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Development and Validation of Lane Keeping Assist Algorithm for advanced Driver Assistance System Applications 9

Development and Validation of Lane Keeping Assist Algorithm ...

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9th international conference on Smart computing and Communications, ICSCC 2023

作者： Anand Padmanabhan, A. Khan, Jihas Prakash, Amal Nair, Arjun R. Amrita Vishwa Vidyapeetham Department of Mechanical Engineering Amritapuri India Transportation Business Unit Tata Elxsi Trivandrum India

ISBN: (纸本)9798350314090

Over the past decade, there has been a significant change in trends in the automotive industry. The number of vehicles equipped with advanced driver assistance system (ADAS) are increasing on the roads, but their level of autonomy is showing a gradual increase over time. The important part that is required to have reliable ADAS is that it must process all the data coming from different sensors efficiently. One of the most important ADAS algorithms is intended for lane keeping assist system (LKAS) and lane departure warning (LDW). This paper looks forward to employ LKAS algorithm that aids the user to have a real-world visual experience with a 3D visualization. Most of this algorithm testing are done on straight road scenes but in this paper a clothoid road structure is adapted and is visualized using the tool. © 2023 ieee.

关键词： advanced driver assistance systems

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Design and Implementation of Parallel Deterministic Network System 4

Design and Implementation of Parallel Deterministic Network ...

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4th international conference on Computer Science, Electronic Information Engineering and intelligent Control Technology, CEI 2024

作者： Zhao, Xuyang Zhang, Xingming Zhu, Jun Li, Heyuan Zhang, Fujun Chen, Xia The Intelligent Computing Infrastructure Innovation Center Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350378542

With the current development of industrial Internet technology, the number of interconnected industrial control devices is increasing, the throughput of key data in industrial Internet networks is increasing, and the requirements for deterministic data processing are increasing. Traditional industrial control systems generally use low-performance single-core processors, which operate the collected industrial data in a single-core and single-threaded manner, resulting in poor key data processing capabilities and a lack of data scheduling capabilities. With the development of industrial Internet technology and deterministic network technology, the parallel data processing capabilities of multi-core SoCs can effectively meet the needs of industrial real-time networks. The data flow of the industrial Internet network uses custom data identification and a parallel data processing mechanism. The data faces macro operations such as data packet matching, parsing, and forwarding to establish models, provide accurate execution mechanisms for devices in the time dimension, and effectively ensure high throughput and low-latency deterministic transmission of industrial control data. © 2024 ieee.

关键词： System-on-chip

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Massive IRS-MIMO-RSMA with Polarization Multiplexing: An Enhanced SIC-Free Approach

Massive IRS-MIMO-RSMA with Polarization Multiplexing: An Enh...

引用

ieee international conference on Communications (ieee ICC)

作者： de Sena, Arthur S. da Costa, Daniel B. Nardelli, Pedro H. J. Bader, Faouzi Debbah, Merouane Technol Innovat Inst United Arab Emirates Abu Dhabi U Arab Emirates Lappeenranta Lahti Univ Technol Lappeenranta Finland

ISBN: (纸本)9781538674628

Dual-polarized rate-splitting multiple access (RSMA) with polarization multiplexing has appeared recently as an attractive downlink transmission technique for dual-polarized massive multiple-input multiple-output (MIMO) systems. Dual-polarized RSMA does not require successive interference cancellation (SIC), which avoids practical issues of imperfect SIC decoding. Nevertheless, depolarization phenomena can still degrade the performance of this featured technique. In this work, we exploit the advanced capabilities of a dual-polarized intelligent reflecting surface (IRS) for unleashing an enhanced RSMA polarization multiplexing. To optimize the IRSs, we develop a multi-objective Frank-Wolfe-based algorithm for jointly mitigating cross-polar interference and improving the reception of common and private data streams at multiple users. Representative simulation examples demonstrate that dual-polarized IRS-MIMO-RSMA can efficiently mitigate detrimental depolarization phenomena and outperform conventional systems.

关键词： Dual-polarization intelligent reflecting surfaces rate-splitting multiple access massive MIMO

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Integrating data and Rules: A Hybrid Approach for Robust Lane Change Intention Prediction Under Distribution Shift 27

Integrating Data and Rules: A Hybrid Approach for Robust Lan...

引用

27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Teng, Yao Long Naing, Htet Cai, Wentong College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798331505929

Lane change intention prediction is critical in improving road safety. In general, this task is achieved via two distinct approaches: The data-based approach and the rule-based approach. Generally, the former outperforms the latter under in-distribution scenarios. However, under out-distribution scenarios, data-based methods tend to perform extremely poorly. Rule-based methods are robust to such problems since they are formulated based on theory. Instead of relying exclusively on each approach, it is more advantageous to combine them to exploit both of their strengths. Thus, in this paper a physics guided hybrid model is proposed for lane change intention prediction to improve model robustness under distribution shift. The effectiveness of the proposed model is demonstrated in the experimental results where the proposed model outperforms rule-based models under in-distribution scenarios by 4%, and outperforms both rule-based models and data-based models in out-distribution scenarios by 3% and 60% respectively in the highD dataset. © 2024 ieee.

关键词： Prediction models

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Manipulator's Control System with Application of the Machine Learning 11

Manipulator's Control System with Application of the Machine...

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11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Kondratenko, Yuriy Sichevskyi, Stanislav Kondratenko, Galyna Sidenko, Ievgen Petro Mohyla Black Sea Natl Univ Intelligent Informat Syst Dept 68th Desantnyk Str10 UA-54003 Kolaiv Ukraine

ISBN: (纸本)9781665426053

This paper provides an analytical review of the subject area, approaches and methods for real-time control systems with application of the machine learning. The review helps to choose approaches to control robotic devices, methods of artificial intelligence to perform pattern recognition and classification, technologies to design a control system and simulate a robotic device. The design and software implementation stage involved the creation of a structural diagram and description of the selected technologies, training a neural network for recognition and classification of geometric objects, software implementation of control system components. The control system was implemented using the Swift programming language and other technologies, and the configuration and creation of a neural network using the CreateML framework.

关键词： real-time system device control system manipulator arm robotics pattern recognition and classification artificial intelligence

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Neural Networks in Workforce Planning and Management

Neural Networks in Workforce Planning and Management

引用

2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Shetty, Pallavi Pushpa, Ch. Sharonu Khan, Gazala Amanullah Joshi, Deeksha Lourens, Melanie Dhanraj, Joshuva Arockia Department of Mca India Mlrit Department of Data Science Dundigal Hyderabad India Indira Institute of Management Sciences Vishnupuri Nanded India School of Management Graphic Era Hill University Uttarakhand Haldwani India Durban University of Technology Faculty of Management Sciences South Africa Lovely Professional University Research and Development Cell Punjab Phagwara144411 India

ISBN: (纸本)9798331543617

The primary objective of this study is to examine the utilization of neural networks in workforce planning and management, with an emphasis on their capacity to enhance labor allocation, anticipate personnel requirements, and enhance overall organizational efficiency. Neural networks, a type of artificial intelligence, are extremely appropriate for managing the complexities of human management because of their robust capabilities in data processing and pattern detection. By examining historical data and current trends, these algorithms can identify areas where skills are lacking, forecast the need for workers, and suggest the optimal employment levels. By incorporating neural networks into workforce planning, we can achieve a workforce that is more adaptable and efficient, as well as improve employee satisfaction, reduce costs, and improve the precision of our decision-making. © 2024 ieee.

关键词： artificial intelligence labor allocation Neural networks organisational efficiency predictive analytics workforce management workforce planning

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Real-Time Optimization of Banking Processes using data Processing and Machine Learning 4

Real-Time Optimization of Banking Processes using Data Proce...

引用

4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Kondam, Anusha Logeshwaran, J. JPMorgan Chase Co United States Christ University Department of Computer Science Karnataka Bengaluru560029 India

ISBN: (纸本)9798331529635

Optimizing processes in the banking industry is largely responsible for growth, which allows it to become more efficient. As the trend of data generation is increasing in financial institutions, there is a dire requirement for real-time data processing along with machine learning approaches to cater to banking processes most efficiently. It analyses these massive data sets on a rolling basis to produce insights and assist in making informed decisions. The bank can then use this to find inefficiencies quickly, determine where they would be at risk, and if anything should be changed or modified. On the other hand, Machine learning is a way to learn algorithms from data and predictive modules for optimal results based on input parameters/without being directly programmed. Using real-time data processing by machine learning allows banks to constantly diagnose and move changes toward achieving optimal performance. Algorithms can, for instance, detect fraudulent activities in real time, which minimizes losses banks sustain due to fraud. In addition, with machine learning, banks can create tailor-made services for each customer (e.g. personalized investment products or levels of credit risk individually), anticipate the behavior and needs of their customers, and give them just-at-time contentment =). This can improve customer satisfaction and loyalty. © 2024 ieee.

关键词： Adversarial machine learning

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Design and Implementation of a Web-based Collaborative Manufacturing System Based on Knowledge Graph 4

Design and Implementation of a Web-based Collaborative Manuf...

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4th ieee international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Yuan, Meng Wu, Huaiyu Shen, Zhen Han, Yunjun Zhou, Jiehan Yue, Zhijian Xiong, Gang Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266109 China University of Oulu Faculty of Information Technology and Electrical Engineering OuluTS352 Finland

ISBN: (纸本)9798350349252

Collaborative manufacturing is central to agile, intelligent, and cloud-based manufacturing paradigms. This paper presents the design and implementation of a web-based collaborative manufacturing system based on a knowledge graph (KG). The system achieves the full spectrum of knowledge extraction, fusion, and reasoning across the industrial chain, thereby enhancing efficiency and reducing operational costs. The proposed system constructs an industrial chain KG and multi-agent systems, offering comprehensive digital services across the manufacturing value chain. It supports the integration of business processes, data, and standard information for enterprise groups, presenting collaborative manufacturing data of the industrial chain in real-time visualization. The paper further discusses a method for real-time perception of anomalies in the collaborative manufacturing process, utilizing expert knowledge and data-driven insights to construct an operational risk KG. The system's architecture, workflow, and functional design are detailed, along with application cases in visual dynamic management. The conclusion underscores the system's potential to improve supply-demand chain management and operational efficiency in collaborative manufacturing. © 2024 ieee.

关键词： Smart manufacturing

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Weather Prediction from Historical data using Big data Technologies 5

Weather Prediction from Historical Data using Big Data Techn...

引用

5th international conference on IoT Based Control Networks and intelligent systems, ICICNIS 2024

作者： Choudha, Shaurya Singh, Aniruddh Kumar, Aman Gouthaman, P. School of Computing College of Engineering and Technology Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur603203 India

ISBN: (纸本)9798331518097

Weather forecasting is an important function in a variety of activities such as agriculture, environment monitoring, and disaster management among others. Today with the development of technology, vast amounts of meteorological data are able to be collected and analyzed with the intention of improving prediction accuracy. The purpose of this work is to analyze the contribution of Big data frameworks such as, Hadoop, MapReduce and Apache Spark in effective weather prediction processes. In a distributed system, historical weather data, satellite images and sensor readings are kept and manipulated through the HDFS storage. For effective data manipulation, MapReduce and Spark are used along with Hive and Pig for searching large datasets. Predictive analytics are performed using machine learning algorithms included in the MLlib package of Spark. These results are presented using the Tableau application, which shows the speed and accuracy of this Big data approach in comparison to classical ones. The proposed work ends with a look into the prospects of Big data in the field of weather forecasting and relevant problems in this area. © 2024 ieee.

关键词： MapReduce

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Utilizing Big data for Personalized Nutrition Interventions and Health Outcomes

Utilizing Big Data for Personalized Nutrition Interventions ...

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Labhane, Suchitra Marwaha, Sanjana Sharma Shastri, Swati Raj, Nihal Tomar, Mayank Koshal, Avadhesh Kumar Datta Meghe Institute of Management Studies Department of Mba Atrey Layout Maharashtra Nagpur440022 India Amity University Department of Amity Institute of Social Sciences Uttar Pradesh Noida201313 India Motherhood University Department of Facultyof Science Uttarakhand Haridwar247661 India

ISBN: (纸本)9798331543617

The use of big data in the context of proven nutrition interventions and their effects on health issues is examined in this exploratory work. We want to tailor dietary recommendations to individual needs by leveraging big databases, which include health records, hereditary information, and healthy behaviors. Big data analytics integration makes it possible to find distinctive health connections and beneficial patterns, which in turn makes it possible to express personalized dietary recommendations that can improve health problems. Our research shows how big data may change conventional nutritional knowledge and lead to a more individualized strategy that takes specific nature and life elements into account. The results show notable improvements in the treatment of habitual conditions, the enhancement of salutary recommendations, and the promotion of general well-being. To enhance proven nutrition, this study highlights the value of interdisciplinary collaboration by fusing data wisdom, nutrition, and healthcare moxie. Refining data integration techniques and investigating the long-term benefits of supported nutrition programs on various populations are examples of potential exploration directions. © 2024 ieee.

关键词： Electronic health record

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