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Automated Vickers hardness system with closed-loop control for polishing pads for semiconductor production

Instrumentation science and Technology 2024年第4期52卷 401-414页

作者： Huy, Le Nam Quoc Hoa, Le Ngoc Quynh Haq, Muhamad Amirul Huynh, Tan-Thanh Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei Taiwan Nanyang Technological University Singapore Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan School of Applied Chemistry Tra Vinh University Viet Nam

Chemical mechanical polishing plays a pivotal role in enhancing the topography of wafers during semiconductor fabrication. The hardness of polishing pads holds great significance as it determines the material removal rate during the planarization process. Hence, precise determination of polishing pad hardness at both micro and macro scales is essential for optimizing the semiconductor polishing process, ensuring the prevention of wafer erosion or dishing. In this study, an automated system was developed for determining Vickers hardness of microporous polishing pads employing a closed-loop control method. The system was calibrated based on the relationship between load-unloading force and displacement observed in Vickers indentation experiments. The designed apparatus is not only successfully verified with standard values for Vickers reference blocks with less than 2% error, but it also demonstrated a smaller average relative error percentage than the commercial system using traditional open-loop measurement method. The closed-loop Vickers hardness apparatus proves to be a promising alternative for measuring hardness accurately, particularly in samples with noisy surfaces, rough surfaces, and porous microstructures. © 2023 Taylor & Francis Group, LLC.

关键词： Fabrication

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Multi-strategy Hybrid Coati Optimizer:A Case Study of Prediction of Average Daily Electricity Consumption in China

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Journal of Bionic engineering 2024年第5期21卷 2540-2568页

作者： Gang Hu Sa Wang Essam H.Houssein Department of Applied Mathematics Xi’an University of TechnologyXi’an710054China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China Faculty of Computers and Information Minia UniversityMinia61519Egypt

The power sector is an important factor in ensuring the development of the national *** simulation and prediction of power consumption help achieve the balance between power generation and power *** this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity ***,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning *** improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the ***,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test ***,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting *** experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.

关键词： Forecast of average daily electricity consumption in China Coati optimization algorithm Three-parameter combination optimization model Circle chaotic mapping

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YARN Schedulers for Hadoop MapReduce Jobs: Design Goals, Issues and Taxonomy

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Recent Advances in computer science and Communications 2023年第6期16卷 44-55页

作者： Kotikam, Gnanendra Selvaraj, Lokesh Department of Information and Communication Engineering Anna University Tamil Nadu Chennai India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore641062 India

Objective: Big Data processing is a demanding task, and several big data processing frameworks have emerged in recent decades. The performance of these frameworks is greatly dependent on resource management models. Methods: YARN is one of such models which acts as a resource management layer and provides computational resources for execution engines (Spark, MapReduce, storm, etc.) through its schedulers. The most important aspect of resource management is job scheduling. Results: In this paper, we first present the design goal of YARN real-life schedulers (FIFO, Capacity, and Fair) for the MapReduce engine. Later, we discuss the scheduling issues of the Hadoop MapReduce cluster. Conclusion: Many efforts have been carried out in the literature to address issues of data locality, heterogeneity, straggling, skew mitigation, stragglers and fairness in Hadoop MapReduce scheduling. Lastly, we present the taxonomy of different scheduling algorithms available in the literature based on some factors like environment, scope, approach, objective and addressed issues. © 2023 Bentham science Publishers.

关键词： Energy utilization

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LSTM-XGBoost: An Ensemble Model for Blood Demand Distribution Forecasting – A Case Study in Zakho City, Kurdistan Region, Iraq

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Operations Research Forum 2025年第1期6卷 1-22页

作者： Zebari, Rizgar R. Zebari, Gheyath M. Al-zebari, Adel Mohammed, Marwan Aziz Department of Computer Science College of Science Knowledge University Erbil 44001 Iraq Department of Information Technology Technical College of Informatics Akre University for Applied Sciences Kurdistan Akre Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil 44001 Iraq

A safe and adequate blood supply is essential for healthcare systems to function effectively. Accurately forecasting blood demand plays a key role in efficient inventory management and resource allocation. Traditional forecasting methods, like moving averages and ARIMA models, often fall short due to the complexity of factors influencing blood demand. This study introduces an innovative hybrid ensemble model that combines Long Short-Term Memory (LSTM) networks with XGBoost, harnessing their combined strengths to enhance forecasting accuracy. By analyzing blood donation data from the Zakho Blood Bank (January 1, 2015—July 22, 2022), the model outperforms individual LSTM and XGBoost models, excelling in metrics such as Mean Square Error (MSE) and Mean Absolute Error (MAE). These findings underscore the potential of advanced machine learning techniques to improve healthcare supply chain management and ensure the timely availability of critical blood supplies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Blood banking Blood demand forecasting Healthcare supply chain LSTM network Machine learning XGBoost

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SMS Spam Detection using Advance Naive-Bayes Approach

SMS Spam Detection using Advance Naive-Bayes Approach

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Arora, Yojna Gupta, Neha Rathore, Yogesh Singh Bansal, Nidhi Mehta, Aina Chadha, Shikha Kumar, Ashwani Sharda University Department of Computer Science and Engineering Greater Noida India Symbiosis University of Applied Sciences School of Computer Science & Information Technology Madhya Pradesh Indore India Sharda University School of Engineering and Technology Department of Computer Science and Applications Uttar Pradesh Greater Noida India School of Engineering and Technology Department of Computer Science & Engineering Haryana Faridabad India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331508685

Recently, with increased use of mobile phones, it has transformed into a multibillion-dollar Short Message Service or SMS. However, the drop in the cost of messaging services has led to an increased number of unsolicited commercial messages, or spam, being delivered to mobile phones. Several reasons exist for which traditional algorithms used in email filtering may not efficiently classify the spam in SMS such as the absence of strong SMS spam databases, with the short length and features of the message, the informal language used, etc. This paper uses a real SMS spam database After preprocessing the data and extraction of relevant features, Naïve Bayes machine learning techniques is applied to get the results. Then it is analyzed using confusion matrix, accuracy, precision and recall values. A learning curve with clearly defined relationship between the training and testing data set and overall error at the end. © 2025 IEEE.

关键词： Adversarial machine learning

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Sustainable Solutions for Emerging Issues in Wireless Technology

Sustainable Solutions for Emerging Issues in Wireless Techno...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Al-Dmour, Nidal A. Alnawayseh, Saif. E. A. Ihsan, Anaum Irfan, Muhammad Kousar, Shagufta Ali, Dilawer Abbas, Tahir College of Engineering Department of Computer Engineering Mutah University Jordan College of Engineering Department of Electrical Engineering Mutah University Al-Karak Jordan Lahore Pakistan Multan Pakistan Applied Science Research Center Applied Science Private University Amman Jordan The TIMES Institute Department of Computer Science Multan60000 Pakistan

ISBN: (纸本)9798350369106

The rapid evolution of wireless technology has transformed communication, offering unprecedented accessibility and convenience. However, significant challenges like spectrum scarcity, security vulnerabilities, and environmental concerns must be addressed for sustainable growth. This paper provides an overview of these issues, including the impact of regulatory barriers and increased data demand on network infrastructure. It also suggests measures to support the development of effective wireless communication policies, ensuring sustainable progress as technologies advance towards 5G, MIMO, and IoT. The goal is to identify improvements for more robust wireless communication systems. © 2024 IEEE.

关键词： 5G Accessibility Communication IOT Network Infrastructure Security Wireless technology

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VisionReli6G: Enhancing 6G Wireless Reliability with CNN-LSTM for LOS Blockage Prediction 2024

VisionReli6G: Enhancing 6G Wireless Reliability with CNN-LST...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Bajaj, Sagar Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, Shabbir N. School of Engineering and Applied Science Ahmedabad University Ahmedabad India Department of Computer Science and Engineering SVNIT Surat India Department of Electrical Engineering IIT Bombay Mumbai India

ISBN: (纸本)9798400709722

The mmWave and sub-THz signals rely on Line-of-Sight (LOS) links for higher throughput. Blocking these links can lead to a sudden drop in the received SNR and increase the latency of the communication network. We propose a CNN-LSTM pipeline designed to enhance the reliability of 6G wireless networks by predicting LOS blockages in advance using a sequential stream of RGB images. The proposed solution's efficiency is demonstrated by comparing key performance indicators between the baseline (YOLO-GRU) and the developed pipelines (ResNet-LSTM, ResNeXt-LSTM). The results are validated on the first real-world 6G communication dataset, DeepSense 6G, which contains diverse data modalities captured at different locations and times of the day. The Top-1 accuracy for future 1, 5, and 10 instances of scenario 22 yields an average increase of 10.63% and 12.60% with the Resnet-LSTM and ResNeXt-LSTM pipelines respectively compared to the baseline YOLO-GRU model. The F1 score of scenarios 17-22 for future-1 time window prediction increased by an average of 7.58% and 8.13% for both pipelines respectively. © 2024 Copyright held by the owner/author(s).

关键词： Phase locked loops

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Enhanced Form-Based SPARQL Builder: Effortless Access to Temporal Medical Data for Users Without Technology Knowledge 17

Enhanced Form-Based SPARQL Builder: Effortless Access to Tem...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Vcelak, Petr Mika, Ondrej Kryl, Martin Kleckova, Jana University of West Bohemia Faculty of Applied Sciences Department of Computer Science and Engineering Plzen Czech Republic

ISBN: (纸本)9798331507398

Accessing complex medical data, especially temporal information, presents a significant challenge for non-technical users, including healthcare professionals not versed in technology or query languages like SPARQL. This paper introduces an enhanced form-based SPARQL query builder that offers effortless access to temporal medical data. The described system simplifies constructing SPARQL queries into an intuitive, form-driven interface that abstracts and hides the technical complexities. Novel enhancement is primarily the user's ability to filter and order data with column constraints. By focusing on usability, the system empowers users to retrieve temporal medical information without needing in-depth knowledge of database systems or SPARQL syntax. We evaluate the system's usability and performance by testing it with non-technical healthcare professionals. Results demonstrate that users can easily navigate the interface, significantly reduce the time spent querying the database, and effectively retrieve data. This approach benefits from FAIR data principles and enhances data reusability in healthcare settings for reaching accuracy and precision for follow-up analysis in evidence-based research, facilitating better decision-making that can improve future patient outcomes. © 2024 IEEE.

关键词： Spatio-temporal data

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Noise Pollution Detection and Notification Using Flet and Arduino

Noise Pollution Detection and Notification Using Flet and Ar...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Kala, I. Swathy, S. Vishnu Priya, V. Harshavarthan, P.K. PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350378177

This project seeks to identify urban, traffic, and industrial noise hazards. This is accomplished by Arduino, which uses sound sensors to recognize and differentiate between light and loud noise. With the help of the ThingSpeak server, the noise level data for different time is also tracked. The proj ect extends its benefits to the users by notifying users via mail if they are in an area with high noise levels. The Flet User Interface framework was used in the development of this web application. © 2024 IEEE.

关键词： Background noise

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Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification

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computers, Materials & Continua 2023年第4期75卷 149-164页

作者： K.Kalyani Sara A Althubiti Mohammed Altaf Ahmed ELaxmi Lydia Seifedine Kadry Neunggyu Han Yunyoung Nam Department of Computer Science Dr.Nalli Kuppusamy Arts College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613003India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Lepartment of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea

Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. computer-Aided Diagnostic (CAD) tool saves time and effort indiagnosing melanoma compared to existing medical approaches. In this background,there is a need exists to design an automated classification modelfor melanoma that can utilize deep and rich feature datasets of an imagefor disease classification. The current study develops an Intelligent ArithmeticOptimization with Ensemble Deep Transfer Learning Based MelanomaClassification (IAOEDTT-MC) model. The proposed IAOEDTT-MC modelfocuses on identification and classification of melanoma from dermoscopicimages. To accomplish this, IAOEDTT-MC model applies image preprocessingat the initial stage in which Gabor Filtering (GF) technique is *** addition, U-Net segmentation approach is employed to segment the lesionregions in dermoscopic images. Besides, an ensemble of DL models includingResNet50 and ElasticNet models is applied in this study. Moreover, AOalgorithm with Gated Recurrent Unit (GRU) method is utilized for identificationand classification of melanoma. The proposed IAOEDTT-MC methodwas experimentally validated with the help of benchmark datasets and theproposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.

关键词： Skin cancer deep learning melanoma classification dermoscopy computer aided diagnosis

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