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Flexible and Transportable Illumination Control System for Machine Vision Applications 10th

Flexible and Transportable Illumination Control System for M...

引用

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

作者： Gusic, Muhamed Sokic, Emir Univ Sarajevo Dept Automat Control & Elect Fac Elect Engn Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783031662706;9783031662713

During the development of machine vision setups for industrial applications, namely automatic visual inspection systems, the type of required lighting and correct illumination position and intensity are often not known in advance. In order to solve a specific machine vision task, an engineer needs to propose and implement a suitable image processing algorithm and design the accompanying lighting system. Testing different illumination concepts, especially "in the field", using off-the-shelf lighting systems is time consuming. The paper presents a custom-made, cost effective, transportable and flexible system which may be used for testing different illumination techniques and simultaneously control multiple lighting modules. In addition to the hardware part, the developed software used for control the system is presented in the paper. The proposed device usefulness is demonstrated by solving a suitable multi-light digital image processing task.

关键词： machine vision visual inspection illumination control system embedded systems digital image processing

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Decision-Making Framework for Supplier Selection Using an Integrated Approach of Dempster-Shafer Theory and Maximum Entropy Principle 1

引用

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

作者： Bisht, Garima Pal, A. K. GB Pant Univ Agr & Technol Dept Math Stat & Comp Sci Pantnagar 263145 Uttarakhand India

ISBN: (数字)9789819984794

ISBN: (纸本)9789819984787;9789819984794

The process of supplier selection plays a crucial role in determining the success and competitiveness of organizations in today's dynamic business environment. To make informed decisions, decision-makers often rely on robust and efficient methods that consider multiple criteria simultaneously. In this regard, this paper presents a novel approach that combines the Dempster-Shafer theory and the Maximum Entropy Principle to address the supplier selection problem effectively. Dempster-Shafer theory, rooted in evidence theory, provides a powerful framework for handling uncertainty and incomplete information in decision-making. It allows decision-makers to represent and combine evidence from multiple sources, leading to more reliable and rational decisions. On the other hand, the Maximum Entropy Principle, a principle of statistical inference, is widely used for incorporating prior knowledge into decision-making processes. Our proposed approach harnesses the strengths of both the Dempster-Shafer theory and the Maximum Entropy Principle to enhance the supplier selection process. The decision-making framework is designed to handle the inherent uncertainty, subjectivity, and imprecision associated with the supplier selection problem. The flexibility and robustness of the method are demonstrated by the comparative and sensitivity analysis.

关键词： Dempster-Shafer theory Cobb-Douglas utility function Maximum entropy principle

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EDiTOK: Energy-Efficient Distributed Transmission Optimization-Based Kalman-Zlib Approaches for Monitoring Smart Farming Irrigation in Internet of Things networks 1

引用

5th International Conference on Communication and Intelligent systems (ICCIS)

作者： Hasan, Zahraa Yaseen Idrees, Ali Kadhum Univ Babylon Dept Software Babylon Iraq

ISBN: (数字)9789819720538

ISBN: (纸本)9789819720521;9789819720538

Water scarcity is a major global problem and directly impacts agriculture. Both growth in the population and the need for food exacerbate this issue. Therefore, mitigating water scarcity in irrigation systems is one of the most important challenges to be faced in the current era, for smart irrigation systems based on the critical function of Internet of Things (IoT) important role in optimizing water use in agriculture and landscaping. This article proposes an energy-efficient distributed transmission optimization for monitoring smart farming irrigation based on the Kalman-Zlib approaches (EDiTOK) in IoT networks. The EDiTOK approach operates at the sensor device level. This approach involves a prediction method to determine whether or not to transmit the most recent set of data readings depending on the similarity threshold value and then implement a lossless compression method to decrease the size of a given data set before transmitting them to the gateway node. Numerous experiments utilize actual sensor data from farming fields. The results demonstrate that the proposed EDiTOK approach achieved satisfactory results compared to other proposed methods regarding energy use, the quantity of sent readings, the proportion of data reduction, and the accuracy of the data.

关键词： IoT Agriculture precision Kalman filter Lossless compression Energy efficiency Data reduction

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Forced Nonlinear Bending Vibrations of Beams with Two Breathing Cracks

Forced Nonlinear Bending Vibrations of Beams with Two Breath...

引用

International Scientific and Technical Conference on Integrated Computer Technologies in Mechanical Engineering - Synergetic Engineering (ICTM)

作者： Avramov, K. Malyshev, S. Miroshnikov, V Hariachevska, I Natl Aerosp Univ N Ye Zhukovsky KhAI Kharkiv Ukraine Natl Acad Sci Ukraine Anatolii Pidhornyi Inst Mech Engn Problems Kharkiv Ukraine Kharkiv Natl Univ Radio Elect Kharkiv Ukraine Natl Tech Univ Kharkiv Polytech Inst Kharkiv Ukraine Kharkov Natl Univ Kharkiv Ukraine

ISBN: (纸本)9783031614149;9783031614156

The nonlinear bending vibrations of the cracked beams are studied. The beam structure has two breathing cracks, which are described by two contact parameters. The nonlinear beam vibrations are described by finite degrees of freedom nonlinear dynamical system, which are derived by using the Galerkin technique. The numeric continuation method is implemented to study the nonlinear forced vibrations of beams. The bifurcation behavior of forced vibrations is analyzed numerically. The period-doubling bifurcations and the Naimark-Sacker bifurcations of the cracked beam are analyzed.

关键词： nonlinear bending vibrations cracked beam bifurcation behavior

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An Overview on Testing Big Data Applications 9th

An Overview on Testing Big Data Applications

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

作者： Staegemann, Daniel Ataei, Pouya Lautenschlaeger, Erik Pohl, Matthias Haertel, Christian Daase, Christian Volk, Matthias Abdallah, Mohammad Turowski, Klaus Otto von Guericke Univ Univ Pl 2 D-39106 Magdeburg Germany Auckland Univ Technol 55 Wellesley St East Auckland 1010 New Zealand IU Int Univ Appl Sci Mulheimer Str 38 D-53604 Bad Honnef Germany Al Zaytoonah Univ Jordan Queen Alia Airport St 594 Amman 11733 Jordan

ISBN: (纸本)9789819732982;9789819732999

Big data respectively big data analytics is one of the defining technological developments of our time and directly or indirectly influences countless aspects of daily life in today's society. Yet, for organizations to reap the corresponding benefits of implementing big data analytics, besides other factors, it is necessary to assure that the respective applications are functioning as intended. Hence, their testing is a very important aspect that is, however, oftentimes somewhat overlooked. To increase its accessibility, in the publication at hand, this topic is addressed. For this purpose, in the first step, the typical components of big data applications are identified, and their general nature is outlined. Then, subsequently, suitable approaches for the components' testing are discussed. In doing so, researchers and practitioners alike shall be provided with a starting point when it comes to the discussion of big data quality assurance and how it can be implemented in their own endeavors.

关键词： Big data Testing Quality assurance Big data engineering Software engineering Overview

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Performance Comparisons of Association Rule Learning Algorithms on a Supermarket Data 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Alawadh, Monerah Mohammed Barnawi, Ahmed Mohammed King Abdulaziz Univ Jeddah Saudi Arabia

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

In today's business environment, sustaining growth and profitability is never a guarantee. This concept of instability encourages business owners to seek new opportunities using technological and scientific advances to grasp the sustainability and maximizing profit. Nowadays, Machine Learning is helping the retail sector in many ways. Such improved life cycles of products, services, and business models. Association Rule Learning (ARL) is an unsupervised machine learning technique having great potential to extract interesting and useful associations and/or correlations among frequent item sets in transactional dataset. ARL techniques are widely employed in a variety of applications, including web click data, medical diagnostics, consumer behavior analysis, bioinformatics, and many more. In this paper, we conduct a comprehensive performance analysis of six distinct ARL algorithms, which was carried out to find the best fit algorithm for our data. The data is a large transactional dataset of one of the biggest supermarket chains in Saudi Arabia. Those algorithms are;Apriori, FP-Growth, GCD, Top-K Rules, TNR, and Closed AR. Note that these algorithms are uniquely different in terms of the number of datasets scanning which influences performance. The evaluation study shows that FP-growth algorithm based on FP-Tree outperformed other algorithms. FP-growth was more efficient and scalable than other algorithms for the given data in terms of memory usage and processing time.

关键词： Association Rule Learning Algorithm Comparison Machine Learning Apriori pattern reconstruction FP-Growth Algorithm

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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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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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A Viability Study of Pickup and Delivery Locations Using Genetic Algorithm in Intelligent Transportation systems

A Viability Study of Pickup and Delivery Locations Using Gen...

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Intelligent systems Conference (IntelliSys)

作者： Lucq, Delphine Lopez, Angel J. Aghezzaf, El-Houssaine Semanjski, Ivana Gautama, Sidharta Univ Ghent Dept Ind Syst Engn & Prod Design Ghent Belgium ESPOL Polytech Univ Dept Elect & Comp Engn Guayaquil 090150 Ecuador FlandersMake UGent Corelab ISyE Kortrijk Belgium

ISBN: (纸本)9783031477171;9783031477188

Due to the increasing popularity of e-commerce and retail warehouses, city center retailers are having a rough time. This translates into a decreasing trend in city visitors and consequently, in retailers. Shop&Hop, established in the city of Ghent, wanted to address this phenomenon. The main purpose of the concept is to attract more people to shop in Ghent. Shop&Hop makes it possible for shoppers to leave their shopping bag in the store where they just made a purchase, after which the bag is transported to the desired parking lot. Unfortunately, the concept did not reach the hoped-for success and it was put on hold. The aim of this work is to analyze to what extent the operational choices, inherent to the original concept, affect the viability of this initiative. In this way, adaptions, which could increase the chance on viability, can be formulated. By relying on methods from the field of Operations Research, a mathematical model formulation and an accompanying genetic algorithm are developed and validated. During the subsequent viability study, two operational aspects are studied. First, the influence of the spatiality of the concept is mapped. Second, the impact of delivery time differentiation is studied. The viability study has shown that a concept, implementing aggregated pickup points, park-and-rides as delivery points and delivery time differentiation, has an increased chance on viability, compared to the original concept. Finally, it could be concluded that a Shop&Hop implementation in a certain city is heavily dependent on the urban planning of the city regarding park-and-ride locations.

关键词： Routing Genetic algorithms City logistics Pickup and delivery

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Enhancing Municipal Fleet Management in Smart Cities Through 5G Integration 21st

Enhancing Municipal Fleet Management in Smart Cities Through...

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21st International Conference on Smart Technologies and Education (STE) on Smart Technologies for a Sustainable Future

作者： Traboulsi, Salam Uckelmann, Dieter Univ Appl Sci Schellingstr 24 D-70174 Stuttgart Germany

ISBN: (纸本)9783031619045;9783031619052

This paper presents a revolutionary research initiative focused on improving municipal fleet management in smart cities by leveraging the power of 5G technology. Through adopting 5G, the municipality gains access to real-time communication, precise vehicle tracking, optimized route planning, and improved operational efficiency. Delving into the realm of 5G technology, the study examines its multiple applications in fleet management, including real-time monitoring and predictive maintenance strategies. The initial findings establish a robust foundation, laying the groundwork for forthcoming initiatives aimed at enhancing municipal fleet operations in smart cities through the seamless integration of 5G connectivity. Our objective is to amplify the impact of our work, making a substantial contribution to the continuous evolution of smart city infrastructures. Subsequent research endeavors will delve even deeper into the expansive potential of 5G, exploring its applications in fleet optimization, intelligent transportation systems, and innovative solutions for sustainable mobility.

关键词： Smart Cities 5G Technology Municipal Fleet Management Predictive Maintenance

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