Division Scheme Research Articles

Comprehensive evaluation and reservoir classification in the Quan 3 Member of the Cretaceous Quantou Formation in the Fuxin Uplift, Songliao Basin

Continental tight oil sandstone reservoirs are developed in the Cretaceous Quentou Formation in the Songliao Basin, China. At present, there is still a lack of research on the reservoir microstructures, reservoir physical properties, and the division scheme of reservoir types in the Quan 3 Member of the Quanzhou Formation. Therefore, in this paper, taking the Quan 3 Member in the Fuxin Uplift Belt of the Songliao Basin as an example, the microscopic pore structure characteristics of tight oil sandstones have been systematically studied, and the classification standard of tight sandstones has been formulated. Furthermore, the sweet spots of the main production layers are predicted. The results show that the I sandstone group in the Quan 3 Member in the study area belongs to shallow water delta facies. Feldspar lithic fine sandstones are developed in the target layer, and calcareous sandstone is locally developed. Moreover, the mian pore types of the target layer include dissolved intergranular and intragranular pores, followed by primary intergranular pores, while micro-fractures are occasionally seen. According to the mercury intrusion test results, the pore-throat structures of the reservoir in the Quan 3 Member are divided into four types: 1) small-pore medium-throat type (point bar and delta distributary channel), 2) small-pore micro-throat type (point bar), 3) small-pore micro-throat type (natural levee), 4) micro-pore micro-throat type (river floodplain and inter-tributary bay). The lower limits of the physical properties of the effective reservoirs in the Quan 3 Member has been determined: the porosity is 10% and the permeability is 0.1 mD. Finally, combined with the study of the mercury intrusion curves, the physical properties and the sedimentary facies, the classification standard of the Quan 3 Member reservoirs was formulated. For the I sandstone group, the sweet spots of the Type I reservoirs are mainly developed in the Fuyu Oilfield in the southeast areas. The sandstones in this area suffered less compaction, and primary and secondary pores suffered from late dissolution are mainly developed.

Three-dimensional topology optimization of Ni-YSZ anode for solid oxide fuel cells via multiphase level-set method

In the present study, numerical model is developed to optimize the 3D multiphase electrode microstructure for SOFC, e.g. nickel-yttria-stabilized zirconia (Ni-YSZ) anode. During the optimization, Ni-YSZ microstructure is deformed in order to maximize the total reaction current. Multiphase level-set (MLS) model is applied to represent the Ni-YSZ anode microstructure, and adjoint method is applied for the sensitivity analysis. In addition, in order to improve the computational accuracy and to make the optimized microstructure manufacturable, a radius constraint is imposed to ensure that the local particle radii are kept larger than a target value (Rtarget), where the interface is updated only when the local radius value is larger than Rtarget. It is also confirmed that the optimized microstructure and performance of the Ni-YSZ anode are independent of the initial structures and the phase division schemes. According to the optimization results, the optimized electrochemical performances are improved, and the particle and pore radii of the microstructure successfully remained larger than the target values. The optimized Ni-YSZ microstructures are composed of Ni particles embedded into a YSZ scaffold which has a pillar-like shape along the anode thickness direction.

Equilibrium optimizer with divided population based on distance and its application in feature selection problems

Effective machine learning relies on feature selection (FS) to preprocess the data to search for the best feature subset among all feature combinations, which is a global optimization problem (GOPs). The equilibrium optimizer (EO) is a physical-based meta-heuristic that is promising in solving global optimization problems but still exhibits unbalanced exploration and exploitation; low convergence accuracy; and can easily fall into local optima. This paper proposes an equilibrium optimizer with divided population based on distance (DEO) to improve EO’s global optimization abilities and to use it to solve FS problems. First, we propose an adaptive population division strategy with beta distribution, which makes DEO divide populations by particles’ distance weights and dynamic adjustment of the number of particles in the population, facilitating the algorithm to achieve a balance between exploration and exploitation. Second, we add distance factor to the exploitation population update to control the step size, increasing convergence accuracy. Third, the exponential term in the EO algorithm is introduced into the exploration population update as a disturbance factor to enhance the ability to jump out of a local optimum. Finally, twenty benchmark functions are used to verify the effectiveness of the improved strategy. Numerical experiments based on CEC’17 and FS experiments based on 14 UCI datasets are conducted to validate DEO’s global optimization and feature selection capabilities, respectively. Experimental results demonstrate that the proposed DEO significantly outperforms seven state-of-the-art algorithms and five other modified algorithms in terms of the optimization-seeking accuracy and convergence speed. Additionally, DEO also obtains overall better fitness as well as shorter running time than the other seven algorithms in FS problems.

RMGen: A Tri-Layer Vehicular Trajectory Data Generation Model Exploring Urban Region Division and Mobility Pattern

As an important branch of the Internet of Things (IoT), the Internet of Vehicles (IoV) has attracted extensive attention in the research field. To deeply study the IoV and build a vehicle spatiotemporal interaction network, it is necessary to use the trajectory data of private cars. However, due to privacy and security protection policies and other reasons, the data set of private cars cannot be obtained, which hinders the research on the social attributes of vehicles in the IoV. Most of the previous work generated the same type of data, and how to generate private car data sets from various existing data sets is a huge challenge. In this paper, we propose a tri-layer framework to solve this problem. First, we propose a novel region division scheme that considers detailed inter-region relations connected by traffic flux. Second, a new spatial-temporal interaction model is developed to estimate the traffic flow between two regions. Third, we devise an evaluation pipeline to validate generation results from microscopic and macroscopic perspectives. Qualitative and quantitative results demonstrate that the data generated in heavy density scenarios can provide strong data support for downstream IoV and mobility research tasks.

Resource Sharing for Cellular-Assisted D2D Communications With Imperfect CSI: A Many-to-Many Strategy

The rising demand for higher access rate and higher data rate applications will lead to the shortage of available spectrum resources. In this article, we propose a scalable framework to share resources and design a reasonable and effective resource management scheme to solve the resource allocation and power control problems of device-to-device (D2D) communication in dense scenarios. First, in order to maximize system throughput and user access rate by using many-to-many resource sharing to improve spectrum utilization. Second, considering imperfect channel state information, we propose a subcell division strategy based on location information, with the set partitioning algorithm to model intracell users as multiple many-to-many resource sharing sets satisfy the constraints. Finally, the problems of interference accumulation and low computational efficiency, a distributed power optimization algorithm based on quadratic transform is devised, which solves the multiratio fractional planning problem through the three steps of convex relaxation, fractional programming and alternating optimization. Experimental results show that the system throughput and D2D user access rate can be significantly enhanced with the proposed resource sharing framework and resource management scheme, especially in systems with a dense D2D deployment.

Domain selective two-stage beamforming in 3D massive MIMO

In this paper, a two-stage beamforming scheme called domain selective neighbor-based joint spatial division and multiplexing (DS-NJSDM) scheme is proposed to reduce the overhead of pilot training and uplink feedback in Frequency Division Duplexing (FDD) three-dimensional (3D) massive multi-input multi-output (MIMO) systems. Inspired by the Kronecker product structure of the 3D channel model, a DS pre-beamformer is proposed to sparsify the channel matrix, which selects horizontal or vertical pre-beamformer to mitigate the interference from non-neighbors. The signal space can be effectively utilized with the DS pre-beamformer, and the complexity of beamforming is significantly reduced. After the pre-beamformer, the zero entries of the downlink channel matrix are randomly distributed, making pilot design difficult. Then, a pilot allocation scheme is proposed to design the pilot matrix. Simulation results are provided to validate the superiority of the proposed scheme in enhancing the spectral efficiency over the existing schemes.

Saliency-Enabled Coding Unit Partitioning and Quantization Control for Versatile Video Coding

The latest video coding standard, versatile video coding (VVC), has greatly improved coding efficiency over its predecessor standard high efficiency video coding (HEVC), but at the expense of sharply increased complexity. In the context of perceptual video coding (PVC), the visual saliency model that utilizes the characteristics of the human visual system to improve coding efficiency has become a reliable method due to advances in computer performance and visual algorithms. In this paper, a novel VVC optimization scheme compliant PVC framework is proposed, which consists of fast coding unit (CU) partition algorithm and quantization control algorithm. Firstly, based on the visual saliency model, we proposed a fast CU division scheme, including the redetermination of the CU division depth by calculating Scharr operator and variance, as well as the executive decision for intra sub-partitions (ISP), to reduce the coding complexity. Secondly, a quantization control algorithm is proposed by adjusting the quantization parameter based on multi-level classification of saliency values at the CU level to reduce the bitrate. In comparison with the reference model, experimental results indicate that the proposed method can reduce about 47.19% computational complexity and achieve a bitrate saving of 3.68% on average. Meanwhile, the proposed algorithm has reasonable peak signal-to-noise ratio losses and nearly the same subjective perceptual quality.

The Strategi Komunikasi Divisi P2M Badan Narkotika Nasional Kabupaten Nganjuk Dalam Membangun Ketahanan Keluarga Anti Narkoba

The P2M Division is a division of the National Narcotics Agency that is tasked with prevention and community empowerment, which functions to expand information related to the dangers of using drugs, preventing the use of narcotics and how to deal with them if they have fallen into narcotics. The type of research used is descriptive qualitative by applying data collection methods through in-depth interviews, observation, and documentation. Technical data analysis includes data collection, presentation, data reduction, and drawing conclusions. The purpose of this study is to find out the communication strategy of the prevention and community empowerment division of the National Narcotics Agency of Nganjuk Regency in building anti-drug family resilience. The results obtained from this study are that the implementation of the communication strategy of the P2M division of the BNN in Nganjuk Regency to conduct counseling in building anti-drug family resilience is to apply the 5-step communication strategy model proposed by Hafied Cangara.

Efficient Supervised Image Clustering Based on Density Division and Graph Neural Networks

In recent research, supervised image clustering based on Graph Neural Networks (GNN) connectivity prediction has demonstrated considerable improvements over traditional clustering algorithms. However, existing supervised image clustering algorithms are usually time-consuming and limit their applications. In order to infer the connectivity between image instances, they usually created a subgraph for each image instance. Due to the creation and process of a large number of subgraphs as the input of GNN, the computation overheads are enormous. To address the high computation overhead problem in the GNN connectivity prediction, we present a time-efficient and effective GNN-based supervised clustering framework based on density division namely DDC-GNN. DDC-GNN divides all image instances into high-density parts and low-density parts, and only performs GNN subgraph connectivity prediction on the low-density parts, resulting in a significant reduction in redundant calculations. We test two typical models in the GNN connectivity prediction module in the DDC-GNN framework, which are the graph convolutional networks (GCN)-based model and the graph auto-encoder (GAE)-based model. Meanwhile, adaptive subgraphs are generated to ensure sufficient contextual information extraction for low-density parts instead of the fixed-size subgraphs. According to the experiments on different datasets, DDC-GNN achieves higher accuracy and is almost five times quicker than those without the density division strategy.

Fully Polynomial-Time Approximation Schemes for Fair Rent Division

We study the problem of fair rent division that entails splitting the rent and allocating the rooms of an apartment among agents in a fair manner (i.e., under the imposed rents, no agent has a strictly stronger preference for any other agent’s room). The utility functions specify the cardinal preferences of the agents for the rooms for every possible room rent. Although envy-free solutions are guaranteed to exist under reasonably general utility functions, efficient algorithms for finding them were known only for quasilinear utilities. This work addresses this notable gap and develops a fully polynomial-time approximation scheme for fair rent division with minimal assumptions on the utility functions. Envy-free solutions correspond to equilibria of a two-sided matching market with monetary transfers; hence, this work also provides efficient algorithms for finding approximate equilibria in such markets. We complement the algorithmic results by proving that the fair rent division problem lies in the intersection of the complexity classes polynomial parity arguments on directed graphs and polynomial local search.

Carbon Isotope Stratigraphy of the Cambrian System in the Eastern Tarim Basin, China

AbstractThe ultra‐deep Cambrian System in the Tarim Basin is an important field for petroleum exploration, while fine division of the Cambrian strata remains controversial. In recent years, carbon isotope stratigraphy of the Cambrian System has been established and widely used. Here, we report an integrated profile of carbonate and organic carbon isotopic values (δ13Ccarb and δ13Corg) from cuttings of the Tadong2 Well in the eastern Tarim Basin. Three carbon isotope anomalies of BACE, ROECE and SPICE were recognized on the δ13Ccarb profile. Three apogees and a nadir on the δ13Corg profile and the onset of ROECE on the δ13Ccarb profile were suggested as boundaries of the present four series of the Cambrian System. Suggested boundaries are easily identifiable on the gamma logging profile and is consistent with the previous division scheme, based on biostratigraphic evidence in outcrop sections. Abnormal carbon cycle perturbations and organic carbon burials during the BACE and SPICE events might be related to the reduction and expansion of a huge dissolved organic carbon reservoir in the deep ocean of the ancient Tarim Basin.

Challenges from the disastrous COVID-19 pandemic: exposure to opportunities for branchless banking in Malaysia

Branchless banking is a new digitalization of artificial intelligence (AI) for future banking services. The rapid digitalization and technological advancement of financial institutions (FI) have changed due to the COVID-19 pandemic. Such branchless banks are expected to offer financial customers meaningful access whilst promoting responsible usage of suitable and affordable financial solutions. The objectives of this paper are to identify CSF to conduct branchless banking for bank performance during the attack of the COVID-19 pandemic in Malaysia. These CSFs are important to reinforce the new business model recommended by the central bank of Malaysia (BNM). Through this study five CSFs have been identified which are customers do not physically attend to the bank for their banking needs, it is more efficient, saves time, secured, and low cost. As the industrial revolution (IR) 4.0 approaches, public acceptance, innovation, and strong financial services, as well as demand of digital payment will take over the country. National savings bank (BSN) was officially the first bank in Malaysia to conduct branchless banking through agent banking. In line with the BNM division strategy 2011–2020, BSN had launched banking beyond branches via BSN agents who offer banking services on behalf of BSN.

Surrogate-Assisted Differential Evolution With Region Division for Expensive Optimization Problems With Discontinuous Responses

A considerable number of surrogate-assisted evolutionary algorithms (SAEAs) have been developed to solve expensive optimization problems (EOPs) with continuous objective functions. However, in the real-world applications, we may face EOPs with discontinuous objective functions, which are also called EOPs with discontinuous responses (EOPDRs). Indeed, EOPDRs pose a great challenge to current SAEAs. In this article, a surrogate-assisted differential evolution (DE) algorithm with region division is proposed, named ReDSADE. ReDSADE includes three main strategies: 1) the region division strategy; 2) the Kriging-based search; and 3) the radial basis function (RBF)-based local search. In the region division strategy, we define a new distance measure, called the objective-decision distance. Based on this distance, the evaluated solutions are partitioned into several clusters, and several support vector machine (SVM) classifiers are trained to classify them. These SVM classifiers divide the decision space into several subregions, with the aim of making the objective function continuous in them. In the Kriging-based search, a Kriging model is established in each subregion and combined with DE to search for the optimal solution. In the RBF-based local search, DE is coupled with RBF to search around the best solution found so far, thus accelerating the convergence. By combining these three strategies, ReDSADE is able to solve EOPDRs with limited function evaluations. Three sets of test problems and a real-world application are utilized to verify the effectiveness of ReDSADE. The results demonstrate that ReDSADE exhibits good convergence accuracy and convergence speed.

ЭФФЕКТИВНОЕ ИСПОЛЬЗОВАНИЕ ГЕОРЕСУРСНОГО ПОТЕНЦИАЛА ГЛУБОКИХ РУДНЫХ КАРЬЕРОВ И ОБОСНОВАНИЕ ВЫБОРА СРЕДСТВ МЕХАНИЗАЦИИ ПО ПРИРОДНО-ТЕХНОЛОГИЧЕСКИМ ЗОНАМ МЕСТОРОЖДЕНИЯ

The scientific paper reviews operation and operating conditions of deep pits. The division of the quarry field into natural-technological areas and their classification have been made. The work also provides schemes of division of the open pit field into conventional natural-technological zones and of complex mechanization of natural-technological zone by the cyclic action equipment. The dependence of the rate of mining decline on the width of the working site has been maintained. Strategic technical solutions to help manage mining manufacture and complex continuous planning and design of mining operations as well as division of working space into pit zones, have been elaborated. A nomogram for determining the parameters of excavation blocks based on the rate of decline of mining works for different sizes of excavators and a structural scheme of complex mechanization of natural and technological zones by cyclic-flow technology have been provided.

Effective data management strategy and RDD weight cache replacement strategy in Spark

With the dramatic increase in internet users and their demand for real-time network performance, Spark has distributed computing environment has emerged. It is widely used due to its high-performance caching mechanism and high scalability. In the face of the unpredictability of data access patterns in the current big data environment, the data shuffling phase is prone to the problems of under-utilization of Spark cluster resources, high computational latency, and high task processing latency. Based on this, this paper proposes an intermediate data management strategy based on the data shuffling phase. Firstly, the size of the data generated in the data shuffling phase of the Spark platform is predicted by random sampling. The strength division strategy obtains the skewed data degree to obtain the part with excessive skew deviation. Finally, the adaptive data management strategy is applied to perform the corresponding computation tasks by the data deviation. In addition, to improve the response time, memory usage, and computation latency of Spark applications, an adaptive cache replacement algorithm based on RDD partition weights is proposed, which takes into account the influence of four weight factors such as computation cost, usage times, partition size and life cycle of RDDs by reasonably calculating the RDD partition weight values. Compared with the current mainstream baseline algorithms, the data management algorithm based on the data mash-up phase proposed in this paper can effectively reduce resource usage and computational response latency. The RDD-based partition weighted adaptive cache replacement algorithm proposed in this paper can fully use memory resources and effectively reduce the problem of resource wastage.

A new method to select frequency band for vibration signal demodulation and condition estimation of rolling bearings

The narrowband amplitude demodulation of vibration signals is widely used to extract components carrying information about rolling bearing faults for condition estimation. However, precisely selecting the frequency band determines the quality of demodulation. Although transient detection based methods have achieved satisfactory performance in some scenarios, they may be severely disturbed by strong random impulses or harmonics. Additionally, frequency division strategy is always considered as rough. Aiming at these problems, a new method is proposed to select the optimal frequency band by combining the frequency division strategy, genetic algorithm (GA) optimization, and an automatic fault characteristic order (FCO) search algorithm. Aimed at a signal, its sub-signals are extracted according to the frequency division strategy and the order tracing technology is implemented to obtain the order spectrum of each sub-signal at first. Then, the FCO and its multiples (FCOs) are automatically searched from each order spectrum, and their amplitude summation is calculated. Afterwards, the ratio of FCOs summation to noise amplitude (RFN) is measured as the basis to construct an RFNgram for selecting the primary optimal frequency band Finally, GA is implemented to select the optimized optimal frequency band where the signal is demodulated for estimating the bearing condition. RFN measures both the impulsiveness and periodicity of signals, and GA optimizes the result from a RFNgram, essentially making up for the shortcomings of previous methods under the condition of multiple interferences. RFN is measured through the order spectrum, which also allows it to be applicable to varying speeds, thus achieving a more comprehensive range of industrial applications. Three case studies are implemented to present the superiority of the proposed method in condition estimation for rolling bearings by comparison with four classical or advanced methods.

ML-Assisted Equalization for 50-Gb/s/λ O-Band CWDM Transmission Over 100-km SMF

We propose and demonstrate a bidirectional Vanilla recurrent neural network (Vanilla-RNN) based equalization scheme for O-band coarse wavelength division multiplexed (CWDM) transmission. Based on a 4 × 50-Gb/s intensity modulation and direct detection (IM/DD) system, we demonstrate the significantly better bit error rate (BER) performance of the Vanilla-RNN scheme over the conventional decision feedback equalizer (DFE) for both Nyquist on-off keying (OOK) and Nyquist 4-ary pulse amplitude modulation (PAM4) formats. It is shown that the Vanilla-RNN equalizer is capable of compensating for both linear and nonlinear impairments induced by the transceiver and the single-mode fiber (SMF). As a result, up to 100-km and 75-km SMF transmission can be achieved for OOK and PAM4 transmission, respectively. Furthermore, through the comparison with other equalization schemes, including the linear equalizer, 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> -order Volterra equalizer, and Volterra+DFE, it is demonstrated that the Vanilla-RNN equalizer achieves the best BER performance. In the meantime, it also exhibits lower implementation complexity when compared to Volterra-based schemes. Our results show that the Vanilla-RNN scheme is a viable solution for realizing simple and effective equalization. This work serves as an exploration and offers useful insights for future implementations of reach-extended O-band CWDM IM/DD systems.

Multi-Model-Based Predictive Control for Divisional Regulation in the Direct Air-Cooling Condenser

Flow distortions caused by ambient wind can have complex negative effects on the performance of direct air-cooling condensers, which use air as their cooling medium. A control-oriented model of the direct air-cooling condenser model, considering fan volumetric effectiveness and plume recirculation rate, was developed, and its linearization model was derived. The influences of fan volumetric effectiveness and plume recirculation rate on backpressure were analyzed, and the optimal backpressure was calculated. To improve both the transient performance and steady-state energy saving of the condenser, a multi-model-based predictive control strategy was proposed to divisionally adjust the fan array. Four division schemes of the direct air-cooling fan array constituted the local models, and in each division scheme, axial fans were divided into three groups according to the wind direction: windward fans, leeward fans, and other fans. The simulation results showed that the turbine backpressure can be increased by 15 kPa under the influence of plume recirculation and the reduction of the fan volumetric efficiency. The fan division adjustment strategy can achieve satisfactory control performance with switching rules.

Explainable Shapley-Based Allocation (Student Abstract)

The Shapley value is one of the most important normative division scheme in cooperative game theory, satisfying basic axioms. However, some allocation according to the Shapley value may seem unfair to humans. In this paper, we develop an automatic method that generates intuitive explanations for a Shapley-based payoff allocation, which utilizes the basic axioms. Given a coalitional game, our method decomposes it to sub-games, for which it is easy to generate verbal explanations, and shows that the given game is composed of the sub-games. Since the payoff allocation for each sub-game is perceived as fair, the Shapley-based payoff allocation for the given game should seem fair as well. We run an experiment with 210 human participants and show that when applying our method, humans perceive Shapley-based payoff allocation as significantly more fair than when using a general standard explanation.

The heterogeneity of lithofacies types, combination modes, and sedimentary model of lacustrine shale restricted by high‐frequency sequence

The second Member of the Funing Formation in the Haian Sag of the Subei Basin is a set of organic‐rich fine‐grained sedimentary rocks, which is a key interval for shale oil exploration. Under the premise of establishing a high‐frequency sequence framework, the fine description of the lithofacies change characteristics of the continental fine‐grained sedimentary rocks is the basis for the prediction of shale oil favourable areas and the optimization of the production horizons. This study is established through wavelet transformation of the natural gamma curve of the high‐frequency sequence framework of the second Member of the Funing Formation, using thin‐section observation, total organic carbon analysis, and X‐ray diffraction experiments to study the lithofacies types and characteristics of fine‐grained sedimentary rocks, and discuss the combination of lithofacies under the high‐frequency sequence framework Mode and influencing factors. The results show that the second Member of Funing Formation is composed of three‐level sequence of low‐level system tract (LST), transgressive system tract (TST), and early high‐level system tract (EHST), corresponding to 6 fourth‐level sequences and 10 fifth‐level sequences. According to the division scheme of “abundance of organic matter + mineral composition + sedimentary structure”, it is found that 12 types of lithofacies were developed in the fine‐grained sedimentary rocks of the second Member of Funing Formation. The lithofacies type is affected by the palaeoenvironment. The organic matter facies are mainly controlled by palaeo‐water depth and palaeo‐redox conditions, the components are mainly controlled by palaeo‐water depth and palaeo‐salinity, and the tectonic facies are mainly controlled by palaeo‐redox and palaeo‐salinity. The lithofacies are regularly superimposed in the high‐frequency sequence framework, and three combination modes of A, B, and C are developed. Among them, the type A lithofacies are developed in the low‐stand system tract, and the type B lithofacies are developed in the transgressive system. The C‐type lithofacies assemblage developed near the largest flooding surface and the early highstand system tract, and combined with palaeoenvironment analysis to establish a lithofacies sedimentary model.