Effect of local material properties on tapping mode atomic force microscopy

The phase image produced by Atomic Force Microscopy (AFM) is very important in the study of surface topography and properties. The phase difference of different domains on a surface is due to the different tip-sample interaction forces which are a consequence of different local properties. By simulating the AFM imaging procedure and the tip-sample interactions with variable viscosity and modulus, the effect of local material properties on phase lag was studied. These simulations showed that both elastic and viscous properties have an influence on the phase lag. For hard, elastic materials the dominant interaction force is the elastic force, and for soft, viscoelastic materials the viscous force is dominant. The phase lag between the probe response and the activation force is higher for soft viscoelstic domains. With the mathematical model it was demonstrated that the phase contrast between viscoelastic materials and silicon can be used to predict the local viscosity and elastic modulus. Experiments were done on a surface with a silicon domain which is hard and elastic and different viscoelastic domains. The experimental results of polybutadiene and polystyrene agree well with the simulation. The model was also applied to a block copolymer of butadiene and styrene and crystalline and amorphous polylactic acid. Finally, it is demonstrated that the AFM can detect materials properties beneath the surface

SpreadCluster: Recovering Versioned Spreadsheets through Similarity-Based Clustering

Version information plays an important role in spreadsheet understanding, maintaining and quality improving. However, end users rarely use version control tools to document spreadsheet version information. Thus, the spreadsheet version information is missing, and different versions of a spreadsheet coexist as individual and similar spreadsheets. Existing approaches try to recover spreadsheet version information through clustering these similar spreadsheets based on spreadsheet filenames or related email conversation. However, the applicability and accuracy of existing clustering approaches are limited due to the necessary information (e.g., filenames and email conversation) is usually missing. We inspected the versioned spreadsheets in VEnron, which is extracted from the Enron Corporation. In VEnron, the different versions of a spreadsheet are clustered into an evolution group. We observed that the versioned spreadsheets in each evolution group exhibit certain common features (e.g., similar table headers and worksheet names). Based on this observation, we proposed an automatic clustering algorithm, SpreadCluster. SpreadCluster learns the criteria of features from the versioned spreadsheets in VEnron, and then automatically clusters spreadsheets with the similar features into the same evolution group. We applied SpreadCluster on all spreadsheets in the Enron corpus. The evaluation result shows that SpreadCluster could cluster spreadsheets with higher precision and recall rate than the filename-based approach used by VEnron. Based on the clustering result by SpreadCluster, we further created a new versioned spreadsheet corpus VEnron2, which is much bigger than VEnron. We also applied SpreadCluster on the other two spreadsheet corpora FUSE and EUSES. The results show that SpreadCluster can cluster the versioned spreadsheets in these two corpora with high precision.Comment: 12 pages, MSR 201

Experimental Investigation on the Vertical Distribution of Cohesive Sediment Concentration in Weak Dynamical Flow

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Development of multiobjective optimization techniques for sonic boom minimization

An optimization procedure is developed for the simultaneous improvement of the aerodynamic and sonic boom characteristics of high speed aircraft. From a sonic boom perspective, it is desirable to minimize the first peak in the overpressure signal at a specified distance away from the aircraft. From aerodynamic point of view, the aerodynamic drag coefficient ratio must be minimized while maintaining the lift coefficient at desired level. The optimization procedure is applied to wing-body configurations related to high speed aircraft. The objectives of this current research are: (1) development of a multiobjective optimization procedure for aerospace vehicles with the integration of sonic boom and aerodynamic performance criteria; and (2) development of semi-analytical approach for calculating sonic boom design sensitivities

Research on Semiconductor Chip Grade Classification and Real-Time Evaluation Method Based on Hybrid Artificial Intelligence Technology

Semiconductor chips are widely used in various industries, making the classification of their quality grades and real-time evaluation crucial for ensuring optimal performance and reliability. This paper presents a semiconductor chip grade classification and real-time evaluation method based on hybrid artificial intelligence techniques, effectively improving the accuracy and efficiency of the classification process. Through extensive experiments on real-world data sets, the method demonstrated superior performance in terms of classification accuracy, real-time evaluation, and generalization capabilities compared to traditional methods

A lightweight dual-branch semantic segmentation network for enhanced obstacle detection in ship navigation

Semantic segmentation is essential for ship navigation as it enables the identification and understanding of semantic regions, thereby enhancing the navigational capabilities of smart ships. However, current deep learning techniques encounter challenges in balancing model size and segmentation accuracy due to the complexity of water surface features. In response, we propose a novel lightweight dual-branch semantic segmentation network. The model initially utilizes a specially designed dual-branch backbone to independently extract local details and global semantics from water surface images. The detail branch compresses and reconstructs feature information to mitigate interference from water dynamics, while the semantic branch efficiently expands the receptive field to capture global object relationships. Additionally, we introduce an aggregation module that holistically guides the feature responses to facilitate the sufficient aggregation of dual-branch information. Furthermore, a cascaded fusion approach is proposed to restore diminished localization precision, while also ensuring fusion accuracy by leveraging the segmentation attributes of deep features. Experimental results on visible light datasets from real navigation scenarios demonstrate that our network achieves approximately a 10% improvement in obstacle detection precision compared to existing advanced maritime models. Moreover, within the domain of the latest lightweight and real-time research, our network attains an optimal balance among accuracy, parameter efficiency, and real-time performance. This contributes to enhancing the navigation safety of intelligent vessels and promotes adaptability for onboard deployment