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The heating, ventilation and air-conditioning (HVAC) system accounts for substantial energy use in buildings, whereas a large group of occupants are still not actually feeling comfortable staying inside. This poses the issue of developing energy-efficient HVAC control, i.e., reduce energy use (cost) while simultaneously enhancing human comfort. This paper pursues the objective and studies the stochastic optimal HVAC control subject to uncertain thermal demand (i.e., the weather and occupancy etc). Particularly, we involve the elaborate predicted mean vote (PMV) thermal comfort model in the optimization. The problem is computationally challenging due to the non-linear and non-analytical constraints imposed by the system dynamics and PMV model. We make the following contributions to address it. First, we formulate the problem as a Markov decision process (MDP) which is a desirable modeling technique capable of handling the complexities. Second, we propose a gradient-based learning (GB-L) method for progressively learning a stochastic control policy off-line and store it for on-line execution. Third, we prove the learning method converge to the optimal policies theoretically, and its performance (i.e., energy cost, thermal comfort and on-line computation) for HVAC control via simulations. The comparisons with the existing model predictive control based relaxation (MPC-R) method which is assumed with accurate future information and supposed to provide the near-optimal bounds, show that though there exists some performance loss in energy cost reduction (i.e., 6.5%), the proposed method can enable efficient on-line implementation (less than 1 second) and provide high probability of thermal comfort under uncertainties., 11 pages, 10 figures

This article studies a scalable control method for multizone heating, ventilation, and air-conditioning (HVAC) systems to optimize the energy cost for maintaining thermal comfort (TC) and indoor air quality (IAQ) (represented by CO2) simultaneously. This problem is computationally challenging due to the complex system dynamics, various spatial and temporal couplings, as well as multiple control variables to be coordinated. To address the challenges, we propose a two-level distributed method (TLDM) with an upper level and lower level control integrated. The upper level computes zone mass flow rates for maintaining zone TC with minimal energy cost, and then, the lower level strategically regulates zone mass flow rates and the ventilation rate to achieve IAQ while preserving the near energy-saving performance of upper level. As both the upper and the lower level computation are deployed in a distributed manner, the proposed method is scalable and computationally efficient. The near-optimal performance of the method in energy cost saving is demonstrated through comparison with the centralized method. In addition, the comparisons with the existing distributed method show that our method can provide IAQ with only little increase of energy cost, while the latter fails. Moreover, we demonstrate that our method outperforms the demand-controlled ventilation (DCVs) strategies for IAQ management with about 8%–10% energy cost reduction. Note to Practitioners: The high portion of building energy consumption has motivated the energy saving for heating, ventilation, and air-conditioning (HVAC) systems. Concurrently, the living standards for indoor environment are rising among the occupants. Nevertheless, the status quo on improving building energy efficiency has mostly focused on maintaining thermal comfort (such as temperature), and the indoor air quality (IAQ) (usually represented by CO2 level) has been seldom incorporated. In our previous work with the similar setting, we observed that the CO2 levels will surge beyond tolerance during the high occupancy periods if only thermal comfort (TC) is considered for HVAC control. This deduces the IAQ and TC should be jointly considered while pursuing the energy cost saving target and thus studied in this article. This task is computationally cumbersome due to the complex system dynamics (thermal and CO2) and tight correlations among the different control components (variable air volume and fresh air damper). To cope with these challenges, this work develops a two-level distributed computation paradigm for HVAC systems based on problem structures. Specifically, the upper level control (ULC) first calculates zone mass flow rates for maintaining comfortable zone temperature with minimal energy cost, and then, the lower level strategically regulates the computed zone mass flow rates as well as ventilation rate to satisfy IAQ while preserving the near energy-saving performance of the ULC. As both the upper and lower level calculations can be implemented in a distributed manner, the proposed method is scalable to large multizone deployment. The method’s performance both in maintaining comfort (i.e., TC and IAQ) and energy cost saving is demonstrated via simulations in comparisons with the centralized method, the distributed token-based scheduling strategy, and the demand-controlled ventilation strategies.

This paper develops a fully distributed framework to investigate the cooperative behavior of multiagent systems in the presence of distributed denial-of-service (DoS) attacks launched by multiple adversaries. In such an insecure network environment, two kinds of communication schemes, that is, sample-data and event-triggered communication schemes, are discussed. Then, a fully distributed control protocol with strong robustness and high scalability is well designed. This protocol guarantees asymptotic consensus against distributed DoS attacks. In this paper, "fully" emphasizes that the eigenvalue information of the Laplacian matrix is not required in the design of both the control protocol and event conditions. For the event-triggered case, two effective dynamical event-triggered schemes are proposed, which are independent of any global information. Such event-triggered schemes do not exhibit Zeno behavior even in the insecure environment. Finally, a simulation example is provided to verify the effectiveness of theoretical analysis. Economic Development Board (EDB) Ministry of Education (MOE) This work was supported in part by the Singapore Economic Development Board through EIRP under Grant S14-1172-NRF EIRP-IHL, in part by the Singapore Ministry of Education Academic Research Fund Tier 1 under Grant RG180/17 (2017-T1-002-158), in part by the Research Grants Council of the Hong Kong under Grant CityU 11200717 and Grant CityU 7005029, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20180367, in part by the National Natural Science Foundation of China under Grant 61803082, in part by the Fundamental Research Funds for the Central Universities, in part by the Alexander von Humboldt Foundation of Germany, and in part by the ZhiShan Youth Scholar Program from Southeast University.

This paper studies secure cooperative event-triggered control of linear multiagent systems under denial-of-service (DoS) attacks. The DoS attacks refer to interruptions of communication channels carried out by an intelligent adversary. We consider a class of time-sequence-based DoS attacks allowed to occur aperiodically. Then, an explicit analysis of the frequency and duration of DoS attacks is investigated for both secure leaderless and leader-following consensus problems. A resilient cooperative event-triggered control scheme is developed and scheduling of controller updating times is determined in the presence of DoS attacks. It is shown that based on the proposed distributed algorithms, all the agents can achieve secure consensus exponentially. The effectiveness of the developed methods is illustrated through three case studies: 1) multiple robot coordination; 2) distributed voltage regulation of power networks; and 3) distributed cooperative control of unstable dynamic systems. Economic Development Board (EDB) Ministry of Education (MOE) This work was in part supported by the Singapore Economic Development Board through EIRP under Grant S14-1172-NRF EIRP-IHL, and in part by the Singapore Ministry of Education Academic Research Fund Tier 1 RG180/17(2017-T1-002-158).

Objective To establish the relationship between pulse wave transit time (PWTT) before anaesthesia induction and blood pressure variability (BPV) during anaesthesia induction. Methods This prospective observational cohort study enrolled consecutive patients that underwent elective surgery. Invasive arterial pressure, electrocardiography, pulse oximetry, heart rate and bispectral index were monitored. PWTT and BPV were measured with special software. Anaesthesia was induced with propofol, sufentanil and rocuronium. Results A total of 54 patients were included in this study. There was no correlation between BPV and the dose of propofol, sufentanil and rocuronium during anaesthesia induction. Bivariate linear regression analysis demonstrated that PWTT ( r = –0.54), age ( r = 0.34) and systolic blood pressure ( r = 0.31) significantly correlated with systolic blood pressure variability (SBPV). Only PWTT ( r = –0.38) was significantly correlated with diastolic blood pressure variability (DBPV). Patients were stratified into high PWTT and low PWTT groups according to the mean PWTT value (96.8 ± 17.2 ms). Compared with the high PWTT group, the SBPV of the low PWTT group increased significantly by 3.4%. The DBPV of the low PWTT group increased significantly by 2.1% compared with the high PWTT group. Conclusions PWTT, assessed before anaesthesia induction, may be an effective predictor of haemodynamic fluctuations during anaesthesia induction.

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.

The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. Intersubject correlation (ISC) analysis of functional magnetic resonance imaging (fMRI) data is a widely used method that can measure neural responses to naturalistic stimuli that are consistent across subjects. However, interdependent correlation values in ISC artificially inflated the degrees of freedom, which hinders the investigation of individual differences. Besides, the existing ISC model mainly focus on similarities between subjects but fails to distinguish neural responses to different stimuli features. To estimate large-scale brain networks evoked with naturalistic stimuli, we propose a novel analytic framework to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In the framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. Tensor component analysis (TCA) will then reveal spatially and temporally shared components, i.e., naturalistic stimuli evoked networks, their temporal courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with TCA, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of TCA algorithm. We demonstrate the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also apply the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are identified evoked by naturalistic movie viewing.

This paper proposes a novel weakly-supervised defect segmentation approach for the visual inspection of LCD array images by a joint application of known weakly-supervised segmentation and unsupervised anomaly segmentation technique. Potential defect regions are firstly identified via active heatmap and masked out. Generative CAE trained via a GAN framework is further applied to create defect-free contents within the masked regions. A comparison between the generated image and the original image leads to precise defect segmentation. Our experiment over LCD inspection images showed that the proposed approach achieved comparable segmentation performance to the fully supervised FCN model, justifying its applicability for serious industry scenarios. Although the approach was motivated by and validated for the visual inspection in the LCD panel industry, it is conceptually a general method and has the potential to be applied in wide areas.

In diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Most of the existing state-of-the-art approaches rely on hand-crafted features and multi-modality polysomnography (PSG) data, where prior knowledge is compulsory and high computation cost can be expected. Besides, few studies are able to obtain high accuracy sleep staging using raw single-channel electroencephalogram (EEG). To overcome these shortcomings, this paper proposes an end-to-end framework with a deep neural network, namely SingleChannelNet, for automatic sleep stage classification based on raw single-channel EEG. The proposed model utilizes a 90s epoch as the textual input and employs two multi-convolution blocks and several max-average pooling layers to learn different scales of feature representations. To demonstrate the efficiency of the proposed model, we evaluate our model using different raw single-channel EEGs (C4/A1 and Fpz-Cz) on two different datasets (CC-SHS and Sleep-EDF datasets). Experimental results show that the proposed architecture can achieve better over-all accuracy and Cohen’s kappa (CCSHS: 90.2%-86.5%, Sleep-EDF: 86.1%-80.5%) compared with state-of-the-art approaches. Additionally, the proposed model can learn features automatically for sleep stage classification using different single-channel EEGs with distinct sampling rates from different datasets without using any hand-engineered features.

In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis. Model order selection (MOS) algorithms have been used to determine the number of estimated components. However, simulations show that even when the model order equals the number of simulated signal sources, traditional ICA algorithms may misestimate the spatial maps of the signal sources. In principle, increasing model order will consider more potential information in the estimation, and should therefore produce more accurate results. However, this strategy may not work for fMRI because large-scale networks are widely spatially distributed and thus have increased mutual information with noise. As such, conventional ICA algorithms with high model orders may not extract these components at all. This conflict makes the selection of model order a problem. We present a new strategy for model order free ICA, called Snowball ICA, that obviates these issues. The algorithm collects all information for each network from fMRI data without the limitations of network scale. Using simulations and in vivo resting-state fMRI data, our results show that component estimation using Snowball ICA is more accurate than traditional ICA. The Snowball ICA software is available at https://github.com/GHu-DUT/Snowball-ICA. peerReviewed

This brief studies an energy management (EM) problem with unknown dynamics of consumer appliances. A two-level optimization model is established between the utility company and the consumers. In this model, the utility company maximizes its profit by setting the electricity price, and the consumers respond to the price by regulating power usage to minimize their costs. The aforementioned process is performed in multiple stages. In each stage, the consumer response is formulated as a constrained optimization problem, which can be transformed into an unconstrained optimization problem using the penalty function method, and then an extremum seeking control (ESC) algorithm is developed to search for the quasi-optimal power consumption of the consumers. The ESC algorithm has noncontinuous first and second derivatives with respect to the variables. We propose an approximation method to make the ESC algorithm continuous and prove that the algorithm is semiglobally practically asymptotically (SPA) stable. After the consumer response in the same stage, the utility company updates the electricity price by the particle swarm optimization (PSO) algorithm. Then, we give an EM algorithm that integrates the ESC with PSO. In simulations, the algorithm is applied to achieve EM of heating, ventilation, and air conditioning (HVAC) systems, and the results show that the algorithm can converge to a neighborhood of the optimal solution and reduce the peak load and daily load.

It has been well acknowledged that buildings account for a large proportion of the world’s energy consumption. In particular, about 40%-50% of the building’s energy is consumed by the heating, ventilation and air-conditioning (HVAC). However, the energy use of buildings, especially the HVAC system, is far from being efficient. There still exists a dramatic potential to save energy through improve building energy efficiency. Therefore, this paper studies the control of HVAC system for multi-zone buildings with the objective to reduce the energy consumption cost of the HVAC system while satisfying the zone thermal comfort requirements. In particular, the thermal coupling due to the heat transfer between the adjacent zones are incorporated in the optimization. Considering that a centralized method is generally computationally prohibitive for buildings with an increasing number of zones, an efficient decentralized approach is developed, based on the Accelerated Distributed Augmented Lagrangian (ADAL) method [1]. To evaluate the performance of this decentralized approach, we first compare it with a centralized method, in which the optimal solution of a small-scale problem can be obtained. We find that this decentralized approach can almost approach the optimal solution of the problem. Further, to evaluate the performance and scalability, this decentralized approach is compared with the Distributed Token-Based Scheduling Strategy (DTBSS) [2], which has been demonstrated with a satisfactory performance in reducing the energy consumption of the HVAC system and in improving scalability. The numeric results reveal that when the number of zones in the buildings is relatively small (less than 20), the two decentralized methods can achieve a comparable performance regarding the cost of the HVAC system. However, with an increase of the number of zones in the building, the proposed decentralized approach demonstrates better performance with a considerable reduction of the total cost. Moreover, the average computation time of the two decentralized methods are compared in the case studies. The numeric results shows that the two decentralized methods are both computationally efficient. However, the decentralized approach proposed in this paper demonstrate better scalability with less average computation required. National Research Foundation (NRF) Submitted/Accepted version This work was supported by the Republic of Singapore’s National Research Foundation through a Grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program. BEARS has been established by the University of California at Berkeley, Berkeley, as a Center for Intellectual Excellence in Research and Education in Singapore.

Independent component analysis (ICA) on group-level voxel-based morphometry (VBM) produces the coefficient matrix and the component matrix. The former contains variability among multiple subjects for further statistical analysis, and the latter reveals spatial maps common for all subjects. ICA algorithms converge to local optimization points in practice and the mostly applied stability investigation approach examines the stability of the extracted components. We found that the practically stable components do not guarantee to produce the practically stable coefficients of ICA decomposition for the further statistical analysis. Consequently, we proposed a novel approach including two steps: (1), the stability index for the coefficient matrix and the stability index for the component matrix were examined, respectively; (2) the two indices were multiplied to analyze the stability of ICA decomposition. The proposed approach was used to study the sMRI data of Type II diabetes mellitus group and the healthy control group (HC). Group differences in VBM were found in the superior temporal gyrus. Besides, it was revealed that the VBMs of the region of the HC group were significantly correlated with Montreal Cognitive Assessment (MoCA) describing the level of cognitive disorder. In contrast to the widely applied approach to investigating the stability of the extracted components for ICA decomposition, we proposed to examine the stability of ICA decomposition by fusion the stability of both coefficient matrix and the component matrix. Therefore, the proposed approach can examine the stability of ICA decomposition sufficiently.

This paper focuses on the jointly operational optimization of a network of buildings while considering energy exchange among them. Based on the operation model of each individual building, coordinated information and strategies of the energy exchange among all buildings are used to maximally reduce the energy cost of all the buildings while reducing the complexity of the network operation. The optimization problem of a network of multiple buildings is formulated as a stochastic mixed integer programming problem, and a Lagrangian relaxation-based decentralized algorithm is developed to solve this problem. Based on this method, a price mechanism for the energy exchange among buildings which can guarantee the fairness of all the buildings is developed by introducing Lagrangian multipliers as the coordinated signals for the decentralized optimization. The performance of the proposed method is discussed based on case studies of a network of four buildings in Singapore. Numerical results show that the method can not only achieve significant energy cost savings for all the buildings, but also provide huge ability to accommodate the uncertainties in the network. It is also found that the proposed price mechanism for the energy exchange provides incentives for the buildings to participate in the joint optimization.

In the existing control problems for the leader–follower multiagent systems, the leader's dynamics are independent of the followers. In this paper, we consider a tracking control problem for an interconnected leader–follower multiagent system, where the leader's dynamics are interconnected with the states of the followers. It is shown that such a problem can be used to model the autonomous power sharing problem of grid-connected microgrids. In order to solve this problem, a distributed control law using only local information is synthesized. Simulation results are provided to show the effectiveness of the proposed control law.

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.

Air temperature and speed play a critical role in the thermal sensation of comfort felt by occupants, especially in the tropics. It is of great practical interest to coordinate air conditioning and mechanical ventilation (ACMV) system and personal fans so as to enhance building demand response (DR) capability while minimizing energy cost in response to a specific electricity price signal and maintaining a thermal comfort level. In this paper, an optimization problem of coordinating ACMV and personal fans is addressed, which captures the coupling between ACMV and fans. A Lagrangian relaxation-based algorithm is developed to solve the problem by individually solving the subproblems of ACMV and personal fans with Lagrangian multipliers as the coordinated signals. This algorithm can separate the calculation of the cooling effect from the optimization procedure, so we do not have to solve the problem using a non-analytical model for evaluating the cooling effect provided by the fans. The performance of the proposed method is evaluated and validated using experimental and simulation results. Both the results show that coordinating ACMV and fans can substantially enhance building DR capability, save energy cost, and also improve customized thermal comfort microenvironment.

This paper focuses on the problem of vision-based leader–follower formation control of mobile robots. The proposed adaptive controller only requires the image information from an uncalibrated perspective camera mounted at any position and orientation (attitude) on the follower robot. Furthermore, the approach does not depend on the relative position measurement and communication between the leader and follower. First, a new real-time observer is developed to estimate the unknown intrinsic and extrinsic camera parameters as well as the unknown coefficients of the plane where the feature point moves relative to the camera frame. Second, the Lyapunov method is employed to prove the stability of the closed-loop system, where it is shown that convergence of the image error is guaranteed. Finally, the performance of the approach is demonstrated through physical experiments and experimental results.

The cooperative output regulation problem of linear multi-agent systems using distributed observer approach has been solved under the assumption that each follower knows the system matrix of the leader system. To remove this assumption, in this paper, a distributed control scheme utilizing the adaptive distributed observer has been proposed, which consists of three steps. First, an adaptive distributed observer is designed to estimate both the system matrix and the state of the leader system. Second, an adaptive algorithm is developed to calculate the solutions of the regulator equations online. Third, both adaptive state feedback and adaptive measurement output feedback controllers are synthesized to solve the cooperative output regulation problem without the assumption that each follower knows the system matrix of the leader system.

Due to the inherently distributed and heterogeneous nature of the microgrids, distributed control can be a promising approach to improve the stability, reliability, and scalability of the microgrids compared with centralized control strategies. This paper studies the distributed reactive power sharing problem for a microgrid with connected ac inverters. Under the standard decoupling approximation for bus angle differences, the reactive power flow of each inverter is dependent on the voltage amplitudes of its neighboring inverters connected by electrical power lines. Using the Lyapunov approach, a novel distributed voltage controller with nonlinear state feedback is proposed for reactive power sharing of the microgrid. It is proved that the inverters can achieve accurate reactive power sharing under the proposed controller if the communication network of inverters is connected. Then, by introducing the sampling and holding scheme, we extend the proposed controller to the nonlinear state feedback control with event-triggered communication among inverters. The new event-triggered control approach can dramatically reduce the amount of communication of the microgrid, and significantly relax the requirement for precise real-time information transmission among the inverters. Both the proposed controllers are validated by simulations on a group of inverters with time-varying loads.

This paper aims to solve the trajectory tracking problem of manipulator and mobile robot by using proximal policy optimization (PPO) with generalized advantage estimation (GAE). We adopt a distributed framework of PPO to improve the speed of sample collection and reduce the correlation of transitions when updating the networks. Random reference state initialization is used during the training process to enable the robot to learn effectively from the reference trajectory. Additionaly, we introduce Long Short-term Memory (LSTM) to represent the actor and critic, and buffers to store the cell state and hidden state of LSTM for the initialization of each episode, which helps solve the problem of inaccurate inital LSTM states. Distributed PPO with fully-connected neural networks and LSTM is utilized to train the manipulator and mobile robot with an exponential reward function to track the given trajectories. Numerical simulations are provided to demonstrate the effectiveness of the proposed method. It can be seen that distributed PPO with LSTM can improve the tracking performance.

Although China is one of the largest recipients of inward investment and one of the largest outward investors, it is challenging for researchers to collect evidence about investors’ information needs and the decision usefulness of reporting in this emerging economy. The authors discuss results from a survey of professional investors in China. They find that accounting information, particularly where it is forward looking, is most important for an investment decision or an investment recommendation. Buy-side analysts favour accounting information while sell-side analysts view non-accounting information as more important. An overwhelming majority consider accounting information to have both confirmative and predictive value.

In electricity markets, it is quite possible for market participants to make strategies to harvest their maximal profits without considering others. The result of this is usually at the expense of the overall social profit loss, which will impair the sustainability of society in the long term. Hence, in this paper, we discuss how to improve the social profit of the market based on demand response (DR) management. In the proposed model, we analyse two sorts of interaction mechanisms: Nash equilibrium (NE) and Stackelberg equilibrium (SE) among utility companies (UCs) and user-UC interactions, respectively. Users focus on maximizing their own profits by determining the optimal energy purchasing strategies from different UCs. At the UC side, we consider two types of UCs: governmental UC (g-UC) and normal UC. A g-UC acts as the market leader and focuses on optimizing the social profit of the market on behalf of the government; normal UCs play games with their rivals and seek NE solutions. Additionally, an independent system operator (ISO) is established to provide pricing guidance to g-UC so as to improve the social profit without changing the “selfish instinct” of normal UCs. The feasibility of the proposed algorithm is verified by mathematical analysis and numerical simulation.

Background Stability of spatial components is frequently used as a post-hoc selection criteria for choosing the dimensionality of an independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. Although the stability of the ICA temporal courses differs from that of spatial components, temporal stability has not been considered during dimensionality decisions. New method The current study aims to (1) develop an algorithm to incorporate temporal course stability into dimensionality selection and (2) test the impact of temporal course on the stability of the ICA decomposition of fMRI data via tensor clustering. Resting state fMRI data were analyzed with two popular ICA algorithms, InfomaxICA and FastICA, using our new method and results were compared with model order selection based on spatial or temporal criteria alone. Results Hierarchical clustering indicated that the stability of the ICA decomposition incorporating spatiotemporal tensor information performed similarly when compared to current best practice. However, we found that component spatiotemporal stability and convergence of the model varied significantly with model order. Considering both may lead to methodological improvements for determining ICA model order. Selected components were also significantly associated with relevant behavioral variables. Comparison with Existing Method: The Kullback–Leibler information criterion algorithm suggests the optimal model order for group ICA is 40, compared to the proposed method with an optimal model order of 20. Conclusion The current study sheds new light on the importance of temporal course variability in ICA of fMRI data.

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. Note to Practitioners - Fault detection and diagnosis (FDD) for smart buildings by addressing the fact that FDD systems are of great importance for saving energy and improving occupancy comfort levels and building safety levels. Existing FDD methods are mainly based on the assumption that sensor data are complete and reliable, which are rarely true in real practice. To solve the building FDD problem with incomplete data, in this article, the adjacent information recovery (AIR) filter is proposed to recover the missing data before applying FDD methods. The AIR filter takes the time series adjacency information into consideration via hidden Markov model (HMM) and includes the channel adjacency information with the collaborating filtering technique. National Research Foundation (NRF) This work was supported in part by the National Research Foundation, Prime Minister’s Office, Singapore, through the Energy Innovation Research Programme (EIRP) for Building Energy Efficiency Grant Call, administered by the Building and Construction Authority, under Grant NRF2013EWT-EIRP004-051 and in part by the Republic of Singapore’s National Research Foundation under its Campus for Research Excellence and Technological Enterprise (CREATE) Programme through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program

A triple band-notched ultrawide band (UWB) antenna is presented to avoid the interference of services working in the UWB band, such as WLAN, WiMAX and X-band satellite systems. The arc H-shaped slot on the radiating patch creates a low frequency notched band, while the other two band-notched bands are formed by cutting narrow slots on the ground plane. The presented antenna can operate on the ultrawide band efficiently and inhibit interference from three different kinds of narrow band communication systems. The simulation and measurement results show that the antenna has excellent band-notched function on the rejectband and almost omnidirectional radiation pattern on the passband.

In this paper, a distributed extremum seeking scheme is proposed to find the solution of a nonmodel-based distributed optimization problem among networked agents. The agents are supposed to have measurements of the local cost functions and constraint functions. However, no explicit expressions on the cost functions, the constraint functions, or their gradients are available. The design of the distributed extremum seeking scheme is based on the saddle point dynamics. Stability analysis is conducted via using averaging analysis, Lyapunov stability analysis, and the concept of saddle point. It is shown that the solution to the distributed optimization problem is semiglobally practically asymptotically stable under the proposed extremum seeking law. An application of the proposed extremum seeking method to energy consumption control for the electricity consumers in smart grid is discussed. Simulation results on energy consumption control of a network of heating ventilation and air conditioning systems are provided to validate the proposed distributed extremum seeker.

For the battery energy storage system (BESS) consisting of multiple battery packages, package-level state-of-charge (SOC) balancing can provide safety redundancy in protecting battery packages from overcharging or overdischarging, and maintain the maximum power capacity of the overall BESS. In this paper, a distributed control scheme is proposed for package-level SOC balancing of a grid-connected BESS. The proposed control scheme comprises three parts. First, each battery package shares its SOC with its neighboring battery packages over the communication network. Based on the SOCs of its own and its neighbors, each battery package is equipped with an energy coordinator. Second, for each battery package, a distributed observer is employed to estimate the average desired power output, which is used to determine the local reference power output together with the energy coordinator. Third, local current controller is synthesized for each battery package to achieve local reference power output tracking. Comprehensive case studies are conducted to evaluate the effectiveness of the proposed control scheme.

Chillers contribute to a significant part of the building energy consumption. In order to save energy and improve the performance of building automation systems, there is an increasing need for chiller fault detection and diagnosis (FDD). This paper proposes a two-stage data-driven FDD strategy which formulates the chiller fault detection and diagnosis task as a multi-class classification problem. Linear Discriminant Analysis (LDA) is adopted to project the high dimensional data into a lower dimensional space so as to achieve maximum class separation and original class information maintenance. At the first stage, a fault is detected and diagnosed if the monitoring data set is the closest to one of the predefined fault clusters and within the predefined Manhattan distance range of the corresponding fault. At the second stage, fault severity level is recognized by comparing the monitoring data set with the corresponding predefined severity level clusters. The fault is diagnosed as at a particular severity level if it is the closest to the corresponding severity level cluster. The proposed strategy is validated by the experimental data of ASHRAE Research Project 1043 (RP-1043). Results show that the data-driven FDD strategy using LDA can detect and diagnose chiller faults effectively.

This paper addresses the cooperative control problem of AC microgrids consisting of both voltage-controlled voltage-source inverters and current-controlled voltage-source inverters. The voltage-controlled voltage-source inverters dictate the frequency and voltage of the microgrid and the current-controlled voltage-source inverters provide quick and decoupled active and reactive power support to the microgrid. To handle the nonlinear and heterogeneous dynamics of the inverters, we have developed a distributed feedforward approach based on the cooperative control of multi-agent systems. The proposed controller presents three advantages in comparison with the existing works. First, all the inverters share one communication network, and the locations of the inverters over the communication network can be arbitrarily sited as long as all the inverters are connected. Second, the selection of the control gains does not rely on either the whole microgrid dynamics or the topology of the communication network. Third, the inverters’ dynamics after input-output feedback linearization are allowed to be different. The effectiveness of the proposed controller is verified by the simulation study of a microgrid system for three cases, i.e., frequency and voltage restoration after islanding, active and reactive power adjustment after abrupt load changes and system performance subject to communication link failure and restoration.

In this paper, a unified tracking and regulation visual servoing strategy is proposed for a wheeled mobile robot equipped with an onboard camera, wherein the 2-1/2-D visual servoing scheme helps to keep targets in the camera’s field of view. Specifically, a novel composite error vector, including both image signals and rotational angles, is defined and then calculated by comparing the feature points of the unmodeled target object from the current image, the reference image, and the desired sequence of images. After that, an error transformation is designed to construct the chained-form error system, for which a time-varying continuous controller is designed to accomplish both tracking and regulation tasks. As shown by Lyapunov analysis, the proposed unified controller achieves asymptotic stability even in the presence of uncertainties regarding object model and depth information. Both simulation and experimental results are collected to investigate the feasibility of the proposed approach.

A compound control strategy is proposed for frequency regulation of source-grid-load systems in which power sources, power grids, and loads are all participating in the process. Here, power sources are conventional thermal generators, including new energy power generations, and loads are composed of energy storage units (ESUs) and grid-friendly appliances (GFAs). The proposed control scheme includes two levels of operations, with the upper level to be a model predictive control (MPC) for generators and the lower level to be a distributed leader-following consensus control strategy for multiple ESUs. For new energy power generations, the power outputs are restricted on a constant value during a sampling period based on a predicted generating curve. GFAs respond to the system frequency by regulating their active power consumption. Simulations on a single power system and three interconnected area power systems are provided to verify the effectiveness of the proposed compound control strategy.

With more educational business absorbed into information management system at universities, traditional information management platform seems unable to provide efficient service for teaching and research. Some universities then resort to cloud computing platform. In view of the problems existing in the traditional information platform, this study presented an information management framework designed with cloud technology, and introduced the security techniques for its protection.

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.

The papers in this special section focus on technological and system developments in designing power grids. The power grid is a large interconnected infrastructure for delivering electricity from power plants to end users. Now, traditional grids are facing kinds of challenges, and the world is proposing to modernize legacy and make strides toward smart grid. It is widely recognized that demand response is the core feature of smart grid, which can be formally defined as “changes in electric use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized. With the support of the advanced information and communication technologies, demand response is able to improve the efficiency, reliability, economics, and sustainability of power generation, distribution, and utilization.

The electricity price forecasting (EPF) is essential for decision-making mechanisms of market participants to survive in the deregulated and competing commercial environment. Due to special features of the electricity such as seasonality, the constant balance between production and consumption required by the system, and environmental dependencies, electricity prices generally shows extreme volatility and price spikes with the heteroscedasticity. This paper provides a survey of main EPF methodologies and the ultimate goal of this survey is to provide readers insights and guidelines for choosing different EPF techniques for day-ahead electricity markets. For each type of method, we briefly introduce its principle and then describe how it is applied in EPF. Many new EPF techniques developed recently are also discussed, especially the artificial intelligence forecasting methods. The pros and cons of each type of method are provided in a final table so that users can pay attention to when choosing them. In the final section, several promising methods and potential directions for further exploration are presented.

This paper presents a nonlinear model for the climate control of temperature, relative humidity and CO 2 concentration in a multi-zone HVAC system. A constrained optimization problem has been formulated to meet the objectives of good thermal comfort and energy optimization. Soft constraints have been introduced to increase the feasible region and provide balance between these two objectives. A distributed model predictive control (MPC) algorithm based on projected subgradient method is proposed to design the controller for the HVAC system. The effectiveness of the proposed algorithm is verified by comparing with the existing works through numerical simulation.

This paper proposes a distributed MPC strategy for bilinear HVAC systems with coupled dynamics and constraints. These couplings arise from the heat transfer between adjacent zones and the limited total supply energy and airflow rate. To handle the coupled dynamics and constraints, a decoupled method is needed for the distributed computation of the online MPC problem. Besides the coupling issue, the bilinear model is also a fundamental obstacle to efficient online computation. To solve this problem, a convex relaxation method is developed in this paper. The performance of the proposed MPC strategy is demonstrated by a numerical example.

This paper provides a study of the optimal scheduling of building operation to minimize its energy cost under building operation uncertainties. Opposed to the usual way that describes thermal comfort using a static range of air temperature, the optimization of a tradeoff between energy cost and thermal comfort predicted mean vote (PMV) index is addressed in this paper. In order to integrate the calculation of the PMV index with the optimization procedure, we develop a sufficiently accurate approximation of the original PMV model which is computationally efficient. We develop a model-based periodic event-triggered mechanism (ETM) to handle the uncertainties in the building operation. Upon the triggering of predefined events, the ETM determines whether the optimal strategy should be recalculated. In this way, the communication and computational resources required can be significantly reduced. Numerical results show that the ETM method is robust with respect to the uncertainties in prediction errors and results in a reduction of more than 60% in computation without perceivable degradation in system performance as compared to a typical closed-loop model predictive control.

Demand side management is well known as an effective way to reduce the power mismatch between stochastic supply and demand in a microgrid. Since thermal comfort of occupants is mainly determined by air temperature and speed, we explore the fact that building demand flexibility could be enhanced by coordination of air conditioning and mechanical ventilation (ACMV) system and cooling fans. The coordinated scheduling of these energy devices is addressed in this paper to minimize the power mismatch between the supply and demand in a high renewable penetration microgrid. A receding horizon-based framework is developed to address the uncertainties in both the supply and demand. Based on this framework, we have to solve the problem at each sampling stage with the future evolution of the system dynamics. Due to the coupling between ACMVs and fans and a large number of these devices in the microgrid, the problem may not be tractable. So we develop a Lagrangian relaxation-based algorithm to efficiently solve the problem at each stage. Numerical results show the effectiveness of the proposed method based on the actual data.

© 2017 IEEE. The paper addresses the problem of efficiently deploying sensors in spatial environments, e.g. smart buildings, for the purpose of monitoring environmental phenomena. By modelling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality criterion 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 not affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment problem is then solved by a practically and feasibly polynomial algorithm, where its solutions are guaranteed. The proposed approaches were implemented in a real tested space in a university building, where the obtained results are highly promising.

This paper studies secure leader-following consensus of linear multi-agent systems under a directed communication network subject to Denial-of-Service (DoS) attacks. We consider time-sequence-based DoS attacks allowed to occur aperiodically in an unknown attack strategy. The frequency and duration of DoS attacks are analyzed and investigated for a secure leader-following consensus problem. A distributed event-triggered control law is developed with scheduling of controller updating times determined in the presence of DoS attacks. It is proven that the agents can achieve secure leader-following consensus exponentially under the proposed distributed control scheme. The effectiveness of the developed methods is illustrated through two case studies: 1) distributed multi-robot coordination, and 2) distributed voltage regulation of power networks.

Building sector is known to be the largest energy consumer sector in the world and approximately half of the energy consumed by buildings is attributed to heating, ventilating and air-conditioning (HVAC). Intelligent HVAC control technique such as Model Predictive Control (MPC) has shown up to 40% of energy savings through extensive simulations and experiments. However, a good quality building model is required by the MPC technique. Although many software are available for building model simulation, little documentation is found regarding the complete process of model development and verification of a multizone building using TRNSYS. In addition, the process of multizone building modelling tends to be time consuming regardless of the software environments. This paper aims to address the above issues by presenting a tutorial-type case study of the development and verification of a multizone building HVAC model using TRNSYS. The paper also presents a generalized procedure in terms of a flow chart for generic multizone building HVAC modelling and some suggestions from the authors which could be helpful for faster modelling especially for new TRNSYS users.

Sensors are pervasively deployed on mobile devices with the development of Internet of Things technology. Value-added services are innovated and developed by analyzing data streams from massive number of mobile sensors in online mode. Due to dynamic working condition of mobile sensors and the high data rate, back end analytic services confront incoming streams with large rate fluctuation and out-of-order time series. This puts forward special challenges in service implementation for commercial applications, where good reliability/scalability performance is a must. In this paper, a data ingestion and scheduling framework is proposed to enable large-scale tempo-spatial streams analysis in a reliable and cost-effective way. A case study on a real world application adopting this framework is introduced and its pilot result is presented.

This paper studies event-triggered secure cooperative control of linear multi-agent systems under Denial-of-Service (DoS) attacks. The DoS attacks refer to interruptions of communication on the control channels carried out by an intelligent adversary. We consider time-sequence-based DoS attacks allowed to occur aperiodically in an unknown attack strategy. The frequency and duration of DoS attacks are analyzed and investigated for a secure average consensus problem. A distributed event-triggered control law is developed and scheduling of controller updating times is determined in the presence of DoS attacks. It is shown that under the proposed distributed control scheme, the agents can achieve secure consensus exponentially. The effectiveness of the developed methods is illustrated through example and numerical simulations on multi-robot coordination.

This paper considers the cooperative output regulation problem for a class of heterogeneous multi-agent systems with linearly parameterizable nonlinear matching uncertainties, which can be used to model electrical energy storage systems. Such a problem cannot be solved by the existing control approaches due to the heterogeneity and multi-input multi-output property of the agents' dynamics. To overcome the technical difficulties, we propose two distributed observer based control approaches which integrate the distributed observer/adaptive distributed observer techniques with the certainty equivalent adaptive control design. Sufficient conditions are given on the convergence of the estimated errors of the uncertain system parameters. Simulation results show the application of the proposed control approaches to the energy management of a battery energy storage system.

This brief proposes a pricing-based energy control strategy to remove the peak load for smart grid. According to the price, energy consumers control their energy consumption to make a tradeoff between the electricity cost and the load curtailment cost. The consumers are interactive with each other because of pricing based on the total load. We formulate the interactions among the consumers into a noncooperative game and give a sufficient condition to ensure a unique equilibrium in the game. We develop a distributed energy control algorithm and provide a sufficient convergence condition of the algorithm. The energy control algorithm starts at the beginning of each time slot, e.g., 15 min. Finally, the energy control strategy is applied to control the energy consumption of the consumers with heating ventilation air conditioning systems. The numerical results show that the energy control strategy is effective in removing the peak load and matching supply with demand, and the energy control algorithm can converge to the equilibrium.

This study conceptualises the monitoring power of independent directors (IDs) as consisting of specialist expertise or competence, incentives, balancing power and diligence. It then empirically investigates the influence of IDs’ monitoring power on internal control quality (ICQ), which is proxied by the voluntary disclosure of auditors’ reports on internal control and financial restatements released by China's A-share firms during 2006–2010. We find that the combined ID monitoring power index has a positive and significant effect on ICQ, which is robust to different proxies for ICQ, and that its components are also positively and significantly associated with either or (in most cases) both measures of ICQ. Overall, our evidence indicates that IDs’ monitoring power plays a positive role in improving ICQ in China.

This paper considers the power sharing problem for a grid-connected AC microgrid consisting of multiple dispatchable distributed generators. The microgrid is modeled as a multi-agent system and the power sharing problem is formulated as an interconnected leader-following consensus problem in the sense that the leader is affected by the fol- lowers passively. To solve this interconnected leader-following consensus problem, a distributed control scheme is proposed. First, each distributed generator is equipped with a distributed observer to communicate with each other and agree on a global reference signal, based on which the local reference power output is determined in proportion to its power capacity. Second, local tracking controller is designed for each distributed generator to achieve local reference power output tracking. It is proven that, under this control scheme, power sharing among all the distributed generators can be accomplished autonomously. Simulation results are presented to validate the effectiveness of the proposed control scheme.