【Abstract】Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and head-worn wearables, and perform seamless transitions from the physical world to a mixed world with digital entities. These MAR systems support user experiences using MAR devices to provide universal access to digital content. Over the past 20 years, several MAR systems have been developed, however, the studies and design of MAR frameworks have not yet been systematically reviewed from the perspective of user-centric design. This article presents the first effort of surveying existing MAR frameworks (count: 37) and further discusses the latest studies on MAR through a top-down approach: (1) MAR applications; (2) MAR visualisation techniques adaptive to user mobility and contexts; (3) systematic evaluation of MAR frameworks, including supported platforms and corresponding features such as tracking, feature extraction, and sensing capabilities; and (4) underlying machine learning approaches supporting intelligent operations within MAR systems. Finally, we summarise the development of emerging research fields and the current state-of-the-art and discuss the important open challenges and possible theoretical and technical directions. This survey aims to benefit both researchers and MAR system developers alike.
【Abstract】Emerging distributed cloud architectures, e.g., fog and mobile edge computing, are playing an increasingly important role in the efficient delivery of real-time stream-processing applications (also referred to as augmented information services), such as industrial automation and metaverse experiences (e.g., extended reality, immersive gaming). While such applications require processed streams to be shared and simultaneously consumed by multiple users/devices, existing technologies lack efficient mechanisms to deal with their inherent multicast nature, leading to unnecessary traffic redundancy and network congestion. In this paper, we establish a unified framework for distributed cloud network control with generalized (mixed-cast) traffic flows that allows optimizing the distributed execution of the required packet processing, forwarding, and replication operations. We first characterize the enlarged multicast network stability region under the new control framework (with respect to its unicast counterpart). We then design a novel queuing system that allows scheduling data packets according to their current destination sets, and leverage Lyapunov drift-plus-penalty control theory to develop the first fully decentralized, throughput- and cost-optimal algorithm for multicast flow control. Numerical experiments validate analytical results and demonstrate the performance gain of the proposed design over existing network control policies.
【Abstract】The formulation of the Vehicle to Grid (V2G) scheme should ideally consider both Electric Vehicle (EV) and building owners' viewpoints. From the building owner's viewpoint, the EVs should be present in the building and participate consistently to deliver the required power. From the EV owners' viewpoint, participating in the V2G scheme should provide economic incentives while not compromising their travel needs. However, those proposed V2G algorithms up to this point in time had not considered any corrections required in response to unplanned changes in the EV travel plan. In this paper, a Two-stage optimization technique is proposed to determine the charging and discharging schedule for EVs participating in a vehicle-to-grid (V2G) programme at an office building. The EV owners' travel convenience is focused with more attention in the proposed model by giving them two V2G options. Firstly, day-ahead optimization (DAO) is applied, based on the expected building load profile and EV behaviour, the optimal charging or discharging control of the EVs is obtained in order to save electricity bills by minimizing the maximum demand of the building. Subsequently, a real-time optimization (RTO) is performed to adjust the V2G operation based on actual vehicle behaviours which usually deviate from their estimations. Simulations are conducted and the preliminary results show that the proposed technique is able to adjust the EV charging or discharging in real-time with the aid of the day-ahead stage. The proposed model manages to capture a new ideal optimal maximum demand point and maintain the EV SOC profile as planned from the DAO stage in real-time when prediction deviation occurs. In addition, a comprehensive cost-benefit analysis in utilizing V2G in peak load reduction is also performed to gain insight into the potential savings and discharging rewards attributable to the building and EV owner respectively.
【Abstract】Economic dispatch problem (EDP) is a fundamental optimization problem in power system operation, which aims at minimizing the total generation cost. In fact, the power grid is becoming a cyber-physical power system (CPPS). There-fore, the quality of communication is a key point. In this paper, considering two important factors, i.e., time delays and channel noises, a fully distributed consensus based algorithm is proposed for solving EDP. The critical maximum allowable upper bounds of heterogeneous communication delays and self-delays are obtained. It should be pointed out that the proposed algorithm can be robust against the time-varying delays and channel noises considering generator constraints. In addition, even with time-varying delays and channel noises, the power balance of supply and demand is not broken during the optimization. Several simulation studies are presented to validate the correctness and superiority of the developed results.
【Abstract】This paper is concerned with the distributed secure balancing control problem for battery energy storage systems (BESSs) in a Direct Current (DC) microgrid. The Denial-of-service (DoS) attacks, which appear in a random way, are taken into account during the data exchange of individual BESS units. By introducing a well-designed compensation term, an improved distributed consensus algorithm is developed to deal with the state-of-charge (SOC) balance issue in the presence of randomly occurring cyber attacks. With the aid of the Lyapunov stability theory, sufficient conditions are established for the networked control system such that the balance of the SOC for each BESS unit is achieved under certain constraints. The effectiveness of the proposed balance scheme is verified by a series of illustrative simulation experiments.
【Abstract】In a rapidly changing modern society, the construction industry is facing various issues, including the Fourth Industrial Revolution and climate change. Research on convergence between technologies such as artificial intelligence, AR/VR, IoT, and metaverse, and sustainable technologies such as green buildings and eco-friendly energy is being attempted in each field. The most important thing in the development of these technologies will be the interoperability of data. BIM is a technology that can effectively store data regardless of the size of a building or the amount of information and can be shared and stored without loss of data through an open format called IFC (industry foundation classes). This study aims to present a plan to generate alternatives and evaluate energy performance by analyzing the shape of the envelope for amorphous buildings through IFC. Design elements were derived through analysis of previous studies, and alternatives were automated by developing interfaces that can generate shapes according to the derived design elements. The generated alternatives can be compared and analyzed through the analysis of building energy by developing an evaluation system based on IFC. Based on the quantitative results in the initial design stage, the reliability of the design proposal considering the performance of the building is improved, and the process and cost can be predicted in advance; thus, it is expected to be an efficient decision support tool.
【Keywords】industry foundation classes (IFC); building information modeling (BIM); parametric modeling; energy performance evaluation; alternatives evaluation
【Abstract】This paper proposes a novel state estimation algorithm, called the distributed Frobenius-norm finite memory interacting multiple model (DFFM-IMM) estimation algorithm, for mobile robot localization in wireless sensor networks (WSNs). The proposed algorithm involves finite memory estimation based on recent finite measurements; such estimation facilitates robust localization in cases of missing measurements and robot kidnapping. Furthermore, the proposed algorithm employs IMM, which facilitates accurate localization if a mobile robot abruptly changes its speed and course. Notably, average-consensus-based distributed processing renders the proposed DFFM-IMM algorithm computationally efficient, and hence, real-time processing for very short sampling times of the WSN is possible. The proposed algorithm's performance is demonstrated by comparing it with a centralized Frobenius-norm finite memory IMM (CFFM-IMM) estimation algorithm and a localization algorithm on the basis of simulations and experiments.
【Abstract】The Chinese stock market exhibits many characteristics that deviate from the efficient market hypothesis and the trading volume contains a great deal of complexity information that the price cannot reflect. Do small or big orders drive trading volume? We studied the complex behavior of different orders from a microstructure perspective. We used ETF data of the CSI300, SSE50, and CSI500 indices and divided transactions into big and small orders. A multifractal detrended fluctuation analysis (MFDFA) method was used to study persistence. It was found that the persistence of small orders was stronger than that of big orders, which was caused by correlation with time. A multiscale composite complexity synchronization (MCCS) method was used to study the synchronization of orders and total volume. It was found that small orders drove selling-out transactions in the CSI300 market and that big orders drove selling-out transactions in the CSI500 market. Our findings are useful for understanding the microstructure of the trading volume in the Chinese market.
【Abstract】Industry 4.0 affects nearly every aspect of life by making it more technologically advanced, creative, environmentally friendly and ultimately, more interconnected. It also represents the beginning of the interconnectedness and metaverse associated with Industry 5.0. This issue is becoming decisive for advancement in all areas of life, including science. The primary goal of this study is to concisely explain how current Industry 4.0 trends might interact with existing work systems in global value chains to accelerate their operational activity in the context of firms from the Visegrad Four (V4) nations. Through an examination of the digital abilities in these nations, the purpose of the study is also to demonstrate how well citizens, employees, and end users are able to comprehend the problem at hand. The most recent resources for the topics are covered in the first section of the work. The next one uses graphic analysis and mutual comparison methods, generally comparing existing data over time; it is secondary research, and through these methods the Industry 4.0 applications can significantly speed up the work process itself when compared to the traditional lean process, primarily because of its digital structure. It is difficult to predict which of the V4 will be digitally prepared, as the precedent shifts are based on distinct indicators; therefore, it is crucial that all V4 nations expand their digital adaptability dramatically each year, primarily as a result of spending on scientific research, and education that is organised appropriately. The extra value of this effort may be attributed to how lean processes are intertwined with the Industry 4.0 trend's digital experience, which already includes the Industry 5.0 trend's artificial intelligence and metaverse, which represent the potential for further research in the future.
【Abstract】Introduction: Augmented Reality (AR) has demonstrated a potentially wide range of benefits and educational applications in the virtual health ecosystem. The concept of real-time data acquisition, machine learning aided processing, and visualization, is a foreseen ambition to leverage AR applications in the healthcare sector. This breakthrough with immersive technologies like AR, mixed reality (MR), virtual reality (VR), or extended reality (XR) will hopefully initiate a new surgical era: that of the use of the so-called surgical metaverse. Main text: This paper focuses on the future use of AR in breast surgery education describing two potential applications (surgical remote telementoring and impalpable breast cancer localization using AR), along with the technical needs to make it possible.Conclusion: Surgical telementoring and impalpable tumors non-invasive localization are two examples that can have success in the future provided the improvements in both data transformation and infrastructures are capable to overcome the current challenges and limitations.
