Global Energy Optimization Research Articles

Reconstructing compact building models from 3D indoor point cloud with curved surfaces via global energy optimization

The automatic reconstruction of indoor interiors from scanned 3D point clouds has emerged as a substantial challenge. Most indoor reconstruction methods typically focus on planar structures rather than curved surfaces. Certain primitive representation-based approaches reconstruct curved surfaces by fitting and replacing regularity primitives, but they suffer from generating compact, watertight models solely through primitive replacement when occlusion and varying densities are present. This paper addresses indoor curved surface reconstruction by employing a hypothesis and selection strategy under global energy optimization (GEO). Hypothesized cells are generated through a combination of points and supervoxels with adaptive resolutions, thereby enhancing their geometric precision and integrity. Additionally, the optimal cell is selected through iterative refinement and clustering processing steps under GEO, ensuring that the reconstructed model accurately adheres to the target interior structure while maintaining compactness. A series of experiments demonstrate the effectiveness and feasibility of the proposed method.

A highly accurate quantum optimization algorithm for CT image reconstruction based on sinogram patterns

Computed tomography (CT) has been developed as a nondestructive technique for observing minute internal images in samples. It has been difficult to obtain photorealistic (clean or clear) CT images due to various unwanted artifacts generated during the CT scanning process, along with the limitations of back-projection algorithms. Recently, an iterative optimization algorithm has been developed that uses an entire sinogram to reduce errors caused by artifacts. In this paper, we introduce a new quantum algorithm for reconstructing CT images. This algorithm can be used with any type of light source as long as the projection is defined. Assuming an experimental sinogram produced by a Radon transform, to find the CT image of this sinogram, we express the CT image as a combination of qubits. After acquiring the Radon transform of the undetermined CT image, we combine the actual sinogram and the optimized qubits. The global energy optimization value used here can determine the value of qubits through a gate model quantum computer or quantum annealer. In particular, the new algorithm can also be used for cone-beam CT image reconstruction and for medical imaging.

Low‐voltage DC building distribution and utilization system and its implementation in China southern grid

AbstractThe demand‐side DC electricity‐using equipment and newly integrated renewables are driving the transformation of power distribution and utilization mode. The building system based on DC technology is emerging as a promising option. In the low‐voltage DC building distribution and utilization system (LVDCBDUS), global energy optimization management and operational control arrangement are key components. To obtain exemplary achievements of those, two different DC building energy management system (DC BEMS) integration schemes are investigated according to the respective features and application‐required functions of various system networking structures. Centralized and decentralized control strategies are presented and discussed for buildings with AC–DC transformation and newly built LVDCBDUSs. On this basis, the centralized DC BEMS and operational control strategy are applied to the first multi‐scenario low‐carbon city‐based future building project—Shenzhen IBR Future Complex. The operation data are recorded and analysed. Problems encountered during the implementation are summarized, and requirements of converter equipment, new technologies and marketization are further discussed to promote the high‐quality development of the LVDCBDUS.

A two-phase total optimization on aircraft stand assignment and tow-tractor routing considering energy-saving and attributes

The continued air traffic growth leads to more fuel consumption for the total aviation operation. Studying how to achieve energy-saving operations through a more reasonable strategy under the existing technology is still significant. This study first constructs a two-stage global operational energy optimization model with stand assignment and GSE routing problems, then distinguishes the scheduling of ground vehicles based on task attributes through a mathematical approach and also improves the initialization and cross-variance approach of the NSGA-II algorithm to reduce the number of infeasible solutions and improve the solution speed. A case study is carried out with the operational data of a Chinese airport, and the results show that the strategy can help achieve about a 5% of reduction in operational fuel consumption. This study can provide theoretical support and a technical basis for airport management to formulate relevant strategies.

Global energy and leakage optimization in water distribution systems from water treatment plants to customer taps

Delivering drinking water from water treatment plants (WTPs) to customer taps results in significant energy consumption and water loss due to leakage. The energy consumption involved in this process includes both the energy used in the water distribution networks (WDNs) and buildings. Existing research primarily focuses on optimizing energy consumption in WDNs while ignoring the energy consumption at buildings. Building height and water supply mode are crucial factors that impact energy consumption. A building hydraulic model involving various supply modes coupled with the WDN hydraulic model is developed to calculate the energy consumption and leakage from WTPs to taps. An optimization model is then proposed to minimize expenses by placing regional pump stations (RPSs) and scheduling pumps in WTPs. The application of the proposed method to a realistic WDN resulted in a 1.17% decrease in energy consumption and a 6.98% reduction in water leakage compared to traditional methods.

Neural network atomistic potentials for global energy minima search in carbon clusters.

The global energy optimization problem is an acute and important problem in chemistry. It is crucial to know the geometry of the lowest energy isomer (global minimum, GM) of a given compound for the evaluation of its chemical and physical properties. This problem is especially relevant for atomic clusters. Due to the exponential growth of the number of local minima geometries with the increase of the number of atoms in the cluster, it is important to find a computationally efficient and reliable method to navigate the energy landscape and locate a true global minima structure. Newly developed neural network (NN) atomistic potentials offer a numerically efficient and relatively accurate approach for molecular structure optimization. An important question that needs to be answered is "Can NN potentials, trained on a given set, represent the potential energy surface (PES) of a neighboring domain?". In this work, we tested the applicability of ANI-1ccx and ANI-nr NN atomistic potentials for the global minima optimization of carbon clusters Cn (n = 3-10). We showed that with the introduction of the cluster connectivity restriction and consequent DFT or ab initio calculations, ANI-1ccx and ANI-nr can be considered as robust PES pre-samplers that can capture the GM structure even for large clusters such as C20.

Indoor 3D Point Cloud Segmentation Based on Multi-Constraint Graph Clustering

Indoor scene point cloud segmentation plays an essential role in 3D reconstruction and scene classification. This paper proposes a multi-constraint graph clustering method (MCGC) for indoor scene segmentation. The MCGC method considers multi-constraints, including extracted structural planes, local surface convexity, and color information of objects for indoor segmentation. Firstly, the raw point cloud is partitioned into surface patches, and we propose a robust plane extraction method to extract the main structural planes of the indoor scene. Then, the match between the surface patches and the structural planes is achieved by global energy optimization. Next, we closely integrate multiple constraints mentioned above to design a graph clustering algorithm to partition cluttered indoor scenes into object parts. Finally, we present a post-refinement step to filter outliers. We conducted experiments on a benchmark RGB-D dataset and a real indoor laser-scanned dataset to perform numerous qualitative and quantitative evaluation experiments, the results of which have verified the effectiveness of the MCGC method. Compared with state-of-the-art methods, MCGC can deal with the segmentation of indoor scenes more efficiently and restore more details of indoor structures. The segment precision and the segment recall of experimental results reach 70% on average. In addition, a great advantage of the MCGC method is that the speed of processing point clouds is very fast; it takes about 1.38 s to segment scene data of 1 million points. It significantly reduces the computation overhead of scene point cloud data and achieves real-time scene segmentation.

An Efficient Plane-Segmentation Method for Indoor Point Clouds Based on Countability of Saliency Directions

This paper proposes an efficient approach for the plane segmentation of indoor and corridor scenes. Specifically, the proposed method first uses voxels to pre-segment the scene and establishes the topological relationship between neighboring voxels. The voxel normal vectors are projected onto the surface of a Gaussian sphere based on the corresponding directions to achieve fast plane grouping using a variant of the K-means approach. To improve the segmentation integration, we propose releasing the points from the specified voxels and establishing second-order relationships between different primitives. We then introduce a global energy-optimization strategy that considers the unity and pairwise potentials while including high-order sequences to improve the over-segmentation problem. Three benchmark methods are introduced to evaluate the properties of the proposed approach by using the ISPRS benchmark datasets and self-collected in-house. The results of our experiments and the comparisons indicate that the proposed method can return reliable segmentation with precision over 72% even with the low-cost sensor, and provide the best performances in terms of the precision and recall rate compared to the benchmark methods.

Surface Extraction and Segmentation From 3-D Underwater Sub-Bottom Point Clouds Using Enhancement Filtering and Global Energy Optimization

Surface Extraction and Segmentation From 3-D Underwater Sub-Bottom Point Clouds Using Enhancement Filtering and Global Energy Optimization

Contrast-Aware Color Consistency Correction for Multiple Images

Color consistency optimization for multiple images is a challenging task in remote sensing and computer vision. To ensure that the visual quality of corrected images is satisfactory, not only should the color discrepancies between multiple images be invisible, but also the contrast of individual images should be visually appealing. Most color correction approaches focus on eliminating drastic color discrepancies, but ignore the problem of image contrast enhancement. Some color correction approaches even tend to degrade the image contrast to ensure that the image tones are consistent especially when the contrast of input images is low. To solve this problem, we present a contrast-aware color consistency correction approach in this paper. We attempt to eliminate the drastic color differences and enhance the contrast of input images simultaneously. We creatively integrate the problems of color consistency correction and image contrast enhancement into the same global energy optimization framework, and we also design a special cost function to minimize the color discrepancies and enhance the image contrast using the original color information. Thus, although the contrast of input images is low, our approach can still generate the corrected images with consistent tones and visually appealing contrast. At last, we select several challenging datasets to evaluate our approach. The experimental results visually and quantitatively demonstrate the effectiveness and superiority of the proposed contrast-aware color consistency correction approach. The results also demonstrate that our approach significantly outperforms the existing approaches, especially when the contrast of the input images is low.

Indoor interior segmentation with curved surfaces via global energy optimization

Most existing indoor interior segmentation methods typically focus on planar structures rather than curved structures. Random sample consensus-based methods perform curved surface segmentation via regularity model fitting but suffer from spurious model generation when noise and outliers are present due to the uncertainty in the random sampling. This paper formulates indoor interior segmentation by fitting representative models and matching each primary cell with corresponding model simultaneously. The models are fitted by a combination of points and supervoxels with adaptive resolutions instead of just points, guaranteeing the correctness of sampling on the same surface and avoiding spurious models. Cell-to-model matching is achieved by iterative refinement/clustering under the global energy optimization method, which ensures optimal overall segmentations. Experimental tests demonstrate the effectiveness of our method in dealing with both planar and nonplanar surfaces, resulting in performance metrics of approximately 0.75 for the structure F1-score and over 0.9 for edge precision and recall.

Global energy efficiency improvement of redundant hydraulic manipulator with dynamic programming

Open-loop controlled hydraulic manipulators are still dominating in construction sites and forest fields thanks to little reliance on the expertise of human operators in manipulator control, which however exist large inlet/outlet and other energy losses during working cycles. And the situation further deteriorates with the increasing number of joints. This paper discusses the problem of global energy optimization of three-degrees-of-freedom (3-DOF) hydraulic manipulator in case of plane motion where exists an exceeding DOF for motion redundancy. Different from conventional energy optimization methods applied to electric driven manipulators, the proposed global energy-optimized dynamic programming (DP) algorithm models the whole system at the hydraulic level, which contains the dynamic characteristics of pressure-flow coupling between cylinders. The energy consumption models of constant pressure (CP), load-sensing (LS) and electrohydraulic load-sensing (ELS) systems are built in terms of cost functions formulated in the proposed algorithm together with penalty function containing physical actuator constraints at position, velocity and acceleration level. The DP algorithm is tested in various cases, and the convergence is proved consistent. To highlight the effectiveness of the proposed algorithm, the gradient projection (GP) method and the actuator velocity minimum norm (MA) method are introduced as a contrast. The results of the numerical examples of end-effector paths using the mechanical-hydraulic coupling AMESim model demonstrate that the DP algorithm saves around 10.49% hydraulic energy consumption in the CP system while saving around 76.55% in the LS system. Experiments performed on a typical-structured hydraulic manipulator further validate the effectiveness of the proposed algorithm, with approximately 4.61% energy saving in the CP system and 29.89% in the LS system. The energy-saving ratio is even larger in the ELS system, with around 80.03% comparing the max value. These contribute to the energy optimization resolution of redundant hydraulic manipulators to a degree.

Efficient point cloud segmentation approach using energy optimization with geometric features for 3D scene understanding.

Efficient and quick extraction of unknown objects in cluttered 3D scenes plays a significant role in robotics tasks such as object search, grasping, and manipulation. This paper describes a geometric-based unsupervised approach for the segmentation of cluttered scenes into objects. The proposed method first over-segments the raw point clouds into supervoxels to provide a more natural representation of 3D point clouds and reduce the computational cost with a minimal loss of geometric information. Then the fully connected local area linkage graph is used to distinguish between planar and nonplanar adjacent patches. Then the initial segmentation is completed utilizing the geometric features and local surface convexities. After the initial segmentation, many subgraphs are generated, each of which represents an individual object or part of it. Finally, we use the plane extracted from the scene to refine the initial segmentation result under the framework of global energy optimization. Experiments on the Object Cluttered Indoor Dataset dataset indicate that the proposed method can outperform the representative segmentation algorithms in terms of weighted overlap and accuracy, while our method has good robustness and real-time performance.

Exergy analysis and dynamic control of chemical looping combustion for power generation system

Chemical looping combustion (CLC) is an efficient technology for power generation system (PGS) and CO2 capture. Regarding carbon deposition, this work presents a global energy optimization strategy by integrating CLC with PGS process (CLC-PGS) based on exergy analysis. First, the CLC-PGS system is simulated by using Aspen Plus software and its steady-state simulation results are used for global exergy analysis. Second, the carbon deposition phenomena resulting in insufficient contact between oxygen carrier (OC) and methane (CH4) and thus low conversion of CH4 is considered in the CLC-PGS optimization. OC circulation rate is increased to address carbon deposition problem and alleviate exergy destruction along with fuel reactor (FR) temperature, the ratio of CH4 to steam (XCTS), and the ratio of CH4 to OC (XCTO) optimized simultaneously by exergy analysis. Finally, the pressure and liquid level of drum in heat recover steam generator (HRSG) are selected as key variables by transfer entropy method for control scheme design. The dynamic behaviors of three alternative control schemes are compared by Aspen Dynamics software, with the control scheme II resulting the best according to the stability indicators in the face of ±10% disturbances.

In Search of Non-covalent Inhibitors of SARS-CoV-2 Main Protease: Computer Aided Drug Design Using Docking and Quantum Chemistry

Two stages virtual screening of a database containing several thousand low molecular weight organic compounds is performed with the goal to find inhibitors of SARS-CoV-2 main protease. Overall near 41000 different 3D molecular structures have been generated from the initial molecules taking into account several conformers of most molecules. At the first stage the classical SOL docking program is used to determine most promising candidates to become inhibitors. SOL employs the MMFF94 force field, the genetic algorithm (GA) of the global energy optimization, takes into account the desolvation effect arising upon protein-ligand binding and the internal stress energy of the ligand. Parameters of GA are selected to perform the meticulous global optimization, and for docking of one ligand several hours on one computing core are needed on the average. The main protease model is constructed on the base of the protein structure from the Protein Data Bank complex 6W63. More than 1000 ligands structures have been selected for further postprocessing. The SOL score values of these ligands are more negative than the threshold of –6.3 kcal/mol obtained for the native X77 ligand docking. Subsequent calculation of the protein-ligand binding enthalpy by the PM7 quantum-chemical semiempirical method with COSMO solvent model have narrowed down the number of best candidates. Finally, the diverse set of 20 most perspective candidates for the in vitro validation are selected.

Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based Regression

Point cloud segmentation for planar surface detection is a valid problem of automatic laser scans analysis. It is widely exploited for many industrial remote sensing tasks, such as LIDAR city scanning, creating inventories of buildings, or object reconstruction. Many current methods rely on robustly calculated covariance and centroid for plane model estimation or global energy optimization. This is coupled with point cloud division strategies, based on uniform or regular space subdivision. These approaches result in many redundant divisions, plane maladjustments caused by outliers, and excessive number of processing iterations. In this paper, a new robust method of point clouds segmentation, based on histogram-driven hierarchical space division, inspired by kd-tree is presented. The proposed partition method produces results with a smaller oversegmentation rate. Moreover, state-of-the-art partitions often lead to nodes of low cardinality, which results in the rejection of many points. In the proposed method, the point rejection rate was reduced. Point cloud subdivision is followed by resilient plane estimation, using Mahalanobis distance with respect to seven cardinal points. These points were established based on eigenvectors of the covariance matrix of the considered point cluster. The proposed method shows high robustness and yields good quality metrics, much faster than a FAST-MCD approach. The overall results indicate improvements in terms of plane precision, plane recall, under-, and the over- segmentation rate with respect to the reference benchmark methods. Plane precision for the S3DIS dataset increased on average by 2.6pp and plane recall- by 3pp. Both over- and under- segmentation rates fell by 3.2pp and 4.3pp.

HPM-TDP: An efficient hierarchical PatchMatch depth estimation approach using tree dynamic programming

Accurate and efficient estimation of the dense depth information from a pair of stereo images is a key step for many applications such as digital surface model production, 3D reconstruction and visualization, autonomous driving, and robotic navigation. Although great progress has been achieved in stereo matching over the past decade, the matching difficulties in poor and repetitive texture regions remain an issue. Aiming at solving the shortcomings of the current methods, this paper proposes HPM-TDP, which is an efficient hierarchical PatchMatch depth estimation approach that integrates a coarse-to-fine image pyramid strategy with a continuous Markov random field (MRF)-based global energy optimization framework, and minimizes the energy function by combining a hierarchical PatchMatch (HPM) framework and local α-expansion based tree dynamic programming (TDP). Firstly, the coarse-to-fine image pyramid strategy is integrated with the PatchMatch filter algorithm to quickly generate the hierarchical disparity plane prior for initializing each pixel’s disparity plane of the energy function optimization. Secondly, a multi-resolution cost aggregation strategy is adopted to boost the robustness of the matching cost function in the poor and repetitive texture areas. Finally, the HPM framework and local α-expansion based TDP are adopted to solve the non-submodular energy optimization problem, resulting in a globally optimized disparity plane map. Three benchmark datasets—the Middlebury 3.0, KITTI 2015, and Vaihingen datasets—were used to test the performance of HPM-TDP. The comprehensive experimental results demonstrate that HPM-TDP obtains a good performance on all datasets in terms of the (“Out-Noc”, “Avg-Noc”, “Out-All”, “Avg-All”) of (15.45%, 4.16px, 24.26%, 12.14px) and (5.46%, 1.20px, 6.55%, 1.54px) for Middlebury 3.0 and KITTI 2015 training datasets, and the (“Out-All”, “Avg-All”) of (26.32%, 4.04px) for Vaihingen dataset, respectively.

Stereoscopic image stitching with rectangular boundaries

This paper proposes a novel algorithm for stereoscopic image stitching, which aims to produce stereoscopic panoramas with rectangular boundaries. As a result, it provides wider field of view and better viewing experience for users. To achieve this, we formulate stereoscopic image stitching and boundary rectangling in a global optimization framework that simultaneously handles feature alignment, disparity consistency and boundary regularity. Given two (or more) stereoscopic images with overlapping content, each containing two views (for left and right eyes), we represent each view using a mesh and our algorithm contains three main steps: We first perform a global optimization to stitch all the left views and right views simultaneously, which ensures feature alignment and disparity consistency. Then, with the optimized vertices in each view, we extract the irregular boundary in the stereoscopic panorama, by performing polygon Boolean operations in left and right views, and construct the rectangular boundary constraints. Finally, through a global energy optimization, we warp left and right views according to feature alignment, disparity consistency and rectangular boundary constraints. To show the effectiveness of our method, we further extend our method to disparity adjustment and stereoscopic stitching with large horizon. Experimental results show that our method can produce visually pleasing stereoscopic panoramas without noticeable distortion or visual fatigue, thus resulting in satisfactory 3D viewing experience.

Disparity Refinement in Depth Discontinuity Using Robustly Matched Straight Lines for Digital Surface Model Generation

Dense image matching using remote sensing images is of particular importance for providing ground geometric data for object modeling and analysis. These methods normally operate in a rectified (epipolar) space producing disparity images that are correlated with the final three-dimensional geometry. However, the resulting digital surface models can be problematic on discontinuities of the terrain object, e.g., building edges, which largely limit their practical use for highly accurate object modeling. Existing works put forth efforts for dealing with this issue by defining better edge constraints and more global energy optimization, while intended to improve the disparity maps, it might be challenging to leverage the result of all the pixels through a single energy minimization, leading to either oversmoothed object boundaries or noisy surfaces. In this paper, we propose an intuitive method that integrates straight line primitives to enhance the disparity map. For each matched line, we perform a local discontinuity analysis and propose an intensity-based weighting method for a local plane fitting using iteratively solved weighted least squares adjustment, such that straightness of the object's edges (e.g., buildings) can be preserved, as the straight lines are detected and matched. Experiments on both aerial and satellite dataset show that the proposed method yields visually clear object edges and has achieved higher accuracy (2-3 pixels improvement) around edges than DSM generated from typical stereo matching result (i.e., from semiglobal matching).

Decentralized and Collaborative Scheduling Approach for Active Distribution Network with Multiple Virtual Power Plants

In order to build an active distribution system with multi virtual power plants (VPP), a decentralized two-stage stochastic dispatching model based on synchronous alternating direction multiplier method (SADMM) was proposed in this paper. Through the integration of distributed energy and large-scale electric vehicles (EV) in the distribution network by VPP group, coordinative complementarity, and global optimization were realized. On the premise of energy autonomy management of active distribution network (AND) and VPP, after ensuring the privacy of stakeholders, the power of tie-line was taken as decoupling variable based on SADMM. Furthermore, without the participation of central coordinators, the optimization models of VPPs and distribution networks were decoupled to achieve fully decentralized optimization. Aiming at minimizing their own operating costs, the VPPs aggregate distributed energy and large-scale EVs within their jurisdiction to interact with the upper distribution network. On the premise of keeping operation safe, the upper distribution network formulated the energy interaction plan with each VPP, and then, the global energy optimization management of the entire distribution system and the decentralized autonomy of each VPP were achieved. In order to improve the stochastic uncertainty of distributed renewable energy output, a two-stage stochastic optimization method including pre-scheduling stage and rescheduling stage was adopted. The pre-scheduling stage was used to arrange charging and discharging plans of EV agents and output plans of micro gas turbines. The rescheduling stage was used to adjust the spare resources of micro gas turbines to deal with the uncertainty of distributed wind and light. An example of active distribution system with multi-VPPs was constructed by using the improved IEEE 33-bus system, then the validity of the model was verified.