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With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection.

This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure.

This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs.

The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate.

The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security.

This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.

The purpose of this study is to substantiate the matrix approach to digitalization of management objects based on identification of relevant qualitative characteristics of these objects and its dichotomies, which allowing determine the quantity and quality of their main variants, as well as the relationships between them.

Methods of classification and typology are selected as study methods, and binary matrices are used as the tool to determine the main variants of management objects, assign binary codes to it and form codes of more complex management objects on its basis, depending on the content of study tasks.

The main results of study include the classification of organization components; variants for choosing qualitative characteristics of chains components; adjusted content of methodology of qualitative research of management objects; sequences of “up” and “down” digitization of these objects; actual qualitative characteristics of e components of management objects and dichotomies; and variants of forming of ciphers of these objects.

The use of study results allows to reduce the complexity of substantiating and making managerial decisions in organization and supply chains, to structure these decisions by man-agement levels and positions and to reduce costs, time and lost profits for fulfilling orders of end consumers of products and/or services.

The originality of this study is confirmed by the substantiation of choice and use of actual qualitative characteristics of management objects and its dichotomies, which allow obtaining two variants of these objects and assigning them binary codes processed using computer software for management activities.

Briefly reviews previous literature by the author before presenting an original 12 step system integration protocol designed to ensure the success of companies or countries in their efforts to develop and market new products. Looks at the issues from different strategic levels such as corporate, international, military and economic. Presents 31 case studies, including the success of Japan in microchips to the failure of Xerox to sell its invention of the Alto personal computer 3 years before Apple: from the success in DNA and Superconductor research to the success of Sunbeam in inventing and marketing food processors: and from the daring invention and production of atomic energy for survival to the successes of sewing machine inventor Howe in co‐operating on patents to compete in markets. Includes 306 questions and answers in order to qualify concepts introduced.

This paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.

Because of taking on the spatiotemporal characteristics, panel data can well-describe and depict the systematic and dynamic of the decision objects. However, it is difficult for traditional panel data analysis methods to efficiently extract information and rules implied in panel data. To effectively deal with panel data clustering problem, according to the spatiotemporal characteristics of panel data, from the three dimensions of absolute amount level, increasing amount level and volatility level, the authors define the conception of the comprehensive distance between decision objects, and then construct a novel grey incidence analysis clustering approach for panel data and study its computing mechanism of threshold value by exploiting the thought and method of three-way decisions; finally, the authors take a case of the clustering problems on the regional high-tech industrialization in China to illustrate the validity and rationality of the proposed model.

The results show that the proposed model can objectively determine the threshold value of clustering and achieve the extraction of information and rules inherent in the data panel.

The novel model proposed in the paper can well-describe and resolve panel data clustering problem and efficiently extract information and rules implied in panel data.

The proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.

A common approach to predicting the price of residential properties uses the hedonic price model and its spatial extensions. Within the hedonic approach, real estate prices are decomposed into internal characteristics of an apartment, apartment characteristics and external characteristics. To account for the unobserved quality of the surrounding environment, price models include spatial price correlation factors, where the distance is usually measured as the distance in geographic space. In determining the price, a seller focuses not only on the observed and unobserved factors of the apartment and its environment but also on the prices of similar marketed objects that can be selected both by geographic proximity and by characteristics similarity. The purpose of this study is to show the latter point empirically.

This study uses an ensemble clustering approach to measure objects' proximity and test whether the proximity of objects in the property characteristics space along with spatial correlation explain the significant variation in prices.

In this paper, the pricing behaviour of sellers in a reselling market in Perm, Russia is studied. This study shows that the price transmission mechanism includes both geographic and characteristics spaces.

After testing on market data, the proposed framework for the distance construct could be used to obtain higher predictive power for price predictive models and construction of automated valuation services.

This study tests the higher explanatory power of the model that includes both the distance measured in geographic and property characteristics spaces.

Gives introductory remarks about chapter 1 of this group of 31 papers, from ISEF 1999 Proceedings, in the methodologies for field analysis, in the electromagnetic community. Observes that computer package implementation theory contributes to clarification. Discusses the areas covered by some of the papers ‐ such as artificial intelligence using fuzzy logic. Includes applications such as permanent magnets and looks at eddy current problems. States the finite element method is currently the most popular method used for field computation. Closes by pointing out the amalgam of topics.

Presently, 6 Degree of Freedom (6DOF) visual pose measurement methods enjoy popularity in the industrial sector. However, challenges persist in accurately measuring the visual pose of blank and rough metal casts. Therefore, this paper introduces a 6DOF pose measurement method utilizing stereo vision, and aims to the 6DOF pose measurement of blank and rough metal casts.

This paper studies the 6DOF pose measurement of metal casts from three aspects: sample enhancement of industrial objects, optimization of detector and attention mechanism. Virtual reality technology is used for sample enhancement of metal casts, which solves the problem of large-scale sample sampling in industrial application. The method also includes a novel deep learning detector that uses multiple key points on the object surface as regression objects to detect industrial objects with rotation characteristics. By introducing a mixed paths attention module, the detection accuracy of the detector and the convergence speed of the training are improved.

The experimental results show that the proposed method has a better detection effect for metal casts with smaller size scaling and rotation characteristics.

A method for 6DOF pose measurement of industrial objects is proposed, which realizes the pose measurement and grasping of metal blanks and rough machined casts by industrial robots.

The purpose of this paper is to guide companies in conducting benchmarking studies of their manufacturing processes by viewing across industries, locations and products. In particular, the proposed framework can help corporate decision makers in terms of production footprint and site location studies. The level of benchmarking performance can be measured by evaluating defined benchmarking evaluation profiles.

This paper develops a tool to operationalize value-added manufacturing processes for benchmarking evaluations. In this context, an object-oriented database structure has been developed for the business areas such as product development, manufacturing and assembly. This paper focuses on manufacturing processes. Furthermore, a framework for applying high-performance benchmarking has been developed and applied in a case study.

This paper shows that object class-oriented modeling approach can be applied to manufacturing processes. The higher the degree of independence in terms of locations, industry sectors and products, the more powerful thus a higher performance of benchmarking is achieved. The performance level of benchmarking has been defined by proving and demonstrating higher and lower performance levels. The high-performance benchmarking tool has been successfully applied to a production footprint case study.

This paper takes up the superiority of process benchmarking that has been the focus of numerous research papers on benchmarking techniques in the past. The potential of process benchmarking has been enhanced and operationalized as a tool. A classification logic for benchmarking evaluation profiles has been developed and integrated in the overall tool set. The model helps decision makers to configure their benchmarking studies tailored to their strategic entrepreneurial questions and to guide them to achieve a higher benchmarking performance level.

Introduces papers from this area of expertise from the ISEF 1999 Proceedings. States the goal herein is one of identifying devices or systems able to provide prescribed performance. Notes that 18 papers from the Symposium are grouped in the area of automated optimal design. Describes the main challenges that condition computational electromagnetism’s future development. Concludes by itemizing the range of applications from small activators to optimization of induction heating systems in this third chapter.