We propose a gesture recognition system that leverages existing WiFi infrastructures and learns gestures from Channel State Information (CSI) measurements. Having developed an innovative OpenWrt-based platform for commercial WiFi devices to extract CSI data, we propose a novel deep Siamese representation learning architecture for one-shot gesture recognition. Technically, our model extends the capacity of spatio-temporal patterns learning for the standard Siamese structure by incorporating convolutional and bidirectional recurrent neural networks. More importantly, the representation learning is ameliorated by our Siamese framework and transferable pairwise loss which helps to remove structured noise such as individual heterogeneity and various measurement conditions during domain-different training. Meanwhile, our Siamese model also enables one-shot learning for higher availability in reality. We prototype our system on commercial WiFi routers. The experiments demonstrate that our model outperforms state-of-the-art solutions for temporal-spatial representation learning and achieves satisfactory results under one-shot conditions.

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) achieves encouraging results recently, but the time and labor overhead of constructing a dense WiFi radio map remain the key bottleneck that hinders it for real word large-scale implementation. In this paper, we propose, WiGAN, an automatic fine-grained indoor ratio map construction and adaptation scheme empowered by Gaussian Process Regression conditioned Least Squares Generative Adversarial Networks (GPR-GAN) with a mobile robot. Firstly, we develop a mobile robotic platform that constructs the spatial map and radio map simultaneously in the easy accessed free space. GPR-GAN firstly establishes a GPR model using the real RSS measurements collected by our robotic platform via LiDAR SLAM in the free space. Then, the outputs of GPR are adopted as the input of GAN’s generator. The learning objective of GAN is to synthesize realistic RSS data in constrained space where 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.

The power system has been incorporating increasing amount of unconventional generations and loads such as distributed renewable resources, electric vehicles, and controllable loads. The induced dynamic and stochastic power flow require high-resolution monitoring technology and agile decision support techniques for system diagnosis and control. This paper discusses the application of micro-phasor measurement unit (PMU) data for power distribution network event detection. A novel datadriven event detection method, namely Hidden Structure Semi- Supervised Machine (HS $^{3}$ M), is established. HS $^{3}$ M only requires partial expert knowledge: it combines unlabeled data and partly labeled data in a large margin learning objective to bridge the gap between supervised learning, semi-supervised learning, and learning with hidden structures. To optimize the non-convex learning objective, a novel global optimization algorithm, namely Parametric Dual Optimization Procedure (PDOP), is established through its equivalence to a concave programming. Finally, the proposed method is validated on an actual distribution feeder with installed PMUs, and the result justifies the effectiveness of the learning-based event detection framework, as well as its potential to serve as one

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.

Cascading failures pose a significant security threat to networked systems, with recent global incidents underscoring their destructive potential. The security threat of cascading failures has always existed, but the evolution of cyber–physical systems (CPSs) has introduced novel dimensions to cascading failures, intensifying their threats owing to the intricate fusion of cyber and physical domains. Addressing these threats requires a nuanced understanding achieved through failure modeling and vulnerability analysis. By analyzing the historical failures in different CPSs, the cascading failure in CPSs is comprehensively defined as a complicated propagation process in coupled cyber and physical systems, initialized by natural accidents or human interference, which exhibits a progressive evolution within the networked structure and ultimately results in unexpected large-scale systemic failures. Subsequently, this study advances the development of instructions for modeling cascading failures and conducting vulnerability analyses within CPSs. The examination also delves into the core challenges inherent in these methodologies. Moreover, a comprehensive survey and classification of extant research methodologies and solutions are undertaken, accompanied by a concise evaluation of their advancements and limitations. To validate the performance of these methodologies, numerical experiments are conducted to ascertain their distinct features. In conclusion, this article advocates for future research initiatives, particularly emphasizing the exploration of uncertainty analysis, defense strategies, and verification platforms. By addressing these areas, the resilience of CPSs against cascading failures can be significantly enhanced.

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.