Integrating energy storage systems (ESSs) into the railway power flow controller (RPFC) offers a promising path to enhance the interaction capability and connection compatibility between the railway power systems (RPSs) and the utility. Despite its technical advantages, full consideration must be given to the media-type & configuration of ESS, the rating of RPFC, and the control framework that can compatibly integrate the control & planning for cost-efficiency improvement. For this problem, a novel concept that takes the real-time control and off-line configuration approach in a unified framework is proposed for hybrid ESS-integrated RPFC (HESS-RPFC) in this paper, in which the comprehensive techno-economic performances of the whole system are taken into account. First, a unified analytical power regulation framework is established for HESS-RPFC, in which the functions of energy management (EM) and power quality control are seamlessly integrated. To realize EM, improve RPS’s grid-connection compatibility, and reduce the rating of RPFC, a double-layer power regulation strategy of HESS-RPFC is developed on the proposed framework. Subsequently, a lifetime net benefit-oriented optimization model is formulated and solved, through which, the optimal rating configuration of the whole system and HESS’s mode-triggering thresholds can be obtained. Finally, a comprehensive performance evaluation based on real-measured data is conducted, revealing that HESS-RPFC with the proposed method provides superior economic benefits over single-media ESS schemes, ensures satisfactory grid-connection compatibility for the RPS, and reduces the RPFC’s rating by nearly 46%. Additionally, the real-time execution feasibility of the proposal is demonstrated by hardware-in-the-loop tests.

RPFC, as a promising technical route for railway power regulation, its rating determinization, and the related flexible power regulation perplex designers and hinder large-scale applications, especially when the RBE is nonnegligible. In this article, a general power factors full-dimensionally hunted power distribution strategy (PFFDH-PDS), applicable for both offline design and real-time control, is proposed. Two key methods are developed: full-dimensionally hunting the power factors (PFs) under the developed mathematic framework, from which the global optimal compensating power and RPFC's rating can be worked out; and precisely partitioning two-phase loads on their PF-P and PF-PF distributing panels; in both methods, RBE is considered. By adopting PFFDH-PDS, RPFC's rating can be reduced by almost 60% compared to the conventional method, while the system has satisfactory grid-connection compatibility. Finally, the effectiveness and performance of the proposal are verified and conducted by real-measured data.

We pose the decompose-and-pack or DAP problem, which tightly combines shape decomposition and packing. While in general, DAP seeks to decompose an input shape into a small number of parts which can be efficiently packed, our focus is geared towards 3D printing. The goal is to optimally decompose-and-pack a 3D object into a printing volume to minimize support material, build time, and assembly cost. We present Dapper , a global optimization algorithm for the DAP problem which can be applied to both powder- and FDM-based 3D printing. The solution search is top-down and iterative. Starting with a coarse decomposition of the input shape into few initial parts, we progressively pack a pile in the printing volume, by iteratively docking parts, possibly while introducing cuts, onto the pile. Exploration of the search space is via a prioritized and bounded beam search , with breadth and depth pruning guided by local and global DAP objectives. A key feature of Dapper is that it works with pyramidal primitives, which are packing- and printing-friendly. Pyramidal shapes are also more general than boxes to reduce part counts, while still maintaining a suitable level of simplicity to facilitate DAP optimization. We demonstrate printing efficiency gains achieved by Dapper, compare to state-of-the-art alternatives, and show how fabrication criteria such as cut area and part size can be easily incorporated into our solution framework to produce more physically plausible fabrications.

We present an algorithm for hierarchical and layered analysis of irregular facades, seeking a high-level understanding of facade structures. By introducing layering into the analysis, we no longer view a facade as a flat structure, but allow it to be structurally separated into depth layers, enabling more compact and natural interpretations of building facades. Computationally, we perform a symmetry-driven search for an optimal hierarchical decomposition defined by split and layering operations applied to an input facade. The objective is symmetry maximization , i.e., to maximize the sum of symmetry of the substructures resulting from recursive decomposition. To this end, we propose a novel integral symmetry measure, which behaves well at both ends of the symmetry spectrum by accounting for all partial symmetries in a discrete structure. Our analysis results in a structural representation, which can be utilized for structural editing and exploration of building facades.

We present an algorithm for interactive structure-preserving retargeting of irregular 3D architecture models, offering the modeler an easy-to-use tool to quickly generate a variety of 3D models that resemble an input piece in its structural style. Working on a more global and structural level of the input, our technique allows and even encourages replication of its structural elements, while taking into account their semantics and expected geometric interrelations such as alignments and adjacency. The algorithm performs automatic replication and scaling of these elements while preserving their structures. Instead of formulating and solving a complex constrained optimization, we decompose the input model into a set of sequences, each of which is a 1D structure that is relatively straightforward to retarget. As the sequences are retargeted in turn, they progressively constrain the retargeting of the remaining sequences. We demonstrate interactivity and variability of results from our retargeting algorithm using many examples modeled after real-world architectures exhibiting various forms of irregularity.

At first, the paper introduces the most prevailing 3D model benchmark, the Princeton shape benchmark. Deficiencies emerged in the benchmark are then discussed in depth, which are concluded as: 1) models belonging to the same category are not exactly similar according to their shapes, and 2) category similarity is totally ignored. To overcome those shortcomings, the paper proposes a new model classification method, based on which a novel retrieval performance metric, GSSS (get score from similarity sequence), is designed and discussed. Experimental results have shown that GSSS is better than the precision-recall benchmark on most occasions.