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We propose a photonic quasi-crystal fiber which has five ring-shaped cores in the cross section. It realizes the multiplexing of 6 vortex modes. The fiber is based on SiO2 and has an octuple photonic quasi-crystal structure. It achieves four independent dual-core coupling pairs with the optimization of geometric parameters. By adjusting the thickness of five ring-shaped cores, the fundamental mode (HE11) in the small side ring-shaped fiber cores couple to different higher order vector modes (TE01, HE21, TM01, HE31) in the large center ring-shaped fiber core, which makes the light field transfer from small side cores to large center core and realizes the multiplexing of 6 vortex modes (TE01, $\text{OAM}_{ \pm 1,1}^ \pm $, TM01, $\text{OAM}_{ \pm 2,1}^ \pm $). The transmission characteristics are analyzed with supermode theory. The $\Delta {n}_{\text{eff}}$ of adjacent vector modes is larger than ${10}^{ - 4}$ due to the thin center ring core. The coupling length (${L}_c$) keeps in the order of ${10}^{ - 2}$ m between 1500--1600 nm and the coupling efficiency is larger than 90% in 1550 nm for four dual-core coupling pairs. We believe this design will have great potential in the field of all-fiber vortex mode division multiplexing.

Zeroing neural network (ZNN) is an effective method to calculate time-varying problems. However, the ZNN and its extensions separately addressed the robustness and the convergence. To simultaneously promote the robustness and finite-time convergence, a nonlinearly activated integration-enhanced ZNN (NIEZNN) model based on a coalescent activation function (C-AF) has been designed for solving the time-varying Sylvester equation in various noise situations. The C-AF with an optimized structure is convenient for simulations and calculations, which promotes NIEZNN accelerates convergence speed without remarkable efficiency loss. The robustness and the finite-time convergence of the NIEZNN model have been proved in theoretical analyses. Furthermore, the upper bounds of convergence time of the NIEZNN model and the noise-attached NIEZNN model have been deduced in theory. At last, numerical comparative results and the application to mobile manipulator have validated the efficiency and superiority of the NIEZNN model based on the designed coalescent activation function.

Building a dense correspondence between two images is a fundamental vision problem. Most existing methods use local features, but global features cannot be ignored. Local features are often not enough to disambiguate similar regions without global features. Computing relevant features between images requires structural relationship and the importance of local features. For that, We propose novel multi-scale attention and structural relation graph (MASRG) for local feature matching. The MASRG adopts an overall architecture that first builds coarse-level matches on a coarse feature map and then refines fine matches on a fine-level feature map. We propose a structural relation graph module and a multi-scale attention module. We introduce global context information into the overall architecture. Using global information to separately assist in learning the structural information between local descriptors, the features of different receptive fields, and the importance of modeling single local information, a limited number of possible matches can be obtained with high confidence. Finally, the matching relationship is predicted. In this way, the network significantly improves the matching reliability and localization accuracy. Our proposed method has 5.6%, 6.7%, and 6.3% performance increases over the baseline method(See I) under different conditions in the HPatches. Extensive experiments on three large-scale datasets (i.e., HPatches, InLoc, and Aachen Day-Night v1.1) demonstrate that our proposed MASRG method is superior to state-of-the-art local feature matching approaches.

With the increasing deployment of network technologies in industrial control systems (ICSs), cybersecurity has become a challenge in ICSs. Cybersecurity risk assessment (CRA) plays an important role in cybersecurity protection of ICSs. However, the weights of risk indices are constants in traditional CRA methods, and they do not fully consider the requirements of risk identification. In this paper, we define a novel order- $\alpha $ divergence measure for interval-valued intuitionistic fuzzy numbers (IVIFNs) and further develop a novel CRA approach for ICSs based on the proposed divergence measure under an interval-valued intuitionistic fuzzy environment to contribute to the research gap. First, an order- $\alpha $ divergence measure for IVIFNs is defined considering flexibility and robustness of divergence measures with the parameter. Next, a variable weight-based CRA approach for ICSs is developed. In this approach, IVIFNs are adopted to describe evaluation values of risk indices. The weights of risk indices are variable weight vectors and they are determined by the relative divergence closeness. Integration approaches of each node and each attack path in attack-defense trees (ADTs) are proposed based on the operations of IVIFNs, and risk scores of each attack path are calculated by using the score function. Finally, we apply the proposed method to the CRA of a civil aviation fuel supply automatic control system and verify its effectiveness and advantages by comparing it with other methods. This method can dynamically adjust the weights of risk indices considering the relationship between each risk index and the highest risk, and therefore, it can more effectively recognize the highest risk of ICSs than the traditional CRA method. In addition, it can also match the risk attitude of decision-makers by adjusting the parameter $\alpha $ .

We propose a ring-core photonic quasi-crystal fiber (RC-PQCF) featuring a ring-shaped fiber core and two symmetrical SiO2 stress-applying parts (SAPs). By optimizing the mole percentage of GeO2 and geometrical parameters of the fiber, the design supports 34 full vector polarization modes (FV-PMs). The effective refractive index difference ($\Delta {n_{eff}}$) of adjacent FV-PMs is larger than $1.07 \times {10^{ - 4}}$ at 1550 nm. The confinement loss (${\mathrm{\alpha }}$) of FV-PMs is less than ${10^{ - 6}}$, which is sufficient to confine the light field in the ring-core. Through numerical analysis, broadband performance is investigated subsequently in the 1500–1600 nm. The dispersion (${D_\lambda }$) of FV-PMs is less than $138.14{\mathrm{\ ps}} \cdot {\mathrm{n}}{{\mathrm{m}}^{ - 1}} \cdot {\mathrm{k}}{{\mathrm{m}}^{ - 1}}$ and maintains a flat trend. The mode field area (${A_{eff}}$) of FV-PMs is larger compared to single mode fibers and the nonlinear coefficient (${\mathrm{\gamma }}$) of FV-PMs is within ($6.97 \times {10^{ - 4}}$, $1.54 \times {10^{ - 3}}$) ${{\mathrm{m}}^{ - 1}} \cdot {{\mathrm{W}}^{ - 1}}{\mathrm{\ }}$ in the 1500–1600 nm. The fiber is a promising design for mode division multiplexing (MDM) that supports the MIMO-free processing and improves the transmission capacity and spectral efficiency.

Fault diagnosis based on the expert system (ES) is still a research topic of manufacturing in Industry 4.0 because of the stronger interpretability. As the core component of the ES, fault diagnosis accuracy is positively correlated to the precision of the knowledge base. But it is difficult for users to understand the knowledge obtained from the original dataset utilizing the existing knowledge extraction method. Therefore, it is of great significance to extract easy-to-understand and exact rules from the NN framework. This paper proposes a hybrid extraction framework to perform the rule extraction for overcoming this drawback. First, an improved adaptive genetic algorithm (GA) using a logistic function, namely LAGA, is proposed to solve the traditional GA’s insufficient prediction performance issue. Compared with the other three mainstream adaptive GAs, the experiment results of optimizing six selected test functions by these GA variants show that the LAGA algorithm’s convergence accuracy and speed have been greatly improved, especially for high latitude functions. On this basis, a rule extraction method based on the symbol rule and NN, namely the LAGA-BP framework, is discussed in this manuscript to classify the real-valued attributes. This framework obtains hidden knowledge (knowledge refinement process) by NN and further transforms the acquired hidden knowledge into more easy-to-understand rule knowledge (rule extraction process). The execution of the LAGA-BP framework could be separated into two phases. The first phase is to optimize a back propagation NN (BPNN) using the LAGA and refine prediction classification knowledge over the optimized BPNN. In the second phase, an attribute reduction algorithm using multi-layered NN (SD algorithm) based on two different superposed networks is used in this framework to reduce data-set attributes and then uses the K-means clustering algorithm to extract the if-then rule from the simplified attributes. The Wisconsin breast cancer dataset is used as a case study to reveal the correctness and robustness of the proposed LAGA-BP method. Consulting relevant medical personnel and referencing relevant data shows that the rules extracted using this method help verify the diagnosis results, thus verifying the proposed framework’s feasibility and practicality.

In this paper, a new high efficiency soft-switching non-isolated three-port converter (TPC) is proposed. In the conventional TPC, three switches are required to process power in different directions between inputs and output. Providing soft-switching condition for all switches using a low number of auxiliary components is a challenging task. In this paper, the conventional TPC topology is modified by changing its structure and adding a simple auxiliary circuit such that all switches operate under soft-switching condition in all operating modes. To reduce the volume of the converter, the magnetic components are integrated using a coupled inductor in the auxiliary soft-switching cell. In this paper, various converter operating modes are presented, and design considerations are discussed. Finally, a prototype 200 W, 100 V converter is implemented in the laboratory, and the theoretical analysis is validated by the experimental results.

Due to the high penetration of renewable energy resources in microgrids (MGs), the grid inertia becomes low which leads to the grid to be vulnerable to large disturbances. The energy storage devices can play an important role to enhance the inertia of MGs. However, due to the high investment cost of storages or their dp/dt limitation, the installed energy storages cannot cover the challenge of high df/dt. A prominent solution to solve the problem is to use the inertia response of the wind generators. However, relatively high second frequency nadir is the main drawback of using the inertia response of the wind generators which may impose an extensive disturbance to MGs. Accordingly, a coordinated operation strategy for MGs between wind generator and hybrid energy storage (HES) system is proposed in this paper. In addition, to improve the inertia response of the MG; providing high-quality communication infrastructures with low delay and increasing the Ultracapacitor capacity have been paid attention. In this paper, the costs of the installed Ultracapacitor and quality of communication services are defined as the operation cost. Guaranteeing enough frequency damping for the MG with low operation cost are two conflict objectives. Therefore, a multi-objective optimization method is used to set the controllers’ values and reduce the operation cost. The results confirmed that the effectiveness of the proposed strategy to control hybrid power storage in coordination with the wind generator and the frequency recovery process is improved. Also, employing the optimum values guaranteed the frequency damping effectively with low operation cost. The Integral Absolute Error (IAE) value and operation cost are reduced by 13.6% and 32%, respectively. Also, the simulation results show that the maximum MG frequency deviation and maximum df/dt is well compatible with different standards in the presence of load perturbations and different wind speeds.

In this paper, to solve the hard-switching operation problem and the lack of existing a bidirectional power flow path from output to the energy storage device of the conventional three-port converter, a new soft-switched bidirectional multi-port converter is proposed. A large number of switches, different power flow paths which vary in each operating mode, and changing the outputs and their power levels are some of the challenges to provide soft-switching conditions in multi-port converters. In the proposed converter, by changing the topology of the conventional three-port converter, the bidirectional power exchangeability between ports is provided. Moreover, a soft-switching cell is added, which can operate independently from the output power levels. Less number of components with low volume is one of the important features of this multi-port converter. Due to the soft-switching operation of the proposed converter, the size of passive components and heat-sink is reduced. In addition, by the use of coupled inductors in the soft-switching cell, only one magnetic core is used and thus, single-stage power conversion is achieved and conduction loss is reduced. In this paper, the converter operating modes are presented, and design considerations are discussed. Finally, a 200 W–200 V prototype is implemented, and the theoretical analysis is validated by the experimental results.

This paper presents a novel design of step-up DC-DC converters whose merits are: (i) The continuity of the input current has been kept; (ii) The polarity of the output voltage has been kept positive which provides the same ground of the input source and load; (iii) The low voltage gain of the quadratic converters has been solved that it can increase the input voltage to 10 times more by the low value of the duty cycle; (iv) Apart from the high value of the voltage gain, the semiconductors’ voltage and current stresses were lower than the output voltage and input current of the converter which are the highest value of the voltage and current respectively and semiconductor based components do not suffer from high value of the current/voltage stresses; (v) Additionally, the voltage/current stresses are low, and the efficiency is good according to its 90 percent value. The analysis of the non-ideal voltage gain has been done and its better function has been deduced by comparing it with the recently proposed non-isolated topologies. Additionally, the non-isolated voltage gain has been studied for different output power levels. The efficiency has been extracted and discussed for varying duty cycles and output power based on ignoring some losses. Experimental results and simulation outcomes from the PLECS software have been compared along with theoretical relationships. The prototype of the topology has been tested at 100 W output power, 100 V output voltage, and 10 V input voltage.

The eigenmodes of an inverse-parabolic graded index fiber under torsion were investigated by using the finite element method simulation. These eigenstates correspond to orbital angular momentum modes except for the two polarized-vortex modes. The design of appropriate core parameters can lead to the difference in the effective refractive indexes between all adjacent modes being greater than 10-4 over a bandwidth of 1.5 to 1.6 μm, which is expected to be an ideal method for the generation of multiple vortex modes in few-mode fiber for optical communication. The results showed that the separation of these modes changes with twist rate. Compared to the twisted air-hole ring-core fiber, the optimal twist rate of this fiber was found to be an order of magnitude smaller, which provides a more feasible scheme for the combination of mode division multiplexing and wavelength division multiplexing. We speculate that this study may have promising prospects in various applications such as particle manipulation, imaging, and quantum communication.

Understanding the change of river islands is essential for water conservancy construction, channel navigation safety, and industrial/agricultural production. Yangtze River is the largest river in China; however, there are few long-term monitoring results on the changes of Yangtze River islands. To fill this gap, Landsat data available on the Google Earth Engine (GEE) were used to obtain the annual changes of the Yangtze River islands located from the lower reaches of the Three Gorges Dam (TGD) to the Yangtze River delta during 1986-2017. Moreover, we focused on the changes of those islands before and after the construction of the TGD. Annual areas of all river islands were calculated and spatial-temporal pattern of Yangtze River islands were analyzed. Our satellite observation results show continuous expansions of Yangtze River islands after the construction of the TGD in 2003. For the total 138 river islands, in three stages: before 2003, after 2003 and in the 32-year study period, 68, 118, and 124 river islands showed increasing trends, with the net increase of 286.47 km2, 417.49 km2, and 665.62 km2. For the total 92 river island groups (138 islands), percentages of river island expansion are 41.30% (before 2003), 80.43% (after 2003) and 88.04% (1986-2017). Moreover, we found 10 newly developed river islands, with a net expansion of 87.42 km2 and an expansion rate of 2.73 km2/year. The significant decrease (2-4 m) in water level before and after the TGD might be the main factor that results in the observed expansion of Yangtze islands downstream the TGD. We also highlighted the importance of using all available Landsat imagery for tracking the changes of river islands. Our satellite-derived dataset provide reference data for the land use planning and environmental protection in the Yangtze River drainage basin.

High frequency oscillations (HFOs) in intracranial electroencephalography (iEEG) recordings are a promising clinical biomarker that can help define the epileptogenic regions in the brain. The aim of this study is to characterize the spatial and temporal distribution of HFOs in channel-wise instead of event-level as usual and to develop an automated the seizure onset zone (SOZ) identification by using a support vector machine (SVM) approach on the channel-wise features in a short-term recording. In this work, five consecutive patients with medically intractable epilepsy were enrolled. For each patient, ten-minute segments were defined from two hours of iEEG recordings during sleep state. A total of 17 channel-wise features including 6 rate-based, 6 duration-based, 3 amplitude-based, and 2 power-based features of HFOs were extracted from each 10-min segment, which including ripples (Rs, 80-250 Hz) and fast ripples (FRs, 250-500Hz) were detected automatically using validated detectors. Each channel-wise feature was ranked by using the Student's t-test method and the most distinctive features were selected to explore the characteristics of HFOs in each channel. A supervised-learning based SVM classifier with the selected channel-wise features or their combinations was developed to identify each channel within the independently clinician-defined SOZ or not. Over 3,816 chanel-10-min segments of iEEG recordings, the evaluated accuracy, sensitivity, and specificity of the proposed approach with the optimal combination of top five ranked features for SOZ identification are 86.6%, 73.0%, and 94.1%, respectively, for ten-fold cross-validation, and 86.0%, 79.2 %, and 91.8%, respectively, for the leave-1-out cross-validation. Compared with the recently reported SOZ detectors based on event-wise feature of HFOs, the channel-wise features and the combination with machine learning approach demonstrate its feasibility in SOZ identification with a relative higher performance and potentially reduce the time needed currently for long-term recording and manual inspection.

In actual operation, photovoltaic arrays inevitably encounter shadow problems, resulting in a power output decline and multiple peaks in the output characteristics. The effect of partial shade can be satisfactorily reduced by photovoltaic array reconfiguration. Almost all current reconfiguration techniques are based on the irradiance equalization principle. However, by analyzing the output characteristics of photovoltaic array, it is found that the irradiance equalization principle improves the output power by increasing only the minimum row current without considering the effect of the voltage, so the reconfiguration techniques based on this principle cannot obtain the global optimal configuration under some partial shading conditions. In order to maximize the output power of photovoltaic array under any partial shaded condition, this paper proposes a reconfiguration strategy based on direct power evaluation. In this approach, the reconfiguration problem is formulated as a 0-1 multi-knapsack problem, and a novel mathematical model is established to directly evaluate the maximum output power of photovoltaic array. Then, the optimal reconfiguration scheme is determined by solving the mathematical model. Finally, the effectiveness of the proposed reconfiguration strategy is proved in theory and simulations.

Complex-valued time-varying Sylvester equation (CVTVSE) has been successfully applied into mathematics and control domain. However, the computation load of solving CVTVSE will rise significantly with the increase of sampling rate, and it is a challenging job to tackle the CVTVSE online. In this paper, a new sign-multi-power activation function is designed. Based on this new activation function, an improved complex-valued Zhang neural network (ICZNN) model for tackling the CVTVSE is established. Furthermore, the strict proof for the maximum time of global convergence of the ICZNN is given in detail. A total of two numerical experiments are employed to verify the performance of the proposed ICZNN model, and the results show that, as compared with the previous Zhang neural network (ZNN) models with different nonlinear activation functions, this ICZNN model with the sign-multi-power activation function has a faster convergence speed to tackle the CVTVSE.

High-frequency oscillations (HFOs) of 80~500 Hz in the intracranial electroencephalogram (iEEG) recordings are considered as a reliable marker for epileptic location. However, a significant challenge to the clinical use of HFOs is due to the time-consuming procedure of visually identifying them. A new methodology is presented in this paper for the automated detection of HFOs based on their 2D time-frequency map employing the short-time energy (STE) estimation and the convolutional neural network (CNN) classification algorithm. The effectiveness and usefulness of the proposed method are evaluated using the clinical iEEG data acquired from five patients (28.4 ± 13.0 years) with medically intractable epilepsy. The proposed methodology presents the following significant advantages: 1) compared with the recently reported HFOs detector based on the CNN using only the 1D temporal EEG signal, the proposed method achieves a higher accuracy using the deep CNN classifier on 2D time-frequency map of HFOs, of which the evaluated sensitivity and false discovery rate (FDR) for identifying ripples are 88.16% and 12.58%, respectively, and the corresponding sensitivity and FDR are 93.37% and 8.1% for detecting fast ripples, respectively; 2) it is capable of automatically extracting the shared features of HFOs events of different patients and would be much robust, unlike other automated methodologies proposed in the literature where the characteristics of HFOs were extracted manually on the basis of researchers' knowledge, which, probably, is prone to observer bias; and 3) with the proposed STE estimation, all suspicious ripples and fast ripples could be initially found out and transformed into time-frequency map for subsequently CNN-based classification, rather than transforming and classifying the raw data, thus requiring a lower computational resource. In addition, the time occurrence of each transient event of the HFOs can be identified to be potentially useful for further seizure analysis. In conclusion, this automated detection of the HFOs combing the STE and the CNN could allow analyzing large amounts of data in a short time while assuring a relatively higher accuracy and, thus, would potentially serve to provide a clinically useful tool.

In this paper, an improved Zhang neural network (IZNN) is proposed by using a kind of novel nonlinear activation function to solve the complex-valued systems of linear equation. Compared with the previous ZNN models, the convergence rate of the IZNN model has been accelerated. To do so, a kind of novel nonlinear activation function is first proposed to establish the novel recurrent neural network. Then, the corresponding maximum convergent time is given according to the randomly generated initial error vector, and the theoretical proof is described in detail in this paper. Finally, the experiment results illustrate that the new recurrent neural network using the proposed activation function has higher convergence rate than the previous neural networks using the linear activation function or the tunable activation function.

Wide area monitoring (WAM) offers many opportunities to improve the performance of power system protection. This paper presents some of these opportunities and the motivation for their development. This methods include monitoring the suitability of relay characteristics, supervisory control of backup protection, more adaptive and intelligent system protection and the creation of novel system integrity protection scheme. The speed of response required for primary protection means that the role WAM in enhancing protection is limited to backup and system protection. The opportunities offered by WAM for enhancing protection are attractive because of the emerging challenges faced by the modern power system protection. The increasingly variable operating conditions of power systems are making it ever more difficult to select relay characteristics that will be a suitable compromise for all loading conditions and contingencies. The maloperation of relays has contributed to the inception and evolution of 70% of blackouts, thus the supervision of the backup protection may prove a valuable tool for preventing or limiting the scale of blackouts. The increasing interconnection and complexity of modern power systems has made them more vulnerable to wide area disturbances and this has contributed to several recent blackouts. The proper management of these wide area disturbances is beyond the scope of most of the existing protection and new, adaptive system integrity protection schemes are needed to protect power system security.