Global Energy Optimization Research Articles

RECONSTRUCTION OF 3D MODELS FROM POINT CLOUDS WITH HYBRID REPRESENTATION

Abstract. The three-dimensional (3D) reconstruction of urban buildings from point clouds has long been an active topic in applications related to human activities. However, due to the structures significantly differ in terms of complexity, the task of 3D reconstruction remains a challenging issue especially for the freeform surfaces. In this paper, we present a new reconstruction algorithm which allows the 3D-models of building as a combination of regular structures and irregular surfaces, where the regular structures are parameterized plane primitives and the irregular surfaces are expressed as meshes. The extraction of irregular surfaces starts with an over-segmented method for the unstructured point data, a region growing approach based the adjacent graph of super-voxels is then applied to collapse these super-voxels, and the freeform surfaces can be clustered from the voxels filtered by a thickness threshold. To achieve these regular planar primitives, the remaining voxels with a larger flatness will be further divided into multiscale super-voxels as basic units, and the final segmented planes are enriched and refined in a mutually reinforcing manner under the framework of a global energy optimization. We have implemented the proposed algorithms and mainly tested on two point clouds that differ in point density and urban characteristic, and experimental results on complex building structures illustrated the efficacy of the proposed framework.

An efficient global energy optimization approach for robust 3D plane segmentation of point clouds

Automatic 3D plane segmentation is necessary for many applications including point cloud registration, building information model (BIM) reconstruction, simultaneous localization and mapping (SLAM), and point cloud compression. However, most of the existing 3D plane segmentation methods still suffer from low precision and recall, and inaccurate and incomplete boundaries, especially for low-quality point clouds collected by RGB-D sensors. To overcome these challenges, this paper formulates the plane segmentation problem as a global energy optimization because it is robust to high levels of noise and clutter. First, the proposed method divides the raw point cloud into multiscale supervoxels, and considers planar supervoxels and individual points corresponding to nonplanar supervoxels as basic units. Then, an efficient hybrid region growing algorithm is utilized to generate initial plane set by incrementally merging adjacent basic units with similar features. Next, the initial plane set is further enriched and refined in a mutually reinforcing manner under the framework of global energy optimization. Finally, the performances of the proposed method are evaluated with respect to six metrics (i.e., plane precision, plane recall, under-segmentation rate, over-segmentation rate, boundary precision, and boundary recall) on two benchmark datasets. Comprehensive experiments demonstrate that the proposed method obtained good performances both in high-quality TLS point clouds (i.e., SEMANTIC3D.NET dataset) and low-quality RGB-D point clouds (i.e., S3DIS dataset) with six metrics of (94.2%, 95.1%, 2.9%, 3.8%, 93.6%, 94.1%) and (90.4%, 91.4%, 8.2%, 7.6%, 90.8%, 91.7%) respectively.

Energy and Delay Optimization of Heterogeneous Multicore Wireless Multimedia Sensor Nodes by Adaptive Genetic‐Simulated Annealing Algorithm

Energy efficiency and delay optimization are significant for the proliferation of wireless multimedia sensor network (WMSN). In this article, an energy‐efficient, delay‐efficient, hardware and software cooptimization platform is researched to minimize the energy cost while guaranteeing the deadline of the real‐time WMSN tasks. First, a multicore reconfigurable WMSN hardware platform is designed and implemented. This platform uses both the heterogeneous multicore architecture and the dynamic voltage and frequency scaling (DVFS) technique. By this means, the nodes can adjust the hardware characteristics dynamically in terms of the software run‐time contexts. Consequently, the software can be executed more efficiently with less energy cost and shorter execution time. Then, based on this hardware platform, an energy and delay multiobjective optimization algorithm and a DVFS adaption algorithm are investigated. These algorithms aim to search out the global energy optimization solution within the acceptable calculation time and strip the time redundancy in the task executing process. Thus, the energy efficiency of the WMSN node can be improved significantly even under strict constraint of the execution time. Simulation and real‐world experiments proved that the proposed approaches can decrease the energy cost by more than 29% compared to the traditional single‐core WMSN node. Moreover, the node can react quickly to the time‐sensitive events.

Proteome-Wide Modeling of Transmembrane Alpha-Helical Homodimers by TMDOCK

A novel energy-based method, TMDOCK, has been developed for ab initio modeling of 3D structures of parallel homodimers in membranes. Instead of exploring the entire conformational space, the method employs a fast template-driven global energy optimization procedure that combines the knowledge of geometrically feasible packing arrangements and local energy minimization with a novel force field. Ranking and selection of optimal dimerization modes is based on the calculated free energy of α-helix association (ΔGassoc) which includes van der Waals, hydrogen bonding and dipole-dipole interactions between helices, side chain conformational entropy, and solvation energy in the anisotropic lipid environment. The fast version of the TMDOCK method is available through the web server (http://membranome.org/tm_server.php).TMDOCK successfully reproduced 26 experimental dimeric structures formed by transmembrane (TM) α-helices of 21 single-pass membrane proteins (including 4 mutants) with Cα atom r.m.s.d. from 1.0 to 3.3 A. Additional testing demonstrated consistency of calculated dimer structures with published TOXCAT, mutagenesis, or other experimental data for more than 80 bitopic proteins. Subsequent application of the method to 6041 bitopic proteins from six proteomes included in the Membranome database (membranome.org) shows that ∼70% of the proteins tend to form stable dimers with ΔGassoc<-3 kcal/mol. Results of modeling indicate three different types of dimerization behavior of TM α-helices. A unique dimerization mode is usually observed for TM domains carrying a single GxxxG-like motif, interhelical disulfide bridges, or polar residues in the middle of a helix. Proteins with several GxxxG-like motifs usually have a few dimerization modes with similar energies. Proteins with Phe-rich sequences often show multiple helix association modes. The preferred modes of dimerization are frequently evolutionarily conserved (e.g. in cadherin, HLA histocompatibility antigen, receptor tyrosine kinase, and cytochrome P450 families), but they can also change even after a single mutation.

Global Energy Minimization and Optimization of Multi-Target Tracking

The recent multiple target tracking methods aim to obtain the best possible number of trajectories within the time frame, and few constraints have been set to handle the wide area of trajectories by discrete mapping. In this novel approach of multi target tracking, energy terms are formulated to attain the global optimization which includes the entire representation of the issues such as target tracking, operational representation, collision handling and trajectory processing. Furthermore, two optimization strategies such as the gradient descent which is performed on multiple feature space to obtain local minima of a density function from the given sample of data and gradient ascent which is carried out to achieve a likelihood matching of the target and used to handle the partial evidence of the image, and also uncertainty of the various targets are minimized. The experimentation is performed on the openly available dataset and the mean target tracking accuracy and precision is studied to validate the proposed tracking.

Comparative analysis of multiple target tracking methods

Many applications such as intelligent transportation, video surveillance, robotics of computer vision mainly depend on task of multiple object tracking. It includes the process of detection, classifications and tracking. The main focus of the study is to develop an efficient and effective multiple target tracking methods to solve the issues of illumination changes, occlusions and affinity matching. Accordingly, the various multiple target tracking methods are tested and evaluated using the metrics on publicly available datasets from which it is obvious that the outcome of the global energy minimization and optimization techniques is comparatively better than any other existing techniques in all aspects. This comparative study work will also help in better understanding of the problem, knowledge of the methods and experimental evaluation skill for further research works.

Optimal Electric and Heat Energy Management of Multi-Microgrids with Sequentially-Coordinated Operations

We propose an optimal electric and heat energy management for a cooperative multi-microgrid community. The sequentially-coordinated operation for heat energy is proposed in order to distribute the computational burden as an extension of “Optimal Energy Management of Multi-Microgrids with Sequentially Coordinated Operations” and is following the sequentially-coordinated operations for electric energy in it. This sequentially-coordinated operation for heat energy is mathematically modeled and how to obtain the global heat energy optimization solution in the cooperative multi-microgrid community is presented. The global heat energy optimization is achieved for the cooperative community by adjusting the combined electric and heat energy production amounts of combined heat and power (CHP) generators and the heat energy production amount of heat only boilers (HOBs) which satisfy all heat loads, as well as optimize the external electric energy trading in order to minimize the unnecessary cost from the external electric trading, and/or maximize the profit from the external electric trading. To validate the proposed mathematical energy management models, a simulation study is also conducted.

Global image completion with joint sparse patch selection and optimal seam synthesis

This paper presented a global exemplar-based image completion method for filling large missing or unwanted regions in an image. Based on three proposed completion rules, image completion problem is formulated as a global discrete optimization problem with a well-defined energy function. The energy function can evaluate image consistency globally and is minimized with an expectation-maximization (EM) like algorithm, which considers patch matching and patch synthesis in a unified way. In the algorithm, M step and E step are achieved by sparse patch subspace searching and optimal seam synthesis respectively. The patch subspace is learned with the statistics of geometric transformation relationships of similar patches. Moreover, E step combines image patch synthesis and coherent correction simultaneously. We analyzed the proposed global energy function and optimization method in theory. Simulation comparisons with other state-of-the-art methods show the superiority of our proposed method in ensuring global coherent and avoiding image blurring.

3D OCTREE BASED WATERTIGHT MESH GENERATION FROM UBIQUITOUS DATA

Abstract. Despite of the popularity of Delauney structure for mesh generation, octree based approaches remain an interesting solution for a first step surface reconstruction. In this paper, we propose a generic framework for a octree cell based mesh generation. Its input is a set of Lidar-based 3D measurements or other inputs which are formulated as a set of mass functions that characterize the level of confidence on the occupancy of each octree’s leaf. The output is a binary segmentation of the space between occupied and empty areas by taking into account the uncertainty of data. To this end, the problem is then reduced to a global energy optimization framework efficiently optimized with a min-cut approach. We use the approach for producing a large scale surface reconstruction algorithm by merging data from ubiquitous sources like airborne, terrestrial Lidar data, occupancy map and extra cues. Once the surface is computed, a solution is proposed for texturing the mesh.

Optimal Energy Management of Multi-Microgrids with Sequentially Coordinated Operations

We propose an optimal electric energy management of a cooperative multi-microgrid community with sequentially coordinated operations. The sequentially coordinated operations are suggested to distribute computational burden and yet to make the optimal 24 energy management of multi-microgrids possible. The sequential operations are mathematically modeled to find the optimal operation conditions and illustrated with physical interpretation of how to achieve optimal energy management in the cooperative multi-microgrid community. This global electric energy optimization of the cooperative community is realized by the ancillary internal trading between the microgrids in the cooperative community which reduces the extra cost from unnecessary external trading by adjusting the electric energy production amounts of combined heat and power (CHP) generators and amounts of both internal and external electric energy trading of the cooperative community. A simulation study is also conducted to validate the proposed mathematical energy management models.

Interactive Cosegmentation Using Global and Local Energy Optimization.

We propose a novel interactive cosegmentation method using global and local energy optimization. The global energy includes two terms: 1) the global scribbled energy and 2) the interimage energy. The first one utilizes the user scribbles to build the Gaussian mixture model and improve the cosegmentation performance. The second one is a global constraint, which attempts to match the histograms of common objects. To minimize the local energy, we apply the spline regression to learn the smoothness in a local neighborhood. This energy optimization can be converted into a constrained quadratic programming problem. To reduce the computational complexity, we propose an iterative optimization algorithm to decompose this optimization problem into several subproblems. The experimental results show that our method outperforms the state-of-the-art unsupervised cosegmentation and interactive cosegmentation methods on the iCoseg and MSRC benchmark data sets.

Interactive image recoloring by combining global and local optimization

We propose a novel interactive image recoloring method by combining global and local optimization. Our approach assumes that each pixel is a linear transform of its neighbors which can be in spatial or feature space. Corresponding, a new framework for combining global and local energy optimization is designed and derived. By taking advantage of global and local color propagation, our approach requires only a few user scribbles to produce the high-quality results. We show various experimental results and comparisons on image recoloring. Compared with the state-of-the-art methods, our approach produces higher-quality results with only a small amount of user interaction than those only consider local propagation or global propagation approaches.

Fast and Memory-Efficient Topological Denoising of 2D and 3D Scalar Fields.

Data acquisition, numerical inaccuracies, and sampling often introduce noise in measurements and simulations. Removing this noise is often necessary for efficient analysis and visualization of this data, yet many denoising techniques change the minima and maxima of a scalar field. For example, the extrema can appear or disappear, spatially move, and change their value. This can lead to wrong interpretations of the data, e.g., when the maximum temperature over an area is falsely reported being a few degrees cooler because the denoising method is unaware of these features. Recently, a topological denoising technique based on a global energy optimization was proposed, which allows the topology-controlled denoising of 2D scalar fields. While this method preserves the minima and maxima, it is constrained by the size of the data. We extend this work to large 2D data and medium-sized 3D data by introducing a novel domain decomposition approach. It allows processing small patches of the domain independently while still avoiding the introduction of new critical points. Furthermore, we propose an iterative refinement of the solution, which decreases the optimization energy compared to the previous approach and therefore gives smoother results that are closer to the input. We illustrate our technique on synthetic and real-world 2D and 3D data sets that highlight potential applications.

A hybrid dynamic programming-rule based algorithm for real-time energy optimization of plug-in hybrid electric bus

The optimization of the control strategy of a plug-in hybrid electric bus (PHEB) for the repeatedly driven bus route is a key technique to improve the fuel economy. The widely used rule-based (RB) control strategy is lacking in the global optimization property, while the global optimization algorithms have an unacceptable computation complexity for real-time application. Therefore, a novel hybrid dynamic programming-rule based (DPRB) algorithm is brought forward to solve the global energy optimization problem in a real-time controller of PHEB. Firstly, a control grid is built up for a given typical city bus route, according to the station locations and discrete levels of battery state of charge (SOC). Moreover, the decision variables for the energy optimization at each point of the control grid might be deduced from an off-line dynamic programming (DP) with the historical running information of the driving cycle. Meanwhile, the genetic algorithm (GA) is adopted to replace the quantization process of DP permissible control set to reduce the computation burden. Secondly, with the optimized decision variables as control parameters according to the position and battery SOC of a PHEB, a RB control is used as an implementable controller for the energy management. Simulation results demonstrate that the proposed DPRB might distribute electric energy more reasonably throughout the bus route, compared with the optimized RB. The proposed hybrid algorithm might give a practicable solution, which is a tradeoff between the applicability of RB and the global optimization property of DP.

A global energy optimization framework for 2.1D sketch extraction from monocular images

The 2.1D sketch is a layered image representation, which assigns a partial depth ordering of over-segmented regions in a monocular image. This paper presents a global optimization framework for inferring the 2.1D sketch from a monocular image. Our method only uses over-segmented image regions (i.e., superpixels) as input, without any information of objects in the image, since (1) segmenting objects in images is a difficult problem on its own and (2) the objective of our proposed method is to be generic as an initial module useful for downstream high-level vision tasks. This paper formulates the inference of the 2.1D sketch using a global energy optimization framework. The proposed energy function consists of two components: (1) one is defined based on the local partial ordering relations (i.e., figure-ground) between two adjacent over-segmented regions, which captures the marginal information of the global partial depth ordering and (2) the other is defined based on the same depth layer relations among all the over-segmented regions, which groups regions of the same object to account for the over-segmentation issues. A hybrid evolution algorithm is utilized to minimize the global energy function efficiently. In experiments, we evaluated our method on a test data set containing 100 diverse real images from Berkeley segmentation data set (BSDS500) with the annotated ground truth. Experimental results show that our method can infer the 2.1D sketch with high accuracy.

Optimal Velocity Control for a Battery Electric Vehicle Driven by Permanent Magnet Synchronous Motors

The permanent magnet synchronous motor (PMSM) has high efficiency and high torque density. Field oriented control (FOC) is usually used in the motor to achieve maximum efficiency control. In the electric vehicle (EV) application, the PMSM efficiency model, combined with the EV and road load system model, is used to study the optimal energy-saving control strategy, which is significant for the economic operation of EVs. With the help of GPS, IMU, and other information technologies, the road conditions can be measured in advance. Based on this information, the optimal velocity of the EV driven by PMSM can be obtained through the analytical algorithm according to the efficiency model of PMSM and the vehicle dynamic model in simple road conditions. In complex road conditions, considering the dynamic characteristics, the economic operating velocity trajectory of the EV can be obtained through the dynamic programming (DP) algorithm. Simulation and experimental results show that the minimum energy consumption and global energy optimization can be achieved when the EV operates in the economic operation area.

Optimal control of the transient emissions and the fuel efficiency of a diesel hybrid electric vehicle

As an important technical innovation which has emerged in the automotive industry, powertrain hybridization has been considered as one of the most effective ways for addressing transportation energy consumption and environmental concerns. However, owing to the complex physical and chemical nature of the engine combustion process, and the dynamic interactions between the engine and the alternative power source, it is difficult to optimize simultaneously and precisely both the engine fuel consumption and the emissions, especially during transient operations. Targeted at achieving both global energy optimization and reduction in the transient emissions, a control-oriented diesel engine emission model is first investigated and identified using a large number of experimental data. On this basis, a two-mode hybrid energy management strategy is proposed. In the ‘fuel-efficiency-improving’ mode, a dynamic programming algorithm is used to seek global optimization of the fuel economy and to ensure sustainability of the battery’s state of charge over any given driving cycle while, in the ‘emission-reducing’ mode, the management strategy utilizes a linear quadratic regulator to optimize locally the surging emissions due to undesired engine torque transients. At the end of each emission-reducing mode, the locally optimized engine operation is driven back to match the globally optimized trajectory, i.e. the initial states of the next fuel-efficiency-improving mode. This seamless integration of the two modes will realize a reduction in the local transient emissions without losing optimality of the global fuel efficiency and sustainability of the battery’s state of charge. Experimental results demonstrate that the proposed modeling and control strategy can considerably reduce the local soot emissions of a power-split diesel hybrid electric vehicle but can still maintain a high fuel efficiency and a high state of charge of the battery.

An advanced energy management framework to promote energy awareness

Abstract Increasing energy costs, new environmental legislation, and concerns over energy security are driving efforts to increase industrial energy efficiency across the European Union and the world. Manufacturers are keen to identify the most cost-effective techniques to increase energy efficiency in their factories. To achieve the desired efficiency improvements, energy use should be measured in more detail and in real-time, to derive an awareness of the energy use patterns of every part of the manufacturing system. In this paper, we propose a framework for energy monitoring and management in the factory. This will allow decision support systems and enterprise services to take into consideration the energy used by each individual productive asset and related energy using processes, to facilitate both global and local energy optimization. The proposed framework incorporates standards for energy data exchange, on-line energy data analysis, performance measurement and display of energy usage.

GalaxyDock: Protein–Ligand Docking with Flexible Protein Side-chains

An important issue in developing protein-ligand docking methods is how to incorporate receptor flexibility. Consideration of receptor flexibility using an ensemble of precompiled receptor conformations or by employing an effectively enlarged binding pocket has been reported to be useful. However, direct consideration of receptor flexibility during energy optimization of the docked conformation has been less popular because of the large increase in computational complexity. In this paper, we present a new docking program called GalaxyDock that accounts for the flexibility of preselected receptor side-chains by global optimization of an AutoDock-based energy function trained for flexible side-chain docking. This method was tested on 3 sets of protein-ligand complexes (HIV-PR, LXRβ, cAPK) and a diverse set of 16 proteins that involve side-chain conformational changes upon ligand binding. The cross-docking tests show that the performance of GalaxyDock is higher or comparable to previous flexible docking methods tested on the same sets, increasing the binding conformation prediction accuracy by 10%-60% compared to rigid-receptor docking. This encouraging result suggests that this powerful global energy optimization method may be further extended to incorporate larger magnitudes of receptor flexibility in the future. The program is available at http://galaxy.seoklab.org/softwares/galaxydock.html .

Exemplar-Based Image Inpainting Using Multiscale Graph Cuts

We present a novel formulation of exemplar-based inpainting as a global energy optimization problem, written in terms of the offset map. The proposed energy function combines a data attachment term that ensures the continuity of reconstruction at the boundary of the inpainting domain with a smoothness term that ensures a visually coherent reconstruction inside the hole. This formulation is adapted to obtain a global minimum using the graph cuts algorithm. To reduce the computational complexity, we propose an efficient multiscale graph cuts algorithm. To compensate the loss of information at low resolution levels, we use a feature representation computed at the original image resolution. This permits alleviation of the ambiguity induced by comparing only color information when the image is represented at low resolution levels. Our experiments show how well the proposed algorithm performs compared with other recent algorithms.