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2022 ◽  
Vol 9 ◽  
Author(s):  
Hongjun Fan ◽  
Xiaoqing Zhao ◽  
Xu Liang ◽  
Quansheng Miao ◽  
Yongnian Jin ◽  
...  

The identification of the “sweet spot” of low-permeability sandstone reservoirs is a basic research topic in the exploration and development of oil and gas fields. Lithology identification, reservoir classification based on the pore structure and physical properties, and petrophysical facies classification are common methods for low-permeability reservoir classification, but their classification effect needs to be improved. The low-permeability reservoir is characterized by low rock physical properties, small porosity and permeability distribution range, and strong heterogeneity between layers. The seepage capacity and productivity of the reservoir vary considerably. Moreover, the logging response characteristics and resistivity value are similar for low-permeability reservoirs. In addition to physical properties and oil bearing, they are also affected by factors such as complex lithology, pore structure, and other factors, making it difficult for division of reservoir petrophysical facies and “sweet spot” identification. In this study, the logging values between low-porosity and -permeability reservoirs in the Paleozoic Es3 reservoir in the M field of the Bohai Sea, and between natural gamma rays and triple porosity reservoirs are similar. Resistivity is strongly influenced by physical properties, oil content, pore structure, and clay content, and the productivity difference is obvious. In order to improve the identification accuracy of “sweet spot,” a semi-supervised learning model for petrophysical facies division is proposed. The influence of lithology and physical properties on resistivity was removed by using an artificial neural network to predict resistivity R0 saturated with pure water. Based on the logging data, the automatic clustering MRGC algorithm was used to optimize the sensitive parameters and divide the logging facies to establish the unsupervised clustering model. Then using the divided results of mercury injection data, core cast thin layers, and logging faces, the characteristics of diagenetic types, pore structure, and logging response were integrated to identify rock petrophysical facies and establish a supervised identification model. A semi-supervised learning model based on the combination of “unsupervised supervised” was extended to the whole region training prediction for “sweet spot” identification, and the prediction results of the model were in good agreement with the actual results.


2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Xiaocheng Li ◽  
Zhaoli Liu ◽  
Fangzhen Ge

It is a global issue to set up a practical, sensitive, and useful model to eradicate or mitigate the coronavirus disease 2019 (COVID-19). Taking Central China’s Hubei Province for example, three models were established. Firstly, a susceptible-probable-infectious-recovered (SPIR) model was proposed to predict the monthly number of confirmed and susceptible cases in each city. Next, an epidemic prefecture clustering model was set up to find proper vaccine delivery sites, according to the distance of each city. Finally, a dynamic material delivery optimization model was established for multiple epidemic prefectures, aiming to speed up vaccine production and storage in each delivery site.


Author(s):  
Sai Ji ◽  
Jun Li ◽  
Zijun Wu ◽  
Yicheng Xu

In this paper, we propose a so-called capacitated min–max correlation clustering model, a natural variant of the min–max correlation clustering problem. As our main contribution, we present an integer programming and its integrality gap analysis for the proposed model. Furthermore, we provide two approximation algorithms for the model, one of which is a bi-criteria approximation algorithm and the other is based on LP-rounding technique.


Author(s):  
Fabiola Talavera-Mendoza ◽  
Carlos E. Atencio-Torres ◽  
Henry del Carpio ◽  
David A. Deza ◽  
Alexander R. Cayro

Online learning offers opportunities responding to their different individual and group learning needs by leaving digital traces that allow tracking their experiences at the user level. This study aims to examine the perceived usability of the gamified educational platform called (ELORS) in relation to online behaviour. As well as analyse the clustering models in terms of their high and low level of engagement through their interaction metrics. A quantitative, descriptive correlational approach and an educational data analysis design was adopted through the K-means algorithm. The participants were 51 students in mathematics in the second year of secondary education. An instrument was used to evaluate usability and behavioural metrics, analysing 1065 interactions with 57 activities. The results showed advantages in usability and grouping. The level of usability achieved depends on the interaction of the users with the different learning objects and their moderate relationship in their interactions. In relation to the centroids, two groups are evidenced by number of attempts and interactions, identifying students with low levels of participation in the minority. A significant finding is given in relation to the preference of redeeming virtual values in gold from the diamonds collected. The perspective of the analysis allows identifying the potential of the gamified platform to work online in the formation of mathematical competence according to the current educational curriculum.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xujie Ren ◽  
Tao Shang ◽  
Yatong Jiang ◽  
Jianwei Liu

In the era of big data, next-generation sequencing produces a large amount of genomic data. With these genetic sequence data, research in biology fields will be further advanced. However, the growth of data scale often leads to privacy issues. Even if the data is not open, it is still possible for an attacker to steal private information by a member inference attack. In this paper, we proposed a private profile hidden Markov model (PHMM) with differential identifiability for gene sequence clustering. By adding random noise into the model, the probability of identifying individuals in the database is limited. The gene sequences could be unsupervised clustered without labels according to the output scores of private PHMM. The variation of the divergence distance in the experimental results shows that the addition of noise makes the profile hidden Markov model distort to a certain extent, and the maximum divergence distance can reach 15.47 when the amount of data is small. Also, the cosine similarity comparison of the clustering model before and after adding noise shows that as the privacy parameters changes, the clustering model distorts at a low or high level, which makes it defend the member inference attack.


2021 ◽  
pp. 1-14
Author(s):  
Qingjiang Xiao ◽  
Shiqiang Du ◽  
Yao Yu ◽  
Yixuan Huang ◽  
Jinmei Song

In recent years, tensor-Singular Value Decomposition (t-SVD) based tensor nuclear norm has achieved remarkable progress in multi-view subspace clustering. However, most existing clustering methods still have the following shortcomings: (a) It has no meaning in practical applications for singular values to be treated equally. (b) They often ignore that data samples in the real world usually exist in multiple nonlinear subspaces. In order to solve the above shortcomings, we propose a hyper-Laplacian regularized multi-view subspace clustering model that joints representation learning and weighted tensor nuclear norm constraint, namely JWHMSC. Specifically, in the JWHMSC model, firstly, in order to capture the global structure between different views, the subspace representation matrices of all views are stacked into a low-rank constrained tensor. Secondly, hyper-Laplace graph regularization is adopted to preserve the local geometric structure embedded in the high-dimensional ambient space. Thirdly, considering the prior information of singular values, the weighted tensor nuclear norm (WTNN) based on t-SVD is introduced to treat singular values differently, which makes the JWHMSC more accurately obtain the sample distribution of classification information. Finally, representation learning, WTNN constraint and hyper-Laplacian graph regularization constraint are integrated into a framework to obtain the overall optimal solution of the algorithm. Compared with the state-of-the-art method, the experimental results on eight benchmark datasets show the good performance of the proposed method JWHMSC in multi-view clustering.


Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 37
Author(s):  
Tariq Qayyum ◽  
Zouheir Trabelsi ◽  
Asad Malik ◽  
Kadhim Hayawi

Unmanned aerial vehicles (UAVs) play an important role in facilitating data collection in remote areas due to their remote mobility. The collected data require processing close to the end-user to support delay-sensitive applications. In this paper, we proposed a data collection scheme and scheduling framework for smart farms. We categorized the proposed model into two phases: data collection and data scheduling. In the data collection phase, the IoT sensors are deployed randomly to form a cluster based on their RSSI. The UAV calculates an optimum trajectory in order to gather data from all clusters. The UAV offloads the data to the nearest base station. In the second phase, the BS finds the optimally available fog node based on efficiency, response rate, and availability to send workload for processing. The proposed framework is implemented in OMNeT++ and compared with existing work in terms of energy and network delay.


2021 ◽  
Vol 9 (12) ◽  
pp. 1458
Author(s):  
Taewoong Hwang ◽  
Ik-Hyun Youn

The collision avoidance system is one of the core systems of MASS (Maritime Autonomous Surface Ships). The collision avoidance system was validated using scenario-based experiments. However, the scenarios for the validation were designed based on COLREG (International Regulations for Preventing Collisions at Sea) or are arbitrary. Therefore, the purpose of this study is to identify and systematize objective navigation situation scenarios for the validation of autonomous ship collision avoidance algorithms. A data-driven approach was applied to collect 12-month Automatic Identification System data in the west sea of Korea, to extract the ship’s trajectory, and to hierarchically cluster the data according to navigation situations. Consequently, we obtained the hierarchy of navigation situations and the frequency of each navigation situation for ships that sailed the west coast of Korea during one year. The results are expected to be applied to develop a collision avoidance test environment for MASS.


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