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This article addresses the finite-time nonfragile synchronization control of time-varying delays T-S fuzzy discrete chaotic network systems(TSFDCNSs) with random deception attacks. Aiming at the security threats caused by network attacks in network system, a nonfragile feedback controller is designed considering random deception attacks. In order to reduce communication resource consumption effectively, an adaptive event triggering method is proposed based on adaptive threshold parameters. Under the Lyapunov-Krasovskii functional and linear matrix inequality, we derive sufficient conditions for the finite-time synchronization of TSFDCNSs. Finally, two examples are given to validate the proposed modes.

Accurate classification and prediction of remote vehicle positions contribute significantly to the functionality of Advanced Driver Assistance Systems (ADAS). This essential information offers vital details about surrounding vehicles that are critically important in vehicle control decisions in order to avoid collisions and optimize vehicle performance. This paper investigates two deep learning neural network (DLNN) models: Long Short-Term Memory (LSTM) and Transformer-based deep neural networks, and effective features extracted from V2V communication signals for accurate detection and prediction of remote vehicle positions. Instead of predicting vehicle trajectories, this research focuses on detection and prediction of the remote vehicle positions characterized in 8 classes of locations immediately surrounding a host vehicle. These position classification and prediction results can be readily used by the host vehicle in making decisions involving lane change, making safe turns and overtaking, executing proper yielding, etc. We show through extensive experiments that the proposed DLNN models, LSTM and Transformers, are capable of effectively modeling the underlying dynamics of the 8 vehicle positioning classes, and providing situational awareness with the predicted remote vehicle positions. The experiments were conducted on V2V communication data collected from 69 real-world driving trips. Experimental results demonstrate that the proposed LSTM and the transformer-based DLNN systems outperform multilayer perceptron (MLP) systems by a large margin for both detection and prediction of remote vehicle position classes. The average prediction accuracy of the transformed-based DLNN systems using proposed geometric features combined with host and remote vehicle’s location and speed outperformed the LSTM systems by more than 7%.

With the ever-increasing penetration level of renewable energy and emerging electricity demand, it is very important to ensure the safe and reliable operation of the modern transmission system with strong stochastics and dynamics. Exceeding transmission limits will lead to line overloading, equipment damage, voltage instability, and even cascading failures. Therefore, it is of great significance to accurately evaluate the total transfer capability (TTC) in real-time. This paper proposes a novel ensemble model-based method to learn the nonlinear mapping between online operating conditions and TTCs of key transmission corridors. Massive representative operational samples are generated using a high-fidelity power system security analysis engine involving a variety of operating conditions, and thermal stability, static voltage security, and transient stability are considered. After feature extraction and label labeling of these samples, the dataset was split into training and test sets. The samples were used to predict TTC using three types of models: Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). The hyperparameters of these models were optimized through random search and 50% discounted cross-validation methods to enhance the accuracy of TTC estimation. Finally, the optimized models were combined using the stacking method. In this approach, LGBM, XGBoost, and RF served as base models, while LGBM functioned as the meta-model, leveraging the strengths of each model. The proposed method was validated on a 500-node network model with real-world operational characteristics, demonstrating improved TTC evaluation accuracy and higher efficiency.

To evaluate the ability of space optical systems to suppress off-axis stray light, this paper proposes a stray light testing method for large-field of view, multispectral spatial optical systems based on point source transmittance (PST). And a stray light testing platform was developed using a high-brightness simulated light source, large-aperture off-axis reflective collimator, high-precision positioning mechanism and a double column tank to evaluate the stray light PST index of spatial optical system. On the basis of theoretical analyses, a set of calibration lenses and stray light elimination structures such as hoods, baffle and stop are designed for the accuracy calibration of stray light testing systems. The theoretical PST values of the calibration lens at different off-axis angles are analyzed by Trace Pro software simulation and compared with the measured values to calibrate the accuracy of the system. The testing results show that the PST measurement range of the system reaches $10^{-3}\sim 10^{-10}$ when the off-axis angles of the calibration lens are in the range of $\pm 5^{\circ }\sim \pm 60^{\circ }$ . The stray light test system has the advantages of wide working band, high automation and large dynamic range, and its test results can be used in the correction of lens hood and other applications.

In the past, the liver tumors were reported manually in an unstructured format. There actually exists much valuable knowledge in these reports for further disease risk assessment, disease recognition and treatment recommendation. Yet, it is not easy to read and mine knowledge from the unstructured reports. Hence, how to extract the knowledge from these biomedical reports effectively and efficiently has been a challenging issue in the past decades. Although a set of Natural Language Processing techniques were proposed for Bio-medical information retrieval, few related works were made on transforming the unstructured CT liver-tumor reports into structured ones. To aim at this issue, in this paper, we propose a two-stage report structuring method by integrating effective Natural Language Processing (NLP) and interpretable machine learning. For the first stage, the candidate keywords in unstructured reports are extracted. Next, the feature keywords are determined by the feature-selection technique. For the second stage, the well-known multi-classifiers are performed, and finally the reports are labeled in a refined structure format. Further, the factor keywords in the classification model are filtered to interpret the performance. In overall, the proposed report structuring method generates a hierarchical data structure, including the common features and refined features in the $1^{\mathrm {st}}$ and $2^{\mathrm {nd}}$ levels/stages, respectively. To reveal the performance of proposed method, a set of evaluations were conducted and the results show that, the proposed method is more promising than the fashion neural networks such as Bert (Bidirectional Encoder Representations from Transformers) in terms of effectiveness and efficiency.

A homomorphic encryption (HE) scheme is an advanced encryption technology which allows any user receiving ciphertexts to perform computations over them in a public manner. An important application of an HE scheme is a private delegating computation where clients encrypt their secret data, send the ciphertexts to a (computationally powerful) server who perform computations over encrypted data. In this application, one of the crucial problems is that the delegated server might be not trusted one and in this case, we cannot believe that a server always returns correct computation results. To solve this problem, Lai et al. (ESORICS 2014) proposed a verifiable homomorphic encryption (VHE) as a core primitive realizing private and verifiable secure delegating computation. However, their VHE scheme only supports homomorphic evaluation over ciphertexts generated by a single user. In this paper, we propose a formalization and its construction of multi-key verifiable homomorphic encryption (MVHE), which is a new cryptographic primitive for realizing private and verifiable delegated computation in the multi-client setting. Our construction can be obtained by combining a multi-key homomorphic encryption scheme and a multi-key homomorphic encrypted authentication scheme, which is also a new primitive provided in this work.

The optimal design of power converters often requires a huge number of simulations and numeric analyses to determine the optimal parameters. This process is time-consuming and results in a high computational cost. Therefore, this paper proposes a deep reinforcement learning (DRL)-based optimization algorithm to optimize the design parameters for power converters using a deep neural network (DNN)-based surrogate model. The surrogate model of power converters can quickly estimate the power efficiency from input parameters without requiring any simulation. The proposed optimization model includes two major steps. In the first step, the surrogate model is trained offline using a large dataset. In the second step, a soft actor-critic-based optimization model interacts with the surrogate model from step 1 to determine the optimal values of design parameters in power converters. Unlike deep Q learning-based methods, the proposed method is able to handle large state and action spaces. In addition, using entropy-regularized reinforcement learning, our proposed method can accelerate and stabilize the learning process and also prevent trapping in local optima. Finally, to show the effectiveness of the proposed method, the performance of different optimization algorithms is compared, considering over ten power converter topologies.

Network reconfiguration is a significant strategy to enhance the resiliences of distribution systems against contingencies. This paper proposes a two-stage robust network reconfiguration model to maximize the power supply under the uncertain failures of both lines and renewable generators. In stage 1, before the contingencies are realized, the network topology is reconfigured into several subsystems supplied by conventional generators. Then the contingencies are launched to the reconfigured distribution system, inducing load curtailments. In stage 2, the flexible resources, e.g., conventional and renewable generators, and energy storages, are dispatched to guarantee the maximal power supply under the worst-case contingencies. To solve the two-stage robust model, we develop a column & constraint generation (C&CG) algorithm. The network reconfiguration decision is made in the master problem according to an increasing amount of worst-case scenarios generated by the subproblem. Simulation results demonstrate the performance of the network reconfiguration decision of the two-stage robust model and verify the effectiveness of the C&CG algorithm to deal with the proposed model.

Due to the limitations of current technology and budget, a single satellite sensor cannot obtain high spatiotemporal resolution remote sensing images. Therefore, remote sensing image spatio-temporal fusion technology is considered as an effective solution and has attracted extensive attention. In the field of deep learning, due to the fixed size of the perception field of a convolutional neural network, it is impossible to model the correlation of global features, and the features extracted only through convolution operation lack the ability to capture long-distance features. At the same time, complex fusion methods cannot better integrate temporal and spatial features. In order to solve these problems, we propose a multistage remote sensing image spatio-temporal fusion model based on texture transformer and convolutional neural network. The model combines the advantages of transformer and convolutional network, uses a lightweight convolution network to extract spatial features and temporal discrepancy features, uses transformer to learn global temporal correlation, and finally, fuses temporal features with spatial features. In order to make full use of the features obtained in different stages, we design a cross-stage adaptive fusion module CSAFM. The module adopts the self-attention mechanism to adaptively integrate the features of different scales while considering the temporal and spatial characteristics. To test the robustness of the model, the experiments are carried out on three datasets of CIA, LGC, and DX. Compared with five typical spatio-temporal fusion algorithms, we obtain excellent results, which prove the superiority of MSFusion model.

The performance of AlGaN-based light-emitting diodes (LEDs) emitting at UVA–UVC regions can be severely compromised due to the polarization difference (ΔP) between the last quantum barrier (LQB) and the electron blocking layer (EBL). In this work, the different situations of the bandgap difference (ΔEg) and ΔP of InAlN/AlGaN and AlGaN/AlGaN heterojunctions fully strained on GaN and AlN substrates are discussed. It shows that the InAlN/AlGaN heterojunctions could produce positive or negative sheet charges at the heterointerface under ΔEg >0, which could not be realized by the conventional AlGaN/AlGaN heterojunctions. To demonstrate and utilize the feature, the polarization-modulated InAlN LQBs with 0.14–0.16 indium compositions of 320 nm UVB LEDs are designed and investigated. It is observed that the InAlN LQBs could replace the conventional AlGaN LQB to improve electron confinement and hole injection by affecting effective barrier heights. By modulating the LQB/EBL polarization using InAlN, the proposed UV LED has a 32% enhancement in internal quantum efficiency and lower efficiency droop (from 16.9% to 0.7%) compared with the conventional one without modulation. The operation voltage at the same current also significantly decreases. The improvement of optical output power and wall plug efficiency at 60 mA in proposed structures are near 90% and 100%, respectively. This study provides a novel and highly effective methodology for development of high efficiency UV LEDs.

Hyperspectral anomaly detection has drawn much attention in recent years. In this paper, in order to effectively extract anomalies in hyperspectral images, a novel sparse-representation based hyperspectral anomaly detection method via adaptive background sub-dictionaries is proposed. Firstly, a background estimation strategy is proposed to provide representative background information. Based on the estimated background, a global dictionary is constructed by utilizing K-means clustering algorithm. Next, Several active atoms are selected from the global dictionary to form a sub-dictionary to adaptively approximate the local region in each dual-window. This sub-dictionary construction strategy can remove potential anomaly contamination in local regions. Finally, a re-weighting strategy is proposed to enhance the performance of sparse-representation-based anomaly detector. Experimental results demonstrate that our method can effectively extract anomalies and suppress background simultaneously.

In this paper, we propose a new modulation scheme for wireless communications called high-dimensional orthogonal frequency division multiplexing with in-phase/quadrature index modulation (HD-OFDM-IQ-IM). In the proposed scheme, coordinate values of a symbol of the HD signal constellations are separated. A part of the coordinate values of an HD symbol are mapped to the active in-phase component subcarriers and the rest are mapped to the active quadrature component subcarriers in each OFDM subblock. A simple rule to design the HD constellation using the Gray coding and mapping is also presented. The method increases the minimum Euclidean distance between symbols of an HD signal constellation by increasing the Hamming distance of the binary sequences. As the dimension of the constellation increases, more index patterns can be produced, thus improving spectral and energy efficiency of the HD-OFDM-IQ-IM scheme. A feasible selection of subcarrier component active patterns can also produce additional index patterns to enhance spectral efficiency without additional energy consumption. In the receiver, both maximum likelihood (ML) and reduced-complexity log-likelihood ratio (LLR) detection can be exploited. As a result, the proposed scheme has better bit error performance than the conventional OFDM system with IM techniques in the frequency selective Rayleigh fading channel. In particular, it is noted that the bit error rate (BER) in the high signal-to-noise ratio (SNR) region is tightly limited by the upper bound on bit error probability derived in this paper.

Most aluminum alloys, especially the 7xxx series, are incompatible with laser additive manufacturing due to many factors. In-situ process monitoring technology offers an excellent opportunity for process understanding and control, which improves the component quality and repeatability of AM. In this study, emission spectroscopy and plume imaging were utilized to monitor and understand the laser-directed energy deposition of Al7075 alloy. Characteristics of plasma were analyzed using spectra and plume image signals. Emission lines of Al I, Mg I, and Cr I observed in spectra signals demonstrated that primary elements in Al7075 were evaporated and excited during the AM process forming a plasma plume. The plasma plume intensity increased significantly as the laser power increasing. The periodic fluctuation of plasma was revealed using plume images, indicating that laser powers inputted into the target surface were unstable due to the laser and plasma interaction. The elemental evaporation and inconsistent laser input are the main challenges for Al7075 AM and are potentially monitored by plasma signals. Methods of signal process and feature extraction for spectra and plume images were developed. Experimental results showed that the aluminum emission spectral energy, the average baseline spectral intensity, and the plume area are practical for rough surface monitoring. Correlations between spectral features and key printing parameters were developed, demonstrating an excellent response of spectra features to the laser power and laser scanning speed. This investigation revealed the potentials and challenges of emission spectroscopy in the in-situ monitoring of laser AM, making progress towards intelligent additive manufacturing.

An Industrial Control System (ICS) adversary often takes different actions to exploit vulnerabilities, pass the border between Information Technology (IT) and Operational Technology (OT) networks, and launch a targeted attack against OT networks. Detecting these threat actions in early phases before the final stage of the attacks can be executed against industrial endpoints can help prevent adversaries from achieving their goals. Threat hunting in IT networks has been previously studied, and several hunting methods have been proposed. However, these methods are not sufficient for ICSs, as the integration of industrial legacy systems with advanced IT networks has introduced new types of vulnerabilities and changed the behaviour of attacks. The lack of a unified hunting solution for integrated IT and OT networks is the gap that is considered in our paper. The contribution of this paper is an ICS Threat Hunting Framework (ICS-THF) which focuses on detecting cyber threats against ICS devices in the earliest phases of the attack lifecycle. ICS-THF consists of three stages, threat hunting triggers, threat hunting, and cyber threat intelligence. The threat hunting trigger stage identifies events or external resources that can trigger the hunting stage. The hunting stage uses a combination of the MITRE ATT&CK Matrix and a Diamond model of intrusion analysis to generate a hunting hypothesis and to predict the future behaviour of the adversary. This hypothesis will be validated by analysing Diamond models of threat actions. Finally, the cyber threat intelligence stage is responsible for generating Indicators of Compromise (IoCs) to be used for future threat hunting. The Black Energy 3 malware, PLC-Blaster malware, and SWaT dataset are used in this paper to evaluate the efficiency of the proposed framework.

The extension from centimeter wave frequencies to millimeter wave (mmWave) frequencies has triggered an enormous transformation in terms of radio access architecture for future wireless networks, and it has therefore empowered unlimited opportunities for the user-oriented services and applications. Besides mmWave as a driving element, beamforming (BF) will be incorporated as a key enabling technology for the future wireless networks. In this paper, we propose a positioning-aided beamforming (PA-BF) framework for enhanced downlink communications in a cloud-oriented mmWave mobile networks. We show that the proposed PA-BF achieves a higher effective transmit ratio that is equivalent to a lower initial access latency than the conventional codebook-based BF, which in turn manifests its capability to support high-velocity mobile users. We also analyze the impact of positioning accuracies on the performance of PA-BF and discuss the trade-offs between different BF strategies with varied system parameters. Our simulation results demonstrate that, with a narrow beam phased array and reasonably good positioning accuracy, the PA-BF framework is capable of achieving higher spectral efficiency than the considered codebook-based BF especially at higher velocities.

In this study, we propose a method that combines Bayesian network, structural equation modeling, and importance-performance analyses to evaluate and improve the service quality of crowded metros from the point of service components. First, service components that influence the service satisfaction of crowded metros are investigated, and 7 service components and 27 factors are collected from the perspective of metro daily operation. Correspondingly, 8,011 sample questioners are collected using both online (i.e. microblog and WeChat) and face-to-face survey methods. The usage of these service components is more practical for metro managers to improve service quality than the factor analysis methods used in previous studies. Second, a hybrid method combining Bayesian network and structural equation modeling is proposed to establish the relationship between service quality and these service components and factors. This hybrid method can learn the uncertain relationship directly from the data, explain the psychological element, and pinpoint the crucial service component. It is concluded that security check and waiting for boarding have the greatest effect on the overall satisfaction. With the help of importance-performance analyses, some practical suggestions (e.g., theme stations, women-only carriages, and one-day ticket) are proposed to optimize bottleneck service components and thus improve the service quality of daily metro operation. Further, heterogeneity of service quality satisfaction for various groups of respondents is discussed, which contributes to effective strategies for satisfaction improvement.

The evolution of the fifth-generation (5G)/ beyond 5G (B5G) mobile communication networks greatly depends on the seamless integration of terrestrial and sky networks over advanced multi-tier heterogeneous network (HetNet) infrastructure. By using the huge potential of unmanned aerial vehicles (UAVs) and mm-Wave, we develop a novel and practical multi-UAV assisted multi-tier HetNet model which consists of clustered aerial UAV base stations (U-BSs) and Poisson point process (PPP) based ground BSs (G-BSs) for emergency and crowded hotspot area. In a typical cluster, both ground user equipments (GUEs) and UAVs are modeled as Poisson cluster process (PCP) and scattered around the common cluster center. Specially, the analysis is conducted by adding two additional tiers which consist of multiple intra-cluster U-BSs and single G-BS in representative cluster so that the point sets of U-BSs and G-BSs are partitioned into two sub-sets, respectively, and a 4-tier network model is formed. To find the contribution of the inter-cluster associations on the performance of networks, we construct 2-tier and 4-tier association schemes. The 2-tier association allows a GUE associated only with intra-cluster U-BSs and G-BS, while the 4-tier association allows all inter/intra-cluster U-BSs and G-BSs can be associated. Then, according to stochastic geometry methods, statistical description of the downlink interferences experienced at a typical GUE is derived. This derivation yields the downlink coverage probabilities and total network throughput are obtained. It is achieved that association probabilities greatly depend on the distribution of daughter point process. UAVs' height and the distributions of U-BSs and GUEs impose non-monotonous impact on coverage probabilities. Due to severe inter-cluster interference, the achievable average area throughput is not always increasing with SINR so that there is an optimal SINR threshold. In addition, our analysis finds the proposed 4-tier association scheme outperforms the simple 2-tier one in terms of the average area throughput.

Job shop scheduling problem (JSP) is a combinatorial optimization problem, which has been widely studied due to its strong theoretical and background for application. However, in previous studies on the traditional JSP, the optimization objective is mainly relative to time, such as makespan, flow time, tardiness, earliness, and workload. With the advent of green manufacturing, energy consumption should be considered in the JSP. Therefore, a low-carbon JSP is studied in this paper. Due to the NP-hard nature, a meta-heuristic algorithm, bat algorithm (BA), is considered in this paper. According to the characteristics of the problem, a kind of bi-population-based discrete BA (BDBA) is proposed to minimize the sum of the energy consumption cost and the completion-time cost. A parallel searching mechanism is first introduced to the algorithm, by which the population is divided into two sub-populations to, respectively, adjust the job permutation and the processing speed of each machine. Three communication strategies are used to implement the cooperation between the sub-populations. In addition, due to the fact that the original BA was developed to deal with the continuous problems, a modified discrete updating approach is proposed to make the BA algorithm directly work in a discrete domain. Finally, extensive simulations have been conducted to test the effectiveness of the proposed BDBA algorithm. The experimental data demonstrate that the proposed BDBA is effective in solving the low-carbon JSP under study.

In satellite navigation receivers, the hardware delays of code and carrier are different because of their different tracking processes, which is called as the code-carrier hardware delay difference. This difference depends on the condition of receivers and varies from one to another. Therefore, in the carrier phase RTK (real-time kinematic) position process, the bias of code-carrier hardware delay difference between the base receiver and the moving receiver will cause the baseline resolution error. In this article, we analyze the code-carrier hardware delay difference, and the theoretical derivation shows that the baseline resolution error increases with the bias as well as the signal Doppler frequency. For a static RTK user on the ground, a microsecond level bias leads to a baseline resolution error in millimeter level, compared to a centimeter level error caused by the LEO satellite whose speed can reach several kilometers per second. In that case, the bias needs to be calibrated for those high precision RTK applications. However, there are few researches to calibrate the receiver carrier hardware delay because the carrier phase measurement integral ambiguity is difficult to be measured. In this article, we propose a method to measure and calibrate the bias of code-carrier hardware delay difference between different receivers. Real data test results show that the proposed method can effectively eliminate the RTK baseline resolution error caused by the bias of code-carrier hardware delay differences between the base receiver and the moving receiver.

The development of cognitive radio and electronic warfare brings new challenges to radar electronic reconnaissance, the recognition of radar signal plays an extreme important role in radar electronic reconnaissance. In order to realize the reliable recognition of radar signal at the condition of low signal-to-noise ratio (SNR), we propose a new radar signal recognition system based on non-negative matrix factorization network (NMFN) and ensemble learning, which can recognize radar signals including BPSK, LFM, NLFM, COSTAS, FRANK, P1, P2, P3 and P4. First, we explore feature extractor based on convolutional neural network (CNN), which applies transfer learning to solve the problem of small sample size. Second, we propose non-negative matrix factorization network to extract features, which can reduce the redundant information. Third, we develop feature fusion algorithm based on stacked autoencoder (SAE), which can acquire essential expression of features and reduce dimension of features. Finally, we propose improved artificial bee colony algorithm (IABC) as the strategy of ensemble learning, which can improve the recognition rate. The simulation results show that the recognition rates reach 94.23% at -4 dB, 99.82% at 6 dB.

AlGaN-based deep UV (DUV) LEDs generally employ a p-type electron blocking layer (EBL) to suppress electron overflow. However, Al-rich III-nitride EBL can result in challenging p-doping and large valence band barrier for hole injection as well as epitaxial complexity. As a result, wall plug efficiency (WPE) can be compromised. Our systematic studies of band diagram and carrier concentration reveal that carrier concentrations in the quantum well and electron overflow can be significantly impacted because of the slope variation of the quantum barrier (QB) conduction and valence bands, which in turn influence radiative recombination and optical output power. Remarkably, grading the Al composition from 0.60 to 0.70 for the 12-nm-thick AlGaN QB of the DUV LED without the EBL can lead to 13.5% higher output power and similar level of overflown electron concentration (~1 × 1015/cm3) as opposed to the conventional DUV LED with the p-type EBL. This paradigm is significant for the pursuit of higher WPE or shorter emission wavelength for DUV LEDs and lasers, as it provides a new direction for addressing electron overflow and hole injection issues.

Domain adaptation (DA), a particular case of transfer learning, is an effective technology for learning a discriminative model in scenarios where the data from the training (source) and the testing (target) domains share common class labels but follow different distributions. The differences between domains, called domain shifts, are caused by variations in the acquisition devices and environmental conditions, such as changing illuminations, pose, and collecting-device noises, that are related to a specific domain, denoted as domain-specific noises in this paper. The research on stacked denoising autoencoder (SDA) has demonstrated that noise-robust features could be learned through training a model to reduce the man-made (simulated) noises. However, little research has been conducted to learn the domain-invariant features through training SDA to reduce the domain-specific noises from the real word. In this paper, we propose a novel variant of SDA for DA, called the stacked local constraint auto-encoder (SLC-AE), which aims to learn domain-invariant features through iteratively optimizing the SDA and the low-dimensional manifold. The core idea behind the SLC-AE is that both the source and target samples are corrupted due to the domain-specific noises, and each corrupted sample could be de-noised by calculating the weighted sum of its neighbor samples defined on the intrinsic manifold. Because the neighbor samples on the intrinsic manifold are semantically similar, their weighted sum preserves the generic information and reduces the domain-specific noises. To properly evaluate the performance of the SLC-AE, we conducted extensive experiments using seven benchmark data sets, i.e., MNIST, USPS, COIL20, SYN SIGNS, GTSRB, MSRC and VOC 2007. Compared to twelve different state-of-the-art methods, the experimental results demonstrated that the proposed SLC-AE model made significant improvement over the performance of SDA and achieved the best average performance on the seven data sets.

As more and more attention is paid to green manufacturing, production scheduling has been proved to be an efficient method for the reduction of environmental pollution. It is well-known that the flexible job shop scheduling problem (FJSP) is a very complex combinatorial optimization problem with strong theoretical and background for application. However, the problem has been extensively investigated and historically concerned with some indicators related to time, e.g., flow time, makespan, and workload. In this study, an energy-conscious FJSP is investigated with the consideration of the energy consumption. First, a mathematical model of the energy-conscious FJSP is built with the objective of optimizing the sum of the energy consumption cost and the completion-time cost. Due to the fact that the basic water wave optimization (WWO) was developed for various continuous problems, a discrete water wave optimization (DWWO) algorithm is proposed to solve the model. In our DWWO algorithm, a three-string encoding approach is first adopted to represent each individual wave. To make the algorithm adapt for the considered scheduling problem, three discrete evolutionary operations are redesigned according to the characteristics of the problem, i.e., propagation, refraction, and breaking. Finally, extensive experimental simulations are conducted to test the proposed DWWO algorithm. The comparison results demonstrate that the proposed DWWO algorithm is efficient for the energy-conscious FJSP.

A switching state sequences (SSSs)-based modulation scheme along with a dead-time effect compensation method is proposed in this paper. In the proposed modulation scheme, the converter's switching states changing sequence in one carrier period, i.e., the SSS, is treated as the basic unit to control the T-type 5L-ANPC converter. Because there are only two possible voltage levels in each SSS, the converter can be controlled as a traditional two-level converter within each SSS. Thus, a switching pulse modification-based method is proposed to compensate the dead-time effect within each SSS. Moreover, an SSS transition rule and the transition diagram are furtherly presented to avoid the dead-time effect between different SSSs. Consequently, both the output voltage delay and the unwanted voltage glitches that caused by the dead-time effect are avoided in the whole modulation range. Besides, a balancing strategy is proposed to balance the flying capacitor voltage based on the charging abilities of SSSs. The performance and effectiveness of the proposed modulation scheme are validated by both simulation and experimental results.

Rail transit (RT) has been favored by passengers because it effectively alleviates the problems of dense population, housing shortages, small natural areas, and serious air pollution in urban centers. In this paper, we propose a framework that combines statistical analysis, fuzzy theory, and the technique for order preference by similarity to an ideal solution (TOPSIS) to evaluate the service quality of RT. First, the passenger perception of service quality is modeled as trapezoidal fuzzy numbers from the fuzzy theory, which solves the uncertainty problem of passenger perception that how factors affect service quality. Next, a case study that evaluates the service quality of the Beijing metro system is proposed using the fuzzy TOPSIS method. During the evaluation process, 8011 surveys are collected from 16 metro lines operated by Beijing Metro Operating Company Ltd. The evaluation results show that transfers, in-vehicle experience, and ticket purchases or recharges are the three factors that passengers find most unsatisfactory about metro travel and that need to be greatly improved in the future construction and management of metros. Furthermore, we analyze the stableness of the fuzzy TOPSIS method by the ranking change of service quality for a line from different comparison sets of metro lines. Finally, we provide suggestions and guidance for the optimization of RT infrastructure and investment.

We demonstrated a passively Q-switched Yb-doped fiber laser in an all-fiber configuration that used a piece of a Yb-doped fiber with a smaller core than the gain fiber as a saturable absorber. The laser generated stable output pulses at 1064 nm with a narrow line width of less than 0.2 nm. The Q-switched pulses have a pulse width of 140 ns and pulse energy of 141 μJ at a repetition rate of 100 kHz. The peak power of ~ 1 kW and high slope efficiency of 51% were obtained. The repetition rate of this laser can be varied from a few kHz to 100 kHz with a potential of reaching up to 250 kHz. Stimulated Raman scattering was observed, though at 70 dB below the laser emission. The estimated stimulated Raman threshold is 6.2 kW, which allows this laser to further scale up the power. Because of its high peak power and adequate average power, the laser can be used as a stand-alone module for some applications in material processing. It can be also used as a seed laser for further power amplification.