Currently, the amount of both information and knowledge is growing rapidly, and many problems faced by enterprises, organizations and individuals are characterized by non-linearity, high uncertainty and dynamism. Problems with relatively definite goals and domains and uncertain or insufficient boundary conditions leading to unclear problem expression and difficult goal achievement are called ill-defined problems. In this research, based on the method of constructing the extension model and solving the contradictory problem in Extenics, the method of constructing the initial extension model of the ill-defined problems and the method of solving the single-goal ill-defined problems are proposed by using the extensible analysis method and the extension transformation method. Finally, the feasibility of the method is demonstrated by a case study. Since this study applies a combination of formal and quantitative methods, it also lays the foundation for the intelligent solution of single-goal ill-defined problems and the study of multi-goal ill-defined problems.

There are many factors involved in the evaluation of coal mine dust concentration, and the output evaluation results lack comprehensiveness when the single type of dust concentration is taken as the evaluation standard. In this research, a comprehensive evaluation method of coal mine dust concentration is established by using the superiority evaluation method in the extension innovation method, and the corresponding extension intelligent evaluation software is developed on the C++platform. First, many kinds of dust in the environment is analyzed, the data queried to determine the measurement indicators and the harmfulness of various kinds of dust, and the weight coefficient set, then an appropriate correlation function selected to calculate the degree of dust concentration in the environment meeting the requirements. Finally, a relatively comprehensive conclusion is obtained through the calculation and analysis of the comprehensive correlation function, by which the hazard of the coal mine dust concentration in a certain environment is comprehensively evaluated. The calculation and analysis process adopts the self-designed extension intelligent evaluation software of the coal mine dust concentration, which can intelligently output coping strategies according to the calculation results.

The method of transforming bridge is an effective method in Extenics used to solve antithetical problems. The study addresses the problem in the application of the transforming bridge, refines and improves the existing transforming bridge, optimizes the general steps, and applies it to the planning and design of a golf course to obtain a variety of effective design strategies. The further refinement of the transforming bridge will provide more specific methods for the solution of antithetical problems in various fields, and has very important application value for the formal and quantitative study of antithetical problems.

To address the scheduling problem of multi-department cooperative treatment of patients under the condition of limited medical resources in a hospital, this paper proposes a collaborative treatment scheduling algorithm based on intelligent optimization. The proposed algorithm regards the cooperative treatment scheduling of doctors, nurses and patients in different scenarios as a multi-role cooperative control problem. In order to optimize the role's access behavior, we propose a decision-making model to guide the role to make the optimal access behavior, and introduce an intelligent optimization algorithm to optimize the decision-making model. For the case scenarios of collaborative treatment of patients, doctor ward rounds, and patient physical examinations, we conduct experiment to compare four scheduling strategies, includingthe random, shortest distance, maximum free space, and decision-making models, and comparatively analyze the performance of the genetic algorithms, particle swarm optimization, simulated annealing, and differential evolution in optimizing the decision-making models. The experimental results demonstrates that the decision-making model based on the differential evolution algorithm performs the best, and the optimized decision-making model can find feasible solutions in the case scenarios and also obtain the optimal scheduling results.

Coarctation of aorta (CoA) is a congenital malformation of the aortic arch with a poor natural prognosis, which requires early intervention and even emergency surgery. Meanwhile, postoperative aortic re-coarctation is still a possible problem. At present, the prediction of aortic re-coarctation is mainly carried out based on the risk factor analysis of doctors on the clinical characteristics of patients combining with echocardiography (Ultra Sound Cardiogram) data, which is easy to be misdiagnosed. In this paper, a multimodal data detection framework based on Swin-Unet network is proposed based on the images of the patient's heart from computed tomography (CT) combining with the patient's clinical data. The framework carries out multimodal feature fusion analysis by combining the Swin-Unet network and the machine learning models, aiming to perform early detection of aortic re-coarctation. The experimental results on the clinical dataset show that our proposed methodeffectively improves the prediction effect of aortic re-coarctation when compared with the traditional prediction methods using clinical data. Particularly, we verifie the risk factors related to re-coarctation, the results of which provides a reference for clinical medicine.

Hepatobiliary stone disease is a common liver disease and has become the main cause of death from non-neoplastic biliary diseases in China, and it is important to achieve interpolated segmentation reconstruction between slices of hepatobiliary ducts. In this research, an end-to-end framework for super-resolution abdominal CT image processing is proposed based on deep correlation mechanism. The framework cascades an inter-slice interpolation network and a segmentation network, in which the ConvLSTM is introduced to enhance the extraction of high-dimensional feature information of hepatobiliary ducts between slices. A novel loss is designed by combining the loss of the interpolation network and the loss of the segmentation network. Experimental results show that the proposed framework is superior to the existing deep learning methods for the segmentation of hepatobiliary ducts, which is beneficial for the 3D reconstruction of hepatobiliary ducts.

In order to improve the performance and efficiency of image segmentation with multilevel threshold of two-dimensional entropy for practical industrial applications, this paper proposes a fast image segmentation method with multilevel threshold of two-dimensional entropy based on ISSA and integral graph. Firstly, we introduce and analyze the sparrow search algorithm (SSA). To address the shortcomings of SSA, such as poor global search ability and easy to fall into local optimal solution, we propose an improved sparrow search algorithm (ISSA) based on Gaussian perturbation strategy with linear decreasing variance and moving strategy with random step size. Then, we further introduce the integral graph method to reduce the calculation amount of the entropy, use the entropy as the fitness function of ISSA to search the optimal threshold, and propose a fast algorithm for image segmentation with multilevel threshold of two-dimensional entropy based on ISSA and integral graph. Finally, we compare the proposed method with the existing segmentation algorithms, and the experimental results show that the proposed method improves the segmentation efficiency of image segmentation with multilevel threshold of two-dimensional entropy in industrial application scenarios.

With the continuous expansion of differential privacy and its applications, its application in the privacy protection field of trajectory data release has received extensive attention. However， most existing research methods use the Kmeans to cluster the trajectory, , which cannot guarantee the final convergence due to the fact that the trajectory datasets under differential privacy constraints are usually disturbed by noise. To addrss this, this paper proposes an orientation control-based differential privacy-preserving trajectory data publishing method. Firstly, a trajectory generalization algorithm based on SKmeans|| clustering is proposed, which updates the centroid via a direction control mechanism during iterative process of clustering, and designs a scoring function in the index mechanism to control the convergence of the centroid, such that the quality of high dimensional data clustering can be improved. Secondly, a trajectory data publishing algorithm based on bounded noise mechanism is designed, which improves the availability of trajectory data after publishing. Meanwhile, the bounded noise mechanism ensures the true count of the hidden trajectory. Finally, the effectiveness of the method proposed in this paper is evaluated by experiments.

In the process of UAV swarm road inspection, there are many problems such as difficulty in UAV route planning, unbalanced utilization of UAVs, and difficulty in determining the distributed airport site. In response, firstly, an inspection map is built to remove the redundant information irrelevant to road inspection. Secondly, route planning and airport site selection are unified in the framework of multi-objective optimization. Thirdly, the particle coding method, particle update rules and particle decoding method are proposed which combine route optimization and airport site selection. Fourthly, several evaluation indexes are proposed comprehensively to evaluate the effect of route planning and airport site selection. The experimental results show that: (1) By this method, the mileage of repeated part in optimized inspection route is less than 7% of the total mileage, and the UAV utilization balance rate is more than 75%. (2) After optimization, the reuse rate of distributed airports has been significantly improved. It shows that the method proposed in this research can plan the route for UAV road inspection task preferably, balance the utilization of UAV, and select a better location as the distributed airport site. It provides a firm foundation for autonomous UAV swarm road inspection system.

The energy-efficient caching strategy and computation offloading design of mobile edge computing (MEC) faces the double randomness challenges, which require to adapt to the time-varying wireless channel state and the dynamic task arrivals. This paper investigates a cache-enabled mobile edge computing system with dynamic tasks arriving at multiple wireless devices. By minimizing the system weighted sum energy over multiple time slots, we optimize the AP's task caching decision and MEC execution, the wireless devices’ local computing and tasks offloading under the caching capacity and computation causality, and the computation deadline constraints. The branch-and-bound (BnB) method is first presented to obtain the globally optimal solution to define the lower bound for practical schemes. Then, a relaxation-based scheme is proposed to efficiently achieve a near-optimal solution. Numerical results show that the proposed relaxation-based scheme achieves a closer performance to the optimal BnB scheme when compared to the benchmark schemes.

This paper investigates the problem of pattern failure safe diagnosis of decentralized fuzzy discrete event systems, and proposes a pattern failure safe diagnosis method based on pattern failure safe codiagnoser. Specifically, we first introduce the concepts of pattern failure and safe diagnosis of pattern failure in fuzzy discrete event systems, and then formalize the notion of safe codiagnosibility of pattern failure for fuzzy discrete event systems. Moreover, to verify the pattern failure safe codiagnosibility of decentralized fuzzy systems, we construct the pattern failure safe codiagnoser is constructed, and developa necessary and sufficient condition for the safe codiagnosibility of pattern failure. By doing these, the pattern failure safe diagnosis of decentralized fuzzy discrete event systems can be performed based on the pattern failure safe codiagnoser.

In this paper, we investigate the problem of consensus in leader-following multi-agent system, where the information of the leader is only accessed by a subset of the following agents. For the part of the follower who cannot obtain the leader’s information, the state of the leader need to be estimated. In addition, considering the discontinuity of obtaining the output information of agents under certain conditions, an impulsive observer is introduced to reduce the sampling times among multiple agents. To achieve this, this paper aims to design a controller based on impulsive observer to achieve the consensus of leader-following multi-agent systems. Firstly, an impulsive full-order observer and a consensus protocol are designed for each follower, so that the follower can use the observer to estimate the leader. Secondly, the dynamic equation of the error system is derived, and the appropriately Lyapunov function is constructed by using the error variables. Finally, the stability of error systems is studied by using the Lyapunov stability theory combining with the linear matrix inequalities, so that the sufficient conditions for the leader-following consensus problem of multi-agent systems can be obtained. Numerical simulation results clearly show the effectiveness of the proposed controller.

For the resonant grounding system of the distribution network with the neutral point grounded through the arc suppression coil, there are problems such as inconspicuous fault current characteristics, difficulty in threshold setting and low accuracy of fault line selection when a single-phase grounding fault occurs. In this research，a threshold-free fault line selection method based on Complementary Ensemble Empirical Mode Decomposition (Complementary Ensemble Empirical Mode Decomposition, CEEMD) and sample entropy is proposed. By analyzing the transient characteristics of the zero-sequence current of the fault line, the high-frequency components of the oscillation and attenuation characteristics are selected as the observation objects. A fast extraction method and fault characterization strategy of high-frequency components based on CEEMD algorithm are proposed, and the reliability of sample entropy based on high-frequency components as a threshold-free line selection criterion is explored. The resonant grounding system model under disturbance under multiple scenarios is simulated and established, and the feasibility of the proposed algorithm in engineering scenarios such as different line positions, fault closing angle, grounding resistance and arc suppression coil compensation degree is verified. Among them, the proposed algorithm can realize fast fault line selection that only collects half-cycle current. In addition, the method does not need to set a threshold value, and is not affected by different operating modes of the system, which is easy for engineering application of multi-scenario fault line selection.

To address the harmonic pollution caused by nonlinear loads, a single-tuned hybrid active power filter (HAPF) with asymmetric topology is proposed, which consists of a three-phase voltage converter and a two-phase LC passive filter. By analyzing the operating principle and characteristics of the new topology, a filtering equivalent circuit is designed. By analyzing the working principle and characteristics of the new topology, a filter equivalent circuit is constructed. The two-phase current with passive filter is used to construct the control quantity of asymmetric third-phase current, which can effectively suppress the three phase harmonic current of the power grid and control the DC-link voltage of the filter. The DC-link voltage is stabilized by Proportional Integral (PI). The topology circuit and control strategy are tested by conducting simulations and experiments, and the experimental results validate the feasibility of the proposed topology and control scheme.

Aiming at the problems such as insufficient ability to detect discharge state and hysteresis of output of existing wire-cut pulse power supply, a self-adjusting pulse power supply based on FPGA is developed. The power supply can accurately identify the discharge state of each processing pulse by high-speed A/D sampling. By adjusting the pulse output frequency in real time according to different discharge states, the harmful discharge state can be suppressed and the effective discharge pulse ratio can be improved. The experimental results show that the machining efficiency of the Connecting Rod Cutting machine is improved by using the self-tuning pulse power supply, compared with that by using the constant frequency pulse power supply. The pulse power supply promotes the development of the reciprocating wire cutting machine to high efficiency, energy saving and automation.

Octylphenol is widely used in coating, rubber and other industries. It is a typical Phenolic Endocrine Disruptors (PEDs). Low concentration of octylphenol in environmental medium may also cause great environmental harm. It is urgent to develop efficient and rapid detection methods to warn and predict its environmental risks. In this study, microspheres were prepared with silver particles as core and octylphenol engram on surface. A method for rapid determination of trace octylphenol by surface enhanced Raman scattering and molecular engram was tested. The results of Transmission Electron Microscope (TEM) , X-ray diffraction (XRD) and elemental Energy Dispersive Spectroscopy (EDS) showed that the silver core octylphenol engram microspheres are the core-shell structure of silica wrapped with shell, octylphenol is imprinted on the surface of the shell, and the shell did not affect the crystal structure of the silver core. The shell thickness affects the selective adsorption of octylphenol and Raman light enhancement effect, the best shell thickness of microspheres is 25 nm. The minimum detection limit of octylphenol by surface enhanced Raman spectroscopy combined with molecular engram is 10⁻¹² mol/L.

In order to study the friction angles between different soils and concrete surface in Guangzhou area, four soil samples and three different slurry-soil mixtures from different regions were put into the modified direct shear apparatus, and direct shear tests were carried out under different normal stresses to obtain the friction angles between soils, slurry-soil mixtures and concrete surfaces. Testing results show that: (1) the friction angle between granite residual remolded dry soil and concrete block surface is 25.2°. The friction angle between granite residual undisturbed soil and concrete block surface is 24.1°. Nansha silt soil and concrete block surface friction angle is 17.2°. Fujian standard dry sand and concrete block surface friction angle is 31.7°; (2) the friction angle between soil and concrete surface depends on the mineral composition of soil, normal pressure, the interlocking between particles, water content, and the interaction between soil and concrete surface. The degree of convexity and concavity changing on concrete surface, the content and gradation of sand particles in soil, the content of clay and mica, unsaturated state of soil, determine the cohesion of concrete and soil shear surface finally; and (3) as slurry weight ratio increasing, frictional coefficient and cohesion are both decreased between slurry-soil mixture and concrete surfaces.

There are a large number of institutional investors in the financial market. The characteristics of institutional investors pursuing high returns and high wealth lead to increasingly fierce market competition. The competitive market environment makes institutional investors not only pursue the maximization of their own wealth, but also pay attention to the wealth gap between them and their competitors. The investment and risk control problem under the interaction of multiple institutional investors' strategies was studied, considering the situation that there are a large number of institutional investors in the market. It is assumed that each investor can invest his wealth in the financial market to realize wealth appreciation, and at the same time transfer the risks he faces to other financial institutions in the market by purchasing insurance and other means. The relative performance described by the difference between the investor's own wealth and the market average wealth is used to describe the market competition. The objective of the investor is to maximize the expected utility of the relative performance at the terminal moment. A multi person investment and risk control game model is constructed under the non-zero sum game framework. Taking the CARA utility function as an example, the optimal investment and risk control strategy under the Nash equilibrium state is obtained by using the stochastic differential game theory and the mean field game theory, and the sensitivity analysis of parameters is carried out. The research finds that: (1) Competition will lead to the rise of venture capital and the weakening of risk control, which will lead to the increase of systemic risk in the financial market; (2) The risk preference and market competition of institutional investors and their competitors will affect the balanced investment and risk control strategy; (3) Earnings fluctuation affects the risk control strategy in the same direction, but this effect is more obvious when the fluctuation is slight. When the fluctuation exceeds a certain degree, the fluctuation has little impact on the risk control strategy. The research provides useful guidance for institutional investors to choose investment and risk control strategies.