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The purpose of this paper is to propose dot sensitivity analysis of ferromagnetic materials for topology optimization in an axi-symmetric magnetostatic system.

The dot sensitivity formula for the axi-symmetric system is derived as a closed form using the continuum shape sensitivity formula. The dot sensitivity method is combined with the level set method to perform topology optimization.

Derived dot sensitivity analysis can generate a ferromagnetic ring torus in a vacant region. Thus, an initial design is not needed for the design material. Two design problems are tested to demonstrate the usefulness of dot sensitivity.

By simultaneously using the shape sensitivity and dot sensitivity, in axi-symmetric magnetic system, the design space is expanded and it includes the interface and the inside of the vacant region. This property can reduce the possibility of local optimum convergence.

The purpose of this paper is to present a new numerical technique, called adaptive level set method, for use with the finite element method.

A conventional level set method using the smeared Heaviside function has been employed for shape and topology optimizations. The smeared Heaviside function yields an indistinct interface boundary, and so can increase computational time and cause numerical errors. The adaptive level set method does not use the smeared Heaviside function. To coincide with the material interface, it processes the zero level as the boundary data of element meshing.

Usefulness and accuracy of shape optimization using the adaptive level set method are shown by comparison to the conventional level set method. A shape optimization procedure using the adaptive level set method is introduced. Numerical examples are employed to explain how the adaptive level set method is applied.

The adaptive level set method is proposed to relieve the interface problem of the conventional level set method. Shape variation in the optimization is calculated from the continuum sensitivity analysis.

The purpose of this study is to propose a novel plane-based mapping method for legged-robot navigation in a stairway environment.

The approach implemented in this study estimates a plane for each step of a stairway using a weighted average of sensor measurements and predictions. It segments planes from point cloud data via random sample consensus (RANSAC). The prediction uses the regular structure of a stairway. When estimating a plane, the algorithm considers the errors introduced by the distance sensor and RANSAC, in addition to stairstep irregularities, by using covariance matrices. The plane coefficients are managed separately with the data structure suggested in this study. In addition, this data structure allows the algorithm to store the information of each stairstep as a single entity.

In the case of a stairway environment, the accuracy delivered by the proposed algorithm was higher than those delivered by traditional mapping methods. The hardware experiment verified the accuracy and applicability of the algorithm.

The proposed algorithm provides accurate stairway-environment mapping and detailed specifications of each stairstep. Using this information, a legged robot can navigate and plan its motion in a stairway environment more efficiently.

This study aims to provide a more complete picture of blockchain development by combining numerous methodologies with diverse data sources, such as academic papers and news articles. This study displays the developmental status of each subject based on the interrelationships of each topic cluster by analyzing high-frequency keywords extracted from the collected data. Moreover, applying above methodologies will help understanding top research topics, authors, venues, institutes and countries. The differences of blockchain research and new are identified.

To identify and find blockchain development linkages, researchers have used search terms such as co-occurrence, bibliographic coupling, co-citation and co-authorship to help us understand the top research topics, authors, venues, institutes and countries. This study also used text mining analysis to identify blockchain articles' primary concepts and semantic structures.

The findings show the fundamental topics based on each topic cluster's links. While “technology”, “transaction”, “privacy and security”, “environment” and “consensus” were most strongly associated with blockchain in research, “platform”, “big data and cloud”, “network”, “healthcare and business” and “authentication” were closely tied to blockchain news. This article classifies blockchain principles into five patterns: hardware and infrastructure, data, networking, applications and consensus. These statistics helped the authors comprehend the top research topics, authors, venues, publication institutes and countries.

Since Web of Science (WoS) and LexisNexis Academic data are used, the study has few sources. Others advise merging foreign datasets. WoS is one of the world's largest and most-used databases for assessing scientific papers.

This study has several uses and benefits. First, key concept discoveries can help academics understand blockchain research trends so they can prioritize research initiatives. Second, bibliographic coupling links academic papers on blockchain. It helps information seekers search and classify the material. Co-citation analysis results can help researchers identify potential partners and leaders in their field. The network's key organizations or countries should be proactive in discovering, proposing and creating new relationships with other organizations or countries, especially those from the journal network's border, to make the overall network more integrated and linked. Prominent members help recruit new authors to organizations or countries and link them to the co-authorship network. This study also used concept-linking analysis to identify blockchain articles' primary concepts and semantic structures. This may lead to new authors developing research ideas or subjects in primary disciplines of inquiry.