Transactive Energy Management with Blockchain Smart Contracts for P2P Multi-Settlement Markets

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Integration of renewables and energy storage, leading to rise of prosumers, has created localized bidirectional flows. As the result, the utility demand has decreased and traditional centralized controller can no longer realize the optimal performance of ever growing distribution systems. To achieve scalable control, exploiting the potential of smart loads and Distributed Energy Resource (DER) controllability, a framework for decentralized Peer-To-Peer (P2P) energy management has been developed to manage localized micro-energy markets. Such decentralized management approach could, in theory, sustain diverse prosumer and utility business models. We have been developing an autonomous decentralized management solution that maximizes the benefit of prosumers while protecting utility assets. This P2P energy trading market leverages Blockchain technology and its Smart Contract framework. This paper presents 1) transactive energy market for P2P multi-settlement markets, 2) architecture of blockchain-based energy management system, 3) smart contract design that solves an economic dispatch problem of DERs to maximize the profit of pro/consumers.

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Multi-parameter distributed fiber sensing with higherorder optical and acoustic modes

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We propose a novel multi-parameter sensing technique based on a Brillouin optical time domain reflectometry in the elliptical-core few-mode fiber, using higher-order optical and acoustic modes. Multiple Brillouin peaks are observed for the backscattering of both the LP01 mode and LP11 mode. We characterize the temperature and strain coefficients for various optical–acoustic mode pairs. By selecting the proper combination of modes pairs, the performance of multi-parameter sensing can be optimized. Distributed sensing of temperature and strain is demonstrated over a 0.5-km elliptical-core few-mode fiber, with the discriminative uncertainty of 0.28°C and 5.81 ?? for temperature and strain, respectively.

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Visual Entailment: A Novel Task for Fine-Grained Image Understanding

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Existing visual reasoning datasets, such as Visual Question Answering (VQA), often suffer from biases conditioned on the question, image or answer distributions. The recently proposed CLEVR dataset addresses these limitations and requires fine-grained reasoning, but the dataset is synthetic and consists of similar objects and sentence structures across the dataset. In this paper, we introduce a new inference task, Visual Entailment (VE) – consisting of image-sentence pairs whereby a premise is defined by an image, rather than a natural language sentence as in traditional Textual Entailment tasks. The goal of a trained VE model is to predict whether the image semantically entails the text. To realize this task, we build a dataset SNLI-VE based on the Stanford Natural Language Inference corpus and Flickr30k dataset. We evaluate various existing VQA baselines and build a model called Explainable Visual Entailment (EVE) system to address the VE task. EVE achieves up to 71% accuracy and outperforms several other state-of-the-art VQA based models. Finally, we demonstrate the explainability of EVE through cross-modal attention visualizations.

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A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

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Nowadays, multivariate time series data are increasingly collected in various real-world systems, e.g., power plants, wearable devices, etc. Anomaly detection and diagnosis in multivariate time series refer to identifying abnormal status in certain time steps and pinpointing the root causes. Building such a system, however, is challenging since it not only requires to capture the temporal dependency in each time series, but also need encode the inter-correlations between different pairs of time series. In addition, the system should be robust to noise and provide operators with different levels of anomaly scores based upon the severity of different incidents. Despite the fact that a number of unsupervised anomaly detection algorithms have been developed, few of them can jointly address these challenges. In this paper, we propose a Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED), to perform anomaly detection and diagnosis in multivariate time series data. Specifically, MSCRED first constructs multi-scale (resolution) signature matrices to characterize multiple levels of the system statuses in different time steps. Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns. Finally, based upon the feature maps which encode the inter-sensor correlations and temporal information, a convolutional decoder is used to reconstruct the input signature matrices and the residual signature matrices are further utilized to detect and diagnose anomalies. Extensive empirical studies based on a synthetic dataset and a real power plant dataset demonstrate that MSCRED can outperform state-of-the-art baseline methods.

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Coherent optical wireless communication link employing orbital angular momentum multiplexing in a ballistic and diffusive scattering medium

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We experimentally investigate the scattering effect on an 80 Gbit/s orbital angular momentum (OAM) multiplexed optical wireless communication link. The power loss, mode purity, cross talk, and bit error rate performance are measured and analyzed for different OAM modes under scattering levels from ballistic to diffusive regions. Results show that (i) power loss is the main impairment in the ballistic scattering, while the mode purities of different OAM modes are not significantly affected; (ii) in the diffusive scattering, however, the performance of an OAM-multiplexed link further suffers from the increased cross talk between the different OAM modes.

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Memory Warps for Long-Term Online Video Representations and Anticipation

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We propose a novel memory-based online video representation that is efficient, accurate and predictive. This is in contrast to prior works that often rely on computationally heavy 3D convolutions, ignore motion when aligning features over time, or operate in an off-line mode to utilize future frames. In particular, our memory (i) holds the feature representation, (ii) is spatially warped over time to compensate for observer and scene motions, (iii) can carry long-term information, and (iv) enables predicting feature representations in future frames. By exploring a variant that operates at multiple temporal scales, we efficiently learn across even longer time horizons. We apply our online framework to object detection in videos, obtaining a large 2.3 times speed-up and losing only 0.9% mAP on ImageNet-VID dataset, compared to prior works that even use future frames. Finally, we demonstrate the predictive property of our representation in two novel detection setups, where features are propagated over time to (i) significantly enhance a real-time detector by more than 10% mAP in a multi-threaded online setup and to (ii) anticipate objects in future frames.

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Attentive Conditional Channel-Recurrent Autoencoding for Attribute-Conditioned Face Synthesis

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Attribute-conditioned face synthesis has many potential use cases, such as to aid the identification of a suspect or a missing person. Building on top of a conditional version of VAE-GAN, we augment the pathways connecting the latent space with channel-recurrent architecture, in order to provide not only improved generation qualities but also interpretable high-level features. In particular, to better achieve the latter, we further propose an attention mechanism over each attribute to indicate the specific latent subset responsible for its modulation. Thanks to the latent semantics formed via the channel-recurrency, we envision a tool that takes the desired attributes as inputs and then performs a 2-stage general-to-specific generation of diverse and realistic faces. Lastly, we incorporate the progressive-growth training scheme to the inference, generation and discriminator networks of our models to facilitate higher resolution outputs. Evaluations are performed through both qualitative visual examination and quantitative metrics, namely inception scores, human preferences, and attribute classification accuracy.

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41.5-Tb/s Transmission Over 549 km of Field Deployed Fiber Using Throughput Optimized Probabilistic-Shaped 144QAM

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We demonstrate high spectral efficiency transmission over 549 km of field-deployed single-mode fiber using probabilistic-shaped 144QAM. We achieved 41.5 Tb/s over the C-band at a spectral efficiency of 9.02 b/s/Hz using 32-Gbaud channels at a channel spacing of 33.33 GHz, and 38.1 Tb/s at a spectral efficiency of 8.28 b/s/Hz using 48-Gbaud channels at a channel spacing of 50 GHz. To the best of our knowledge, these are the highest total capacities and spectral efficiencies reported in a metro field environment using C-band only. In high spectral efficiency transmission, it is necessary to optimize back-to-back performance in order to maximize the link loss margin. Our results are enabled by the joint optimization of constellation shaping and coding overhead to minimize the gap to Shannon’s capacity, transmitter- and receiver-side digital backpropagation, signal clipping optimization, and I/Q imbalance compensation.

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Battery Degradation Temporal Modeling Using LSTM Networks

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Accurate modeling of battery capacity degradation is an important component for both battery manufacturers and energy management systems. In this paper, we develop a battery degradation model using deep learning algorithms. The model is trained with the real data collected from battery storage solutions installed and operated for behind-the-meter customers. In the dataset, battery operation data are recorded at a small scale (five minutes) and battery capacity is measured at every six months. In order to improve the training performance, we apply two preprocessing techniques, namely subsampling and feature extraction on operation data, and also interpolating between capacity measurements at times for which battery operation features are available. We integrate both cyclic and calendar aging processes in a unified framework by extracting the corresponding features from operation data. The proposed model uses LSTM units followed by a fully-connected network to process weekly battery operation features and predicts the capacity degradation. The experimental results show that our method can accurately predict the capacity fading and significantly outperforms baseline models including persistence and autoregressive (AR) models.

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Conditioning Neural Networks: A Case Study of Electrical Load Forecasting

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Machine learning tasks typically involve minimizing a loss function that measures the distance of the model output and the ground-truth. In some applications, in addition to the usual loss function, the output must also satisfy certain requirements for further processing. We call such requirements model conditioning. We investigate cases where the conditioner is not differentiable or cannot be expressed in closed form and, hence, cannot be directly included in the loss function of the machine learning model. We propose to replace the conditioner with a learned dummy model which is applied on the output of the main model. The entire model, composed of the main and dummy models, is trained end-to-end. Throughout training, the dummy model learns to approximate the conditioner and, thus, forces the main model to generate outputs that satisfy the specified requirements. We demonstrate our approach on a use-case of demand charge-aware electricity load forecasting. We show that jointly minimizing the error in forecast load and its demand charge threshold results in significant improvement to existing load forecast methods.

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