algorithm Based on Dynamic User Preference

In recent years, hybrid recommendation techniques based on feature fusion have gained extensive attention in the field of list ranking. Most of them fuse linear and nonlinear models to simultaneously learn the linear and nonlinear features of entities and jointly fit user-item interactions. These methods are based on implicit feedback, which can reduce the difficulty of data collection and the time of data preprocessing, but will lead to the lack of entity interaction depth information due to the lack of user satisfaction. This is equivalent to artificially reducing the entity interaction features, limiting the overall performance of the model. To address this problem, we propose a two-stage recommendation model named A-DNR, short for Attention-based Deep Neural Ranking. In the first stage, user short-term preferences are modeled through an attention mechanism network. Then the user short-term preferences and user long-term preferences are fused into dynamic user preferences. In the second stage, the high-order and low-order feature interactions are modeled by a matrix factorization (MF) model and a multi-layer perceptron (MLP) model, respectively. Then, the features are fused through a fully connected layer, and the vectors are mapped to scores. Finally, a ranking list is output through the scores. Experiments on three real-world datasets (Movielens100K, Movielens1M and Yahoo Movies) show that our proposed model achieves significant improvements compared to existing methods

A Recommendation Algorithm Combining Local and Global Interest Features

Due to the ability of knowledge graph to effectively solve the sparsity problem of collaborative filtering, knowledge graph (KG) has been widely studied and applied as auxiliary information in the field of recommendation systems. However, existing KG-based recommendation methods mainly focus on learning its representation from the neighborhood of target items, ignoring the influence of other items on the target item. The learning focuses on the local feature representation of the target item, which is not sufficient to effectively explore the user’s preference degree for the target item. To address the above issues, in this paper, an approach combining users’ local interest features with global interest features (KGG) is proposed to efficiently explore the user’s preference level for the target item, which learns the user’s local interest features and global interest features for target item through Knowledge Graph Convolutional Network and Generative Adversarial Network (GAN). Specifically, this paper first utilizes the Knowledge Graph Convolutional Network to mine related attributes on the knowledge graph to effectively capture item correlations and obtain the local feature representation of the target item, then uses the matrix factorization method to learn the user’s local interest features for target items. Secondly, it uses GAN to learn the user’s global interest features for target items from the implicit interaction matrix. Finally, a linear fusion layer is designed to effectively fuse the user’s local and global interests towards target items to obtain the final click prediction. Experimental results on three real datasets show that the proposed method not only effectively integrates the user’s local and global interests but also further alleviates the problem of data sparsity. Compared with the current baselines for knowledge graph-based systems, the KGG method achieves a maximum improvement of 8.1% and 7.6% in AUC and ACC, respectively

Deep Collaborative Recommendation Algorithm Based on Attention Mechanism

Representation learning-based collaborative filtering (CF) methods address the linear relationship of user-items with dot products and cannot study the latent nonlinear relationship applied to implicit feedback. Matching function learning-based CF methods directly learn the complicated mapping functions that map user-item pairs to matching scores, which has limitations in identifying low-rank relationships. To this end, we propose a deep collaborative recommendation algorithm based on attention mechanism (DACR). First, before the user-item representations are input into the DNNs, we utilize the attention mechanism to adaptively assign different weights to the user-item representations, which captures the hidden information in implicit feedback. After that, we input the user-item representations with corresponding weights into the representation learning and matching function learning modules. Finally, we concatenate the prediction vectors learned from different dimensions to predict the matching scores. The results show that we can improve the expression ability of the model while taking into account not only the nonlinear information hidden in implicit feedback, but also the low-rank relationships of user-item pairs to obtain more accurate predictions. Through detailed experiments on two datasets, we find that the ranking capability of the DACR model is enhanced compared with other baseline models, and the evaluation metrics HR and NDCG of DACR are increased by 0.88–1.19% and 0.65–1.15%, respectively

Deep Collaborative Recommendation Algorithm Based on Attention Mechanism

Representation learning-based collaborative filtering (CF) methods address the linear relationship of user-items with dot products and cannot study the latent nonlinear relationship applied to implicit feedback. Matching function learning-based CF methods directly learn the complicated mapping functions that map user-item pairs to matching scores, which has limitations in identifying low-rank relationships. To this end, we propose a deep collaborative recommendation algorithm based on attention mechanism (DACR). First, before the user-item representations are input into the DNNs, we utilize the attention mechanism to adaptively assign different weights to the user-item representations, which captures the hidden information in implicit feedback. After that, we input the user-item representations with corresponding weights into the representation learning and matching function learning modules. Finally, we concatenate the prediction vectors learned from different dimensions to predict the matching scores. The results show that we can improve the expression ability of the model while taking into account not only the nonlinear information hidden in implicit feedback, but also the low-rank relationships of user-item pairs to obtain more accurate predictions. Through detailed experiments on two datasets, we find that the ranking capability of the DACR model is enhanced compared with other baseline models, and the evaluation metrics HR and NDCG of DACR are increased by 0.88–1.19% and 0.65–1.15%, respectively

High-Temperature Dielectric Properties of Aluminum Nitride Ceramic for Wireless Passive Sensing Applications

The accurate characterization of the temperature-dependent permittivity of aluminum nitride (AlN) ceramic is quite critical to the application of wireless passive sensors for harsh environments. Since the change of the temperature-dependent permittivity will vary the ceramic-based capacitance, which can be converted into the change of the resonant frequency, an LC resonator, based on AlN ceramic, is prepared by the thick film technology. The dielectric properties of AlN ceramic are measured by the wireless coupling method, and discussed within the temperature range of 12 °C (room temperature) to 600 °C. The results show that the extracted relative permittivity of ceramic at room temperature is 2.3% higher than the nominal value of 9, and increases from 9.21 to 10.79, and the quality factor Q is decreased from 29.77 at room temperature to 3.61 at 600 °C within the temperature range

Mesh model building and migration and accumulation simulation of 3D hydrocarbon carrier system

Migration and accumulation simulation of oil and gas in carrier systems has always been a difficult subject in the quantitative study of petroleum geology. In view of the fact that the traditional geological modeling technology can not establish the interrelation of carriers in three dimensional space, we have proposed a hybrid-dimensional mesh modeling technology consisting of body (stratum), surfaces (faults and unconformities), lines and points, which provides an important research method for the description of geometry of sand bodies, faults and unconformities, the 3D geological modeling of complex tectonic areas, and the simulation of hydrocarbon migration and accumulation. Furthermore, we have advanced a 3D hydrocarbon migration pathway tracking method based on the hybrid-dimensional mesh of the carrier system. The application of this technology in western Luliang Uplift of Junggar Basin shows that the technology can effectively characterize the transport effect of fault planes, unconformities and sand bodies, indicate the hydrocarbon migration pathways, simulate the process of oil accumulation, reservoir adjustment and secondary reservoir formation, predict the hydrocarbon distribution. It is found through the simulation that the areas around the paleo-oil reservoir and covered by migration pathways are favorable sites for oil and gas distribution. Key words: oil and gas migration and accumulation, carrier system, hybrid-dimensional mesh, migration pathway, geological modeling, mesh generation, Junggar Basin, western Luliang Uplif

A High Temperature Capacitive Pressure Sensor Based on Alumina Ceramic for in Situ Measurement at 600 °C

In response to the growing demand for in situ measurement of pressure in high-temperature environments, a high temperature capacitive pressure sensor is presented in this paper. A high-temperature ceramic material-alumina is used for the fabrication of the sensor, and the prototype sensor consists of an inductance, a variable capacitance, and a sealed cavity integrated in the alumina ceramic substrate using a thick-film integrated technology. The experimental results show that the proposed sensor has stability at 850 °C for more than 20 min. The characterization in high-temperature and pressure environments successfully demonstrated sensing capabilities for pressure from 1 to 5 bar up to 600 °C, limited by the sensor test setup. At 600 °C, the sensor achieves a linear characteristic response, and the repeatability error, hysteresis error and zero-point drift of the sensor are 8.3%, 5.05% and 1%, respectively