Search

In recent years, Service Oriented Architecture (SOA) has been increasingly adopted to develop distributed applications in the context of the Internet. To develop reliable SOA-based applications, an important issue is how to ensure the quality of web services. In this paper, we propose a dynamic random testing (DRT) technique for web services, which is an improvement over the widely-practiced random testing (RT) and partition testing (PT). We examine key issues when adapting DRT to the context of SOA, including a framework, guidelines for parameter settings, and a prototype for such an adaptation. Empirical studies are reported where DRT is used to test three real-life web services, and mutation analysis is employed to measure the effectiveness. Our experimental results show that, compared with the three baseline techniques, RT, Adaptive Testing (AT) and Random Partition Testing (RPT), DRT demonstrates higher fault-detection effectiveness with a lower test case selection overhead. Furthermore, the theoretical guidelines of parameter setting for DRT are confirmed to be effective. The proposed DRT and the prototype provide an effective and efficient approach for testing web services. IEEE

The replacement of small cations with bulkier organic cations containing long alkyl chains or benzene rings to form a thin two-dimensional (2D) perovskite passivation layer on three-dimensional (3D) perovskite (2D/3D) has become a promising strategy for improving both the efficiency and stability of perovskite solar cells (PSCs). The 2D layer defines the interfacial chemistry and physics at the 2D/3D bilayer and endows the 2D/3D structure with better chemical and thermal stability. Herein, 2D/3D (CF3-PEA)2FA0.85MA0.15Pb2I7/FA0.85MA0.15PbI3 planar heterojunction perovskite was produced using a facile interfacial ion exchange process. The 2D (CF3-PEA)2FA0.85MA0.15Pb2I7 capping layer can not only passivate the FA0.85MA0.15PbI3 film but also act as super-hydrophobic layer to inhibit water diffusion and significantly enhance the stability. The 2D capping layer can also establish a unique graded band structure at the perovskite/Spiro-OMeTAD interface and lead to p-type doping for Spiro-OMeTAD layer which is beneficial for efficient charge transport. Optimized PSCs based on this 2D/3D heterojunction yield a champion power conversion efficiency (PCE) of 23.1% and improved stability. The device maintains 84% output for 2400 h aging under ambient environmental conditions without encapsulation, and maintains 81% for 200 h under illumination with encapsulation. This work will inspire the design of more fluorinated 2D perovskite interfaces for advanced photovoltaics and beyond.

The hole transport material (HTM) plays an extremely important role to determine the power conversion efficiency (PCE) and the stability of perovskite solar cells (PSCs). Herein, we report an effective strategy to improve the performance of HTMs by introducing −CF3 groups via the rational decorative mode. Upon direct attachment or nonconjugated alkoxyl bridging of −CF3 groups on the terminal diphenylamines, the resulting molecular HTMs, i.e., 2,7-BCzA4CF3 and 2,7-BCzA4OCCF3, show distinct properties. Compared with 2,7-BCzA4CF3, the nonconjugated alkoxyl bridging −CF3 group-based 2,7-BCzA4OCCF3 exhibits better thermal stability, hydrophobicity, and a dramatically upgraded hole mobility by 135.7-fold of magnitude to 1.71 × 10−4 cm2 V−1 S−1. The PSCs with 2,7-BCzA4OCCF3 as HTM exhibit an PCE of up to 20.53% and excellent long-term stability, maintaining 92.57% of their performance for 30 days in air with humidity of 30% without encapsulation. This work provides beneficial guidelines for the design of new HTMs for efficient and stable PSCs.

In a non-emulative precast shear wall structures, joints are the key to influence the overall mechanical performance under seismic actions. While wet joints are widely used in the precast reinforced concrete shear wall structure, they present problems such as collision of steel bars, inconsistency of the post-casting area and importantly, they cannot achieve the best potential of precast concrete structures. Hence, this paper proposes a new dry joint, which is a bolted C-shaped steel plate for the horizontal joint of precast shear walls. Four identical sub-assembly precast shear wall panels bolted with four distinct configurations of steel plates (plain, X-stiffeners, horizontal slots and vertical slots) were tested under reversed cyclic loading. It was discovered that the bolted steel plate with horizontal slots could improve the ductility and energy dissipation of the joint. Numerical models were calibrated by benchmarking with the experimental results. Through a systematic parametric study on the number and spacing of slots, thickness of steel plates and number of bolts, the results indicated that the number of bolts is the critical parameter that will affect initial stiffness, peak strength capacity and ductility of the proposed joint.

Flue gas desulphurized slag was collected as the raw material and purified using the multiple steps of sour purification procedures, which was further prepared as CaSO4 oxygen carrier (OC). Reaction characteristics of the purified desulphurized slag OC with the selected lignite as well as release of the different gaseous sulfur species from the CaSO4 side reactions were investigated in a lab-scale fixed bed reactor. Several main influencing factors, including reaction temperature, OC excess number ϕ and the cycle numbers, were focused. The experimental results indicated that the purified CaSO4 OC was of high reactivity and promising to be applied in the coal-fueled chemical looping combustion process as OC. Furthermore, according to the pros and cons of the three influencing factors on the carbon conversion versus the gaseous sulfur release, the optimized reaction conditions were determined as 900°C and the OC excess number around 1.0. Finally, under this optimized reaction condition, the effect of the cycle numbers was evaluated and found that the continuous release of gaseous sulfur from the CaSO4 side reactions deteriorated the reactivity of the purified CaSO4 OC and its reaction stability was diminished with the five cycles.

With the emergence of a large number of multi-core systems, many 3D routing schemes have been developed for network-on-chips (NoCs) in order to obtain low overhead and high-performance. Nevertheless, it is difficult to possess these characteristics for contemporary 3D routing algorithms. This study presents a new routing algorithm for 3D stacked NoCs based on the repetitive turn concept. The authors propose the high-performance minimum pressure turn model (MPTM) routing algorithm that can be applied to a 3D case. Repetitive prohibited turns are spread in the row and column of the planes and vertical direction in the MPTM routing algorithm with no virtual channels. Besides, the MPTM routing algorithm has minimum routing pressure by exploring the whole 3D space. Considering network average latency and throughput, the results acquired show that the MPTM scheme improves performance over existing work.

Flight testbeds with multiple unmanned aerial vehicles (UAVs) are especially important to support research on multi-vehicle-related algorithms. The existing platforms usually lack a generic and complete solution allowing for software and hardware design. For such a purpose, this paper presents the design and implementation of a comprehensive multi-camera-based testbed for 3-D tracking and control of UAVs. First, the testbed software consists of a multi-camera system and a ground control system, which performs image processing, camera calibration, 3-D reconstruction, pose estimation, and motion control. In the multi-camera system, the positions and orientations of UAVs are first reconstructed by using epipolar geometric constraints and triangulation methods and then filtered by an extended Kalman filter (EKF). In the ground control system, a classical proportional–derivative (PD) controller is designed to receive the navigation data from the multi-camera system and then generates control commands to the target vehicles. Then, the testbed hardware employs smart cameras with field-programmable gate array (FPGA) modules to allow for real-time computation at a frame rate of 100 Hz. Lightweight quadcopter Parrot Bebop drones are chosen as the target UAVs, which does not require any modification to the hardware. Artificial infrared reflective markers are asymmetrically mounted on target vehicles and observed by multiple infrared cameras located around the flight region. Finally, extensive experiments are performed to demonstrate that the proposed testbed is a comprehensive and complete platform with good scalability applicable for research on a variety of advanced guidance, navigation, and control algorithms.

Designing a controller for the docking maneuver in Probe-Drogue Refueling (PDR) is an important but challenging task, due to the complex system model and the high precision requirement. In order to overcome the disadvantage of only feedback control, a feedforward control scheme known as Iterative Learning Control (ILC) is adopted in this paper. First, Additive State Decomposition (ASD) is used to address the tight coupling of input saturation, nonlinearity and the property of NonMinimum Phase (NMP) by separating these features into two subsystems (a primary system and a secondary system). After system decomposition, an adjoint-type ILC is applied to the Linear Time-Invariant (LTI) primary system with NMP to achieve entire output trajectory tracking, whereas state feedback is used to stabilize the secondary system with input saturation. The two controllers designed for the two subsystems can be combined to achieve the original control goal of the PDR system. Furthermore, to compensate for the receiver-independent uncertainties, a correction action is proposed by using the terminal docking error, which can lead to a smaller docking error at the docking moment. Simulation tests have been carried out to demonstrate the performance of the proposed control method, which has some advantages over the traditional derivative-type ILC and adjoint-type ILC in the docking control of PDR. Keywords: Additive state decomposition, Adjoint operator, Docking control, Iterative learning control, Probe-drogue refueling, Stable inversion

This paper presents an empirical study of 5741 bug reports for the Linux kernel from an evolutionary perspective, with the aim of obtaining a deep understanding of bug characteristics in the Linux operating system. Bug classification is performed based on the fault triggering conditions, followed by an analysis of the proportions and evolution of the bug types as well as comparisons among versions, products, and repair locations. In addition, an analysis of regression bugs and the relationship between the types of bugs and the time needed to fix them are presented. Moreover, a procedure for the analysis of bug type characteristics based on complex network metrics is proposed, and four network metrics, i.e., degree, clustering coefficient, betweenness, and closeness, are utilized to further investigate the relationship between bug types and software metrics. In this paper, 22 interesting findings based on the empirical results are revealed, and guidance based on these findings is provided for developers and users.

As innovative nanostructured catalysts, palladium nanoparticles supported on carbon-coated zinc oxide nanowires (PdNPs/C&ZnO-NWs) are widely applied in industrial thermal catalysis, and therefore the real-time insights into their thermal stability and microstructural reconstruction at thermal reaction conditions will greatly extend our knowledge of their physicochemical properties and provide valuable guidance for the applications and designs of future nanocatalysts. The in-situ transmission electron microscopy (TEM) studies revealed the high-temperature resistance to PdNPs aggregations with aids of carbon-coated low-dimensional nanostructures C&ZnO-NWs and elucidated ripening dynamics of PdNPs and the surface reconstruction of C&ZnO-NWs. The operando analysis of PdNPs/C&ZnO-NWs was executed in real-time under working-temperature situations. The aggregations of PdNPs were not observed until reaching a high working temperature of 300 °C and carbon-coated layers could functionally prevent PdNPs from sintering even when ZnO-NWs experienced melting at 500 °C. The time-lapsed TEM investigation proved that the ripening dynamics and time-dependent revolution of PdNPs size are in accordance with the Ostwald ripening process. The subsequent atomic-scale observation revealed the surface reconstruction of ZnO-NWs in the form of ZnO encapsulation over PdNPs and carbon-coated layers during the thermal process, which showed the microscopic evidence of potential deactivation of ZnO-NW-based nanocatalysts in thermal reactions.

Currently, the time of endurance of electric multicopters is still too short for many mission requirements, and optimizing the efficiency of the propulsion system is considered as an effective way to overcome this problem. This paper proposes a practical method to help designers quickly select the optimal products of the propulsion system to maximize the multicopter efficiency under the desired flight condition. First, the modeling methods for the components of the propulsion system are studied respectively to describe the optimization problem mathematically. Second, methods are proposed to find optimal motor and propeller combination with the maximum thrust efficiency according to the given design requirements. Finally, factors that may affect the hovering time of multicopters are analyzed, and the optimal battery parameters are obtained for maximizing the multicopter endurance. Experiments and simulations are performed to demonstrate the effectiveness and practicability of the proposed method.

A particularly good software testing strategy is to achieve the underlying testing goal while solving the problems of tradeoffs between testing effectiveness and efficiency. To improve the fault detection effectiveness of software testing, the principle of feedback control theory was adopted, which motivated the proposal of dynamic random testing (DRT). The main idea behind DRT is using the testing results to guide the test case selection to increase the selection probabilities of the subdomains with higher fault detection rates. Previous works show that DRT strategy can achieve better effectiveness than random testing strategy and random partition testing strategy, and has significantly lower computational costs than adaptive testing strategy. However, the essential factors that affect the performance of DRT, i.e., adjusting parameters, initial profile, and test case classification have not been thoroughly investigated. Besides, some experimental assumptions are inconsistent with real scenarios. Therefore, this paper gives a series of investigations on DRT with a set of practical subject programs. More specifically, the effectiveness and efficiency of DRT are presented, and the extended experiments on DRT with relevant factors are conducted. The results indicate that the effectiveness of DRT is robust to different initial profiles and affected noticeably by the adjusting parameter settings and test case classification methods.

There exist at least two viewpoints on the internal model principle (IMP), namely the cancelation viewpoint and the geometrical viewpoint. However, neither of them is applicable to repetitive contr...

Doping of nanomaterials has become a versatile approach to tailoring their physical and chemical properties, leading to the emerging fields of solotronics and quantum-controlled catalysis. These ex...

The Android operating system (OS) is a sophisticated man-made system and is the dominant OS in the current smartphone market. Due to the accumulation of errors in the system internal state and the incremental consumption of resources, such as the Dalvik heap memory of software applications and the physical memory, software aging is observed frequently and recognized as a chronic problem of Android smartphones. To mitigate this problem, we propose a two-level software rejuvenation, with the two levels referring to software applications and the OS, in this paper. Based on this strategy, a Markov regenerative process model is constructed to evaluate the steady-state availability and to optimize the time required to trigger rejuvenation for Android smartphones. The parameters of the model, such as the degradation rate and failure rate of software applications and the Android OS, are obtained via our testing platform. Experiments on two real Android applications show that the availability of an Android smartphone increases by 10.81% and 10.18% for the two subjects in our experiments, respectively. An empirical study comparing our two-level strategy with one-level strategies (single application-level and system-level rejuvenation) further verifies the effectiveness of our approach.

The core–shell-structured Co–Z@TiO2(N2+O2) catalyst, which was prepared by subsequent pyrolysis and calcination from precursor ZIF-67 coated by amorphous TiO2, was applied in Fischer–Tropsch synthe...

This paper proposes a new algorithm to construct self-organizing radial basis function neural networks (RBFNNs) for aero-engine thrust estimation. The algorithm can not only optimize centers and network size of the RBFNN but also automatically determine the connection weights. To reduce the dimensionality of particle and speed up the optimization process, spreads of an RBFNN are randomly initialized. Its weights are dynamically derived and adjusted by the product between the Moore–Penrose inverse of the hidden layer's outputs and the desired outputs. To optimize the centers and network size of the RBFNN, a strategy named multi-Gbest is adopted. Based on all these strategies, the proposed algorithm can effectively generate self-organizing RBFNNs with high accuracy. The successful application to aero-engine thrust estimation shows the practicability and effectiveness of the proposed algorithm.

Random testing and partition testing are two major families of software testing techniques. They have been compared both theoretically and empirically in numerous studies for decades, and it has been widely acknowledged that they have their own advantages and disadvantages and that their innate characteristics are fairly complementary to each other. Some work has been conducted to develop advanced testing techniques through the integration of random testing and partition testing, attempting to preserve the advantages of both while minimizing their disadvantages. In this paper, we propose a new testing approach, adaptive partition testing , where test cases are randomly selected from some partition whose probability of being selected is adaptively adjusted along the testing process. We particularly develop two algorithms, Markov-chain based adaptive partition testing and reward-punishment based adaptive partition testing , to implement the proposed approach. The former algorithm makes use of Markov matrix to dynamically adjust the probability of a partition to be selected for conducting tests; while the latter is based on a reward and punishment mechanism. We conduct empirical studies to evaluate the performance of the proposed algorithms using ten faulty versions of three large-scale open source programs. Our experimental results show that, compared with two baseline techniques, namely random partition testing (RPT) and dynamic random testing (DRT), our algorithms deliver higher fault-detection effectiveness with lower test case selection overhead. It is demonstrated that the proposed adaptive partition testing is an effective testing approach, taking advantages of both random testing and partition testing.

In this paper, a series of Dy3+/Gd3+ codoped LaF3 (LDG) single crystals were grown by the Bridgman–Stockbarger method. The influence of non-optically-active Gd3+ ions on the luminescence of Dy3+ ions in LaF3 crystals has been explored. Dy3+/Gd3+ ions substitute for the La lattice in LaF3, and the local site symmetry of each subset of Dy3+ will be distorted from C2 to lower symmetry C1. Based on the measured low temperature excitation and emission spectra, it can be proved that Dy3+ ions occupy the same kind of spectroscopic site in Dy3+/Gd3+ codoped LaF3 crystals, and codoped Gd3+ ions do not change the spectroscopic site symmetry of Dy3+. When Gd3+ ions were codoped into the Dy3+:LaF3 crystal, Dy–Gd clusters were formed around single Dy3+ ions to isolate Dy3+ ions from each other and weakened the concentration quenching effect of Dy3+ ions. When 2 at% Dy3+ and 1 at% Gd3+ ions were doped into the LaF3 crystal, the strongest yellow luminescence was obtained under excitation at 349 nm and 453.75 nm, respectively. While doped with 2 at% Dy3+ and 2 at% Gd3+ ions, the LDG crystal exhibited the strongest mid-infrared fluorescence emission intensity. This concludes that the LDG crystals are suitable for achieving a yellow laser (4F9/2 → 6H13/2 transition) pumped using a InGaN blue laser diode (LD) and a mid-infrared laser (6H13/2 → 6H15/2 transition) pumped using a 1280 nm LD.

Service-based systems resource allocation in cloud computing is a key method of meeting service requests because service request workloads and resource demands change over time. When coping with dynamic fluctuating service requests and resource demands, adaptive resource allocation to ensure the quality of service (QoS) with the lowest resource consumption becomes challenging. In cloud computing, services share the same resource pool and compete for critical resources, such as CPU and memory resources. Because services need arbitrary resource combinations, focusing on a single resource may lead to excessive or deficient resource allocations or even service request failures. Due to the shared nature of cloud computing, QoS may be impacted by interference with co-hosted services. In this paper, we propose an adaptive control approach for resource allocation that adaptively reacts to dynamic request workloads and resource demands. The multivariable control is adopted to allocate multiple resources for multiple services according to the dynamic fluctuating requests and considers the interference between co-hosted services, thereby ensuring QoS even if the resource pool is insufficient. The comparative experiments show that the proposed approach can meet service requests and can improve resource utilization regardless of whether the resource pool is sufficient.

Contraction mapping methods do not need to know about the concrete form of plant models like the Lyapunov method. This is the biggest advantage over other methods in the field of iterative learning control for example. However, it is difficult to use such a tool to analyze repetitive control systems. This paper proposes a contraction mapping method based on spectral theory to design a saturated repetitive controller for a class of nonlinear systems, where the derived necessary and sufficient condition on the spectral radius can reduce the conservatism as much as possible. The feasibility of our work is demonstrated through a robotic manipulator tracking example.

In a probe–drogue aerial refueling system, the drogue is affected not only by wind disturbances but also by strong disturbances from the tanker vortex and receiver forebody bow wave. Along with the aerodynamic disturbances acting on the receiver aircraft, it is difficult for the probe to capture the moving drogue in the docking stage. This paper studies the model of the probe–drogue aerial refueling system under aerodynamic disturbances, and proposes docking control method based on iterative learning control to compensate for the docking errors caused by aerodynamic disturbances. For receiver aircraft with different maneuverability, three control strategies are proposed to achieve a trade-off between safety and control precision. Furthermore, a practical method is proposed to predict the initial value of the learning controllers, which can significantly improve the iterative learning speed of the proposed methods. Finally, simulations demonstrate that the proposed control methods are simple and efficient for the docking control of autonomous probe–drogue aerial refueling.

Dynamically tunable ultra-narrowband perfect absorbers are important to next-generation active photonic devices. A composite structure of a graphene pair and a microcavity with Bragg mirrors are proposed for this purpose. Based on the electrically controllable doping of graphene and critical coupling of the incident light, the microcavity–graphene composite structure achieves peak absorptance higher than 99.5%, a relative peak width ( Δ λ / λ 0 ) smaller than 1.1%, and a modulation depth larger than 92.0% throughout the visible-to-mid-infrared range, surpassing other structures in comprehensive performance. By changing the number of the dielectric pairs in the Bragg mirrors, the device can become an amplitude or a spectral modulator. The results are based on the optical constants from experiment data, including the surface conductivity of graphene with relatively low mobility, so they are more useful in practical situations.

A novel two-dimensional (2D) zeolitic imidazolate framework-graphene oxide hybrid nanocomposite (ZIF-L@GO) is designed as an inorganic filler in sulfonated poly(ether ether ketone) (SPEEK). ZIF-L with unique leaf-like morphology is grown in-situ on the GO sheet in aqueous media at room temperature. The terminal imidazole linker in ZIF-L@GO and the –SO3H in SPEEK can form acid-base pairs in the membrane interface to produce low energy proton conduction highway. Benefiting from the unique structural advantage, the hybrid SP-ZIF-L@GO membranes displayed promoted physicochemical and electrochemical performances over the pure SPEEK. The SP-ZIF-L@GO-5 achieved a proton conductivity of 0.265 and 0.0364 S cm−1 at 70 °C-100% RH and 90 °C-40% RH, 1.76- and 6.24-fold higher than pure SPEEK, respectively. Meanwhile, a single cell based on SP-ZIF-L@GO-5 had an output power up to 652.82 mW cm−2 at 60 °C, 1.45 times higher than the pure SPEEK. In addition, the durability test was performed by holding open circuit voltage (OCV) for 24 h. The SP-ZIF-L@GO-5 provided better long-term stability than the pure SPEEK. These superior performance suggests a promising application in PEMFC.

It is very important for completing spectrum data in the wireless communication system. The traditional completion method mainly considered the missing completion operation of sparse data. To solve this problem, we proposed an efficient algorithm based on generative adversarial network (GAN), which can automatically mine the relationship of the data and complete the missing data accurately. The proposed method uses the sensing spectrum waterfall as the input sample of the network structure. We adopt a pre-classification module and generation module based on GAN to generate incomplete spectrum data in multiple jamming patterns. Simulation results show that the proposed algorithm can effectively complete the missing data, and its performance is better than that of the data completion algorithm without a clssifier.

Atomic lattice reconstruction and postcrack edge stacking behavior are observed during the fracture of 2D ReS2., Low-dimensional materials usually exhibit mechanical properties from those of their bulk counterparts. Here, we show in two-dimensional (2D) rhenium disulfide (ReS2) that the fracture processes are dominated by a variety of previously unidentified phenomena, which are not present in bulk materials. Through direct transmission electron microscopy observations at the atomic scale, the structures close to the brittle crack tip zones are clearly revealed. Notably, the lattice reconstructions initiated at the postcrack edges can impose additional strain on the crack tips, modifying the fracture toughness of this material. Moreover, the monatomic thickness allows the restacking of postcrack edges in the shear strain–dominated cracks, which is potentially useful for the rational design of 2D stacking contacts in atomic width. Our studies provide critical insights into the atomistic processes of fracture and unveil the origin of the brittleness in the 2D materials.

The space scientific payload rack is a multifunctional experimental platform, and the requirements of the environmental temperature index are different for diversified experimental modules inside. The air cooling system is an important part of the rack thermal control system. A new type of air cooling system with small size and flexible arrangement is proposed in this paper, that is, micro air ducts with pinhole-sized air vents. The rack physical models of new and traditional air cooling modes are established, respectively. The numerical simulation of the inner air flow is carried out by Ansys Fluent CFD software (Ansys Inc., Canonsburg, PA, USA), which verifies that compared with the traditional method, the temperature field and flow field of the new air cooling method are more uniform, and the heat sources located at the edge of the rack can also be cooled better.

This paper investigates the problem of wireless transmitter identification in the presence of malicious attacker based on radio frequency (RF) fingerprint. A robust wireless transmitter identification scheme is proposed using Generative Adversarial Networks (GAN). First, to build unique RF fingerprint of different wireless transmitter, in-phase (I) and quadrature (Q) imbalance model is introduced. Then, a classifier which consists of multiple discriminators is proposed to detect attacker and classify trusted transmitters based on RF fingerprint. Every discriminator is trained in adversarial learning framework and serves as one binary classifier to verify RF fingerprint. Wasserstein GAN with gradient penalty (WGAN-GP) is modified to train the discriminator. Finally, a simulation dataset is generated to validate the proposed approach. Experimental results reveal that the approach achieves 99.98% accuracy for detecting attacker and an average accuracy of 97.36% for classifying trusted transmitters.

Automated Valet Parking is foreseen as a highly automated function to be implemented on large scale in mass production in the near future. For such function, The authors have developed an innovative system concept using both real time kinematic sensing system and infrastructure IoT (Internet of things) camera to perform AVP function. As the driver activates the AVP function, the infrastructure identifies available parking spots by processing in real time camera frames and as the path is computed, the vehicle executes automated parking maneuvers. This paper introduces the system scheme and system engineering approach as well as key technology bricks to realize AVP function and proves the effectiveness and accuracy of the system through extensive vehicle testing. From system definition to final prototype validation, through Motion control algorithm description or deep learning algorithm tuning method, this paper covers a large spectrum of the engineering domains required to implement such function. Finally a specific Pursuit Model development and calibration approach is elicited for the motion control function and proven in use by AVL Vehicle Prototype platform.

The development of anion exchange membranes (AEMs) is hindered by the trade-off of ionic conductivity, alkaline stability, and mechanical properties. Troger's base polymers (Tb-polymers) are recognized as promising membrane materials to overcome these obstacles. Herein, the AEMs made from Tb-poly(crown ether)s (Tb-PCEs) show good comprehensive performance. The influence of crown ether on the conductivity and alkaline stability of AEMs has been investigated in detail. The formation of hydronium ion-crown ether complexes and an obvious microphase-separated structure formed by the existence of crown ether can enhance the conductivity of the AEMs. The maximum OH- conductivity of 141.5 mS cm-1 is achieved from the Tb-PCEs based AEM (Tb-PCE-1) at 80 °C in ultrapure water. The ion-dipole interaction of the Na+ with crown ether can protect the quaternary ammonium from the attack of OH- to improve the alkaline stability of AEMs. After 675 h of alkaline treatment, the OH- conductivity of Tb-PCE-1 decreases by only 6%. The Tb-PCE-1-based single cell shows a peak power density of 0.202 W cm-2 at 80 °C. The prominent physicochemical properties are attributed to the well-developed microstructure of the Tb-PCEs, as revealed by TEM, AFM, and SAXS observations.

In bulk crystals, the kinetics of dislocations is usually hindered by the twining boundaries (TB) or grain boundaries (GB), rendering the well‐known grain boundary strengthening effects. Nevertheless, here it is found that in 2D rhenium disulfide (ReS2), twinning is much easier than dislocation slip. Consequently, the highly mobile TBs or GBs are inversely pinned by the relatively immobile dislocations. Due to the strong in‐plane covalent bonding, the GBs in high‐symmetry 2D materials such as graphene which consists of defects are immobile at room temperature. In contrast, in monoclinic 2D ReS2 several types of GBs (including TBs) can be readily generated and driven by mechanical loading. A complete library of the GBs in 2D ReS2 is established by the (in situ) atomic‐scale transmission electron microscopy (TEM) characterizations and density functional theory (DFT) calculations. The twinning (shear) stresses for 2D ReS2 are estimated as low as 4–30 MPa, one or two orders of magnitude lower than the traditional bulk materials. Full elucidation on the GB structures and especially the intriguing GB kinetics in such anisotropic 2D materials are of fundamental importance to understand the structure–property relationships and develop strain‐tunable applications for 2D materials in future., A complete library of grain boundaries in 2D rhenium disulfide, classified by the mobile and pinned nature, is established. In 2D rhenium disulfide twinning is much easier than dislocation slip. As a result, the highly mobile twining boundaries (TBs) or grain boundaries (GBs) can be inversely pinned by the relatively immobile dislocations.

Scientific experimental racks are an indispensable supporter in space stations for experiments with regard to meeting different temperature and humidity requirements. The diversity of experiments brings enormous challenges to the thermal control system of racks. This paper presents an indirect coupling thermal control single-phase fluid loop system for scientific experimental racks, along with fuzzy incremental control strategies. A dynamic model of the thermal control system is built, and three control strategies for it, with different inputs and outputs, are simulated. A comparison of the calculated results showed that pump speed and outlet temperature of the cold plate branch are, respectively, the best choice for the control variable and controlled variable in the controller. It showed that an indirect coupling thermal control fluid loop system with a fuzzy incremental controller is feasible for the thermal control of scientific experimental racks in space stations.

Velocity estimation is essential for multicopters to guarantee flight stability and maneuverability. For such a purpose, this paper proposes a new method for multicopter velocity estimation based on visual and inertial information in GPS-denied or confined environments. In this method, no map, artificial landmark of the environment is required, and only the off-the-shelf onboard sensors in a multicopter including a low-cost Inertial Measurement Unit (IMU), a downward-looking monocular camera and an ultrasonic range finder facing downwards are exploited to constitute the vision motion constraint. This constraint connects metric velocity with the point correspondences between successive images in which an efficient approach based on Mean Shift (MS) algorithm is developed to detect outliers and select optimal matching points. Then, it is theoretically verified that the estimation system is observable based on observability analysis. Furthermore, combined with the vision motion constraint and a multicopter dynamic model, the metric velocity is estimated using a standard Linear Kalman Filter (LKF). Finally, the proposed method is tested with a collection of synthetic data from simulation as well as flight experiments using real data from DJI Matrice 100 and Guidance. The simulation and experimental results indicate that the proposed method can accurately estimate the velocity of the multicopter in GPS-denied or confined environments.

It is very important for communication equipment to realize the recognition and prediction of jamming signals in a short time. In the communication environment with variable jamming mode and noise, it is very difficult to achieve rapidly jamming recognition. To solve this problem, this paper constructs a spectrum waterfall containing white gaussian noise signals, designs a jamming pattern recognition method based on deep learning, and realizes rapid recognition of jamming patterns and important parameters. Simulation results show that the training process of this method has considerable accuracy and the test process has high recognition rate.

This paper presents an additive-state-decomposition-based attitude tracking control method for a class of bank-to-turn aerial vehicles subject to unknown disturbances and nonlinear coupling. This method ‘additively’ decomposes the original tracking problem into two more tractable problems, namely a tracking problem for a deterministic nonlinear ‘primary’ system, and a disturbance rejection problem for a linear time-invariant ‘secondary’ system. Based on the decomposition, a backstepping controller is designed for the primary system to track the reference attitude signal, and a proportional-integral controller is applied to the secondary system to compensate for the disturbances. Finally, the two designed controllers are combined to achieve the original control objective. By using additive state decomposition, the proposed control method with two degrees of freedom can consider tracking task and disturbance rejection task respectively. Simulation results illustrate that the proposed controller can track the reference attitude signal and compensate for disturbances meanwhile. Additionally, the ASD-based controller outperforms the traditional backstepping controller in the presence of unknown disturbances and input delay, and the robustness of the full system can be improved by adjusting the controller parameters of the secondary system.

Service disruptions due to deliberate sabotage are serious threats to supply systems. To alleviate the loss of accessibility caused by such disruptions, identifying the system vulnerabilities that would be worth strengthening is a critical problem in critical infrastructure protection. Today's supply systems tend to be organized in a decentralized manner, with different components belonging to different entities, keeping much information private. Therefore, a protection plan must balance its benefits among these entities for universal agreement to be reached. This paper addresses the issue of decentralized supply chain fortification by proposing the R-Interdiction Median problem with Fortification for Decentralized supply systems (D-RIMF). In the D-RIMF, each demand node is private and is a client of a certain facility; each facility evaluates its potential worst-case reduction in accessibility, measured as the increase in service provision costs considering only its own clients, and the objective is to minimize the largest evaluation values. To model the D-RIMF, we introduce a bilevel multiagent framework, in which all facilities and the defender are considered as independent agents. To solve the D-RIMF, both heuristic and optimal algorithms are designed to satisfy different requirements. Finally, the usefulness of the D-RIMF and the performances of the proposed algorithms are observed through simulations performed on typical datasets.

The usefulness of unlabelled test cases in fault localization is explored.An approach using test classification is proposed to utilize unlabelled test cases.A classifier utilizing the domain knowledge of fault localization is designed.Experiments are conducted to demonstrate the usefulness of our approach. In automatic software fault localization techniques, both the coverage and the testing outcomes of the provided test suite are considered to be essential information. The problem occurs when test oracles do not exist. Specifically, the test suite will contain a large number of unlabelled test cases, i.e., test cases whose output is not identified as being either correct (passing) or incorrect (failing). Such unlabelled test cases cannot be directly used, thereby leading to a degradation of localization effectiveness. In this paper, we propose an approach based on test classification to enable the use of unlabelled test cases in localizing faults. In our approach, unlabelled test cases are classified based on their execution information and are then assigned corresponding estimated labels to allow them to be utilized in fault localization. Experimental results show that with the utilization of these newly labelled test cases, the effectiveness of fault localization can indeed be improved.

An online partitioning strategy is presented, in which test cases are selected based on feedback information collected during the testing process. The strategy differs from conventional approaches because the partitioning is performed online rather than off-line and because the partitioning is not based on program code or specifications. It can, therefore, be implemented in the absence of the source code or specification of the program under test. The cost-effectiveness of the proposed strategy has been empirically investigated with a set of subject programs, namely, SPACE, SED, GREP, and the Siemens Suite of Programs. The results demonstrate that the proposed strategy constantly achieves large savings in terms of the total number of test case executions needed to detect all faults.

A typical 6-layered metal-dielectric film structure of SiO2 (57.3 nm)/Cr (2.9 nm)/SiO2 (72.9 nm)/Cr (6.6 nm)/SiO2 (57.6 nm)/Cu (>100.0 nm) was designed and fabricated by magnetron sputtering. It showed a high solar absorption of about 95.8% in the wavelength range of 250–2000 nm, a low thermal emittance of about 0.104 at 600 K and good thermal stability at 673 K after annealing for 12 h in vacuum condition. The angle-dependent reflectance spectra indicated that the proposed 6-layered film structure has a great angular tolerance even when the incident angle increases to 50°. All of the excellent spectral properties and good thermal stability demonstrate the 6- layered planar film structure will be suitable for practical applications of solar-thermal conversion.

In this paper, molecular dynamics simulation methods were used to investigate salt effects on micellization of N-Dodecyl-N,N-Dimethyl-3-Ammonio-1-Propane-sulfonate (SB12-3) in aqueous solution. It is proved that micelle shapes transform from spherical to prolate spherical shape after 30.0 ns simulation with different NaCl and CaCl2 concentration. By comparison with the eccentricity value without salt addition, SB12-3 shows salt tolerance behaviors due to its unique “inner salt” structure of amphiphilic surfactant. Radial distribution function (RDF), hydrophobic groups solvent accessible surface area (SASA), hydrogen bond, and the deuterium order parameter (SCD) are to elucidate salt effects on SB12-3 micelle formation. The radius of micelle is achieved by the related RDF value and in accordance with other researches. The addition of NaCl and CaCl2 didn’t change the structure of micelle significantly and have little effects on the interactions between SB12-3 and water molecule. By comparison with t...

Autopilot systems on unmanned aerial vehicles (UAVs) are safety-critical systems whose requirements on reliability and safety are ever-increasing. However, testing a complex autopilot control system is an expensive and time-consuming task, which requires massive outdoor flight tests during the whole development stage. This paper presents an indoor automatic test platform for autopilot systems aiming to significantly improve the development efficiency and safety level of UAVs. First, a unified modeling framework is proposed for different types of aerial vehicles to make it convenient to share common modeling experience and failure modes. Then, a real-time simulation platform is developed by using automatic code generation and FPGA-based hardware-in-the-loop simulation methods to ensure simulation credibility on software and hardware levels. Finally, an automatic test framework is proposed to traverse test cases during real-time flight simulation and assess the test results. In the verification part, the accuracy and credibility of the simulation platform are verified by comparing the obtained results with experimental results, and several successful applications on multicopters demonstrate the practicability of the proposed platform.

This paper deals with the problem of pose estimation (or motion estimation) for multirotor unmanned aerial vehicles (UAVs) by using only an off-board camera. An extended Kalman filter (EKF) is often adopted to solve this problem. However, the accuracy and robustness of an EKF are limited partly by the usage of an existing linear constant-velocity process model applicable to many rigid objects. For such a reason, a nonlinear constant-velocity process model featured with the characteristics of multirotor UAVs is proposed in this paper, the superiority of which is explained from the perspective of observability. With the new process model and a generic camera model, a practical EKF method suitable for conventional cameras and fish-eye cameras is then proposed. By taking EKF implementation into account, a general correspondence method that could handle any number of feature points is further designed. Simulation and real experiments show that the proposed EKF method is more robust against noise and occlusion than currently employed filtering methods.

For multicopters, failures may abort missions, crash multicopters, and moreover, injure or even kill people. In order to guarantee flight safety, a system of prognostics and health management should be designed to prevent or mitigate unsafe consequences of multicopter failures, where health evaluation is an indispensable module. This paper proposes a health evaluation method of multicopters based on Stochastic Hybrid System (SHS). In the SHS model, different working conditions (health statuses) of multicopters are modeled as discrete states, and system behaviors of different working conditions are modeled as continuous dynamics under discrete states. Then, the health of multicopters is quantitatively measured by a definition of health degree, which is a probability measure describing an extent of system degradation from an expected normal condition. On this basis, the problem of multicopter's health evaluation is transformed to a hybrid state estimation problem. In this case, a modified interacting-multiple-model algorithm is proposed to estimate the real-time distribution of hybrid state, and evaluate multicopter's health. Finally, a case study of multicopter with sensor anomalies is presented to validate the effectiveness of the proposed method.

Health performance prediction of a dynamical system aims at determining the probability or possibility that the system state will remain in a permitted area (safe set) or reach a forbidden area (unsafe set) at a future time instance. This paper proposes a health performance prediction algorithm for large-scale Stochastic Linear Hybrid Systems (SLHS) with small failure probability. In the studied SLHS, the continuous variable evolution is described by a set of stochastic linear differential equations, and the discrete state evolution is modeled by a first-order Markov chain. Furthermore, a safe set of the SLHS is described by a permitted area in the hybrid state space. Given an initial condition, a hybrid state evolution algorithm is proposed referring to the execution of stochastic hybrid systems. On this basis, a concept of health degree is introduced to evaluate the health performance of the studied SLHS. Finally, a multicopter with sensor anomalies is studied to validate the availability and effectiveness of the proposed method.

The mechanical properties of resin-bonded sand mixtures at high temperatures significantly affect the quality of casting. However, the existing instruments for high-temperature performances testing mainly focus on inorganic binder-bonded sands no matter the test items or the atmospheric protection, while the instrumentss specially designed for resin-bonded sand are not yet available. A new instrument for testing the high-temperature performance of resin sand was designed including the confirmation of the testing parameters, loading, measurement and control systems, and the design of the frame shape and heating furnace. This instrument can test the compressive strength, heat tolerance time and restraining load of phenol-formaldehyde resin coated sand, self-hardened furan resin sand, and trimethylamine (TEA)-based resin bonded sand at high temperatures. The developed instrument has a high accuracy offering smaller than 0.3% deviation at a full scale in the measurement of the high temperature compressive strength and the restraining load over the range of 0–6.8 MPa and 0–2,000 N, respectively. The high temperature heat tolerance time range is 0–300 s and its measurement accuracy is ±1 s.

In this technical note, an additive-state-decomposition-based iterative learning control (ILC) method is proposed. Based on this method, the output feedback ILC problem is solved for a class of nonminimum phase (NMP) nonlinear systems with input saturation. This method is to “additively” decompose the output feedback ILC problem into two more tractable subproblems, namely an output feedback ILC problem for a linear time invariant (LTI) system and a state-feedback stabilization problem for a nonlinear system with input saturation. Then, a controller can be designed for each subproblem separately using existing methods, and the two designed controllers are combined together to achieve the original control goal. An illustrative example demonstrates the effectiveness of the proposed method.

Web service selection for multiple users is an important aspect for achieving efficient operations for web service applications. Its aim is to select optimal solutions, in which each abstract web service in the workflow of a web application is bound to its corresponding concrete web service with the optimal quality of service (QoS), for all users based on their QoS requirements for the workflow. There are a lot of approaches to resolve this problem, but they do not consider each user’s different QoS requirements or have prohibitively large overhead for using these approaches. In this paper, we present an approach to significantly improve the efficiency of web service selection by the advanced a-fully polynomial time approximation scheme to calculate the Pareto optimal set, where each solution is not dominated by others. Additionally, this approach reduces its overhead further by adopting artificial bee colony algorithm to select an optimal solution from the Pareto set for each user. Experimental results are presented to show the efficiency of this approach.

We demonstrate that a field-effect transistor (FET) made of few-layer black phosphorus (BP) encapsulated in hexagonal boron nitride (h-BN) in vacuum exhibits a room-temperature hole mobility of 5200 cm2/(Vs), being limited just by the phonon scattering. At cryogenic temperatures, the FET mobility increases up to 45 000 cm2/(Vs), which is five times higher compared to the mobility obtained in earlier reports. The unprecedentedly clean h-BN–BP–h-BN heterostructure exhibits Shubnikov–de Haas oscillations and a quantum Hall effect with Landau level (LL) filling factors down to v = 2 in conventional laboratory magnetic fields. Moreover, carrier density independent effective mass of m* = 0.26 m0 is measured, and a Lande g-factor of g = 2.47 is reported. Furthermore, an indication for a distinct hole transport behavior with up- and down-spin orientations is found.