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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.

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.

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 $K_{\mathrm {pu}}$ . The synchronization coefficient is mainly affected by steady-state operating point and DC voltage integration control parameter $K_{\mathrm {iu}}$ . 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.

Due to the characteristics of uncertainty, multivariate, strong coupling, and non-linearity in the marine alkaline protease MP fermentation process, the realization of high-performance decoupling control has always been the goal. In this thesis, a nonlinear robust decoupling control method based on artificial bee colony algorithm and multiple least squares support vector machine inversion is proposed, which combines the inverse system method with support vector machine theory. First, the “gray box” dynamics model of marine alkaline protease MP fermentation process is developed based on the material balance relationship of the fermentation process, and the existence of the inverse system is analyzed. Second, the inverse model is constructed off-line using least squares support vector machine, and the inverse model is corrected on-line by artificial bee colony algorithm according to the model deviation. Finally, the inverse model is connected in series with the original system to form a combining pseudo-linear system, and according to the characteristics of the pseudo-linear system, the internal model controller is introduced to perform closed-loop control on the system. The simulation results show that the control method can realize dynamic decoupling control of marine alkaline protease fermentation process, and the system has strong parameter robustness and good anti-interference ability.

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 real-world 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.

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.