Learning-Based Real-Time Event Identification Using Rich Real PMU Data

A large-scale deployment of phasor measurement units (PMUs) that reveal the inherent physical laws of power systems from a data perspective enables an enhanced awareness of power system operation. However, the high-granularity and non-stationary nature of PMU time series and imperfect data quality could bring great technical challenges to real-time system event identification. To address these issues, this paper proposes a two-stage learning-based framework. At the first stage, a Markov transition field (MTF) algorithm is exploited to extract the latent data features by encoding temporal dependency and transition statistics of PMU data in graphs. Then, a spatial pyramid pooling (SPP)-aided convolutional neural network (CNN) is established to efficiently and accurately identify operation events. The proposed method fully builds on and is also tested on a large real dataset from several tens of PMU sources (and the corresponding event logs), located across the U.S., with a time span of two consecutive years. The numerical results validate that our method has high identification accuracy while showing good robustness against poor data quality

Deep Representation Learning and Prediction for Forest Wildfires

An average of 8000 forest wildfires occurs each year in Canada burning an average of 2.5M ha/year as reported by the Government of Canada. Given the current rate of climate change, this number is expected to increase each year. Being able to predict how the fires spread would play a critical role in fire risk management. However, given the complexity of the natural processes that influence a fire system, most of the models used for simulating wildfires are computationally expensive and need a high variety of information about the environmental parameters to be able to give good performances. Deep learning algorithms allow computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined in terms of its relation to simpler concepts. We propose a deep learning predictor that uses a Deep Convolutional Auto-Encoder to learn the key structures of a forest wildfire spread from images and a Long Short Term Memory to predict the next phase of the fire. We divided the predictor problem in three phases: find a dataset of wildfires, learning the essential structure of forest fire, and predict the next image. We first present the simulated wildfires dataset and the algorithm we applied on it to make it more suitable to the model. Then we present the Deep Forest Wildfire Auto-Encoder and its implementation using the Caffe framework. Particular attention is given to the design considerations and to the best practice used to implement the model. We also present the design of the Deep Forest Wildfire Predictor, and some possible future variations of it

ディープラーニングを用いた視覚運動学習による適応的な描画行為

早大学位記番号:新7963早稲田大

Data-Efficient Machine Learning with Focus on Transfer Learning

Machine learning (ML) has attracted a significant amount of attention from the artifi- cial intelligence community. ML has shown state-of-art performance in various fields, such as signal processing, healthcare system, and natural language processing (NLP). However, most conventional ML algorithms suffer from three significant difficulties: 1) insufficient high-quality training data, 2) costly training process, and 3) domain dis- crepancy. Therefore, it is important to develop solutions for these problems, so the future of ML will be more sustainable. Recently, a new concept, data-efficient ma- chine learning (DEML), has been proposed to deal with the current bottlenecks of ML. Moreover, transfer learning (TL) has been considered as an effective solution to address the three shortcomings of conventional ML. Furthermore, TL is one of the most active areas in the DEML. Over the past ten years, significant progress has been made in TL. In this dissertation, I propose to address the three problems by developing a software- oriented framework and TL algorithms. Firstly, I introduce a DEML framework and a evaluation system. Moreover, I present two novel TL algorithms and applications on real-world problems. Furthermore, I will first present the first well-defined DEML framework and introduce how it can address the challenges in ML. After that, I will give an updated overview of the state-of-the-art and open challenges in the TL. I will then introduce two novel algorithms for two of the most challenging TL topics: distant domain TL and cross-modality TL (image-text). A detailed algorithm introduction and preliminary results on real-world applications (Covid-19 diagnosis and image clas- sification) will be presented. Then, I will discuss the current trends in TL algorithms and real-world applications. Lastly, I will present the conclusion and future research directions

Material Management Framework utilizing Near Real-Time Monitoring of Construction Operations

Materials management is a vital process in the delivery of construction facilities. Studies by the Construction Industry Institute (CII) have demonstrated that materials and installed equipment can constitute 40– 70% of the total construction hard cost and affect 80% of the project schedule. Despite its significance, most of the construction industry sectors are suffering from poor material management processes including inaccurate warehouse records, over-ordering and large surpluses of material at project completion, poor site storage practices, running out of materials, late deliveries, double-handling of components, out-of-specification material, and out of sequence deliveries which all result in low productivity, delay in construction and cost overruns. Inefficient material management can be attributed to the complex, unstructured, and dynamic nature of the construction industry, which has not been considered in a large number of studies available in this field. The literature reveals that available computer-based materials management systems focus on (1) integration of the materials management functions, and (2) application of Automated Data Collection (ADC) technologies to collect materials localization and tracking data for their computerized materials management systems. Moreover in studies that focused on applying ADC technologies in construction materials management, positioning and tracking critical resources in construction sites, and identifying unique materials received at the job site are the main applications of their used technologies. Even though, various studies have improved materials management processes copiously in the construction industry, the benefits of considering the dynamic nature of construction (in terms of near real-time progress monitoring using state of the art technologies and techniques) and its integration with a dynamic materials management system have been left out. So, in contrast with other studies, this research presents a construction materials management framework capable of considering the dynamic nature of construction projects. It includes a vital component to monitor project progress in near real-time to estimate the installation and consumption of materials. This framework consists of three models: “preconstruction model,” “construction model,” and “data analysis and reporting model.” This framework enables (1) generation of optimized material delivery schedules based on Material Requirement Planning (MRP) and minimum total cost, (2) issuance of material Purchase Orders (POs) according to optimized delivery schedules, (3) tracking the status of POs (Expediting methods), (4) collection and assessment of material data as it arrives on site, (5) considering the inherent dynamics of construction operations by monitoring project progress to update project schedule and estimate near real-time consumption of materials and eventually (6) updating MRP and optimized delivery schedule frequently throughout the construction phase. An optimized material delivery schedule and an optimized purchase schedule with the least cost are generated by the preconstruction model to avoid consequences of early/late purchasing and excess/inadequate purchasing. Accurate assessment of project progress and estimation of installed or consumed materials are essential for an effective construction material management system. The construction model focuses on the collection of near real-time site data using ADC technologies. Project progress is visualized from two different perspectives, comparing as-built with as-planned and comparing various as-built status captured on consecutive points of time. Due to the recent improvements in digital photography and webcams, which made this technology more cost-effective and practical for monitoring project progress, digital imaging (including 360° images) is selected and applied for project progress monitoring in the construction (data acquisition) model. In the last model, which is the data analysis and reporting model, Deep Learning (DL) and image processing algorithms are proposed to visualize and detect actual progress in terms of built elements in near real-time. In contrast with the other studies in which conventional computer vision algorithms are often used to monitor projects progress, in this research, a deep Convolutional Auto-Encoder (CAE) and Mask Region-based Convolutional Neural Network (R-CNN) are utilized to facilitate vision-based indoor and outdoor progress monitoring of construction operations. The updated project schedule based on the actual progress is the output of this model, and it is used as the primary input for the developed material management framework to update MRP, optimized material delivery, and purchase schedules, respectively. Applicability of the models in the developed material management framework has been tested through laboratory and field experiments. The results demonstrated the accuracy and capabilities of the developed models in the framework