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The emerging Insect Intelligent Building (I2B) platform is pioneering a new realm in intelligent buildings. I2B has a distributed and decentralized network structure with intelligent nodes, and the key enabler is an application (APP) that functions to process information from intelligent nodes and accomplish complex control tasks in a decentralized network. To develop APPs for I2B, a proper programming language is the foremost goal; however, existing programming languages cannot be applied directly due to I2B's unique structure and distinction of application domains. This paper aims to provide language support for a direct and friendly development of I2B APPs. We propose a graphical programming language that adapts to the operating characteristics of I2B and users' habits of buildings. Specifically, we first analyze the domain characteristics of the proposed language and present a general programming language model illustrated with a motivating example. Then, we investigate the detailed design, definition, and usage of graphic elements. Additionally, we implement a prototype support tool for the graphical programming language to develop I2B APPs. Moreover, we use the application example of a building's constant pressure control for evaluating the effectiveness of our work. [ABSTRACT FROM AUTHOR]

Insect intelligent building (I2B) is a novel decentralized, flat-structured intelligent building platform with excellent flexibility and scalability. I2B allows users to develop applications that include control strategies for efficiently managing and controlling buildings. However, developing I2B APPs (applications) is considered a challenging and complex task due to the complex structural features and parallel computing models of the I2B platform. Existing studies have been shown to encounter difficulty in supporting a high degree of abstraction and in allowing users to define control scenarios in a concise and comprehensible way. This paper aims to facilitate the development of such applications and to reduce the programming difficulty. We propose Touch, a textual domain-specific language (DSL) that provides a high-level abstraction of I2B APPs. Specifically, we first establish the conceptual programming architecture of the I2B APP, making the application more intuitive by abstracting different levels of physical entities in I2B. Then, we present special language elements to effectively support the parallel computing model of the I2B platform and provide a formal definition of the concrete Touch syntax. We also implement supporting tools for Touch, including a development environment as well as target code generation. Finally, we present experimental results to demonstrate the effectiveness and efficiency of Touch. [ABSTRACT FROM AUTHOR]

Insect Intelligent Building (I2B) is a novel platform of intelligent buildings. The outstanding feature of I2B is the decentralized network structure connected by smart nodes. I2B can employ APPs (applications) developed by various practitioners or programming fans to manage and control buildings. However, due to the unique parallel operation of I2B platform and the popularization of APP developers, there still exists no effective approach to supporting I2B APP development. To deal with the challenges and provide meaningful guidance for describing and developing I2B APP and motivating the prospective programming language design, we propose INR, a programming model for I2B APP development. Three submodels in INR, namely, Individual, Neighborhood, and Region, are defined and implemented, respectively, for describing different task requirements. Moreover, new mechanisms of Tag-based programming and Clustering operation are established to support the plug-and-play and parallel abilities of APPs in I2B. Finally, we apply the programming model into an application case to illustrate the developing pattern of the I2B APP and verify the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Thresholding function is an important part of the wavelet threshold denoising method, which can influence the signal denoising effect significantly. However, some defects are present in the existing methods, such as function discontinuity, fixed bias, and parameters determined by trial and error. In order to solve these problems, a new wavelet thresholding function based on hyperbolic tangent function is proposed in this paper. Firstly, the basic properties of hyperbolic tangent function are analyzed. Then, a new thresholding function with a shape parameter is presented based on hyperbolic tangent function. The continuity, monotonicity, and high-order differentiability of the new function are theoretically proven. Finally, in order to determine the final form of the new function, a shape parameter optimization strategy based on artificial fish swarm algorithm is given in this paper. Mean square error is adopted to construct the objective function, and the optimal shape parameter is achieved by iterative search. At the end of the paper, a simulation experiment is provided to verify the effectiveness of the new function. In the experiment, two benchmark signals are used as test signals. Simulation results show that the proposed function can achieve better denoising effect than the classical hard and soft thresholding functions under different signal types and noise intensities. [ABSTRACT FROM AUTHOR]

Due to simple calculation and good denoising effect, wavelet threshold denoising method has been widely used in signal denoising. In this method, the threshold is an important parameter that affects the denoising effect. In order to improve the denoising effect of the existing methods, a new threshold considering interscale correlation is presented. Firstly, a new correlation index is proposed based on the propagation characteristics of the wavelet coefficients. Then, a threshold determination strategy is obtained using the new index. At the end of the paper, a simulation experiment is given to verify the effectiveness of the proposed method. In the experiment, four benchmark signals are used as test signals. Simulation results show that the proposed method can achieve a good denoising effect under various signal types, noise intensities, and thresholding functions. [ABSTRACT FROM AUTHOR]

Optimal sensor placement (OSP) is an important part in the structural health monitoring. Due to the ability of ensuring the linear independence of the tested modal vectors, the minimum modal assurance criterion (minMAC) is considered as an effective method and is used widely. However, some defects are present in this method, such as the low modal energy and the long computation time. A new OSP method named IAGA-MMAC is presented in this study to settle the issue. First, a modified modal assurance criterion (MMAC) is proposed to improve the modal energy of the selected locations. Then, an improved adaptive genetic algorithm (IAGA), which uses the root mean square of off-diagonal elements in the MMAC matrix as the fitness function, is proposed to enhance computation efficiency. A case study of sensor placement on a numerically simulated wharf structure is provided to verify the effectiveness of the IAGA-MMAC strategy, and two different methods are used as contrast experiments. A comparison of these strategies shows that the optimal results obtained by the IAGA-MMAC method have a high modal strain energy, a quick computational speed, and small off-diagonal elements in the MMAC matrix. [ABSTRACT FROM AUTHOR]