Double Constraints Research Articles

Optimize data association of point cloud to improve the quality of mapping and positioning

Purpose Accurate mapping is crucial for the positioning and navigation of mobile robots. Recent advancements in algorithms and the accuracy of LiDAR sensors have led to a gradual improvement in map quality. However, challenges such as lag in closing loops and vignetting at map boundaries persist due to the discrete and sparse nature of raster map data. The purpose of this study is to reduce the error of map construction and improve the timeliness of closed loop. Design/methodology/approach In this letter, the authors introduce a method for dynamically adjusting point cloud distance constraints to optimize data association (ODA-d), effectively addressing these issues. The authors propose a dynamic threshold optimization method for matching point clouds to submaps during scan matching. Findings Large deviations in LiDAR sensor point cloud data, when incorporated into the submap, can result in irreparable errors in correlation matching and loop closure optimization. By implementing a data association framework with double constraints and dynamically adjusting the matching threshold, the authors significantly enhance submap quality. In addition, the authors introduce a dynamic fusion method that accounts for both submap size and the distance between submaps during the mapping process. ODA-d reduces errors between submaps and facilitates timely loop closure optimization. Originality/value The authors validate the localization accuracy of ODA-d by examining translation and rotation errors across three open data sets. Moreover, the authors compare the quality of map construction in a real-world environment, demonstrating the effectiveness of ODA-d.

Underwater multi-sensor fusion localization with visual-inertial-depth using hybrid residuals and efficient loop closing

Unmanned underwater vehicles (UUVs) need robust localization systems to complete their missions safely. To address the degradation of the localization accuracy of UUVs using visual-based simultaneous localization and mapping (SLAM) in underwater environments. A full SLAM system that fuses visual-inertial-depth measurements using hybrid residuals and efficient loop closing is proposed. Specifically, a novel visual hybrid residual is presented with an adaptive dynamic weighting factor. Reprojection and photometric double constraints are constructed simultaneously using the selected robust features. These constraints are then optimized jointly with the IMU measurements and the relative depth measurements to obtain a more accurate pose. Furthermore, a new three-step image-to-sequence strategy and a convolutional auto-encoder (CAE)-based global image representation are adopted to select candidate frames in order to improve the recall of underwater visual loop closure detection. Quantitative and qualitative analyses of real-world artificial pool and ocean experiments demonstrate the accuracy and effectiveness of the proposed system.

Increasing SAR Imaging Precision for Burden Surface Profile Jointly Using Low-Rank and Sparsity Priors

The synthetic aperture radar (SAR) imaging technique for a frequency-modulated continuous wave (FMCW) has attracted wide attention in the field of burden surface profile measurement. However, the imaging data are virtually under-sampled due to the severely restricted scan time, which prevents the antenna being exposed to high temperatures and heavy dust in the blast furnace (BF) for an extended period. In traditional SAR imaging algorithm research, the insufficient accumulation of scattered energy in reconstructing the burden surface profile leads to lower imaging precision, and the harsh smelting increases the probability of distortion in shape detection. In this study, to address these challenges, a novel rotating SAR imaging algorithm based on the constructed mechanical swing radar system is proposed. This algorithm is inspired by the low-rank property of the sampled signal matrix and the sparsity of burden surface profile images. First, the sparse FMCW signal is modeled, and the position transform matrix, calculated according to the BF dimensions, is embedded into the dictionary matrix. Then, the low-rank and sparsity priors are considered and reformulated as split variables in order to establish a convex optimization problem. Lastly, the augmented Lagrange multiplier (ALM) is employed to solve this problem under double constraints, and the imaging results are obtained using the alternating direction method of multipliers (ADMM). The experimental results demonstrate that, in the subsequent shape detection, the root mean square error (RMSE) is 15.38% lower than the previous algorithm and 15.63% lower under low signal-to-noise (SNR) conditions. In both enclosed and harsh environments, the proposed algorithm is able to achieve higher imaging precision even under high noise. It will be further optimized for speed and reliability, with plans to extend its application to 3D measurements in the future.

Reliability-based thermal-fluid-structural topological optimization of three-dimensional coolant channels in proton exchange membrane fuel cells

In order to minimize the flow resistance in designing the cooling multi-channels of proton exchange membrane fuel cells (PEMFCs) under the double constraints of target thermal reliability index and target structural performance, this work proposes a three-dimensional (3D) thermal-fluid-structural reliability-based topology optimization (RBTO) method for the first time. In the RBTO for PEMFCs’ coolant channels, the position uncertainty of the multi-heat loads on the bipolar plates caused by irregular metal foam skeleton is considered, and the multi-heat load positions are treated as the interval uncertain variables within a certain range. To solve the problem of low computational efficiency caused by the coupling of topological parameter optimization and reliability constraint analysis, the RBTO is equivalently decoupled to a sequence of cycles of deterministic topology optimization (DTO) and inverse reliability assessment, and the latter is completed by performance measure approach (PMA) based on the target reliability index. Results show that RBTO introduces more flow resistance cost than DTO, and the additional flow resistance in RBTO increases with the increase of the target reliability index. The heat concentration caused by a smaller fiber diameter of foam skeletons results in more flow resistance cost of RBTO compared to DTO. When the inlet velocity of the coolant channel varies in the region of 0.05–0.3 m/s, the flow resistance of the multi-channel obtained by RBTO is 1.29–0.96 times as that of the straight multi-channel. In addition, the uneven reactant gas concentration in PEMFC gas channels leads to the uneven distribution of heat flow, and it affects the temperature distribution on bipolar plates, which in turn affects the results of RBTO. The above method and findings can be applied to design the cooling channels of PEMFCs with irregular metal foam gas channels.

Double-Constraint Fuzzy Clustering Algorithm

Given a set of data objects, the fuzzy c-means (FCM) partitional clustering algorithm is favored due to easy implementation, rapid response, and feasible optimization. However, FCM fails to reflect either the importance degree of the individual data objects or that of the clusters. Numerous variants of FCM have been proposed to address these issues. However, most of them cannot effectively apply the available information on data objects or clusters. In this paper, a double-constraint fuzzy clustering algorithm is proposed to reflect the importance degrees of both individual data objects and clusters. By incorporating double constraints into each data object and cluster, the objective function of FCM is reformulated and its realization equation is mathematically conducted. Consequently, the clustering accuracy of FCM is improved by applying the available information on both data objects and clusters. Especially, the proposed algorithm effectively addresses the limitations inherent in the existing variants of FCM. The experimental results validate the effectiveness, implementation, and robustness of the new fuzzy clustering algorithm.

Non-negative Tucker decomposition with double constraints for multiway dimensionality reduction

<p>Nonnegative Tucker decomposition (NTD) is one of the renowned techniques in feature extraction and representation for nonnegative high-dimensional tensor data. The main focus behind the NTD-like model was how to factorize the data to get ahold of a high quality data representation from multidimensional directions. However, existing NTD-like models do not consider relationship and properties between the factor matrix of columns while preserving the geometric structure of the data space. In this paper, we managed to capture nonlinear local features of data space and further enhance expressiveness of the NTD clustering method by syncretizing organically approximately orthogonal constraint and graph regularized constraint. First, based on the uni-side and bi-side approximate orthogonality, we flexibly proposed two novel approximately orthogonal NTD with graph regularized models, which not only in part make the factor matrix tend to be orthogonality, but also preserve the geometrical information from high-dimensional tensor data. Second, we developed the iterative updating algorithm dependent on the multiplicative update rule to solve the proposed models, and provided its convergence and computational complexity. Finally, we used numerical experimental results to demonstrate the effectiveness, robustness, and efficiency of the proposed new methods on the real-world image datasets.</p>

Potential of China's national policies on reducing carbon emissions from coal-fired power plants in the period of the 14th Five-Year Plan

Coal-fired power is one of the largest contributors to China's carbon emissions. To promote its national low-carbon transition ambitions, the Chinese government has issued a series of policies to reduce emissions from coal-fired power plants (CFPP) during its 14th Five-Year Plan (2021-2025). This study mainly focuses on the mitigation potential of related national policies, using global optimization methods with double constraints on different policy implementation extents and power supply security under different scheduled views of national new energy developments. Thereby, 81 scenarios are set, and policy simulations till 2025 are conducted, achieving emission reductions ranging from 0.39 Gt to 1.04 Gt across scenarios. Specifically, if all policies are implemented as planned, they can bring significant changes, 0.64 Gt CO2 cumulative reduction and 25 Mt/GWh emitting efficiency improvement. But the simulated emission-changing trend shows that they may not be sufficient for the nation's target of peaking emissions before 2030, while results in higher-extent scenarios indicate that stronger implementation is required for this target. More relevant recommendations are also provided for subsequent sustainability policies on CFPPs in China.

Temporal–Spatial Variations and Convergence Analysis of Land Use Eco-Efficiency in the Urban Agglomerations of the Yellow River Basin in China

Achieving synergistic development of efficient urban land use and the natural environment is crucial in promoting green urbanization. The assessment of land use eco-efficiency (LUEE) and its temporal–spatial changes provides an effective means of quantifying the relationship between the urban ecological environment and land use. Targeting 55 selected cities in the Yellow River Basin (YRB), in this study, we utilize the Super-EBM method to gauge the LUEE. We explore the temporal patterns and the spatial convergence of LUEE utilizing kernel density estimation and spatial econometric methods. Considering the resource and environmental costs of land use, we assumed the industrial pollutant emissions generated during urban land use as the undesired outputs and designed a framework for measuring the level of LUEE under double constraints, which theoretically revealed the formation process and spatial convergence mechanism of LUEE. The results show the following: (1) Throughout the sample period, the LUEE of the YRB urban agglomeration decreased from 0.158 in 2009 to 0.094 in 2020, indicating a decreasing spatial disparity in LUEE over time. Notably, the Lanxi urban cluster exhibited the largest gap in LUEE, whereas the Guanzhong Plain urban agglomeration displayed the smallest gap. The hyper-variable density exceeded the inter-group gap as the main factor leading to the difference in LUEE. (2) Although the LUEE of urban agglomerations has increased, there still exists a noticeable polarization phenomenon. (3) The LUEE of YRB demonstrates a pattern of conditional convergence and exerts a significant spatial spillover effect. Over time, the LUEE of YRB will tend towards an individual steady state. The findings have implications for strengthening linkage and synergy among cities in YRB, promoting factor integration across administrative regions, and formulating heterogeneous policies.

Cross-Modality Person Re-Identification Algorithm Based on Two-Branch Network

Person re-identification is the technique of identifying the same person in different camera shots, known as ReID for short. Most existing models focus on single-modality person re-identification involving only visible images. However, the visible modality is not suitable for low-light environments or at night, when crime is frequent. In contrast, infrared images can reflect the nighttime environment, and most surveillance systems are equipped with dual-mode cameras that can automatically switch between visible and infrared modalities based on light conditions. In contrast to visible-light cameras, infrared (IR) cameras can still capture enough information from the scene in those dark environments. Therefore, the problem of visible-infrared cross-modality person re-identification (VI-ReID) is proposed. To improve the identification rate of cross-modality person re-identification, a cross-modality person re-identification method based on a two-branch network is proposed. Firstly, we use infrared image colorization technology to convert infrared images into color images to reduce the differences between modalities and propose a visible-infrared cross-modality person re-identification algorithm based on Two-Branch Network with Double Constraints (VI-TBNDC), which consists of two main components: a two-branch network for feature extraction and a double-constrained identity loss for feature learning. The two-branch network extracts the features of both data sets separately, and the double-constrained identity loss ensures that the learned feature representations are discriminative enough to distinguish different people from two different patterns. The effectiveness of the proposed method is verified by extensive experimental analysis, and the method achieves good recognition accuracy on the visible-infrared image person re-identification standard dataset SYSU-MM01.

Spatial planning layout design of national land in the context of rural revitalization based on MOLA model

Abstract In the context of the comprehensive implementation of rural revitalization strategy, ecological protection and sustainable territorial spatial pattern are important elements of spatial planning and ecological civilization construction. With this as the starting point, this paper can use spatial analysis, hierarchical analysis, entropy method, integrated learning and other technical methods to divide the national spatial evaluation units under the MOLA model. Carry out spatial suitability evaluation of national land based on double constraints as well as spatial planning and design. The test results show that the villagers’ trust in the implementation of rural revitalization policies reached 78%, and most of them agree more with various policies on rural construction and development, which illustrates the necessity of territorial spatial planning in rural revitalization. The research data have good score validity when the observed variable of the questionnaire under the experimental test is greater than 0.4, which indicates the spatial planning layout under the proposed model. On the basis of mastering the current situation and problems facing rural revitalization, it not only proposes an effective path, but also scientifically adjusts and optimizes the space of rural areas, providing a boost to promote the construction and sustainable and healthy development of new rural areas.

Intelligent Containment Control With Double Constraints for Cloud-Based Collaborative Manufacturing

Intelligent Containment Control With Double Constraints for Cloud-Based Collaborative Manufacturing

The role of rationality and altruism in rural households' acceptance of straw energy utilization: Evidence from Northeast China

Straw energy utilization has been considered a prosocial practice, which is conducive to increasing the efficiency of agricultural waste resources and improving the ecological environment. Individuals' willingness to engage in such a prosocial technology relies on the double constraints of rational and altruistic factors. This paper develops a theoretical model to understand the function of rationality and altruism in rural households' acceptance of straw energy utilization. Partial least squares structural equation modeling is employed to test and evaluate the research data collected by a cross-sectional survey in Northeast China. Results display that 82.7% of the variance is explained by the proposed model in rural households' acceptance of straw energy utilization. Personal norms, attitude, perceived behavioral control, and subjective norm have significantly positive effects on the willingness to adopt straw energy utilization, and the effects sequentially weaken (i.e., 0.422, 0.245, 0.167, 0.166, respectively). The influence coefficients of rational predictors are 0.578, while the influence coefficients of altruistic predictors are 0.422. Overall, rational predictors exert more significant effects on rural households' acceptance of straw energy utilization than altruistic predictors. In addition, promotion strategies and policies for straw energy utilization are provided based on the motivation analysis in rural households' acceptance.

Rock brittleness index inversion method with constraints of seismic and well logs via a CNN-GRU fusion network based on the spatiotemporal attention mechanism

Rock brittleness is an important parameter for evaluating the fracability of the unconventional oil and gas reservoirs such as tight sandstone and shale oil. Compared with the laboratory measurement, rock brittleness in 3D stratum using the joint inversion of seismic and well logs is more significant for oil exploration and development, but the resolution of the inversion results is low due to the narrowly effective bandwidth of seismic data. Deep neural network (DNN) can establish the complex nonlinear mapping relationship between input and output data to improve the resolution of inversion results, but it has the problem of unstable prediction results in the case of insufficient training samples in the geophysical field. Aiming at the problem, firstly, the ratio of Young ‘s modulus to Poisson ‘s ratio is defined as the rock brittleness index, and an AVO approximation equation with brittleness index is derived, thus establishing the convolution model between the rock brittleness index and seismic data. Secondly, an objective function containing the loss of logging data, seismic data and low-frequency components obtained by the inverse distance weighted interpolation of seismic and well logs is proposed to improve the spatial constraints of seismic and well logs. Then, the spatiotemporal attention mechanism is fused with the convolutional neural network (CNN) and gate recurrent unit (GRU) network to improve the sensitivity of the network proposed to important spatiotemporal characteristics. Finally, a DNN based on the double constraints of convolution model and data is established to solve the inversion stability in the case of small samples. The inversion results of the geological model with sand body superposition and pinchout and the tight sandstone reservoir in Junggar Basin verify that the generalization and prediction accuracy of the network proposed.

Alternating direction method for the fixed point problem of set-valued mappings with second-order cone double constraints

<abstract><p>This paper focuses on solving a class of equilibrium problems, namely, the fixed point problem of set-valued mappings with second-order cone double constraints. Under certain conditions, the variational inequality form of the fixed point problem of set-valued mappings with second-order cone double constraints is obtained by using the generalized saddle point theory three times. The alternating direction method is used to solve the fixed point problem of set-valued mappings with second-order cone double constraints, and the global convergence of the algorithm is proved. Finally, numerical results of solving five examples with an inexact alternating direction method are given, and the feasibility and effectiveness of the algorithm are demonstrated by comparing with other algorithms.</p></abstract>

2D NURBS Curve discretization method with double constraints of area and chord error and contour accuracy prediction modeling

The short segment discretization algorithm for complex curves is one of the critical factors in improving the quality and efficiency of economical CNC machine tools. However, traditional single constraint methods such as equal error and equal chord length algorithms cannot achieve the requirements of both quality and efficiency, and intelligent methods with high algorithmic complexity are unsuitable for traditional machining. To this end, a simple and effective method for generating short segment trajectories called area-chord error double constraint is presented in this paper. The area enclosed by the trace of the short line segment and the original curve is defined as area error, and the semicircle with chord length as the diameter is defined as constrained area. By controlling the ratio of the area error to the constrained area, short line segment separation with variable chord length and error can be achieved according to the curvature characteristics. The influence mechanism of short line segmentation on curves is investigated and the contour accuracy prediction model is established through 70 irregular curves from simple to complex. The experimental results show that this method can retain more geometric features of the original curve on the premise of fewer feature points so as to achieve both processing precision and efficiency.

Measuring the Green Total Factor Productivity in Chinese Aquaculture: A Zofio Index Decomposition

Aquatic products are important sources of protein and food consumption, which are pivotal to solving the problem of food shortage. As the world’s largest producer of aquatic products, China’s aquaculture has developed rapidly. However, a large quantity of pollutants has also been generated in the fish farming process, which is detrimental to the sustainable development of China’s aquaculture. Therefore, under double constraints with regard to resources and the environment, fishery development must achieve cleaner production. Measuring green total factor productivity in aquaculture is fundamental to improving aquaculture production efficiency and reducing pollution emissions. This paper investigated the green total factor productivity in China using the SBM-ML method and analyzed the dynamic evolution of fish farming, measuring its change characteristics with regard to time and spatial differences. The results indicated that the total factor productivity indexes of mariculture and freshwater aquaculture in China are 1.050 and 1.060, respectively. Regionally, mariculture in the East China Sea region has the highest green total factor productivity of 1.072, followed by the South China Sea region with 1.056, and the green total factor productivity in the Yellow Sea region is the lowest—only 1.030. The results of the freshwater aquaculture calculations showed the opposite geographical distribution characteristics in China, with the highest in the western region (1.098), followed by the central region (1.046), with the lowest being in the eastern region (1.038). Evidently, both mariculture and freshwater aquaculture have noticeable spatiotemporal heterogeneity. Relevant policy recommendations are provided to improve the green production efficiency of fishery.

Dynamic Programming BN Structure Learning Algorithm Integrating Double Constraints under Small Sample Condition

The Bayesian Network (BN) structure learning algorithm based on dynamic programming can obtain global optimal solutions. However, when the sample cannot fully contain the information of the real structure, especially when the sample size is small, the obtained structure is inaccurate. Therefore, this paper studies the planning mode and connotation of dynamic programming, restricts its process with edge and path constraints, and proposes a dynamic programming BN structure learning algorithm with double constraints under small sample conditions. The algorithm uses double constraints to limit the planning process of dynamic programming and reduces the planning space. Then, it uses double constraints to limit the selection of the optimal parent node to ensure that the optimal structure conforms to prior knowledge. Finally, the integrating prior-knowledge method and the non-integrating prior-knowledge method are simulated and compared. The simulation results verify the effectiveness of the method proposed and prove that the integrating prior knowledge can significantly improve the efficiency and accuracy of BN structure learning.

A Neighborhood Model with Both Distance and Quantity Constraints for Multilabel Data.

In this paper, a novel distance-based multilabel classification algorithm is proposed. The proposed algorithm combines k-nearest neighbors (kNN) with neighborhood classifier (NC) to impose double constraints on the quantity and distance of the neighbors. In short, the radius constraint is introduced in the kNN model to improve the classification accuracy, and the quantity constraint k is added in the NC model to speed up computing. From the neighbors with the double constraints, the probabilities for each label are estimated by the Bayesian rule, and the classification judgment is made according to the probabilities. Experimental results show that the proposed algorithm has slight advantages over similar algorithms in calculation speed and classification accuracy.

Simulation of PM2.5 Concentration Based on Optimized Indexes of 2D/3D Urban Form

It is well known that urban forms can affect the source distribution and diffusion process of air pollution; however, practical quantitative methods and models on alleviating urban air pollution by optimizing urban form indexes are lacking. Using Chang Sha city as an example, we quantitatively analyzed the PM2.5 concentration distribution in terms of 2D/3D urban form indexes (e.g., land use functionality, landscape pattern, and development intensity). Based on this, the urban form regulation-aided air quality optimization model (UFR-AQOM) was proposed and consequently employed to simulate the scene-dependent PM2.5 concentrations under double constraints from both the index threshold and air quality objectives. The results showed that the high value area of PM2.5 concentration in Chang Sha featured a "one axis and four nodes" spatial pattern. PM2.5 concentrations in grids with commercial or road land applications, high patch density or high Shannon index, and high land plot ratio or low sky openness were shown to be relatively higher. The development intensity indexes had the greatest impact on the spatial variation in PM2.5 concentration, followed by landscape pattern and land use functionality, and the interaction of factor indexes could significantly strengthen their own single contributions. The UFR-AQOM model, taking into account the contribution differences and interactions among different factors, could effectively simulate the spatial variation in PM2.5 concentration in urban areas (R2=0.65,RMSE=1.40 μg·m-3). In order to meet the regulations of PM2.5 standards, the overall management of landscape pattern indexes, such as the integral dimension of the perimeter surface and patch density, should be strengthened in the main urban area of Changsha. Further, the zoned optimization of PM2.5 concentrations could be implemented by controlling the urban land use indexes, such as the industrial land use ratio and water area ratio, as well as the development intensity indexes such as the land use area ratio. These research results provided support for decisions in the optimization of national territory spatial planning indexes targeting air quality improvement.

Ultrasonic Image Optimization based on Double Constraint Algorithm

The study of ultrasonic image deblurring is of high research value and practical significance, which can provide significantly useful medical pathological information for doctors and patients. Ultrasonic images are prone to speckle noise or blur. Real-time deblurring of ultrasonic images is a common problem in medical circles in China and globally. In this paper, based on the causes of ultrasound image degradation and its degradation model, the problem of ultrasound image deblurring is studied in depth. A new regularization constraint model is given, and the algorithm of ultrasound image deblurring with double constraints is effective. Through the experiment, the image quality evaluation index is used to evaluate the experimental results. The improved algorithm proposed in this paper improves the peak signal-to-noise ratio by 7%, the structural similarity value by 6%, the visual information fidelity by 14%, the feature similarity value by 4%, the resolution gain by 4%, and the new blind image quality evaluation value by 22%. The key information of the image can be kept faithfully. Compared with the traditional methods, the proposed method enhances the identification of the focus area in ultrasound images, provides a fast, efficient and complete deblurring method for medical image analysis, and faithfully retains and restores the required information in ultrasound images.