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.

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.

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.

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.

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.

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.

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.

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_0.1O₂ 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.

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 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.

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.

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".

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.