Occupancy models are necessary towards design and operation of smart buildings. Developing an appropriate algorithm to predict occupancy presence will allow a better control and optimization of the ...whole building energy consumption. However, most previous studies of development of such model only focus on commercial buildings. The occupancy model of residential houses are usually based on Time User Survey data. This study focuses on providing a unique data set of four residential houses collected from occupancy sensors. A new inhomogeneous Markov model for occupancy presence prediction is proposed and compared to commonly used models such as Probability Sampling, Artificial Neural Network, and Support Vector Regression. Training periods for the presence prediction are optimized based on change-point analysis of historical data. The study further explores and evaluates the predictive capability of the models by various temporal scenarios, including 15-min ahead, 30-min ahead, 1-hour ahead, and 24-hour ahead forecasts. The spatial-level comparison is additionally conducted by evaluating the prediction accuracy at both room-level and house-level. The final results show that the proposed Markov model outperforms the other methods in terms of an average 5% correctness with 11% maximum difference in 15-min ahead forecast of the occupancy presence. However, there is not much differences observed for 24-hour ahead forecasts.
•Development of a New Markov Model for forecasting occupancy.•Model validation based on field data.•Comparison analysis between new Model and other commonly used statistical models.•New model outperforms other methods by average 5% correctness with 11% maximum difference in one time step ahead forecast.
Real-time occupancy predictions are essential components for the smart buildings in the imminent future. The occupancy information, such as the presence states and the occupants’ number, allows a ...robust control of the indoor environment to enhance the building energy performances. With many current studies focusing on the commercial building occupancy, most researchers modeled either the occupancy presence or the occupants’ number without evaluating the model potentials on both of them. This study focuses on 1) providing a unique data set containing the occupancy for the offices located in the U.S with difference pattern varieties, 2) proposing two methods, then comparing them with four existing methods, and 3) both presence of occupancy and occupancy number are predicted and tested using the approaches proposed in this study. In detail, the paper develops a new moving-window inhomogeneous Markov model based on change point analysis. A hierarchical probability sampling model is modified based on existed models. They are additional compared to well-known models from previous researchers. The study further explores and evaluates the predictive power of the models by various temporal scenarios, including 15-min ahead, 30-min ahead, 1-h ahead, and 24-h ahead forecasts. The final results show that the proposed Markov model outperforms the other methods with a max 22% difference in terms of presence forecasts for 15-min, 30min and 1-h ahead. The proposed Markov model also outperforms other models in occupancy number prediction for all forecast windows with 0.34 RMSE and 0.23 MAE error respectively. However, there is not much performance difference between models for 24-h ahead predictions of occupancy presence forecast.
During the diagnostic workup of lung adenocarcinomas (LAC), pathologists evaluate distinct histological tumor growth patterns. The percentage of each pattern on multiple slides bears prognostic ...significance. To assist with the quantification of growth patterns, we constructed a pipeline equipped with a convolutional neural network (CNN) and soft-voting as the decision function to recognize solid, micropapillary, acinar, and cribriform growth patterns, and non-tumor areas. Slides of primary LAC were obtained from Cedars-Sinai Medical Center (CSMC), the Military Institute of Medicine in Warsaw and the TCGA portal. Several CNN models trained with 19,924 image tiles extracted from 78 slides (MIMW and CSMC) were evaluated on 128 test slides from the three sites by F1-score and accuracy using manual tumor annotations by pathologist. The best CNN yielded F1-scores of 0.91 (solid), 0.76 (micropapillary), 0.74 (acinar), 0.6 (cribriform), and 0.96 (non-tumor) respectively. The overall accuracy of distinguishing the five tissue classes was 89.24%. Slide-based accuracy in the CSMC set (88.5%) was significantly better (p < 2.3E-4) than the accuracy in the MIMW (84.2%) and TCGA (84%) sets due to superior slide quality. Our model can work side-by-side with a pathologist to accurately quantify the percentages of growth patterns in tumors with mixed LAC patterns.
Hexagonal close-packed (hcp) metals such as Mg, Ti, and Zr are lightweight and/or durable metals with critical structural applications in the automotive (Mg), aerospace (Ti), and nuclear (Zr) ...industries. The hcp structure, however, brings significant complications in the mechanisms of plastic deformation, strengthening, and ductility, and these complications pose significant challenges in advancing the science and engineering of these metals. In hcp metals, generalized plasticity requires the activation of slip on pyramidal planes, but the structure, motion, and cross-slip of the associated 〈c + a〉 dislocations are not well established even though they determine ductility and influence strengthening. Here, atomistic simulations in Mg reveal the unusual mechanism of 〈c + a〉 dislocation cross-slip between pyramidal I and II planes, which occurs by cross-slip of the individual partial dislocations. The energy barrier is controlled by a fundamental step/jog energy and the near-core energy difference between pyramidal 〈c + a〉 dislocations. The near-core energy difference can be changed by nonglide stresses, leading to tension–compression asymmetry and even a switch in absolute stability from one glide plane to the other, both features observed experimentally in Mg, Ti, and their alloys. The unique cross-slip mechanism is governed by common features of the generalized stacking fault energy surfaces of hcp pyramidal planes and is thus expected to be generic to all hcp metals. An analytical model is developed to predict the cross-slip barrier as a function of the near-core energy difference and applied stresses and quantifies the controlling features of cross-slip and pyramidal I/II stability across the family of hcp metals.
Previous studies have reported that a series of sensory–motor-related cortical areas are affected when a healthy human is presented with images of tools. This phenomenon has been explained as ...familiar tools launching a memory-retrieval process to provide a basis for using the tools. Consequently, we postulated that this theory may also be applicable if images of tools were replaced with images of daily objects if they are graspable (i.e., manipulable). Therefore, we designed and ran experiments with human volunteers (participants) who were visually presented with images of three different daily objects and recorded their electroencephalography (EEG) synchronously. Additionally, images of these objects being grasped by human hands were presented to the participants. Dynamic functional connectivity between the visual cortex and all the other areas of the brain was estimated to find which of them were influenced by visual stimuli. Next, we compared our results with those of previous studies that investigated brain response when participants looked at tools and concluded that manipulable objects caused similar cerebral activity to tools. We also looked into mu rhythm and found that looking at a manipulable object did not elicit a similar activity to seeing the same object being grasped.
People spend more than 90% of their life time in buildings, which makes occupant behavior one of the leading influences of energy consumption in buildings. Occupancy and occupant behavior, which ...refer to human presence inside buildings and their active interactions with various building system such as lighting, heating, cooling, ventilation, window blinds, and plugs, attract great attention of research with regard to better building design and operation. Due to the stochastic nature of occupant behavior, prior occupancy models vary dramatically in terms of data sampling, spatial and temporal resolution. This paper provides a comprehensive review of the current modeling efforts of occupant behavior, summarizes occupancy models for various applications including building energy performance analysis, building architectural and engineering design, intelligent building operations and building safety design, and presents challenges and areas where future research could be undertaken. In addition, modeling requirement for different applications is analyzed. Furthermore, a few commonly used statistical and data mining models are presented. The purpose of this paper is to provide a modeling reference for future researchers so that a proper method or model can be selected for a specific research purpose.
Significant theoretical efforts have been made to understand the Hall-Petch and inverse Hall-Petch relations of nanocrystalline pure metals, metallic glasses and binary alloy systems. However, only a ...few studies have investigated the Hall-Petch or inverse Hall-Petch relations in high-entropy alloys. In this work, phase stability of single-crystalline CoNiFeAlxCu1-x and uniaxial compression of polycrystalline CoNiFeAlxCu1-x are investigated by molecular dynamics simulation. Calculations of cohesive energies indicate that FCC structured CoNiFeAlxCu1-x is more stable at low Al concentrations (x ≤ 0.4) and BCC structured CoNiFeAlxCu1-x is more stable for high Al concentrations (x > 0.4). Based on the phase stability, FCC structured polycrystalline CoNiFeAl0·3Cu0.7 and BCC structured polycrystalline CoNiFeAl0·7Cu0.3 are constructed to perform uniaxial compression. Hall-Petch and inverse Hall-Petch relations are observed in both FCC and BCC structured polycrystalline CoNiFeAlxCu1-x. The microstructural evolutions of polycrystalline CoNiFeAlxCu1-x reveal that the dominant deformation mechanisms in the Hall-Petch regime of FCC structures are dislocation slip and deformation twinning due to relatively low stacking fault energy and that of BCC structures is phase transformation plasticity. For the inverse Hall-Petch relation, the dominant deformation mechanisms for both FCC and BCC HEAs are the rotation of grains and migration of grain boundaries. It indicates that FCC and BCC HEAs exhibit similar Hall-Petch and inverse Hall-Petch relations with the conventional polycrystalline materials, but its grain size exponent and gradient are quite different from those of pure metals.
The
density matrix renormalization group (DMRG) method has been well-established and has become one of the most accurate numerical methods for the precise electronic structure solution of large ...active spaces. In the past few years, to capture the missing dynamic correlation, various post-DMRG approaches have been proposed through the combination of DMRG and multireference quantum chemical methods or density functional theory. With this in mind, this work provides a brief overview of
DMRG principles and the new developments within post-DMRG methods. For clarity, post-DMRG methods are classified into two main categories depending on whether high-order
-electron reduced density matrices are used, and their merits and disadvantages are properly discussed. Finally, we conclude by discussing unsolved bottlenecks and giving development perspectives of post-DMRG approaches, which are expected to yield quantitative descriptions of complex electronic structures in large strongly correlated molecules and materials.
•Development of a centralized occupancy-based Buildings-to-grid Model Predictive Control (MPC) framework.•Simulation on building clusters and standard IEEE grid systems.•Findings show 50–61% cost ...reduction for BtG integration.
Buildings-to-grid (BtG) integration simulations are becoming prevalent due to the development of smart buildings and smart grid. Buildings are the major energy consumers of the total electricity production worldwide. There is an urgent need to integrate buildings with smart grid operation to accommodate the needs of flexible load controls due to the increasing of renewable energy resources. In the imminent future, smart buildings can contribute to grid stability by changing their overall demand patterns in response to grid operations. Meanwhile, building thermal energy consumption is also maintained by building operators to satisfy occupants’ thermal comforts. However, explicit large-scale demonstrations based on a simulation platform that integrates building occupancy, building physics, and grid physics at community level have not been explored. This study develops an occupancy behavior driven BtG optimization platform that can simulate, predict and optimize indoor temperature and energy consumption of buildings, generator setpoint and deviation while maintaining acceptable grid frequency. Authors have tested the framework on two standard power networks. The results show that the integrated framework can provide potential cost savings up to 60% comparing with the decoupled operation.
Point cloud analysis has drawn much attention in recent years, whereas most existing point-based deep networks ignore the rotation-invariant property of the encoded features, which leads to poor ...performance given 3D shapes with arbitrary rotation. In this paper, we propose a novel rotation-invariant method that embeds both distinctive local and global rotation-invariant information. Specifically, we design a two-branch network that separately extracts purely local and global rotation-invariant features. In the global branch, we leverage canonical transformation to extract global representations, while in the local branch, we utilize hand-crafted geometric features (e.g., relative distances and angles) to embed local representations. To fuse the features from distinct branches, we introduce an attention-based fusion module to adaptively integrate the local-to-global representation by considering the geometry contexts of each point. Particularly, different from existing rotation-invariant works, we further introduce a self-attention unit into the global branch for embedding non-local information and also insert multiple fusion modules into the local branch to emphasize the global features. Extensive experiments on standard benchmarks show that our method achieves consistent and competitive performance on various downstream tasks, and also the best performance on the shape classification task on the ModelNet40 dataset with a 0.8% accuracy gain, compared to state-of-the-art methods. The code and pre-trained models are available at https://github.com/CentauriStar/Rotation-Invariant-Point-Cloud-Analysis.
•The use of fused feature is explored to represent rotation-invariance for point cloud.•Distinctive local and global information is exploited and adaptively fused.•Purely global features are extracted within the entire space of point cloud.•Experimental results are boosted by the deep fusion of local and global features.•Classification accuracy gains 0.8% compared to the state-of-the-art.
PDF
