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Abstract A novel cyber-physical-social system (CPSS) with parallel learning is presented for distributed energy management (DEM) of a microgrid. CPSS is developed by extending the conventional cyber-physical system to the social space with human participation and interaction. Each energy supplier or each energy demander is regarded as a human in the social space, who is able to learn the knowledge, co-operate with others, and make a decision with various preference behaviors. The correlated equilibrium (CE) based general-sum game is employed for realizing the human interaction on the complex optimization subtask, while the novel adaptive consensus algorithm is used for achieving that on the simple optimization subtask with multi-energy balance constraints. A real-world system and multiple virtual artificial systems are introduced for parallel and interactive execution based on the small world network, thus a higher quality optimum of DEM can be rapidly emerged with a high probability. Case studies of a microgrid with 11 energy suppliers and 7 energy demanders demonstrate that the proposed technique can effectively achieve the human-computer collaboration and rapidly obtain a higher quality optimum of DEM compared with other centralized heuristic algorithms. Highlights • A cyber-physical-social system is constructed for distributed energy management. • A game theory with various decision behaviors is proposed for human interaction. • Energy suppliers or demanders can reach a consensus on the incremental cost. • The parallel interactive systems can lead to a lower total operating cost. • The proposed method outperforms other centralized heuristic algorithms for DEM. [ABSTRACT FROM AUTHOR]

A novel series of ZnAl 2 O 4 :Eu3+ aerogels (ZAE) and mullite ceramic phase reinforced ZnAl 2 O 4 :Eu3+ aerogels (MZAE) with high fluorescence thermal stability have been firstly synthesized for the encapsulation of high-power optical devices. However, due to the intrinsic structural brittleness of the aerogel, the structure of ZAE tends to collapse during the heat treatment and the fluorescence performance falls short of expectations. To this end, we propose a simple and effective strategy to enhance the structural rigidity of fluorescent aerogels by introducing the mullite ceramic phase into the network structure of ZAE. This can effectively suppress the agglomeration of Eu3+ caused by the collapse of the structure during the heat treatment, thus enhancing the optical properties of the aerogel. Compared with ZAE, MZAE has higher fluorescence thermal stability. The fluorescence intensity of MZAE at 498 K is still 75 % of that at 298 K, and the chromaticity shift is only 22 × 10−3. [ABSTRACT FROM AUTHOR]

The increase in global wind power installations has also increased the wind turbine density in wind farms. This has made the wake interactions between neighbouring turbines more significant and difficult to describe. Understanding wakes is important to predict the energy production and assess their environmental impacts. Although existing wake description methods can predict the average wind turbine wake under a high wind turbine density, they are unable to identify the wake boundary with an acceptable accuracy and computational cost. Consequently, the role of wake boundaries in modern wind farms has become unclear. To deal with this problem, this paper presents a comprehensive discussion on wind turbine wakes, especially the role of boundary identification in the wind farm planning stage. After a review of existing methods, an approach based on a newly derived mathematical formulation of the velocity field is proposed. Lidar-based field measurements and large-eddy simulations along with actuator line model-based numerical simulations were used to compare different wake boundary identification methods. The results show that the new approach is computationally cost-effective with a 5% increase in accuracy. The new approach also offers significant advantages as wake boundaries become increasingly complex. • The importance of wind turbine wake boundary identification is discussed. • Various wake boundary identification methods are compared for different purposes. • A cost-effective method is presented and demonstrated for its advantages. • The more complicated the wakes, the easier the new method's advantage is shown. [ABSTRACT FROM AUTHOR]

This paper suggests a unified and automated design methodology for synthesizing designs for multi-domain systems, such as mechatronic systems. A multi-domain dynamic system includes a mixture of electrical, mechanical, hydraulic, pneumatic, and/or thermal components, making it difficult use a single design tool to design a system to meet specified performance goals. The multi-domain design approach is not only efficient for mixed-domain problems, but is also useful for addressing separate single-domain design problems with a single tool. Bond graphs (BGs) are domain independent, allow free composition, and are efficient for classification and analysis of models, allowing rapid determination of various types of acceptability or feasibility of candidate designs. This can sharply reduce the time needed for analysis of designs that are infeasible or otherwise unattractive. Genetic programming is well recognized as a powerful tool for open-ended search. The combination of these two powerful methods is therefore an appropriate target for a better system for synthesis of complex multi-domain systems. The approach described here will evolve new designs (represented as BGs) with ever-improving performance, in an iterative loop of synthesis, analysis, and feedback to the synthesis process. The suggested design methodology has been applied here to three design examples. The first is a domain-independent eigenvalue placement design problem that is tested for some sample target sets of eigenvalues. The second is in the electrical domain––design of analog filters to achieve specified performance over a given frequency range. The third is in the electromechanical domain––redesign of a printer drive system to obtain desirable steady-state position of a rotational load. [Copyright &y& Elsevier]

Image dehazing can improve image clarity and visual effect, which plays a pivotal role in many computer vision tasks. Existing dehazing methods are mostly based on a single feature stream and tend to ignore the low-frequency characteristics of haze. In this paper, we propose a dual stream network for image dehazing. To enhance the edge information and texture detail of the image, we construct a frequency stream based on attention octave convolution. We decompose the features into high and low-frequency branches in the frequency stream to obtain different structural information. By adding a residual channel attention block, the attention octave convolution can extract frequency features more efficiently and effectively. Due to the lower resolution of low-frequency features in the frequency stream, the frequency stream features alone are insufficient for recovering the overall content of the image. Therefore, a content stream was added to compensate for the information lost in the frequency stream. By fusing the outputs of two feature streams, the network achieves an enhanced dehazing performance. The results show that our method is superior to other state-of-the-art algorithms in quantitative evaluation and visual impact. • A significant difference between hazy and clear images after low-pass filtering. • Prior information indicates that haze is closer to low-frequency information. • Spatial features are decomposed into high and low-frequency components by Att OctConv. • The content stream uses RCAB to filter out redundant low-frequency information. • Our method is also excellent for underwater image restoration. [ABSTRACT FROM AUTHOR]

Abstract There are a number of government directives and regulations as well as many public schemes on the recycling of batteries, in spite of this; the quantity of batteries that are actually recycled is still very low. Current production capacity cannot meet projected demand of Lithium-ion batteries. To counter this, the reclamation and repurposing of metals like cadmium, Lithium and Zinc from used or spent batteries is the only viable scheme. This is both environmentally friendly and economically feasible. An alternative is the selective chemical leaching in the presence of Sulfuric acid and Sodium metabisulfite. In this paper, the effect of these chemicals as well as the solid-to-liquid ratio and time of retention is comprehensively studied. Experiments are designed for the recovery of Zinc and cadmium from the spend Lithium-ion batteries mix. To maximize the recovery of Zinc and cadmium, the combined genetic programming and simulated annealing approach is proposed. Genetic programming is used for the formulation of functional relationship between recovered metals Zinc and cadmium and the inputs (Solid/Liquid ratio, concentration of Sulfuric acid, mass of Sodium metabisulfite and retention time). The optimal input conditions determined using the simulated annealing algorithm includes Solid/Liquid ratio of 11.7%, 0.86 M Sulfuric acid, 0.56 g/g of Sodium metabisulfite and 45 min of retention time. Three dimensions surface analysis reveals that a lower value of Solid/Liquid ratio favours a better yield. The optimal conditions are validated using experiments. This confirms the efficacy of simulated annealing aided genetic programming techniques as well as the optimal conditions of the metal extraction. Highlights • Problem of maximum extraction of Cadmium and Zinc from spent battery mix is undertaken. • Combined genetic programming and simulated annealing approach is proposed. • Genetic programming models fits the cadmium and zinc output very well. • Simulated annealing optimizes the Genetic programming model to obtain the optimum inputs. [ABSTRACT FROM AUTHOR]

Abstract To achieve an effective coordination between the secondary control and the tertiary control of load frequency control (LFC), a new optimal active power control (OAPC) is constructed for real-timely changing the operating points of distributed energy resources (DERs) and thermostatically controlled loads (TCLs) in an islanded microgrid. A large number of TCLs are integrated as a load aggregator (LA) for participating the secondary control of LFC, which can enhance the dynamic response performance due to their much faster response speeds compared with that of distributed generators. Since OAPC is a nonsmooth and nonlinear optimization with a quite short implementation period, a novel model-free ensemble learning (EL) is proposed to rapidly obtain a high-quality optimal solution for it. EL based OAPC is composed of multiple sub-optimizers and a learning concentrator, where each sub-optimizer is responsible for providing the exploitation and exploration samples to the learning concentrator, while the reinforcement learning based concentrator is mainly used for knowledge learning and knowledge transfer. Case studies are thoroughly carried out to verify the performance of EL based OAPC in an islanded microgrid with 12 DERs and 900 TCLs. Highlights • A new optimal active power control (OAPC) is constructed for an islanded microgrid. • Various thermostatically controlled loads are used for secondary frequency control. • A novel ensemble learning (EL) can rapidly obtain a high-quality optimal solution. • The secondary control and tertiary control are effectively coordinated by OAPC. • The EL based OAPC can achieve a high dynamic response performance of a microgrid. [ABSTRACT FROM AUTHOR]

At present many hospitals have to deal with the patient’s care and nursing for Acute Hypotensive Episodes (AHE) occurring in Intensive Care Units. AHE can cause fainting or shock suddenly, leading to irreversibility organ damage, and even death. Therefore, forecasting of occurrence of AHE is of practical value. However, the prediction of clinical AHE largely depends on the doctors’ experience, which cannot guarantee the high rate of success. It is thus very meaningful for the clinical care to use appropriate methods to predict the AHE with an automatic and reliable method. In this study, a Probability Distribution Patterns Analysis (PDPA) method is presented to solve the time series prediction problem of AHE. In the first phase, the features are extracted from the PDPA in the global and integral time series, and the partial local time series in the fixed time window. In the second phase, the proposed algorithm combining Genetic Algorithm (GA) and Support Vector Machine (SVM), namely GA-SVM is adopted to select the vital features for the effective classification. In order to demonstrate the generality of our method, we also conduct experiments on a classical time series problem-Control Chart Patterns (CCPs) multi-class time series, which is a benchmark problem in the process control. For CCPs problem, the experimental results demonstrate that the proposed method outperforms several traditional methods. The obtained accuracy is 98.65%, which is superior to listed previous works using the same CCPs model. For AHE classification and forecasting, the methodology is applied in two data sets, a small data set (37 records) and a big one (2892 records). The test accuracy of 89.19%, sensitivity of 91.67%, specificity of 88% in the small data set, and a test accuracy of 80.76%, sensitivity of 78.19%, specificity of 81.51% in the big data set are achieved with the classification model. [ABSTRACT FROM AUTHOR]

Abstract: The efficient expression of exogenous gene in mitochondria of photosynthetic organism has been an insurmountable problem. In this study, the pBsLPNCG was constructed by inserting the egfp gene into a site between TERMINVREP-Left repeats and the cob gene in a fragment of mitochondrial DNA of Chlamydomonas reinhardtii CC-124 and introduced into the mitochondria of respiratory deficient dum-1 mutation of C. reinhardtii CC-2654. Sequencing and DNA Southern analyses revealed that egfp gene had been integrated into the mitochondrial genome of transgenic algae as expected and no other copy of egfp existed in their nucleic genome. Both the fluorescence detection and Western blot analysis confirmed the presence of eGFP protein in the transgenic algae; it indicated that the egfp gene was successfully expressed in the mitochondria of C. reinhardtii. [Copyright &y& Elsevier]

Abstract: Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight benchmark problems have demonstrated that the proposed method can perform information aggregation efficiently in data modeling. [Copyright &y& Elsevier]

Phase-sensitive optical time domain reflectometer (Φ-OTDR) can detect the occurrence of external vibration, but lacks of ability to classify multiple similar events. With the help of machine learning method, the events can be basically classified. An event recognition method based on deep learning and traditional classifier is proposed in this paper to further improve the classification accuracy. Firstly, the temporal-spatial data matrix from Φ-OTDR is directly sent to convolutional neural network (CNN) and the data features can be obtained automatically. Then these data features are transmitted to a traditional classifier for further classification. The traditional classifier can find a better classification hyperplane to improve the classification accuracy. In this paper, the most suitable classifier is support vector machine (SVM). As CNN works as a black box, T-distributed stochastic neighbor embedding (T-SNE) method and gradient-weighted class activation mapping (Grad-CAM) method are applied to visualize CNN's working process, which illustrates the correctness of feature extraction. Experimental results based on 11,997 images under eight event categories show that the classification accuracy can be improved by the traditional classifier. The CNN+SVM strategy performs the best and reaches 94.17 % accuracy, which offers 2.04 % improvement compared with using CNN alone. [ABSTRACT FROM AUTHOR]

• Propose a sampling-based asymptotically optimal path planning algorithm. • The proposed algorithm guarantees a fast convergence rate. • Theoretical proof of asymptotic optimality and fast convergence rate is given. During the last decade, sampling-based algorithms for path planning have gained considerable attention. The RRT*, a variant of RRT (rapidly-exploring random trees), is of particular concern to researchers due to its asymptotic optimality. However, the limits of the slow convergence rate of RRT* makes it inefficient for applications. For the purposes of overcoming these limitations, this paper proposes a novel algorithm, PQ-RRT*, which combines the strengths of P-RRT* (potential functions based RRT*) and Quick-RRT*. PQ-RRT* guarantees a fast convergence to an optimal solution and generates a better initial solution. The asymptotic optimality and fast convergence of the proposed algorithm are proved in this paper. Comparisons of PQ-RRT* with P-RRT* and Quick-RRT* in four benchmarks verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposed a method for discriminating two close perturbations, whose interval is less than spatial resolution, in Φ-OTDR system. The mathematical model under two close perturbations is studied and the frequency features of temporal signal from every sensing positions are obtained. The model shows that if the two close perturbations have different frequencies, they can be distinguished through spatial-frequency analysis. When the two perturbations have even the same frequency, they can still be distinguished by inspecting higher-order harmonic frequencies. Both computer simulation and experiments have been carried out to support the effectiveness of this method. [ABSTRACT FROM AUTHOR]