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Carbon-based microassemblies (CMs) have attracted significant attention in numerous applications due to their unique hierarchical structures and delicate building blocks, especially when hollow carbon spheres (HCSs) are reasonably introduced into the construction. Herein, a new design for novel HCSs-combined CMs is proposed. Remarkably, the HCSs are linear carbon bubbles linked one-by-one, arranging into necklaces decorating on the graphene microfolds. Detailed thermal analysis confirm that high temperatures straighten the linked carbon bubbles into bamboo-like carbon nanofibers, evidently due to the attenuation of doping degree. Benefiting from the abundant active sites of carbon bubbles, the obtained CMs exhibit satisfactory electrocatalytic activity for oxygen reduction reactions. This work establishes a bridge to precisely control the synthesis of carbon-based hierarchical architectures.

The growing applications of wearable electronics, electronic textiles, and biomedical devices have sparked explosive demand for high-performance flexible sensors. Herein, we report a facile approach for fabricating a highly sensitive carbon hybrid fiber, which is composed of a graphene fiber skeleton and carbon nanotube (CNT) branches. In this hierarchical fiber, in situ grown CNTs prohibit the stacking of graphene sheets and bridge graphene layers simultaneously, making the hybrid fiber fluffy and conductive. Due to the well-designed architecture, the assembled fiber sensor exhibits satisfactory performance with a high gauge factor (up to 1127), a fast response time (less than 70 ms), and excellent reliability and stability (>2000 cycles). This work provides a feasible and scalable pathway for the fabrication of ultrasensitive fiber-based sensors, achieving the full realization of monitoring human physiological signals and architecting a real-time human-machine controlling system. Moreover, these practical sensors are used to monitor the sitting posture to prevent cervical spondylosis and lumbar disc herniation.

A scalable synthesis of high refractive index, optically transparent photopolymers from a family of low-viscosity multifunctional thiol and alkyne monomers via thiol-yne "click" is described herein. The monomers designed to incorporate high refractive index cores consisting of aryl and sulfide groups with high intrinsic molar refraction were synthesized starting from commercially available low-cost raw materials. The low-viscosity (

This study presents an integrated control scheme of a diesel engine which aims to ensure quick torque response while limiting the average NOx emission below the certain specification. To start with, the sensitivity analysis is performed to recognize the key factors determined to balance emission, torque and fuel consumption. Considering the strong nonlinearity of the diesel engine, a multi-input multi-output (MIMO) linear parameter varying (LPV) model is developed. Unlike the conventional state-depended LPV modelling process, the proposed LPV model is built based on a kernel function with a novel form of state-free predictive equation so that the system outputs are directly computed. The control objective of coordinating torque tracking and emission reduction is then accomplished by designing a model predictive controller that regulates the fuel injection quantity, fuel injection angle, exhaust gas recirculation (EGR) rate and the opening degree of variable geometry turbine (VGT) valve. Finally, the developed LPV model and the MPC controller are testified with a high-fidelity commercial diesel engine model. The results show that the LPV-MPC controller is able to satisfy the NOx emission specification and the engine torque tracks the desired reference fast in transients, while the control variables are suitably actuated within the actuator constraints.

This paper proposes a personalized adaptive cruise control (ACC) system based on driving style recognition and model predictive control (MPC) to meet different driving styles while ensuring car-following, comfort and fuel-economy performances. To obtain the controller parameters corresponding to different driving styles, a set of real vehicle experiments are conducted to collect driving data of 66 randomly recruited drivers, then the experimental data is clustered through unsupervised machine learning method. On the basis of it, a driving style classifier is designed by supervised machine learning method, which can be used to recognize the driving style of drivers online. Then, the control problem of the personalized ACC system is described as a multi-objective optimization problem which is solved by MPC method. The simulation results show that the proposed personalized ACC system can meet the requirements of different driving styles and guarantee various performances.

Carbon hybrids (CHs) with controllable synthesis and hierarchical structures have been considered as promising materials to satisfy the explosive energy consuming. Here, a novel strategy is demonstrated to design hierarchical CHs by reductive-catalytic chemical vapor deposition (RCCVD). The graphene oxide sheets self-assemble into microfiber skeleton during wet-spinning, combining with in situ growth of carbon hybrid nanofibers by the catalytic species from RCCVD. The derivatives from RCCVD exhibit diverse microstructures of nanocaps, nanofibers and nanorods under different synthesis temperatures. Ammonia (NH3), hydrogen cyanide (HCN) and cyanic acid (HCNO) are released efficiently as the reductant by the synergy of ion-contained graphene oxide and melamine. The microassemblies display satisfactory performance as LIB anodes ascribed to the well-designed micro-nano hierarchical structures. This work provides a feasible strategy to synthesize functional carbon-based hierarchical assemblies.

A high-performance holographic recording medium was developed based on a unique combination of photoinitiated thiol–ene click chemistry and functional, linear polymers used as binders. Allyl reacti...

This study investigated the fatigue crack propagation (FCP) behavior of different regions (base metal (BM), heat-affected zone (HAZ) and weld metal (WM)) of dissimilar metal welded joints of L360MS pipeline steel with Inconel 625 welding wire as the filler material. The effect of the microstructure on FCP was discussed. The results indicated that the fatigue crack growth rates (da/dN) for the WM were lower than those for the BM and the HAZ under the same stress ratio. The microstructures of these regions had different characteristics, such as equiaxed austenite, ferrite and pearlite, which could explain the observed differences in properties. For instance, the FCP path revealed that the existence of ductile equiaxed grains and coarse columnar grains caused the WM to have higher fatigue crack growth resistance and a more tortuous crack propagation path than the BM and the HAZ. The microstructure, grain orientation, strain distribution and grain boundaries around the fatigue crack in the HAZ specimens that propagated through the WM and the fusion line were analyzed by electron backscatter diffraction (EBSD). The results showed that the fatigue cracks followed a zigzag path into the HAZ along the high-angle Type II boundary and Type I boundary in the WM.

In this work, chabazite SSZ-13 samples with different cations were synthesised by hydrothermal method and thoroughly analysed using various characterisation techniques. The role of types of cations in CO2 and CO adsorption measurements was investigated. The adsorption isotherms were fitted by Langmuir, Langmuir-Freundlich and Toth adsorption models, respectively. The result shows that the Langmuir-Freundlich model is the most suitable for the adsorption isotherms of SSZ-13 molecular sieves. The adsorption isotherms of the fitted CO2 and CO were analysed to study the high silicon SSZ-13 samples of alkali metal cations Li+, Na+, K+ and Cs+ for ion exchange. The result shows that Na+ has the highest adsorption capacity, but the adsorption selectivity is extremely low. K+ has the lowest adsorption capacity, but it has excellent adsorption selectivity, which may be attributed to its large cation radius and the special position of cations in CHA skeleton structure, resulting in the formation of ‘molecular trapdoor’ effect. In addition, Cs+ has a larger ionic radius than K+, but the adsorption selectivity is not significantly improved, similar to Li+. It can be determined that the ‘molecular trapdoor’ effect has a significant relationship with the position of cations in the framework.

In optical sensors and imagers, high gain that leads to high sensitivity and high signal to noise ratio (SNR) is often desirable. One popular approach is avalanche photomultiplication initiated by impact ionization in an avalanche photodiode or similar devices and the other approach is active pixel sensor (APS) with in-pixel amplifier. However, the former requires high electric field which induces high shot noise and the latter needs a multiple-transistor pixel circuit which compromises the fill factor and consequently, reduces the SNR. This work proposes and summarizes our recent efforts taken to achieve high gain optical sensors through subthreshold operation of photodiode-gated transistors.

Eliminating the influence of cogging torque and model uncertainty on the tracking control of a dc motor when its speed varies nonperiodically is a challenge. In this paper, an adaptive robust triple-step control method is proposed for compensating cogging torque and model uncertainty. First, a new presentation of the cogging torque and a simplified model of the friction torque are presented to facilitate the online estimation of the unknown model parameters. The load torque, motor disturbance, and model errors are considered as model uncertainty. Based on these considerations, a control-oriented model that contains unknown parameters and model uncertainty is obtained. Second, benefitting from the new presentation, an adaptive algorithm is employed to identify the unknown parameters online. The model uncertainty is estimated by an extended state observer. Third, the model-based triple-step nonlinear method is extended to a system with both parameter uncertainty and model uncertainty, and an adaptive robust triple-step nonlinear controller is derived. The robust stability of the closed-loop system is proven in the framework of Lyapunov theory. Finally, the effectiveness and the satisfactory control performance of this controller are evaluated through comparative experiments on a J60LYS05 motor.

Point of interest (POI) data can provide a clear spatial location and accurate attributes for geoscience research. The traditional assessment of Beautiful China construction (BCC) has relied on statistical materials, which have shortcomings in terms of timeliness, authenticity, efficiency, and accuracy. Referring to the theoretical framework of the Zhongke Beauty Index, we built an evaluation index system and technical process based on POI data. In terms of the Inner Mongolia Autonomous Region (IMAR), 5.09 million POIs were collected using the web crawler technique, and the Beautiful Inner Mongolia construction evaluation and analysis were performed. The results show the following: (1) POI data can be used to establish an evaluation index system for the construction of Beautiful Inner Mongolia on the county scale, in the dimensions of industrial development, social harmony, and institutional improvement, it shows especially good application prospects. (2) The Beautiful Inner Mongolia index in 2020 was 0.22. Among the five dimensions, the industrial development index was the highest, while the cultural heritage index was the lowest. We found significant spatial differences in the dimensions of cultural heritage as well as social harmony. (3) The areas in the IMAR with a low-level construction were mostly industrial and mining areas, agricultural counties, and other economically developing areas, among which the Baiyunebo mining area and Xianghuangqi and Shiguai areas had the lowest comprehensive beauty index values. (4) We also found large numerical disparities in the level of Beautiful Inner Mongolia construction between municipal districts and banners/counties, and the ranking of each region was affected by the population and coverage areas of administrative units. After verification, we found an overall good consistency between the evaluation indexes proposed in this paper and a previous study. Therefore, this paper provides a new perspective and an effective method for the application of Internet big data in economic and social evaluation work.

Electrowetting display (EWD) is the most potential technology among new electronic paper technologies. It not only has the advantages of electrophoretic display (EPD) technology but also can realize color video playback. Therefore, this technology has been widely studied in recent years. Driving waveform is a voltage sequence which can drive pixels to display gray scales in EWDs. As one of the key technologies, it directly affects the display effect of pixels. In this paper, we give a review of the display principle of EWDs and the research status of driving waveforms. At the same time, the contact angle hysteresis, charge trapping, and oil splitting are also reviewed, which can provide a reference value for designing driving waveforms.

As an atomically thin semiconductor, 2D molybdenum disulfide (MoS2) has demonstrated great potential in realizing next-generation logic circuits, radio-frequency (RF) devices and flexible electronics. Although various methods have been performed to improve the high-frequency characteristics of MoS2 RF transistors, the impact of the back-gate bias on dual-gate MoS2 RF transistors is still unexplored. In this work, we study the effect of back-gate control on the static and RF performance metrics of MoS2 high-frequency transistors. By using high-quality chemical vapor deposited bilayer MoS2 as channel material, high-performance top-gate transistors with on/off ratio of 107 and on-current up to 179 μA/μm at room temperature were realized. With the back-gate modulation, the source and drain contact resistances decrease to 1.99 kΩ∙μm at Vbg = 3 V, and the corresponding on-current increases to 278 μA/μm. Furthermore, both cut-off frequency and maximum oscillation frequency improves as the back-gate voltage increases to 3 V. In addition, a maximum intrinsic fmax of 29.7 GHz was achieved, which is as high as 2.1 times the fmax without the back-gate bias. This work provides significant insights into the influence of back-gate voltage on MoS2 RF transistors and presents the potential of dual-gate MoS2 RF transistors for future high-frequency applications.

Bacillus sp. DL-2 was isolated from the deep sea of the Western Pacific and further utilized as novel biocatalysts to efficiently asymmetrically hydrolyze (±)-1-phenylethyl acetate. After the optimization of hydrolytic reactions, chiral chemicals (R)-1-phenylethanol and (S)-1-phenylethyl acetate were obtained with high optical purities (96% and 99.8%, respectively). Our research is about the asymmeric hydrolysis of (±)-1-phenylethyl acetate using whole-cell biocatalysts. In addition, the optical purity of (S)-1-phenylethyl acetate generated through the kinetic resolution of (±)-1-phenylethyl acetate using the whole-cells of Bacillus sp. DL-2 was the highest report so far. Using the whole cells of deep sea bacterium Bacillus sp. DL-2 as the biocatalysts is an environmentally friendly method and will play critical roles in industrial asymmetric synthesis.

A redox prodrug consisting of camptothecin-conjugating PEG through the diselenide bond was synthesized, and the resultant amphiphilic prodrug was then used to load the curcumin to form the co-delivery system for the synergetic B16 melanoma cells inhibition. The co-delivery system showed a good response to glutataione, and the prodrug could be degraded and the two loaded drugs were then released. Both camptothecin and curcumin could induce the obvious cytotoxicity to B16 melanoma cells. Particularly, the co-delivery strategy showed a synergetic inhibition effect to B16 melanoma cells through an in vitro assay. In vivo assay also showed that the co-delivery system could inhibit B16 tumor growth and showed much better inhibition effect than that of only camptothecin or curcumin used. Moreover, as an injectable drug delivery system, the biocompatibility including the blood compatibility of the co-delivery system was confirmed, suggesting that the redox co-delivery system have a potential application in B16 tumor therapy.

Model predictive control (MPC)-based path following schemes for autonomous cars represent a novel and highly debated control approach. Their high online computational load poses a challenge for practical real-time application in vehicle systems with fast dynamics. This paper proposes an implementation scheme for an MPC path following controller that considers the feasible road region and vehicle shape. Moreover, the model mismatch induced by varying road conditions and small-angle assumptions is considered in the form of a measurable disturbance. To solve the optimization problem for the proposed MPC path following controller, a differential evolution (DE) algorithm is adopted. To verify the computational performance of the proposed implementation scheme, an experimental platform was developed that consists of the Hongqi autonomous car HQ430, various sensors, and systems for communication and data processing. The experimental results indicate that the proposed DE-based implementation strategy for the MPC path following controller achieves good computational performance and satisfactory control performance for path following in autonomous cars.

The engine friction and actuator power are the main factors of the cooling system affecting the fuel economy of a spark ignition (SI) engine. An electrified cooling system containing an electric fan, pump, and thermostat provides an opportunity to reduce fuel consumption. The coolant temperature is always kept at a high fixed value within the safe temperature range to avoid friction losses caused by overcooling; however, the actuator power is not typically considered. Recent publications have attempted to minimize the actuator power and the coolant temperature is maintained in a range. Nevertheless, neither method quantitatively considers both factors. In this paper, the integrated consideration of engine friction and actuator power is presented to minimize engine fuel consumption. The accuracy of a control-oriented model of a cooling system is improved first in an attempt to exert the full potential of the model. Then, the proposed strategy for minimum fuel consumption is constructed as an optimization problem and the improvement of fuel economy obtained by the proposed strategy is evaluated using a causal suboptimal controller and dynamic programming (DP)-based global optimal controller. Compared with a causal coolant temperature tracking controller, the causal suboptimal controller and the global optimal controller based on the proposed strategy both achieve significant improvements. Compared with a global optimal controller for minimum actuator power, the global optimal controller based on the proposed strategy achieves a certain improvement and this effect can increase as the environmental temperature decreases. Finally, a real-time implementation of the proposed strategy on a hardware platform using model predictive control (MPC) with a limited horizon is presented, which shows the feasibility of the proposed strategy.

The precise boost pressure control is the key to guaranteeing reliable turbocharged gasoline engine operation. The closed-loop control requires fast boost pressure tracking performance in wide engine operating range. This paper presents a double closed-loop nonlinear control scheme to improve transient tracking performance. The air-path system of the turbocharged gasoline engine is described by a physics-based, nonlinear, mean value model that presents a long regulation channel from the control input, waste-gate-opening, to the control output, boost pressure and brings challenges for the controller design with satisfied tracking performance. To address this problem, the outer loop treats the turbine speed as a downstream control demand and adjusts it by using a feedforward-feedback control to achieve fast boost pressure tracking. Subsequently, a disturbance-observer-based nonlinear inner-loop controller is designed to regulate the wastegate that stabilizes the turbine speed tracking error while handles the uncertainties due to operating variation. The stability of the whole closed-loop system is analyzed by the Lyapunov stability theory and the robustness against operating variation is discussed based on input to state stability (ISS). The effectiveness of the proposed controller is validated through a higher fidelity model in AMESim and its performances are compared with well-tuned baseline controllers.

It is very important and necessary to carry out the research on improving the lighting protection level for the strong lighting area, especially for the distribution line. For this propose, the paper focus on studying the lighting protection level using arrester and grounding line with10kV distribution line. Take Qingyuan as an example, the characteristics of lighting activity is analyzed. The simulation model applying arrester and grounding line to protect 10kV distribution line is set up, respectively. And the protecting effect of the two method is compared. Besides, the economic efficiency is also studied. Based on the simulation and the economic analysis, a synthesis lighting protection measure is presented, which can greatly improve the lighting protection level of 10kV distribution line.

The regulations for diesel vehicles are expected to become increasingly more stringent. However, how to coordinate eco-driving, the diesel engine and the urea selective catalytic reduction (urea-SCR) system for fuel economy improvement and emissions reduction remains a formidable challenge. In this paper, a sequential optimization control method with three stages is designed. In stage I, the optimal driving force and braking force are obtained by solving a nonlinear optimization problem of tracking the vehicle velocity profile. In stage II, a real-time reference estimation model is designed to provide the optimal ammonia coverage ratio target. In stage III, to implement the driving force requirement and ammonia coverage ratio target, an integrated engine and urea-SCR system control method and a distributed method are proposed, respectively. The distributed method consists of a fuel injection controller utilizing a data-driven predictive method and a NH3 dosing controller utilizing nonlinear model predictive control (NMPC). The control model of the integrated method is represented by the first-order ammonia coverage ratio dynamics only. The results show that the fuel consumption is improved by 5.3% and the PM emission is reduced by 11.56% during the partial transient acceleration process, and that the fuel consumption and emissions of the integrated control method can accomplish the level of the distributed method and achieve the trade-off between the multi-objective. However, the integrated control method incurs an average computational time penalty of 15.47%.

This paper proposes an electrochemical-thermal coupling model for state of charge (SOC) estimation of lithium-ion batteries (LIBs). In this paper, to solve two numerical problems in the quasi two-dimensional model of LIBs, a simplified one-dimensional model is built. Considering that the parameters in the model are affected by temperature, the internal parameters of the battery which obey the Arrhenius equation are introduced. Based on the simplified one-dimensional model, the electrochemical-thermal coupling model is built. Finally, an adaptive SOC observer based on electrochemical-thermal coupling model is designed. The simulation results show that the electrochemical-thermal coupling model can be effectively applied to SOC estimation.

To reduce toxic engine emissions and meet the strict regulations, split injection has been adopted to optimize engine performance and fast-idle has been selected as the researching condition. Two sets of experiments have been conducted to investigate the effect of split injection on particulate number (PN) emissions at fast-idle. The first set is conducted in a constant volume vessel to get detailed spray characteristics, while the second is conducted in engine bench analyzing the effect of split injection on real engine PN emissions. Establishing direct connection from spray characteristics and real engine PN emissions is the main objective of this paper. Higher ambient temperature can reduce spray angle, while spray tip penetration is insensitive to experimental parameters. Long injection pulse width combined with short height of damper (HOD) will increase the possibility to impingement. Total PN concentration of four injection modes all decreases at first and then increases with increasing coolant temperature, and split injection can obviously reduce total PN to 50% from single injection. With increasing coolant temperature, accumulation mode particles all decrease while nucleation mode particles show different trends under four injection modes. In addition, the last injection within split injection has obvious influence on PN emissions.

The spatial distribution and dynamic changes of the forests in Primorsky Krai, Russia, are of great significance for regional ecological security and sustainable economic and societal development. With the support of the Google Earth Engine cloud computing platform, we first synthesized yearly Landsat surface reflectance images of the best quality of the research area and then used the random forest method to calculate the forest classification probability of the study area year by year from 1998 to 2015. Furthermore, we used a time−series segmentation algorithm to perform temporal trajectory segmentation for forest classification probability estimation, and determined the spatial and temporal distribution characteristics and change laws of the forest. We extended the existing algorithms and parameters of forest classification probability trajectory analysis, achieving a high overall accuracy (86.2%) in forest change detection in the study area. The extended method can accurately capture the time node information of the changes. In the present research we observed: (1) that from 1998 to 2015, the forest area of the whole district showed a net loss state, with a loss area of 0.56 × 106 ha, of which the cumulative forest disturbance area reached 1.12 × 106 ha, and the cumulative forest recovery area reached 0.55 × 106 ha; and (2) that more than 90% of the forest change occurred in areas with a slope of less than 18°, at a distance of less than 20 km from settlements, and at a distance of less than 10 km from roads. The forest disturbance monitoring results are consistent with the changes in official statistical results over time, but there was a 20% overestimation. The technical method we extended in this study can be used as a reference for large−scale and high−precision dynamic monitoring of the forests in Russia’s Far East and other regions of the world; it also provides a basis for estimating illegal timber harvesting and determining the appropriate amount of forest harvested.

In this paper, a neural-network-based nonlinear model predictive control (NMPC) scheme is investigated to realize coordinated control over the throttle and wastegate of a turbocharged gasoline engine of a passenger vehicle. First, due to the presence of MAPs and the complex structure of the turbocharged engine, establishing a mechanism model for controller design is very complicated. Benefiting from a large amount of experimental data, a predictive model is learned by a neural network to predict the future dynamics of the engine air-path system, and the accuracy of this model is verified. Second, to address the system constraints and coupling, a nonlinear model predictive controller is proposed to track the desired intake manifold pressure and boost pressure for meeting the engine torque demand. Third, quantum-behaved particle swarm optimization (QPSO) is applied for optimization of the NMPC objective function to obtain a more accurate solution. Finally, the performance of the control system is tested using the commercial simulation software AMESim.

Variable geometry turbocharger (VGT) is a key technology and has become a development trend in the diesel engine industry because it can reduce turbo lag and improve fuel economy. In this paper, nonlinear model predictive control (NMPC) method for VGT is developed to solve the air system control problem of turbocharged diesel engine. Firstly, a model of the turbocharged diesel engine was built in AMESim. Secondly, the desired torque is converted into the desired exhaust manifold pressure through the look-up table. Thirdly, The VGT controller based on NMPC is designed to achieve the desired exhaust manifold pressure tracking while considering constraints on the system input and output. Finally, the effectiveness of the VGT controller is verified.

This paper focus on the topic of reducing the random noise in the output data of MEMS gyroscope and improving the accuracy of MEMS gyroscope. Aiming at the problems that the previous methods are not suitable for dynamic data and difficult to realize real-time online processing, a direct modeling method is proposed to establish the output data model of MEMS gyroscope. Based on this model, the kalman filter is designed to process the output data. The experimental results show that the proposed method has good performance on both static data and dynamic data.

The work of this paper focuses on oxygen excess ratio control, for the oxygen excess ratio has a great influence on the performance of the proton exchange membrane (PEM) fuel cell. To model dynamics of air supply system and oxygen excess ratio, a validated three-order model is used for controller design. Because the pressure of the cathode is unmeasurable in the actual system, a state observer is designed to observe the cathode pressure based on the third-order model. A controller combining with feedforward control and feedback control based on LQR method and map that can output the equilibrium state, feedforward control signal and feedback gain calculated off-line at different equilibrium points through the input of the expected oxygen excess ratio and load current is then designed. At last, oxygen excess ratio control strategy under different working conditions is realized and the simulation results verify the effectiveness of the proposed controller.

Precise electronic throttle control is a basic problem of engine control. This paper identifies the model parameters based on input PWM, throttle output angle and angular velocity. But the angular velocity can not be measured directly. Therefore, the kalman filter is applied to calculate the angular velocity. A hybrid optimization algorithm is used to search the parameters of throttle model which minimizes the errors between model and real throttle output angle and angular velocity. The controller is designed based on identified parameters and triple-step nonlinear control design. Experimental results indicate the effectiveness of the identified parameters and the controller.

In extreme driving situations, the coupling between the longitudinal and lateral vehicle motion becomes significant with the highly nonlinear tire forces and influences vehicle overall stability. To address the above problem, a model predictive controller is proposed for four wheels independent motor-drive electric vehicles. Firstly, a LuGre combined-slip tire model is developed to describe the coupling nonlinearity of the tire. The variation of longitudinal velocity is considered a disturbance term in the vehicle dynamics model to consider its effect on vehicle stability. Then, the multiple objectives, including tracking the reference values of the yaw rate and lateral velocity, suppressing the tire slip ratios, and reducing the torque energy consumptions, are balanced by the additional torque generated by the designed model-based controller. The safety and actuator constraints are considered concurrently. Finally, the proposed controller is tested via co-simulation with CarSim and MATLAB/Simulink, and a hardware-in-loop system with extreme maneuvers. The results show that the handling performance, longitudinal and lateral stability are effectively improved under extreme conditions.

Model predictive control of discrete-time nonlinear systems with incremental input constraints is proposed in this paper. Firstly, the existence of the terminal set and terminal penalty is proven on the assumption that the considered system is twice continuously differentiable. Secondly, properties of the optimal cost function are exploited. It shows that the optimal cost function is positive semi-definite, continuous at the equilibrium and monotonically decreasing along the predicted trajectory. The systems under control converge to the equilibrium since the optimal cost function is monotonically decreasing. Thirdly, stability of nonlinear systems is proven in terms of the classical Lyapunov Theorem, where an upper bound of the optimal cost function in the terminal set is chosen as a candidate Lyapuonv function. Finally, the system is asymptotically stable since the system state converges to the equilibrium and the system is stable.

Understanding how complex urban factors affect the Urban Heat Island (UHI) is crucial for assessing the impacts of urban planning and environmental management on the thermal environment. This paper investigates the relationships between two-dimensional (2D) and three-dimensional (3D) factors and land surface temperatures (LST) within the Olympic Area of Beijing in different seasons, using the boosted regression tree (BRT) model. The BRT model captures the specific contributions of each urban factor to LST in each season and across a continuum of magnitudes for this factor. The results show that these relationships are complex and highly nonlinear. The four most common dominant factors are the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI), a gravity index for parks (GPI), and average building height (BH). The most important factor in spring is NDBI, with a 45.5% contribution rate. In the other seasons, NDVI is the dominant factor, with contributions of 40% in summer, 21% in autumn, and 19% in winter. NDVI has an overall negative impact on LST in spring and summer, with a quadratic nonlinear decreasing curve, but a positive one in autumn and winter. The 2D land-use variables are most strongly related to LST in summer and spring, but 3D building-related variables have stronger impacts in colder weather. The Sky View Factor (SVF), a 3D measure of urban morphology, has also strong impacts in summer and winter. Both a building-based and a DSM-based SVFs are computed. The latter accounts for buildings, bridges, and trees. In contrast to a building-based SVF, the DSM-based SVF reduces LST when it varies between 0 and 0.75, reflecting the effects of high-density tree canopies that increase shades and evapotranspiration while blocking sky view. The marginal effect curves produced by the BRT are often characterized by thresholds. For instance, the maximal NDVI effect in summer takes place when NDVI = 0.7, suggesting that a very intense green coverage is not necessary to achieve maximal thermal results. Implications for urban planning and environmental management are outlined, including the increased use of evergreen trees that provide thermal benefits in both summer and winter.

A 8–10 bit resolution-reconfigurable SAR ADC for bioelectronics application is proposed. The ADC consists of resolution-reconfigurable binary-weighted capacitive DAC, a two-stage full dynamic comparator, and the resolution-control SAR logic. Simulated in 180 nm CMOS process, the ADC achieves the 49.77/55.65/61.36 dB signal-to-noise and distortion ratio (SNDR) and 67.56/71.94/78.41 dB spurious-free dynamic range (SFDR) at 100 kS/s sampling rate with the power consumption of $0.81/0.91/ 1.01 \mu \mathrm{W}$.

Recently, the increasing shrub-encroached grassland in the Mongolian Plateau partly indicates grassland quality decline and degradation. Accurate shrub identification and regional difference analysis in shrub-encroached grassland are significant for ecological degradation research. Object-oriented filter (OOF) and digital surface model (DSM)-digital terrain model (DTM) analyses were combined to establish a high-accuracy automatic shrub identification algorithm (CODA), which made full use of remote sensing products by unmanned aircraft systems (UASs). The results show that: (1) The overall accuracy of CODA in the Grain for Green test area is 89.96%, which is higher than that of OOF (84.52%) and DSM-DTM (78.44%), mainly due to the effective elimination of interference factors (such as shrub-like highland, well-grown grassland in terrain-depression area, etc.) by CODA. (2) The accuracy (87.5%) of CODA in the typical steppe test area is lower than that (92.5%) in the desert steppe test area, which may be related to the higher community structure complexity of typical steppe. Besides, the shrub density is smaller, and the regional difference is more massive in the typical steppe test area. (3) The ground sampling distance for best CODA accuracy in the Grain for Green test area is about 15 cm, while it is below 3 cm in the typical and desert steppe test area.

In this paper, the control targets are the low rate into the cathode and the pressure of the cathode. Firstly, the typical structure of the proton-exchange membrane fuel cell (PEMFC) air supply subsystem is briefly introduced and a simplified multi-input and multi-output (MIMO) air supply subsystem of a PEMFC is proposed. Then optimize the tracking targets under different load currents and establish a map between load current and tracking targets. Finally a nonlinear controller called the triple-step method is designed and the stability of the system is proved by Lyapunov stability theory. The simulation results show good performance that the system can quickly track the targets with no steady-state error when tracking targets and load current step-change.

Based on grid percentage land cover data of 1988, 1995, 2000, 2005, and 2010, the spatial and temporal variation of urban and construction land in Chengdu City was analyzed using spatial analysis and statistical techniques based on ArcGIS, excel and other software. The results showed that: In 2010, the urban and construction land area reached 1841.98 km2, and mainly distributed in the central area of Chengdu City. During 1988-2010, the urban and construction land in Chengdu showed increasing trend, with the largest increase occurring during 2005-2010. After 2000, the city entered a period of rapid expansion, and the turn-in area increased significantly. The turn-out area was largest during 1988-1995, benefiting from land consolidation and transformation at this stage. Magnitude of the changes in urban and construction land showed increase trend during 1988-2010, especially after 2000. In the future, it is suggested to speed up the development in surrounding counties (districts) and to rationalize the allocation of urban land use. In central urban areas, refined planning and transformation should be paid more attention to meet the needs of future urban development.

In the existing stochastic gradient matching pursuit algorithm, the preliminary atomic set includes atoms that do not fully match the original signal. This weakens the reconstruction capability and increases the computational complexity. To solve these two problems, a new method is proposed. Firstly, a weak selection threshold method is proposed to select the atoms that best match the original signal. If the absolute gradient coefficients were greater than the product of the maximum absolute gradient coefficient and the threshold that was set according to the experiments, then we selected the atoms that corresponded to the absolute gradient coefficients as the preliminary atoms. Secondly, if the scale of the current candidate atomic set was equal to the previous support atomic set, then the loop was exited, otherwise, the loop was continued. Finally, before the transition estimation of the original signal was calculated, we determined whether the number of columns of the candidate atomic set was smaller than the number of rows of the measurement matrix. If this condition was satisfied, then the current candidate atomic set could be regarded as the support atomic set and the loop was continued, otherwise, the loop was exited. The simulation results showed that the proposed method has better reconstruction performance than the stochastic gradient algorithms when the original signals were a one-dimensional sparse signal, a two-dimensional image signal, and a low-rank matrix signal.

In urea selective catalytic reduction (urea-SCR) systems, theammonia coverage ratio is an important parameter for satisfyingemission regulations. However, this parameter cannot be directly measured byonboard sensors. Most of observersand tracking controllers for the ammonia coverage ratio in theexisting literature were designed separately and lacked an overallrobustness analysis. In this paper, an observing and tracking controller (AOTC), along with its overall design andstability analysis, is proposed under input-to-state stability (ISS)theory. The proposed AOTC strategy consists of a sliding modeobserver based on ${\rm~NH}_3$ concentration dynamics and a trackingcontroller based on the observer dynamics. The stability is discussed considering theuncertainties about the conversion of urea to ${\rm~NH}_3$ and the cross-sensitivityof the ${\rm~NO}_x$ sensor, and the principle for adjusting the control parameters is provided. A few transientsimulations are conducted to evaluate the effectiveness of theproposed AOTC strategy.

Functional areas are the basic spatial units in which cities or development zones implement urban plans and provide functions. Internet map big data technology provides a new method for the identification and spatial analysis of functional areas. Based on the POI (point of interest) data from AMap (a map application of AutoNavi) from 2017, this paper proposes an urban functional areas recognition and analysis method based on the frequency density and the ratio of POI function types. It takes the Guangzhou Economic and Technological Development Zone as a case study to analyze the main function and spatial distribution characteristics of the detailed functional areas. The research shows the following: (1) The POI frequency density index and the function type ratio can effectively distinguish the functions of the grid units and analyze the spatial distribution characteristics of a complex functional area. (2) The single functional area is the most common area type in the Guangzhou Economic and Technological Development Zone. The largest proportion of all areas is allocated to traditional manufacturing industry functional areas, followed by high-tech enterprises, catering and entertainment, real estate, and education and health care, in descending order. The smallest proportion is allocated to finance and insurance functional areas. (3) The current layout of the functional areas in the Guangzhou Economic and Technological Development Zone conforms to the overall requirements and planning objectives of the central and local government. The layout and agglomeration of different blocks within the economic development zone are consistent with local industry&rsquo, s target orientation and development history.

Limited research has been published on land changes and their driving mechanisms in Central Asia, but this area is an important ecologically sensitive area. Supported by Google Earth Engine (GEE), this study used Landsat satellite imagery and selected the random forest algorithm to perform land classification and obtain the annual land cover datasets of Central Asia from 2001 to 2017. Based on the temporal datasets, the distributions and dynamic trends of land cover were summarized, and the key factors driving land changes were analyzed. The results show that (1) the obtained land datasets are reliable and highly accurate, with an overall accuracy of 0.90 ± 0.01. (2) Grassland and bareland are the two most prominent land cover types, with area proportions of 45.0% and 32.9% in 2017, respectively. Over the past 17 years, bareland has displayed an overall reduction, decreasing by 2.6% overall. Natural vegetation (grassland, forest, and shrubland), cultivated land, water bodies and wetlands have displayed increasing trends at different rates. (3) The amount of precipitation and degree of drought are the driving factors that affect natural vegetation. The changes in cultivated land are mainly affected by precipitation and anthropogenic drivers. The effects of increasing urban populations and expanding industrial development are the factors driving the expansion of urban regions. The advantages and uncertainties arising from the land mapping and change detection method and the complexity of the driving mechanisms are also discussed.

Land cover and its dynamic information is the basis for characterizing surface conditions, supporting land resource management and optimization, and assessing the impacts of climate change and human activities. In land cover information extraction, the traditional convolutional neural network (CNN) method has several problems, such as the inability to be applied to multispectral and hyperspectral satellite imagery, the weak generalization ability of the model and the difficulty of automating the construction of a training database. To solve these problems, this study proposes a new type of deep convolutional neural network based on Landsat-8 Operational Land Imager (OLI) imagery. The network integrates cascaded cross-channel parametric pooling and average pooling layer, applies a hierarchical sampling strategy to realize the automatic construction of the training dataset, determines the technical scheme of model-related parameters, and finally performs the automatic classification of remote sensing images. This study used the new type of deep convolutional neural network to extract land cover information from Qinhuangdao City, Hebei Province, and compared the experimental results with those obtained by traditional methods. The results show that: (1) The proposed deep convolutional neural network (DCNN) model can automatically construct the training dataset and classify images. This model performs the classification of multispectral and hyperspectral satellite images using deep neural networks, which improves the generalization ability of the model and simplifies the application of the model. (2) The proposed DCNN model provides the best classification results in the Qinhuangdao area. The overall accuracy of the land cover data obtained is 82.0%, and the kappa coefficient is 0.76. The overall accuracy is improved by 5% and 14% compared to the support vector machine method and the maximum likelihood classification method, respectively.

Rho zeolite was successfully synthesized by hydrothermal synthesis adopting 18-crown-6 as template, and the cations (H+, Na+, K+, Cs+, Cd2+ and Pb2+) of different radii were introduced into the framework of Rho by ion exchange. The samples were characterized by XRD, SEM-EDX, ICP and N2-BET. The adsorption behavior of CO2 and CH4 on cation-exchanged Rho was measured, and the adsorption isotherm was fitted by Langmuir equation to study the adsorption performance of these zeolites. The adsorption results show that the pore size of Rho is not solely dependent on the cation radius, the different positions of the cations are also more crucial in pore size adjustment. In all researched Rho, the introduction of Cd2+ and Pb2+ can more significantly reduce the pore size, and completely restrict the entry of CH4, thereby further improving the CO2/CH4 selectivity. Therein, Cd-Rho has the best adsorption performance, with considerable CO2 adsorption capacity, and extremely high CO2/CH4 selectivity and CO2 working capacity, which provides a new clue for the development of adsorbents for CO2 and CH4 separation in PSA process.

Owing to the dynamic operation mode of urea selective catalytic reduction (urea-SCR) systems, advanced control strategies are required to improve urea dosing control. A new control-oriented model presentation of urea-SCR systems is developed in this study. A novel controller based on the triple-step non-linear method is designed. The controller drives the non-linear system with time-varying parameters to track the variable ammonia coverage ratio. Unlike the existing triple-step non-linear method, the third design procedure in the proposed method is adjusted as an [Formula: see text] error feedback control. The proposed method provides a concise design process so that derivation of the control law can be simple and straightforward. The robustness of the controller against measurement noises and system uncertainties is analysed. A transient simulation is conducted to evaluate the effectiveness of the proposed control strategy.

For gasoline direct injection (GDI) engines, obtaining desired rail pressure has become a key prerequisite of fuel injection quantity precise control. Since improved control performance and robustness will translate into better combustion stability, fuel economy and emissions. This paper develops a feedforward-feedback controller based on a nonlinear model of fuel rail system, where nominal feedforward control is derived by differential flatness and fuel rail pressure is selected as flat output. In feedback part, to simplify the design, the system is linearized on equilibrium point using Taylor series expansion, and Lyapunov method is used to ensure stability of closed-loop error system. Finally, the proposed controller is verified by fuel rail system simulation model established in AMESim software package, the simulation results in different pressure references demonstrate the effectiveness of the control system.

For hybrid electric vehicles with traction motors installed behind the transmission, motor torque can be used to compensate for traction loss during gear shift. Thus, engine speed control can be used to achieve speed synchronization without disengaging the clutch. To improve the speed tracking performance during gear shift, an engine speed control system is designed based on gain-scheduling PID and PD controllers and Smith predictor. Gain-scheduling PID and PD controllers are used to address the system nonlinearities and Smith predictor is used to compensate the intake-to-power delay. An accurate engine model is built, and the delay time of intake-to-power is identified. By intensive off-line calibration, the control settling time is less than 1 s, and the range of tracking error is 0 to +20 rpm, which satisfy the performance requirements of the synchronizer engagement during gear shift of AMT(Automated Manual Transmission).

An energy-efficient switching scheme for successive approximation register (SAR) analogue-to-digital converter (ADC) is proposed. Based on closed-loop charge recycling method, the proposed switching scheme achieves 100% less switching energy over the conventional switching scheme. Furthermore, the common-mode voltage of the comparator is always V cm and only two reference voltages are used.

In this age of Facebook, Instagram, and Twitter, there is rapidly growing interest in understanding network-enabled opinion dynamics in large groups of autonomous agents. The phenomena of opinion polarization, the spread of propaganda and fake news, and the manipulation of sentiment is of interest to large numbers of organizations and people. Whether it is the more nefarious players such as foreign governments that are attempting to sway elections or it is more open and above board, such as researchers who want to make large groups of people aware of helpful innovations, what is at stake is often significant. In this paper, we review opinion dynamics including the extensions of many classical models as well as some new models that deepen understanding. For example, we look at models that track the evolution of an individual’s power, that include noise, and that feature sequentially dependent topics, to name a few. While the first papers studying opinion dynamics appeared over 60 years ago, there is still a great deal of room for innovation and exploration. We believe that the political climate and the extraordinary (even unprecedented) events in the sphere of politics in the last few years will inspire new interest and new ideas. It is our aim to help those interested researchers understand what has already been explored in a significant portion of the field of opinion dynamics. We believe that in doing this, it will become clear that there is still much to be done.

Nighttime light images record the brightness of the Earth surface, indicating the scope and intensity of human activities. However, there are few studies on the long-term changes in global nighttime lights. In this paper, the authors constructed a long time series (1992~2017) nighttime light dataset combining the Defense Meteorological Satellites Program/Operational Linescan System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data sources and observed the following: (1) Global nighttime lights have become brighter. The global nighttime brightness in 2017 was 2.2 times that of 1992. Approximately 40.3% of the lighted area was significantly brightened, and an area of 1.3 &times, 107 km2 transitioned from an unlighted area to a lighted area. (2) Approximately 85.7% of the nighttime light increase occurred in the low-brightness zone (LBZ). Therefore, global brightness has become more uniform than before. (3) China, India, and the United States have led the global lighting trend. The increase in Chinese nighttime lights is the largest, with an average annual growth of 6.48%, followed by the light growth in India, while the United States has the largest brightened area. (4) The changes in nighttime lights in developing countries (e.g., China and India) are closely and positively related to their electricity consumption, industrial added value and gross domestic product (GDP). The shift of the LBZ center from Asia to Africa indicates the intercontinental transition of poverty.

For electric vehicles with in-wheel motors, the torque distribution strategy is used to manipulate their dynamics to reduce energy consumption and ensure safety. Under critical conditions, it is difficult to meet the necessary requirements with a simple torque distribution due to the coupled nonlinear characteristics and corresponding safety constraints. To address these problems, a stability-guaranteed and energy-conserving torque distribution strategy is proposed for the vehicles in an innovative master-slave control framework. Considering the dynamic characteristics of tires on a low-friction-coefficient road, a nonlinear controller is designed to regulate the steering angle of the front wheel and an additional yaw moment in the active safety control layer. According to the driver’s dynamic demand and actuator constraints, a torque distribution controller based on model predictive control theory is designed in the energy-efficiency control layer. The motor efficiency map is used in the objective function to reduce energy consumption while improving and balancing motor efficiency. The proposed torque distribution strategy managed to show an increment of 4.50%, 0.80% with previous in energy saving under double lane change and straight acceleration maneuvers respectively, while the power loss does not exceed 0.08%.