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This research serves to advance the development of engineering computational fluid dynamics (CFD) computing efficiency for the analysis of pressurized water reactor (PWR) core using rod-type fuel assemblies with mixing vanes (one kind of typical PWR core). In this research, a CFD scheme based on the reconstruction of the initial fine flow field (RIF CFD scheme) is proposed and analyzed. The RIF scheme is based on the quantitative regulation of flow velocities in the rod-type PWR core and the principle that the CFD computing efficiency can be improved greatly by a perfect initialization. In this paper, it is discovered that the RIF scheme can significantly improve the computing efficiency of the CFD computation for the rod-type PWR core. Furthermore, the RIF scheme also can reduce the computing resources needed for effective data storage of the large fluid domain in a rod-type PWR core. Moreover, a flow-ranking RIF CFD scheme is also designed based on the ranking of the flow rate, which enhances the utilization of the flow field with a closed flow rate to reconstruct the fine flow field. The flow-ranking RIF CFD scheme also proved to be very effective in improving the CFD efficiency for the rod-type PWR core.

This paper proposes a finite-time adaptive neural network (NN) position tracking control method for the permanent magnet synchronous motor (PM SM) servo system with full-state constraints. NNs are utilized to compensate the parameter uncertainties and load torque disturbances of the PMSM servo system. Multiple barrier Lyapunov functions are introduced in the backstepping based control design, such that the rotor position, the rotor angular velocity and the currents of the d-q axis are constrained in given compact sets. In addition, the finite-time control technique is adopted to the control design, which can accelerate the convergence speed and improve the robustness of the PMSM servo system. Under the proposed control algorithm, the position tracking control is realized in finite time, and the tracking error can converge to a small neighborhood of the origin. Finally, simulation results are presented to show the effectiveness of the developed control approach, and some comparisons are given to show the rapid and accurate position tracking control performance.

In view of the problems of low precision, poor quality, and long time of gait feature recognition due to the influence of human body movement environment on the recognition process of the current gait feature recognition method of human body movement blurred image, a new method of gait feature recognition based on graph neural network (GNN) method is proposed. The gait features of human movement blurred images were extracted, and the fusion clustering recognition of the GNN algorithm was used to locate the gait features of human movement blurred images. The gait features of human body movement blurred images were located by the GNN method. According to the contour feature point info of the human body movement blurred image, the standard deviation of gait feature location of the human body movement blurred image was calculated, the gait feature of the blurred image of human body movement was reconstructed, and the gait recognition of the human body movement blurred image was achieved. The results show that the extraction of human movement is good, with high positioning confidence, good recognition quality, average recognition accuracy of 92%, and greatly shortened recognition time.

In this paper, the coupling of fine-mesh computational fluid dynamics (CFD) thermal-hydraulics (TH) code and neutronics code is achieved using the Ansys Fluent User Defined Function (UDF) for code development, including parallel meshing mapping, data computation, and data transfer. Also, some CFD schemes are designed for mesh mapping and data transfer to guarantee physical conservation in the coupling computation. Because there is no rigorous research that gives robust guidance on the various CFD schemes that must be obtained before the fine-mesh coupling computation, this work presents a quantitative analysis of the CFD meshing and mapping schemes to improve the accuracy of the value and location of key physical prediction. Furthermore, the effect of the sub-pin scale coupling computation is also studied. It is observed that even the pin-resolved coupling computation can also create a large deviation in the maximum value and spatial locations, which also proves the significance of the research on mesh mapping and data transfer for CFD code in a coupling computation.

A rapid-spreading epidemic of COVID-19 hit China at the end of 2019, resulting in unignorable social and economic damage in the epicenter, Wuhan. POIs capture the microscopic behavior of citizens, providing valuable information to understand city reactions toward the epidemic. Leveraging large-scale check-in records, we analyze the POI visit trends over the epidemic period and normal times. We demonstrate that COVID-19 greatly influences the society, where most POIs demonstrate more than 60% of visit drops during the city lockdown period. Among them, Tourist Attractions received greatest impact with a 78.8% drop. Entertainment, Food, Medical and Shopping are sensible to the disease before lockdown, and we identify these "early birds" to investigate the public reaction in the early stage of the epidemic. We further analyze the revival trends, generating four different revival patterns that correlated with the necessity of POI functions. Finally, we analyze the perseverance during the COVID-19, finding no large-scale closures compared with the tremendous visit drop. The strong resilience in Wuhan supports the rapid recovery of society. These findings are important for researchers, industries, and governments to understand the city respondence under severe epidemic, proposing better regulations to respond, control, and prevent public emergencies.

In this paper, a finite-time composite adaptive fuzzy control approach is proposed for position tracking of permanent magnet synchronous motors (PMSMs) with uncertain parameters and load torque disturbances. Based on the approximate ability of fuzzy logic systems (FLSs), a novel finite-time adaptive fuzzy control is developed, where dynamic surface control with error compensation mechanism is incorporated in the control design. A serial-parallel estimation model of PMSM is established. The prediction error between PMSM and the serial-parallel estimation model is utilized to construct the adaptive law of FLSs, which can improve the approximate accuracy of FLSs. Moreover, by introducing a new fuzzy adaptive law, the algebraic loop problem in the existing backstepping based control approaches is overcome. It is proven that all signals of closed-loop systems are uniformly ultimately bounded, and the position tracking error can converge to a small neighborhood of the origin in finite time. Finally, the simulation results are presented to show the effectiveness of the proposed control approach, and some comparisons are given to show the rapid and accurate position tracking performance.

JADE is a differential evolution (DE) algorithm and has been shown to be very competitive in comparison with other evolutionary optimization algorithms. However, it suffers from the premature convergence problem and is easily trapped into local optima. This article presents a novel JADE variant by incorporating chaotic local search (CLS) mechanisms into JADE to alleviate this problem. Taking advantages of the ergodicity and nonrepetitious nature of chaos, it can diversify the population and thus has a chance to explore a huge search space. Because of the inherent local exploitation ability, its embedded CLS can exploit a small region to refine solutions obtained by JADE. Hence, it can well balance the exploration and exploitation in a search process and further improve its performance. Four kinds of its CLS incorporation schemes are studied. Multiple chaotic maps are individually, randomly, parallelly, and memory-selectively incorporated into CLS. Experimental and statistical analyses are performed on a set of 53 benchmark functions and four real-world optimization problems. Results show that it has a superior performance in comparison with JADE and some other state-of-the-art optimization algorithms.

This research presents a multi-vehicle control strategy combining the merging control and platoon control to deal with the merging scenario in the freeway ramp entrance. In the merging controller, a selection algorithm is presented firstly to determine the merging sequence based on the initial states of the vehicles in the cooperative area. Subsequently, the optimized longitudinal trajectory of the facilitating vehicle and on-ramp vehicle is addressed by solving the Hamiltonian function. As for the platoon controller, string stability is analyzed to attenuate the impact of merging on the mainline traffic, considering the influence of communication time delay. Smooth mode switching is achieved by introducing the tracking policy of platoon controller to determine the terminal states of the facilitating vehicle. The proposed strategy is simulated at two typical merging scenarios, where two on-ramp vehicles attempt to cut into a six-vehicular platoon, to validate its feasibility and advantage.

The main work of this paper focuses on identifying the heat flux in inverse problem of two-dimensional nonhomogeneous parabolic equation, which has wide application in the industrial field such as steel-making and continuous casting. Firstly, the existence of the weak solution of the inverse problem is discussed. With the help of forward solution and dual equation, this paper proves the Lipchitz continuity of the cost function and derives the Lipchitz constant. Furthermore, in order to accelerate the convergence rate and reduce the running time, this paper presents a sufficient descent Levenberg–Marquard algorithm with adaptive parameter (SD-LMAP) to solve this inverse problem. At last, compared with other methods, the results of simulation experiment show that this algorithm can obviously reduce the running time and iterative number.

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task designed to predict the sentiment polarity of each aspect term in a text. Recent research mainly uses neural networks to model text and utilizes attention mechanisms to interact for associate aspect terms and context to obtain more effective feature representation. However, the general attention mechanism is easy to lose the original information. Besides, in the multi-aspect text, the sentiment information of other aspect terms interferes with the sentiment analysis of the current aspect term likely. In this paper, we propose two models named RA-CNN and RAO-CNN for ABSA tasks. In RA-CNN, we apply CNN to model the aspect term and utilize a specially designed residual attention mechanism to interact with the text. Based on the RA-CNN, RAO-CNN adds other aspect terms module, which can reduce interference of sentiment information related to other aspect terms in the multi-aspect text. To verify the proposed models’ effectiveness, we conduct a large number of experiments and comparisons on seven public datasets. Experimental results show that our proposed models are useful and achieve state-of-the-art results.

True Random Number Generators (TRNGs) create a hardware-based, non-deterministic noise that is used for generating keys, initialization vectors, and nonces in a variety of applications requiring cryptographic protection. A compromised TRNG may lead to a system-wide loss of security. In this brief, we show that an attack combining power analysis with bitstream modification is capable of classifying the output bits of a TRNG implemented in FPGAs from a single power measurement. We demonstrate the attack on the example of an open source AIS-20/31 compliant ring oscillator-based TRNG implemented in Xilinx Artix-7 28nm FPGAs. The combined attack opens a new attack vector which makes possible what is not achievable with pure bitstream modification or side-channel analysis.

In this letter, a novel concept for the dielectric resonator functioning as a differential antenna and as an independently controllable dual-band filter simultaneously is proposed for the first time. The multifunctional design takes full advantage of the virtual and reflection grounds of the differential dielectric resonator antenna (DDRA). The antenna and filter functions share a single module and a physical ground, making the proposed design compact and cost effective. In addition to high isolation between the two functions, the filter part shows two independently adjustable passbands. For demonstration, a prototype is designed, fabricated, and measured. The DDRA is designed at 2.5 GHz with an impedance bandwidth of 2.7% and the realized gain of 4.5 dBi. The dual-band filter operates at the center frequencies of 2.16 GHz and 3.51 GHz with 3 dB bandwidths of 13% and 7%, respectively. A good agreement is observed between the simulated and measured results.

Reinforcement learning (RL) aims at searching the best policy model for decision making, and has been shown powerful for sequential recommendations. The training of the policy by RL, however, is placed in an environment. In many real-world applications, the policy training in the real environment can cause an unbearable cost due to the exploration. Environment estimation from the past data is thus an appealing way to release the power of RL in these applications. The estimation of the environment is, basically, to extract the causal effect model from the data. However, real-world applications are often too complex to offer fully observable environment information. Therefore, quite possibly there are unobserved variables lying behind the data, which can obstruct an effective estimation of the environment. In this paper, by treating the hidden variables as a hidden policy, we propose a partially-observed multi-agent environment estimation (POMEE) approach to learn the partially-observed environment. To make a better extraction of the causal relationship between actions and rewards, we design a deep uplift inference network (DUIN) model to learn the causal effects of different actions. By implementing the environment model in the DUIN structure, we propose a POMEE with uplift inference (POMEE-UI) approach to generate a partially-observed environment with a causal reward mechanism. We analyze the effect of our method in both artificial and real-world environments. We first use an artificial recommender environment, abstracted from a real-world application, to verify the effectiveness of POMEE-UI. We then test POMEE-UI in the real application of Didi Chuxing. Experiment results show that POMEE-UI can effectively estimate the hidden variables, leading to a more reliable virtual environment. The online A/B testing results show that POMEE can derive a well-performing recommender policy in the real-world application.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies worldwide. However, the mechanisms underlying the acquisition of the metastatic potential in PDAC has not been well understood. In this study, we identified a total of 154 genes upregulated in primary tissues of PDAC with liver metastasis using the Genome Cancer Atlas (TCGA) and GSE151580 cohorts. The epithelial-mesenchymal transition and glycolysis were enriched by the liver metastasis-related genes, indicating that the liver metastasis-related genes might be functionally relevant to liver metastasis in PDAC. Moreover, we also found that the liver metastasis-related genes were primarily regulated at epigenetic level. Particularly, SFN, a cell cycle checkpoint protein, and KRT19, a marker gene for ductal cells, were predicted to be regulated by multiple methylation sites at the promoter. Clinically, we for the first time defined a liver metastasis score (LMS), which was derived from liver metastasis-related genes, and closely associated with clinical characteristics such as disease type and tumor grade, in PDAC. Furthermore, we also divided the samples into high and low LMS groups using three cohorts with long-term follow-up (TCGA, GSE71729, and E-MTAB-6134), which exhibited significantly different prognostic outcomes across three PDAC cohorts, suggesting that the LMS might be a good indicator for risk stratification in PDAC. Furthermore, we also found that the liver metastasis-related genes were primarily expressed in malignant ductal cells by integrative analysis of the bulk and single-cell gene expression data. Moreover, the malignant ductal cells and M0 macrophages were highly correlated with LMS, indicating that the two cell types might function as tumor-promoting cells in PDAC. In summary, the systematic analysis for the first time defined an LMS score to evaluate the risk of liver metastasis in PDAC and revealed that malignant ductal cells might promote PDAC liver metastasis, which greatly improves our understanding of the liver metastasis-related genes, their underlying mechanisms, and association with prognosis in PDAC.

The positioning technology based on receive signal strength (RSS) fingerprints has become one of the hottest research spots with its advantages of simple deployment, low cost, and single parameter. However, in the limited space, the multipath and shadowing, result in poor separability of the fingerprint data, and low accuracy of target localization. In this paper, a novel RSS fingerprints positioning algorithm that is based on fuzzy kernel clustering SVM is proposed to combat the multipath and shadowing effects. The first step of the proposed positioning algorithm is to use kernel function to map the traditional fingerprints sample data to high-dimensional feature space to generate fuzzy classes. The second step is to generate binary-class SVM of fuzzy class based on the relationship between classes and internal discrete information of each class. After that, we can use the binary fuzzy class SVM to dichotomize the classified fingerprints in the first step, and combine these dichotomous SVMs into a handstand classification binary tree. And thus, the proposed positioning algorithm achieves quick and accurate positioning. Experimental results show that the positioning accuracy and locating stability of proposed positioning algorithm are improved by 38.73% and 59.26%, respectively, compared with the traditional RSS fingerprints algorithm.

A sub-sampling phase-locked loop (SSPLL) with loop bandwidth calibration is presented. By using a sub-sampling phase detector with gain calibration and a pulse width control circuit, the loop bandwidth deviation of the SSPLL can be reduced. This SSPLL is fabricated in a 40 nm CMOS process and its core area is 0.15mm2. The power consumption of the SSPLL is 5.81mW from a supply of 1.1V. The reference frequency is 75 MHz and the output frequency range of the SSPLL is 2.4~3.0GHz. The measured rms jitter is 2.02ps at the output frequency of 3.0GHz. With the calibration, the largest loop bandwidth deviation from 3.5MHz among five samples is reduced from −71.4% to −18.5% at 3.0GHz.

Traditional open-switch fault diagnosis methods suffer from poor rapidity or robustness. To solve this issue, a new differential current observer-based fault diagnosis method is proposed in this article. With the designed differential observer, fault symptoms (residuals) can be generated and adopted for fault diagnosis easily. Considering that the residuals are sensitive to motor operating condition in the conventional model-based method due to model error, an adaptive fault detection threshold is designed. As a result, the false detection and missed detection caused by the change of working condition can be avoided, and stronger robustness against speed, load, and parameter variations can be achieved with superior rapidity compared with existing methods. Finally, the rapidity and robustness of the proposed fault diagnosis method are verified sufficiently through experimental results.

The classification tasks for numerical or categorical data have been well developed. However, the data collected in the real world are frequently the mixed type containing numerical and categorical values, and how to classify the mixed data quickly and efficiently is a critical yet challenging task. Existing classification models for mixed data usually treat the mixed data processing and subsequent classification as two independent phases, without considering their compatibility. By fusing the mixed data processing into a classification algorithm, this paper proposes an extended version of RBF-ELM (Radial Basis Function-Extreme Learning Machine), a Mixed Data RBF-ELM method (MD-RBF-ELM for short), which can achieve direct, fast, and efficient classification for mixed data. Specifically, a distance metric method for mixed data is firstly designed to calculate the distances between the input data and the RBF centers, and then these distances are used to train the network structure and weights of MD-RBF-ELM, thereby realizing the fusion of data processing with model learning. In addition, to alleviate the problem of MD-RBF-ELM’s unstable performance caused by randomly selecting the RBF centers, we propose an improved density peak clustering algorithm and use it to select the optimal RBF centers automatically and adaptively. Extensive experimental results on 34 data sets demonstrate that MD-RBF-ELM significantly enhances the classification performance (increasing 2.37% for F1-score, up to 14/34 for the number of best results, and reaching 2.4/8 for the averaged ranks), compared with seven state-of-the-art competitors.

Accurate and stable prediction of NOx emissions from diesel vehicles plays a crucial role in the establishment of virtual NOx sensors and the development and design of diesel engines. This paper presents a method for estimating transient NOx emissions by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a long- and short-term memory neural network (LSTM). First, the CEEMDAN algorithm is used to reduce the non-stationarity and volatility of the transient NOx emission data to obtain multiple subseries with different frequencies. Secondly, a predictive model is developed for each subsequence using an LSTM neural network. Finally, the results of each subsequence prediction are summed to obtain the final prediction. The proposed model uses NOx emission data generated by an EU IV diesel bus during real road driving. The results show that (1) The use of CEEMDAN can effectively improve the smoothness of NOx transient emission data, as well as facilitate more effective extraction of internal characteristics and variations of the raw data. (2) LSTM has better learning and prediction capability for transient changes in NOx emissions. (3) The results of CEEMDAN-LSTM for RMSE, R2, MAE and NRMSE are 46.11,0.98, 29.82 and 2.71, respectively, which are better than the other model with improved prediction performance.

A gravitational search algorithm &#x0028 GSA &#x0029 uses gravitational force among individuals to evolve population. Though GSA is an effective population-based algorithm, it exhibits low search performance and premature convergence. To ameliorate these issues, this work proposes a multi-layered GSA called MLGSA. Inspired by the two-layered structure of GSA, four layers consisting of population, iteration-best, personal-best and global-best layers are constructed. Hierarchical interactions among four layers are dynamically implemented in different search stages to greatly improve both exploration and exploitation abilities of population. Performance comparison between MLGSA and nine existing GSA variants on twenty-nine CEC2017 test functions with low, medium and high dimensions demonstrates that MLGSA is the most competitive one. It is also compared with four particle swarm optimization variants to verify its excellent performance. Moreover, the analysis of hierarchical interactions is discussed to illustrate the influence of a complete hierarchy on its performance. The relationship between its population diversity and fitness diversity is analyzed to clarify its search performance. Its computational complexity is given to show its efficiency. Finally, it is applied to twenty-two CEC2011 real-world optimization problems to show its practicality.

Yield optimization aims at finding microwave filter designs with high yield under fabrication tolerance. The electromagnetic (EM) simulation-based yield optimization methods are computationally expensive because a large number of EM simulations is required. Moreover, the microwave filter design usually requires several performance objectives to be met, which is not considered by the current yield optimization methods for microwave filters. In this paper, an efficient yield-constrained optimization using polynomial chaos surrogates (YCOPCS) is employed for microwave filters considering multiple objectives. In the YCOPCS method, the low-cost and high-accuracy of polynomial chaos is used as a surrogate. An efficient yield-constrained design framework is implemented to obtain the optimal design solution. Two numerical examples demonstrate the performance of the YCOPCS method, including a coupling matrix model of a fourth-order filter with cascaded quadruplet topology and an EM simulation model of a microwave waveguide bandpass filter. The numerical results show that the YCOPCS method can obtain the filter designs with higher yield and reduce EM simulations by 80% compared to Monte Carlo-based yield optimization in all testing examples.

This article presents the first dedicated system-on-chip (SoC) that supports full data analysis workflow for genetic variant discovery for next-generation sequencing (NGS). The SoC implements four major steps: preprocessing, short-read mapping, haplotype calling, and variant calling. By adopting the sBWT-based short read mapping and Genome Analysis ToolKit haplotype caller-based variant calling algorithms, this work achieves a comparable precision as the software solutions. The dedicated hardware architecture achieves high parallelism with low hardware complexity. In this SoC, the multitask sorting engine and the dynamic programming processing engine are proposed for essential computing tasks for NGS data analysis. An ARC processor is integrated to facilitate IO interfaces, including a DDR3 PHY. Fabricated in a 28-nm technology, the chip area is 12 mm2. It dissipates 975 mW at a clock frequency of 400 MHz from a 0.9-V supply. The SoC is able to complete variant discovery for the whole human genome within 37 min. Compared with an optimized graphic processing unit solution, this work is 66 $\times $ faster and also achieves an $1.59\times 10^{4}$ times higher energy efficiency and 3086 $\times $ higher area efficiency. Verified with the FDA standardized benchmark, the SoC achieves a precision of 99.6%. The prototype system demonstrates the analysis procedure in real time for on-site DNA sequencing.

Advances in wind power system modeling have produced widespread socioeconomic benefits for alleviating global environmental problems. However, previous studies mainly payed attention to point forecasts of wind power system, with the absence of its uncertainty quantification analysis and outlier detection, which cannot facilitate further development in this field. In this paper, a novel monitoring-forecasting system, including the analysis module, the outlier detection module, the probabilistic forecasting module, and the evaluation module, is proposed for uncertainty modeling of wind power. In the analysis module, recurrence analysis techniques are developed, aiming at characterizing complicated patterns of wind power. Furthermore, the interval partitioning-based isolation forest algorithm, which can effectively address the effects of swamping and masking, is first developed in the outlier detection module for wind power. Superior to the traditional point forecasting method that cannot perform quantitative characterization of the intrinsic uncertainties in wind power forecasting, an advanced probabilistic forecasting method based on Gaussian process regression (GPR) with an optimal kernel function scenario, cooperating with a feature selection method, is first presented in the probabilistic forecasting module, indicating that the forecast skill of GPR is significantly enhanced. Finally, the proposed system is validated using real wind power data with high resolution from Spain in the evaluation module, solidly demonstrating its high reliability and flexibility compared to benchmarks considered in this study.

This study presents an evolutionary game to model interactions among stakeholders with potential conflicts, including the operational enterprise of incineration plant, the local government, and the residents nearby. System dynamics is used to simulate the change of strategic actions corresponding to the three players, in order to seek for the evolutionary stability strategies. A numerical case is proposed to demonstrate the game theory application, in which the impacts of governmental incentive and punishment on the player’s actions are investigated. The results indicated that administrative penalty is effective not only in motivating the enterprises to upgrade treatment facilities for ensuring environmental quality but also in helping the local government and residents to approach dominant strategies. Policy implications are given based on the results to lay out a foundation for the alleviation of the conflicts.

Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties. To understand the practical behaviors of EAs theoretically, there are a series of efforts devoted to analyzing the running time of EAs for optimization under uncertainties. Existing studies mainly focus on noisy and dynamic optimization, while another common type of uncertain optimization, i.e., robust optimization, has been rarely touched. In this paper, we analyze the expected running time of the (1+1)-EA solving robust linear optimization problems (i.e., linear problems under robust scenarios) with a cardinality constraint $k$. Two common robust scenarios, i.e., deletion-robust and worst-case, are considered. Particularly, we derive tight ranges of the robust parameter $d$ or budget $k$ allowing the (1+1)-EA to find an optimal solution in polynomial running time, which disclose the potential of EAs for robust optimization., Comment: 17 pages, 1 table

In this paper, a fast and effective method based on multiple image features and a weighted K-means clustering algorithm is proposed to achieve the automatic grading of apples. The method provides a novel way of using four images (top, bottom and two sides) and average gray values for each apple to distinguish between the apple defects, stem and calyx. Furthermore, weighted features (MCSAD (maximum cross-sectional average diameter), circularity, PRA (proportion of red area) and defect regions) were carefully selected according to the requirements of the national apple grading standard, which improves the practicality of the proposed method. Finally, qualitative and quantitative evaluation results demonstrate that the total accuracy of the proposed multi-feature grading method is greater than 96%, which provides encouragement for the additional research and implementation of multi-feature automatic grading for the fruit industry.

In this article, an optimal control problem (OCP) for the power management system is proposed for the more electric aircraft (MEA) with consideration of the dynamics in the generators and the energy storage systems. Electrical power components included in the electrical power system (EPS) of MEA such as the generator, the bus, the rectifier, and the battery system are modeled in this OCP. Based on this formulation, a three-stage optimization architecture is introduced to obtain the optimal power references and the optimal control policies. In the first stage, an optimization problem for the power scheduling and allocation is solved as a mixed integer quadratic programming problem and the optimal power allocation schemes (power references) are obtained. In the second stage, an OCP to minimize the fluctuations in power generation systems is proposed and solved via an off-policy integral reinforcement learning approach. In the third stage, the voltage restoration is applied to maintain the main-bus voltage in the onboard high voltage dc system. Extensive case studies are carried out to illustrate the effectiveness of our proposed methodology. Simulations show that the optimal power references are obtained based on various requirements, and the optimal power control policy is achieved based on off-policy iterations.

Electrical resistivity is one of the fundamental physical properties of rocks, and its relationship with fluid saturation is widely used in reservoir evaluation. The interpretation of electrical logs usually relies on the results of rock resistivity tests of cores in the laboratory. In the laboratory, core samples are commonly cut into cylinders, and the resistivity is measured along the axial direction. To determine the rock resistivity along different directions, samples are cut into cubes and tested along three perpendicular directions to obtain resistivity data. The expensive and complicated preparation process of cubic samples and the custom holder requirements limit the use of these tests. We have analyzed the advantages and disadvantages of rock resistivity measurements of cylindrical and cubic samples. To overcome the abovementioned difficulties, we develop a core holder for radial resistivity measurements and a radial resistivity measurement method for cylindrical samples. Based on the conformal transformation of the complex variable function theory, we establish a mathematical model of radial resistivity. We develop a measurement method of radial resistivity according to the mathematical model and verify this approach with isotropic cores. The theoretical calculation results agree with the experimental results. With the radial resistivity measurement method, we effectively test the resistivity anisotropy of shale. This method has more advantages than existing methods in terms of the sample preparation and realization of various displacement levels and angles. Hence, the proposed method can be widely used.

In view of the fact that on flat panel display industrial production line, the power consumption is very important and determined by their different driving modes and light-emitting principles. Based on the industrial control systems, this paper analyses the power consumption of flat panel display, studies the main factors affecting its power consumption and analyses the main methods to improve light efficiency and reduce power consumption. This paper also discusses the focus issues in the current standard of flat panel display power measurement at home and abroad. According to the characteristics of different flat panel displays, this paper discusses the dual indexes of power consumption and efficiency in power measurement, and how to consider the influence of screen brightness and angle of view in the measurement.

DC/DC converters play an important role in electrical systems. The anomalous state of a dc/dc converter has a major impact on the operation of the back-end components and the entire electrical system. To effectively recognize an anomalous state, particularly for dc/dc converters with unknown circuit structures, an online anomaly detection method that involves statistical feature estimation using Gaussian process regression (GPR) and a genetic algorithm (GA) is proposed. In the proposed method, the normal output range is built upon the dc/dc normal output signal using GPR, and seven statistical features are considered as the detection indexes. In the detection process, the output signal of the dc/dc converter is acquired, and the corresponding extreme values of statistical features are calculated by using GA, which can effectively reduce the operation time and the calculation consumption. The working state of the dc/dc converter is distinguished according to these features. Simulation and hardware experimental results validate the practicability and effectiveness of the proposed method.

Urban sensing is one of the fundamental building blocks of urban computing. It uses various types of sensors deployed in different geospatial locations to continuously and cooperatively monitor the natural and cultural environment in urban areas. Nevertheless, issues such as uneven distribution, low sampling rate and high failure ratio of sensors often make their readings less reliable. This paper provides an innovative framework to detect the noise data as well as to repair them from a spatial-temporal causality perspective rather than to deal with them individually. This can be achieved by connecting data through monitored objects, using the Skip-gram model to estimate spatial correlation and long short-term memory to estimate temporal correlation. The framework consists of three major modules: 1) a space embedded Bidirectional Long Short-Term Memory (BiLSTM)-based sequence labeling module to detect the noise data and the latent missing data; 2) a space embedded BiLSTM-based sequence predicting module calculating the value of the missing data; 3) an object characteristics fusion repairing module to correct the spatial and temporal dislocation sensory data. The approach is evaluated with real-world data collected by over 3 000 electronic traffic bayonet devices in a citywide scale of a medium-sized city in China, and the result is superior to those of several referenced approaches. With a 12.9% improvement in data accuracy over the raw data, the proposed framework plays a significant role in various real-world use cases in urban governance, such as criminal investigation, traffic violation monitoring, and equipment maintenance.

Aspect-based sentiment analysis can predict the sentiment polarity of specific aspect terms in the text. Compared to general sentiment analysis, it extracts more useful information and analyzes the sentiment more accurately in the comment text. Many previous approaches use long short-term memory networks with attention mechanisms to directly learn aspect-specific representations and model comment text. However, these methods always ignore the importance of the aspect terms position and interactive information between the aspect terms and other words. To address these issues, we propose an improved model based on convolutional neural networks. First, a novel relative position encode layer can integrate the relative position information of specific aspect terms validly in a text. Second, by using the aspect attention mechanism, the semantic relationship between aspect terms and words in the text is fully considered. To verify the effectiveness of the proposed models, we conduct a large number of experiments and comparisons on seven public datasets. The experimental results show that this model outperforms to other state-of-the-art methods.

Existing gain estimation methods for radio frequency (RF) antennas often rely on rigorous and expensive experimental facilities or are only implemented for classic structure. They perform limited application scopes. To address the challenges, this study provides an accessible method for gain estimation of heteromorphic RF identification (RFID) antennas. There are three main innovations in the proposed method. An estimation framework is proposed based on neuro-space mapping technique which effectively reduces the time consumption and avoids laborsome measurement processes. A diverse extraction integration strategy is designed for training data acquisition, to balance the estimation accuracy and the training data size. A new adaptive particle swarm optimiser embedded with scale elaboration strategy is developed, which tackles the approximation problem from the gain estimation model to the gain from high-fidelity simulations. The proposed method is tested by four types of RF antennas. Simulations results demonstrate the method possesses high accuracy and strong applicability.

Based on the research background of bearing compression deformation of filling gangue in deep mines, athree-directional compression testing system of gangue was developed independently to carry out...

Purpose Focusing on the problem that the visual detection algorithm of navigation path line in intelligent harvester robot is susceptible to interference and low accuracy, a navigation path detection algorithm based on improved random sampling consensus is proposed. Design/methodology/approach First, inverse perspective mapping was applied to the original images of rice or wheat to restore the three-dimensional spatial geometric relationship between rice or wheat rows. Second, set the target region and enhance the image to highlight the difference between harvested and unharvested rice or wheat regions. Median filter is used to remove the intercrop gap interference and improve the anti-interference ability of rice or wheat image segmentation. The third step is to apply the method of maximum variance to thresholding the rice or wheat images in the operation area. The image is further segmented with the single-point region growth, and the harvesting boundary corner is detected to improve the accuracy of the harvesting boundary recognition. Finally, fitting the harvesting boundary corner point as the navigation path line improves the real-time performance of crop image processing. Findings The experimental results demonstrate that the improved random sampling consensus with an average success rate of 94.6% has higher reliability than the least square method, probabilistic Hough and traditional random sampling consensus detection. It can extract the navigation line of the intelligent combine robot in real time at an average speed of 57.1 ms/frame. Originality/value In the precision agriculture technology, the accurate identification of the navigation path of the intelligent combine robot is the key to realize accurate positioning. In the vision navigation system of harvester, the extraction of navigation line is its core and key, which determines the speed and precision of navigation.

Although a large number of solutions have been proposed to handle imbalanced classification problems over past decades, many researches pointed out that imbalanced problem does not degrade learning performance by its own but together with other factors. One of these factors is the overlapping problem which plays an even larger role in the classification performance deterioration but is always ignored in previous study. In this paper, we propose a density-based adaptive k nearest neighbor method, namely DBANN, which can handle imbalanced and overlapping problems simultaneously. To do so, a simple but effective distance adjustment strategy is developed to adaptively find the most reliable query neighbors. Concretely, we first partition training data into six parts by density-based method. Next, for each part, we modify distance metric by considering both local and global distribution. Finally, output is made by the query neighbors selected in the new distance metric. Noticeably, the query neighbors of DBANN are adaptively changed according to the degree of imbalance and overlap. To show the validity of our proposed method, experiments are carried out on 16 synthetic datasets and 41 real-world datasets. The results supported by the proper statistical tests show that our proposed method significantly outperforms the state-of-the-art methods.

The blade is a crucial part of wind turbine for generating electricity and prone to damage due to harsh external environment. Accurate damage detection of wind turbine blade (WTB) is still a prominent challenge. This paper presents an acoustical detection method for damage identification of the WTB based on pattern recognition. In the proposed method, sound pulse extraction of the WTB is first investigated through physical method in combination with the filter and sliding window. Subsequently, the wavelet packet energy ratios of acoustic signal are introduced to characterize the discrepancy between intact and cracked sound pulses, and the support vector data description (SVDD) model is built for WTB damage detection. Besides, an improved incremental learning method is presented and employed to adaptively update the SVDD model, which aims at simplifying calculation procedure. Finally, the performance of proposed method is evaluated using experimental data collected from the WTBs with both intact and damaged conditions in commercial wind farms. It is demonstrated that proposed method has improvement in prediction accuracy compared to previous incremental SVDD models and performs the best on training time.

This paper empirically investigates the impact of economic growth and industrial structure on China's Nitrogen Oxides (NOx) emissions reduction in a comprehensive Kaya-PLS framework. It is found that the influencing coefficients of economic growth and industrial structure are 0.27 and 2.53, respectively. It indicates that economic growth and industrial structure do bring positive effects on China's NOx emissions, and industrial structure plays a much greater role than economic growth in the process of NOx emissions reduction in China. Moreover, we incorporate additional variables involving energy consumption structure, energy intensity, population and cement production to augment the NOx emissions function. In addition, six scenarios are set to calculate China's NOx emissions for 2019–2030. The prediction results show that China's NOx emissions decreased by 9.11% on average during 2019–2030 because of the decline of the proportion of secondary industry. It means that the decrease of proportion of secondary industry would make a significant decrease for China's NOx emissions. What's more, China's NOx emissions in six scenarios in 2020 are not more than 15.74 million tons, indicating that it is realistic to achieve China's reduction target on NOx emissions according to the 13th Five-Year Plan. Therefore, the government should adjust the energy structure and industrial structure as soon as possible to control China's NOx emissions and improve air quality.

In traditional maximum torque per ampere (MTPA) method based on signal injection, fixed wave amplitude and constant integral coefficient are employed, which actually degrade the optimal current angle tracking performance in variable conditions, especially under heavy loads. In order to solve this problem, an improved MTPA method based on amplitude-adjustable square wave injection is proposed. First, the relationships among optimal current angle, square wave amplitude and electromagnetic torque are analyzed. The analysis results indicate that if fixed wave amplitude is adopted, the fluctuations in the obtained current angle increases when load torque rises. Based on this relationship, an amplitude-adjustable square wave virtual signal injection MTPA method is proposed. By adjusting square wave amplitude according to the load torque, steady-state fluctuations of the obtained current angle can be reduced compared to conventional method under heavy loads. Meanwhile, to improve the MTPA dynamic response under load change conditions, an adaptive fuzzy control method is proposed for adjusting integral coefficient. Finally, the validity of the improved MTPA method is verified through experimental results.

Nonreciprocal waveguides play an important role in microwave and optical systems. Here we present a new concept for a one-way meta-waveguide: a waveguide that supports a guided mode in one direction while waves traveling in the opposite direction leak out into the waveguide cladding. This is achieved by placing a metasurface atop of a ferrite slab, and judiciously phase matching two radiant and one subradiant meta-waveguide modes in order to enhance the magneto-optical response and to create an accidental Dirac cone in the reciprocal space. By applying an in-plane magnetic field, the reciprocal and nonreciprocal contributions to magneto-optical response can interfere constructively/destructively depending on the propagation direction, thereby shifting the apex of the Dirac cone and creating a one-way propagation bandgap. The idea of one-way leaky-cladding waveguide provides an alternative way of designing nonreciprocal devices that do not require a gap in the Dirac cone.

Side information has been shown useful for improving the performance of matrix completion applications. However, in most cases, only partial side information of either the column or row space is available. In this work, we propose a novel regularization based model to incorporate partial side information in matrix completion. We provide theoretical guarantees to ensure the success of the proposed model. It is proved that the proposed model achieves the state-of-the-art sample complexity when the given partial side information is exact, and an error bound for inexact partial side information is also provided. Moreover, we provide a deterministic rule for the selection of regularization parameter. We conduct extensive experiments on both synthetic and real-world data-sets. Experimental results show that our model succeeds to incorporate partial side information, and outperforms the state-of-the-art models on most data-sets.

Many advanced lattice based cryptosystems require to sample lattice points from Gaussian distributions. One challenge for this task is that all current algorithms resort to floating-point arithmetic (FPA) at some point, which has numerous drawbacks in practice: it requires numerical stability analysis, extra storage for high-precision, lazy/backtracking techniques for efficiency, and may suffer from weak determinism which can completely break certain schemes. In this paper, we give techniques to implement Gaussian sampling over general lattices without using FPA. To this end, we revisit the approach of Peikert, using perturbation sampling. Peikert’s approach uses continuous Gaussian sampling and some decomposition \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf {\Sigma }= \mathbf {A}\mathbf {A}^t$$\end{document}Σ=AAt of the target covariance matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf {\Sigma }$$\end{document}Σ. The suggested decomposition, e.g. the Cholesky decomposition, gives rise to a square matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf {A}$$\end{document}A with real (not integer) entries. Our idea, in a nutshell, is to replace this decomposition by an integral one. While there is in general no integer solution if we restrict \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf {A}$$\end{document}A to being a square matrix, we show that such a decomposition can be efficiently found by allowing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf {A}$$\end{document}A to be wider (say \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n \times 9n$$\end{document}n×9n). This can be viewed as an extension of Lagrange’s four-square theorem to matrices. In addition, we adapt our integral decomposition algorithm to the ring setting: for power-of-2 cyclotomics, we can exploit the tower of rings structure for improved complexity and compactness.

Due to the discovery of innovative and practical neural architectures, deep learning has achieved bright successes in many fields, such as computer vision, natural language processing, recommendation systems, etc. To reach high performance, researchers have to adjust neural architectures and choose training tricks very carefully. The manual trial-and-error process for discovering the best neural network configuration consumes plenty of manpower. The neural architecture search (NAS) aims to alleviate this issue by automatically configuring neural networks. Recently, the rapid development of NAS has shown significant achievements. Novel neural network architectures that outperform the state-of-the-art handcrafted networks have been discovered in image classification benchmarks. In this paper, we survey NAS from a technical view. By summarizing the previous NAS approaches, we drew a picture of NAS for readers including problem definition, search approaches, progress towards practical applications and possible future directions. We hope that this paper can help beginners start their researches on NAS.

In this paper, we propose a novel post-bond through-silicon via (TSV) test method for post-bond TSV manufacture test. The proposed method can measure the RGC parameters of TSVs using switched-capacitor circuits and calibration method and detect TSV leakage fault, open fault, high-resistance fault, and delay fault based on the measured RGC parameters. The test process has a high resolution and is robust. The fault-detection effectiveness is evaluated via HSPICE simulations. The results of the TSV capacitance measurement have a relative error within 1%, the TSV resistance measurement results have a relative error within 5%, and the TSV conductance measurement results have a relative error within 8.5%. We also perform an Monte Carlo simulation to prove that even under process variation, the proposed method can obtain a TSV capacitance within an absolute error of ±3 fF, obtain a TSV resistance with a relative error of no more than 18.4%, and obtain a TSV conductance of less than 100 ${\mu \Omega }^{{-1}}$ with a relative error of no more than 13%.

Biomass gasification is an attractive option for producing high-quality syngas (H2+CO) due to its environmental advantages and economic benefits. However, due to some technical problems such as tar formation, biomass gasification has not yet been able to achieve its purpose. The purpose of this work was to study the catalytic activity of coal-bottom ash for fuel gas production and tar elimination. Effect of gasification parameters including reaction temperature (700–900 °C), equivalence ratio, EQR (0.15–0.3) and steam-to-biomass ratio, SBR (0.34–1.02) and catalyst loading (5.0–13 wt %) on gas distribution, lower heating value (LHV) of gas steam, tar content, gas yield and H2/CO ratio was studied. The tar content remarkably decreased from 3.81 g/Nm3 to 0.97 g/Nm3 by increasing char-bottom ash from 5.0 wt% to 13.0 wt%. H2/CO significantly increased from 1.12 to 1.54 as the char-bottom ash content in the fuel increased from 5.0 wt% to 13.0 wt%.

In noisy evolutionary optimization, sampling is a common strategy to deal with noise, which evaluates the fitness of a solution multiple times (called sample size) independently and then uses the average to approximate the true fitness. Previous studies mainly focused on the empirical design of efficient sampling strategies, and the few theoretical analyses mainly proved the effectiveness of sampling with a fixed sample size in some situations. There are many fundamental theoretical issues to be addressed. In this paper, we first investigate the effect of sample size. By analyzing the (1+1)-EA on noisy LeadingOnes, we show that as the sample size increases, the running time can reduce from exponential to polynomial, but then return to exponential. This discloses that a proper sample size is crucial in practice. Then, we investigate what other strategies can work when sampling with any fixed sample size fails. By two illustrative examples, we prove that using parent populations can be better, and if using parent populations is also ineffective, adaptive sampling (i.e., sampling with an adaptive sample size) can work.

At present, the primary technical deterrent to the use of strawberry harvesting robots is the low harvest rate, and there is a need to improve the accuracy and real-time performance of the localization algorithms to detect the picking point on the strawberry stem. The pose estimation of the fruit target (the direction of the fruit axis) can improve the accuracy of the localization algorithm. This study proposes a novel harvesting robot for the ridge-planted strawberries as well as a fruit pose estimator called rotated YOLO (R-YOLO), which significantly improves the localization precision of the picking points. First, the lightweight network Mobilenet-V1 was used to replace the convolution neural network as the backbone network for feature extraction. The simplified network structure substantially increased the operating speed. Second, the rotation angle parameter $\alpha $ was used to label the training set and set the anchors; the rotation of the bounding boxes of the target fruits was predicted using logistic regression with the rotated anchors. The test results of a set of 100 strawberry images showed that the proposed model's average recognition rate to be 94.43% and the recall rate to be 93.46%. Eighteen frames per second (FPS) were processed on the embedded controller of the robot, demonstrating good real-time performance. Compared with several other target detection methods used for the fruit harvesting robots, the proposed model exhibited better performance in terms of real-time detection and localization accuracy of the picking points. Field test results showed that the harvesting success rate reached 84.35% in modified situations. The results of this study provide technical support for improving the target detection of the embedded controller of harvesting robots.

Dendritic neuron model (DNM) that utilizes a single dendritic neuron to emulate the information processing in human brains has been successfully applied to classification, approximation, and prediction fields. However, it is still a challenging task to improve the performance for forecasting the time series with outliers. A time series is a collection of data ordered in time. An outlier in time series as an input can affect prediction performance. Thus, in this study, we first propose a novel median dendritic neuron model (MDNM) for prediction to deal with time series with outliers. In MDNM, DNM and median function are combined to achieve better forecasting results. Moreover, states of matter search (SMS) algorithm is used to train MDNM. Twenty real time series and a benchmark time series are adopted to verify the performance of MDNM. The comparison between the MDNM and DNM suggests that MDNM is effective for forecasting time series with outliers.

As an important component of music information retrieval, music genre classification attracts great attentions these years. Benefitting from the outstanding performance of deep neural networks in computer vision, some researchers apply CNN on music genre classification tasks with audio spectrograms as input instead, which has similarities with RGB images. These methods are based on a latent assumption that spectrums with different temporal steps have equal importance. However, it goes against the theory of processing bottleneck in psychology as well as our observation from audio spectrograms. By considering the differences of spectrums, we propose a new model incorporating with attention mechanism based on Bidirectional Recurrent Neural Network. Furthermore, two attention-based models (serial attention and parallelized attention) are implemented in this paper. Comparing with serial attention, parallelized attention is more flexible and gets better results in our experiments. Especially, the CNN-based parallelized attention models with taking STFT spectrograms as input outperform the previous work.

Gravitational search algorithm (GSA) inspired from physics emulates gravitational forces to guide particles' search. It has been successfully applied to diverse optimization problems. However, its search performance is limited by its inherent mechanism where gravitational constant plays an important role in gravitational forces among particles. To improve it, this paper uses chaotic neural oscillators to adjust its gravitational constant, named GSA-CNO. Chaotic neural oscillators can generate various chaotic states according to their parameter settings. Thus, we select four kinds of chaotic neural oscillators to form distinctive chaotic characteristics. Experimental results show that chaotic neural oscillators effectively tune the gravitational constant such that GSA-CNO has good performance and stability against four GSA variants on functions. Three real-world optimization problems demonstrate the promising practicality of GSA-CNO.