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Reconstructing 3D face from sparse points is an ill-posed problem. While there already exits available solutions addressing this problem, to our knowledge, we propose a better-performed approach which can robustly reconstruct fine 3D face shape. Our method includes two modules: face model establishment based on probabilistic principal component analysis (PPCA) trained in an unsupervised manner to learn transformation between landmarks and point cloud in their low-dimensional representation, and 3D face reconstruction based on learned relation between them to reconstruct fine face shape. Overall, our method considers the probability of face shape and learns more useful information of 3D face shape. We compare our method with 3 typical and state-of-the-art methods on 2 datasets and the effectiveness of our method is demonstrated generally. Further experiments on datasets with noise of different intensities show the stability of our method.

Deep exploration of user-side flexibility resources is crucial for large-scale renewable energy consumption. This paper proposed a typical integrated energy system (IES) that comprehensively includes wind power, photovoltaic, thermal power, combined heat and power, hybrid energy storage, and flexible load and constructed the system’s unified power flow model based on the heat current method. On this basis, the regulation capabilities of different typical industrial and residential flexible loads were considered the symmetrical source-type load, which can transfer load and align user demand with the peaks and valleys of renewable energy generation, thus achieving power-energy decoupling. This contributes effectively to renewable energy accommodation capacity when the total electrical energy consumption remains constant. In both typical industrial and residential load scenarios, flexible load reduces integrated costs, increases renewable energy consumption, lowers peak thermal power generation, and decreases the requirement for a battery energy storage system (BESS). Besides, on typical industrial and residential load days, smoothing thermal power generation necessitates 12% and 18% flexible load, respectively, while replacing BESS requires 18% and 23% flexible load, respectively. Therefore, we can obtain the feasible operation ranges of symmetrical source-type load and provide suggestions for configuration capacity design of demand response in integrated energy systems.

3D non-rigid shape similarity is a meaningful and challenging task in deformable shape analysis. In this paper, we present a 3D non-rigid shape similarity measure framework based on Laplace-Beltrami operator which achieves the state-of-the-art performance in shape analysis tasks. The presented framework is used to measure 3D non-rigid shape similarity by calculating the Fréchet distance between the shape spectral distances distribution curves extracting geometry and topology information of shapes. Here, the wave diffusion distance within shape spectral distances is selected because it can describe the shape with high accuracy and does not depend on the time parameter. In addition, our framework is more flexible and computationally efficient: it can be generalized to any distance distribution curves and different distances between the shape distances distribution curves. Experiment results show that the proposed framework can measure 3D non-rigid shape similarity accurately and robustly on benchmarks and have good performance in 3D non-rigid shape retrieval.

We present an effective spectral matching method based on a shape association graph for finding region correspondences between two cel animation keyframes. We formulate the correspondence problem as an adapted quadratic assignment problem, which comprehensively considers both the intrinsic geometric and topology of regions to find the globally optimal correspondence. To simultaneously represent the geometric and topological similarities between regions, we propose a shape association graph (SAG), whose node attributes indicate the geometric distance between regions, and whose edge attributes indicate the topological distance between combined region pairs. We convert topological distance to geometric distance between geometric objects with topological features of the pairs, and introduce Kendall shape space to calculate the intrinsic geometric distance. By utilizing the spectral properties of the affinity matrix induced by the SAG, our approach can efficiently extract globally optimal region correspondences, even if shapes have inconsistent topology and severe deformation. It is also robust to shapes undergoing similarity transformations, and compatible with parallel computing techniques.

Recently, human motion prediction has gained significant attention and achieved notable success. However, current methods primarily rely on training and testing with ideal datasets, overlooking the impact of variations in the viewing distance and viewing angle, which are commonly encountered in practical scenarios. In this study, we address the issue of model invariance by ensuring robust performance despite variations in view distances and angles. To achieve this, we employed Riemannian geometry methods to constrain the learning process of neural networks, enabling the prediction of invariances using a simple network. Furthermore, this enhances the application of motion prediction in various scenarios. Our framework uses Riemannian geometry to encode motion into a novel motion space to achieve prediction with an invariant viewing distance and angle using a simple network. Specifically, the specified path transport square-root velocity function is proposed to aid in removing the view-angle equivalence class and encode motion sequences into a flattened space. Motion coding by the geometry method linearizes the optimization problem in a non-flattened space and effectively extracts motion information, allowing the proposed method to achieve competitive performance using a simple network. Experimental results on Human 3.6M and CMU MoCap demonstrate that the proposed framework has competitive performance and invariance to the viewing distance and viewing angle.

Integrated modeling and operation optimization of building energy systems is significant for improving the energy utilization efficiency and reducing carbon emission. This paper introduces the standardized thermal resistance to construct an overall heat current model of the building cooling system with coupled heat transfer, mass transfer, and energy conversion processes. Based on the heat current model, we derive the holistic thermal energy transfer and conversion constraints on the system level and reduce the intermediate parameters of the system model. Moreover, the genetic algorithm is introduced to optimize the system operation conditions under the given system structure parameters. The optimization results provide the optimal mass flow distribution of cooling water, return water, and ambient air and meanwhile show that the compressor power consumption can reach 76.5% of the total system power consumption. The change of user behavior by raising the room temperature to 4°C can reduce the total system power consumption by 20%. The results are in line with the theoretical reality and prove the feasibility and effectiveness of the method proposed in this paper, which provides a practical reference for the energy‐saving operation of the building cooling system. This article focuses on building cooling systems and extends the standardized thermal resistance model to an integrated model of building cooling systems that includes coupling of heat and mass transfer and work processes. Genetic algorithms are used to optimize the operation of the system under all operating conditions.

Various heating disturbances and faults in the heating network are necessary to be controlled and handled by some intelligent heating strategies with the increasing complexity of the heating network. This paper constructed the dynamic modeling of the heating system using standard thermal resistance and obtained a dynamic heat current model of the heating system. On this basis, we analyzed the heat transfer performance of the heating system. Five disturbances are selected, including the behavior of users, indoor heat source, heat exchanger heat transfer performance deterioration, pipe blockage, and pipe leakage. The effects of different disturbances on the overall system and user side water supply temperature were obtained by establishing a dynamic model for segmented heating pipelines. Feasible control methods for the heating network and load side are sorted out, mainly changing the water supply's temperature and the water supply's flow rate to reduce the water supply's fluctuation. Five specific control strategies are proposed for five types of disturbances. Comparing the case without control and the case with control, the results show that the fluctuation of water supply temperature is significantly reduced, and the control strategies can reduce the impact of disturbances on the heat network system and customers and improve the comfort of customers in the presence of disturbances. under the disturbance of pipeline leakage, the method proposed in this article reduces the temperature fluctuation amplitude by 75% and the fluctuation duration by 60%. Proposed a dynamic thermal power flow model using the heat current method and analyzed different types of disturbances, and improved the stability of system operation by reducing the amplitude of temperature fluctuations and the duration of disturbances combining prediction methods.

Efficient and reliable utilization of renewable energy at the user's end is the key to achieving a low‐carbon life. This paper proposed a new distributed energy system around the comprehensive utilization of solar energy by integrating solid oxide fuel cell (SOFC), energy storage equipment, photovoltaic thermal (PVT) collector, and heat pump. By integrating the use of SOFC and PVT, we can further minimize reliance on fossil fuels, while employing the coupling of PVT and heat pump effectively mitigates the inherent challenges of solar energy's variability and intermittency, all while enhancing overall system efficiency. On this basis, we apply the heat current method to construct a cross‐scale heat current model of the components and the system by considering the energy transfer, conversion, and storage characteristics of the system. By employing this model, we simulate the system's operation throughout an entire typical day, assess the COP enhancement of the PVT‐coupled heat pump system, analyze the influence of diverse operating conditions on daily system performance, and evaluate the economy of the energy storage devices in the system. Proposal for a new distributed energy system by integrating SOFC, energy storage equipment, PVT and heat pump. The authors applied the heat current method to construct a cross‐scale model of the components and the system by considering the energy transfer and conversion of the system. They simulated and analyzed the system performance under various energy demands.

Chinese calligraphy is a charismatic ancient art form with high artistic value in Chinese culture. Virtual calligraphy learning system is a research hotspot in recent years. In such system, a judging mechanism for user’s practice result is quite important. Sometimes, user’s handwritten character is not that standard, the size and position are not fixed, and the whole character may be even askew, which brings difficulty for its evaluation. In this paper, we propose an approach by using DBSCs (disk B-spline curves) vectorization and ICP (iterative closest point) algorithm, which cannot only evaluate a calligraphic character without knowing what it is, but also deal with the above problems commendably. Firstly we find the promising candidate characters from the database according to the angular difference relations as quickly as possible. Then we check these vectorized candidates by using ICP algorithm based upon the skeleton, hence finding out the best matching character. Finally a comprehensive evaluation involving global (the whole character) and local (strokes) similarities is implemented, and a final composited evaluation score can be worked out.

The physically based deformable curve models are widely used to simulate thin one-dimensional objects in computer graphics, interactive simulation, and surgery simulation. These models consider objects to be rods described by an adapted frame curve that contains the rod’s centerline as well as the orthonormal material frame of each point on the centerline. However, they pose challenges including fine discretization, redundancy in modeling slender rods, and maintaining accuracy and stability. In this paper, we propose a novel physically based deformable B-spline curve model that regards curves as rods consisting of parallel fibers and derive elastic potential energy only from curves’ representations. Therefore, our model does not take rotation-based adapted frames into consideration and reduces degree of freedom. Our model divides the curves into infinitesimal elements in parameter space and derives the analytical relationship between elastic potential energy function and curves’ representations through the change of total length of infinitesimal elements’ fibers. Our model can support material attributes in the real world and maintain the reality and stability of the solution. We employ isogeometric analysis to solve the dynamic equations derived from our deformable model as isogeometric analysis is suitable to solve the dynamic equations of parametric models. We compare the scenarios in the real world, our model’s simulation results, and other model’s results to demonstrate the reality of our models. The results are in line with expectation. We design several examples to demonstrate our models’ applications.