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Simulation of 6-DOF Modular Arm-Z Manipulator in MATLAB Simulink 28th

Simulation of 6-DOF Modular Arm-Z Manipulator in MATLAB Simu...

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2024 International Conference Automation

作者： Jarek, Michal Zawalski, Krzysztof Falkowski, Piotr Zawidzka, Ela Zawidzki, Machi Ind Res Inst Automat & Measurements PIAP Warsaw Poland Warsaw Univ Technol Warsaw Poland Inst Fundamental Technol Res PAN Warsaw Poland

ISBN: (纸本)9783031782657;9783031782664

Arm-Z is a concept of a hyper-redundant manipulator based on linearly joined sequence of congruent units. Each unit has only one degree of freedom (1-DOF) - the twist relative to the previous unit. This paper presents the approach to design of modular robotic manipulator based on computer simulations. The approach is rather standard nowadays, however, it is applied to rather unique robotic system. The CAD model of the module structure for a manipulator with 6 degrees of freedom and respective Denavit-Hartenberg notation are presented. Simulink - a MATLAB-based graphical programming environment was used to determine the torques of individual modules. The results of three simulations are presented and discussed. Finally, based on critical observations, the drive and gear for the module of Arm-Z system were selected.

关键词： Simulation Robotic arm Manipulator Arm-Z MATLAB Simulink

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Proposal to Improve Risk Management in New Product Development Projects in an Automotive Company 12th

Proposal to Improve Risk Management in New Product Developme...

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Laranjeiro, Ines Tereso, Anabela Faria, Joao Univ Minho Masters Ind Engn & Management Guimaraes Portugal Bosch Car Multimedia Portugal SA Rua Max Grundig Braga Portugal Univ Minho ALGORITMI Res Ctr LASI P-4710057 Guimaraes Portugal

ISBN: (纸本)9783031602207;9783031602214

Risk Management is a crucial domain within Project Management for organizations, and it plays a fundamental role in the automotive sector, ensuring a competitive position in today's dynamic and constantly evolving environment. This research was carried out at Bosch Car Multimedia Portugal, with the main objective of analyzing and identifying areas for improvement in Risk Management practices in New Product Development projects of the company. As a result of the study, several measures were proposed to enhance and optimize the Risk Management process. The research strategy employed was a case study, incorporating various techniques for gathering the necessary information for both the theoretical foundation and the analysis of the current situation in the company, for the project under analysis. The research aimed to foster a culture of Risk Management within the project team, considering their needs, limitations, and expectations. It is important to promote Risk Management routines, ensure team involvement, and implement good practices that make the entire process effective, practical, and smooth. Following the comparative analysis between the observed reality and the theory, a model approach for Risk Management, tailored to the specific project, was proposed. Additionally, other suggestions were presented to facilitate and enhance the implementation of Risk Management practices within the company, as well as to broaden the overall knowledge of teams on the subject matter. The importance of cooperative and proactive work, the promotion of a risk-aware culture, and continuous investment in process improvement and team dynamics were highlighted.

关键词： Project Management Risk Management New Product Development

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Promising AI Applications in Power systems: Explainable AI (XAI), Transformers, LLMs 10th

Promising AI Applications in Power Systems: Explainable AI (...

引用

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

作者： Lukianykhin, Oleh Shendryk, Vira Shendryk, Sergii Malekian, Reza Sumy State Univ Dept Informat Technol Sumy Ukraine Malmo Univ Dept Comp Sci & Media Technol Malmo Sweden Malmo Univ Internet Things & People Res Ctr Malmo Sweden Sumy Natl Agr Univ Dept Cybernet & Informat Sumy Ukraine

ISBN: (纸本)9783031662706;9783031662713

This paper aims to analyze and identify the most promising opportunities for Artificial Intelligence (AI) applications in the Power systems (PS) domain. It identifies major challenges faced in PS and explores the corresponding technical tasks: forecasting and optimal control. Then, the paper investigates the key AI techniques commonly employed in PS for these tasks, e.g. reinforcement learning (RL) and time series forecasting. It also highlights promising methods with great potential in advancing PS solutions: attention-based models (Transformers, LLMs) and explainable AI (XAI) approaches. This study's primary contribution lies in identifying critical research gaps in AI for PS, highlighting areas where research and development may have the biggest impact. Additionally, the paper provides a structured literature overview, serving as a valuable resource for researchers and practitioners in the field.

关键词： Power System Artificial Intelligence Explainable AI Transformers Large Language Models

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Failure Modeling of Industrial Electric Motors Using Unsupervised Learning Methods 28th

Failure Modeling of Industrial Electric Motors Using Unsuper...

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2024 International Conference Automation

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Natl Univ Food Technol UA-01033 Kyiv Ukraine Natl Univ Life & Environm Sci Ukraine UA-03041 Kyiv Ukraine State Natl Univ Trade & Econ UA-02156 Kyiv Ukraine

ISBN: (纸本)9783031782657;9783031782664

The task of detecting breakdowns helps to improve the reliability of industrial devices through early identification of problems and their timely resolution, which helps to improve the resource and energy efficiency of production as a whole. When modeling breakdowns of industrial electric motors, the following characteristics are primarily taken into account: average load, rotational frequency, power supply voltage, current consumption, leakage current, collected from data from a traditional automated control system and breakdown logs of repair teams of an industrial enterprise. The model for detecting reliability and breakdowns of electric motors in the work is built on an anomaly detection algorithm using unsupervised machine learning methods. Its effectiveness has been successfully tested using many evaluation metrics. It can be used for a range of tasks and applications in equipment management and maintenance, visualization, forecasting and management decision support.

关键词： unsupervised learning Gaussian Mixture Model motor modelling breakdown reliability

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Explainable AI for Intrusion Detection systems: A Model Development and Experts' Evaluation

Explainable AI for Intrusion Detection Systems: A Model Deve...

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Intelligent systems Conference

作者： Durojaye, Henry Naiseh, Mohammad Bournemouth Univ Talbot England

ISBN: (纸本)9783031664274;9783031664281

This study sought to develop a transparent machine learning model for network intrusion detection that domain experts would trust for security decision-making. Intrusion detection systems using machine learning have shown promise but often lack interpretability, undermining user trust and deployment. A hybrid Random Forest/XGBoost classifier achieved over 99% accuracy and F1 score, outperforming previous literature. Post-hoc LIME explanations provided feature effect transparency. Nine domain experts from technical roles then evaluated the model's reliability, explainability, and trustworthiness through a standardised process. While over half found the model reliable, one-third expressed uncertainty. Responses on performance explanations and trustworthiness assessments also varied thus suggesting opportunities to strengthen reliability communications and consolidate diverse perspectives. To optimise user confidence and model deployment, refinements targeting consistent explainability across audiences were proposed. Overall, high predictive performance validated effectiveness, but variable viewpoints from evaluations indicated the need to bolster reliability and trust explanations. With continued iterative evaluation and enhancements, this research framework holds promise for developing interpretable machine learning solutions trusted for complex security decision-making.

关键词： Explainable AI Trustworthy AI Intrusion detection systems

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Object Detection and Tracking Approach for Traffic Monitoring 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Barekar, Praful, V Singh, Kavita R. Yeshwantrao Chavan Coll Engn Comp Technol Dept Nagpur Maharashtra India

ISBN: (数字)9789819713233

ISBN: (纸本)9789819713226;9789819713233

In present day, many object detection algorithms are available. These computer vision-based object detection algorithms help to detect, locate and trace an object from an image or a video. It requires high speed and accuracy along with efficiency when it is concerned with real-time systems. In machine learning and computer vision, object detection is considered to be an essential research area, also widely implemented in various sectors such as robotic navigation and intelligent video. The traditional approach to object identification consists of the steps as partitioning, clustering, feature extraction and classification. The solution is dependent of manual annotation, which leads to increase in the cost of the algorithm. Due to the diversity of objects, multiple models are needed for feature detection. As a result, classical object detection algorithms have poor generalizability, low detection accuracy, slow operating rate and low robustness. Object detection strategies are widly categorized as one-step and two-step object detection strategies. We are presenting a comparative analysis on object detection algorithms from two categories, i.e. single shot feed forward object detection algorithms and region proposal-based object detection algorithms. Under single shot feed forward, YOLOv7 is being is used along with pretrained weights trained on MS-COCO dataset from scratch. On the contrary, Mask R-CNN is being used to compare with, with its pretrained weights. This study presents a comparative analysis of YOLOv7 and Mask R-CNN in the context of accuracy, memory footprint and processing speed by retraining the models on the dataset of images and videos obtained through real-time systems in constrained.

关键词： Object detection Object tracking YOLOv7 Mask R-CNN

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Multimodal Face and Ear Recognition Using Feature Level and Score Level Fusion Approach

Multimodal Face and Ear Recognition Using Feature Level and ...

引用

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

作者： Resmi, K. R. Joseph, Amitha George, Bindu Christ Deemed Univ Bengaluru India Santhigiri Coll Thodupuzha Kerala India

ISBN: (纸本)9789819984756;9789819984763

Recent years have seen a significant increase in attention in multimodal biometric systems for personal identification especially in unconstrained environments. This paper presents a multimodal recognition system by combining feature level fusion of ear and profile face images. Multimodal biometric systems by combining face and ear can be used in an extensive range of applications because we can capture both the biometrics in a non-intrusive manner. Local texture feature descriptor, BSIF is used to extract discriminative features from biometric templates. Feature level and score level fusion is experimented to improve the performance of the system. Experimental results on different public datasets like GTAV, FEI, etc., show that the proposed method gives better performance in recognition results than individual modality.

关键词： Multimodal biometrics Face Ear Feature level fusion Score level fusion BSIF

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The Barriers to AI Adoption in Supply Chains: Case of Moroccan Companies 7th

The Barriers to AI Adoption in Supply Chains: Case of Morocc...

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7th International Conference on Logistics Operations Management (GOL)

作者： Barakat, Ouafa Bouanba, Nouhayla Ibn Zohr Univ Natl Sch Business & Management Agadir 80000 Morocco

ISBN: (纸本)9783031686276;9783031686283

Artificial intelligence (AI) is quickly becoming one of the most important new technologies. Its adoption has tremendous potential for businesses. They can use it to increase profits, save costs in the supply chain, provide outstanding client service, and make their supply chains smarter. However, several companies find it difficult to fully realize all the advantages of this technology. This research aims to investigate the major barriers influencing the adoption of AI in Moroccan companies. In this paper, we used the qualitative approach to explore the main barriers to AI adoption in our country. Twenty semi-structured interviews were conducted with managers in Moroccan companies, and data analysis was conducted using the IRAMUTEQ analysis software in addition to content analysis. Our results will highlight six barriers to AI adoption related to two contexts: the technological and the organizational ones. These barriers are: training and assistance, complexity, funding, managerial support, technical capability and company size. We used TOE (Technological-Organizational- Environmental) theory to cluster our findings into the two adoption dimensions.

关键词： Artificial Intelligence Supply Chain Barriers Moroccan companies TOE theory

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Energy Optimization Through Capacitor Banks Using the K Factor 13th

Energy Optimization Through Capacitor Banks Using the K Fact...

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13th Computer Science Online Conference

作者： Nino Mayuri, Jhuver Espinoza Jamanca, Ian Zarate Segura, Guillermo Univ Tecnol Peru Lima Peru

ISBN: (纸本)9783031702846;9783031702853

In this work we will compare 2 of the most common forms of energy compensation in order to determine which is the best option, we will analyze the normal mode, where the engineering analysis is used and the other mode which makes use of the K-factor table, both are used for energy compensation, but we are looking for a way to do it in an easier, faster and more efficient way, also, we will present the comparison of before and after the compensation of capacitor banks using the above methods and thus achieve highlight the effectiveness and efficiency of using the K-factor table.

关键词： capacitor bank design selection energy flow energy optimization non-linear loads

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Complexity Classification of Complex-Weighted Counting Acyclic Constraint Satisfaction Problems 1

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Computing Conference

作者： Yamakami, Tomoyuki Univ Fukui Fac Engn 3-9-1 Bunkyo Fukui 9108507 Japan

ISBN: (数字)9783031622731

ISBN: (纸本)9783031622755;9783031622731;9783031622724

We study the computational complexity of counting constraint satisfaction problems (#CSPs) whose constraints assign complex numbers to Boolean inputs when the corresponding constraint hypergraphs are acyclic. These problems are called acyclic #CSPs or succinctly, #ACSPs. We wish to determine the computational complexity of all such #ACSPs when arbitrary unary constraints are freely available. Depending on whether we further allow or disallow the free use of the specific constraint XOR (binary disequality), we present two complexity classifications of the #ACSPs according to the types of constraints used for the problems. When XOR is freely available, we first obtain a complete dichotomy classification. On the contrary, when XOR is not available for free, we then obtain a trichotomy classification. To deal with an acyclic nature of constraints in those classifications, we develop a new technical tool called acyclic-T-constructibility or AT-constructibility, and we exploit it to analyze a complexity upper bound of each #ACSPs.

关键词： Auxiliary pushdown automata #LOGCFL Counting constraint satisfaction problem #ACSP Complexity classification Acyclic hypergraph Acyclic-T-constructibility

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