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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.

Multi-agent consensus has been widely applied in engineering. A novel protocol that can achieve an average state consensus for multi-agent systems in finite time is presented in this paper. The proposed protocol contains a non-linear and a linear term. The state consensus is achieved in finite time by the non-linear term and convergence performance is improved by the linear term to some degree. The protocol can be applied to systems with a switching topology as long as the communication graph is always undirected and connected. The upper bound of convergence time is obtained. The relationship between convergence time and protocol parameter, communication topology and initial state is analysed. Lastly, simulations are conducted to verify the effectiveness of the results.

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.

Context: Software Fuzzy Self-Adaptation (SFSA) is a fuzzy control-based software self-adaptation paradigm proposed to deal with the fuzzy uncertainty existing in self-adaptive software. However, as many software engineers lack fuzzy control knowledge, it is difficult for them to design and model this kind of fuzzy self-adaptive software (F-SAS). Therefore, efficient and effective modeling technologies and tools are needed for the SFSA framework. Objective: This paper aims to identify modeling requirements of F-SAS and to provide a modeling framework to specify, design and model F-SAS systems. Such a framework can simplify modeling process of F-SAS and improve the accessibility of software engineers to the SFSA paradigm. Method: This study proposes a modeling framework called Fuzzy self-Adaptation ModEling (FAME). By extending UML, FAME creates three types of modeling views. An analysis view called Fuzzy Case Diagram is created to specify the fuzzy self-adaptation goal and the realization processes of this goal. A structure view called Fuzzy Class Diagram is created to describe the fuzzy concepts and structural characteristics of F-SAS. A behavior view called Fuzzy Sequence Diagram is created to depict the dynamic behaviors of the F-SAS systems. The framework is implemented as a plug-in of Enterprise Architect. Results: We demonstrate the effectiveness and efficiency of the proposed approach by carrying out a subject-based empirical evaluation. The results show that FAME framework can improve modeling quality of F-SAS systems by 44.38% and shorten modeling time of F-SAS systems by 38.41% in comparison with traditional UML. Thus, FAME can considerably ease the modeling process of F-SAS systems. Conclusion: FAME framework incorporates the SFSA concepts into standard UML. Therefore, it provides a direct support to model SFSA characteristics and improves the accessibility of software engineers to the SFSA paradigm. Furthermore, it behaves a good example and provides good references for modeling domain-specific software systems.

The determination of threshold and the construction of thresholding function would directly affect the signal denoising quality in wavelet transform denoising techniques. However, some deficiencies exist in the conventional methods, such as fixed threshold value and the inflexible thresholding functions. To overcome the defects of the traditional wavelet thresholding techniques, a modified particle swarm optimization (MPSO) algorithm-based parametric wavelet thresholding approach is proposed for signal denoising. Firstly, a kind of parametric wavelet thresholding function construction method is proposed on the basis of conventional thresholding functions. With mathematical derivation, the properties of the constructed function are proved. Three dynamic adjustment strategies are then employed to modify the PSO algorithm. The mean square error (MSE) between the original signal and the reconstructed signal is minimized by the MPSO algorithm. Finally, the performances of the proposed approach and the existing methods are simulated by denoising four benchmark signals with different noise levels. The simulation results show that the proposed MPSO-based parametric wavelet thresholding can obtain lower MSE, higher signal-to-noise ratio, and noise suppression ratio compared to the other algorithms. Besides, the denoising visual results also indicate the superiority of the proposed approach in terms of the signal denoising capability.

To solve the high labor and maintenance cost problems in actual engineering, a decentralized heating, ventilation, and air-conditioning (HVAC) system is configured following its physical layout. In a decentralized HVAC control system, each of the updated smart equipment can communicate with the adjacent nodes collaboratively to fulfill the load requirement. Furthermore, to achieve the global optimal operation of an HVAC system, a fully distributed constrained optimization is formulated. In this paper, a fully distributed genetic algorithm (GA) is developed to solve the proposed constrained optimization. The proposed method is confirmed to be effective to realize the global optimization of HVAC system through simulation study.

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.

This paper studies the problem of stabilising a sampled-data switched linear system by quantised feedback asynchronously switched controllers. The idea of a quantised feedback asynchronously switched control strategy originates in earlier work reflecting actual system characteristic of switching and quantising, respectively. A quantised scheme is designed depending on switching time using dynamic quantiser. When sampling time, system switching time and controller switching time are all not uniform, the proposed switching controllers guarantee the system to be finite-time stable by a piecewise Lyapunov function and the average dwell-time method. Simulation examples are provided to show the effectiveness of the developed 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.

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.

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.

Optimal sensor placement is a key issue in the structural health monitoring of large-scale structures. However, some aspects in existing approaches require improvement, such as the empirical and unreliable selection of mode and sensor numbers and time-consuming computation. A novel improved particle swarm optimization (IPSO) algorithm is proposed to address these problems. The approach firstly employs the cumulative effective modal mass participation ratio to select mode number. Three strategies are then adopted to improve the PSO algorithm. Finally, the IPSO algorithm is utilized to determine the optimal sensors number and configurations. A case study of a latticed shell model is implemented to verify the feasibility of the proposed algorithm and four different PSO algorithms. The effective independence method is also taken as a contrast experiment. The comparison results show that the optimal placement schemes obtained by the PSO algorithms are valid, and the proposed IPSO algorithm has better enhancement in convergence speed and precision.

A huge amount of information and identification accuracy in large civil engineering structural damage identification has not been addressed yet. To efficiently solve this problem, a new damage identification method based on rough set and integrated neural network is first proposed. In brief, rough set was used to reduce attributes so as to decrease spatial dimensions of data and extract effective features. And then the reduced attributes will be put into the sub-neural network. The sub-neural network can give the preliminary diagnosis from different aspects of damage. The decision fusion network will give the final damage identification results. The identification examples show that this method can simplify the redundant information to reduce the neural network model, making full use of the range of information to effectively improve the accuracy of structural damage identification.

Monitoring school violence is critical for the prevention of juvenile delinquency and promotion of social harmony. Pioneering approaches employ always-on-body sensors or cameras with limited surveillance area, which cannot provide ubiquitous violence monitoring. In this paper, we present Wi-Dog, a non-invasive physical violence monitoring scheme based on commodity WiFi infrastructures. The key intuition is that violence-induced WiFi signals convey informative characteristics of intensity, irregularity and continuity. To identify school violence from violence-alike actions (e.g., jump, lie down and run), we develop a precise noise reduction method by selecting sensitive antenna pair and subcarriers. Moreover, a wavelet-entropy-based segmentation method is proposed to detect movement transitions in the distance, and the complete local-global analysis is further adopted to improve overall performance. We implemented Wi-Dog using commercial WiFi devices and evaluated it in real indoor environments. Experimental results demonstrate the effectiveness of Wi-Dog with average detection accuracy of 0.9.

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.

In order to solve the problems of complicated wiring and inconvenient operation existing in traditional pressure sensor, based on the surface acoustic wave (SAW) theory, a wireless passive pressure sensor with hollow cylinder sealing beam structure is designed in this paper. The sensor is resistant to electromagnetic interference, and the temperature compensation is adopted to obtain the strain that the pressure causes exactly. In addition, the mechanical properties analysis and finite element simulation analysis about the structure is done to verify its effectiveness in this paper. Based on the principle of SAW, this sensor can form a complete sense of passive wireless sensor system. Compared to traditional pressure sensor, the sensor designed in this paper has a high accuracy and application flexibility.

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.

Considering the deficiencies and limitations of Multidimensional Scaling -based (MDS-MAP) localization algorithm, a new MDS-mass spring-based (MDS-MS) localization algorithm was proposed by analyzing the MDS-MAP algorithm and Mass Spring Optimization-based (MSO) positioning algorithm. Moreover, for different ranging error, network connectivity and anchor node density, MDS-MAP, MSO, MDS-MAP(P) and MDS-MS four localization algorithms were simulated and compared. The simulation results show that MDS-MS-based algorithm possesses strong robustness and high positioning precision, and is fit for localization in sparse network topology and irregular network topology.

By analyzing the advantages and disadvantages of existing routing protocol of underwater acoustic sensor network, combining with underwater acoustic channel model, the author proposes a novel adaptive routing protocol based on the location of the nodes (LARP). The routing protocol can select forward route adaptively according to the level of data packets and the dynamic characteristics of underwater environment. Therefore, it can reduce the delay of network transmission, save the network energy consumption and improve the quality of the network communication effectively. Finally, through the simulation of four different routing mechanism: HH-VBF, VBF, MPR and LARP, our routing protocol's superiority is verified.

Field Service Security (FSS) is a systematic project which requires coordination of various departments to ensure the flight safe. At present data of FSS can't provide effective support for OLAP (Online Analytical Process), data mining or decision support because of its inconsistence and dispersion, so bring Data Warehouse technology into FSS is of great of importance. This paper presents how the FSS Data Warehouse is designed and implemented by an improved method-normalized dimensional modeling.

Self-adaptive software is expected to adjust itself attributes or structures at runtime in response to changes. Aiming at addressing some challenging problems such as difficult mathematically modeling software using the current control theoretical methods, we propose a novel fuzzy-control-based approach to achieve self-adaptive software, which is presented as framework of fuzzy self-adaptive software (FFSAS). In this framework, the general model, the implementation architecture, and the design methodology are put forward and discussed in detail. The fuzzy-control-based approach is evaluated with a news-website case study.