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The thermal stress imbalance is a challenge for the reliability and in-service lifetime of modular multilevel converters, especially in traction drive applications. The finite control set model predictive control (FCS-MPC) can offer an effective solution to this issue, but it is faced with two difficulties, that is the weight allocation of electrical and thermal variables with different natures in control objectives functions, and dynamic weighting factor adjustment during the switching of variable operations. Therefore, an electro-thermal performance-based FCS-MPC (ETP-FCS-MPC) for dynamic thermal balance control is proposed, which can not only achieve thermal stress balance in converters with more intuitive and time saving ways, but also offer dynamic compromise schemes between the electrical and thermal performance of converters in the speed switching process. First of all, dynamic electrical and thermal models of converters are presented. Then, the absolute error-based electrical control objective and the energy loss variance-based thermal control objective are designed. Moreover, a performance-based normalized control objective function and an outer loop control feedback-based dynamic weighting factor are proposed. Finally, HIL-based test is carried out to verify the effectiveness of the proposed method, the test results show the merits of the proposed method.

This paper aims to address the problem of poor diversity and convergence for traditional evolutionary algorithms in solving multi-objective optimization problems and proposes a cellular differential evolutionary algorithm with double-stage external population-leading. This algorithm divides the maintenance of external population diversity into double stages and introduces new disturbance in the mutation operation to avoid the algorithm falling into a local optimum. In the first stage, the external population is maintained according to the rank and k-nearest neighbor distance. The second stage adopts the external population retention strategy of multi-objective cellular differential algorithm that only retains non-dominated solutions. In both stages, the external population has complete feedback, which means the solutions from the external population randomly replace existing individuals in the two-dimensional grid population after every iteration. Tests on fifteen benchmark functions show that the new algorithm can obtain more uniform Pareto front and competitive convergence results than the other four typical algorithms. Finally, the feasibility and effectiveness of this algorithm are verified by the case study of cycloid speed reducer.

After tertiary recovery from the oilfields, improving the production of the remaining hydrocarbon is always challenging. To significantly improve oil recovery, a heterogeneous composite flooding system has been developed with preformed particle gels (PPG) and polymers according to the technical approach of plugging and flooding combination. In addition, an oil saturation monitoring device and a large-scale 3D physical model were designed to better evaluate the performance of the technique. The evaluation results show that the viscosity, stability, and elasticity of the heterogeneous composite flooding system are better than the single polymer system. In addition, both systems exhibit pseudoplastic fluid characteristics and follow the principle of shear thinning. The results of seepage experiments showed that PPG migrates alternately in porous media in the manner of “piling plugging-pressure increasing-deformation migration”. The heterogeneous composite system can migrate to the depths of the oil layer, which improves the injection profile. In the visualization experiment, the heterogeneous composite system preferentially flowed into the high-permeability layer, which increased the seepage resistance and forced the subsequent fluid to flow into the medium and low permeability layers. The average saturation of the high, medium, and low permeability layers decreased by 4.74%, 9.51%, and 17.12%, respectively, and the recovery factor was further improved by 13.56% after the polymer flooding.

Additional file 1.

Though provenance has long played a major role in the context of art and archaeology (in terms of lineage or pedigree), more recently it has become more important for data in various sectors such as finance and medicine. It is not just about the origin or creator of data but also what sort of operations have been performed by whom and in what context, especially when it comes to security and privacy. As provenance research on security stays at its initial stage,some open problems and research challenges have been identified for provenance research, specifically in terms of security issues. Access control involving provenance is treated as a primary security issue, and is the main area to which we are trying to contribute. Integrity and non-repudiation should also be ensured for provenance. This thesis mainly focuses on preserving the security of provenance as well as utilising provenance as conditions to control proper access to data. The contributions of the thesis are illustrated as follows: We propose three frameworks of access control policies on provenance. The Partition-based Access Control Policy Language on Provenance is tailored based on our extended provenance model (OPM+). The fine-grained policies determine access for provenance, based on our defined provenance partitions instead of whole provenance graphs. Moreover, Algorithms for merging policy results and transferring provenance graphs according to policy results are provided as well. Following this, The Provenance-based Access Control policies employs provenance partitions as conditions to evaluate accessibility for data. Our proposed policies distinguish different types of attributes extracted from provenance, where the result of each policy is a value in the "four-valued" decisions set. Policy algebras for the "four-valued" decision set are tailored accordingly. Further, to provide a comprehensive scope for access control policies involving provenance, Purpose-based Access Policies on provenance are proposed. This defines allowed/prohibited access purposes for data based on attributes in provenance. A series of corresponding internal and external policy algebras is provided to merge purpose sets. We also provide two cryptographic schemes to implement access control policies involving provenance. One scheme implements Provenance-based file Classification Policies which sort files based on given provenance partitions (keywords) from their provenance. The scheme enables to search given provenance partitions in the ciphertext of provenance as well as check authentication of users. The other scheme is derived from attribute-based access control encryption schemes. It allows data owners to encrypt data based on Provenance-based Access Control policies.

This paper proposes a pressure-type generator that collects human mechanical energy by stepping, a prototype is finally designed and manufactured. The proposed pressure-type generator has a volume of 82.8 c m 3 when subjected to pressure. The average output power can reach to 97 mW when under the walking speed of 4 km/h. As a result we can get the power density of 1.17 m W / c m 3 , higher than most electromagnetically powered devices. Unlike some other generators, this pressure-type generator uses magnets as the recovery device, making the durability of the device better. And also, the wearable design is completely made to let the device can be comfortable used. Finally, we did a series of tests to prove that the device can have great power output performance in the low frequency environment of the human foot movement.

This paper presents the design, implementation, and characterization of a hardware platform applicable to a self-powered wireless sensor network (WSN) node. Its primary design objective is to devise a hybrid energy harvesting system to extend the operational lifetime of WSN node after they are deployed in the field environment. Besides the implementation of optimal components (microcontroller, sensor, radio frequency (RF) transceiver, and others) to achieve the lowest power consumption, it is also necessary to consider the sources of energy instead of the frequent recharging or replacement of batteries. Therefore, the platform incorporates a multisource energy harvesting module to collect energy from the surrounding environment, including wind, solar radiation, and thermal energy. The platform also includes an energy storage module through a super-capacitor, RF transceiver module, and the primary microcontroller module. Experimental results showed that the WSN node system with appropriate integration will reserve sufficient energy and meet the long-term power supply requirements of the WSN node without batteries in the field environment. The experimental results and empirical measurements taken over nine days demonstrated that the average daily generating capacity was 7805.09 J, which is far more than the energy consumption of the WSN node (about 2972.88 J).

Detailed experiment description and more measurement results

We proposed a novel Raman distributed temperature sensor (RDTS) based on intensity-modulated pulse compression technique. The spatial resolution and temperature resolution are 1.6 m and 1.8 °C at a range of 24 km.

Inflow and outflow processes are common phenomena in daily life. Many types of research have been conducted to study the features of the outflow process, especially in scenarios with a single room or a straight corridor. A few scholars have paid attention to the movement characteristics of pedestrian inflow. Further explorations are still under great demand. In this contribution, a set of pre-conducted experiments are used to analyze the characteristics of the pedestrian inflow process with inactive persons. In these experiments, inactive persons were required to randomly cease within the room, leading to intensive detour behavior of pedestrians. The characteristics are carefully investigated using gradient analysis and curl analysis. To mimic the aforementioned inflow process, static global field is constructed to heuristically navigate a social force based microscopic model. The proposed model can reproduce the self-organized phenomena in the experiments. Our work can help understand the field feature of the pedestrian inflow process with inactive persons. High chaos level areas can be marked out providing practical information for managers.

Physically vitrifying single-element metallic glass requires ultrahigh cooling rates, which are still unachievable for most of the closest-packed metals. Here, we report a facile synthetic strategy for creating mono-atomic palladium metallic glass nanoparticles with a purity of 99.35 +/- 0.23 at% from palladium-silicon liquid droplets using a cooling rate below 1000 K/s. In-situ environmental transmission electron microscopy directly detected the leaching of silicon. Further hydrogen absorption experiment showed that this palladium metallic glass expanded little upon hydrogen uptake, exhibiting a great potential application for hydrogen separation. Our results provide insight into the formation of mono-atomic metallic glass at nanoscale., Comment: Nano Res. (2022)

On one hand, aiming at the prominent problem of improving the reliability of Faster-than-Nyquist (FTN) wireless transmission, abandoning the channel estimation, we propose the Radial Basis Function Least Squares Support Vector Machine (RBF LSSVM) algorithm based on Intelligent Signal Processing for FTN equalization and form FTN time domain transverse filter equalizer. The model of FTN wireless communication system based on equalization of LSSVM algorithm is established by adding 50-bit training sample sequence module. On the other hand, in the FTN transmission BPSK modulation system receiving terminal, we propose joint research equalization of LSSVM algorithm and adaptive channel decoding scheme of Chase algorithm, to improve the reliable transmission performance of FTN wireless communication. The threshold value is 20, and the adaptive 2-D Turbo Product Codes (TPC) encoding and decoding is simulated by four iterations. The BER performance of FTN wireless communication combined with LSSVM algorithm and TPC adaptive decoding is simulated.

This paper introduces an analysis and calculation method to determine voltage bands, which are voltage regions of possible global maximum power points (GMPPs) for stand-alone photovoltaic (PV) systems under non-uniform irradiance conditions. Non-repeated irradiance cases is analyzed to obtain GMPPs according to electrical characteristics of a series-parallel-connected PV array. A clustering method is used to process these points to build voltage bands. Slope coefficients compensate for the influence of the temperature. Simulation results shows the influence of the PV array configuration and the PV module type on the voltage band method. The proposed method can be applied in designing global maximum power point tracking methods and analyzing the approximate regions of GMPPs.

It is a crucial mechanism of access control to determine that data can only be accessed for allowed purposes. To achieve this mechanism, we propose purpose-based access policies in this paper. Different from provenance-based policies that determine if a piece of data can be accessed or not, purpose-based access policies determines for what purposes can data be accessed. Particularly, the purposes can be classified as different sensitivity levels. For the first time, We tailor policy algebras to include internal and external policy operators for hierarchical purposes, in order to merge purpose sets generated by individual policies. We also created external policy algebras to merge policies from multi-parties. With different types' testing experiments, our model is proved to be feasible and practical.

To deal with various datasets over different complexity, this paper presents an self-adaptive learning model that combines the proposed Dynamic Connected Neural Decision Networks (DNDN) and a new pruning method--Dynamic Soft Pruning (DSP). DNDN is a combination of random forests and deep neural networks that enjoys both the advantages of strong classification capability of tree-like structure and representation learning capability of network structure. Based on Deep Neural Decision Forests (DNDF), this paper adopts an end-to-end training approach by representing the classification distribution with multiple randomly initialized softmax layers, which further allows an ensemble of multiple random forests attached to layers of neural network with different depth. We also propose a soft pruning method DSP to reduce the redundant connections of the network adaptively to avoid over-fitting simple dataset. The model demonstrates no performance loss compared with unpruned models and even higher robustness over different data and feature distribution. Extensive experiments on different datasets demonstrate the superiority of the proposed model over other popular algorithms in solving classification tasks.

Data security and privacy is an important but challenging problem in cloud computing. One of the security concerns from cloud users is how to efficiently verify the integrity of their data stored on the cloud server. Third Party Auditing (TPA) is a new technique proposed in recent years to achieve this goal. In a recent paper (IEEE Transactions on Computers 62(2): 362-375 (2013)), Wang et al. proposed a highly efficient and scalable TPA protocol and also a Zero Knowledge Public Auditing protocol which can prevent offline guessing attacks. However, in this paper, we point out several security weaknesses in Wang et al's protocols: first, we show that an attacker can arbitrarily modify the cloud data without being detected by the auditor in the integrity checking process, and the attacker can achieve this goal even without knowing the content of the cloud data or any verification metadata maintained by the cloud server; secondly, we show that the Zero Knowledge Public Auditing protocol cannot achieve its design goal, that is to prevent offline guessing attacks., Comment: arXiv admin note: text overlap with arXiv:1912.00446

With the wide application of cloud storage, cloud security has become a crucial concern. Related works have addressed security issues such as data confidentiality and integrity, which ensure that the remotely stored data are well maintained by the cloud. However, how to define zero-knowledge proof algorithms for stored data integrity check has not been formally defined and investigated. We believe that it is important that the cloud server is unable to reveal any useful information about the stored data. In this paper, we introduce a novel definition of data privacy for integrity checks, which describes very high security of a zero-knowledge proof. We found that all other existing remote integrity proofs do not capture this feature. We provide a comprehensive study of data privacy and an integrity check algorithm that captures data integrity, confidentiality, privacy, and soundness.

In the regression problem, L1 and L2 are the most commonly used loss functions, which produce mean predictions with different biases. However, the predictions are neither robust nor adequate enough since they only capture a few conditional distributions instead of the whole distribution, especially for small datasets. To address this problem, we proposed arbitrary quantile modeling to regulate the prediction, which achieved better performance compared to traditional loss functions. More specifically, a new distribution regression method, Deep Distribution Regression (DDR), is proposed to estimate arbitrary quantiles of the response variable. Our DDR method consists of two models: a Q model, which predicts the corresponding value for arbitrary quantile, and an F model, which predicts the corresponding quantile for arbitrary value. Furthermore, the duality between Q and F models enables us to design a novel loss function for joint training and perform a dual inference mechanism. Our experiments demonstrate that our DDR-joint and DDR-disjoint methods outperform previous methods such as AdaBoost, random forest, LightGBM, and neural networks both in terms of mean and quantile prediction.

Honeycomb sandwich materials have been widely used in aerospace industry as secondary structural materials or interior panels. Recently the research of full thermoplastic honeycomb sandwich materials has been of interest due to their recyclability. In their production, they usually demand an adhesive-free process, namely fusion bonding, to connect thermoplastic honeycomb core and face sheets. It is a heat induced process where a parameter of temperature should be well controlled to guarantee the product quality. This article presents a mathematical model of heat conduction to analyze the transient temperature distribution from heating tools towards inner part of the core under fusion bonding conditions. In order to simplify the complexity of 3D honeycomb geometry, a homogenization method is used to obtain average thermal properties of the honeycomb along the major heat flux direction. The model is validated by comparing with the results of in-situ temperature measurement during fusion bonding. The presented model can also be applied to analyzing general out-of-plane heat conduction through honeycomb sandwich structures made from other materials.

This paper studies a kind of sliding mode fault tolerant control method which used in aircraft engine sensor failure occurs. Firstly, reconstruct the fault signal of the fault sensor by using state observer. Secondly, design the sliding mode controller which can realize the processing for sensor fault, and proving the asymptotic stability of the system based on the Lyapunov stability theory. Finally, prove the aero-engine control system based on sliding mode has a certain fault tolerance capability through the simulation. In order to verify the effect of the fault tolerance by sliding mode controller, also introduced the PID controller and the H ∞ controller in the same fault signal to fault tolerant control, results are compared to illustrate that the sliding mode fault tolerant controller could improve the flight performance in the sensor fault cases.