Most measuring principles for surface topography measurement cannot be broadly used within machine tools due to the rough environment featuring vibrations, dust, cooling liquid, or temperature gradients. Having to mea...
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Most measuring principles for surface topography measurement cannot be broadly used within machine tools due to the rough environment featuring vibrations, dust, cooling liquid, or temperature gradients. Having to measure outside of the machine tool results in large quality control loops. The angular-resolved scattering light sensor is an established measuring principle which can be used directly in the manufacturing environment and thus allows small quality control loops and a resource-efficient manufacturing since deviations in the manufacturing process can be detected almost immediately. For the evaluation of the surface characteristics, the angular distribution of the surface within a lightspot is typically characterized by its center of gravity (M-parameter) and its variance (Aq-parameter) as described in the guideline VDA 2009. These two parameters provide only little information about the angular distribution and the workpiece quality and for certain, function-oriented applications and expanded dataanalysis can be beneficial to allow the monitoring in more manufacturing systems with high benefitss for resource-efficient manufacturing. We suggest using additional parameters of the angular distribution and perform a correlation analysis with the machining parameters of an exemplified manufacturing process to find parameters that feature a more pronounced correlation with the process parameters than Aq and thus allow an improved monitoring of the manufacturing process in this paper. The example process chosen is a grinding process with different tool grit sizes, tool surface speeds, feed rates, and coolant air flow power. With this case study it can be demonstrated how a more comprehensive analysis of the scattering of surface angles can lead to an improved process monitoring since the correlation between the process parameters and the parameters of the angular distribution shows how sensitively different deviations in the process can be detected, e.g. when the
Digital forensics is important in investigating cybercrimes and in proving the digital evidence. However, conventional forensic storage systems have defects such as data manipulation, unauthorized access, and unreliab...
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ISBN:
(数字)9798331512088
ISBN:
(纸本)9798331512095
Digital forensics is important in investigating cybercrimes and in proving the digital evidence. However, conventional forensic storage systems have defects such as data manipulation, unauthorized access, and unreliability of the system especially in cloud computing environment. In order to address these challenges, this paper proposes a Digital Forensic Architecture with Authentication and Optimal Key Generation Encryption (DFA-AOKGE) as a secure framework that is meant to enhance the security, integrity and credibility of forensic investigations. The DFA-AOKGE model performs the storage and transport of the forensic data in a blockchain-based architecture across different node to avoid the risks of a single point of failure. This way ensures that critical information is easily retrievable and nobody can alter the proof. Also, a Robust Block Verification System (RBVS) is proposed to validate the digital evidence, detect any attempt of alteration and sustain the information integrity throughout the investigation process. For enhancing the security, the model applies an Improved Equilibrium Optimizer (EEO) for generating cryptographic keys to make the encryption more effective against brute force attacks. Also, the MHE allows for operations on the encrypted forensic data without the need to decrypt the data, thus preserving the data’s privacy during the analysisprocess. As a result, the DFA-AOKGE integrates these sophisticated mechanisms to provide a comprehensive and secure digital forensic approach for the cloud computing environment where the threats such as hacking and data leakage are frequent.
The proceedings contain 39 papers. The special focus in this conference is on Green, Pervasive, and Cloud Computing. The topics include: Chinese Medical Named Entity Recognition Based on Pre-training Model;a Func...
ISBN:
(纸本)9789819998920
The proceedings contain 39 papers. The special focus in this conference is on Green, Pervasive, and Cloud Computing. The topics include: Chinese Medical Named Entity Recognition Based on Pre-training Model;a Function Fitting System Based on Genetic Algorithm;a Rumor Detection Model Fused with User Feature Information;Design and Implementation of a Green Credit Risk control Model Based on SecureBoost and Improved-TCA Algorithm;unsupervised Concept Drift Detection Based on Stacked Autoencoder and Page-Hinckley Test;an Industrial Robot Path Planning Method Based on Improved Whale Optimization Algorithm;intrusion Detection System Based on Adversarial Domain Adaptation Algorithm;Small-Sample Coal-Rock Recognition Model Based on MFSC and Siamese Neural Network;elemental Attention Mechanism-Guided Progressive Rain Removal Algorithm;a data Security Protection Method for Deep Neural Network Model Based on Mobility and Sharing;A Method for Small Object Contamination Detection of Lentinula Edodes Logs Integrating SPD-Conv and Structural Reparameterization;a Study of Sketch Drawing process Comparation with Different Painting Experience via Eye Movements analysis;review of Deep Learning-Based Entity Alignment Methods;VMD-AC-LSTM: An Accurate Prediction Method for Solar Irradiance;anomaly Detection of Industrial data Based on Multivariate Multi Scale analysis;research on Script-Based Software Component Development;Integration Model of Deep Forgery Video Detection Based on rPPG and Spatiotemporal Signal.
Owing to the nature of traffic and architecture of Wireless Seismic data Acquisition (WSDA) networks also referred to as Wireless Geophone Networks (WGN), we propose a model that analytically investigates the performa...
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ISBN:
(纸本)9781665471039
Owing to the nature of traffic and architecture of Wireless Seismic data Acquisition (WSDA) networks also referred to as Wireless Geophone Networks (WGN), we propose a model that analytically investigates the performance of IEEE 802.11 protocol for single-hop ad hoc WGNs under unsaturated traffic and non-ideal channel conditions. Although several IEEE 802.11 models have been presented in literature, some inaccuracies exist with respect to modeling IEEE 802.11-based WGNs. Our model focuses primarily on singling out the inaccuracies in modeling the backoff procedure and packet drop probability as some of the deviance with the existing literature. Expressions for MAC delay, throughput, collision probability, and average duration a node spends during the backoff procedure before decrementing its counter were proposed. Furthermore, the model investigates an optimal number of geophones that could be supported within a subnetwork based on the proposed WGN architecture in [16]. The model was evaluated analytically in MATLAB and validated using simulation in OMNeT++ discrete event simulator.
Ultrasonic measurement of ultra-thin films, ranging from a few nanometers to tens of nanometers, has proven to be a reliable and non-destructive technique widely used in semiconductor manufacturing. Precise process co...
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ISBN:
(数字)9798331531003
ISBN:
(纸本)9798331531010
Ultrasonic measurement of ultra-thin films, ranging from a few nanometers to tens of nanometers, has proven to be a reliable and non-destructive technique widely used in semiconductor manufacturing. Precise processcontrol at each step of semiconductor manufacturing is crucial for the performance of semiconductor devices. In this paper, we demonstrate that machine learning (ML) can effectively address the challenges of ultrasonic measurement for multi-layer thin films, where traditional modeling techniques may fall short. We also show that Principal Component analysis (PCA) and Discrete Fourier Transform (DFT) can be employed for feature extraction in dataprocessing for ML to improve efficiency while perfectly retaining the most significant features.
Head and neck cancers (HNC) encompass a spectrum of malignancies affecting the oral cavity, throat, and adjacent regions. For treating HNC patients, radiation therapy (RT) is often required for managing the disease. H...
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ISBN:
(纸本)9781510685925;9781510685932
Head and neck cancers (HNC) encompass a spectrum of malignancies affecting the oral cavity, throat, and adjacent regions. For treating HNC patients, radiation therapy (RT) is often required for managing the disease. However, the accurate prognosis and personalized RT of HNC remain challenging, given the limited number of training data, varied anatomical sites and heterogeneous patient-specific responses to treatment. While investigations into patients’ electronic health records (EHR) data and image data have shown improved prognostic value compared to the widely used tumor staging based method, most of the models for RT outcome have predominantly focused on the prediction of a single specific outcome. These models have provided valuable insights, however, they neglect the interconnected nature of different clinical outcomes and the potential for shared representation learning across multiple tasks or labels. In addition, for deep learning-based prediction, scant attention has been given to deep model interpretation analysis to elucidate the decision-making process. These oversights limit the models’ ability to harness the full spectrum of available data, potentially obscuring critical insights into the multifaceted nature of cancer progression and treatment response.
The way to provide energy supply for electric passenger vehicles by adopting the power exchange mode is an important guarantee to promote the development of the Electric Passenger Vehicles (EPVs) industry. This paper ...
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Existing algorithms for predicting milling chatter have not been widely adopted in industry since they require specialized instruments to measure the stability inputs. This study describes how the machining process fo...
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The Generative Artificial Intelligence chatbots (GAI chatbots) have emerged as promising tools in various domains, including higher education, so this study aims to investigate the role of Bard, a newly developed GAI ...
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The utilization of 3D scanning and reverse engineering techniques has revolutionized quality control practices across various industries. These technologies play a pivotal role in accurately assessing and documenting ...
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