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In multi-label learning, feature selection is an essential preprocessing module, which can be exploited a more compact and precise representation of instances. Most of existing multi-label feature selection methods are either converted into multiple single-label feature selection methods or directly utilize half-baked label information, thus it is difficult for them to obtain a discriminative feature subset across multiple labels. To tackle this problem, we propose multi-label feature selection with local discriminant model and label correlations. First, for each instance, a local clique comprising this instance and its neighboring instances is constructed, and a local discriminant model for each local clique is integrated globally to evaluate the clustering performance of all instances. Second, in terms of clustering results, we explore high-order label correlations to reduce the impact of half-baked label information. Finally, we combine l2,1-norm regularization to design the objective function to achieve multi-label feature selection. Comprehensive experiments are conducted on twelve real-world multi-label data sets, and results demonstrate the effectiveness of the proposed method in comparison with several representative methods.

The Laplacian eigenmaps (LE) is one of the most commonly used nonlinear dimensionality reduction methods and aims to find a low-dimensional representation to preserve the topological relationship between sample points in the original data. However, the l2-norm based loss function makes LE unable to preserve the relationship in many cases. Additionally, the topological relationship does not represent the real intrinsic structure of data. For example, the overemphasis of the topological relationship by LE easily breaks the manifold structure into multiple local areas in the embedding space, which makes the spectral clustering analysis of multi-manifold data more difficult to carry out. To solve this problem, we propose the soft adaptive loss based LE (SALE). With the soft adaptive loss, SALE can adaptively emphasize the topological relationship between sample points and the clustering structure of data. The model is tested and validated on UCI, face and gene expression data sets, and compared with some state-of-the-art models. The experimental results show that the method is robust to noise.

In the literature, a lot of work focused on studying intermittent control problems via feedback control strategy. No study on the intermittent control problems via sliding mode control method has been reported so far. This paper studies the problem of fast synchronization between two complex dynamical networks via aperiodically intermittent control and sliding mode control. In order to achieve fast synchronization of complex dynamical networks by using aperiodically intermittent sliding mode controller, new differential inequalities are derived firstly. After that, some sufficient finite-time synchronization criteria and finite-time achieving slide mode surface are obtained based on finite-time stability theory, aperiodically intermittent sliding mode control technique and constructing Lyapunov function. Finally, an example is provided to verify the effectiveness of the proposed theoretical methods.

In this paper, the finite-time synchronization problem of complex dynamic networks with time delay is studied via aperiodically intermittent control. By compared with the existed results concerning aperiodically intermittent control, some new results are obtained to guarantee the synchronization of networks in a finite time. Especially, a new lemma is proposed to reduce the convergence time. In addition, based on aperiodically intermittent control scheme, the essential condition ensuring finite-time synchronization of dynamic networks is also obtained, and the convergence time is closely related to the topological structure of networks and the maximum ratio of the rest width to the aperiodic time span. Finally, a numerical example is provided to verify the validness of the proposed theoretical results.

Determining a systematic whole-building retrofit plan for envelope components and indoor appliances to achieve targets such as cost savings and policy compliance is a challenging task. To be specific, the systematic whole-building retrofit problem, when solved by an optimization approach, is highly complicated. It is sometimes even impossible to find a solution with given computation resources and algorithms. In addition, a costly comprehensive bottom-up audit is required to establish the parameters of the problem. This study presents two models to reduce the complexity of the systematic whole-building retrofit optimization problem. Firstly, the proposed models use the grouping concept to optimize the retrofit of subsystems in a building instead of individual components/appliances, which reduces the dimension of the problem effectively. Secondly, the models use so-called ‘notch test’ data, which are sampled and verified savings of an intervention, to eliminate the need for bottom-up energy audits. This further simplifies the retrofit optimization problem and reduces the retrofit cost. The models are based on our previous work and aim at energy savings maximization and payback period minimization, considering the green building policy and tax incentive initiatives. A case study shows that about 2530 MWh energy savings and an A rating from the energy performance certificate standard can be obtained with a payback period of 59 months, which verifies the feasibility and effectiveness of the models proposed.

To promote sustainable development and expedite the progress on moving to a green building sector, the government of South Africa has developed an energy performance certificate (EPC) standard for buildings. A building is required to obtain a certain rating from the EPC in order to comply with the country's green building policy. Therefore, finding optimal retrofit plans for existing buildings are essential given the high investments involved in the retrofit of buildings that do not currently comply with the policy. This paper presents an optimization model to help decision makers to identify the best combination of retrofit options for buildings to ensure policy compliance in the most cost-effective way. The model determines optimal retrofit plans for a whole building in a systematic manner, taking into account both the envelope components and the indoor facilities. A roof top PV system is utilized to reduce the usage of electricity produced from fossil fuels. The model breaks down the long-term investment into yearly short-term investments that are more attractive to investors. Tax incentive program available in the country is taken into account to offset the long payback period of the investment. Economic analysis is also built into the model to help decision makers to make informed decisions. The retrofit of an existing office building is taken as a case study. The results show that 761.6 MWh energy savings and an A rating from the EPC can be obtained with a payback period of 70 months, which demonstrates the effectiveness of the model developed.

The segmentation of a point cloud is one of the key technologies for three-dimensional reconstruction, and the segmentation from three-dimensional views can facilitate reverse engineering. In this paper, we propose a self-adaptive segmentation algorithm, which can address challenges related to the region-growing algorithm, such as inconsistent or excessive segmentation. Our algorithm consists of two main steps: automatic selection of seed points according to extracted features and segmentation of the points using an improved region-growing algorithm. The benefits of our approach are the ability to select seed points without user intervention and the reduction of the influence of noise. We demonstrate the robustness and effectiveness of our algorithm on different point cloud models and the results show that the segmentation accuracy rate achieves 96%.

Retrofitting existing buildings with energy-efficient facilities is an effective method to improve their energy efficiency, especially for old buildings. A multi-objective optimization model for building envelope retrofitting is presented. Envelope components including windows, external walls and roofs are considered to be retrofitted. Installation of a rooftop solar panel system is also taken into consideration in this study. Rooftop solar panels are modeled with their degradation and a maintenance scheme is studied for sustainability of energy and its long-term effect on the retrofitting plan. The purpose is to make the best use of financial investment to maximize energy savings and economic benefits. In particular, net present value, the payback period and energy savings are taken as the main performance indicators of the retrofitting plan. The multi-objective optimization problem is formulated as a non-linear integer programming problem and solved by a weighted sum method. Results of applying the designed retrofitting plan to a 50-year-old building consisting of 66 apartments demonstrated the effectiveness of the proposed model.

Like single-label learning, multi-label learning also suffers from the curse of dimensionality. Due to the existence of high-dimensional data, feature selection as a preprocessing tool always plays a key position in the multi-label learning. Although a variety of multi-label feature selection methods have been developed, they neglect to consider the redundancy of features, thereby degrading learning performance. To address this problem, we present a novel multi-label feature selection approach with uncorrelated regression and adaptive spectral graph. Specifically, we first construct a manifold framework with uncorrelated regression model to hunt for uncorrelated yet discriminative features, which also utilize the low-dimensional representation based on feature space to fit the label distribution. Then, a spectral graph term based on information entropy is incorporated into the manifold framework, so as to ensure the local geometric structure of data in subsequent learning process. Following the above principles, we design an objective function to achieve multi-label feature selection, and propose a detailed optimization method. Comprehensive experiments are conducted on multiple public multi-label data sets, the results show that our method is superior to other compared methods.

To reduce the fluctuation of renewable energy (RE) supply and improve the economic efficiency of the power grid, the energy local area network (ELAN), which is a subnetwork of the energy internet (EI), plays an important role in specific regions. Electric vehicles (EVs), as virtual energy storage (VES) in ELANs, are helpful to decrease the fluctuations of RE supply. However, how to use EVs in ELANs is a complex issue, considering the uncertainties of EVs’ charging demand, the forecast data errors of RE sources, etc. In this paper, a typical ELAN structure is established, taking into account RE sources, load response system, and a distributed energy storage (DES) system including EVs. A two-step optimization framework for ELAN scheduling problem is proposed. A global optimization model based on forecast data is built to maximize the income of ELAN, and an online local optimization model is introduced to minimize the correction cost utilizing prior knowledge. Finally, the proposed two-step optimization framework is applied to a series of real-world ELAN scheduling problems. The results show that DES system with EVs can reduce the volatility of RE supply evidently, and the proposed method is able to maximize the income of the ELAN efficiently.

The rise of skeletal skinning technology has provided great convenience for animators. At the same time, it improves the efficiency of animation production. However, the deformation resulting from this technology suffers from some undesirable effects, which require manual improvement. In this paper, we propose an approach addressing the problem of creating believable mesh-based skin deformation. In this approach, the skin is first deformed with a classic linear blend skinning approach, which usually lead to artifacts like the candy-wrapper effect or volume loss. Then we enforce the geometric constraints which displace the positions of the vertices to mimic the behavior of the skin and achieve effects like volume preservation. At last, we adopt the finite element method to handle large deformed elements which could accelerate the system’s convergence rate. This approach is easy to implement and has a high skinning efficiency without affecting the simulating effect.

Raw data of point cloud is often noisy and with\ud topological defects (such as holes), which cause problems including faulty connection and inaccurate structure. As\ud a result, the surface reconstruction of point cloud data is\ud a highly challenging problem. This work proposes a novel\ud method, which improves the surface quality compared with\ud existing methods. Our method combines both the local detailed\ud features and the global topological information during the\ud reconstruction process. To facilitate the feature refinement,\ud we first pre-process the point cloud data by relocating each\ud point, upsampling the point data, and optimizing normals to\ud enhance the features and geometric details. We then identify\ud the topological information by segmenting the geometry and\ud constructing curve skeletons for each part and guide the surface reconstruction with the skeletons by minimal user interaction. We demonstrate the effectiveness of our methods with various examples, where our reconstruction can fill out missing data and preserve sharp features.

Reducing energy consumption of buildings has become an effective way of mitigating carbon footprint because of the high energy intensity of the building sector. Retrofitting existing buildings to be more energy efficient is an integral part of an energy efficient building sector given the slow construction rate of new buildings. In this study, an optimization model for building envelope retrofit is presented to help decision makers to obtain an optimal retrofit plan with a given budget. The envelope components considered for retrofit are windows, walls and roof. Installation of a PV power supply system is also built into the optimization model in view of the rich solar resource in South Africa. An additional objective of this study is to obtain at least a grade D rating from the established energy performance certificate (EPC) program in South Africa to ensure the continued suitability of the building with respect of the EPC standard and green building policy. A case study of a public office building in South Africa is carried out to illustrate the effectiveness and benefits of the model presented.

This paper presents a novel approach for bas-relief generation and synthesis. In contrast to previous methods, we divide this problem into two parts: the selection of the best view and arrangement of the relief layout. Taking these into account, we incorporate the visual saliency and photographic composition rules into the bas-relief generation. Additionally, a nonlinear compression function is used to compress the models, and finally, we implement surface parameterization by directly manipulating the mesh triangles to generate curved surface bas-relief. We validate our approach through a variety of models. The results indicate that the proposed approach is effective to adapt different types of target surface with topology unchanged. Comparing with conventional methods, our approach is able to effectively produce bas-relief with a reasonable layout and distinct details.