State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Linking human-building interactions in shared offices with personality traits

Occupant behavior influences office building energy performance. The level of human-building interactions (HBIs) in shared offices strongly influences building energy use and occupant well-being. This study explored the link between occupant personality types and their behaviors of sharing energy and environment control systems and interactions with their colleagues. Inspired by the Five-Factor Model (FFM), we classified HBI behaviors into four dimensions: willingness to share control, knowledge of control, group decision behavior, and adaptive strategies. These four variables can be mapped to the four personality traits proposed by the FFM: agreeableness, openness, extraversion, and conscientiousness. Our cluster analysis identified six behavioral patterns: average (17.7%), reserved (15.3%), environmentally friendly (16.6%), role model (24.2%), self-centered (17.2%), and mechanist (9.0%). We further applied association rules, a widely utilized machine learning technique, to discover how demographics, building-related contextual factors, and perception-attitudinal factors influence HBI behaviors. Country, control feature accessibility, and group dynamics were found to be the three most influential factors that determine occupants’ HBI behaviors. The study provides insights about building design and operation, as well as policy to promote socially and environmentally desirable HBI behaviors in a shared office environment

Derivation of Electroweak Chiral Lagrangian from One Family Technicolor Model

Based on previous studies deriving the chiral Lagrangian for pseudo scalar mesons from the first principle of QCD in the path integral formalism, we derive the electroweak chiral Lagrangian and dynamically compute all its coefficients from the one family technicolor model. The numerical results of the $p^4$ order coefficients obtained in this paper are proportional to the technicolor number $N_{\rm TC}$ and the technifermion number $N_{\rm TF}$, which agrees with the arguments in previous works, and which confirms the reliability of this dynamical computation.Comment: 6 page

Quakes in Solid Quark Stars

A starquake mechanism for pulsar glitches is developed in the solid quark star model. It is found that the general glitch natures (i.e., the glitch amplitudes and the time intervals) could be reproduced if solid quark matter, with high baryon density but low temperature, has properties of shear modulus \mu = 10^{30~34} erg/cm^3 and critical stress \sigma_c = 10^{18~24} erg/cm^3. The post-glitch behavior may represent a kind of damped oscillations.Comment: 11 pages, 4 figures (but Fig.3 is lost), a complete version can be obtained by http://vega.bac.pku.edu.cn/~rxxu/publications/index_P.htm, a new version to be published on Astroparticle Physic

Fast Determination of Lycopene Content and Soluble Solid Content of Cherry Tomatoes Using Metal Oxide Sensors Based Electronic Nose

Lycopene content (LC) and soluble solid content (SSC) are important quality indicators for cherry tomatoes. This study attempted simultaneous analysis of inner quality of cherry tomato by Electronic nose (E-nose) using multivariate analysis. E-nose was used for data acquisition, the response signals were regressed by multiple linear regression (MLR) and partial least square regression (PLS) to build predictive models. The performances of the predictive models were tested according to root mean square and correlation coefficient (R2) in the training set and prediction set. The results showed that MLR models were superior to PLS model, with higher value of R2 and lower values of for RMSE firmness, pH, SSC, and LC. Together with MLR, E-nose could be used to obtain firmness, pH, soluble solid and lycopene contents in cherry tomatoes

Modelling and control of the flame temperature distribution using probability density function shaping

This paper presents three control algorithms for the output probability density function (PDF) control of the 2D and 3D flame distribution systems. For the 2D flame distribution systems, control methods for both static and dynamic flame systems are presented, where at first the temperature distribution of the gas jet flames along the cross-section is approximated. Then the flame energy distribution (FED) is obtained as the output to be controlled by using a B-spline expansion technique. The general static output PDF control algorithm is used in the 2D static flame system, where the dynamic system consists of a static temperature model of gas jet flames and a second-order actuator. This leads to a second-order closed-loop system, where a singular state space model is used to describe the dynamics with the weights of the B-spline functions as the state variables. Finally, a predictive control algorithm is designed for such an output PDF system. For the 3D flame distribution systems, all the temperature values of the flames are firstly mapped into one temperature plane, and the shape of the temperature distribution on this plane can then be controlled by the 3D flame control method proposed in this paper. Three cases are studied for the proposed control methods and desired simulation results have been obtained

Fully gapped superconducting state in Au2Pb: a natural candidate for topological superconductor

We measured the ultra-low-temperature specific heat and thermal conductivity of Au$_2$Pb single crystal, a possible three-dimensional Dirac semimetal with a superconducting transition temperature $T_c \approx$ 1.05 K. The electronic specific heat can be fitted by a two-band s-wave model, which gives the gap amplitudes $\Delta_1$(0)/$k_BT_c$ = 1.38 and $\Delta_2$(0)/$k_BT_c$ = 5.25. From the thermal conductivity measurements, a negligible residual linear term $\kappa_0/T$ in zero field and a slow field dependence of $\kappa_0/T$ at low field are obtained. These results suggest that Au$_2$Pb has a fully gapped superconducting state in the bulk, which is a necessary condition for topological superconductor if Au$_2$Pb is indeed one.Comment: 6 pages, 4 figure