A robust tracking control problem for linear discrete-time systems with a prescribed convergence rate is considered. The robust tracking problem is formulated by utilizing robust output regulation and is subsequently solved by reinforcement learning with integration of the prescribed convergence rate. The learned controller ensures that the tracking error asymptotically converges to zero, meanwhile it is robust to uncertain system dynamics. The proposed convergence rate design is data-driven in the sense that it does not depend on the time for the system evolution or the accurate system model.

Lignin has a wide range of sources, low price, and complex structure. It is a renewable phenolic polymer in nature. It can be transformed into energy, chemicals, and functional materials, and has a potential application prospect. The research progress of multifunctional lignin materials is reviewed, especially based on typical examples, and the progress of lignin in the fields of energy, environment, sensing and carbon peak and carbon neutralization discussed. Finally, the problems and potential development direction of multifunctional lignin materials are proposed based on our knowledge. Solutions and theoretical support are provided for the applications of multifunctional lignin-based materials.

With the development of flexible devices and wearable devices, as well as the improvement of human environmental awareness, the development of flexible energy storage devices with high energy efficiency, high energy and power density and green environmental protection has attracted increasing interest. However, the traditional organic polymers, precious metals and other materials are not only expensive but also facing the crisis of resource depletion, so it is difficult to meet the development needs of flexible devices. Biomass resources are abundant, renewable, environmentally friendly, biocompatible and inexpensive, which are ideal precursors for the preparation of flexible energy storage materials. A comprehensive review is conducted on the preparation and synthesis of biomass-based flexible electrode materials, solid electrolyte and separator, and their applications in supercapacitors, metal-air batteries, lithium-ion batteries and lithium-sulfur batteries.

The structure and molecular composition complexity of lignin make it difficult to be extracted from the biomass. In addition, the traditional fractionation methods usually lead to degradation and condensation of lignin with low purity, which hinders its valorization. Therefore, it is urgent to develop a new and environmentally friendly method for lignin fractionation. As a promising and green solvent for lignin fractionation, deep eutectic solvent (DES) has been a hotspot in the fields of biorefinery and accumulated many excellent achievements. Many active groups, including aryl, phenolic hydroxyl, alcohol hydroxyl and carbon based conjugate double bond, exist in molecular structure of lignin, leading to high activity for preparation of some functional materials, which has a strong industrial application prospect. Based on the our latest researches, the latest research progress in lignin fractionation by DES, the preparation of lignin-based functional materials and the application of lignin phenolic resin, lignin-based slow/controlled release fertilizer and lignin-based biodegradable material are concluded in the study.

Lithium-ion batteries (LIBs) are widely used in many fields such as electronic products and vehicles due to their long-term cycling and no-memory effect. With the rapid development of domestic electric vehicles, higher requirements are put forward for the energy density, safety performance, cost, thermal stability, and cycle life of lithium-ion batteries. However, the battery performances depend deeply on the improvement of electrode materials. As the key component of LIBs, the cathode materials will directly affect the battery performances. Therefore, the nickel-rich LiNi _0.8Co _0.1Mn _0.1O ₂ cathode (hereinafter referred to as NCM811) has attracted more and more attention owing to its extremely high discharge capacity (> 200 mAh/g). However, the poor thermal stability, cycle rate, and safety of NCM811 restrict its large-scale application in practice. Combining the crystal structure, synthesis method and current main problems of NCM811, the improving technology is summarized for electrochemical performance of nickel-rich NCM811 in recent years, focusing on the surface coating, ion doping and additive modification.

How does intelligence emerge? What kinds of dynamical behaviors are intertwined with intelligence and how do we control them? Concerning with these two issues, relevant studies on intelligent control are briefly surveyed from an integrated viewpoint of complex network and dynamic systems. Presented first are fundamental concepts and challenging questions that arise from the interdisciplinary research areas of complex networks, dynamical systems, neuroscience, and intelligent control. An overview of research progress on intelligent control is further presented, emphasizing pinning control, hybrid control, adaptive control, and the controllability of complex networks. Moreover, potential applications of complex network dynamics and intelligent control in the fields of brain science and machine behavior are briefly discussed, with an outlook at possible research directions.

The locating and sizing planning of battery swapping stations for electric vehicles is a complex nonlinear problem with multiple variables and constraints, which is hard to be solved by general mathematical methods. A joint method of Voronoi diagram and improved bats algorithm (IBA) is proposed to solve the problem. Considering the influence of centralized charging station, a multi-objective decision model considering annual infrastructure investment costs, location satisfaction and annual battery swapping costs is established. In order to transform the multi-objective decision model into single-objective weight model, the method of fuzzy entropy weight is introduced to avoid the shortcomings of both subjective weighting and objective weighting. An improved bats algorithm (IBA) is proposed, which introduces inertia weight and considers the speed update of individual extremum, taking the minimum weighted gross of the multi-objective decision as the condition, combining with the Voronoi diagram which divides the service area of each battery swapping station, and the optimal solution of locating and sizing planning of battery swapping stations is obtained. The simulation results prove the rationality and feasibility of the model and improved algorithm.

Optical coherence tomography (OCT) is an emerging imaging technique that measures internal structures of transparent and semitransparent objects, e.g. polymers, ceramics, and composites, with micrometer resolution and millimeter range. After combining with the method of phase-contrast, the technique is also available for measuring nanometer level displacement field and micro-strain level deformation field inside objects due to the high sensitivity of interference phase. Since it can be employed for characterizing and testing full-field mechanical behaviors of inside materials, the technique has gained rapid development and more attention in the last decade, which has already become a hot topic in non-destructive testing. In this review, the basic principle of phase-contrast OCT was firstly described, some typical applications were then introduced, e.g. depth-resolved thermal deformation measurement of multilayer structure, curing processing visualization inside polymer, and micro defect identification inside materials, and some future developments were finally discussed.

Hilbert transform is an important tool for signal analysis and processing. Due to the singularity of the Cauchy kernel at the origin, the calculation of the Hilbert transform becomes very difficult. Recently, researchers firstly propose the AFD (Adaptive Fourier Decomposition) method which uses complex analysis to calculate the Hilbert transform. The AFD method adaptively approximates the analytical signal through the linear combination of the parameterized Szegö kernel to obtain original signals' Hilbert transform. Compared with the traditional method, the AFD method can give approximate analytical expressions and thus has a wider application. However, when using the principle of maximum selection to select parameters, the AFD method needs to exhaust all points of the unit disk which is time-consuming. Sparse Bayesian learning has been a hot spot in machine learning research in recent years. The Szegö kernel-based complex sparse Bayesian learning algorithm can provide a sparse rational approximation. A complex sparse Bayesian learning algorithm will be proposed based on the Szegö kernel to calculate the Hilbert transform. This method has the same advantages as the AFD method, and an iterative optimization can be performed without parameter control. The calculation speed of the proposed method is fast. Experimental results show that the proposed method is effective.

Aiming at the problem of multi vehicle cooperative control in an environment with connected and automated vehicles, a cooperative lane changing framework is proposed based on multi cluster system. The selection conditions of virtual leader are given. Connected and Automated Vehicles (CAVs) select neighbors, leader and virtual leader as the state evolution of control protocol through distributed clustering algorithm. On this basis, a space control algorithm for cluster space allocation and a leader-follower control protocol based on cluster system are designed to make the target vehicles extend the longitudinal distance between adjacent vehicles to achieve safe lane changing distance. Theoretical analysis verifies that the proposed control protocol can guarantee the local consistency within the cluster and the group consistency among clusters by solving multiple differential equations. The simulation results show that the proposed algorithm and control protocol can make the multi clusters converge uniformly and Cavs maintain an expected safe distance to drive stably in a sparse formation. As a result, CAVs change safely and accurately to the target lane.

Flow is a new concept in modern TRIZ theory, and flow analysis is a tool for systematic analysis of problems based on flow formation. However, formal research is insufficient and is difficult to be applied to intelligent design. Based on the elementary theory of extenics, a formal research on flow and flow analysis is conducted. It takes the material flow in the technical system as the research object, and uses elementary and composite elements to formally express the objects, behaviors, functions and other elements involved in the flow. Based on the structure and other elements, the extension flow model is established, and then the beneficial flow or the harmful flow is identified according to the flow analysis method. Finally, taking a company’s charging power supply production process defect as an example, the method is used to establish an extension flow model and identify the beneficial flow and the harmful flow. This research lays a good foundation for further obtaining extension knowledge research to improve defect flow.

Whether China's new energy automobile industry can be successfully overturned has a significant demonstration effect on boosting the transformation of green economy and achieving sustainable development, while the discontinuity and abrupt change of disruptive innovation bring great challenges to Chinese automobile enterprises. Choosing byd, tesla and Toyota, the three representatives of new energy vehicles (NEV) enterprises, as analysis objects, and starting from the initial target market selection, technical path, the path of market diffusion and dimension, this research sums up the experience of case enterprise of disruptive innovation, figures out three disruptive innovation models, and analyzes the key elements of the models. The findings are as follows: (1) byd adopts the environmentally supportive disruptive innovation model, while tesla adopts the trans-boundary fusion disruptive innovation model, and Toyota adopts the collaborative innovative disruptive innovation model. (2) The three disruptive innovation models are constructed based on different perspectives. Driven by different types of opportunity Windows, they will follow different competitive evolution paths and face different evolutionary barriers. The findings of this research suggest that many different disruptive innovation patterns may occur simultaneously in the same industry.

Automated guided vehicle (AGV) autonomous path planning is an important part of the logistics system in discrete manufacturing smart factories. AGV can greatly improve the intelligence and automation capabilities of discrete smart manufacturing. The traditional AGV navigation method has a low degree of freedom. The autonomous path planning of AGV is studied under the scenario of discrete manufacturing smart factories, and deep reinforcement learning methods applied to improve the freedom of autonomous path planning. A neural network structure for multi-modal environmental information perception is designed, and the path planning policy of AGV under global obstacles introduced to the path planning in the complex discrete manufacturing smart factory scenario, thereby realizing the AGVs end-to-end path planning from environmental perception to action for decision making. The experimental results show that AGV can independently plan paths in the complex and unknown intelligent logistics system environment of discrete manufacturing smart factories, and has a high success rate and obstacle avoidance ability.

The combination of online and offline teaching mode is a trend of teaching in the future. The previous researches show that learners’ learning behavior can directly affect the learning results, so the research focuses on studying the influence level of learning behavior on academic performance. For the current common evaluation models, there are still some problems such as low credibility and weak interpretability. In this study, the Bayesian network (BN) based on evidential reasoning can effectively solve this problem. Compared with the commonly used machine model and deep learning model, it shows a lower error rate and a stronger interpretability.

Systematic Innovation Method (SIM) is a kind of method to guide innovation in technology, commerce and industry, and to elevate the capability of independent innovation. After 1990s, SIM had been developed well in theory development, tools integration, computer aided innovation, etc. Researches indicate that SIM is of good effectiveness in applicability, effects and benefits of knowledge, social and intellectual. For the complicated system, difficulties in learning and using, vague mechanism on its effectiveness, and being hard to promote, all this hazards the propagation of TRIZ. Based on the researches above, it is believed that future research spots on SIM will mainly be the mechanism of its effectiveness, and methods integration and improvement, of the "AI engineer".

In order to scientifically evaluate and decide the optimal multimodal combination scheme of driver's emotion perception device, based on the extension design multi-functional product creation method, the detection function of emotion perception device is analyzed by using the divergence analysis method of basic element theory, and the matter-element like model of specific product established. The relationship between the three installation positions of a single detection device is analyzed, and a series of reorganization schemes are developed. The optimization evaluation method is used to screen them, and an effective combination scheme of multimodal driver emotion perception device is determined.

In order to reduce the adverse effects of large-scale use of fossil fuels on the environment, make full use of renewable resources, and improve the defects in the road performance of traditional petroleum asphalt, a kind of biomass asphalt with the advantages of renewable, large reserves and green environmental protection was introduced. In recent years, most of the domestic and foreign research focused on the preparation process and modification effect of bioasphalt. However, the reaction mechanism between biomass modifier and commonly used modifier in composite modification is still less, and the modification research and evaluation method of matrix asphalt need to be further explored. Therefore, the preparation process of biomass modified materials and bio-asphalt is reviewed, its modification effect analyzed, the modification effect of existing commonly used modifiers and biomass composite on asphalt compared, and finally the content of this paper summarized and condensed, the existing preparation process and modification effect summarized and evaluated, the problems existing in the research of bio-asphalt put forward, and the development trend of bio-asphalt prospected.

The marketing effect estimation is an important issue for precision marketing. Although the causal effect estimation has provided the research framework for this problem, the existing method mainly focuses on the coarse-grained causal effect estimation on a group of users. When dealing with the fine-grained evaluation problem on an individual user, the existing methods usually fail to solve the challenges raised by the user temporal feature modeling and high dimensional confounder selection. To address the above challenges, a fine-grained deep confounder balancing marketing effect estimation method is proposed. Firstly, a long and short memory network is introduced to model the users’ temporal features. Secondly, a neural network with multi-sparse-connected layers is devised to construct the confounders balancing weight network for both temporal and non-temporal features. Finally, the marketing effect is evaluated using the balancing weight learned in the previous stage.It shows that the time feature modeling and nearest neighbor matching ideas proposed in this paper can reduce the deviation of the effect estimation and improve the stability, it has reference significance for the optimization of marketing strategy.

Recent advances in 6th Generation (6G) mobile networks can meet the needs of deeper intelligent communication. Meanwhile, it also brings great challenges to the security and privacy preservation of user data. Federated learning is emerging as a distributed learning method to preserve privacy by enabling users to train machine learning models locally and requiring the users to upload only model parameters instead of sending original data to the server. However, the users with mobile devices may be unwilling to participate in federated learning tasks due to considerable overhead for computation and communication, which degrades performance and hinders the application of federated learning. In this study, an iterative double auction is designed based on resource cooperation scheme to incentivize the mobile devices to contribute their resources in federated learning tasks, where model trainers act as sellers and task requesters as buyers. Access point determines optimal training time cost and payment according to the prices offered by the sellers and the buyers. The goal of our incentivizing resource cooperation scheme is to maximize the total utility of federated learning market under information asymmetry. Numerical results show that the proposed scheme can converge to the optimal solution, and also can significantly improve model accuracy and degrade model loss value.

Task allocation is a common and important problem in personnel management. In the process of allocation, it is often affected by many different constraints. One of them is the cool down time constraint. Due to the cool down time in personnel allocation or resource scheduling, it is difficult to allocate and optimize the many to many tasks in human-computer cooperation, and the collaboration effect drops sharply. The group multirole assignment (GMRA) is used to formalize the problem, and the integer programming used to decouple and eliminate the cool down time constraints. After that, IBM ILOG CPLEX optimization package (CPLEX) is used to optimize the team execution. In addition, in the large-scale simulation experiment, the necessary conditions are used to reduce the solution space quickly, and the second level accurate solution is achieved. The generality, efficiency and reliability of the model and method are further demonstrated.

In the area of feature selection, most existing methods cannot simultaneously capture the importance of different features and enforce orthogonal constraint on the projected subspace to improve the discrimination of selected features. To deal with this issue, a new unsupervised feature selection method called joint graph embedding and feature weighting (JGEFW) is proposed. To be specific, graph-embedding local structure learning is leveraged to obtain the similarity matrix and the cluster indicator matrix. Moreover, the weight matrix is learned through the orthogonal regression, to select the discriminative and non-redundant features. At last, an alternating iteration algorithm is developed for solving the proposed objective function. Finally, experimental results about clustering evaluation metrics on four publicly available datasets demonstrate that JGEFW is better than others in most cases.

In order to solve the problems of low success rate and long planning time of mobile robot in complex environment, a mobile robot docking position optimization algorithm is proposed based on environment information preprocessing. Firstly, the workspace of the manipulator is analyzed, and the evaluation criteria of grasping difficulty are obtained. The positions of objects, obstacles and mobile chassis in the environment are simplified as points and projected on the xy plane. According to the evaluation criteria of grasping difficulty, the optimized chassis docking position of the mobile robot is obtained. For the obstacle avoidance problem of the manipulator, the rapidly-exploring random trees (RRT) algorithm is used to realize the robot end, connecting rod and obstacle. Finally, through the simulation and experiments in the motion capture system, it is found that using the parking position optimization algorithm of mobile robot can increase the success rate of grasping objects and the speed of grasping planning .

Conventional master/slave-based data processing frameworks are vulnerable to single point of failure and performance bottlenecks of the master node. In contrast, blockchain systems adopt a decentralized framework and are capable of aggregating enormous computing resources. A blockchain-based data processing framework is proposed that utilizes the advantages of the blockchain for solving the drawbacks of the centralized framework. In this framework, the blockchain stores the task information and the adopted proof of useful work consensus enables nodes to process tasks using their computing resources, while competing for the leader (who dispatches pending tasks to the blockchain). Extensive simulations show that the proposed framework is better than the centralized framework in terms of the throughput and the task response time.

Flexible grippers are widely used in micromanipulation system due to their capability of micro/nano resolution. However, it cannot avoid damaging the grasped object for the gripper cannot provide a constant output force or the limited range constant force. To solve these issues, a flexible gripper with constant force characteristic was designed based on an amplification module and a constant force module with series- structure. Based on the pseudo-rigid body method, the stiffness and magnification ratio model of the bridge type mechanism and the lever mechanism were established. The relationship between the force and displacement of the inclined guide beam was obtained with 42.5 N constant output force and 370 μm output range. Finally, the effect of each parameter on the constant force characteristics was analyzed with MATLAB. The research results can provide some theoretical guidance for the configuration design of flexible constant force grippers.

To study the way to realize the intrinsic safety of production system, the method of realizing the intrinsic safety of production system by establishing artificial intelligence management system is proposed. Firstly, the concept and problems of intrinsic safety are discussed, including the functions and characteristics of human, machine, environment and management subsystems in the production system, and their obstacles to the realization of intrinsic safety also studied; secondly, the feasibility of realizing intrinsic safety by artificial intelligence is discussed, and the structure of artificial intelligence production system established. Compared with the original structure, the operator disappears, the role of manager changes and increases feedback mechanism and system complexity is decreased. Finally, the way to realize intrinsic safety is discussed, that is to build an artificial intelligence management system, which has the characteristics of double cycle and self-learning. Although the theory of fault pattern recognition and fault knowledge base is not mature, it is feasible to realize the intrinsic safety of production process by establishing artificial intelligence management system.

Compute first networking (CFN) is a latest distributed framework that intelligently allocates computing resources for edge computing according to computing load and network status. It requires real-time visibility of available statuses of local or remote computing resources. To the best of our knowledge, thisis the first endeavor to propose a centralized fault-detection protocol called CFN-Watchdog to well meet this CFN requirement and timely recycle resources occupied by faults. The impact of various parameters (e.g., detection thresholds, task processing time, and network delay) on the Watchdog performance is then theoretically analyzed. Extensive simulations verify the effectiveness of our proposed protocol and the accuracy of our theoretical model. This study is very helpful to optimize parameter configurations and better design fault-detection protocols for edge computing.

With the continuous development of rural economy, rural energy consumption is gradually changing from biomass energy to electricity. Therefore, identifying the potential driving factors of rural electricity carbon emissions from the perspective of rural areas is an important prerequisite for promoting rural low-carbon development and achieving carbon emission reduction goals. In this study, Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and ridge regression are used to analyze the influencing factors of carbon emissions from rural electricity consumption in Guangdong Province from 2005 to 2018. The results show that urbanization level, agricultural economic development level, effective irrigation area, agricultural mechanization level, agricultural fixed assets investment, income level and housing conditions are positively correlated with carbon emissions from rural power consumption. Among them, the per capita disposable income of rural residents is the most important factor affecting the carbon emission of rural electricity. In addition, there is a weak decoupling relationship between agricultural economic development and rural electricity carbon emissions. Finally, policy suggestions are put forward on how to reduce rural electricity carbon emissions in Guangdong Province.

In order to study the influence of block-scale surface morphological characteristics on the urban thermal and humid environment, seven study typical neighborhoods in Guangzhou are selected based on the classification method of local climate zone and the surface morphological characteristics of them parametrically expressed. And field measurement and ENVI-met simulation are used to analyze the spatial and temporal distribution characteristics of thermal and humid parameters under different block-scale surface morphological characteristics. The results show that the neighborhoods with compact high-rise buildings present lower air temperature, while compact low-rise district presents a high temperature and low humidity thermal and humid environment. Besides, the neighborhoods with open space have more balanced air temperature and relative humidity distribution with less spatial variability. In addition, the accuracy of the results of ENVI-met software simulating the thermal and humid environment in the neighborhoods of hot-humid regions is verified based on the field measured data, and the results show that ENVI-met can predict the characteristics of thermal and humid environment in different neighborhoods of hot-humid regions better overall, but the simulation accuracy for compact low-rise districts is low.

In order to clearly quantify and evaluate the complex issues in the interior design of self-driving car, and improve design efficiency, extension is applied to the interior design of self-driving car, and the definition of interior design problems proposed for self-driving car methods, conjugate analysis, generating algorithm and constructing an evaluation index system for the interior design of unmanned vehicles. Taking the self-driving car interior design based on the 2030 90s parents, children and pet dogs traveling together in the suburbs as an example, through the self-driving car interior design scheme generation algorithm based on the extension theory, three effective designs of creativity are first obtained, and the designs expressed to make them concrete into a design plan, and a goodness evaluation finally performed to obtain the optimal plan. The design results verify the feasibility and effectiveness of the method.

The computational models for geophysical fluid dynamics are computationally enormously expensive to employ in tasks such as data assimilation and uncertainty quantification. Naturally, surrogate models seeking to alleviate the computational burden has been proposed. Researchers have started applying artificial intelligence and machine learning algorithms, particularly artificial neural networks, to build data-driven surrogate models for geophysical flows. The performance of the neural network highly relies upon their architecture design and selection of hyper-parameters. These neural networks are usually manually crafted through trial and error to maximize their performance. This often demands specialized knowledge of the underlying neural network as well as the domain problems of interest. This limitation can be addressed by using an evolutionary algorithm to automatically design and select optimal hyper-parameters of the neural network. In this study, the genetic algorithm is applied to effectively design the long short-term memory (LSTM) neural network to build the forecasting model of the temperature in the NOAA sea-surface temperature data set.

In order to reduce the fuzziness of using scientific effect in TRIZ, and to accurately and clearly describe the transformation between input and output in the effect, a formal expression method based on Extenics is proposed to study scientific effect. First, the concepts of extension transformation, conductive effect and scientific effect are briefly introduced, and then a formal quantitative research on the relationship between seven important physical effect and extension transformation and conductive effect is carried out, establishing the various objects involved in physical effect Matter-element model, and establishing the correlation between matter-element according to the domain knowledge and correlation rules in Extenics. An active transformation of matter-element in the correlation relationship is implemented based on the knowledge of active transformation and conductive transformation, obtaining the corresponding conductive transformation. Then according to the calculation formula of physical effect and conductive effect in the domain knowledge, the conversion formula of physical effect and conductive effect is obtained. Finally, methods are summarized to establish the general relationship between physical effects and extension transformation and conductive effect. This study describes the main contents of various physical effects accurately and in detail by formal methods, so that the mechanism of various physical effects and the conversion relationship between input and output can be understood. A basis and methods are also provided for solving the contradictions in the field of engineering technology combined with physical effects and extension transformation, and a new idea is provided for finding new scientific effects.

In view of the relatively fixed general solution and standard solution of TRIZ substance field analysis in solving innovation problems, combined with the formalization and extension characteristics of element model, a substance field extension innovation method integrating element model and substance field analysis is proposed. Taking the general solution as an example, the basic element model and its extended analysis method of the substance field model and the general solution are established, the substance field extension solution process of the innovative problem is given, and the scope of the general solution to the problem is expanded. Finally, the forming element of oil-water annular flow is designed by using the material field extension innovation method. It proves that the material field extension innovation method is feasible to guide the product innovation design, and provides a reference for practical application.

An approach of intelligent fitting model of emotional modeling primitives is proposed to solve the limitations of the traditional product design methods that are difficult to carry out the overall design and the interference between the appearance design and the mechanical structure design. By using the principle of visual perception to analyze consumer needs, the principle of visual perception is determined as an effective way to connect perceptual psychology and modeling elements. Combining with extension theory, a method for generating modeling elements based on emotional images is given. Analyzing the influence of product mechanical structure on appearance modeling, and combining extension transformation ideas and parametric design software, a modeling fitting concept is proposed, a product modeling method system is constructed based on user emotional collaborative structural design, and new ideas for product modeling design provided. Finally, the feasibility of the method is verified through an example of trocar.

In order to achieve higher consistency between objective and subjective evaluation of image quality, a full-reference image quality evaluation method based on a synergetic strategy between superpixel and visual saliency is proposed. This method obtains the image quality score by pooling four similarities of the image features. The four similarities are: the superpixel-based local luminance similarity, the superpixel-based local chrominance similarity, the visual saliency similarity, and the Scharr gradient similarity. In particular, a parameter correction model is proposed to adaptively adjust the parameters of each similarity, to address the problem of determining the parameters of each similarity empirically. The visual saliency is introduced to design a weighting function to fuse the four similarities to obtain the global quality score. A large number of comparative experiments show that the proposed method outperforms the baseline full-reference image quality assessment methods, and has a higher correlation with the subjective evaluation.

Horizontal bounding box representation in traditional object detection is not appropriate for ubiquitous oriented objects in aerial images because of the variant perspective, the crowded, cluttered and oriented objects. Therefore, a one-stage pose-based oriented object detection network is proposed, which represents oriented object as different pose and detect the oriented objects by locating the center and regressing four offsets between center and four vertices. Meanwhile, an adaptive feature pyramid network with learnable weights is utilized to automatically select more discriminative features. Moreover, according to the high resolution of aerial images, selective sampling strategy is proposed to improve the efficiency of network training and alleviate the imbalance problem of positive and negative samples. The proposed method achieves 74.85 mAP on oriented detection task of DOTA dataset, which outperforms the existing one-stage or even two-stage methods. The qualitative and quantitative comparative experiments show that the proposed pose-based oriented object detection network is simple and has competitive detection performance.

The large-scale HFS (Hybrid Flow Shop) system has the characteristics of large scale and many resource constraints, which causes dimension disasters during job scheduling, which results in difficult search and solution problems. Addressing such problems, an improved genetic algorithm is proposed, based on the MPN (Manufacturing Petri Net) model, with the makespan as the optimization goal. Firstly, the structure of the chromosome is redefined, and the corresponding algorithm of chromosome arrangement segment used to compress the search space. Secondly, the particle swarm optimization (PSO) optimization mechanism is used to guide the direction of chromosome optimization in the chromosome crossover operator, and the simulated annealing algorithm (SAA) mechanism used in the chromosome mutation operator to prevent the premature convergence of the genetic algorithm, and then after each population iteration, the current maximum individual uses the neighborhood search mechanism to try to improve the fitness of the best individual. Finally, the experimental data shows that the improved genetic algorithm has made great improvements in the optimization of the solution.

The dynamic characteristics of a three-dimensional(3D) flexible rotating beam is studied. Using the absolute node coordinate formulation and geometric nonlinear deformation hypothesis, the nonlinear dynamic model of 3D flexible beam is established based on the general continuum theory. Based on the perturbation theory and the floating-point coordinate method, the vibration frequency domain analysis model of the rotating flexible beam is established by using the linearization technique. Then, the time domain simulation of the free single pendulum of the flexible beam is carried out, and the frequency domain characteristics of the 3D flexible rotating beam at different speeds are analyzed. The results show that as the rotation speed increases, its dynamic stiffening phenomenon is obvious, and the frequency corresponding to the mode shape of the vertical rotation plane and the torsional and tensile modes will gradually be transformed into low-order frequencies, thereby affecting the vibration characteristics of the flexible beam.

It is an important part to determine the deformation and tensile force of geosynthetic reinforcement (GR) in the design of piled embankments, but some simplifications such as the uniform tension and a circular arc or quadratic parabola shape of the deformed GR have been used in the current methods. Under plain strain condition, the equilibrium equations of GR are derived under arbitrarily distributed loads, and it is found that the deformed GR has a circular shape and the uniform tension only if the friction acting on the GR is ignored and the normal load is in uniform distribution. Furthermore, the deformation and tension of GR can't be determined only by using the equilibrium equations due to the imperfection of their boundary conditions. A simple numerical method to determine the deformation and tensile force of GR is presented on the basis of the combination of the equilibrium equations with the deformation consistency (the arc length obtained by the equilibrium equations is equal to that by the tensile force plus its initial length) of GR. The rationality and practicability are validated by the four examples.

In order to comprehensively promote the construction of ecological civilization and strengthen the protection of ecological environment, the government and enterprises are actively exploring effective measures to encourage individual green consumption. With the increasing popularity of gamified applications, more and more attention has been paid to the research on green consumption behavior nudged by gamification. However, a systematic theoretical framework has not yet been formed. This review first systematically reviews the elements, design framework and affordances of gamification, then analyzes and sorts out the research status of green consumption from three aspects: motivation, attitude and behavior, and puts forward the concept of indirect green consumption behavior according to the characteristics of green consumption. Finally, based on the research and analysis of relevant literature on green consumption behavior nudged by gamification, this review constructs an overall research framework for exploring green consumption behavior nudged by gamification. It emphasizes that gamification has a boosting effect on people’s green consumption motivation, attitude and behavior, providing research ideas for the studies in related fields.

The reconfigurable intelligent surface (RIS) is an effective technique that can improve wireless signal propagation environment and enhance communication security by adjusting the reflected signal through its controlled reflecting elements. The RIS-assisted secure communication in a non-orthogonal multiple access system is considered, where multiple users simultaneously send confidential information to a single-antenna base station in a non-orthogonal multiple access way in the presence of a single-antenna eavesdropper. Our objective is to maximize the total security rate of the system by optimizing the transmit power of the users and the phase shifts of the reflecting elements of the RIS while satisfying the constraints of transmit power and the quality-of-service for each user, as well as the constant modulus constraint of each reflecting element of the RIS. Although the considered optimization problem is non-convex and difficult to solve, an efficient algorithm based on the alternate optimization and semidefinite relaxation techniques is proposed to obtain a near-optimal solution. Simulation results validate that as compared with two benchmark schemes, the proposed algorithm significantly improves the total security rate of the system. It provides a reference for the secure communication research of reconfigurable intelligent surface assisted non orthogonal multiple access system.