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Air handling units (AHUs) are essential for the regulation and circulation of indoor air in modern buildings and for providing a comfortable environment. However, the performance of AHUs can be easily affected by various sensor faults during long-term operation. To precisely detect and diagnose AHU faults, many researchers have applied model-based and data-driven approaches based on existing centralized architectures. However, such methods require comprehensive knowledge of physical constraints or sufficient labeled data and thus are impractical in real scenarios. Inspired by the merits of emerging decentralized systems, we implement insect intelligent building (I2B), a fully decentralized architecture, for intelligent buildings to improve building operation performance. In addition, to ensure the AHU efficiency, we optimize the process of AHU fault detection and diagnosis and propose a fully distributed voting strategy using only adjacent computing process nodes (CPNs) in a decentralized architecture. Extensive experiments were conducted to analyze the performance of the proposed method in different real scenarios. Based on a comparison with related state-of-the-art methods, the experimental results highlight the superiority of our method, which provides high accuracy, plug-in capability and strong adaptability.

The acoustic diffusion equation model has been widely applied in various scenarios, but a larger prediction error exists when applied to underground spaces, showing a significantly lower characteristic of the sound pressure level in the later stage compared to field tests since underground spaces have a more closed acoustic environment. Therefore, we analyze the characteristics of underground spaces differentiating from aboveground spaces when applying the model and propose an improved model from the perspective of energy balance. The energy neglected in the calculation of the acoustic diffusion equation model is compensated in long channel underground spaces named “acoustic escape compensation”. A simulation and two field experiments are conducted to verify the effectiveness of the proposed compensation strategy in long-channel underground spaces. The mean square error is used to evaluate the differences between the classical model and the improved model, which shows a numerical improvement of 1.3 in the underground field test. The results show that the improved model is more suitable for describing underground spaces. The proposed strategy provides an effective extension of the acoustic diffusion equation model to solve the problem of sound field prediction and management in underground spaces.

Fully distributed intelligent building systems can be used to effectively reduce the complexity of building automation systems and improve the efficiency of the operation and maintenance management because of its self-organization, flexibility, and robustness. However, the parallel computing mode, dynamic network topology, and complex node interaction logic make application development complex, time-consuming, and challenging. To address the development difficulties of fully distributed intelligent building system applications, this paper proposes a user-friendly programming language called SwarmL. Concretely, SwarmL (1) establishes a language model, an overall framework, and an abstract syntax that intuitively describes the static physical objects and dynamic execution mechanisms of a fully distributed intelligent building system, (2) proposes a physical field-oriented variable that adapts the programming model to the distributed architectures by employing a serial programming style in accordance with human thinking to program parallel applications of fully distributed intelligent building systems for reducing programming difficulty, (3) designs a computational scope-based communication mechanism that separates the computational logic from the node interaction logic, thus adapting to dynamically changing network topologies and supporting the generalized development of the fully distributed intelligent building system applications, and (4) implements an integrated development tool that supports program editing and object code generation. To validate SwarmL, an example application of a real scenario and a subject-based experiment are explored. The results demonstrate that SwarmL can effectively reduce the programming difficulty and improve the development efficiency of fully distributed intelligent building system applications. SwarmL enables building users to quickly understand and master the development methods of application tasks in fully distributed intelligent building systems, and supports the intuitive description and generalized, efficient development of application tasks. The created SwarmL support tool supports the downloading and deployment of applications for fully distributed intelligent building systems, which can improve the efficiency of building control management and promote the application and popularization of new intelligent building systems.

Test case prioritization is a method to prioritize test cases to improve the testing efficiency of service-oriented workflow applications. Existing prioritization methods prioritize test cases in different application environments according to different metrics (for example, statement coverage, and path coverage). Web services are orchestrated by service-oriented workflow applications to provide different functions, especially in cloud-based mobile systems. As a result, those applications need more precise scheduling to run test cases that can detect faults earlier. Unfortunately, most regression test case prioritization studies in service-oriented workflow applications neglect the use of activity dependency, which is an important factor that affects test case prioritization. By analyzing the dependences between activities, the modification effects of activities on the modified version of service-oriented workflow applications are calculated. On this basis, this paper proposes a new prioritization method for regression test cases. Experimental results show that our method is more effective than the traditional coverage-based technique in testing case priority.

To solve the high labor and maintenance cost while save energy problems in actual engineering, a decentralized control structure and optimization method for the heating, ventilation, and air conditioning (HVAC) systems are proposed. In this decentralized HVAC control system, each updated smart equipment is connected according to the physical correlations and can communicate with adjacent nodes collaboratively. Furthermore, a novel fully distributed and self-organizing swarm intelligence optimization algorithm is presented. And the algorithm runs in each smart equipment to achieve the optimal operation and avoid the conflict among correlated equipment. Moreover, the convergence property of the novel method is analyzed theoretically. This method is confirmed effective for realizing the global optimal operation of HVAC system through simulation results and hardware tests on actual engineering.

Due to the high-fidelity mapping of the physical buildings and the intelligent performance shown in their lifecycle, digital twins (DTs) have gained increasing attention in the building sector. Although digital twins based on building information modeling (BIM) have become a hot research topic, existing works emphasize the digitization of building static and dynamic information and lack a unified consideration of the inherent physical mechanisms and interactive behaviors of buildings. To this end, this paper proposes a foundation model for building digital twins which realizes the unification of building static information, physical mechanisms and interaction patterns. The conceptual framework of the model is given first and then formal modeling and verification with time automata theory are performed to demonstrate the plausibility of the model. Finally, a practical digital twin of a chiller is developed based on the proposed foundation model as an example, thus, indicating its effectiveness and credibility.

Data-aware processes play a crucial role in various IT systems, including requirement elicitation, domain analysis, software design, and system execution. Due to frequent changes in business environments and continual internal adjustments of enterprises, data-aware processes are increasingly evolved into multiple process variants. The detection of differences between variants can be related to process mapping, process integration, or process substitution. A critical step of the procedure is to investigate the data-aware process consistency. Unfortunately, existing studies only provide a simple "yes" or "no" answer or look for an answer purely from the control flow perspective. The objective of this paper is to propose a systematic solution for effective measurement of consistency between data-aware processes. First, we identify essential activity constraints which reside in data-aware processes. Then, we introduce a novel concept of activity constraint graph (ACG) and propose an algorithm for constructing ACGs. Finally, we use ACGs to measure the data-aware process consistency on a scale from 0 to 1. Our technique has been implemented in a prototype tool, and extensive experiments using both real and synthetic datasets are conducted to evaluate the accuracy, distribution of consistency degrees, and capacity of difference detection of our approach. Results show that our approach is more accurate, generates a finer distribution of consistency degrees, and detects differences more effectively than other state-of-the-art approaches.

Abstract We present a novel finite-time average consensus protocol based on event-triggered control strategy for multiagent systems. The system stability is proved. The lower bound of the interevent time is obtained to guarantee that there is no Zeno behavior. Moreover, the upper bound of the convergence time is obtained. The relationship between the convergence time and protocol parameter with initial state is analyzed. Lastly, simulations are conducted to verify the effectiveness of the results.

Multi-robot consensus has been extensively applied in robotics. In this study, a new protocol is proposed to solve the finite-time average consensus problem. The protocol can improve the convergence rate. The upper bound of the convergence time is obtained. Analysis shows that there exists a limit value of the convergence time when the disagreement of initial states tends to be infinitely large, and the value is irrelevant to the initial states. The relationship between convergence time and initial states, communication topology, parameter is analysed. Lastly, the effectiveness of the results is verified by simulations.

Gesture recognition acts as a key enabler for user-friendly human-computer interfaces (HCI). To bridge the human-computer barrier, numerous efforts have been devoted to designing accurate fine-grained gesture recognition systems. Recent advances in wireless sensing hold promise for a ubiquitous, non-invasive and low-cost system with existing Wi-Fi infrastructures. In this paper, we propose DeepNum, which enables fine-grained finger gesture recognition with only a pair of commercial Wi-Fi devices. The key insight of DeepNum is to incorporate the quintessence of deep learning-based image processing so as to better depict the influence induced by subtle finger movements. In particular, we make multiple efforts to transfer sensitive Channel State Information (CSI) into depth radio images, including antenna selection, gesture segmentation and image construction, followed by noisy image purification using high-dimensional relations. To fulfill the restrictive size requirements of deep learning model, we propose a novel region-selection method to constrain the image size and select qualified regions with dominant color and texture features. Finally, a 7-layer Convolutional Neural Network (CNN) and SoftMax function are adopted to achieve automatic feature extraction and accurate gesture classification. Experimental results demonstrate the excellent performance of DeepNum, which recognizes 10 finger gestures with overall accuracy of 98% in three typical indoor scenarios.

A novel quartz micromachined gyroscope is proposed in this paper. The novel gyroscope is realized by quartz anisotropic wet etching and 3-dimensional electrodes deposition. In the quartz wet etching process, the quality of Cr/Au mask films affecting the process are studied by experiment. An excellent mask film with 100 Å Cr and 2000 Å Au is achieved by optimization of experimental parameters. Crystal facets after etching seriously affect the following sidewall electrodes deposition process and the structure’s mechanical behaviours. Removal of crystal facets is successfully implemented by increasing etching time based on etching rate ratios between facets and crystal planes. In the electrodes deposition process, an aperture mask evaporation method is employed to prepare electrodes on 3-dimensional surfaces of the gyroscope structure. The alignments among the aperture masks are realized by the ABM™ Mask Aligner System. Based on the processes described above, a z–axis quartz gyroscope is fabricated successfully.

Optimal sensor placement is an important part in the structural health monitoring of bridge structures. However, some defects are present in the existing methods, such as the focus on a single optimal index, the selection of modal order and sensor number based on experience, and the long computation time. A hybrid optimization strategy named MSE-AGA is proposed in this study to address these problems. The approach firstly selects modal order using modal participation factor. Then, the modal strain energy method is adopted to conduct the initial sensor placement. Finally, the adaptive genetic algorithm (AGA) is utilized to determine the optimal number and locations of the sensors, which uses the root mean square of off-diagonal elements in the modal assurance criterion matrix as the fitness function. A case study of sensor placement on a numerically simulated bridge structure is provided to verify the effectiveness of the MSE-AGA strategy, and the AGA method without initial placement is used as a contrast experiment. A comparison of these strategies shows that the optimal results obtained by the MSE-AGA method have a high modal strain energy index, a short computation time, and small off-diagonal elements in the modal assurance criterion matrix.