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The purpose of this paper is to present a scan matching simultaneous localization and mapping (SLAM) algorithm based on particle filter to generate the grid map online. It mainly focuses on reducing the memory consumption and alleviating the loop closure problem.

The proposed method alleviates the loop closure problem by improving the accuracy of the robot’s pose. First, two improvements were applied to enhance the accuracy of the hill climbing scan matching. Second, a particle filter was used to maintain the diversity of the robot’s pose and then to supply potential seeds to the hill climbing scan matching to ensure that the best match point was the global optimum. The proposed method reduces the memory consumption by maintaining only a single grid map.

Simulation and experimental results have proved that this method can build a consistent map of a complex environment. Meanwhile, it reduced the memory consumption and alleviates the loop closure problem.

In this paper, a new SLAM algorithm has been proposed. It can reduce the memory consumption and alleviate the loop closure problem without lowering the accuracy of the generated grid map.

For autonomous vehicles, trajectory prediction of surrounding vehicles is beneficial to improving the situational awareness of dynamic and stochastic traffic environments, which is a crucial and indispensable element to realize highly automated driving.

In this paper, the overall framework consists of two parts: first, a novel driver characteristic and intention estimation (DCIE) model is built to indicate the higher-level information of the vehicle using its low-level motion variables; then, according to the estimation results of the DCIE model, a classified Gaussian process model is established for probabilistic vehicle trajectory prediction under different motion patterns.

The whole method is later applied and analyzed in the highway lane-change scenarios with the parameters of models learned from the public naturalistic driving data set. Compared with other traditional methods, the performance of this proposed approach is proved superior, demonstrated by the higher accuracy in the long prediction horizon and a more reasonable description of uncertainty.

This hierarchical approach is proposed to make trajectory prediction accurately both in the short term and long term, which can also deal with the uncertainties caused by the perception system or indeterminate vehicle behaviors.

The formation of unmanned aerial vehicle (UAV) swarm plays a critical role in numerous applications, such as unmanned agriculture, environmental monitoring and cooperative fencing. Meanwhile, the self-organized swarm model exhibits excellent performance in amorphous formation flight, and its collective motion pattern displays great potential in dense obstacle avoidance. The paper aims to realize the formation maintenance of UAVs while combining the advantage of the self-organized swarm model in avoiding dense obstacles. Thereby enhancing the flexibility, adaptability and safety of UAV swarms in dense and unpredictable scenarios.

In this paper, a self-organized formation (SOF) swarm model with a constrained coordination mechanism is proposed. A global information-based formation rule is designed to flexibly maintain the formation. A constraint coordination mechanism is designed to resolve the problem of constraint conflicts between formation rules and self-organized behavior rules. The model introduces a new obstacle avoidance rule to prevent deadlocks. Extensive experiments including simulations, real flights and comparative experiments are conducted to evaluate the performance of the model.

The simulation results show that SOF swarm enables the formation elastically to dense obstacles. Compared to the Vasarhelyi model, swarm performance metrics are improved. For example, the task completion time of SOF swarm is reduced by 16%, 28% and 39% across the three obstacle densities, and the order of SOF swarm is improved by 4%, 13% and 18%, respectively. The proposed model is also validated with a swarm of seven quadcopters that can successfully navigate and maintain formation in a real-world indoor environment with dense obstacles. Video at: https://youtu.be/V8hYgOHxWls.

The proposed formation rule is based on global information construction, which presents challenges in terms of communication overhead in distributed systems.

An SOF swarm model is proposed, which achieves formation maintenance by incorporating formation rule and constraint coordination mechanism and improves obstacle avoidance performance by introducing a new obstacle avoidance rule. After real UAVs verification, the model is feasible for practical deployment and provides a new solution to the formation flight and formation maintenance problems encountered in dense environments.

Because submaps including a subset of the global map contain more environmental information, submap-based graph simultaneous localization and mapping (SLAM) has been studied by many researchers. In most of those studies, helpful environmental information was not taken into consideration when designed the termination criterion of the submap construction process. After optimizing the graph, cumulative error within the submaps was also ignored. To address those problems, this paper aims to propose a two-level optimized graph-based SLAM algorithm.

Submaps are updated by extended Kalman filter SLAM while no geometric-shaped landmark models are needed; raw laser scans are treated as landmarks. A more reasonable criterion called the uncertainty index is proposed to combine with the size of the submap to terminate the submap construction process. After a submap is completed and a loop closure is found, a two-level optimization process is performed to minimize the loop closure error and the accumulated error within the submaps.

Simulation and experimental results indicate that the estimated error of the proposed algorithm is small, and the maps generated are consistent whether in global or local.

The proposed method is robust to sparse pedestrians and can be adapted to most indoor environments.

In this paper, a two-level optimized graph-based SLAM algorithm is proposed.

This paper aims to optimize cable-stayed force in asymmetric one-tower cable-stayed bridge formation using an improved particle swarm algorithm. It compares results with the traditional unconstrained minimum bending energy method.

This paper proposes an improved particle swarm algorithm to optimize cable-stayed force in bridge formation. It formulates a quadratic programming mathematical model considering the sum of bending energies of the main girder and bridge tower as the objective function. Constraints include displacements, stresses, cable-stayed force, and uniformity. The algorithm is applied to optimize the formation of an asymmetrical single-tower cable-stayed bridge, combining it with the finite element method.

The study’s findings reveal significant improvements over the minimum bending energy method. Results show that the structural displacement and internal force are within constraints, the maximum bending moment of the main girder decreases, resulting in smoother linear shape and more even internal force distribution. Additionally, the tower top offset decreases, and the bending moment change at the tower-beam junction is reduced. Moreover, diagonal cable force and cable force increase uniformly with cable length growth.

The improved particle swarm algorithm offers simplicity, effectiveness, and practicality in optimizing bridge-forming cable-staying force. It eliminates the need for arbitrary manual cable adjustments seen in traditional methods and effectively addresses the optimization challenge in asymmetric cable-stayed bridges.

The purpose of this paper is to accomplish the low cost mass‐production of flexible radio frequency identification (RFID) tag inlays.

An anisotropic conductive paste (ACP) is prepared by mixing uniform micro‐sized spherical conductive particles, latent curing agent and other additives into a thermoset epoxy resin. RFID tag inlays are assembled with the paste through flip‐chip technology. The microstructural analysis of bonded joints, bond strength testing, and high‐temperature and humidity aging testing are employed to evaluate the performance of the inlays.

It was found that the chips are hard assembled on the antennae by the ACP. Flexible RFID tag inlays assembled using the presented method have good reliability when working under high frequency (13.56 MHz) conditions.

The method presented is a promising new way for packaging flexible RFID tag inlays with ACP. Through the use of flip‐chip technology, large‐scale production is possible with low manufacturing costs.

The paper details a simple way to prepare an anisotropic conductive paste and to assemble flexible RFID tag inlays. The technique uses flip‐chip technology with the paste as the electrical and mechanical interconnection material. It presents a simple and fast method of assembly for flexible RFID tag inlays on a large‐scale with low cost.

This study aims to revisit the relationship between Shariah-compliant firms and earnings management. Specifically, the authors examine whether Shariah-certified firms have lower earnings management than non-Shariah-certified firms and how often a firm must hold its certification to observe considerably reduced earnings management. This study also explores how senior management ethnic dualism affects the association of Shariah certification and earnings management.

The authors analyze the hypothesized association between Shariah certification and earnings management using a panel regression model and several robustness tests, including the Heckman selection model. The sample consists of 547 nonfinancial firms listed on the Bursa Malaysia stock exchange, with 5,478 firm-year observations over the 2001–2016 sample period.

Shariah certification is found to mitigate earnings management, particularly for firms that consistently retain their Shariah status. The longer firms retain their Shariah certification continually, the lower the earnings management. Additionally, the results indicate that the negative impact of Shariah certification on earnings management is driven by ethnic duality when a specific ethnic group dominates the top management.

Firms’ commitment to religious-based screening and continuation of certification plays a significant role in improving earnings quality. Firms are committed to abiding by the Shariah code of conduct instead of using the Shariah status for reputation purposes to attract investors.

For investors, the continuous compliance status is a crucial indicator of a firm’s commitment to comply with Shariah principles and to mitigate earnings management. Regarding policy implications, Shariah-compliance guidelines can constrain earnings manipulation, especially among firms lacking ethnic diversity.

The study shows that Shariah certification must be maintained consecutively to reduce earnings management. Shariah certification’s governance function is crucial in ethnically homogeneous firms, primarily when one ethnic group dominates the senior management.

This paper aims to understand the role that money plays in polygamous marriages among the Hui ethnic group in Northwest China.

This study conducted in-depth interviews, focus group discussions and observations based on snowball sampling of individuals who voluntarily agreed to participate from June to December 2010, and during the summer of 2011, in Qinghai in Northwest China. Follow-up interviews and observations were conducted in 2015.

This study examines how love and money intersect and work together to sustain the participants’ polygamous marriages. The study concludes that material desires unite love with money to make love consumable. With the help of money, love between the sexes is transformed into desirable consumption through economic activities associated with leisure, gift giving and religious beliefs to articulate individualism

This study is the first to explore money’s role in the experience of polygamy among the Hui ethnic group in China.

It is a huge technical and engineering challenge to realize the precise assembly of thousands of large optics in high power solid-state laser system. Using the 400-mm aperture-sized transport mirror as a case, this paper aims to present an intelligent numerical computation methodology for mounting performance analysis and modeling of large optics in a high-power laser system for inertial confinement fusion (ICF).

Fundamental principles of modeling and analysis of the transport mirror surface distortion are proposed, and a genetic algorithm-based computation framework is proposed to evaluate and optimize the assembly and mounting performance of large laser optics.

The stringent specifications of large ICF optics place very tight constraints upon the transport mirror’s assembly and mounts. The operational requirements on surface distortion [peak-to-valley and root mean square (RMS)] can be met as it is appropriately assembled by the close loop of assembly-inspection-optimization-fastening. In the end, the experimental study validates the reliability and effectiveness of the transport mirror mounting method.

In the assembly design and mounting performance evaluation of large laser optics, the whole study has the advantages of accurate evaluation and intelligent optimization on nano-level optical surface distortion, which provides a fundamental methodology for precise assembly and mounting of large ICF optics.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.