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

Generic photonic integrated linear operator processor

Photonic integration platforms have been explored extensively for optical computing with the aim of breaking the speed and power efficiency limitations of traditional digital electronic computers. Current technologies typically focus on implementing a single computation iteration optically while leaving the intermediate processing in the electronic domain, which are still limited by the electronic bottlenecks. Few explorations have been made of all-optical recursive architectures for computations on integrated photonic platforms. Here we propose a generic photonic integrated linear operator processor based on an all-optical recursive system that supports linear operations ranging from matrix computations to solving equations. We demonstrate the first all-optical on-chip matrix inversion system and use this to solve integral and differential equations. The absence of electronic processing during multiple iterations indicates the potential for an orders-of-magnitudes speed enhancement of this all-optical computing approach compared to electronic computers. We realize matrix inversions, Fredholm integral equations of the second kind, 2^{nd} order ordinary differential equations, and Poisson equations using the generic photonic integrated linear operator processor

Polarization-dependent nonlinear optical microscopy methods for the analysis of crystals and biological tissues

The ability to solve a high-resolution protein structure is largely dependent on the successful generation and identification of protein crystals prior to X-ray diffraction (XRD). For novel protein targets, high-throughput crystallography often involves generation of multiple targets and thousands of crystallization trials per target to generate diffraction-quality crystals. Second harmonic generation (SHG) imaging has been developed as a fast, non-destructive and sensitive method for the selective identification of protein crystals, even in highly scattering environments. Polarization-dependent SHG microscopy methods were developed to assess the presence of multidomain crystals to provide a handle on crystal quality. In addition, polarization-dependent two-photon excited fluorescence (TPEF) microscopy was developed as a complementary method to SHG, providing selectivity based on the presence of protein and crystalline order, thereby reducing the potential for false negatives and positives that can arise with SHG and conventional TPEF imaging. Novel instrumentation, data acquisition methods, and data analysis techniques were developed for quantitative polarization-modulated SHG microscopy at imaging speeds up to video rate, offering significantly greater signal to noise ratios compared to polarization modulation through the manual rotation of wave plates. Quantitative polarization-dependent SHG imaging was extended to the analysis of collagen structures in biological tissues, where local-frame second order susceptibility tensors were solved for every pixel within an image of collagenous tissue and combined with ab initio modeling to assess internal ordering of collagen fibers in different tissue types

Theory of Quantum Path Computing with Fourier Optics and Future Applications for Quantum Supremacy, Neural Networks and Nonlinear Schr\"odinger Equations

The scalability, error correction and practical problem solving are important challenges for quantum computing (QC) as more emphasized by quantum supremacy (QS) experiments. Quantum path computing (QPC), recently introduced for linear optic based QCs (LOQCs) as an unconventional design, targets to obtain scalability and practical problem solving. It samples the intensity from the interference of exponentially increasing number of propagation paths obtained in multi-plane diffraction (MPD) of classical particle sources. QPC exploits MPD based quantum temporal correlations of the paths and freely entangled projections a<t different time instants, for the first time, with the classical light source and intensity measurement while not requiring photon interactions or single photon sources and receivers. In this article, photonic QPC is defined, theoretically modeled and numerically analyzed for arbitrary Fourier optical or quadratic phase set-ups while utilizing both Gaussian and Hermite-Gaussian source laser modes. Problem solving capabilities already including partial sum of Riemann theta functions are extended. Important future applications, implementation challenges and open issues such as universal computation and quantum circuit implementations determining the scope of QC capabilities are discussed. The applications include QS experiments reaching more than $2^{100}$ Feynman paths, quantum neuron implementations and solutions of nonlinear Schr\"odinger equation.Comment: This is the author accepted copy of the original article published and fully edited in https://www.nature.com/articles/s41598-020-67364-

Advanced in-situ layer-wise quality control for laser-based additive manufacturing using image sequence analysis

Quality assurance has been one of the major challenges in laser-based additive manufacturing (AM) processes. This study proposes a novel process modeling methodology for layer-wise in-situ quality monitoring based on image series analysis. An image-based autoregressive (AR) model has been proposed based on the image registration function between consecutively observed thermal images. Image registration is used to extract melt pool location and orientation change between consecutive images, which contains sensing stability information. Subsequently, a Gaussian process model is used to characterize the spatial correlation within the error matrix. Finally, the extracted features from the aforementioned processes are jointly used for layer-wise quality monitoring. A case study of a thin wall fabrication by a Directed Laser Deposition (DLD) process is used to demonstrate the effectiveness of the proposed methodology

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world