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A novel decentralized method for optimal load distribution in an isolated power system is proposed. In contrast to the traditional centralized method, the load economic dispatch is distributed to every smart distributed generator (DG) without the need for a monitoring host. Similar to the structure, mechanism and characteristics of biological communities, a smart DG node can communicate with adjacent nodes and operate collaboratively to complete the optimal operation of an isolated power system. The task is formulated as a decentralized optimization with a number of local constraints and is solved by a penalty function method. With the economic dispatch algorithm computed in all of the DG nodes in parallel, a new, fully distributed flat control network is established. The convergence property of the novel method is analysed theoretically. Simulation results on an illustrative system provide support for the validity of the proposed method.

This study proposes a novel decentralized sensor fault detection and self-repair method for heating, ventilation and air-conditioning systems. From the perspective of network structure, sensor fault diagnosis in heating, ventilation and air-conditioning systems is distributed to the updated smart sensors without the monitoring host, which is necessary in the traditional centralized method. A fully distributed flat sensor network is established based on fundamental physical equations. Similar to the structure, mechanism and characteristics of biological communities, a smart sensor needs only to communicate with adjacent nodes and operate collaboratively to complete sensor fault detection and self-repair tasks. These tasks are formulated as a constrained optimization and are solved by a decentralized algorithm with a penalty function executed in all the sensor nodes in parallel. The diagnosis model introduces an exponential function method to determine the precise location and undertake self-repair of a fault node. Simulation results on a chilled water system illustrate the effectiveness of the proposed method. Practical application: The traditional sensor fault detection and diagnosis methods for heating, ventilation and air-conditioning systems are based on a centralized structure with several deficiencies, such as high maintenance and labor costs, link congestion and operational lag. This study presents a decentralized sensor network structure and exponential-function-based method that possess the advantages of plug-and-play, rapid deployment, high flexibility and convenience for engineering implementation without having to build a central monitor. The efficiency and effectiveness of the proposed method are demonstrated via a case study.

Vibration monitoring signals are widely used for damage alarming among the structural health monitoring system. However, these signals are easily corrupted by the environmental noise in the collecting that hampers the accuracy and reliability of measured results. In this paper, a modified artificial bee colony (MABC) algorithm-based wavelet thresholding method has been proposed for noise reduction in the real measured vibration signals. Kent chaotic map and general opposition-based learning strategies are firstly adopted to initialize the colony. Tournament selection mechanism is then employed to choose the food source. Finally, the Kent chaotic search is applied to exploit the global optimum solution according to the current optimal value. Moreover, a generalized cross validation (GCV) based fitness function is constructed without requiring foreknowledge of the noise-free signals. A physical model experiment for a high-piled wharf structure is implemented to verify the feasibility of the proposed signal denoising approach. Particle swarm optimization (PSO) algorithm, basic artificial bee colony (BABC) algorithm and Logistic chaos artificial bee colony (LABC) algorithm and are also taken as contrast tests. Comparison results demonstrate that the proposed algorithm outperforms the other algorithms in terms of convergence speed and precision, and can effectively reduce the noise from the measured vibration signals of the high-piled wharf structure.

The mainstream detection methods Faster R-CNN and SSD are mainly designed for general dataset, but do not emphasize the detection effect of small targets and can not to achieve higher average detection accuracy on general dataset. In order to overcome the problem, we present a target detection method based on hierarchical and multi-scale convolutional neural network aiming at the detection task of maritime targets in complex scenario. To enhance the detection capability of small targets, we extract proposals of different scales in the multi-resolution convolution feature map in the region proposal network. To further improve the detection accuracy, we add an object detection network. The convolution feature maps with high-resolution are used to extract the targets, then an upsampling layer is added to enhance the resolution of the feature maps. The region proposal network and object detection network are then combined to realize the accurate detection of the target. The experiment results demonstrate that the proposed method achieves good detection results in maritime targets dataset, and the accuracy of target detection outperforms those of the mainstream detection methods.

In practice, the communication links between any two agents may not always be available. Based on this, the finite-time average consensus problems in multi-agent systems with switching topology are studied in this paper. The multi-agent systems with switching topology are divided into four types according to the ways of topology interconnection firstly. Then, the finite-time average consensus problems on two of the most common types are studied, and the upper bound of the convergence time is obtained. The asymptotical average consensus problem on another system is explained simply. Lastly, simulations are conducted to verify the effectiveness of the results.

A decentralized control structure is introduced into the heating, ventilation, and air conditioning (HVAC) system to solve the high maintenance and labor cost problem in actual engineering. Based on this new control system, a decentralized optimization method is presented for sensor fault repair and optimal group control of HVAC equipment. Convergence property of the novel method is theoretically analyzed considering both convex and nonconvex systems with constraints. In this decentralized control system, traditional device is fitted with a control chip such that it becomes a smart device. The smart device can communicate and operate collaboratively with the other devices to accomplish some designated tasks. The effectiveness of the presented method is verified by simulations and hardware tests.

In health monitoring of long-span structures, proper arrangement of sensors is a key point because of the need to acquire effective structural health information with limited testing resources. This study proposes a novel approach called dual-structure coding and mutation particle swarm optimization (DSC-MPSO) algorithm for the sensor placement. The cumulative effective modal mass participation factor is firstly derived to select the main contributions modes. A novel method combining dual-structure coding with the mutation operator is then utilized to determine the optimal sensors configurations. Finally, the feasibility of the DSC-MPSO algorithm is verified by optimizing the sensors locations for a long-span cable-stayed bridge. The effective independence method, genetic algorithm and standard particle swarm optimization algorithm are taken as contrast experiments. The simulation results show that the proposed algorithm in this paper could improve the convergence speed and precision. Accordingly, the method is effective in solving optimal sensor placement problems.

Excitation makes a great influence on the wavelet energy distribution of the response signal, this deficiency leads that the traditional structural damage identification method based on wavelet energy has a low precision. In order to solve this problem, a new structural damage identification method based on wavelet packet energy entropy (WPEE) of impulse response is presented in this paper. Firstly, natural excitation technique (NExT) is adopted to extract structural impulse response. Then, WPEE of the impulse response is computed, and the change rate of WPEE is used to construct the structural damage index. An experiment of damage identification on a pile structure is provided to verify the effectiveness of the proposed method. Experiment results show that this method can accurately identify the single damage and multi-damage.

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.

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.

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.

As Cyber-Physical Systems (CPS) operate in open, dynamic and diverse environments, they need to be self-adaptive to address the uncertainty challenges. And it is urgent to study self-adaptive software intensive Cyber-Physical Systems (self-adaptive CPS). As self-adaptive CPS interact closely with the environments and users, their requirements analysis is particularly challenging. General requirement models (e.g., goal models and problem frames) of CPS or self-adaptive software cannot be directly applied to self-adaptive CPS. In this paper, we present the Adapt-Requirement Model, which integrates goal models and problem frames, and further extends both models with self-adaptive CPS concepts. The integrated approach incorporates advantages of both goal models and problem frames, and can specify adaptation objectives of stakeholders as well as dynamic contexts of the system. Moreover, we create a UML Profile to represent Adapt-Requirement Model, and create a supporting tool for it. The UML profile and supporting tool has established continuity and integratability between Adapt-Requirement Model and UML design models. The applicability of our approach is illustrated using an example of Smart Building Firefighting System.

Multi-agent consensus has been widely applied in engineering. The multi-agent consensus problem is studied in this paper. The protocol based on the event-triggered control is presented. The stability of the system is proved. The decay factor is introduced to the threshold function. Then the relationship between the decay factor and the trigger frequency, the convergence time and the inter-event time is studied emphatically. Lastly, simulations are conducted to verify the effectiveness of the conclusions.

Number gestures play essential parts in our daily communication and have attracted academic interests in developing Human-Computer Interface. In this paper, we resort to the fine-grained Channel State Information (CSI) in the 802.11n standard to recognize number gestures. The intuition is that certain gestures can affect wireless environment in a specific formation and thus generate unique features. Unfortunately, the majority of CSI-based technologies only extracted coarse grained features to recognize macro-movements. Besides, it can be time-consuming to select the most discriminative feature as salient evidence. In this paper, we present a device-free number gesture recognition approach based on deep learning, named DeNum. First, we explore the sensibility of both the amplitude and phase information of de-noised CSI values to action transitions. Then the amplitude difference is utilized to detect the finishing points of actions through multiple sliding windows. To extract discriminative features from both the amplitude and phase information over three antennas, a 4-layer deep learning model is adopted after obtaining number gesture information. Finally, a Support Vector Machine (SVM) algorithm is applied for gesture classification. We conduct extensive experiments on commercial Wi-Fi devices with different experimental parameters. Experimental results demonstrate the presented approach can achieve the average accuracy of 94% in current office scenario.

In this paper, finite time state consensus problem for networks of dynamic agent is discussed, a new protocol guaranteeing the state of multi-agent reaching an consensus in finite time is proposed. The protocol contains both a nonlinear term and a linear term, the nonlinear term can guarantee that the consensus can be realized in finite time, and the linear term can reduce the convergence time. The upper bound of convergence time is obtained, the relationship among the convergence time with the communication topology and the initial state is analyzed. Finally, several simulations are presented to show the effectiveness of the results.

Uncertainty has posed great challenges to the development and application of self-adaptive software (SAS). To handle uncertainty underneath SAS, the technique of fuzzy control method has been employed to model and develop SASs. Practices prove that fuzzy logic is powerful to handle uncertainty, especially fuzzy uncertainty, within SAS. However, fuzzy control based SAS needs software developers to set fuzzy rules of the system, which is rather experience-dependent and heavily increases development burden of software engineers. To some extent, the effect of handling uncertainty depends on experiences of software engineers. Besides, fuzzy control based SAS realizes self-adaptation logic using fixed fuzzy rules, lacking the ability to adapt to large changes (e.g., scenario switches). In order to make up the above shortages of fuzzy control based SAS, we present the Fuzzy-Learning SAS, attempting to construct self-adaptation logic using self-learning fuzzy neural network. By incorporating the model of fuzzy neural network, Fuzzy-Learning models SAS with two feedback loops, i.e., the self-adaptation loop and the self-learning loop, enabling SASs with the ability of adapting to dynamic changes and the ability of automatically constructing self-adaptation logic. We have experimentally evaluated effectiveness and efficiency of Fuzzy-Learning SAS with a motivating example. The experiment results confirmed that Fuzzy-Learning SAS can improve the effect of handling uncertainty and alleviate the development burden of software engineers with ill knowledge of fuzzy control. Besides, Fuzzy-Learning SAS can adapt to large changes (e.g., scenario switches) with the self-learning ability.

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.