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As the continuous expansion of robot grinding objects, the traditional manual teaching and offline programming methods can no longer meet people’s requirements for grinding trajectories. With the help of a 3D scanner, model data can be acquired quickly and accurately, which facilitates trajectory planning and effective grinding for workpieces. In this paper, a point cloud hole repair method is proposed based on B-spline surface. It integrates point cloud segmentation, feature point removal, smoothing and hole repair, aiming to solve the hole problem in steel helmet scanning. In addition, to generate a uniform grinding trajectory, a planning method is proposed based on an improved B-spline curve. This method combines curve homogenization with equidistant offset and uses k-nearest neighbor search. Iteratively identify and eliminate unreliable point data to determine the best point of contact. The effectiveness of the two methods is verified by trajectory planning on three types of steel helmets.

Based on applying the hybrid metal walls and half-wavelength diagonal open-slots, a four-port square patch antenna (SPA) having high port isolation (>20 dB) and low radiation correlation (envelope correlation coefficients < 0.01) for 2.4/5/6 GHz (2.4-2.5/5.15-7.125 GHz) WiFi-6E $4\times 4$ MIMO access-point application is presented. The four-port SPA is an all-metal structure with a small-size square patch (50 mm $\times50$ mm, about $0.41\lambda \times 0.41\lambda $ at 2.45 GHz) mounted 11 mm above a ground plane. Between the square patch and the ground plane, the hybrid metal walls consisting of an outer shorting metal wall and an inner gap-coupled metal wall are placed along the patch’s two centerlines to create four isolated resonant quadrants for the four ports. Each resonant quadrant can support the TM $_{\mathrm {1/2,1/2}}$ mode or the TM $_{\mathrm {1/2,1/2}}$ -like mode in the 2.4 GHz and 5/6 GHz bands. Owing to the inner gap-coupled metal wall, enhanced port isolation between two adjacent ports can be obtained. Additionally, enhanced port isolation between two opposite ports is obtained by embedding two half-wavelength open-slots (length about $0.5\lambda $ at 2.45 GHz) along the patch’s two diagonal lines. The decoupling hybrid metal walls and half-wavelength diagonal open-slots together enhance the port isolation of the four ports to be larger than 20 dB over the 2.4 GHz and 5/6 GHz bands. The antenna structure and operation principle of the proposed four-port SPA are described. Experimental results of the fabricated prototype are also presented.

Currently, the surface topology at large domestic airports with increasing traffic flow is becoming more complex. Taxiing conflicts grow as well. This paper proposed a strategy based on aircraft priority to solve the problem. Under the premise of ensuring aircraft priority, this strategy resolved the problem reasonably and improved the safety and efficiency of airport operation. With the purpose of minimizing time cost of all aircraft, an aircraft taxiing model was built. Simulation results showed the advantages of this strategy. Compared with a common conflict resolution strategy, taxiing routes of 9 aircraft changed. The total waiting time was reduced by 50% and the running time was reduced by 43.6s. In addition, to ensure taxiing efficiency of high-priority aircraft, low-priority aircraft tended to choose more tortuous routes when choosing suboptimal alternative routes.

Carbon fiber reinforced polymer (CFRP) is an anisotropic material with outstanding tensile strength in the axial direction but low compressive strength in the radial direction. In the process of line construction, such as wire crimping and conductor clamping, radial pressure failure and slip failure easily occur. This paper applies finite element analysis (FEA) to the parameters influencing the radial stress and displacement in a CFRP core to research the failure modes of the CFRP core. The selected parameters are the interference of the CFRP core, the friction coefficient between the core and inner wedge, and the angle of the inner wedge. The finite element method is applied to analyze a CFRP core with a self-tightening clamp to find the optimum condition parameters so that the CFRP core experiences neither slip failure nor radial pressure failure. From the study, lower interference and larger friction coefficient and angle lead to lower radial stress. The interference has the largest effect on the mitigation of radial pressure failure. For larger interference, a larger friction coefficient and angle mitigate slip failure. According to the analytic results, an interference between 0.02 mm and 0.025 mm and an angle larger than 3°, coupled with a friction coefficient larger than 0.3, best stop slip failure and radial pressure failure.

During the long-term operation of high-voltage cross-linked polyethylene (XLPE) cables, the cable will gradually deteriorate under the effect of various aging factors. Thermal aging is one of the key factors directly related to the long-term operation reliability of the XLPE cables. In this paper, the thermal aging samples are prepared for PDC measurement. Based on the polymer trap theory and the extended Debye model, the shape of PDC curve, depolarization charge, parameters of the extended Debye model, aging factor (A), elongation at break retention rate (EB%) and their relationships under different thermal aging degrees are analyzed. The results show that the depolarization current curve and the depolarization charge can preliminarily judge the aging condition of the XLPE, but it is not accurate enough. $R_{3}$ and $\tau _{3}$ of the branch, with the largest time constant must be the most sensitive to cable aging. With the increase of aging degree, the A shows an upward trend, and has a nonlinear relationship with EB%. Under the aging temperature of 140 °C, after 15–25 days of aging, the A suggests the samples are in the moderate to severe aging stage, and the PDC test should be performed on carried out for the cables with that have been used for 21 years of operation to evaluate the insulation aging condition. The study reveals that the PDC-based parameters can be serviced as an effective tool for the aging condition prediction of XLPE cable insulation.

Dual femtosecond laser ranging (DFLR) is an enabling absolute distance measurement technique which is advantageous of high measurement precision, fast update rate, and large unambiguous range. Peak detection, which requires repeated online solution of nonlinear curve fitting, is a key module of the DFLR system and its performance affects the accuracy and real-time of the ranging system. In addition, for long baseline measurements based on satellite-borne platforms, the DFLR system has requirements in high integration, low cost, and small size. This paper presents a peak detection implementation on a field-programmable gate array (FPGA) that employs a Broyden-Fletcher-Goldfarb-Shanno (BFGS) method to handle nonlinear curve fitting. FPGA is used to explore the possibilities of parallel architecture for the acceleration of peak detection, and realize the miniaturization of the system. The detailed architecture design of the peak detection module using BFGS method (PD-BFGS) and two hardware structures are proposed. Then, the PD-BFGS module is applied to a DFLR system and evaluated with experiments on the absolute distance measurement. The experimental results indicate that the PD-BFGS based on FPGA effectively reduces the peak detection error by 42.81%, compared with the peak detection module using Caruana's method. For the DFLR system, the ranging error is reduced by 63.63% and the real-time updating of the ranging results is guaranteed.

Uniform plant row spacing in a paddy field is a critical requirement for rice seedling transplanting, as it affects subsequent field management and the crop yield. However, current transplanters are not able to meet this requirement due to the lack of accurate navigation systems. In this study, a plant row detection algorithm was developed to serve as a navigation system of a rice transplanter. The algorithm was based on the convolutional neural network (CNN) to identify and locate rice seedlings from field images. The agglomerative hierarchical clustering (AHC) was used to group rice seedlings into seedling rows which were then used to determine the navigation parameters. The accuracies of the navigation parameters were evaluated using test images. Results showed that the CNN-based algorithm successfully detected rice seedlings from field images and generated a reference line which was used to determine navigation parameters (lateral distance and travel angle). Compared with mean absolute errors (MAE) test results, the CNN-based algorithm resulted in a deviation of 8.5 mm for the lateral distance and 0.50° for the travel angle, over the six intra-row seedling spacings tested. Relative to the test results, the CNN-based algorithm had 62% lower error for the lateral distance and 57% lower error for the travel angle when compared to a classical algorithm. These results demonstrated that the proposed algorithm had reasonably good accuracy and can be used for the rice transplanter navigation in real-time.

As large-scale direct-drive wind turbine generator set is connected to the grid, the power system will face problems such as reduced inertia and insufficient frequency modulation capability. The control of wind turbine virtual inertia is an important way to solve this problem. Therefore, this paper takes the direct-drive wind power generation system as the research object, draws lessons from the multi-time scale modeling idea, and based on the electrical torque analysis, establishes the DC voltage time model with the wind turbine virtual inertia control. Based on this, inertia damping characteristics of direct-drive wind power grid-connected system are analyzed. The results show that the dynamic characteristic parameters of the system are affected by many factors, among which the equivalent inertia coefficient of the system is mainly affected by DC capacitance, DC bus voltage and wind turbine virtual inertia control parameters. Damping coefficient is mainly affected by steady-state operating point and DC voltage proportional control parameter Kpu. The synchronization coefficient is mainly affected by steady-state operating point and DC voltage integration control parameter Kiu. The correctness of mechanism analysis of inertia damping characteristics of the whole system is verified by simulation, which provides a certain theoretical reference for inertia damping research of electronic power system.

Changes in the dissolved oxygen concentration of the ocean have important implications for marine ecosystems and global climate change. However, limited by measurement techniques, the hydrology data is not always complete. Thus, accurate prediction on marine dissolved oxygen concentration (MDOC), is a powerful supplement to the current observation data. Deep neural network is a powerful model to do the prediction, while it is usually difficult and time-consuming to tune its structure. Meanwhile, deep jointly informed neural network (DJINN) provides a user friendly method to tune the structure of neural networks. In this article, a deep learning-based model called marine deep jointly informed neural network (M-DJINN), is proposed to predict MDOC. M-DJINN improves DJINN performance via initializing the weights of neural network using a zero-mean Gaussian distribution with a variance related to the number of neurons in the neighbor layer. In M-DJINN, to get an ideal deep neural network structure, users only need to tune the tree number and max tree depth. While predicting MDOC on World Ocean Database 2013(WOD13) data with M-DJINN, the novel model proves better than DJINN on both accuracy and convergency. When the max tree depth is set to 10, the mean squared error (MSE) is reduced by 17.6% compared with DJINN. It can be concluded from the experiments that with enough training data, the performance of M-DJINN may keep improving by deepening the neural networks.

In this paper, we present a machine learning classifier which is used for pedestrian detection based on XGBoost. Our approach, the Genetic Algorithm is introduced to optimize the parameter tuning process during training an XGBoost model. In order to improve the classification accuracy, HOG and LBP features are used to describe pedestrians in a way of tandem fusion, then input into GA-XGBoost classifier proposed in this paper to form a new static image pedestrian detection algorithm. The pedestrian feature extraction and machine learning are decoupled by storing the extracted pedestrian feature as feature files in the experiment, so that training can be exacuted many times and algorithms can be camparied conveniently. Experimental show that our pedestrian detection algorithm has improved the accuracy of pedestrian detection in the static image. The Area Under the ROC Curve (AUC) value reaches 0.9913.

It is always difficult to evacuate crowds in public places like subway stations. The traditional crowd behavior simulation models often ignore two important issues in crowd evacuation: pedestrian tracking and individual differences. To solve the problem, this paper combines social force model (SFM) with deep learning into a novel pedestrian detection method. Firstly, several deep learning algorithms for pedestrian detection were compared, and the best ones for sparse and dense crowds were determined. Next, the pedestrian positions in a real video were acquired by the selected algorithms, and converted into actual coordinates in the scene. Then, the evacuation process was simulated with our method and the SFM based on these coordinates. The results show that our model output closer-to-reality results than the SFM. The research findings shed important new light on evacuation in crowded areas.

Through-wall human detection has vital and widely used applications for anti-terrorism, anti-explosion, and post-disaster relief. The through-wall human-target recognition using ultra-wideband radar-based technology was established in recent research. With the recent development of deep learning algorithms, classification algorithms have demonstrated a dynamic aptitude to learn important characteristics of the dataset by utilizing only a few sample sets. This paper focuses on studying the detection of a human target’s status behind wall in small sample conditions. In the deep learning network model, the autoencoder algorithm is chosen here to classify and identify human targets behind walls. Through automatic acquiring of the knowledge of inherent characteristics in the data, the autoencoder algorithm can extract the concise data-feature representations. Based on the autoencoder network, we add the denoising encoder and sparsity constraints to extract more efficient feature representations, thereby improving the classification and identification rates. In this paper, we classify and identify the behind-wall human-target states separately under single and multiple sensors under a small-sample condition, and then compare the results with those of other classification algorithms. The results illustrate that the use of multiple sensors is more effective than the use of a single sensor and that the adopted autoencoder algorithm enables more effective detection of human targets behind walls than other algorithms.

Paroxysmal diseases of inpatients are globally recognized as one of the top challenges in medicine. Poor clinical outcomes are primarily caused by delayed recognition, especially due to diverse clinical diagnostic criteria with complex manifestations, irregular episodes, and already overloaded clinical activities. With the proliferation of measuring devices and increased computational capabilities, cyber-physical characterization plays an increasingly important role in many domains to provide enabling technologies. This paper presents a cyber-physical system (CPS) framework to assist physicians in making earlier diagnoses of paroxysmal sympathetic hyperactivity based on existing medical knowledge. We propose a configurable diagnostic knowledge model to characterize clinical criteria to reduce domain knowledge deficiency between physicians and computer scientists. We present a component-based medical CPS framework to employ the knowledge models and integrate medical devices. Our approach aims to relieve medical staff from the heavy burden of clinical activities and to provide timely decision support. We evaluate our approach on 128 realworld clinical cases. Compared with the state-of-the-art approach, the results demonstrate that we enable early detection in 11.02% more patients and detect the condition 16.57 hours earlier on average.

The marine biological enzyme fermentation process is a time-varying, nonlinear, uncertain, and multivariable system, so high performance decoupling control is a target to seek. A decoupling control method based on a fuzzy neural-network inverse system for the marine biological enzyme fermentation process is proposed, in which the inverse system linearizing method of the nonlinear system is combined with the nonlinear identifying technology of the fuzzy neural network. First, based on the characteristics of the fermentation process, the model of a fermentation system is obtained, and the reversibility of the system is proved. Second, the ath-order inverse system of the fuzzy neural network is constructed and connected in series with the fermentation system to be a pseudo-linear compound system. Finally, closed-loop control is carried out to obtain good control performance by introducing an expert controller. The simulation experiments demonstrate that good control performance (high accuracy and good robust) can be obtained in the multivariable fermentation process based on the fuzzy neural-network inverse system.

Cryptographic hash functions have become the basis of modern network computing for identity authorization and secure computing; protocol consistency of cryptographic hash functions is one of the most important properties that affect the security and correctness of cryptographic implementations, and protocol consistency should be well proven before being applied in practice. Software verification has seen substantial application in safety-critical areas and has shown the ability to deliver better quality assurance for modern software; thus, applying software verification to a protocol consistency proof for cryptographic hash functions is a reasonable approach to prove their correctness. Verification of protocol consistency of cryptographic hash functions includes modeling of the cryptographic protocol and program analysis of the cryptographic implementation; these require a dedicated cryptographic implementation model that preserves the semantics of the code, efficient analysis of cryptographic operations on arrays and bits, and the ability to verify large-scale implementations. In this paper, we propose a fully automatic software verification framework, VeriHash, that brings software verification to protocol consistency proofs for cryptographic hash function implementations. It solves the above challenges by introducing a novel cryptographic model design for modeling the semantics of cryptographic hash function implementations, extended array theories for analysis of operations, and compositional verification for scalability. We evaluated our verification framework on two SHA-3 cryptographic hash function implementations: the winner of the NIST SHA-3 competition, Keccack; and an open-source hash program, RHash. We successfully verified the core parts of the two implementations and reproduced a bug in the published edition of RHash.

Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy, the comprehending- intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accuracy. To improve forecasting accuracy, this paper focuses on two aspects: ①proposing a novel hybrid method using Boosting algorithm and a multi-step forecast approach to improve the forecasting capacity of traditional ARMA model; ②calculating the existing error bounds of the proposed method. To validate the effectiveness of the novel hybrid method, one-year period of real data are used for test, which were collected from three operating wind farms in the east coast of Jiangsu Province, China. Meanwhile conventional ARMA model and persistence model are both used as benchmarks with which the proposed method is compared. Test results show that the proposed method achieves a more accurate forecast.