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Large-Area Ag/Cu Pastes Sintering on Cu Base Plates for Power Module Packaging

Large-Area Ag/Cu Pastes Sintering on Cu Base Plates for Powe...

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22nd International Conference on Electronics Packaging, ICEP 2023

作者： Tsai, Chin-Hao Steiner, Felix Ishikawa, Dai Kao, C. Robert Blank, Thomas Institute for Data Processing and Engineering Karlsruhe Institute of Technology Germany National Taiwan University Department of Materials Science and Engineering Taiwan Resonac Corporation Ltd. Inorganic Materials Research Dept. Japan

ISBN: (纸本)9784991191152

In this research, Ag, Cu, mixed Ag-In, and mixed Ag-SAC sinter pastes are used for large-area bonding in second-level power module packaging. Due to the large sintering area, different patterns with/without drying channels are tested in the pressure/pressureless sinter process. Besides, various bonding conditions, such as temperature and pressure, are investigated. To analyze large-area sintering effects, scanning acoustic microscopy is used to observe the interface of joints. Finally, the reliability of the large area sintered joints is assessed by thermal shock and bending tests. To conclude, this study explores the feasibility of multiple sintering pastes for large-area sintering. The results reveal that these pastes exhibit reliable properties under proper bonding conditions. © 2023 Japan Institute of Electronics Packaging.

关键词： Sintering

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Redefining the Landscape of Wire Arc Additive Manufacturing: Pioneering Innovations for Residual Stress Mitigation and Process Efficiency

Redefining the Landscape of Wire Arc Additive Manufacturing:...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Abdullah, Falah Hassan Aswini, Kilaru Manjunatha Thethi, H. Pal Parmar, Ashish Bhavani, B. Ganga National University of Science and Technology College of Science Department of Forensic Sciences Dhi Qar64001 Iraq Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India St. Martin's Engineering College Dept. of It Telangana Secunderabad India

ISBN: (纸本)9798350373783

This study introduces a comprehensive framework for Wire Arc Additive Manufacturing (WAAM) optimization, incorporating five key algorithms: Adaptive Layer Thickness Control, In-Situ Stress Monitoring, Dual-Wire Deposition, Thermal Management Strategies, and Machine Learning for Process Control. The Adaptive Layer Thickness Control Algorithm dynamically adjusts layer thickness based on geometric features and real-time feedback, contributing to stress mitigation. The In-Situ Stress Monitoring Algorithm ensures real-time stress distribution optimization. The Dual-Wire Deposition Algorithm introduces simultaneous deposition of two materials, optimizing multi-material properties. Thermal Management Strategies Algorithm focuses on controlling temperature gradient and adaptive cooling strategies. Machine Learning for Process Control Algorithm integrates input from preceding algorithms to predict optimal parameters, ensuring adaptive layer control and overall process optimization. Evaluation against existing methods demonstrates superior outcomes in residual stress reduction, geometric accuracy improvement, material integrity enhancement, process stability, build rate improvement, and energy efficiency. Visualizations and comparative tables confirm the proposed method's holistic excellence in WAAM. © 2024 IEEE.

关键词： Process control

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An Evolutionary Algorithm for the Travelling Sales Man problem

An Evolutionary Algorithm for the Travelling Sales Man probl...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Shaik Mohammad Waseem Jeshwanth N L Lingesh A Matta Jayanth Bhargavi C H Batini Dhanwanth Dept. of Computer Science and Engineering Panimalar Institute of Technology (Anna University) Chennai India Dept. of Artificial Intelligence and Data Science Panimalar Institute of Technology (Anna University) Chennai India Dept. of Computer Science and Engineering Sai Spurthi Institute of Technology India Dept. of AI Engineering Jönköping University Jönköping Sweden

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study explores the use of Genetic Algorithms (GA) to solve the NP-hard combinatorial optimization problem known as the Travelling Salesman Problem (TSP). The suggested GA, called GA-P, performs better than conventional techniques because it uses a new randomized crossover threshold. According to experimental assessments, GA-P outperforms a normal Genetic Algorithm with fixed crossover (GA-N, 628.6) and approaches the best Branch and Bound solutions (B&B, 560.6) with an average cost of 617.4 for 16-node graphs. With average execution times of 0.1347 seconds for 16 nodes, GA-P also demonstrates constant computational efficiency, outperforming B&B's exponentially growing temporal complexity (169.37 seconds for 16 nodes). In order to balance solution quality and computational cost, hyperparameter tweaking revealed the ideal values of population percentage (pp = 1.1), crossover proportion (cp = 0.8), mutation threshold (mt = 0.3), and exploration probability (ep = 0.2). The results show that GA-P is a scalable and efficient substitute for TSP, with room for improvement via parallelization and exploratory techniques.

关键词： Renewable energy sources Costs Evolutionary computation Computational efficiency Complexity theory Optimization Tuning Genetic algorithms

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Decentralized Network for Cloud Storage 2

Decentralized Network for Cloud Storage

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2nd Edition of IEEE Delhi Section Owned Conference, DELCON 2023

作者： Chirag, N. Kshitij, K. Gupta, Gaurav Sir M Visvesvaraya Institute of Technology Dept. of Information Science Engineering Bangalore India Shoolini University Yogananda School of Artificial Intelligence Computer and Data Sciences H.P Solan India

ISBN: (纸本)9798350322064

The amount of data loss on corporate servers in cloud environments has increased significantly. In the cloud, there are many security compromises and account hijackings, resulting in severe vulnerabilities for the service. Cloud computing is one of the most popular on-demand services. Over the past decade, cloud computing has been incorporated into various industries, including health care and the military. Security has, however, been a major concern with cloud computing. There have been many approaches and security implications implemented. However, when it comes to the security and privacy of data, there are loopholes. By decentralizing the cloud, which is currently a centralized entity, the authors attempt to improve cloud security. It is possible to solve these problems by deploying a decentralized cloud. Client software encrypts, fragments data, and then a client engine distributes the data among a number of hosting nodes (servers) spread throughout the world. As a result, the user has complete ownership of his data. In their study, the authors found that decentralizing cloud solved most of the problems of centralized cloud, since it increases the security of data by fragmenting and encrypting fragments. © 2023 IEEE.

关键词： Cloud security

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Earthquake Damage Prediction and Rapid Assessment of Building Damage Using Machine Learning.

Earthquake Damage Prediction and Rapid Assessment of Buildin...

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Advancement in Technology (ICONAT), International Conference for

作者： Yuvaraj Natarajan Gitanjali Wadhwa Preethi Akshaya Ranganathan Karthika Natarajan Dept. of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Dept. of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India

A quick forecast of the region’s structural damage following an earthquake is crucial for community relief and rescue efforts. Following an earthquake, getting detailed information about buildings in seismic disaster zones is frequently impossible. The information available is essential to estimate seismic damage to urban structures accurately. To address the aforementioned issues, we propose a novel method for quickly assessing building damage through extensive use of field research and data collected from earth observation. A model is created and trained to predict building damage from an earthquake. The algorithm used to indicate the building damage from the quake is Elastic net regression. The elastic net is a regularized regression method used to predict building damage from an earthquake. The proposed Elastic net regression is validated with standard accuracy and effectiveness compared with the Random forest and decision tree methods. The proposed model’s outcomes show that elastic net regression is the most effective predictive model for predicting building damage.

关键词： Measurement Machine learning algorithms Buildings Earthquakes Predictive models Prediction algorithms data models

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Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GRU

Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GR...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： K. Mouthami N. Yuvaraj K.K. Thilaheswaran K.J. Lokeshvar Dept. of Computer Science Engineering KPR Institute of Engineering and Technology Coimbatore India Dept. of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India

Sentiment analysis is a vast subject to explore in natural language processing (NLP) techniques. The film reviews were analyzed and segregated into positive, neutral, and negative reviews. The proposed model examines two distinct datasets, one with multi-class labels and the other with binary-type class labels. The preprocessing is done by using the bag of words and skip-gram word2vec, variety of classifiers, including Two state Gated recurrent units (TS-GRU), was used for binary classification, and the Bidirectional Long Short-Term Memory (Bi-LSTM for the multi-class situation was implemented. Bi-LSTM was utilized in the model, in which symmetric Bi-LSTM replaces the LSTM approaches to get around the high computational cost of training the normal LSTM. The Bi-LSTM has equivalent accuracy to the LSTM while significantly reducing computational costs. Sentiment analysis offers a glimpse of how consumers feel about certain movies. This endeavor is undertaken via the study of natural language processing, in which sentiment analysis is a branch which examines how words are arranged and utilized to extract meaning from user writing. This helps to investigate further the methods for deciphering such data thanks to significant advancements in machine learning techniques; with Keras API and the Internet Movie database (IMDb) dataset, this study compares single and multi-branch CNN with Bidirectional LSTMs with different kernel sizes. GRU achieved 98.24% of accuracy, and bi-LSTM achieved 98.65% of accuracy.

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An Analysis of Human Factors in Malware Attacks

An Analysis of Human Factors in Malware Attacks

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International Conference on Computer and Applications (ICCA)

作者： Jawza Alotaibi Jihad M. Al Ja'am Mohammed Hoda Dept. of Computing and Data Science Oryx Universal College in Partnership with Liverpool John Moores University Doha Qatar Dept. of computer Science and Engineering Kuwait College of Science and Technology Doha Kuwait

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

This research study investigates human responses to malware attacks, focusing on attacker motivations, individual reactions, and key factors influencing user vulnerability and resilience. Using a structured survey questionnaire, the study gathered data from 104 participants with diverse demographics across various occupations. The data was analyzed using SPSS statistical software and Python-based machine learning algorithms. The survey included demographic information and responses to cybersecurity-related questions about human behavior and experience. Machine learning techniques identified ten critical factors as significant predictors of vulnerability to malware attacks. The prediction model indicated that 55 out of 104 participants were vulnerable, with higher vulnerability observed among female participants and those in non-IT professions. This study contributes valuable insights into the human factors influencing cybersecurity resilience, providing a foundation for developing effective strategies to mitigate malware attack vulnerability.

关键词： Surveys Training Machine learning algorithms Human factors Organizations Machine learning Predictive models Particle measurements Malware Resilience

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Enhanced Building Prediction for Smart City Surveillance

Enhanced Building Prediction for Smart City Surveillance

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Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), International Conference on

作者： Yuvaraj Natarajan Gitanjali Wadhwa Lekshmipriya Saravanan Mathivathani Natarajan Dept. of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Dept. of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India

Nearly three-quarters of the total power used in the world is consumed by the built environment sector. Therefore, looking for viable methods to reduce building energy needs and decrease negative environmental repercussions is vital. By incorporating intelligence into applications, everyone in the current situation has become intelligent, which lessens the load of constant interruption or human control. By using Machine to Machine interactions, intelligence enables the ability to connect more real-time parameters and make moral judgments in perilous circumstances. The current study examined the framework, approach, and performance of currently available AI-based techniques. According to the research, choosing the best machine and deep learning model for a given task can be difficult. Among the primary areas of study in relation to the vast amounts of data collected is the viability in the practice of the used algorithms, both standard machine learning and the more recent branch known as deep learning. Present a quantitative analysis contrasting the effectiveness of conventional deep learning and machine learning strategies using the direction of human movement in detecting the applications based on the analogue pyroelectric infrared sensor (PIR) outputs. Here, Random Forest is used as the machine learning method because of its great prediction accuracy. Over deeper datasets, deeper CNNs outperform shallow models as a deep learning method. The study demonstrates that traditional Machine Learning performs with detecting values within the first few seconds, improving real-time detection. Additionally, it might be used on more smart buildings without upgrades.

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Earthquake Damage Prediction and Rapid Assessment of Building Damage Using Deep Learning

Earthquake Damage Prediction and Rapid Assessment of Buildin...

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Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), International Conference on

One of the most expensive natural disasters that affect people, earthquakes occur suddenly. As a result, earthquake prediction has grown in importance and difficulty for humanity. Although numerous existing approaches attempt to handle this problem, the majority rely on seismic indicators created by geologists or feature vectors extracted by the deep learning techniques to describe an earthquake for the earthquake prediction. Combining these two categories of characteristics to enhance ultimate Earthquake prediction performance is still a challenge. In order to achieve this, we put forth a deep learning model that successfully fuses explicit and implicit information for earthquake prediction. Here, we use a Convolutional Neural Network to extract implicit features while using eight precursory pattern-based indicators as the explicit features. After that, an attention-based approach is suggested to effectively combine these two categories of traits. A dynamic loss function is the additionally created to address the imbalance of category in seismic data. On a test picture dataset, the performances of multiple CNN models are contrasted. In order to classify earthquake damage, the model is further developed as a web application. Damage assessment values, which are determined using the Convolutional Neural Network model and gradient-weighted class activation mappings, are used to determine the extent of the damage. The web-based program can efficiently and automatically categorize earthquake-related structural damage, making it appropriate for decision-making in emergency response, resource allocation, and policy formation.

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Advancing Eye Disease and Glaucoma Diagnosis: A Comprehensive Survey of Deep Learning, Machine Learning, and Hybrid Approaches

Advancing Eye Disease and Glaucoma Diagnosis: A Comprehensiv...

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Smart Technologies, Communication and Robotics (STCR)

作者： B. Venkata Suresh Reddy Bhanu Prakash Battula Computer Science & Engineering JNTUK Kakinada Kakinada India Dept. of CSE-Data Science KKR & KSR Institute of Technology & Sciences Guntur India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

Glaucoma stands as a serious global health issue among eye diseases because the condition leads to permanent blindness when proper diagnosis and treatment are not made in sufficient time. The detection and classification capabilities for these conditions have been significantly improved by recent developments in computational techniques that combine deep learning and traditional machine learning and hybrid models alongside clinical assessments. The survey explores 54 research studies regarding eye disease diagnosis which the authors divide into four distinct sections. Deep learning algorithms particularly CNN variants achieved outstanding success (93.5%) when used for retinal image segmentation together with disease classification tasks. Hybrid models equipped with machine learning and deep learning capabilities reached the maximum accuracy level at 95% for diagnosing diseases. SVMs and other traditional machine learning algorithms delivered accurate results for structured datasets through their performance while medical evaluations helped identify disease trajectories and disease elements. The study presents a thorough overview of the advantages along with drawbacks and potential applications for these analysis techniques in detecting and treating glaucoma along with other eye conditions.

关键词： Deep learning Glaucoma Surveys Image segmentation Machine learning algorithms Accuracy Retina Trajectory Robots Medical diagnostic imaging

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