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The purpose of this paper is to solve the azimuth sensitivity of a high-resolution range profile (HRRP), which is one of the biggest obstacles faced by a radar automatic target recognition (RATR) system.

Aimed at addressing the shortcomings of the equal angular-sector segmentation based on the scatterer model, an adaptive angular-sector segmentation is proposed on the basis of grey incidence analysis (GIA).

The main conclusions reached are as follows. First, the adaptive angular-sector segmentation in terms of GIA is suitable for RATR based on the HRRP; and, second, the adaptive angular-sector segmentation based on the type-B degree of grey incidence model is better than the Deng-Si degree of grey incidence model and the degree of grey slope incidence model.

The outcome obtained in this paper can be selected for the RATR application.

This paper has been built on the basis of previous research achievements, and a new RATR method of adaptive angular-sector segmentation is presented based on the GIA.

In the high-altitude, high-voltage electromagnetic interference operation environment, due to the parameters perturbation for robot control model caused by uncertainties and disturbances, and with the poor effective of the conventional proportional–integral–derivative (PID) control to parameters perturbation system, the mathematical model of power cable live operation robot joint PID closed-loop control system is established.

The corresponding joint motion robust PID control method is also proposed based on Kharitonov theory, the system robust stability conditions including the sufficient and necessary conditions are deduced and obtained and the solving process of robust PID control parameters stability region is provided.

Finally, the simulation research on robot joint motion PID control system is also launched in MATLAB environment based on Kharitonov theory. The results show that the conventional PID control obtains better control effect only to nominal model but is ineffective to parameter perturbation system, while robust PID obtains sound control effect to parameter perturbation system. Compared with H8 robust PID, the Kharitonov robust PID has better control effect which meet the system design requirements of joint motor quickly response, high tracking accuracy and sound stability. Finally, the validity and engineering practicability are verified by 220-kV living replacing damper operation experiment.

This paper has described the development of a damper replacement power cable live maintenance robot experimental prototype, which greatly improves operation efficiency and deals with the safety problem of operation in a high-voltage environment. A general manipulator motion control model of the power cable robot is established; the Kharitonov theory-based parameter perturbation robust motion control method of damper replacement robot is also obtained. Through the simulation comparison, it is verified that the Kharitonov control has more superiority for dealing with the parameter perturbation systems under the premise of ensuring the stability motion. The field experiment has further confirmed the engineering practicability.

The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell.

In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making.

The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest.

In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.

Allocation of complex crowdsourcing tasks, which typically include heterogeneous attributes such as value, difficulty, skill required, effort required and deadline, is still a challenging open problem. In recent years, agent-based crowdsourcing approaches focusing on recommendations or incentives have emerged to dynamically match workers with diverse characteristics to tasks to achieve high collective productivity. However, existing approaches are mostly designed based on expert knowledge grounded in well-established theoretical frameworks. They often fail to leverage on user-generated data to capture the complex interaction of crowdsourcing participants’ behaviours. This paper aims to address this challenge.

The paper proposes a policy network plus reputation network (PNRN) approach which combines supervised learning and reinforcement learning to imitate human task allocation strategies which beat artificial intelligence strategies in this large-scale empirical study. The proposed approach incorporates a policy network for the selection of task allocation strategies and a reputation network for calculating the trends of worker reputation fluctuations. Then, by iteratively applying the policy network and reputation network, a multi-round allocation strategy is proposed.

PNRN has been trained and evaluated using a large-scale real human task allocation strategy data set derived from the Agile Manager game with close to 500,000 decision records from 1,144 players in over 9,000 game sessions. Extensive experiments demonstrate the validity and efficiency of computational complex crowdsourcing task allocation strategy learned from human participants.

The paper can give a better task allocation strategy in the crowdsourcing systems.

The purpose of this paper is to provide the historical background of genealogical records and analyze the value of Chinese genealogical research through the study of names and genealogical resources.

The paper examines the historical evolution and value of Chinese genealogical records, with the focus on researching the Islamic Chinese names used by the people living in Guilin. The highlight of this paper includes the analysis and evolution of the Islamic Chinese names commonly adopted by the local people in Guilin. It concludes with the recommendations on emphasizing and making the best use of genealogical records to enhance the research value of Chinese overseas studies.

The paper covers the history of Islam and describes how the religion was introduced into China, as well as Muslims' ethnicity and identity. It also places focus on the importance of building a research collection in Asian history and Chinese genealogy.

This research study has a strong subject focus on Chinese genealogy, Asian history, and Islamic Chinese surnames. It is a narrow field that few researchers have delved into.

The results of this study will assist students, researchers, and the general public in tracing the origin of their surnames and developing their interest in the social and historical value of Chinese local history and genealogies.

The study of Chinese surnames is, by itself, a particular field for researching the social and political implications of contemporary Chinese society during the time the family members lived.

Very little research has been done in the area of Chinese local history and genealogy. The paper would be of value to researchers such as historians, sociologists, ethnologists and archaeologists, as well as students and anyone interested in researching a surname origin, its history and evolution.

The purpose of this study was to validate the feasibility of the proposed microstructure-based model by comparing the simulation results with experimental data. The study also aimed to investigate the relationship between the orientation of graphite flakes and the failure behavior of the material under compressive loads as well as the effect of image size on the accuracy of stress–strain behavior predictions.

This paper presents a microstructure-based model that utilizes the finite element method (FEM) combined with representative volume elements (RVE) to simulate the hardening and failure behavior of ferrite-pearlite matrix gray cast iron under uniaxial loading conditions. The material was first analyzed using optical microscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) to identify the different phases and their characteristics. High-resolution SEM images of the undeformed material microstructure were then converted into finite element meshes using OOF2 software. The Johnson–Cook (J–C) model, along with a damage model, was employed in Abaqus FEA software to estimate the elastic and elastoplastic behavior under assumed plane stress conditions.

The findings indicate that crack initiation and propagation in gray cast iron begin at the interface between graphite particles and the pearlitic matrix, with microcrack networks extending into the metal matrix, eventually coalescing to cause material failure. The ferritic phase within the material contributes some ductility, thereby delaying crack initiation.

This study introduces a novel approach by integrating microstructural analysis with FEM and RVE techniques to accurately model the hardening and failure behavior of gray cast iron under uniaxial loading. The incorporation of high-resolution SEM images into finite element meshes, combined with the J–C model and damage assessment in Abaqus, provides a comprehensive method for predicting material performance. This approach enhances the understanding of the microstructural influences on crack initiation and propagation, offering valuable insights for improving the design and durability of gray cast iron components.