Division Method Research Articles

Intelligent setting of ORP under multiple operation modes

In the cobalt removal process, the setting value of oxidation-reduction potential (ORP) directly affects cobalt remioval performance. Consider the multimode feature, an intelligent setting strategy for ORP is proposed. First, a basic ORP setting model is obtained based on mechanism knowledge. Then, an adaptive operation mode division method based on the parameter similarity is proposed. The model parameters for each mode are identified by an intelligent optimization algorithm. Finally, an intelligent online setting framework for ORP is constructed. The experiments verify the validity of the model.

A Location-Privacy-Aware Taxi-Hailing System: Adaptive Differential Privacy-Based Dynamic Incentive Method

Nowadays location-based service (LBS) has become an important service in people’s daily life. Online taxi-hailing system (DiDi, Uber, etc.) is one of the most common LBS system. As the scale of online taxi services continues to expand, some problems have gradually emerged. Specifically, the uncertainty of taxis and passengers makes it difficult to match them effectively, and the passengers’ location privacy will be threatened from both internal and external of the system. In this paper, we first proposed a Bayesian-based location privacy inference attack method from external adversary’s point of view. After that, an adaptive differential privacy-based dynamic incentive method was proposed. Firstly, an adaptivity clocking area division method was proposed to resist the internal privacy threat. Secondly, a dynamic incentive bidding method was proposed to deal with the trading issue. Thirdly, an exponential-based matching method was proposed to resist the external inference privacy threat. Further, theoretical proofs show that the proposed method satisfy the privacy properties of k-anonymity, 2μ1ε-differential privacy, and the economic properties of incentive compatibility, individual rationality. Finally, the experimental results show that the inference attack would achieve a maximum attack success rate of 95% while ensuring the accuracy within 150 meters, and the proposed adaptive differential privacy-based dynamic incentive method can not only provide a good economic performance in terms of satisfaction ratio, social welfare and travel distance, but also can resist internal privacy threat with less than 1% of privacy leakage probability and reduce the success rate of external privacy inference attacks to 25%.

Order Reduction using Basic Characteristics and Factor Division Method

Abstract: The authors propose a mixed method for reducing the order of the high order dynamic systems. In this method, the denominator polynomial of the reduced order model is obtained by using the basic characteristics of the higher order system which are maintained in the reduced model while the coefficients of the numerator are obtained by using factor division method. This method is fundamentally simple and generates stable reduced models if the original high-order system is stable. The proposed method is illustrated with the help of the numerical example taken from the literature.

Variable optimization domain-based cooperative energy management strategy for connected plug-in hybrid electric vehicles

The design of energy management strategy (EMS) that can be applied in real-time with nearly optimal fuel economy for connected plug-in hybrid electric vehicles (C-PHEVs) is an urgent need at present, especially in the context where the benefits of rapid development of intelligent transportation system are increasingly evident. Equivalent consumption minimization strategy (ECMS) is able to achieve the desired performance, on the premise that the equivalent factor (EF) is adjusted reasonably. To achieve this goal, a variable optimization domain-based cooperative EMS for C-PHEVs is proposed in this paper. First, an offline sub-cycle division method is designed, which is effectively utilized to construct a fitting function to output reference EF based on the latest driving information. Second, the theoretical relationship between EF and operating efficiency of the driving components is explored, and the variable optimization domain of EF is designed to avoid inefficient operation of engine. Third, the EF optimization is then simplified into a quadratic problem within the designed domain, which is rapidly solved online with the golden section search algorithm. Finally, the comparison work is conducted under two real-world driving cycles. The results exhibit that the proposed strategy improves the fuel economy of C-PHEV by 6.440 % and 7.936 % over that using the conventional adaptive ECMS (CA-ECMS), respectively. The average engine fuel consumption rates of the proposed strategy are significantly reduced compared with that of CA-ECMS. Moreover, the effectiveness of the proposed strategy is verified on the experiment platform, demonstrating its application prospects.

Data and knowledge collaborative-driven fault identification and self-healing control action inference framework for blast furnace

The blast furnace reactor has a large volume, and smelting occurs at high temperatures and high-pressure environments. The blast furnace reactor has a large volume, and smelting occurs at high temperatures and high-pressure environments. Considering the small samples of fault data and non-stationarity of the production process with a considerable time lag, it is difficult to diagnose and eliminate the faults timely. This paper proposes for the first time a data and knowledge collaborative-driven framework to identify fault and perform self-healing control. Following the adaptive two-scale sequence division method, the prior knowledge matrix refined K2 algorithm is used to construct Bayesian networks to model the control process. When a new abnormal condition arises, the most similar historical sequence will be found, and inference on the corresponding Bayesian network can lead to fault identification results and self-healing control action. The proposed framework has achieved satisfactory results in simulation experiments and real data experiments. In the simulation data, the Bayesian network (BN) modeling achieved an accuracy of 98.75%, and the time required for structure learning of the BN was reduced by over 75.69%. In real data, the accuracy of identifying blast furnace faults reached 97.1%, and the results of self-healing control reasoning surpassed those of traditional BN reasoning methods in terms of correctness, order, completeness, and necessity.

Analysis of Lightweight Structure Mesh Topology of Geodesic Domes

This paper presents two methods of shaping the mesh topology of lightweight structures as spherical domes. The two given methods of dividing the initial face of the polyhedra determine the obtained structures, which differ in the way of connecting the nodal points. These points were obtained by applying the algorithm for calculating spherical coordinates presented in the paper, which were then converted to the Cartesian system using transformation formulas. Two models of dome structures are presented, based on a 4608-hedron according to the first division method, and on a 4704-hedron, using the second proposed method with numerical analysis. Thus, the novelty of this paper is an implementation of the formulas and algorithms from geodesic domes based on the regular dodecahedron to the regular octahedron, which has not been presented so far. The choice of the shape of the structure has impacts on sustainable development, dictated by structural and visual considerations, leading to the design of a light structure with low consumption of construction material (steel), which can undoubtedly be helpful when making the final structure shape. In addition, according to this research, it can be concluded that using the first method to create a geodesic dome mesh is more straightforward, safer, and requires less design experience.

Radial projection-based adaptive sampling strategies for surrogate-assisted many-objective optimization

Intelligent manufacturing and industrial control systems frequently encounter expensive many-objective optimization problems (EMaOPs). Surrogate-assisted evolutionary algorithms (SAEAs) build predictive models to substitute the expensive fitness evaluation, enabling them to solve optimization more efficiently. In SAEAs, to enhance the exploration of optimization and the generalization of the surrogate model, the diversity of infill offspring and training database should be well-maintained, which is challenging in high-dimensional spaces or problems with disconnected Pareto front. This paper suggests a radial projection-based surrogate-assisted framework for solving EMaOPs. The radial projection can map the high-dimensional objective space into a 2-dimensional radial space. Based on this, a dynamic quadratic division method is proposed to enhance the diversity of solutions. Furthermore, an adaptive infill sampling criterion is introduced based on the distribution of selected convergent solutions, and a training database updating strategy is designed under the premise of maintaining its diversity and the model training efficiency. The presented framework exhibits a notable level of flexibility and adaptability as it can be effortlessly combined with other multi-objective optimization algorithms. Several experimental results on a set of expensive multi/many-objective test problems have demonstrated the superiority of the framework.

Contactless wheat foreign material monitoring and localization with passive RFID tag arrays

In the early stage of harvesting, transportation, and storage, the grain could be mingled with clods from the field, metal pieces from aging machines, and other objects of foreign material, which will greatly influence the grain quality and food security. In this paper, we propose a novel radio frequency (RF) sensing system termed TagSee, which utilizes passive RFID tag arrays to sense the objects buried in wheat. The goal is to simultaneously sense the presence of foreign materials, locate their locations in the 3D space and sense their types, to automatically monitor the stored grain. Specifically, we use RFID received signal strength (RSS) and phase as features for foreign material detection. To design the TagSee system, we first introduce a sensing space division method. Then, an Euclidean Distance Ratio (EDR) algorithm and a heuristic method are proposed to achieve high localization success accuracy. A multi-class SVM method is used to sense different materials properties based on the impact of different foreign materials on signal features. Experimental results show that TagSee can effectively detect foreign materials in stored grain and achieve a centimeter-level localization success accuracy, and can recognize four types of objects in grain.

Low-carbon optimal scheduling for multi-source power systems based on source-load matching under active demand response

To reduce the source-load uncertainty and carbon emission levels of the power system, this study proposes a novel low-carbon economic stochastic optimization scheduling model. Firstly, to partially compensate for the instability of wind power (WP) and photovoltaic power (PV) generation, enhance energy efficiency, and reduce system carbon emissions, the introduction of concentrating solar power (CSP) as one of the power sources on the generation side. Secondly, to optimize load characteristics, a peak–valley time period division method based on fuzzy theory and an active demand response model for electric load using the Logistic fuzzy function are introduced on the load side. Thirdly, taking into account the role of the carbon trading market, a ladder-type carbon trading mechanism (LCTM) is introduced to control carbon emissions and reduce overall economic costs. Finally, considering WP-PV output fluctuations, the degree of matching between power output and load, WP-PV utilization, system economic efficiency, and carbon trading factors, with the objective of maximizing the tracking of the load curve and minimizing comprehensive system economic costs, a multi-objective stochastic optimized scheduling model for power systems is established. A multi-objective Runge–Kutta algorithm based on a population-based parallel search mechanism (PSMORUN) is proposed to solve the active demand response model and the low-carbon stochastic optimization scheduling model. Additionally, constraint repair techniques are employed to handle the complex constraints of the models. To verify the effectiveness of the proposed model, a 10-generator power system, including a WP farm, a PV plant, and a CSP plant, is used as a test case for simulation experiments and compared with other multi-objective scheduling models. The experimental results show that the proposed stochastic optimal scheduling model has higher safety, better economy, and lower carbon emissions.

Urban Air-Quality Estimation Using Visual Cues and a Deep Convolutional Neural Network in Bengaluru (Bangalore), India.

Mobile monitoring provides robust measurements of air pollution. However, resource constraints often limit the number of measurements so that assessments cannot be obtained in all locations of interest. In response, surrogate measurement methodologies, such as videos and images, have been suggested. Previous studies of air pollution and images have used static images (e.g., satellite images or Google Street View images). The current study was designed to develop deep learning methodologies to infer on-road pollutant concentrations from videos acquired with dashboard cameras. Fifty hours of on-road measurements of four pollutants (black carbon, particle number concentration, PM2.5 mass concentration, carbon dioxide) in Bengaluru, India, were analyzed. The analysis of each video frame involved identifying objects and determining motion (by segmentation and optical flow). Based on these visual cues, a regression convolutional neural network (CNN) was used to deduce pollution concentrations. The findings showed that the CNN approach outperformed several other machine learning (ML) techniques and more conventional analyses (e.g., linear regression). The CO2 prediction model achieved a normalized root-mean-square error of 10-13.7% for the different train-validation division methods. The results here thus contribute to the literature by using video and the relative motion of on-screen objects rather than static images and by implementing a rapid-analysis approach enabling analysis of the video in real time. These methods can be applied to other mobile-monitoring campaigns since the only additional equipment they require is an inexpensive dashboard camera.

An Improved 3D OPC Method for the Fabrication of High-Fidelity Micro Fresnel Lenses.

Based on three-dimensional optical proximity correction (3D OPC), recent advancements in 3D lithography have enabled the high-fidelity customization of 3D micro-optical elements. However, the micron-to-millimeter-scale structures represented by the Fresnel lens design bring more stringent requirements for 3D OPC, which poses significant challenges to the accuracy of models and the efficiency of algorithms. Thus, a lithographic model based on optical imaging and photochemical reaction curves is developed in this paper, and a subdomain division method with a statistics principle is proposed to improve the efficiency and accuracy of 3D OPC. Both the simulation and the experimental results show the superiority of the proposed 3D OPC method in the fabrication of Fresnel lenses. The computation memory requirements of the 3D OPC are reduced to below 1%, and the profile error of the fabricated Fresnel lens is reduced 79.98%. Applying the Fresnel lenses to an imaging system, the average peak signal to noise ratio (PSNR) of the image is increased by 18.92%, and the average contrast of the image is enhanced by 36%. We believe that the proposed 3D OPC method can be extended to the fabrication of vision-correcting ophthalmological lenses.

Research on the construction of a visualization platform for customer demand analysis based on big data technology

Abstract In this paper, from the MC optimization oriented to customer demand, we use big data technology to optimize the model, and with the help of the fuzzy cluster analysis method, we convert the variable types of customer demand indexes into different clustering effects. Fuzzy cluster analysis is used to establish the mapping relationship between customer demand, functional requirements of the product, and design parameters. Use the idea of customer demand analysis and transformation and the module division method to build the framework system of product configuration design and complete the construction of a customer demand-oriented product configuration visualization platform. By dividing different customer requirements, the best classification of customer requirements is obtained, and the technical optimization design of washing machine products is taken as an example to analyze the practicability of the platform constructed in this paper. Among the 12 technical characteristics of the washing machine, the importance of EG 11 is 0.1395, the importance of EG 1 is 0.1116, and the importance of EG 5 is 0.1017, which indicates that customers are most concerned about the energy-saving function of the product, and thus the enterprise should design the product based on the customer needs to satisfy the customer’s demands.

TAEF: A Task Allocation-Based Ensemble Fuzzing Framework for Optimizing the Advantages of Heterogeneous Fuzzers

Ensemble fuzzing in parallel with heterogeneous fuzzers has been proposed to leverage the advantages of diverse fuzzers and improve testing efficiency. However, in current ensemble fuzzing methods, the collaboration among different fuzzers is achieved solely by synchronizing the seeds discovered by each fuzzer. This results in a high likelihood of different fuzzers choosing the same seeds and creating a large number of equivalent testcases, thus reducing overall fuzzing efficiency. Meanwhile, the existing task division method proposed by AFLTeam is highly coupled with the fuzzer specially designed for it, making it challenging to apply to ensemble fuzzing directly. So, in this paper, we proposed a callgraph-based task division method suitable for ensemble fuzzing. Firstly, we divided the target program’s callgraph into subgraphs (subtasks) balancing expected workloads. Then, we divided the global seed corpus into subcorpora, each corresponding to a subtask, making fuzzers easily accept the subtasks. Finally, we designed synchronization mechanisms for coverage bitmaps and seeds to realize the collaborative fuzzing among different fuzzers and a cyclic subtask scheduling strategy to fully leverage the benefits of ensemble fuzzing. We implemented a prototype called TAEF. The evaluation results show that in the best-case scenario, our method has up to 24% more branch coverage than previous work.

The Influence of Mountain Height and Distance on Shape Factor of Wind Load of Plastic Tunnel

Due to their soft structure and covering material, plastic greenhouses are vulnerable to wind disasters, causing large-scale damage and huge economic losses. The wind load of greenhouses depends on the surface wind pressure distribution, which is different for greenhouses located in valleys from those in plain areas. To study the wind pressure distribution law for various regions of greenhouses built in valleys, mountain and greenhouse models have been built by Computational Fluid Dynamics, in which the length direction of the greenhouse is perpendicular to the valley and the wind direction is parallel to the valley. In the analysis, the verified turbulence model and grid division method are both introduced, and the effect of the height and distance of mountains is considered. According to the distribution law of wind pressure, the greenhouse’s surface is partitioned, and the variation law of the shape factor of wind load on a plastic tunnel is analyzed. Then, the calculation model for the shape factor of the wind load on the greenhouse located in a valley is proposed. The conclusions show that: (a) When the wind inflow direction angle is parallel to the valley, the distribution pattern of wind pressure on the surface of the greenhouse is similar to that on the plain regardless of the distance and height of the mountains, while the values of the wind pressure are greatly affected by the mountain height and distance. The distance between mountains has greater influence than the effect of mountain height. (b) The shape factor of wind load on the suction area of the greenhouse decreases as the distance of mountains increases, while the shape factor on the pressure area of the greenhouse increases with the increase in the distance. It can be seen that the valley effect is non-negligible. The narrower and deeper the valley, the greater the wind pressure effect. (c) When the ratio of the distance between the foot of the mountain and the greenhouse d to the height of the mountain H is less than 5, i.e., d/H < 5, the ratio of the distance to the height has a significant impact on the shape factor of wind load on the greenhouse. When d/H is close to 10, the shape factor of the wind load in the valley area is close to that in the plain area, and the effect of the ratio between the height and the distance is negligible. (d) The proposed calculation model can be used to calculate the effect of mountain height and distance on the shape factor of wind load. The research results can be used in the wind resistance design of plastic greenhouses in valley areas, and can also provide some data support for the revision of the greenhouse structural load code.

MULTI-SCALE DYNAMIC PARTITIONING SYSTEM OF URBAN SPATIAL UNITS

Abstract. As the spatial structure of cities becomes increasingly complex and sustainable development goals are promoted, society places higher demands on the management and planning of cities. As the basic unit of urban analysis, the combination pattern and scale shape of urban spatial units are crucial for rational management and planning of cities. However, existing urban analysis management systems often adopt a prefabricated fixed cell division method, which is difficult to meet the needs of high precision and multi-scale analysis of urban information. Therefore, this paper proposes an interactive dynamic partitioning technology, and designs a multi-scale dynamic partitioning system for urban spatial units (SUPS), in order to meet the diverse needs of urban management and planning. The system consists of a data management module, a spatial unit module, an integration module and a visualization module. The system not only realises the multi-scale dynamic partitioning of spatial units in the form of interactive operation, but also obtains more detailed identification results by applying the multi-scale spatial units to the identification of urban functional areas, verifying the effectiveness and feasibility of the interactive multi-scale dynamic partitioning of spatial units, and providing a new technical support for fine urban management.

Application of High-density Resistivity Method in Fine Division of Quaternary Geological Structure in the Starting Area of Xiongan New Area

In the starting area of Xiongan New Area, the high-density resistivity method is used for fine detection of the deep-lying strata 0m ∽ 200m in the study area. The stratigraphic and lithologic framework of 0 m-200 m depth has been established. The results show that the middle and Late Pleistocene strata are rich in fine sand, and no hidden ground fissures have occurred. It is proved that the high-density resistivity method is effective and feasible in urban planning and construction geological exploration, and it can provide data support for the further construction of Xiongan New Area.

The Effectiveness of Student Team Achievement Division Method with Gitmind in the Teaching Writing for Junior High School Students

Writing is one of the language skills that middle school students need to have. The purpose of this study was to determine the effectiveness of using the Student Group Performance Method with Gitmind to improve the writing ability of 7th grade students at Junior High School. In this study, a quasi-experimental design was used. The sample includes two classes (groups), an experimental class of 25 students and a control class of 24 students. This study uses the ANCOVA formula to analyze data. Research shows that instructional students who use Gitmind and the Student Team Performance Division method have better academic results. It is supported by the estimated marginal mean for the two groups, which is 56. 963 for the grammar translation method and 78. 474 for students taught using the Student Group Achievement Division with Gitmind method.

The Humanity of Plato's Eleatic Science of Politics

In the first section of Plato's Statesman, the Eleatic Stranger uses two methods to try to uncover the science of politics. The first method is that of diairesis — a dialectical method of division and classification. This method ultimately ends in failure and, in turn, the Stranger adopts a second method, which consists of an elaborate myth. In this article, I provide an account of the relationship between the diairesis and the myth and posit that together they teach that a humane account of the whole is required in order to understand the nature of politics.

Modeling and reproduction of sound field by moving complex sound source

Previously, Wave Field Synthesis (WFS), Near-field Compensated Higher-Order Ambisonics (NFC-HOA), Boundary Surface Control (BoSC), and the Spectral Division Method (SDM) have been proposed as sound field synthesis methods. These methods are based on matching the acoustic physical quantities at the boundary of the reproduced sound field with those of the desired sound field. Sound field synthesis techniques can be classified into data-based and model-based methods. The former aim at reproducing sound fields captured by microphone arrays, while the latter aim at synthesizing sound fields based on mathematical models. Most existing sound field reproduction methods only consider simple sound source models, such as static point sources, instead of sound sources with complex radiation properties. To generate a more realistic sound image, this study investigated the formulation of a sound field generated by a sound source with complex radiation properties moving along arbitrary trajectories in a spherical harmonic domain, and reproduced the sound field using loudspeaker arrays.

Reverse time migration surface offset gathers by attribute migration and constrained least-squares inversion

Abstract Surface offset gathers (SOGs) are crucial for velocity updating in seismic data migration and muting the stretched waveform in shallow parts of migrated seismic data. Kirchhoff-based migration methods can output SOGs because they take every seismic trace as the input. As a result, the imaging results can be rearranged according to the offset value of the input data to obtain SOGs. However, regarding more accurate wave equation methods (such as reverse time migration, RTM), the SOGs cannot be obtained directly with a single migration calculation. Attribute migration is an easy-to-implement method to output SOGs of wave equation migration methods. It calculates the offset value of each imaging point by dividing the migration result modulated by the offset attribute with the conventional migration result. However, the division error may cause the calculation result outside the given offset value. This paper proposes a constrained least-squares inversion method for stable calculation of the RTM offset range. The method ensures that the calculated results are within the given offset range. We tested the method against the direct division and least-squares methods without constraints using the Marmousi model and a real 3D dataset. We show that the SOGs obtained by the constrained least-squares inversion attribute migration method had the same characteristics as those derived using the division-based attribute migration method, but they also had higher vertical resolution, better energy distribution, and improved lateral continuity. In a further study, we expect to develop a stable and direct method to efficiently calculate SOGs for RTM and an iterative closed loop for RTM velocity updating.