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Design and Validation of a Learning Factory with Adaptive Human-Robot Collaboration 14th

Design and Validation of a Learning Factory with Adaptive Hu...

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14th International Conference on Learning Factories (CLF)

作者： Rueckert, Patrick Arndt, Johannes Kinder, Anna Charlotte Kunkel, Till Oja, Gunnar Omameh, Leona Tracht, Kirsten Univ Bremen Badgasteiner St 1 D-28359 Bremen Germany

ISBN: (纸本)9783031654107;9783031654114

Technological developments in the field of human-robot collaboration have great potential for manufacturing companies. Due to ever faster development cycles, the fit between competences acquired in vocational training and actually required skills and abilities in the workplace is decreasing. The Bremen Institute for Mechanical Engineering (bime) has designed and implemented a learning factory that enables a team of four employees to explore collaborative and cooperative human-robot interaction in an assembly line scenario. The concept is based on an experimental modular assembly line that allows a free configuration of the arrangement of assembly stations and allows different degrees of automation. Based on a visual part recognition, the robot can react to human movements and the presence of components. In particular, the interaction between human and machine is in focus. The influence on the cycle times of a flow assembly in human-robot teams and the corresponding learning effects are investigated.

关键词： Learning Factory Human-Robot Collaboration Assembly

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Statistical Analysis of Stakeholders Perception on Adoption of AI/ML in Sustainable Agricultural Practices in Rural Development 9th

Statistical Analysis of Stakeholders Perception on Adoption ...

引用

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

作者： Taiwo, Gbadegesin Adetayo Akinwole, Tinuade Oluwatosin Ogundepo, Olumide Benjamin Univ Salford Sch Sci Engn & Environm Manchester Lancs England Univ Ilorin Dept Stat Ilorin Kwara States Nigeria

ISBN: (纸本)9789819733019;9789819733026

The integration of artificial intelligence (AI) and machine-learning (ML) techniques is swiftly revolutionizing global agriculture. These advanced technologies find applications across various domains, including automated monitoring of crops and livestock, as well as predictive analytics for precision agriculture. However, the perspective and assessment of stakeholders on the adaption of these technologies to agriculture have not been evaluated. Hence, a survey with 145 stakeholders from Shonga farms in Nigeria to gather their perspectives on the implementation of AI and ML was conducted. By analyzing their responses across 11 factors, it was found that there is positive support for adopting AI/ML in areas such as training, infrastructure, productivity and economic outcomes. However, some shortcomings in terms of resources, policy frameworks, sustainability measures, and equitable access were identified. The variation in responses highlights the challenges related to training and infrastructure that need to be addressed. To fully unlock the potential of these technologies in improving productivity, sustainability, economic viability, and community well-being at Shonga farms targeted investments and policy improvements are necessary. This empirical evidence provides insights, for applying emerging technologies to promote sustainable rural development while ensuring inclusivity.

关键词： Artificial intelligence Sustainable agriculture Rural development

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Stock Market Prediction Using ML Module

Stock Market Prediction Using ML Module

引用

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

作者： Jathe, Sonal Chaudhari, D. N. JDIET Dept Comp Sci & Engn Yavatmal India

ISBN: (纸本)9789819984756;9789819984763

Predicting how stock prices will go in the future has always been difficult. The efficient market hypothesis asserts that no predictive framework can be constructed that can effectively predict the movement of stock prices, yet this assertion is false. The seemingly random movement patterns of the stock price time series can be anticipated with a high degree of accuracy. Selecting the right variables, using the right methods for transforming variables, and fine-tuning the right model parameters are all required for risk-adjusted prediction models. In this research, we introduce a very accurate and reliable approach for predicting stock prices with risk analysis, using methods from statistics and machine learning. We utilize daily stock price data obtained at five-minute intervals from a credible company trading on India's National Stock Exchange (NSE). Granular data collected over the course of a day is aggregated, and the total amount of information is used to train and develop forecasting models. We argue that the integration of statistical and machine learning techniques into the model-building process has the potential to learn from the unpredictable fluctuations in stock prices with much greater success and much less uncertainty. As a result of this efficient training, models for predicting stock price fluctuations and short-term trends will be very robust and risk-averse. When creating models for regression and classification, we employ statistical and machine learning techniques. Significant data on the effectiveness of these models has been released, and it has been subjected to rigorous analysis.

关键词： Analyzing risks Making forecasts Applying machine learning to the stock market

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Real-Time Live Insult Analysis on Twitter-X Social Media Platform 2nd

Real-Time Live Insult Analysis on Twitter-X Social Media Pla...

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2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Sahin, Fatih Istanbul Topkapi Univ Software Engn Dept TR-34662 Istanbul Turkiye

ISBN: (纸本)9783031628801;9783031628818

Real-time insult analysis could help platforms identify and filter out harmful content promptly, promoting a healthier online environment and reducing the spread of negativity. Identifying and addressing insults in real-time, platforms can enhance user experience by fostering more civil and respectful interactions among users. It requires robust algorithms capable of accurately detecting insults across various languages and contexts while ensuring user privacy and minimizing false positives. In this study, sentiment analysis was conducted using Turkish Twitter data. The data was analyzed by applying data analysis methods and Naive Bayes (NB), K-Nearest Neighbor (KNN), SupportVector Machine (SVM), Logistic Regression (LR), Random Forest, and Decision Tree. They were classified as positive and negative using classification algorithms. The performance of these classifiers was evaluated with the F1 score. According to the results, the Logistic Regression classifier performed with the highest F1 score (85%). Among other classifiers, SVM(84%), Random Forest (83%), Naive Bayes (83%), and Decision Tree (80%) achieved F1 scores. This study aimed to compare classifiers when performing sentiment analysis from Turkish Twitter data.

关键词： Twitter Tweet Analysis Data Mining Text Mining Sentiment Analysis

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The Influence of Thermal Stresses on the Load Distribution in Cycloidal Reducers 1

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3rd International Symposium on Industrial Engineering and Automation

作者： Blagojevic, Mirko Vasic, Milan Dizdar, Samir Tuka, Smajo Josimovic, Milos Univ Kragujevac Fac Engn Kragujevac 34000 Serbia Acad Appl Tech Studies Belgrade Coll Appl Engn Sci Pozarevac Belgrade Serbia Berdiz Consulting AB Gothenburg Sweden

ISBN: (数字)9783031704628

ISBN: (纸本)9783031704611;9783031704628

High precision reducers (HPRs) are being increasingly used in modern industrial systems, especially in industrial robots. That is why the number of installed cycloidal reducers (the most commonly used HPRs) is increasing year after year. In order to design a cycloidal reducer, to determine its efficiency and distribution of stresses and deformations, it is necessary to know the intensity and character of the load. A number of papers dealing with this subject have been published. They analyse the influence of different parameters on the load distribution in cycloidal reducers, such as clearances, modification of the cycloidal disc tooth profile, etc. This paper analyses the influence of thermal stresses (which occur during operation) on distribution of contact forces between the central rollers and the cycloidal disc teeth. This analysis is very important because high operating temperatures affect the size of clearances between the elements that transfer the load and, thus, the number of elements in contact. Intensities of thermal sources were determined analytically (Matlab), while the contact forces were determined numerically using the Ansys software. The obtained results show that the influence of temperature on distribution of contact forces cannot be ignored, especially with frequent starts, stops and considerable overloads.

关键词： Cycloidal Reducer Contact Forces Thermal Analysis

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Towards Type-3 Fuzzy Control Stability with a Fuzzy Lyapunov Approach: The Case of a Robotic Manipulator

Towards Type-3 Fuzzy Control Stability with a Fuzzy Lyapunov...

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Castillo, Oscar Melin, Patricia Tijuana Inst Technol TecNM Tijuana 22379 BC Mexico

ISBN: (纸本)9783031671913;9783031671920

Stability is one of the relevant and key topics in Control. Type-3 Fuzzy Logic is a potential striking area for realizing fuzzy control. This article utilizes a Fuzzy Lyapunov approach, originally proposed for type-1 fuzzy control, to initially formulate a stable type-2 fuzzy controller. Then we propose an elevation from the Type-2 to Type-3, maintaining stability, with the fuzzy Lyapunov approach, of the corresponding fuzzy controller. The main contribution is to offer a first step on how to move forward with providing stability for type-3 fuzzy controllers. As a case study we consider a robotic manipulator to illustrate the proposal. The presented results demonstrate the potential of the type-3 fuzzy Lyapunov extension in the control area.

关键词： Type-3 Fuzzy Control Stability Fuzzy Lyapunov

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Fractal Dimension Measurement Uncertainty 10th

Fractal Dimension Measurement Uncertainty

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

作者： Tonkonogyi, Volodymyr Holofieieva, Maryna Dasic, Predrag Klimov, Sergii Buriachenko, Oleksii Odessa Natl Polytech Univ ONPU Shevchenko Ave Odessa Ukraine Engn Acad Serbia IAS Belgrade Serbia SaTCIP Publisher Ltd Vrnjacka Banja Serbia

ISBN: (纸本)9783031662676;9783031662683

When designing complex systems, the problem of optimization is particularly relevant, that boils down to the solution of many inverse difficulties in developing a model of an object and its research. The using of complex objects in automatic design systems, which contain elements that jointly solve some general task of standard inverse calculation from specified system parameters to design characteristics or production technology, is also complicated. During such calculations, the use of such a characteristic parameter as the fractal dimension of the system elements allows one to significantly simplify the models used.. The principle of hierarchical organization of fractal-type models, in which it is possible to ascertain the absence of influence of scale on the properties of the object, i.e. extrapolation of the results of studying the properties of one of the scale levels to others is often found in nature and certainly manifests itself in material structures and functions that characterize the distribution of physical quantities in the space-time continuum. Despite its promise, its use in modeling the fractal dimension is complicated by issues of the accuracy of determining this parameter by various methods. The article is devoted to the development of a method for calculating the uncertainty of fractal dimension measurement, which is fully discribe the measuring results.

关键词： fractal fractal dimension measurement accuracy measurement uncertainty

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Improved Horse Racing Algorithm-Based Incremental Conductance Method for Mpp Tracking of PV systems 7th

Improved Horse Racing Algorithm-Based Incremental Conductanc...

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7th International Conference on Green Technology and Sustainable Development

作者： Ying-Yi Hong Ngo, Sy Hoang-Phuong Van Chung Yuan Christian Univ Dept Elect Engn Taoyuan 32023 Taiwan Thu Dau Mot Univ Inst Engn & Technol Thu Dau Mot Vietnam

ISBN: (纸本)9783031762314;9783031762321

The paper proposes a new solution using a metaheuristic technique that integrates a horse racing game-based algorithm and the incremental conductance (IC) method to overcome the problem of partial shading on the performance of a photovoltaic power system. This method emphasizes improving the accuracy and response time of the maximum power point tracking (MPPT) system under real weather conditions. By using an algorithm based on a horse racing game, the system can quickly achieve the global maximum power more accurately, helping to optimize solar panel performance while dealing with fluctuations in the surrounding environment. Simulation results show that the proposed method has significantly better performance than conventional IC in tracking and finding global MPP. The convergence time of the proposed method is also improved, and significantly reduced compared to traditional IC algorithm, showing the flexibility and effectiveness of the proposed method in the realistic conditions.

关键词： Global optimum Horse racing game Incremental conductance Maximum power point tracking

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Fusing Temporal and Contextual Features for Enhanced Traffic Volume Prediction 12th

Fusing Temporal and Contextual Features for Enhanced Traffic...

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

作者： Balderas-Diaz, Sara Guerrero-Contreras, Gabriel Munoz, Andres Boubeta-Puig, Juan Univ Cadiz Dept Comp Sci & Engn Ave Univ Cadiz 10 Puerto Real 11519 Cadiz Spain

ISBN: (纸本)9783031602177;9783031602184

Time-series analysis plays a crucial role in extracting meaningful patterns from sequential data, serving to gain insights into temporal trends. Specifically, the burgeoning urbanization trend accentuates the urgency of traffic forecasting in modern cities due to its socio-economic and environmental impact. While Deep Learning techniques, notably Long Short-Term Memory (LSTM) networks, have shown promise in predicting traffic, they often overlook contextual factors like weather and holidays. To address this challenge, this paper proposes a hybrid model combining LSTM with categorical and continuous data to forecast traffic volume on the Interstate 94 American highway. Incorporating weather, temporal patterns, and holidays, this study explores the performance of a model that includes contextual factors against standalone LSTM. The results show superior predictive accuracy for the proposed hybrid model. SHapley Additive exPlanations (SHAP) analysis reveals the influence of diverse features, emphasizing the significance of contextual attributes in enhancing traffic prediction.

关键词： time-series analysis traffic forecasting hybrid neural network LSTM contextual factors

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Intelligent Traffic Safety Management on a Section of a Multi-lane Motorway 10th

Intelligent Traffic Safety Management on a Section of a Mult...

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

作者： Mandzuka, Sadko Vidovic, Kresimir Kos, Goran Dedic, Luka Univ Zagreb Fac Traff & Transport Sci Vukeliceva 4 Zagreb 10000 Croatia Ericsson Nikola Tesla Krapinska 45 Zagreb 10000 Croatia Inst Tourism Zagreb 10000 Croatia PROMETIS Ltd Cvijete Zuzoric 5 Zagreb Croatia

ISBN: (纸本)9783031662706;9783031662713

The paper describes model of intelligent traffic safety management on a section of a multi-lane motorway. System is based on estimation accident risk that uses real-time data of traffic flow on motorway section. Advanced system for giving warning messages to drivers will be analysed, too. As one of the examples, it is described warning system when the use of mobile devices by the driver is detected.

关键词： intelligent transport system multi-lane motorway road safety traffic micro-simulation warning system

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