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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Yue Xiao Tuo Zhou Guojun Liu Xuanren Chen Xiangji Kong Yu Zhang Longlong Liu Shunong diao CNPC Engineering Technology R&D Co. LTD PetroChina International Exploration and Development Co. LTD Overseas Drilling and Completion Technology Center Beijing China PetroChina International Exploration and Development Co. LTD Beijing China Changchun University of Technology Changchun China China University of Petroleum (Beijing) Beijing China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

The optimal selection of drilling rigs is crucial for the rational arrangement of drilling rigs in multi operation teams, the improvement of moving operation management level, and the enhancement of drilling operation quality and efficiency. At present, the optimal selection of drilling rigs only considers whether the drilling meets the needs of the drilling project and the moving distance, and cannot comprehensively and scientifically select drilling rigs, which is not conducive to the rational configuration evaluation of oilfield drilling rig operation. This article comprehensively considers key indicators such as drilling efficiency, time efficiency, moving cycle, moving cost, and moving distance, innovatively forms a pentagonal drilling rig optimization coordinate system, establishes drilling rig optimization rate, comprehensive standard deviation, drilling rig optimization comprehensive standard deviation and other indicators, and forms a drilling rig optimization model for drilling. Taking Block B as an example, the priority of Block B with three drilling rigs to be optimized calculated using the model is ${\mathrm {r}}1\gt {\mathrm {r}}2\gt {\mathrm {r}}3$.The optimal selection rate of drilling r1 is the highest, the comprehensive standard deviation is smaller, and the comprehensive index of the best and more stable drilling performance is the highest, which is 0.0058. Therefore, r1 should be selected for drilling. The drilling rig optimization model proposed in this paper provides a process reference for drilling rig optimization, quantifies various indicators, and provides a theoretical basis for new well drilling rig optimization, project company drilling rig operation plan formulation, and improving project company drilling rig management level.

关键词： drilling Costs Uncertainty Optimization models Computational modeling Stability criteria Companies Complexity theory Standards Optimization

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PId air pressure controller in a positive pressure screening cabinet (PPS-Cabinet) for healthcare operator screening of SArS-CoV-2 infection

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Perioperative Care and Operating room Management 2025年 40卷 100497-100497页

作者： Kaewluan, Sommas Unsomsri, Nathawat Wiriyasart, Songkran Biofuel and Bioenergy Technology Research and Development Laboratory (BBT R&D) Department of Mechanical Engineering Faculty of Engineering Srinakharinwirot University 63 Rangsit-Nakhonnayok Rd. Ongkharak Nakhon-nayok 26120 Thailand Thermal Solution and Energy Technology Research and Development Laboratory (TSET R&D) Department of Mechanical Engineering Faculty of Engineering Srinakharinwirot University 63 Rangsit-Nakhonnayok Rd. Ongkharak Nakhon-nayok 26120 Thailand

This paper presents the implementation of a PId pressure controller for a Positive Pressure Screening Cabinet (PPS Cabinet), designed to reduce the risk of COVId-19 transmission to healthcare personnel. The PPS Cabinet was designed, constructed, and tested to optimize internal pressure by adjusting the proportional (K_p), integral (K_i), and derivative (K_d) gains of the PId controller. In the experimental setup, the cabinet has a total volume of 2.88 m³. A PId controller integrated with a Programmable Logic Controller (PLC) receives a 0–10 V analog signal from a pressure transducer and is programmed using a standard PId algorithm. The K_p, K_i, and K_d values were determined using the Ziegler-Nichols tuning method. Testing scenarios included opening and closing the cabinet door, both with and without an airlock chamber. results indicate that the PId controller effectively regulates cabinet pressure, with optimal tuning values identified as K_p = 0.6, K_i = 0.4, and K_d = 0.4. While the PPS Cabinet without an airlock is deemed suitable for practical use, door opening causes a pressure drop below 0 Pa, posing a significant risk to healthcare workers. However, incorporating an airlock chamber maintains pressure above 0 Pa, thereby enhancing safety and reducing the likelihood of COVId-19 transmission to operators. © 2025 Elsevier Inc.

关键词： COVId-19 PId PLC Positive pressure screening cabinet Pressure controller

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A Precision Enhancement deep Learning Framework for Package Substrate defect detection

A Precision Enhancement Deep Learning Framework for Package ...

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International Microsystems, Packaging, Assembly and Circuits technology (IMPACT)

作者： Yi-Chung Hsu Wong-Shian Huang AI & Advanced Optical Technology Development Division Corporate R&D Advanced Semiconductor Engineering (ASE) Inc. Zhubei City Hsinchu County 302 Taiwan R.O.C.

In the manufacturing industry, producing a large quantity of highquality products every day is the top priority. recently, the integration of Automated Optical Inspection (AOI) equipment and deep Neural Network (dNN) defect image predictions has demonstrated a significant reduction in manual effort required for defect inspection in manufacturing industrial applications. However, during substrate manufacturing, changes in in-process conditions can lead to unexpected variations in defects, such as combinations of multiple defects or anomalies that humans are not familiar with. Consequently, mainstream dNN-based object detection (Od) methods have yet to achieve the desired accuracy for detecting these defects images in production line, and hence human visual inspections remain essential. In this paper, we propose a defect detection framework to both reduce the need for human examination and maintain the high quality of normal products for downstream processes. This method integrates three dNN methods: Od, anomaly detection (Ad), and auxiliary classification (AC). Od constructs hyperplanes for bounding-box objects and classes. Ad measures distances between embedding vectors trained from normal samples to detect anomalies. AC operates by centering the object proposed by Od within a cropped image and re-judging the object's class, which can refine Od's anchor-based training. We conducted an ASE manufacturing dataset experiment, and our method reduced human examination by 28%, with only a 2.8% underkill rate. Combining Od and AC achieved 93.21% defect classification accuracy, surpassing mainstream accuracy 89.74%. With the assistance of our new framework, we are able to simultaneously reduce the manual efforts for examination and increase the accuracy of defect classification. When applied in MFG production lines, this technology can surely assist our factory operation, enabling us to better meet a smart factory requirements.

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Space-filling experimental design for efficient Bayesian optimization

Space-filling experimental design for efficient Bayesian opt...

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Joint e-Manufacturing and design Collaboration Symposium (eMdC) & International Symposium on Semiconductor Manufacturing (ISSM)

作者： Shigeru Kinoshita Yuta Inoue Tetsuro Watanabe Kosuke Ikeda Seiwa Nishio Atsushi Teruya Naoki Sakai Takashi Goda Frontier Technology R&D Institute KIOXIA Corporation Yokohama-shi Japan Advanced Memory Development Center KIOXIA Corporation Yokkaichi-shi Japan Graduate School of Engineering The University of Tokyo Bunkyo-ku Japan

ISBN: (数字)9798331510589

ISBN: (纸本)9798331510596

In this study, we propose a space-filling Latin hypercube design with small fill distance and large separation radius based on simulated annealing for efficient Bayesian optimization. Through an example focused on optimizing high aspect ratio hole etching, we demonstrate that initial sampling with the proposed design is advantageous compared to conventional designs in terms of the convergence of Bayesian optimization with fewer experiments.

关键词： Wet etching Atomic layer deposition Simulated annealing Semiconductor device manufacture Hypercubes Bayes methods Optimization Convergence

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development of a Task Allocation Framework for Agricultural Machines with Algorithm Selection and Hyper-Parameter Tuning

SSRN

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SSrN 2024年

作者： Hayakawa, Kenta Miyashita, Shunsuke Fujiwara, Nagahiro Takamido, ryota Ota, Jun Department of Precision Engineering School of Engineering The University of Tokyo Japan Technology Innovation R&D Dept. II Research & Development Head-quarters KUBOTA Corporation Japan School of Engineering The University of Tokyo Japan

The increasing global demand for food has highlighted the importance of enhancing agricultural productivity. This necessity has spurred interest in smart agriculture, which incorporates advanced technology. A significant challenge within this domain involves optimizing task assignment and route planning for multiple agricultural machines to maximize their efficiency. Although numerous methods have been proposed in prior research, a comprehensive task allocation system that can accommodate diverse constraints and objective functions has yet to be developed. This study introduces a framework for such a system and demonstrates its effectiveness through computer simulations. These simulations highlight the most suitable optimization methods and hyperparameters, taking into account the scale of farmland and computational time constraints. Specifically, environments such as small-scale farms or those with sufficient computational time are found to benefit from simulated annealing. In contrast, large-scale farms or scenarios requiring rapid computations are more apt for local search techniques. Furthermore, genetic algorithms (GA) prove advantageous in large-scale farming contexts, whereas ant colony optimization (ACO) is less effective when compared to other algorithms. © 2024, The Authors. All rights reserved.

关键词： Ant colony optimization

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Lithology-controlled stress variations of Longmaxi shale–Example of an appraisal wellbore in the Changning area

Rock Mechanics Bulletin

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rock Mechanics Bulletin 2022年第1期1卷 53-68页

作者： Xiaodong Ma Shihuai Zhang Xiaowen Zhang Jiandong Liu Juan Jin Wei Cheng Weidong Jiang Guangming Zhang Zhaowei Chen Mark d.Zoback ETH Zürich Department of Earth SciencesSwitzerland Research Institute of Petroleum Exploration&Development PetroChinaChina Key Laboratory of Oil&Gas Production CNPCChina CNPC Engineering Technology R&D Company Limited China Stanford University Department of GeophysicsUnited States

The Longmaxi shale is an extensive,prolific unconventional play in southwestern *** development in the Changning area is affected by ineffective hydraulic fracturing(HF)stimulation,fault reactivation and casing *** is suspected that the stress contrast within and between the shale reservoirs and the formations above and below matters to hydraulic fracture propagation and reservoir *** this end,the Longmaxi shale in the Changning area deserves a dedicated quantification of the in situ stress state and its *** this study,we re-visit the available data from one of the play’s first appraisal wellbores(X01)for an integrated geomechanics study,focusing on profiling the stress across the Longmaxi and its adjacent *** geophysical logs and other stress indicators,we re-interpret its stress profile in the context of lithological *** resulting stress variations are modeled primarily through a viscoplastic stress relaxation framework,compared with the results via the frictional equilibrium and an elastic theory(the Extended Eaton model).We offer some discussions on the differences and similarities of these stress profiling methods,and examine their applicability to Longmaxi shale in the Changning *** objective is to connect the lithology-controlled stress variations to the first-order complexities(HF ineffectiveness and fault reactivation)that have been observed in the area to date.

关键词： China shale formations

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Blockchain technology for Enhanced Transparency, Efficiency, and Security in Supply Chain Management 3

Blockchain Technology for Enhanced Transparency, Efficiency,...

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3rd International Conference on Automation, Computing and renewable Systems, ICACrS 2024

作者： Krishna, Santhosh B.V. Lakshmi, M. Vanitha Sathyanarayanan, S. Balaji, L. Harshith, C.M. Kaliappan, S. New Horizon College of Engineering Bangalore India Department of ECE School of Engineering Presidency University Bengaluru India Sri Sathya Sai University for Human Excellence Karnataka Gulbarga India Dept of ECE School of Electrical and Communication Vel Tech Rangarajan Dr. Sagunthala R&D institute of Science and Technology Avadi Chennai600062 India Division of Research and Development Lovely Professional University Punjab India

ISBN: (纸本)9798331532420

Though innovation in barcodes, GPS, and IoT sensors has enhanced the scope of modern supply chains to track assets to a larger extent, it raises a critical challenge for centralized systems in terms of data breaches, unauthorized access, and manipulation. Blockchain technology serves as an optimum decentralized and tamper-proof solution for further improvement of data integrity, security, and transparency. This paper discusses the integration of blockchain into supply chains its benefits and challenges and its potential to change global supply chain operations at its very core. In a nutshell, the system proposed outlines a blockchain-based framework using IoT, GPS, and smart contracts that guarantee traceability, security, and efficiency throughout the supply chain in real time. The results are showing high improvement in transparency, security, and operational efficiency, which will certainly lead to its usage cases in varied sectors. © 2024 IEEE.

关键词： data integrity

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A review of the Literature on the Effects of Big data and Machine Learning on digital Transformation in Marketing 4

A Review of the Literature on the Effects of Big Data and Ma...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Thangamayan, S. Vijai, C. Singh, Navdeep durga, Vijaya Aravinda, K. Sabah, Hawraa Ali Saveetha Institute of Medical and Technical Sciences Saveetha School of Law Department of Research and Development Chennai India Administration Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Commerce and Business Chennai62 India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350307757

The purpose of this in-depth investigation is to investigate the considerable influence that machine learning (ML) and big data have had on the digital transformation that has occurred in the marketplace. Conventional, intuition-based marketing is being replaced by data-driven strategies that make use of real-time analytics and predictive modelling to enhance customer experience and operational efficiency. This paper reviews a variety of studies to demonstrate how these methods are replacing traditional marketing. Personalized marketing, dynamic strategy adjustments, and deeper customer behaviors insights have all become available as a result of the integration of these technologies, despite the fact that technology has also brought forth severe ethical and privacy concerns. It is discussed in the assessment that it is necessary to find a middle ground between taking into consideration these ethical concerns and making use of big data in order to achieve a competitive advantage. When everything is taken into consideration, the findings shed light on the revolutionary potential of Big data and machine learning in marketing. They also advocate for responsible data governance and a paradigm shift towards methodologies that are more flexible, effective, and customer-focused. © 2024 IEEE.

关键词： Consumer behavior

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A Sustainable renewable Energy Assessment Framework Using Novel Machine Learning Algorithm 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： das, Gurumukh Singh, Gurdeep Zindani, divya Shanmugasundar, G. Chohan, Jasgurpreet Singh Kalita, Kanak Uttar Pradesh Agra282 005 India Punjab Ludhiana141013 India College of Engineering Kalavakkam India Department of Mechanical Engineering Sri Sairam Institute of Technology Chennai600044 India Department of Mechanical Engineering University Centre for Research and Development Chandigarh University Mohali140413 India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India Jadara University Research Center Jadara University Irbid Jordan

ISBN: (数字)9783031733215

ISBN: (纸本)9783031733208

In the quest for sustainable energy solutions, accurate prediction of renewable energy outputs is essential for the seamless integration of these resources into the energy grid. This paper presents a Squirrel Search-Optimized Gradient Boosted decision Tree (SS-GBdT) model, designed to enhance the precision of renewable energy forecasts. The SS-GBdT model leverages the search capabilities of the squirrel search algorithm to optimize the parameters of gradient boosted decision trees, thereby improving their predictive performance. Comparative analyses against established forecasting methods such as dNN, BiLSTM, CNN-BiLSTM, and AB-Net are conducted, with the SS-GBdT model consistently demonstrating superior accuracy, lower error metrics, and an overall robust performance. The findings suggest that the SS-GBdT model could be an invaluable asset for energy sector stakeholders, aiding in the advancement of reliable and efficient renewable energy systems. This study contributes to the growing body of knowledge in renewable energy forecasting and underscores the potential of advanced machine learning techniques in fostering sustainable energy transitions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Clean energy

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A decision-Making Framework for Financial Trading Using Linear Tree-Based Kernel Support Vector Machine Classifier 7th

A Decision-Making Framework for Financial Trading Using Line...

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： das, Gurumukh Singh, Gurdeep Zindani, divya Shanmugasundar, G. Chohan, Jasgurpreet Singh Kalita, Kanak Uttar Pradesh Agra282 005 India Punjab Ludhiana141013 India College of Engineering Kalavakkam India Department of Mechanical Engineering Sri Sai Ram Institute of Technology Chennai600044 India Department of Mechanical Engineering University Centre for Research and Development Chandigarh University Mohali140413 India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India Jadara University Research Center Jadara University Irbid Jordan

ISBN: (纸本)9783031733239

In today's rapidly evolving financial landscape, numerous investors and hedge funds have gravitated towards stock markets, captivated by their extensive range of instruments and lucrative investment opportunities. despite their allure, navigating the stock market landscape is intricate. It demands financial traders to remain vigilant, identifying thriving firms and diligently tracking stock price oscillations to devise potent trading strategies. In recent times, cutting-edge technological advancements like artificial intelligence and machine learning have revolutionized stock price predictions. Machine learning (ML) algorithms, especially Support Vector Machines (SVM), have become instrumental in predicting stock movements. However, an often-underacknowledged limitation of SVM is its susceptibility to overfit in the presence of noisy and intricate datasets. Addressing this gap, our research introduces the innovative Linear Tree-based Kernel Support Vector Machine (LT-KSVM) for enhanced stock market price forecasts. We empirically validated our approach by creating a portfolio of stocks from the Saudi Stock Exchange (Tadawul). Simulated trades over a span of 3.7 years were deployed to assess the portfolio's predicted performance. We meticulously executed three preprocessing phases to enhance stock price movement forecasts. remarkably, our proposed framework demonstrated astounding potential, boasting over 90% returns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forestry

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