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Implementation and Simulation of the Two-Stage Cycloidal Gearbox Multibody Dynamics Model with Nonlinear Contact Forces 6th

Implementation and Simulation of the Two-Stage Cycloidal Gea...

引用

6th International Conference Mechatronics - Ideas for Industrial Applications

作者： Jaworski, Konrad Manka, Michal AGH Univ Krakow Dept Robot & Mechatron Al A Mickiewicza 30 PL-30059 Krakow Poland

ISBN: (纸本)9783031634437;9783031634444

Cycloidal gearboxes have attracted significant attention of the industry as well scientific community for their applications in robotics as well as in high precision machinery. Due the force distribution of the particular components of the transition system, they can be easily manufactured using wildly available additive manufacturing for prototyping purposes as well as for the enthusiasts. To understand behaviour of this gearboxes number of simulation software's have arisen, differencing in the options which provides base of area of application. State of the art software, Romax from company Hexagon, allows to analyse the transmission systems in terms of its durability, noise, vibration as well as bearing analysis. This approach has its limitations as the analysis is solved only on static cases without evolution in time and due to pre-defined cases, only limited type of structures can be used in the simulations. This work introduces method of implementation and simulation of the inverted two-stage cycloidal gearbox in the Hexagon's software Adams, facilitating nonlinear contact forces as the base of methodology. With this approach, the designed by authors gearbox could be set up as the model and perform the dynamic analysis of the drive. A developed simulation model was tested and then its parameters were optimised based on the measurements of penetration depth of contact forces, which were further used as a method of model verification.

关键词： MBS CAD Transmission system Backlash modelling

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Smart Rack System Using AI 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Thakur, Chandan Palkar, Bhakti KJ Somaiya Coll Engn Comp Mumbai Maharashtra India

ISBN: (数字)9789819713134

ISBN: (纸本)9789819713127;9789819713134

The artificial intelligence (AI) has a significant impact on modern living. Typically, the problem with retail establishments is that the retail manager does not know which rack will be empty and has to be replaced. This problem causes a considerable loss. The purpose of this study is to do grocery item detection using object detection on rack and computer vision techniques. The literature review finds that smart rack systems using AI have the potential to transform the warehousing industry, offering significant benefits over traditional warehouse management systems. These benefits include increased efficiency, reduced costs and improved customer experience. The proposed AI-based smart rack system uses object detection model like YOLOv3, YOLOv4 and YOLOv5 which are trained on our dataset that consists of 10 different grocery items to identify changes to the rack and grocery items in real time.

关键词： Smart rack system Artificial intelligence YOLOv3 YOLOv5 Precision Recall and mAP@50

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An Analysis of the Security of IPv6 Routers in a SOHO Environment and Their Resistance to DDoS Attack 13th

An Analysis of the Security of IPv6 Routers in a SOHO Enviro...

引用

13th Computer Science Online Conference

作者： Huraj, Ladislav Hrinkino, Dominik Simon, Marek Univ Ss Cyril & Methodius Trnava Inst Comp Technol & Informat Trnava 91701 Slovakia

ISBN: (纸本)9783031702990;9783031703003

With the advent of IPv6 networks and the simultaneous increase in DDoS attacks, it is important to examine the resilience of SOHO routers to various aspects of IPv6 networks, DDoS attacks over IPv6 networks, and the ability of routers to provide secure connectivity in this environment. This article presents an analysis of the results of the resistance testing of routers from TP-Link, Mikrotik, and TP-Link with support for alternative OpenWRT firmware over IPv6 networks in order to provide recommendations for improving the security and efficiency of these devices. The conclusions of the article point to differences in router capabilities and security measures. The Mikrotik router and the TP-Link router with OpenWRT offered better configuration and security options. The speed tests showed comparable results between IPv4 and IPv6. The DDoS attack test also showed the routers' resistance to an ICMPv6 flood attack. The recommendations presented in the article emphasize the equal support for IPv4 and IPv6, the implementation of security rules, and the possibility of using alternative firmware.

关键词： IPv6 Security SOHO router Firewall ICMPv6 flood DDoS

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A Method to Handle Concept Drift in Predicting Remaining Useful Life 14th

A Method to Handle Concept Drift in Predicting Remaining Use...

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14th International Conference on Learning Factories (CLF)

作者： Nam, Doan Ngoc Chi Leng, Chan Hian Van Tung, Tran Phyoe, Su Myat Singapore Inst Mfg Technol 5 Cleantech Loop Singapore 6367322 Singapore

ISBN: (纸本)9783031654107;9783031654114

Predictive Maintenance (PdM), especially the prediction of Remaining Useful Life (RUL), has long been recognized as a pivotal capability in the progression towards autonomous manufacturing. However, a primary challenge impeding the scalability and generalization of RUL prediction is the concept drift associated with machines - specifically, the changes in machine data patterns over time due to aging and wear, or sudden changes in production recipes, etc. Such concept drifts compromise the predictive accuracy of static trained machine learning models, thereby posing challenges for RUL prediction. In this paper, we present a method to counteract these concept drifts in the RUL prediction task. We first outline a framework for RUL prediction and then devise a concept drift handler utilizing a gradient descent weighting method. This entire approach is subsequently tested on a datasets from a CNC spindle's bearing at the Model Factory@SIMTech to assess its viability. The developed technology would later be used as an advance feature in the PdM app at our Model Factory for training and engagement with local industry.

关键词： predictive maintenance remaining useful life machine degradation machine learning data mining

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Exploring Arabic Hotel Reviews: Sentiment Insights Through Deep Learning and BERT Transformers Models 4th

Exploring Arabic Hotel Reviews: Sentiment Insights Through D...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Benali, Mohammed Hassan 1st Univ Lab LM2CE FEG Settat Settat Morocco

ISBN: (纸本)9783031686498;9783031686504

An application of natural language processing (NLP) called sentiment analysis (SA) has been a more popular field of study in recent years in both scientific research and the business world. Finding the opinion stated in a subjective statement with numerous methods demonstrating some degree of success in identifying sentiment across numerous datasets is the goal of this activity. An opinion is a personal statement of one's subjective feelings and beliefs. These sensations and ideas might be associated with a certain emotion. Two sentiments have been examined the most: negative and positive. Deep learning and transformer models are the two most recent advancements in sentiment analysis techniques since the attention mechanism in machine learning was introduced. In recent times, transformer-based models, which are attentive encoder-decoder systems, have demonstrated remarkable performance in sentiment analysis and have been regarded as the most advanced model for a variety of languages. Utilizing this property in encoder-decoder architecture has affected transformer models' efficacy in sentiment analysis and other NLP tasks. Even so, there is still room for further enhancement of Arabic sentiment analysis efficiency. As a result, bidirectional encoder representations from transformers (BERT) and deep learning (DL) models such as convolutional neural networks (CNN) and bidirectional long-short-term memory (bi-LSTM) have been tested in this work. To achieve better performance, an ensemble model has also been incorporated. By including the standard BERT tokenizer and transformer models, the suggested method is improved. Different models were tested. When compared to CNN and Bi-Lstm, the experimental investigation demonstrates the effectiveness of the suggested strategy in terms of realistic performance.

关键词： Natural Language Processing Sentiment Analysis Deep Learning Bert language model Modern Standard Arabic Dialectal Arabic

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Intelligent System to Detect Malicious URLs Using Machine-Learning Algorithms 9th

Intelligent System to Detect Malicious URLs Using Machine-Le...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Jeyavadhanam, B. Rebecca Bhuvanan, Mahesh Sihan, Haroon Ahmadzadeh, Sahar Karthick, Gayathri York St John Univ Dept Comp Sci London England

ISBN: (纸本)9789819735556;9789819735563

Digital technology has made significant advancements in recent years, particularly on the Internet. Since most of our activities are now conducted online, this development is of particular significance. The continuous evolution of cyber threats has led to a heightened risk of cyberattacks, driven by the inventive tactics employed by malicious actors. Among these threats, one of the most perilous is the malicious URL, meticulously crafted to illicitly obtain information from unsuspecting novice end users. Such attacks compromise user systems and incur annual financial losses in the billions of dollars. Consequently, there is a growing imperative to fortify website defenses. The principal objective of this study is to develop a machine-learning model capable of discerning between malicious and legitimate URLs based on carefully selected parameters for each category. This research employs a variety of machine learning techniques, including decision tree (DT), logistic regression (LR), multi-layer perceptron (MLP), and naive Bayes (NB), while exploring different hyperparameter configurations to classify URLs as safe or malicious. Upon analyzing the experimental results, it is evident that the 'tanh' activation function of MLP in conjunction with the 'adam' solver achieves the highest accuracy rate of 80.01%. This underscores the effectiveness of our approach in enhancing cybersecurity measures against malicious URLs.

关键词： Malicious Machine learning URL Decision tree Logistic

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Global Electric Car Market 10th

Global Electric Car Market

引用

International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Dudic, Branislav Comenius Univ Fac Management Bratislava 82005 Slovakia

ISBN: (纸本)9783031662706;9783031662713

The world must consider the challenge of innovation in automotive industry because of keeping its position on the market and improving economic situation. Nowadays, automobile producers all around the world invest massively into the development of electric drive of automobiles-electro mobiles. Development of electromobility should bring the creation of new work positions in economy. Statistics show that electric cars will increase their numbers on roads every year till they reach their maximum and will not be the part of combustion engine. Electric cars should be able to substitute gradually cars with conventional combustion engines. Electromobility is becoming an everyday part of people's lives. The number of electric cars is still growing all over the world. Nowadays, electro mobiles are more competitive, endurance distance is longer, there are efforts to unify charging technologies and charging infrastructure is gradually developing in every country. Every state has established its direct and indirect support of electromobility whereby countries all around the world have created the conditions for the market growth of electric automobiles.

关键词： electromobility environment global market electric vehicle production

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Multilevel Monte Carlo Simulation Model for Air Pollution Index Prediction of a Smart Network 6th

Multilevel Monte Carlo Simulation Model for Air Pollution In...

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6th International Conference on Soft Computing and Data Mining (SCDM)

作者： Hassan, Mustafa Hamid Mostafa, Salama A. Ghazali, Rozaida Saringat, Mohd Zainuri Husaini, Noor Aida Mustapha, Aida Jubair, Mohammed Ahmed Hariz, Hussein Muhi Imam Jaafar Al Sadiq Univ Coll Informat Technol Al Muthanna 66002 Iraq Univ Tun Hussin Onn Malaysia Fac Comp Sci & Informat Technol Johor Baharu 84600 Malaysia Tunku Abdul Rahman Univ Management & Technol Fac Comp & Informat Technol Jalan Genting Kelang Kuala Lumpur 53300 Malaysia Univ Tun Hussein Onn Malaysia Fac Appl Sci & Technol Johor Baharu 84500 Malaysia

ISBN: (纸本)9783031669644;9783031669651

Air pollution has become a significant environmental challenge in the 21st century due to widespread industrialization and urbanization worldwide. Effectively reducing it requires precise predictions of air quality. However, existing methods for predicting the Air Pollution Index (API) fail to effectively model short-term variables' dependencies and mostly neglect spatial correlations. Given these limitations, the statistical method emerges as the most suitable choice. Monte Carlo Simulation (MCS) is one of the best short-term time series prediction statistical approaches. This paper proposes a Multilevel Monte Carlo Simulation (MLMCS) model based on the MCS model for forecasting the API. The study covers the analysis of an air pollution dataset that includes the API and ambient air quality of ten locations in Beijing, China. The results show that the MLMCS improves the performance of API prediction compared to the MCS. The MLMCS model has the highest accuracy of 86.45% and the lowest computational time of 3.43 s compared to the MCS model's accuracy of 82.90% and computational time of 7.5 s.

关键词： Air quality prediction Air Pollution Index (API) Suspended Particulate Matter (SPM) Monte Carlo Simulation (MCS)

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An Experimental Study to Perform Bioinformatics Based on Heart Disease Case Study Using Supervised Machine Learning 1

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International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Sehgal, Nikhil Mehra, Harshita Vij, Sonakshi Virmani, Deepali Vivekananda Inst Profess Studies Tech Campus New Delhi India

ISBN: (数字)9789819984763

ISBN: (纸本)9789819984756;9789819984763

Heart diseases are very dangerous and can be detrimental. Especially in today's busy life schedule, people are getting more prone to these. After an extensive literature review, the authors realized that experimental studies in bioinformatics can prove to be very helpful for fast analysis of medical databases like these, without their quality being compromised. To verify this, a dataset was extracted and filtered under the supervision of a cardiologist. A model was then created which was later tested on different machine learning algorithms. The F1 scores, precision, and recall for different algorithms tested were then calculated and compared. This paper aims to showcase an experimental study and verify the significance of distinct machine learning algorithms and to find and study the various features and align them in the best way possible. The paper also aims to perform feature engineering and motivate people to take care of themselves.

关键词： Machine learning Heart diseases Regression Logistic regression Support Vector Machine Decision trees

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Machine Learning-Enhanced Automation for Invoice Reconciliation: OCR-Based Solutions for Accuracy and Efficiency in Logistics Industry

Machine Learning-Enhanced Automation for Invoice Reconciliat...

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Intelligent systems Conference

作者： Yu, Chang Hua Yau, Peter Chun Yu Cao, Qi Seow, Chee Kiat Pang, Patrick Wong, Dennis Univ Glasgow Sch Comp Sci Glasgow Lanark Scotland Macao Polytech Univ Fac Appl Sci Macau Peoples R China

ISBN: (纸本)9783031663352;9783031663369

Invoice reconciliation, a pivotal process in logistics, involves verifying and comparing data on freight bills to ensure accuracy. Manual reconciliation is time-consuming and error-prone, taking up to 10 days. Leveraging OCR technology and automated reconciliation mechanisms is proposed to streamline this process. Challenges include varied invoice formats and field name discrepancies. Arobust comparison algorithm is vital for an effective reconciliation engine. Literature review reveals innovative solutions in Regular Expression Pattern Matching, such as FREME, offering fast and scalable results. Optical Character Recognition studies, like OCRMiner, demonstrate the potential for automated invoice processing but face challenges in handling varying formats. Another approach, the Digitization Process, focuses on transforming invoice text into usable formats but lacks extensive discussion on handling diverse formats. This research aims to address these challenges by developing an OCR-based automated freight invoice reconciliation system. The study explores improvements in OCR accuracy, adaptable algorithms, and effective handling of diverse invoice layouts.

关键词： Automation OCR Digitalization Logistics Machine learning Computer vision

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