Der Europäische Master für Lexikographie 2021 an der Friedrich-Alexander-Universität Erlangen-Nürnberg

Schierholz, Stefan J.

doi:10.1515/lex-2021-0018

Cited by 4 publications

(5 citation statements)

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“…The new variables, as the components, were aggregated by a two-step cluster analysis in SPSS 22. The log similarity value was chosen as the distance measure, and the Bayesian information criterion (BIC) was chosen as the clustering criterion [87]. Taking advantage of the automatic determination of the cluster number by the software, different cluster distributions and cluster combination occupancy ratios could be formed.…”

Section: Identification and Visualization Of Landscape Character Typesmentioning

confidence: 99%

Integrating Heritage and Environment: Characterization of Cultural Landscape in Beijing Great Wall Heritage Area

He,

Chen,

Zhang

2024

Land

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The Great Wall, as a globally important large-scale linear cultural heritage asset, is an example of the integration of architecture and landscape, demonstrating the interaction and feedback between heritage and the environment. In the context of advocating the holistic protection of cultural heritage and surroundings, this study utilizes landscape character assessment (LCA) to identify the landscape character of the Great Wall heritage area. Taking the heritage area of the Great Wall in Beijing, China, as an example, principal component analysis (PCA), two-step clustering, and the eCognition software were used to identify and describe the landscape character types, and the interaction mechanism between heritage and the environment was further explored through the reclassification process. A total of 20 landscape character types and 201 landscape character areas were identified in the study area, and a deep coupling relationship between heritage and the environment and cultural landscape spatial patterns were found in the core heritage area. The heritage and environmental character of linear heritage areas should be integrated so as to protect, manage, and plan cultural heritage areas at the landscape level. This study identifies and describes the character of the coupling of heritage and the environment in the Great Wall area for the first time, expands the types and methods of landscape character assessment, and carries out the exploration to combine natural and cultural elements of large-scale linear cultural heritage areas.

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Section: Identification and Visualization Of Landscape Character Typesmentioning

confidence: 99%

Integrating Heritage and Environment: Characterization of Cultural Landscape in Beijing Great Wall Heritage Area

He,

Chen,

Zhang

2024

Land

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“…Next, an early computation of the number of groups was performed by utilizing that indicator. By identifying the distance's biggest change between the two nearest groups throughout each stage of hierarchical clustering, the early estimation was finally improved [34]. where…”

Section: Clustering Analysismentioning

confidence: 99%

Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques

Le,

Tran,

2023

Sustainability

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Urban traffic accidents pose significant challenges to the sustainability of transportation infrastructure not only in Vietnam but also all over the world. To decrease the frequency of accidents, it is crucial to analyze accident data to determine the relationship between accidents and causes, especially for serious accidents. This study suggests an integrated approach using Geographic Information System (GIS) and Data Mining methods to investigate the features of urban traffic accidents in Hanoi, Vietnam aiming to solve these challenges and enhance the safety and efficiency of urban transportation. Firstly, the dataset was segmented into homogenous clusters using the two-step cluster method. Secondly, the correlation between causes and traffic accidents was examined on the overall dataset as well as on each cluster using the association rule mining (ARM) technique. Finally, the location of accident groups and high-frequency sites of accidents (hotspots) were determined by using GIS techniques. As a result, a five-cluster model was created, which corresponded to five common accident groupings in Hanoi. Moreover, the results of the study also identified the types of accidents, the main causes, the time, and the surrounding areas corresponding to each accident group. In detail, cluster 5 depicted accidents on streets, provincial, and national roads caused by motorbikes making up the highest percentage within the groups, accounting for 29.2%. Speeding and driving in the wrong lane in the afternoon and at night were the main causes in this cluster (Cf ≥ 0.9 and Lt ≥ 1.22). Next, cluster 2 had the second-highest proportion. Cluster 2 presented accidents between a truck/car and a motorbike on national and provincial roads, accounting for 27.8%. Cluster 1 presented accidents between a truck/car and a motorbike on local streets, accounting for 22%. Cluster 3 illustrated accidents between two motorbikes on the country lanes, accounting for 12.3%. Finally, cluster 4 depicted single-vehicle motorbike crashes, with the lowest rate of 8.8%. More importantly, this study also recommended using repeatability criteria for the same type of accidents or causes to determine the location of hotspots. Also, suggestions for improving traffic infrastructure sustainability were proposed. To our knowledge, this is the first time in which these three methods are applied simultaneously for analyzing traffic accidents.

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“…Various studies are available about the usage of dynamically obtained stiffness values, and a few examining static loading. [4], [5], [6], [7] No publications about performing the study on the same specimen after cyclic static loading has been found by the authors. In this research, static and dynamic experiments are performed on the same specimen -a real rotor lamination stack used in an automotive electric drive, to avoid altering the nonlinear behavior of the assembly.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of stiffness measurement methods for lamination stacks in electric machines

Szabó,

Gábor Gazdagh,

Vehovszky

2023

J. Phys.: Conf. Ser.

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With the increasingly strict global regulations on vehicle emissions, the demand for electromobility has risen due to its potential to reduce local emissions. However, designing an electric motor for use in passenger cars poses significant challenges. The e-motor should operate reliably in various climates with good mechanical and electrical characteristics, be lightweight, withstand repetitive thermal and structural loads, be cost-effective, and - last but not least - exhibit good noise and vibration properties. To meet these requirements, virtual testing through simulations has become the most efficient and economical approach, enabling the identification of weaknesses and unwanted behaviors before physical prototyping. This paper focuses on the testing of lamination stacks, a critical component of electric motors. Two methods of stiffness measurement for such parts are compared: static stiffness determination and dynamic analysis. The former involve compressing the specimen and measuring the force-displacement response, while dynamic method uses the restoring force surface method to obtain the stiffness and damping characteristics. The study highlights the importance of considering nonlinearity in stiffness measurements. The stiffness of lamination stacks varies depending on the pretension state, and a significant hysteresis exists between the loading and unloading curves. The paper discusses the experimental procedures for each method. The findings emphasize the necessity of accurate stiffness characterization for different applications, such as structural strength, modal analysis, dynamic analysis, and noise-vibration-harshness (NVH) studies. The research contributes to the development of electric machines by providing insights into effective stiffness measurement techniques for lamination stacks.

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Der Europäische Master für Lexikographie 2021 an der Friedrich-Alexander-Universität Erlangen-Nürnberg

Cited by 4 publications

References 0 publications

Integrating Heritage and Environment: Characterization of Cultural Landscape in Beijing Great Wall Heritage Area

Integrating Heritage and Environment: Characterization of Cultural Landscape in Beijing Great Wall Heritage Area

Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques

Comparative analysis of stiffness measurement methods for lamination stacks in electric machines

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