Driven by inherent uncertainty and the sim-to-real gap, robust reinforcement learning (RL) seeks to improve resilience against the complexity and variability in agent-environment sequential interactions. Despite the existence of a large number of RL benchmarks, there is a lack of standardized benchmarks for robust RL. Current robust RL policies often focus on a specific type of uncertainty and are evaluated in distinct, one-off environments. In this work, we introduce Robust-Gymnasium, a unified modular benchmark designed for robust RL that supports a wide variety of disruptions across all key RL components—agents' observed state and reward, agents' actions, and the environment. Offering over sixty diverse task environments spanning control and robotics, safe RL, and multi-agent RL, it provides an open-source and user-friendly tool for the community to assess current methods and foster the development of robust RL algorithms. In addition, we benchmark existing standard and robust RL algorithms within this framework, uncovering significant deficiencies in each and offering new insights.

Constrained reinforcement learning (RL) seeks high-performance policies under safety constraints. We focus on an offline setting where the agent learns from a fixed dataset—a common requirement in realistic tasks to prevent unsafe exploration. To address this, we propose Diffusion-Regularized Constrained Offline Reinforcement Learning (DRCORL), which first uses a diffusion model to capture the behavioral policy from offline data and then extracts a simplified policy to enable efficient inference. We further apply gradient manipulation for safety adaptation, balancing the reward objective and constraint satisfaction. This approach leverages high-quality offline data while incorporating safety requirements. Empirical results show that DRCORL achieves reliable safety performance, fast inference, and strong reward outcomes across robot learning tasks. Compared to existing safe offline RL methods, it consistently meets cost limits and performs well with the same hyperparameters, indicating practical applicability in real-world scenarios. We open-source our implementation at https://github.com/JamesJunyuGuo/DRCORL.

Reinforcement learning (RL) is a powerful tool for optimal control that has found great success in Atari games, the game of Go, robotic control, and building optimization. RL is also very brittle; agents often overfit to their training environment and fail to generalize to new settings. Unsupervised environment design (UED) has been proposed as a solution to this problem, in which the agent trains in environments that have been specially selected to help it learn. Previous UED algorithms focus on trying to train an RL agent that generalizes across a large distribution of environments. This is not necessarily desirable when we wish to prioritize performance in one environment over others. In this work, we will be examining the setting of robust RL building control, where we wish to train an RL agent that prioritizes performing well in normal weather while still being robust to extreme weather conditions. We demonstrate a novel UED algorithm, ActivePLR, that uses uncertainty-aware neural network architectures to generate new training environments at the limit of the RL agent's ability while being able to prioritize performance in a desired base environment. We show that ActivePLR is able to outperform state-of-the-art UED algorithms in minimizing energy usage while maximizing occupant comfort in the setting of building control.

Safe reinforcement learning (RL) is crucial for deploying RL agents in real-world applications, as it aims to maximize long-term rewards while satisfying safety constraints. However, safe RL often suffers from sample inefficiency, requiring extensive interactions with the environment to learn a safe policy. We propose Efficient Safe Policy Optimization (ESPO), a novel approach that enhances the efficiency of safe RL through sample manipulation. ESPO employs an optimization framework with three modes: maximizing rewards, minimizing costs, and balancing the trade-off between the two. By dynamically adjusting the sampling process based on the observed conflict between reward and safety gradients, ESPO theoretically guarantees convergence, optimization stability, and improved sample complexity bounds. Experiments on the Safety-MuJoCo and Omnisafe benchmarks demonstrate that ESPO significantly outperforms existing primal-based and primal-dual-based baselines in terms of reward maximization and constraint satisfaction. Moreover, ESPO achieves substantial gains in sample efficiency, requiring 25--29\% fewer samples than baselines, and reduces training time by 21--38\%.

Background: At the onset of a pandemic, such as COVID-19, data with proper labeling/attributes corresponding to the new disease might be unavailable or sparse. Machine Learning (ML) models trained with the available data, which is limited in quantity and poor in diversity, will often be biased and inaccurate. At the same time, ML algorithms designed to fight pandemics must have good performance and be developed in a time-sensitive manner. To tackle the challenges of limited data, and label scarcity in the available data, we propose generating conditional synthetic data, to be used alongside real data for developing robust ML models. Methods: We present a hybrid model consisting of a conditional generative flow and a classifier for conditional synthetic data generation. The classifier decouples the feature representation for the condition, which is fed to the flow to extract the local noise. We generate synthetic data by manipulating the local noise with fixed conditional feature representation. We also propose a semi-supervised approach to generate synthetic samples in the absence of labels for a majority of the available data. Results: We performed conditional synthetic generation for chest computed tomography (CT) scans corresponding to normal, COVID-19, and pneumonia afflicted patients. We show that our method significantly outperforms existing models both on qualitative and quantitative performance, and our semi-supervised approach can efficiently synthesize conditional samples under label scarcity. As an example of downstream use of synthetic data, we show improvement in COVID-19 detection from CT scans with conditional synthetic data augmentation.

Modern buildings have multiple distributed electrical resources (DERs) such as renewable energy sources and energy storage devices, flexible loads, and utility connection. Coupled with smart meters and new energy pricing plans, these physical assets present new opportunities for building operators to save energy, reduce operational costs, avoid carbon emissions and improve occupant comfort. The concept of Energy Nodes is proposed to represent the electrical resources and loads within a building and their electrical interactions, which can be programmed/controlled through software. A scaled-down hardware emulation test-bed representing commercial buildings is developed with commercial DER interfaces, controllable loads and Transactive Energy based Energy Management System to realize a cyber-physical Energy Node. Through studies on this test-bed, it is demonstrated that the proposed energy nodes can automatically act based on building operator's economy preferences, save electricity bills, and provide Demand Response service by reacting to electricity price signals from the markets.

Air-conditioning and mechanical ventilation (ACMV), power distribution systems, elevators, and lighting systems are critical electrical assets of a smart building. Based on electrical components of these assets such as electrical motors, transformers, solid-state lighting and power electronic converters, a detailed analysis of failure mechanisms and statistics is presented in this paper. Further, suitable electrical and mechanical signals along with prevalent condition monitoring methods are suggested for condition monitoring of these assets based on the presented statistical analysis. This paper lays down the necessary foundation to understand the operating model of critical electrical equipment in buildings and provides a holistic picture for implementation of a predictive and preventive maintenance paradigm. This will enable the building operators to identify potential failures of building equipment and their evolution in real-time, driving decisions on scheduling repair or replacement of components to minimize downtime, save maintenance costs, improve occupant satisfaction, save energy, and maximize resilience.

Predicting blood glucose concentration facilitates timely preventive measures against health risks induced by abnormal glucose events. Advances in IoT devices, such as continuous blood glucose monitors (CGMs) have made it convenient for measurements of blood glucose in real time. However, accurate and personalized blood glucose concentration prediction is still challenging. Previous inference models yield low-inference accuracy due to the ineffective feature extraction and the limited, imbalanced personal training data. The underlying causal correlations among the blood glucose series are scarcely captured by these models. In this article, we propose CausalBG, a causal recurrent neural network (CausalRNN) deployed on an IoT platform with smartphones and CGM for the accurate and efficient individual blood glucose concentration prediction. CausalBG automatically captures the underlying causal relationships embedded in the blood glucose features through CausalRNN, and efficiently shares the limited personal data among users for the sufficient training via the multitask framework. Evaluations and case studies on 112 users demonstrate that CausalBG significantly outperforms the conventional predictive models on the blood glucose dynamics inference.

Location-based service (LBS) has become an indispensable part of our daily lives. Realizing accurate LBS in indoor environments is still a challenging task. WiFi fingerprinting-based indoor positioning system (IPS) has achieved encouraging results recently, but the time and labor overhead of constructing a dense WiFi radio map remains the key bottleneck that hinders it for real-world large-scale implementation. In this article, we propose WiGAN an automatic fine-grained indoor ratio map construction and the adaptation scheme empowered by the Gaussian process regression conditioned least-squares generative adversarial networks (GPR-GANs) with a mobile robot. First, we develop a mobile robotic platform that constructs the spatial map and radio map simultaneously in the easily accessed free space. GPR-GAN first establishes a Gaussian process regression (GPR) model using the real received signal strength (RSS) measurements collected by our robotic platform via LiDAR SLAM in the free space. Then, the outputs of the GPR are adopted as the input of GAN's generator. The learning objective of GAN is to synthesize realistic RSS data in a constrained space where it has not been covered and model the irregular RSS distributions in complex indoor environments. Real-world experiments were conducted in a real-world indoor environment, which confirms the feasibility, high accuracy, and superiority of WiGAN over existing solutions in terms of both RSS estimation accuracy and localization accuracy.

Due to the development of sensor networks and information technology, data-driven fault detection and diagnosis (FDD) has been made possible with real-time multiple sensor measurements. However, due to inevitable sensor errors or communication failures, the raw data are usually incomplete with corrupted values, lost values, or undetected missing values. In practice, the incomplete data are usually dealt with by directly excluding incomplete measurements and abnormal spikes. In addition, some preprocessing methods, which naively impute data though averaging or smoothing, have also been widely applied. In this article, we address the building FDD problem with incomplete data by proposing a new approach, the adjacent information recovery (AIR) filter. The AIR filter is utilized to deal with the FDD for a typical air handling unit (AHU) system with incomplete data based on the ASHRAE Research Project 1312. Experimental results show that the proposed method improves FDD performance by recovering missing sensor measurements and outperforms the state-of-the-art methods.

The control of heating, ventilation, and air-conditioning (HVAC) systems has raised extensive attention due to their high energy consumption cost and operation patterns far from being energy-efficient. However, most of the existing methods suffer limitations in scalability and computational efficiency for large buildings due to the centralized structures. To compensate for such defects, this article studies the scalable control of multizone HVAC systems with the target to reduce energy cost while maintaining thermal comfort. In particular, the thermal couplings due to heat transfer among the adjacent zones are incorporated, which has been ignored or not well studied due to complexity in the literature. To overcome the computational challenges of the nonlinear and nonconvex problem caused by the complex system dynamics, this article proposes a decentralized approach composed of three main steps: 1) relaxing the bilinear system dynamics; 2) solving the relaxed problem in a decentralized manner using the accelerated distributed augmented Lagrangian (ADAL) method; and 3) recovering the recursive feasibility of the solution. Through a comparison with the centralized method, the suboptimality of this approach is demonstrated. In addition, the superior performance of this approach is illustrated through a comparison with the distributed token-based scheduling strategy (DTBSS). The numerical results imply that for buildings with a relatively small number of zones (less than 20), the two methods are competitive. However, for larger cases, the proposed approach performs better with a considerable reduction both in energy cost and computation time.

This paper addresses the problem of efficiently deploying sensors in spatial environments, e.g., buildings, for the purposes of monitoring spatio-temporal environmental phenomena. By modeling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality-cost function of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is proven not to be affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment optimization problem is then solved by a practical and feasible polynomial algorithm, where its solutions are theoretically proven to be guaranteed. The proposed method is also theoretically and experimentally compared with the existing works. The effectiveness of the proposed algorithm is demonstrated by implementation in a real tested space in a university building, where the obtained results are highly promising.

We propose a novel domain adaptation framework, namely Consensus Adversarial Domain Adaptation (CADA), that gives freedom to both target encoder and source encoder to embed data from both domains into a common domaininvariant feature space until they achieve consensus during adversarial learning. In this manner, the domain discrepancy can be further minimized in the embedded space, yielding more generalizable representations. The framework is also extended to establish a new few-shot domain adaptation scheme (F-CADA), that remarkably enhances the ADA performance by efficiently propagating a few labeled data once available in the target domain. Extensive experiments are conducted on the task of digit recognition across multiple benchmark datasets and a real-world problem involving WiFi-enabled device-free gesture recognition under spatial dynamics. The results show the compelling performance of CADA versus the state-of-the-art unsupervised domain adaptation (UDA) and supervised domain adaptation (SDA) methods. Numerical experiments also demonstrate that F-CADA can significantly improve the adaptation performance even with sparsely labeled data in the target domain.

{\em “How much is my data worth?”} is an increasingly common question posed by organizations and individuals alike. An answer to this question could allow, for instance, fairly distributing profits among multiple data contributors and determining prospective compensation when data breaches happen. In this paper, we study the problem of \emph{data valuation} by utilizing the Shapley value, a popular notion of value which originated in coopoerative game theory. The Shapley value defines a unique payoff scheme that satisfies many desiderata for the notion of data value. However, the Shapley value often requires \emph{exponential} time to compute. To meet this challenge, we propose a repertoire of efficient algorithms for approximating the Shapley value. We also demonstrate the value of each training instance for various benchmark datasets.

Energy game-theoretic frameworks have emerged to be a successful strategy to encourage energy efficient behavior in large scale by leveraging human-in-the-loop strategy. A number of such frameworks have been introduced over the years which formulate the energy saving process as a competitive game with appropriate incentives for energy efficient players. However, prior works involve an incentive design mechanism which is dependent on knowledge of utility functions for all the players in the game, which is hard to compute especially when the number of players is high, common in energy game-theoretic frameworks. Our research proposes that the utilities of players in such a framework can be grouped together to a relatively small number of clusters, and the clusters can then be targeted with tailored incentives. The key to above segmentation analysis is to learn the features leading to human decision making towards energy usage in competitive environments. We propose a novel graphical lasso based approach to perform such segmentation, by studying the feature correlations in a real-world energy social game dataset. To further improve the explainability of the model, we perform causality study using grangers causality. Proposed segmentation analysis results in characteristic clusters demonstrating different energy usage behaviors. We also present avenues to implement intelligent incentive design using proposed segmentation method.

In the research of smart buildings, human activity recognition is an important cornerstone for numerous emerging applications. Although several sensing techniques have been proposed for human activity identification, they require either the user instrumentation or additional infrastructure, that are inconvenient, privacy-intrusive, and expensive. To address these issues, a WiFi-enabled device-free human activity recognition system, namely SmartSense, is proposed in this paper. By upgrading commercial WiFi routers with our designed firmware, fine-grained channel state information (CSI) from PHY layer can be directly extracted from the existing WiFi traffic. In this paper, we propose SmartSense, a device-free human activity recognition system that only leverages existing commercial off-the-shelf WiFi routers. By exploiting the prevalence of WiFi infrastructure in buildings, we developed a novel CSI-enabled Internet of Things platform to collect the CSI measurements from regular data frames. To identify different human activities, a novel machine learning tool, namely multiple kernel semi-representation learning (MKSRL) method is established. MKSRL allows the input of expert domain knowledge in a flexible way and conducts automatic and effective multikernel representation learning for the activity recognition task. Each stage of MKSRL is computationally efficient and theoretically guaranteed, and they can be integrated seamlessly within the reproducing kernel framework for the overall information extraction, representation, and fusion. We conducted experiments to comprehensively evaluate the performance of SmartSense in three common indoor environments. Experimental results validate that SmartSense can provide an activity recognition accuracy of 98%, which achieves significant performance gain over the existing methods.

A fundamental building block toward personalized location-based service and context-aware service in smart buildings is the knowledge about the identity and mobility of users in indoor environments. Conventional user identification systems require the deployment of dedicated infrastructure or the active user involvement. Motivated by the widespread usage of the WiFi-enabled mobile device (MD), e.g., people usually carry at least one MD in their daily lives, in this paper, we propose WinDUA, a WiFi-enabled nonintrusive device and user association scheme to infer user identity and mobility via a novel unsupervised association learning algorithm. First, we utilize our WiFi-based indoor positioning system to obtain the historical location data of each MD using only existing WiFi infrastructure in a nonintrusive manner. Then, we classify all the MDs into two categories: 1) static device (SD) and 2) mobile phone (MP), according to their location variations and overnight presences. Subsequently, we estimate the correct mapping between each SD and its user through hierarchical clustering and location similarity matching between its location and user's personal space. Finally, we make possible pairs of MP and SD according to their duration of coexistence as well as the historical location similarity to associate the owner of each MP. Real-world experiments are conducted in an office, verifying that WinDUA is able to associate the MD to the correct users in a nonintrusive and unsupervised manner.

Given a data set D containing millions of data points and a data consumer who is willing to pay for $ X to train a machine learning (ML) model over D , how should we distribute this $X to each data point to reflect its "value"? In this paper, we define the "relative value of data" via the Shapley value, as it uniquely possesses properties with appealing real-world interpretations, such as fairness, rationality and decentralizability. For general, bounded utility functions, the Shapley value is known to be challenging to compute: to get Shapley values for all N data points, it requires O (2 N ) model evaluations for exact computation and O ( N log N ) for ( ϵ , δ)-approximation. In this paper, we focus on one popular family of ML models relying on K -nearest neighbors ( K NN). The most surprising result is that for unweighted K NN classifiers and regressors, the Shapley value of all N data points can be computed, exactly , in O ( N log N ) time - an exponential improvement on computational complexity! Moreover, for ( ϵ , δ)-approximation, we are able to develop an algorithm based on Locality Sensitive Hashing (LSH) with only sublinear complexity O ( N h ( ϵ , K ) log N ) when ϵ is not too small and K is not too large. We empirically evaluate our algorithms on up to 10 million data points and even our exact algorithm is up to three orders of magnitude faster than the baseline approximation algorithm. The LSH-based approximation algorithm can accelerate the value calculation process even further. We then extend our algorithm to other scenarios such as (1) weighed K NN classifiers, (2) different data points are clustered by different data curators , and (3) there are data analysts providing computation who also requires proper valuation. Some of these extensions, although also being improved exponentially, are less practical for exact computation (e.g., O ( N K ) complexity for weigthed K NN). We thus propose an Monte Carlo approximation algorithm, which is O ( N (log N ) 2 /(log K ) 2 ) times more efficient than the baseline approximation algorithm.

Thanks to the development of sensor networks and information technology, data-driven fault detection and diagnosis (FDD) is getting more and more popular with rich data. In the building FDD field, mature supervised learning algorithms and strategies have been applied to detect and diagnose known faults. However, it is out of the question to collect labeled training data for every possible fault. Thus, there is a necessity to study FDD when the training data for some faults are unavailable. To the authors' best knowledge, few works have reported how to identify “unseen faults.” In this paper, authors propose a novel expert knowledge-based unseen fault identification (EK-UFI) method to identify unseen faults by employing the similarities between known faults and unknown faults. The similarity is captured by incorporating essential expert knowledge that is encoded in the fault gene matrix. The fault gene is integrated with a latent incorporation matrix that transfers knowledge from known faults to unseen faults. With application to a real system, the proposed method is proven to be effective in identifying various building unknown faults with a high accuracy. Note to Practitioners- FDD is of great importance for saving energy and improving occupancy comfort levels and building safety levels. Identifying unseen faults in real application is challenging since: 1) building faults are complicated and confusing while well-labeled fault data is rare; 2) experimental fault data collected in laboratory test beds cannot be directly used as judgment criteria for real buildings; and 3) it is impossible to measure every possible fault ahead of time. Although supervised learning methods have been successfully applied in existing works to solve the building FDD, they could not attack the UFI problem. In this paper, a novel EK-UFI method is proposed to identify unseen faults by employing the similarities between known faults and unknown faults. Experimental results show that the proposed method is essential.

In this study we consider the problem of outlier detection with multiple co-evolving time series data. To capture both the temporal dependence and the inter-series relatedness, a multi-task non-parametric model is proposed, which can be extended to data with a broader exponential family distribution by adopting the notion of Bregman divergence. Albeit convex, the learning problem can be hard as the time series accumulate. In this regards, an efficient randomized block coordinate descent (RBCD) algorithm is proposed. The model and the algorithm is tested with a real-world application, involving outlier detection and event analysis in power distribution networks with high resolution multi-stream measurements. It is shown that the incorporation of inter-series relatedness enables the detection of system level events which would otherwise be unobservable with traditional methods.

We propose AutoID, a human identification system that leverages the measurements from existing WiFi-enabled Internet of Things (IoT) devices and produces the identity estimation via a novel sparse representation learning technique. The key idea is to use the unique fine-grained gait patterns of each person revealed from the WiFi Channel State Information (CSI) measurements, technically referred to as shapelet signatures, as the "fingerprint" for human identification. For this purpose, a novel OpenWrt-based IoT platform is designed to collect CSI data from commercial IoT devices. More importantly, we propose a new optimization-based shapelet learning framework for tensors, namely Convex Clustered Concurrent Shapelet Learning (C3SL), which formulates the learning problem as a convex optimization. The global solution of C3SL can be obtained efficiently with a generalized gradient-based algorithm, and the three concurrent regularization terms reveal the inter-dependence and the clustering effect of the CSI tensor data. Extensive experiments are conducted in multiple real-world indoor environments, showing that AutoID achieves an average human identification accuracy of 91% from a group of 20 people. As a combination of novel sensing and learning platform, AutoID attains substantial progress towards a more accurate, cost-effective and sustainable human identification system for pervasive implementations.

In the era of Internet of Things (IoT), human activity recognition is becoming the vital underpinning for a myriad of emerging applications in smart home and smart buildings. Existing activity recognition approaches require either the deployment of extra infrastructure or the cooperation of occupants to carry dedicated devices, which are expensive, intrusive and inconvenient for pervasive implementation. In this paper, we propose DeepSense, a device-free human activity recognition scheme that can automatically identify common activities via deep learning using only commodity WiFi-enabled IoT devices. We design a novel OpenWrt-based IoT platform to collect Channel State Information (CSI) measurements from commercial IoT devices. Moreover, an innovative deep learning framework, Autoencoder Long-term Recurrent Convolutional Network (AE-LRCN), is proposed. It consists of an autoencoder module, a convolutional neural network (CNN) module and a long short-term memory (LSTM) module, which aims to sanitize the noise in raw CSI data, extract high-level representative features and reveal the inherent temporal dependencies among data for accurate human activity recognition, respectively. All the hyperparameters in AE-LRCN are fine-tuned end-to-end automatically. Extensive experiments are conducted in typical indoor environments and the experimental results demonstrate that DeepSense outperforms existing methods and achieves a 97.6% activity recognition accuracy without human intervention.

Accurate human gesture recognition is becoming a cornerstone for myriad emerging applications in human-computer interaction. Existing gesture recognition systems either require dedicated extra infrastructure or user's active cooperation. Although some WiFi-enabled gesture recognition systems have been proposed, they are vulnerable to environmental dynamics and rely on the tedious data re-labeling and expert knowledge each time being implemented in a new environment. In this paper, we propose a WiFi- enabled device-free adaptive gesture recognition scheme, WiADG, that is able to identify human gestures accurately and consistently under environmental dynamics via adversarial domain adaptation. Firstly, a novel OpenWrt-based IoT platform is developed, enabling the direct collection of Channel State Information (CSI) measurements from commercial IoT devices. After constructing an accurate source classifier with labeled source CSI data via the proposed convolutional neural network in the source domain (original environment), we design an unsupervised domain adaptation scheme to reduce the domain discrepancy between the source and the target domain (new environment) and thus improve the generalization performance of the source classifier. The domain- adversarial objective is to train a generator (target encoder) to map the unlabeled target data to a domain invariant latent feature space so that a domain discriminator cannot distinguish the domain labels of the data. In the phase of implementation, we utilize the trained target encoder to map the target CSI frame to the latent feature space and use the source classifier to identify various gestures performed by the user. We implement WiADG on commercial WiFi routers and conduct experiments in multiple indoor environments. The results validate that WiADG achieves 98% gesture recognition accuracy in the original environment. Furthermore, the proposed unsupervised adversarial domain adaptation is able to enhance the recognition accuracy of WiADG by 25% on average without the needs of labeled data collection and new classifier generation when implements it in new environments.

Human gesture recognition plays a critical role in numerous applications of human-computer interaction. By analyzing how gesture alters the WiFi propagation among WiFi-enabled IoT devices to identify the gestures in a device-free manner could be a promising solution. However, existing methods require tedious data collection and labeling process each time being implemented in a new environment. The classifier constructed by SVM or random forest is vulnerable to spatial dynamics. In this paper, we proposed JADA, a novel unsupervised Joint adversarial domain adaptation (JADA) scheme that realizes accurate and resilient WiFi-enabled device-free gesture recognition without collecting and labeling training data in new environments. After constructing a source encoder and a source classifier in the source domain by convolutional neural network, JADA trains a target encoder and also fine-tunes the source encoder through adversarial learning to map both unlabeled target data and labeled source data to a domain-invariant feature space such that a domain discriminator cannot distinguish the domain labels of the data. After training a shared classifier with the labeled source data while fixing the parameters of the source encoder, we employ the trained target encoder to embed the test target samples into the domain-invariant feature space and infer its class using the shared classifier. We develop a novel Channel State Information (CSI) enabled IoT platform that could obtain fine-grained CSI time series data directly from IoT devices and transform them into CSI frames. Real-world experiments with COTS WiFi routers were conducted in 2 indoor environments. The experimental results demonstrate that JADA achieves 98.75% gesture recognition accuracy in the original environment. Moreover, when the environmental scenario is altered, it is able to reduce the domain discrepancy across domains without collecting any labeled data in the new context.

Smart buildings today are aimed at providing safe, healthy, comfortable, affordable, and beautiful spaces in a carbon and energy-efficient way. They are emerging as complex cyber-physical systems with humans in the loop. Cost, the need to cope with increasing functional complexity, flexibility, fragmentation of the supply chain, and time-to-market pressure are rendering the traditional heuristic and ad hoc design paradigms inefficient and insufficient for the future. In this paper, we present a platform-based methodology for smart building design. Platform-based design (PBD) promotes the reuse of hardware and software on shared infrastructures, enables rapid prototyping of applications, and involves extensive exploration of the design space to optimize design performance. In this paper, we identify, abstract, and formalize components of smart buildings, and present a design flow that maps high-level specifications of desired building applications to their physical implementations under the PBD framework. A case study on the design of on-demand heating, ventilation, and air conditioning (HVAC) systems is presented to demonstrate the use of PBD.

Sensing by proxy (SbP) is proposed in this paper as a sensing paradigm for occupancy detection, where the inference is based on “proxy” measurements such as temperature and CO2 concentrations. The effects of occupants on indoor environments are captured by constitutive models comprising a coupled partial differential equation-ordinary differential equation system that exploits the spatial and physical features. Sensor fusion of multiple environmental parameters is enabled in the proposed framework. We report on experiments conducted under simulated conditions and real-life circumstances, when the variation of occupancy follows a schedule as the ground truth. The inference of the number of occupants in the room based on CO2 concentration at the air return and air supply vents by our approach achieves an overall mean squared error of 0.6044 (fractional person), while the best alternative by Bayes net is 1.2061 (fractional person). Results from the projected ventilation analysis show that SbP can potentially save 55% of total ventilation compared with the traditional fixed schedule ventilation strategy, while at the same time maintain a reasonably comfort profile for the occupants.

Cyber-physical systems have enabled the collection of massive amounts of data in an unprecedented level of spatial and temporal granularity. Publishing these data can prosper big data research, which, in turn, helps improve overall system efficiency and resiliency. The main challenge in data publishing is to ensure the usefulness of published data while providing necessary privacy protection. In our previous work (Jia et al. 2017a), we presented a privacy-preserving data publishing framework (referred to as PAD hereinafter), which can guarantee k -anonymity while achieving better data utility than traditional anonymization techniques. PAD learns the information of interest to data users or features from their interactions with the data publishing system and then customizes data publishing processes to the intended use of data. However, our previous work is only applicable to the case where the desired features are linear in the original data record. In this article, we extend PAD to nonlinear features. Our experiments demonstrate that for various data-driven applications, PAD can achieve enhanced utility while remaining highly resilient to privacy threats.

We consider a class of non-convex learning problems that can be formulated as jointly optimizing regularized hinge loss and a set of auxiliary variables. Such problems encompass but are not limited to various versions of semi-supervised learning,learning with hidden structures, robust learning, etc. Existing methods either suffer from local minima or have to invoke anon-scalable combinatorial search. In this paper, we propose a novel learning procedure, namely Parametric Dual Maximization(PDM), that can approach global optimality efficiently with user specified approximation levels. The building blocks of PDM are two new results: (1) The equivalent convex maximization reformulation derived by parametric analysis.(2) The improvement of local solutions based on a necessary and sufficient condition for global optimality. Experimental results on two representative applications demonstrate the effectiveness of PDM compared to other approaches.

In the era of Internet of Things, crowd counting, which estimates the number of people within a region, becomes the underpinning for many emerging applications, such as occupancy estimation in smart building and queuing management and product placement in shopping center. Existing vision based crowd counting schemes require favorable lighting conditions and also raise privacy concerns. RF based approaches rely on specialized sensors and require users to carry RF devices. Thus, an accurate, reliable and non-intrusive crowd counting scheme is still desired. In this paper, we propose FreeCount, a device-free crowd counting scheme that is able to precisely estimate the number of people within a region using only commodity WiFi routers. To this end, the channel state information (CSI) data in PHY layer is obtained directly by upgrading the router's software. We propose an information theory based feature selection scheme to select the most representative features that are sensitive to human motion. To build a classifier that is robust to temporal and environmental disparities, we adopt transfer kernel learning, which minimizes the difference between the source and target distributions in the reproducing kernel Hilbert space, is adopted to process the real-time CSI feature data. Experiments were conducted in moderate sized rooms and the results demonstrated that FreeCount is able to accurately estimate the number of people with 96% crowd counting accuracy consistently over temporal and environmental variation.

Human activity recognition is becoming the vital underpinning for a myriad of emerging applications in the field of human-computer interaction, mobile computing, and smart grid. Besides the utilization of up-to-date sensing techniques, modern activity recognition systems also require a machine learning (ML) algorithm that leverages the sensory data for identification purposes. In view of the unique characteristics of the measurement data and the ML challenges thereof, we propose a non-intrusive human activity recognition system that only uses existing commodity WiFi routers. The core of our system is a novel multiple kernel representation learning (MKRL) framework that automatically extracts and combines informative patterns from the Channel State Information (CSI) measurements. The MKRL firstly learns a kernel string representation from time, frequency, wavelet, and shape domains with an efficient greedy algorithm. Then it performs information fusion from diverse perspectives based on multi-view kernel learning. Moreover, different stages of MKRL can be seamlessly integrated into a multiple kernel learning framework to build up a robust and comprehensive activity classifier. Extensive experiments are conducted in typical indoor environments and the experimental results demonstrate that the proposed system outperforms existing methods and achieves a 98\% activity recognition accuracy.

This paper presents the design, analysis, and experimental validation of a fault detection and identification (FDI) scheme for dc-dc power electronic converters. The FDI scheme includes two new classes of fault filters: (1) a linear-switched fault detection (FD) filter and (2) a bank of linear-switched fault identification (FI) filters. Both the FD filter and the bank of FI filters have a structure similar to that of Luenberger observer. The FD filter detects a fault event and the bank of FI filters identify a faulted converter component, for instance, failure in switching or passive components. We present simulation and experimental results for a prototype 2 kW solar photovoltaic (PV) boost dc-dc converter system to demonstrate the efficacy of the proposed FDI scheme. The experimental results demonstrate accurate fault detection and identification for a collection of catastrophic component faults in PV dc-dc power converters.

Existing human activity recognition approaches require either the deployment of extra infrastructure or the cooperation of occupants to carry dedicated devices, which are expensive, intrusive and inconvenient for pervasive implementation. In this paper, we propose SmartSense, a device-free human activity recognition system based on a novel machine learning algorithm with existing commercial off-the-shelf (COTS) WiFi routers. By exploiting the prevalence of existing WiFi infrastructure in buildings, we developed a novel OpenWrt based firmware for COTS WiFi routers to collect the CSI measurements from regular data frames. To identify different human activities, an automatic kernel representation learning method, namely auto-HSRL, is established to selection informative Hilbert space patterns from time, frequency, wavelet, and shape domains. A new information fusion tool based on multi-view kernel learning is proposed to combine the representations extracted from diverse perspectives and build up a robust and comprehensive activity classifier. Extensive experiments were conducted in an office and the experimental results demonstrate that SmartSense outperforms existing methods and achieves a 98% activity recognition accuracy.

Detecting dangerous riding behaviors is of great importance to improve bicycling safety. Existing bike safety precautionary measures rely on dedicated infrastructures that incur high installation costs. In this work, we propose BikeMate, a ubiquitous bicycling behavior monitoring system with smartphones. BikeMate invokes smartphone sensors to infer dangerous riding behaviors including lane weaving, standing pedalling and wrong-way riding. For easy adoption, BikeMate leverages transfer learning to reduce the overhead of training models for different users, and applies crowdsourcing to infer legal riding directions without prior knowledge. Experiments with 12 participants show that BikeMate achieves an overall accuracy of 86.8% for lane weaving and standing pedalling detection, and yields a detection accuracy of 90% for wrong-way riding using crowdsourced GPS traces.

Accurate Location Based Service (LBS) is one of the fundamental but crucial services in the era of Internet of Things (IoT). WiFi fingerprinting-based Indoor Positioning System (IPS) has become the most promising solution for indoor LBS. However, the offline calibrated received signal strength (RSS) radio map is unable to provide consistent LBS with high localization accuracy under various environmental dynamics. To address this issue, we propose TKL-WinSMS as a systematic strategy, which is able to realize robust and adaptive indoor localization in dynamic indoor environments. We developed a WiFi-based Non-intrusive Sensing and Monitoring System (WinSMS) that enables WiFi routers as online reference points by extracting real-time RSS readings among them. With these online data and labeled source data from the offline calibrated radio map, we further combine the RSS readings from target mobile devices as unlabeled target data, to design a robust localization model using an emerging transfer learning algorithm, namely transfer kernel learning (TKL). It is able to learn a domain-invariant kernel by directly matching the source and target distributions in the reproducing kernel Hilbert space instead of the raw noisy signal space. The resultant kernel can be used as input for the SVR training procedure. In this manner, the trained localization model can inherit the information from online phase to adaptively enhance the offline calibrated radio map. Extensive experiments were conducted and demonstrated that the proposed TKL- WinSMS is able to improve the localization accuracy by at least 26% compared with existing solutions under various environmental interferences.

Experiments show that operation efficiency and reliability of buildings can greatly benefit from rich and relevant datasets. More specifically, data can be analyzed to detect and diagnose system and component failures that undermine energy efficiency. Among the huge quantity of information, some features are more correlated with the failures than others. However, there has been little research to date focusing on determining the types of data that can optimally support fault detection and diagnosis (FDD). This paper presents a novel optimal feature selection method, named information greedy feature filter (IGFF), to select essential features that benefit building FDD. On one hand, the selection results can serve as reference for configuring sensors in the data collection stage, especially when the measurement resource is limited. On the other hand, with the most informative features selected by the IGFF, the performance of building FDD could be improved and theoretically justified. A case study on air-handling unit (AHU) is conducted based on the dataset of the ASHRAE Research Project 1312. Numerical results show that, compared with several baselines, the FDD performances of conventional classification methods are greatly enhanced by the IGFF.

Occupancy detection for buildings is crucial to improving energy efficiency, user comfort, and space utility. However, existing methods require dedicated system setup, continuous calibration, and frequent maintenance. With the instrumentation of electricity meters in millions of homes and offices, however, power measurement presents a unique opportunity for a non-intrusive and cost-effective way to detect occupant presence. This study develops solutions to the problems when no data or limited data is available for training, as motivated by difficulties in ground truth collection. Experimental evaluations on data from both residential and commercial buildings indicate that the proposed methods for binary occupancy detection are nearly as accurate as models learned with sufficient data, with accuracies of approximately 78 to 93 percent for residences and 90 percent for offices. This study shows that power usage contains valuable and sensitive user information, demonstrating a virtual occupancy sensing approach with minimal system calibration and setup.

WiFi fingerprinting-based indoor positioning system (IPS) has become the most promising solution for indoor localization. However, there are two major drawbacks that hamper its large-scale implementation. First, an offline site survey process is required which is extremely time-consuming and labor-intensive. Second, the RSS fingerprint database built offline is vulnerable to environmental dynamics. To address these issues comprehensively, in this paper, we propose WinIPS, a WiFi-based non-intrusive IPS that enables automatic online radio map construction and adaptation, aiming for calibration-free indoor localization. WinIPS can capture data packets transmitted in existing WiFi traffic and extract the RSS and MAC addresses of both WiFi access points (APs) and mobile devices in a non-intrusive manner. APs can be used as online reference points for radio map construction. A novel Gaussian process regression model is proposed to approximate the non-uniform RSS distribution of an indoor environment. Extensive experiments were conducted, which demonstrated that WinIPS outperforms existing solutions in terms of both RSS estimation accuracy and localization accuracy.

We propose Veto-Consensus Multiple Kernel Learning (VCMKL), a novel way of combining multiple kernels such that one class of samples is described by the logical intersection (consensus) of base kernelized decision rules, whereas the other classes by the union (veto) of their complements. The proposed configuration is a natural fit for domain description and learning with hidden subgroups. We first provide generalization risk bound in terms of the Rademacher complexity of the classifier, and then a large margin multi-ν learning objective with tunable training error bound is formulated. Seeing that the corresponding optimization is non-convex and existing methods severely suffer from local minima, we establish a new algorithm, namely Parametric Dual Descent Procedure (PDDP) that can approach global optimum with guarantees. The bases of PDDP are two theorems that reveal the global convexity and local explicitness of the parameterized dual optimum, for which a series of new techniques for parametric program have been developed. The proposed method is evaluated on extensive set of experiments, and the results show significant improvement over the state-of-the-art approaches.

Maximising electricity production from wind requires improvement of wind turbine reliability. Component failures result in unscheduled or reactive maintenance on turbines which incurs significant downtime and, in turn, increases production cost, ultimately limiting the competitiveness of renewable energy. Thus, a critical task is the early detection of faults. To this end, we present a framework for fault detection using machine learning that uses Supervisory Control and Data Acquisition (SCADA) data from a large 3MW turbine, supplemented with features derived from this data that encapsulate expert knowledge about wind turbines. These new features are created using application domain knowledge that is general to large horizontal-axis wind turbines, including knowledge of the physical quantities measured by sensors, the approximate locations of the sensors, the time series behaviour of the system, and some statistics related to the interpretation of sensor measurements. We then use mRMR feature selection to select the most important of these features. The new feature set is used to train a support vector machine to detect faults. The classification performance using the new feature set is compared to performance using the original feature set. Use of the new feature set achieves an F1-score of 90%, an improvement of 27% compared to the original feature set.

Existing WiFi fingerprinting-based Indoor Positioning System (IPS) suffers from the vulnerability of environmental dynamics. To address this issue, we propose TKL-WinSMS as a systematic strategy, which is able to realize robust and adaptive localization in dynamic indoor environments. We developed a WiFi-based Non-intrusive Sensing and Monitoring System (WinSMS) that enables COTS WiFi routers as online reference points by extracting real-time RSS readings among them. With these online data and labeled source data from the offline calibrated radio map, we further combine the RSS readings from target mobile devices as unlabeled target data, to design a robust localization model using an emerging transfer learning algorithm, namely transfer kernel learning (TKL). It can learn a domain-invariant kernel by directly matching the source and target distributions in the reproducing kernel Hilbert space instead of the raw noisy signal space. By leveraging the resultant kernel as input for the SVR training, the trained localization model can inherit the information from online phase to adaptively enhance the offline calibrated radio map. Extensive experimental results verify the superiority of TKL-WinSMS in terms of localization accuracy compared with existing solutions in dynamic indoor environments.

Metro has become the first choice of traveling for tourists and citizens in metropolis due to its efficiency and convenience. Yet passengers have to rely on metro broadcasts to know their locations because popular localization services (e.g. GPS and wireless localization technologies) are often inaccessible underground. To this end, we propose MetroEye, an intelligent smartphone-based tracking system for metro passengers underground. MetroEye leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and infers the state of passengers (Stop, Running, and Interchange) during an entire metro trip using a Conditional Random Field (CRF) model. MetroEye further provides arrival alarm services based on individual passenger state, and aggregates crowdsourced interchange durations to guide passengers for intelligent metro trip planning. Experimental results within 6 months across over 14 subway trains in 3 major cities demonstrate that MetroEye yields an overall accuracy of 80.5% outperforming the state-of-the-art.

We study causal subset selection with Directed Information as the measure of prediction causality. Two typical tasks, causal sensor placement and covariate selection, are correspondingly formulated into cardinality constrained directed information maximizations. To attack the NP-hard problems, we show that the first problem is submodular while not necessarily monotonic. And the second one is ``nearly'' submodular. To substantiate the idea of approximate submodularity, we introduce a novel quantity, namely submodularity index (SmI), for general set functions. Moreover, we show that based on SmI, greedy algorithm has performance guarantee for the maximization of possibly non-monotonic and non-submodular functions, justifying its usage for a much broader class of problems. We evaluate the theoretical results with several case studies, and also illustrate the application of the subset selection to causal structure learning.

In a data-driven Optimal Control (OP) scheme, one or more involved components, such as objective function, system dynamics, or operation constraints, are described with statistical models and learned from data. In this work, we focus on the machine learning of operation constraints which is rarely addressed in previous research. Although a rich collection of supervised learning methods exist in literature, most of them are not suitable for modeling operation constraints, because their decision rules usually induce undesirable non-linear couplings in system variables. In order to surpass simple linear models while at the same time maintaining compatibility with downstream control applications, we propose to describe system operation requirement by convex piecewise linear machine (CPLM), which does not incur any difficulties in optimization and is directly pluggable. The generalization performance of the proposed classifier is analyzed through bounding its VC-dimension, and a large margin cost sensitive learning objective is formulated with Bayes consistent hinge loss. We solve the training problem by online stochastic gradient descent and propose a mixed integer based initialization method. A case study on Heating, Ventilation and Air Conditioning (HVAC) systems control with comfort requirement is conducted and the results show that CPLM is not only a promising candidate for cost sensitive learning in general, but also enables much better description and exploitation of the system operation region for optimal control purpose.

This paper proposes a cooperative demand response (CDR) scheme for load management in smart grid. The CDR scheme is formulated as a constrained optimization problem that generates a Pareto-optimal response strategy profile for consumers. Comparing with the noncooperative response strategy (i.e., Nash equilibrium) obtained from the one-shot demand management game, the Pareto-optimal response strategy reduces the electricity costs to the consumers. We further develop an incentive-compatible trigger-and-punishment mechanism to avoid the noncooperative behaviors of the selfish consumers. Furthermore, the CDR scheme is applied to achieve load management of industrial refrigerated warehouses. To implement the CDR scheme in large-scale systems, we group the refrigerated warehouses into clusters and utilize the CDR scheme within each cluster. Numerical results demonstrate that the CDR scheme can reduce the electricity costs, drop the electricity prices, and curtail the total energy consumption in comparison with the noncooperative demand response scheme.

This paper proposes a cooperative demand response scheme for price-anticipating buildings in smart grid. The cooperative demand response scheme is formulated as a constrained social optimization problem. We develop a cooperative strategy and obtain a Pareto-optimal solution from the constrained social optimization problem. Comparing with the Nash equilibrium obtained from the one-stage demand management game, the Pareto-optimal solution reduces the electricity costs to all the building managers. We further align this Pareto-optimal solution with the subgame perfect Nash equilibrium of a repeated demand management game and develop an incentive-compatible trigger-and-punishment mechanism to avoid the noncooperative behavior of the building managers. Numerical results demonstrate that the cooperative demand response scheme can reduce the electricity costs, the electricity price, and the total energy consumption.

Sequential or online dimensional reduction is of interests due to the explosion of streaming data based applications and the requirement of adaptive statistical modeling, in many emerging fields, such as the modeling of energy end-use profile. Principal Component Analysis (PCA), is the classical way of dimensional reduction. However, traditional PCA coincides with maximum likelihood interpretation only when data follows Gaussian distribution. The Bregman Divergence was introduced to extend PCA with maximum likelihood in exponential family distribution. In this work, we study this generalized form PCA for Bernoulli variables, which is called Logistic PCA (LPCA). We extend the batch-mode LPCA to a sequential version (SLPCA). The convergence property of this algorithm is discussed compared to the batch version (BLPCA), as well as its performance in reducing the dimension for multivariate binary-state systems. Its application in building energy end-use profile modeling is also investigated.

We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant-carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light level collected during eight typical activities: sitting in lab / cubicle, indoor walking / running, resting after physical activity, climbing stairs, taking elevators, and outdoor walking. Our main contribution is the development of features for activity and location recognition based on environmental measurements, which exploit location- and activity-specific characteristics and capture the trends resulted from the underlying physiological process. The features are statistically shown to have good separability and are also information-rich. Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%. For building applications, this study motivates a sensor fusion paradigm for learning individualized activity, location, and environmental preferences for energy management and user comfort.

In this paper, the modeling of building end-use energy profile is comprehensively investigated. Top-down and Bottom-up approaches are discussed with a focus on the latter for better integration with occupant information. Compared to the Time-Of-Use (TOU) data used in previous Bottom-up models, this work utilizes high frequency sampled appliance power consumption data from wireless sensor network, and hence builds an appliance-data-driven probability based end-use energy profile model. ON/OFF probabilities of appliances are used in this model, to build a non-homogeneous Markov Chain, compared to the duration statistics based model that is widely used in other works. The simulation results show the capability of the model to capture the diversity and variability of different categories of end-use appliance energy profile, which can further help on the design of a modern robust building power system.

Modeling spatial variation is important for statistical analysis. Most existing works model spatial variation as spatially correlated random variables. We discuss process origins of spatial variability, all of which indicate that spatial variation comes from deterministic across-wafer variation, and purely random spatial variation is not significant. We analytically study the impact of across-wafer variation and show how it gives an appearance of correlation. We have developed a new die-level variation model considering deterministic across-wafer variation and derived the range of conditions under which ignoring spatial variation altogether may be acceptable. Experimental results show that for statistical timing and leakage analysis, our model is within 2% and 5% error from exact simulation result, respectively, while the error of the existing distance-based spatial variation model is up to 6.5% and 17%, respectively. Moreover, our new model is also faster than the spatial variation model for statistical timing analysis and faster for statistical leakage analysis.

Modeling spatial variation is important for statistical analysis. Most existing works model spatial variation as spatially correlated random variables. We discuss process origins of spatial variability, all of which indicate that spatial variation comes from deterministic across-wafer variation, and purely random spatial variation is not significant. We analytically study the impact of across-wafer variation and show how it gives an appearance of correlation. We have developed a new dielevel variation model considering deterministic across-wafer variation and derived the range of conditions under which ignoring spatial variation altogether may be acceptable. Experimental results show that our model is within 1% error from exact simulation result while the error of the existing distance-based spatial variation model is up to 8%. Moreover, our new model is also 10X faster than the spatial variation model for Monte-Carlo analysis.

The author presents a brief survey of standard SPC (statistical process control) schemes, and illustrates them through examples taken from the semiconductor industry. These methods range from contamination control to the monitoring of continuous process parameters. It is noted that, even as SPC is transforming IC production, the peculiarities of semiconductor manufacturing technology are transforming SPC. Therefore, the author describes novel SPC applications which are now emerging in semiconductor production. These methods are being developed to monitor the short production runs that are characteristic of flexible manufacturing. Additional SPC techniques suitable for in situ multivariate sensor readings are also discussed.>

It is well known that the integrated circuit (IC) manufacturing process is subject to inherent statistical fluctuations of material quality and equipment performance. Characterization of these fluctuations is important if the performance of an IC manufacturing facility is to be simulated and optimized. In this paper we present a general methodology for determining the statistical moments associated with a set of independently varying parameters, that are principally responsible for the fluctuations in the IC manufacturing process. This methodology has been implemented in a program called PROMETHEUS. A method for the formal statistical verification of the extracted moments is also presented.