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The generation of mathematical models for the analysis of buildings with multiple thermal zones is a large and complex task. Furthermore, the order and complexity of the dynamical model are increased by the number of included thermal zones. To overcome this problem, this paper presents an algorithm to define the mathematical model automatically, using the geometric and physics parameters as inputs. Additionally, the spatial position of each thermal zone must be recorded in an arrangement called a contact matrix. The algorithm for modeling systems with multiple thermal zones is the main contribution of this work. This algorithm is presented in pseudocode format and as an annex, an implementation in MATLAB software. One of the advantages of this methodology is that it allows us to work with parallelepipeds and not necessarily cubic thermal zones. The algorithm allows us to generate mathematical models with symbolic variables, starting from the knowledge of how many thermal zones compose the system and its geometric organization. This information must be organized in a matrix arrangement called a contact matrix. Different arrays of thermal zones were constructed with wooden boxes to verify the functionality of the models generated with the algorithm. Each case provided information that allowed us to adjust the mathematical models and their simulations, obtaining a range of errors between experimental and simulated temperatures from 2.08 to 5.6 , depending on the number of thermal zones studied. [ABSTRACT FROM AUTHOR]

In this paper, we present a framework for performing automatic analysis of Land Use Zones based on Location-Based Social Networks (LBSNs). We model city areas using a hierarchical structure of POIs extracted from foursquare. We encode such structures in kernel machines, e.g., Support Vector Machines, using a new Tree Kernel, i.e., the Hierarchical POI Kernel (HPK), which can take the importance of the individual POIs into account during the substructure matching. This way, HPK projects structures in the space of all their possible substructures such that each dimension corresponds to a semantic structural feature, weighted according to the discriminative power of POIs. We generated four different datasets for the following cities: Barcelona, Lisbon, Amsterdam and Milan, where we trained and tested our models. The results show that our approach largely outperforms previous work and standard baseline built on simple features, such as counts of different POIs. Finally, we apply a mining algorithm to extract the most relevant features (tree fragments) from the implicit TK space according to the weights the kernel machine assigned to them. Our approach can produce an explicit set of representative features that can be used to classify and characterize urban areas. [ABSTRACT FROM AUTHOR]

Development of new building HVAC control algorithms has grown due to needs for energy efficiency and operational flexibility. However, case studies demonstrating new algorithms are largely individualized, making algorithm performance difficult to compare directly. In addition, the effort and expertise required to implement case studies in real or simulated buildings limits rapid prototyping potential. Therefore, this paper presents the Building Optimization Testing Framework (BOPTEST) and associated software for simulation-based benchmarking of building HVAC control algorithms. A containerized run-time environment (RTE) enables rapid, repeatable deployment of common building emulators representing different system types. Emulators use Modelica to represent realistic physical dynamics, embed baseline control, and enable overwriting supervisory and local-loop control signals. Finally, a common set of key performance indicators are calculated within the RTE and reported to the user. This paper details the design and implementation of software and demonstrates its usage to benchmark a Model Predictive Control strategy. [ABSTRACT FROM AUTHOR]

The decision making of a suitable heating, ventilating and air conditioning system's set-point temperature is an energy and environmental challenge in our society. In the present paper, a general framework to define such temperature based on a dynamic adaptive comfort algorithm is proposed. Due to the fact that the thermal comfort of the occupants of a building has different ranges of acceptability, this method is applied to learn such comfort temperature with respect to the running mean temperature and therefore to decide the suitable range of indoor temperature. It is demonstrated that this solution allows to dynamically build an adaptive comfort algorithm, an algorithm based on the human being's thermal adaptability, without applying the traditional theory. The proposed methodology based on the K-Nearest-Neighbour algorithm was tested and compared with data from an experimental thermal comfort field study carried out in a mixed mode building in the south-western area of Spain and with the Support Vector Machine method. The results show that K-Nearest-Neighbour algorithm represents the pattern of thermal comfort data better than the traditional solution and that it is a suitable method to learn the thermal comfort area of a building and to define the set-point temperature for a heating, ventilating and air-conditioning system. [ABSTRACT FROM AUTHOR]

A fusion of inertial navigation system and global positioning system (GPS) has been proposed to implement a robust navigation system for a transporter. In several cases, the GPS signals are not reliable to localize a transporter. In order to overcome this difficulty, a fault detection and isolation (FDI) algorithm has been used to detect and isolate the contaminated satellite signals. When the FDI algorithm is used alone, and the number of reliable satellite signals becomes less than four owing to GPS signal errors, the receiver cannot estimate the position precisely. In this paper, a modified FDI algorithm has been proposed and applied for the navigation of a transporter on a flat yard in order to avoid losing its positioning while it passes through a structured area. In the modified FDI algorithm, signal-to-noise ratio, measurement of quality indicator, and Doppler shift data are incorporated. In order to verify the effectiveness of the proposed algorithm, the navigation experiments have been conducted on a campus using a mobile robot to simulate the transporter on a shipbuilding yard and the results are demonstrated in this paper. [ABSTRACT FROM AUTHOR]

Accurate and efficient methods for large-scale urban reconstruction are of significant importance to the computer vision and computer graphics communities. Although rapid acquisition techniques such as airborne LiDAR have been around for many years, creating a useful and functional virtual environment from such data remains difficult and labor intensive. This is due largely to the necessity in present solutions for data dependent user defined parameters. In this paper we present a new solution for automatically converting large LiDAR data pointcloud into simplified polygonal 3D models. The data is first divided into smaller components which are processed independently and concurrently to extract various metrics about the points. Next, the extracted information is converted into tensors. A robust agglomerate clustering algorithm is proposed to segment the tensors into clusters representing geospatial objects e.g., roads, buildings, etc. Unlike previous methods, the proposed tensor clustering process has no data dependencies and does not require any user-defined parameter. The required parameters are adaptively computed assuming a Weibull distribution for similarity distances. Lastly, to extract boundaries from the clusters a new multi-stage boundary refinement process is developed by reformulating this extraction as a global optimization problem. We have extensively tested our methods on several pointcloud datasets of different resolutions which exhibit significant variability in geospatial characteristics e.g., ground surface inclination, building density, etc and the results are reported. The source code for both tensor clustering and global boundary refinement will be made publicly available with the publication on the author's website. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of cooperative mapping (CM) using datasets collected by multiple users at different times, when the transformation between the users’ starting poses is unknown. Specifically, we formulate CM as a constrained optimization problem, in which each user's independently estimated trajectory and map are merged together by imposing geometric constraints between commonly observed point and line features. Additionally, we provide an algorithm for efficiently solving the CM problem, by taking advantage of its structure. The proposed solution is proven to be batch-least-squares (BLS) optimal over all users’ datasets, while it is less memory demanding and lends itself to parallel implementations. In particular, our solution is shown to be faster than the standard BLS solution, when the overlap between the users’ data is small. Furthermore, our algorithm is resource-aware as it is able to consistently trade accuracy for lower processing cost, by retaining only an informative subset of the common-feature constraints. Experimental results based on visual and inertial measurements collected from multiple users within large buildings are used to assess the performance of the proposed CM algorithm. [ABSTRACT FROM AUTHOR]

In asynchronous crash-prone read/write shared-memory systems there is the notion of a snapshot object, which simulates the behavior of an array of single-writer/multi-reader (SWMR) shared registers that can be read atomically. Processes in the system can access the object invoking (any number of times) two operations, denoted $\mathsf {write}()$ and $\mathsf {snapshot}()$ . A process invokes $\mathsf {write}()$ to update the value of its register in the array. When it invokes $\mathsf {snapshot}()$ , the process obtains the values of all registers, as if it read them simultaneously. It is known that a snapshot object can be implemented on top of SWMR registers, tolerating any number of process failures. Snapshot objects provide a level of abstraction higher than individual SWMR registers, and they simplify the design of applications. Building a snapshot object on an asynchronous crash-prone message-passing system has similar benefits. The object can be implemented by using the known simulations of a SWMR shared memory on top of an asynchronous message-passing system (if less than half the processes can crash), and then build a snapshot object on top of the simulated SWMR memory. This paper presents an algorithm that implements a snapshot object directly on top of the message-passing system, without building an intermediate layer of a SWMR shared memory. To the authors knowledge, the proposed algorithm is the first providing such a direct construction. The algorithm is more efficient than the indirect solution, yet relatively simple. [ABSTRACT FROM AUTHOR]

This paper concerns two polarization orientation parameters of built-up areas derived from the polarimetric synthetic aperture radar (PolSAR) data considering two modeling orthogonal dihedral structures. The first orientation parameter is derived from the well-known circular polarization algorithm with the enrichment of arc distance median filtering using an adaptive neighborhood. The derivation of the second orientation parameter is realized by combining the slope-induced changes in polarimetric orientation angle with the shape-from-shading technique. The combination provides the possibility to measure the incidence angle and the azimuth component of the terrain slopes from the cross-pol SAR intensity image. With reference to the cross scattering model, a doubled cross scattering model (DCSM) is introduced by incorporating the orientation parameters, thus serving to guide the model-based decomposition. Using the DCSM refines the estimation of the cross-pol component by enabling us to further reveal the scattering characteristics of built-up areas. Following a novel criterion, the decomposition is implemented at two layers: one for urban areas and one for nonurban areas. The performance of parameter derivation is demonstrated and evaluated with airborne synthetic aperture radar, uninhabited aerial vehicle synthetic aperture radar, and GF-3 fully PolSAR data over different test sites. The decomposed results are consistent with the reference information provided by the National Land Cover Database 2011 about the land cover classification of test sites and encourage the use of the proposed decomposition scheme for different applications. [ABSTRACT FROM AUTHOR]

Energy disaggregation or non-intrusive load monitoring addresses the issue of extracting device-level energy consumption information by monitoring the aggregated signal at one single measurement point without installing meters on each individual device. Energy disaggregation can be formulated as a source separation problem, where the aggregated signal is expressed as linear combination of basis vectors in a matrix factorization framework. In this paper, an approach based on Sum-to-k constrained non-negative matrix factorization (S2K-NMF) is proposed. By imposing the sum-to-k constraint and the non-negative constraint, S2K-NMF is able to effectively extract perceptually meaningful sources from complex mixtures. The strength of the proposed algorithm is demonstrated through two sets of experiments: Energy disaggregation in a residential smart home; and heating, ventilating, and air conditioning components energy monitoring in an industrial building testbed maintained at the Oak Ridge National Laboratory. Extensive experimental results demonstrate the superior performance of S2K-NMF as compared to state-of-the-art decomposition-based disaggregation algorithms. [ABSTRACT FROM PUBLISHER]

While the Poisson point process (PPP) has been widely employed to model the user distribution in many network design problems, an existing challenge is that it often reveals inaccuracy in small-cell networks. In this paper, instead of employing PPP, we capture the randomness of user equipment (UE) by collecting many their realizations. Specifically, we focus on the millimeter-wave (mmWave) base station (BS) deployment problem in an urban geometry, based on the application of a scenario sampling approach, previously introduced for large-scale optimization, to quantitatively sample a portion of the UE realizations. Motivated by the scenario sampling, a reduced-scale mmWave BS deployment problem is formulated, whose optimal solution is attained by the proposed low-complexity iterative search algorithm. A required number of samples that guarantee a specified majority of the link quality constraints is analyzed. Simulation results verify the scenario sampling theory and the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we solve the optimal thermostat programming problem for consumers with combined demand ($/kW) and time-of-use ($/kWh) pricing plans. We account for energy storage in interior floors and surfaces by using a partial-differential model of diffusion. We consider two types of thermostats: the first can be programmed to vary continuously in time and the second is limited to four constant set-points. Thermostat settings were constrained to lie within a desired interval. Numerical testing shows that the resulting algorithm can reduce monthly electricity bills by up to 25% in the summer with average savings of 9.2% over a variety of building models by using prices from Arizona utility Salt River Project. Furthermore, we examine how optimal thermostat programming affects optimal electricity pricing by using a simplified model of utility generation costs to determine the optimal ratio of demand to time-of-use prices. Our results show that pricing electricity at the marginal cost of generation in this scenario is suboptimal. [ABSTRACT FROM AUTHOR]

Cross-modal retrieval has recently drawn much attention due to the widespread existence of multimodal data. It takes one type of data as the query to retrieve relevant data objects of another type, and generally involves two basic problems: the measure of relevance and coupled feature selection. Most previous methods just focus on solving the first problem. In this paper, we aim to deal with both problems in a novel joint learning framework. To address the first problem, we learn projection matrices to map multimodal data into a common subspace, in which the similarity between different modalities of data can be measured. In the learning procedure, the \ell 21 -norm penalties are imposed on the projection matrices separately to solve the second problem, which selects relevant and discriminative features from different feature spaces simultaneously. A multimodal graph regularization term is further imposed on the projected data,which preserves the inter-modality and intra-modality similarity relationships.An iterative algorithm is presented to solve the proposed joint learning problem, along with its convergence analysis. Experimental results on cross-modal retrieval tasks demonstrate that the proposed method outperforms the state-of-the-art subspace approaches. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to deducing the floor plan of a building using only a set of noisy room dimension measurements that have been obtained indirectly through radar probes. It is not assumed that the set of measurements is complete. The key feature of this approach is a method of relating, via a graph-theoretical construction, a given floor plan topology to a model that depends linearly on a minimal set of parameters. Given a list of floor-plan topologies and a system of realizability constraints, it is possible to formulate this problem as a con- strained least-squares optimization problem. An algorithm for generating all floor plan topologies with a specified number of rooms is presented, as well as an algorithm for deciding if two floor plan representations are isomorphic. The techniques in this paper are intended to serve as an exploration into the theoretical performance bounds on the floor plan estimation problem. [ABSTRACT FROM AUTHOR]

Since 3D city models need to be realistic not only from a bird's point of view, but also from a pedestrian's point of view, the interest in the generation of 3D façade models is increasing.This paper presents two successive algorithms for automatically segmenting building façades scanned by Terrestrial Laser Scanner (TLS) into planar clusters and extracting their contours. Since majority of façade components are planes, the topic of automatic extraction of planar features has been studied.The RANSAC algorithm has been chosen among numerous methods. It is a robust estimator frequently used to compute model parameters from a dataset containing outliers, as it occurs in TLS data. Nevertheless, the RANSAC algorithm has been improved in order to extract the most significant planar clusters describing the main features composing the building façades. Subsequently, a second algorithm has been developed for extracting the contours of these features.The innovative idea presented in this paper is the efficient way to detect the points composing the contours. In order to evaluate the performances of both algorithms, they have successively been applied on samples with different characteristics, i.e. densities, types of façades and size of architectural details. Finally, a quality evaluation based on the comparison of planar clusters and contours obtained manually has been carried out.The results prove that the proposed algorithms deliver qualitative as well as quantitative satisfactory results and confirm that both algorithms are reliable for the forthcoming 3D modelling of building façades. [ABSTRACT FROM AUTHOR]

This paper focuses on the benchmark control problems for seismically excited nonlinear buildings. The benchmark study focuses on three typical steel structures, 3-, 9-, and 20-story buildings designed for the SAC project for the Los Angeles region. This paper reports the application of the active control scheme on the 3- and 20-story benchmark buildings, where the control action is achieved by a fuzzy logic controller. The main advantage of the fuzzy controller is its inherent robustness and ability to handle any nonlinear behavior of the structure. The performance of the controller is validated through the computer simulation on MATLAB. The control scheme satisfies the control constraints and is presented according to the evaluation criteria provided with the benchmark problems for comparison with other schemes. Four different earthquakes (El Centro 1940, Hachinohe 1968, Northridge 1994, and Kobe 1995) will be used in the simulation. [ABSTRACT FROM AUTHOR]

This paper investigates range error measurements over multiple paths in an indoor nonline-of-sight environment as a function of signal bandwidth. The raw range measurements were obtained by transmitting and receiving a signal of 4 GHz bandwidth. Then, through suitable processing of the received pulses, signals with an effective bandwidth from 60 MHz to 4 GHz were generated. The range errors had mean value of about 0.2 m for bandwidths greater than 300 MHz, but peak errors increased to 4–5 m with a 60-MHz bandwidth using a standard leading-edge threshold time-of-arrival (TOA) technique. These latter errors were observed to be related to distortions in the leading edge of the pulse due to multipath propagation. Based on the observations, a new enhanced leading-edge TOA algorithm is developed. The enhanced algorithm uses the initial estimate from the standard leading-edge threshold algorithm, but then uses back-projection to correct the initial estimate. Experimental results demonstrate that the proposed algorithm reduces the mean errors by about 50% at lower bandwidths. As a consequence, TOA positioning systems using the enhanced leading-edge algorithm and operating in the 2.4-GHz and especially the 5.8-GHz Industrial, Scientific and Medical band have position errors only about twice that of ultrawideband systems, despite the much smaller signal bandwidth. [ABSTRACT FROM PUBLISHER]

Cosimulation platforms are necessary to study the interactions of complex systems integrated in future smart grids. The Virtual Grid Integration Laboratory (VirGIL) is a modular cosimulation platform designed to study interactions between demand-response (DR) strategies, building comfort, communication networks, and power system operation. This paper presents the coupling of power systems, buildings, communications, and control under a master algorithm. There are two objectives: first, to use a modular architecture for VirGIL, based on the functional mockup interface (FMI), where several different modules can be added, exchanged, and tested; and second, to use a commercial power system simulation platform, familiar to power system operators, such as DIgSILENT PowerFactory. This will help reduce the barriers to the industry for adopting such platforms, investigate and subsequently deploy DR strategies in their daily operation. VirGIL further introduces the integration of the quantized state system (QSS) methods for simulation in this cosimulation platform. Results on how these systems interact using a real network and consumption data are also presented. [ABSTRACT FROM AUTHOR]

Grouping of buildings based on proximity is a pre-processing step of urban pattern (structure) recognition for contextual cartographic generalization. This paper presents a comparison of grouping algorithms for polygonal buildings in urban blocks. Four clustering algorithms, Minimum Spanning Tree (MST), Density-Based Spatial Clustering Application with Noise (DBSCAN), CHAMELEON and Adaptive Spatial Clustering based on Delaunay Triangulation (ASCDT) are reviewed and analysed to detect building groups. The success of the algorithms is evaluated based on group distribution characteristics (i.e. distribution of the buildings in groups) with two methods: S_Dbw and newly proposed Cluster Assessment Circles. A proximity matrix of the nearest distances between the building polygons, and Delaunay triangulation of building vertices are created as an input for the algorithms. A topographic data-set at 1:25,000 scale is used for the experiments. Urban block polygons are created to constrain the clustering processes from topological aspect. Findings of the experiment demonstrate that DBSCAN and ASCDT are superior to CHAMELEON and MST. Among them, MST has exhibited the worst performance for finding meaningful building groups in urban blocks. [ABSTRACT FROM PUBLISHER]

Indoor semantic floorplan is important for a range of location based service (LBS) applications, attracting many research efforts in several years. In many cases, the out-of-date indoor semantic floorplans would gradually deteriorate and even break down the LBS performance. Thus, it is important to automatically update changed semantics of indoor floorplans caused by environmental variation. However, few research has been focused on the continuous semantic updating problem. This paper presents SISE as a mobile crowdsourcing system that uses a new abstraction for indoor general entities and their semantics, enGraph, to automatically update changed semantics of indoor floorplans using images and inertial data. We first propose efficient methods to generate enGraph. Thus, an image can be associated with an indoor semantic floorplan. Accordingly, we formulate the enGraph matching problem and then propose a quality-based maximum common subgraph matching algorithm so that entities extracted from an image can be corresponded to entities in the indoor semantic floorplan. Furthermore, we propose a quadrant comparison algorithm and a region shrink based localization algorithm to detect and localize changed entities. Thus, the new semantics can be labeled and out-of-date semantics can be removed. Extensive experiments have been conducted on real and synthetic data. Experimental results show that 80 percent of out-of-date semantics of indoor general entities can be updated by SISE. [ABSTRACT FROM AUTHOR]

Controllable building loads have the potential to increase the flexibility of power systems. A key step in developing effective and attainable load control policies is modeling the set of feasible building load profiles. In this paper, we consider buildings whose source of flexibility is their HVAC loads. We propose a data-driven method to empirically estimate a robust feasible region of the load using coarse data, that is, using only total building load and average indoor temperatures. The proposed method uses easy-to-gather coarse data and can be adapted to buildings of any type. The resulting feasible region model is robust to temperature prediction errors and is described by linear constraints. The mathematical simplicity of these constraints makes the proposed model adaptable to many power system applications, for example, economic dispatch, and optimal power flow. We validate our model using data from EnergyPlus and demonstrate its usefulness through a case study in which flexible building loads are used to balance errors of wind power forecasts. [ABSTRACT FROM AUTHOR]

In this paper, a new prediction model for aggregated loads of buildings has been propose. Due to high correlation of prediction performance with related horizons and aggregated more customers, a new strategy is developed to provide a forecasting model based on high accuracy. While, consumption shape of a single users normally has low structure to be exploited and vice versa. So, combining different users develops the relative prediction performance up to special point. Outside this point, no more enhancement in relative performance can be found. This model is useful in optimal power system operation and planning in micro-grids. In this paper, besides the aggregated loads of building, the electric vehicle (EV) impact on network has been considered. While, by considering the load growth prediction and impact of EV adaption on load curves this problem can be an important issue for the power grid operation and management. So, an accurate prediction model is presented in this work which is composed of new feature selection and enhanced support vector machine (ESVM) based forecast engine. Effectiveness of the proposed model is carried out to the part of Budapest city through comparing with other prediction models. Obtained results demonstrate the validity of proposed method. [ABSTRACT FROM AUTHOR]

The inverse of the stiffness matrices of a large group of repeated and regular structures were previously obtained using graph products. Considering the importance of increasing the speed of the computing process and decreasing the amount of computation and dimension of matrices, this paper presents an efficient algorithm that swiftly solves irregular structures composed of regular and irregular parts. The present method is based on generalizing the solution of regular forms to irregular ones. Here, the irregular structure is a regular structure with some additional nodes (irregular part), in comparison with another kind of irregularity concerned with additional members that was discussed previously. The algorithm is subsequently combined with the algorithm of additional members to allow solving all kinds of irregularity. In addition to irregular structures, the method is practical for design and reanalysis of structures, analyzing the existing structures to which new stories or degrees of freedom are added, and solving constructional imperfection problems. [ABSTRACT FROM AUTHOR]

This paper reviews and evaluates four building extraction algorithms including two pixel-based and two object-based methods using a diverse set of very high spatial resolution imagery. The applied images are chosen from different places (the cities of Isfahan, Tehran, and Ankara) and different sensors (QuickBird and GeoEye-1], which are diverse in terms of building shape, size, color, height, alignment, brightness, and density. The results indicate that the performance and the reliability of two object-based algorithms are better than pixel-based algorithms; about 10 percent to 15 percent better for the building detection rate and 6 percent to 10 percent better for the reliability rate. However, in some cases, the detection rate of pixel-based algorithms has been greater than 80 percent, which is a satisfactory result. On the other hand, segmentation errors can cause limitations and errors in the object-based algorithms, so that the commission error of object-based algorithms has been higher than pixel-based algorithms in some cases. [ABSTRACT FROM AUTHOR]

Subspace segmentation is the problem of segmenting (or grouping) a set of n data points into a number of clusters, with each cluster being a (linear) subspace. The recently established algorithms such as Sparse Subspace Clustering (SSC), Low-Rank Representation (LRR) and Low-Rank Subspace Segmentation (LRSS) are effective in terms of segmentation accuracy, but computationally inefficient as they possess a complexity of O(n^{3}) , which is too high to afford for the case where n$ is very large. In this paper we devise a fast subspace segmentation algorithm with complexity of $O(n\log (n))$ . This is achieved by firstly using partial Singular Value Decomposition (SVD) to approximate the solution of LRSS, secondly utilizing Locality Sensitive Hashing (LSH) to build a sparse affinity graph that encodes the subspace memberships, and finally adopting a fast Normalized Cut (NCut) algorithm to produce the final segmentation results. Besides of high efficiency, our algorithm also has comparable effectiveness as the original LRSS method. [ABSTRACT FROM AUTHOR]

The development of high-resolution satellite imaging sensors has enhanced the collection of remotely sensed data and offers a cost-effective way to obtain information over large areas. The main structural features that are to be detected through satellite imaging are roads, buildings, and bridges. Detection of clouds and shadows supports the extraction of these features. However, the type of satellite images provided by different imaging sensors varies widely depending on spatial and spectral resolutions, which makes the process of feature extraction more complicated. This paper presents a detailed analysis of different algorithms developed to extract structural features using a variety of satellite images available for a single region. A comparative study of various techniques is done to justify the best method for the extraction of each feature. [ABSTRACT FROM PUBLISHER]

Increased use of energy in buildings and HVAC systems requires advanced control schemes like model-based control to improve energy efficiency, which in turn requires accurate thermodynamic models of buildings. The Resistive-Capacitive (RC) method is a popular and versatile approach for thermal modeling of buildings. Despite this, it is not easy to find practical solutions of implementation of the RC method. It is the goal of this paper to clarify the RC method and demonstrate simple implementation of this method, especially for multi-zone buildings, which have more potential for energy savings from use of model-based control. This paper provides two contributions. First is a detailed explanation of the RC method, focusing on its use for developing a structure of a model and first-principles approach for estimation of parameters of a model. Second is a demonstration of an algorithm that enables automatic development of the structure of a model from basic information about a building (layout, construction elements) and its combination with data-based parameter estimation. Use of the algorithm is presented with a case-study on industrial multi-zone building, for which such a grey-box model is developed and analyzed. The resulting model is rapidly developed and used in a simulation with the measured data. The outputs of the model are compared with the measured temperatures and they show good fit. [ABSTRACT FROM AUTHOR]

This paper presents a new extremum-seeking controller (ESC) derived from a statistical interpretation of conventional ESC. The proposed ESC replaces the gradient feedback variable with a normalized correlation coefficient. The algorithm, as presented, can be fully configured with knowledge of only the open-loop response time of the system being optimized and the amplitude of the dither signal. Simulation tests show that the new algorithm has fast convergence times compared with other types of ESC algorithms. The paper also shows that the proposed method is not limited to a periodic dither signal and that it can also utilize a stochastic signal to similar efficacy. The purpose of the paper is to describe the new algorithm and present a practical implementation along with results from simulation tests. [ABSTRACT FROM AUTHOR]

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Most of the research on the incentive mechanism design in crowdsourcing has focused on how to allocate sensing tasks to participants to maximize the social welfare. However, none of them consider the coverage holes created by the uneven distribution of participants. As a result, most of the participants in some popular areas compete for tasks, while many tasks in unpopular areas cannot be completed due to the lack of participants. In this paper, we design a movement-based incentive mechanism for crowdsourcing, where participants are stimulated to move to the unpopular areas and complete the sensing tasks in these areas, which benefits both participants and the platform. We formulate a task allocation problem considering controlled mobility. Since the task allocation problem is NP-hard, we propose a greedy algorithm to solve it and design a critical payment policy to guarantee that participants declare their cost truthfully. Theoretical analysis shows that our proposed incentive mechanism satisfies the desired properties of truthfulness, individual rationality, platform profitability, and computational efficiency. Evaluation results show that the proposed movement-based incentive mechanism outperforms the existing solution under various conditions. [ABSTRACT FROM AUTHOR]

Multibaseline phase unwrapping is a critical processing procedure of multibaseline synthetic aperture radar interferometry (InSAR). It has an advantage over single-baseline phase unwrapping in discontinuous-terrain-height estimation. In this paper, the pixels' combination information of multiple InSAR interferograms with different baseline lengths is deeply investigated, and the term ambiguity vector is proposed to represent a pixel's ambiguity number of multiple interferograms. It is also revealed that pixels with the same ambiguity vector have an exclusive pattern among them. A fast cluster-analysis (CA)-based method is proposed for multibaseline phase unwrapping. In this method, all pixels are first clustered into different groups according to their patterns, and then, information of the cluster center is used to unwrap the phases of pixels group by group. Simulation results are shown to verify the effectiveness, efficiency, and noise robustness of CA multibaseline phase-unwrapping method. [ABSTRACT FROM AUTHOR]

A technique for imperfection sensitivity analysis with reference to the geometrically nonlinear analysis of structures is presented. The paper discusses how detailed information on structural behavior can be obtained with less computational cost. The imperfection sensitivity analysis of structures is carried out by detecting the critical states on the equilibrium path relating to the various imperfections. In fact we can investigate the behavior of imperfect structures without considering the imperfect equilibrium curve. In this work we obtained the fold line, the one-dimensional equilibrium subset of limit points relating to different values of imperfection, by asymptotic extrapolation from a known singular point. The evaluation of this singular point on the perfect equilibrium curve is carried out by a path-following algorithm. A procedure that overcomes the ill-conditioning of the systems defined in the critical points is described. This proves to be highly advantageous in terms of computational cost in comparison with classical methods of analysis. The paper investigates the behavior of cylindrical shell. In particular the sensitivity analysis for load imperfections is carried out. The asymptotic extrapolation algorithm is also compared with continuation methods for the analysis of imperfect cylindrical shell. [ABSTRACT FROM AUTHOR]

Partition-wise models offer a flexible approach for modeling complex and multidimensional data and are capable of producing interpretable results. They are based on partitioning the observed data into regions, each of which is modeled with a simple submodel. The success of this approach highly depends on the quality of the partition, as too large a region could lead to a nonsimple submodel, while too small a region could inflate estimation variance. This paper proposes an automatic procedure for choosing the partition (i.e., the number of regions and the boundaries between regions) as well as the submodels for the regions. It is shown that under the assumption of the existence of a true partition, as well as a set of known significant predictors, the proposed partition estimator is statistically consistent. Features of the proposed methodology are highlighted for both regression and classification problems on synthetic and real data. [ABSTRACT FROM PUBLISHER]

"Space allocation" was a central pursuit in postwar research on computing and architecture. Researchers sought an algorithm that could automatically design the most efficient floor plan for a set of activities. In this article, we connect an early algorithm for computing architectural floor plans to the postwar British hospital. We examine how researchers adapted algorithmic methods for floor layout design developed in industrial capitalist settings to the promotion of the British welfare state. Finally, we pay special attention to the agency that certain graphic inscriptions borrowed from mathematics had in validating these particular algorithmic methods as promising avenues for the algorithmic automation of all architectural work. This article situates the automation of hospital design in postwar U.K. at the intersection of building science and healthcare management, with the aim to contribute critical perspectives on algorithmic reifications of work in early computer-aided architectural design systems. [ABSTRACT FROM AUTHOR]

The deployment of model-predictive control (MPC) for a building's energy system is a challenging task due to high computational and modeling costs. In this study, an MPC controller based on EnergyPlus and MATLAB is developed, and its performance is evaluated through a case study in terms of energy savings, optimality of solutions, and computational time. The MPC determines the optimal setpoint trajectories of supply air temperature and chilled water temperature in a simulated office building. A comparison between MPC and rule-based control (RBC) strategies for three test days showed that the MPC achieved 49.7% daily peak load reduction and 17.6% building energy savings, which were doubled compared to RBC. The MPC optimization problem was solved multiple times using the Ant Colony Optimization (ACO) algorithm with different starting points. Results showed that ACO consistently delivered high-quality optimized control sequences, yielding less than a 1% difference in energy savings between the worst and best solutions across all three test days. Moreover, the computational time for solving the MPC problem and obtaining nearly optimal control sequences for a three-hour prediction horizon was observed to be around 22 min. Notably, reasonably good solutions were attained within 15 min by the ACO algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an improved artificial bee colony algorithm named Weighted Global ABC (WGABC) algorithm, which is designed to improve the convergence speed in the search stage of solution search equation. The new method not only considers the effect of global factors on the convergence speed in the search phase, but also provides the expression of global factor weights. Experiment on benchmark functions proved that the algorithm can improve the convergence speed greatly. We arrive at the gas diffusion concentration based on the theory of CFD and then simulate the gas diffusion model with the influence of buildings based on the algorithm. Simulation verified the effectiveness of the WGABC algorithm in improving the convergence speed in optimal deployment scheme of gas sensors. Finally, it is verified that the optimal deployment method based on WGABC algorithm can improve the monitoring efficiency of sensors greatly as compared with the conventional deployment methods. [ABSTRACT FROM AUTHOR]

The paper presents an innovative solution which increases the reliability of determining the coordinates of corners of building structures in the RTN GNSS mode. Having performed the surveys of the base points in real time, it is proposed to use the method of line-line intersection, which results in capturing the Cartesian coordinates X, Y of the corners of buildings. The coordinates which were obtained in this way, are subjected to an innovative solution called the method of vectors translation. This method involves modeling the coordinates obtained by the algorithm developed by the author. As a result, we obtain the Cartesian coordinates X and Y of the corners of building structures, the accuracy and reliability of determining which is on a very high level. [ABSTRACT FROM AUTHOR]

Floor slabs represent a large volume of concrete in buildings. The goal of this research is to achieve a structure that has an optimized bearing capacity. The optimization implies economic efficiency and sustainability. This paper describes a bionic optimization process that is applied in a project of the German Research Foundation (DFG) Priority Programme called "Concrete light. Future concrete structures using bionic, mathematical and engineering formfinding principles". The project involves adaption of three different natural structures that lead to a natural flow of forces. These natural structures are (a) spider webs, (b) hollow parts of bones and (c) geometries of structures such as the bottom side of water lilies or seashells. This scientific paper deals with the implementation of an optimization process for a configuration of reinforcement inspired by a spider web. Evolutionary Algorithms (EAs) are used for the development and optimization of an innovative and useful configuration of reinforcement. The EAs use reproduction, mutation and selection as mechanisms, inspired by biological evolution, to solve technical problems gradient-free. In this project the EA is combined with physical nonlinear finite element analyses. The EA is embedded into a C# application, in which the slab structure is generated and the finite element programme is started. The quality of the results is characterized by the fitness of each individual (reinforcement configuration), which is, for this example, the midspan displacement of the generated slab multiplied by the steel volume per slab. Accordingly, the midspan displacement is to be minimized during the process, with the minimum possible amount of reinforcement. The optimization variables are the angles and the number of rebars per slab. Several constrains need to be included to get comparable results between the developed slabs and the conventional slabs with orthogonally configured reinforcement. This paper presents the results of an optimized reinforcement configuration thus found by EA and comparisons with the behaviour of conventional slabs with a similar reinforcement ratio. [ABSTRACT FROM AUTHOR]

The calculation of the shadows that building environment, building elements or shading devices may cast on the building envelope, plays a key role in load calculations and building energy simulation. Algorithms for solar shading calculations have direct repercussions on the accuracy of the results and the computational times of building simulation tools. This paper presents an improved method for direct solar shading calculations based on the projection of every polygon on a unique receiving surface and incorporates recent and high efficient algorithms to solve polygon intersections. Firstly, the method is shortly described. Secondly, it is validated by comparisons with other methods, experimental results and European standards. Then, a comparison test between the proposed and conventional methods is presented to assess computational speed improvement. Finally, the main advantages of the proposed method are discussed. [ABSTRACT FROM AUTHOR]

Due to the fact that some structural external excitations such as earthquakes and wind forces can not be accurately measured under actual operating conditions, it is necessary to investigate algorithms for structural identification under unknown earthquakes. In this paper, an algorithm based on the extended Kalman estimator approach is proposed for the identification of structural parameters and unknown excitation of tall shear-type buildings with only partial measurements of structural absolute acceleration responses. The equation of motion of a tall shear building under ground motion is established in the absolute co-ordinate system, in which the ground-motion input is applied to the 1st floor of the building as an unknown excitation. Based on substructure approach, the tall building is decomposed into substructures. For each substructure above the 1st floor, substructural relative responses and parameters are identified by extended Kalman estimator. For the substructure containing the 1st floor, it is proposed that substructural extended state vector is firstly identified by the extended Kalman estimator and the unknown excitation is subsequently estimated by least-squares estimation. Then, the 1st story stiffness is estimated based on structural eigenvalue equation and the expansion of the determinate of the structural eigen-matrix. The unknown ground motion is identified by the Newmark-β method. A numerical simulation example demonstrates the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Message type or message cluster extraction is an important task in the analysis of system logs in computer networks. Defining these message types automatically facilitates the automatic analysis of system logs. When the message types that exist in a log file are represented explicitly, they can form the basis for carrying out other automatic application log analysis tasks. In this paper, we introduce a novel algorithm for carrying out message type extraction from event log files. IPLoM, which stands for Iterative Partitioning Log Mining, works through a 4-step process. The first three steps hierarchically partition the event log into groups of event log messages or event clusters. In its fourth and final stage, IPLoM produces a message type description or line format for each of the message clusters. IPLoM is able to find clusters in data irrespective of the frequency of its instances in the data, it scales gracefully in the case of long message type patterns and produces message type descriptions at a level of abstraction, which is preferred by a human observer. Evaluations show that IPLoM outperforms similar algorithms statistically significantly. [ABSTRACT FROM AUTHOR]

Highlight • A model updating method for building structures is presented using modal responses. • A representative value of modal responses, MPR, is proposed and used in the method. • A baseline model is obtained by minimizing MPRs between measurement and model. • Based on the damage scenario for the baseline model, damages are detected. Abstract This paper presents a model updating method for the damage detection of building structures on the basis of modal participation without the need for system identification to extract the modal parameters. The presented method uses the modal participation ratio (MPR), which is defined by a representative value of modal responses extracted from vibration responses measured by sensors mounted on a building structure, as an indicator of the extent of modal contribution. Such MPR extraction assumes that the structure is under ambient excitation; thus, no loading on the structure is required to apply the presented method. In the model updating method, the differences between MPRs extracted from the sensors and MPRs estimated from a model are established as objective functions that are generated corresponding to the number of modes considered. These functions are minimized by a multi-objective optimization algorithm while searching for the optimal properties of a dynamic system, and the finally derived optimal solution is regarded as a baseline model that reflects the actual behaviors of a structure. Through analyses of the variations of the MPRs based on the damage scenarios of the derived baseline model, specific rules for damage localization are derived. In addition, to identify the damage severity of a structure, a damage severity function that describes the relationship between damage severity and MPR variation based on the baseline model is proposed. The proposed method is employed to construct a baseline model and detect the location and severity of the damage of a multi-story structure under ambient excitation. [ABSTRACT FROM AUTHOR]

Highlights • Fast 1D nonlinear dynamic model is validated to predict façade response. • External cladding systems influence stiffness and strength of high-rise structures. • Sensitivity to the façade typology is explored. • Locally, façades decrease stresses in structural key members up to 66.94%. • Globally, façades attenuate top displacements up to 4.94%. Abstract The dynamic interaction between glazed curtain wall stick systems and modern high-rise mega-frame buildings is investigated. In the present paper, four moment resisting frames (MRFs), consisting of thirty- and sixty-storey steel-based prototypes, are designed according to European standards: internal concentrically braced frame (CBF) core, outriggers and belt trusses are adopted to limit inter-storey drift and second order effects. Force-displacement relationships are derived from available full-scale test data performed on non-structural aluminium façade units. Therefore, 3D finite element (FE) models are developed to interpret the physical phenomena involved in façade dynamics: as a result, equivalent 1D nonlinear links are calibrated to simulate these phenomena independently. Nonlinear time history analyses (NLTHAs) are executed to investigate the potential combination of stiffness and strength of such hybrid systems, i.e. achieved through the integration of glazed curtain walls on the MRF lateral force resisting system (LFRs). Local and global performance will be shown in terms of inter-storey drifts and displacement peak profiles, forces and percentage peak variations, highlighting static-to-seismic load ratios in critical members and the sensitivity to the structural height. Conclusions point out that, even if accurately designed according to current standards, the façade omission from the seismic analyses of high-rise structures may lead to a crucial underestimation in the dissipation capacity of the building. [ABSTRACT FROM AUTHOR]

Cluster ensembles have attracted a lot of research interests in recent years, and their applications continue to expand. Among the various algorithms for cluster ensembles, those based on coassociation matrices are probably the ones studied and used the most because coassociation matrices are easy to understand and implement. However, the main limitation of coassociation matrices as the data structure for combining multiple clusterings is the complexity that is at least quadratic to the number of patterns N. In this paper, we propose CA-tree, which is a dendogram-like hierarchical data structure, to facilitate efficient and scalable cluster ensembles for coassociation-matrix-based algorithms. All the properties of the CA-tree are derived from base cluster labels and do not require the access to the original data features. We then apply a threshold to the CA-tree to obtain a set of nodes, which are then used in place of the original patterns for ensemble-clustering algorithms. The experiments demonstrate that the complexity for coassociation-based cluster ensembles can be reduced to close to linear to N with minimal loss on clustering accuracy. [ABSTRACT FROM AUTHOR]

Indoor positioning systems (IPSs) locate objects in closed structures such as office buildings, hospitals, stores, factories, and warehouses, where Global Positioning System devices generally do not work. Most available systems apply wireless concepts, optical tracking, and/or ultrasound. This paper presents a standalone IPS using radio frequency identification (RFID) technology. The concept is based on an object carrying an RFID reader module, which reads low-cost passive tags installed next to the object path. A positioning system using a Kalman filter is proposed. The inputs of the proposed algorithm are the measurements of the backscattered signal power propagated from nearby RFID tags and a tag–path position database. The proposed algorithm first estimates the location of the reader, neglecting tag–reader angle-path loss. Based on the location estimate, an iterative procedure is implemented, targeting the estimation of the tag–reader angle-path loss, where the latter is iteratively compensated from the received signal strength information measurement. Experimental results are presented, illustrating the high performance of the proposed positioning system. [ABSTRACT FROM AUTHOR]

As the applications of wireless sensor networks continue to expand, it is important to support fast and simultaneous data aggregation over multiple regions for advanced data analysis. In this paper, we propose a solution by using a novel distributed data structure called distributed data cube (DDC). A DDC maintains a set of special forms of aggregate values (prefix sum, prefix average, prefix max, and prefix min) in distributed sensor nodes. We will first present fast algorithms to build a DDC within a sharp time bound. Then, we will present efficient distributed query-processing algorithms to handle aggregate queries by using a DDC. For a query region with n sensor nodes, our algorithms can return within O(\sqrtn) time. Finally, extensive simulation studies confirm that a DDC can be built very quickly, which is consistent with the theoretical time bound. The network traffic injected while constructing a DDC is acceptable and also scalable as the network size grows. Query processing on a DDC is fast and energy efficient in terms of the time units needed and the number of messages incurred. [ABSTRACT FROM PUBLISHER]

The article presents abstracts on planning literature which include approaches to empirical work in regional economics, understanding the barriers to social adaptation, and management of large city regions.