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4th International Conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Kulkarni, Nilima Sayyed, Arshadali Soni, Nikhil Pal, Komal Singh, Prashant Computer Science & Engineering Department MIT SoC MIT Art Design and Technology University Pune India

ISBN: (纸本)9781032911571

Throughout the years, automobile companies achieved outstanding progress in manufacturing safe, reliable, and affordable vehicles. Many companies and manufacturers are developing autonomous cars for the future years. However, to achieve this goal they are facing too many challenges in technical and non-technical fields like software complexity, mapping, real-time data analysis, testing, and verification. AI Software installation in cars is costly, and also it affects fuel consumption. So, proposed an autonomous solar car that will run on solar energy, which is available free of cost. NumPy, OpenCV computer vision library, AI, ML, and Python were used for the implementation of the work. The edge detection algorithm takes the input from a camera. LiDAR, WIFI/Bluetooth models, and Ultrasonic sensors have been used. Inculcated OpenCV which has an advanced built-in function that has add-on provisions in extension to the blurred image, a threshold that can go low and high and determines whether there is an edge in the path or not. The input collected from the sensors was analyzed to give directions to the car on where to get a turn. In summary, the proposed autonomous solar car model is inexpensive to build and performs well. © 2025 the Author(s).

关键词： Ultrasonic sensors

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Combining Deep Learning with Knowledge Graph for design Knowledge Acquisition in Conceptual Product design

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computer Modeling in engineering & Sciences 2024年第1期138卷 167-200页

作者： Yuexin Huang Suihuai Yu Jianjie Chu Zhaojing Su Yangfan Cong Hanyu Wang Hao Fan Key Laboratory of Industrial Design and Ergonomics Ministry of Industry and Information TechnologyNorthwestern Polytechnical UniversityXi’an710072China School of Industrial Design Engineering Delft University of TechnologyDelft2628 CEThe Netherlands Department of Industrial Design College of ArtsShandong University of Science and TechnologyTsingtao266590China College of Computer Science and Technology Zhejiang UniversityHangzhou310027China

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product *** study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge ***,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product ***,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction *** comparison verified the effectiveness and accuracy of the proposed knowledge extraction *** case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

关键词： Conceptual product design design knowledge acquisition knowledge graph entity extraction relation extraction

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Development of the Invasion Models in Wireless Sensor Networks Based on the Flows of False Events 16

Development of the Invasion Models in Wireless Sensor Networ...

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16th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2022

作者： Belej, Olexander Spas, Nataliia Artyshchuk, Iryna Nestor, Natalia Lviv Polytechnic National University Department of Computer-Aided Design Lviv Ukraine

ISBN: (纸本)9781665468619

Wireless sensor networks are often used to monitor mobile devices in a specific sensor field from unauthorized intrusion. The research examines intrusions into wireless sensor networks based on the creation of streams of false events. According to the simulation results, the property of the dependence of the wireless sensor network life cycle on the type of error event flow is revealed. Research on the impact of erroneous events on the sensor field has shown that ensuring the mobility of sensor nodes can increase the life cycle of the network. A model of a wireless sensor network is proposed, which is a Poisson sensor field and a flow of events in the form of erroneous streams crossing a given plane. According to the research results, the density distribution from the faulty object to the first sensor, the required sensor node density, and the required sensor node detection radius at a given probability of detecting a faulty object, the number of messages transmitted by the object through the sensor field in the network. © 2022 IEEE.

关键词： Sensor nodes

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Using an Artificial Neural Networks Approach to Assess the Links Among Environmental Protection Expenditure, Energy use and Growth in Finland

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Journal of the Knowledge Economy 2025年 1-29页

作者： Daşbaşı, Bahatdin Barak, Doğan Taşyürek, Murat Arslan, Recep Sinan Faculty of Engineering Architecture and Design Department of Engineering Basic Sciences Kayseri University Mühendislik Kayseri Turkey Faculty of Economics and Administrative Sciences Department of Economics Bingöl University Bingöl Turkey Faculty of Engineering Architecture and Design Department of Computer Engineering Kayseri University Kayseri Turkey

This study uses an artificial neural network (ANN) to forecast Finland's economic growth from 1990–2020. Environmental protection expenditure (EPE), capital accumulation (CAPITAL), labor force (LABOR), renewable energy use (REC) and non-renewable energy use (NREC) that play an essential role in contributing to economic growth are selected as input variables. The variable to be estimated is per capita income (GDP). Data augmentation is made using the data between 1990 and 2020 by predicting the future with the ARIMA statistical model. As a result, the data obtained as only 30 data has been extended to 511 data. The generated data is set to be 80% training and validation data and 20% test data. The training data is used in the training process of the ANN model with five inputs and one output layer. The error values of the designed ANN model for the GDP value forecast are examined. To better analyze the performance of the ANN model, two studies, the best and the worst, are presented. The prediction functions found from the ANN structure for both studies are mathematically presented in matrix form through hyperbolic tangent functions consisting of the dependent variable by GDP and the independent variables by others. The answer to which of the other variables is effective in maximizing the GDP value was investigated with the linear programming maximization problem obtained from these equations. Thanks to these problems analyzed separately for the best and worst performance of ANN, it has been seen that the values EPE, REC and NREC have positive effects on maximizing GDP. Finally, the relationship between GDP and the other five variables is examined by considering the 102 data used for the test in the ANN. GDP has been shown to have a strong positive relationship between EPE, REC and NREC and a very weak negative relationship between CAPITAL and LABOR. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： ANN ARIMA Economic growth Environmental protection expenditure Maximization problem Statistical analysis

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Modeling and Analysis of Parasitic Elements Influence on Dynamic Characteristics of Microelectronic Systems 25

Modeling and Analysis of Parasitic Elements Influence on Dyn...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Koloskov, Dmitry B. Dragunov, Valery P. Gluhov, Alexander V. Department of Semiconductor Devices and Microelectronics Novosibirsk State Technical University Novosibirsk Russia Department of Computer Aided Design Siberian State University of Telecommunications and Information Science Novosibirsk Russia

ISBN: (纸本)9798350389234

The paper examines the degree of influence of parasitic layout elements on the key dynamic characteristics of microelectronic systems. Using a 14-bit digital to analog converter with a conversion frequency of 1 GHz as an example, we analyzed the influence of parasitic capacitances and resistances on such converters’ dynamic characteristics. We made a theoretical estimate of MOS transistor intrinsic parasitic capacitances using most common technology parameters for 180 nm CMOS. We showed that layout parasitic capacitances and resistances jointly increase the settling time of the digital to analog converter signal from 0.8 ns to 2.5 ns and thus reduce the conversion frequency by more than three times to 400 MHz. We concluded that the procedure for reducing the influence of layout parasitic elements is mandatory when designing precision integrated circuits such as high-bit digital to analog and analog to digital converters. Without using special techniques to counteract layout parasitic elements, it is not possible to develop a high-frequency digital to analog converter with a resolution of 14 bits or higher. © 2024 IEEE.

关键词： Mixed signal integrated circuits

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A Comprehensive Evaluation Process by Analytical Hierarchical Process for Identification of Potential Groundwater Zones 2

A Comprehensive Evaluation Process by Analytical Hierarchica...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gudadhe, Amit Anil Reddy, K.T.V. Faculty of Engineering and Technology Computer Science and Design Department Wardha India Faculty of Engineering and Technology Wardha India

ISBN: (纸本)9798331528713

For social and economic development of any region, groundwater plays a very vital role. Surface water infiltration depends on various parameters of the earth. The parameters includes Slope, Geology, Soil Type, Land Use/Land Cover, Rainfall, Drainage Density based on the study area under consideration. There exists various techniques for recognizing of groundwater probable regions. This paper focuses specifically on the recognizing of the subsurface water probable regions by Analytical Hierarchy Process. The steps involved in the process are discussed in detailed and shows how the potential zone identification is based on value of weights evaluated. © 2024 IEEE.

关键词： Land use

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Voice Controlled Operations and Navigations for e-Commerce

Voice Controlled Operations and Navigations for e-Commerce

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Kalangutkar, Aditi Yadav, Radha Hadapad, Priya S. Naik, Vinisha V. Menezes, Valerie Sawant, Shreedatta Agnel Institute of Technology and Design Department of Computer Engineering Goa India

ISBN: (纸本)9798350300826

Voice based operations are increasingly being used by the users in their day to day lives and are being used in various sectors for making their lives easier simply by speaking rather than typing keywords. Performing actions using voice is faster and accessible to the users who are not comfortable with the UI of the application. However, there are very few implementations of these voice-controlled systems on the web and e-commerce. This paper presents a browser extension which can be used on various e-commerce sites and get appropriate responses. This tool will allow users to perform human-to-machine interaction, anytime and anywhere using speech recognition and provide ease to the users without using their fingertips. This tool will be a chrome extension (add-on or plugin), that will take voice as an input and will convert it to text, extract meaning from the text, and then perform a wide variety of tasks including the product search, add to cart, in-app navigations, and other operations through speech. This extension will be a combination of technologies, such as automatic speech recognition, text-to-speech service, and natural language understanding, to engage in natural conversational interactions with humans. © 2023 IEEE.

关键词： Speech recognition

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Preliminary Cognitive Modeling: Comparing Distraction-Based Cyber-Attacks and Alcohol-Related Impairments on Human Drivers 4

Preliminary Cognitive Modeling: Comparing Distraction-Based ...

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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： He, Fan Elhammady, Karim Fischmeister, Sebastian Burns, Catherine M. University Of Waterloo Department Of System Design Engineering WaterlooON Canada University Of Waterloo Department Of Electrical And Computer Engineering WaterlooON Canada

ISBN: (纸本)9798350315790

This research investigates the vulnerability and resilience of human cognitive processes in the context of rapidly advancing driving technologies susceptible to multi-modal cyber-attacks. By simulating scenarios where Advanced Driver Assistance Systems (ADAS) are attacked to distract drivers, we aim to understand the decision-making process and cognitive limits affecting safe driving and to devise a systematic approach to assess cognitive impairments. Our simulations of three generic attack models revealed that attacks requiring complex cognitive processing pose greater risks than mere distractions. To assess human driver vulnerabilities in this rapidly evolving technological landscape, we utilized Blood Alcohol Concentration (BAC) levels as a benchmark. © 2024 IEEE.

关键词： Autonomous vehicles

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A comprehensive review of effective parameters to improve the performance of the Savonius turbine using a computational model and comparison with practical results

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Water-Energy Nexus 2024年第1期7卷 266-274页

作者： Jebelli, Ali Lotfi, Nafiseh Saeid Zare, Mohammad Yagoub, Mustapha C.E. Department of Engineering and Design Robotic Century Inc Alberta Canada Faculty of New Science and Technologies Tehran University Tehran Iran School of Electrical Engineering and Computer Science University of Ottawa ON Canada

Amidst growing global concerns over climate change and escalating greenhouse gas emissions from fossil fuels, the pursuit of renewable energy sources has become critical. This study focuses on harnessing hydropower using Savonius turbines, which are known for their efficiency in generating energy at lower flow rates. However, the intrinsic low efficiency of these turbines necessitates precise optimization tailored to specific river or channel conditions. In this research, we optimized the performance of Savonius turbines by analyzing key parameters such as the height-to-diameter ratio, blade twist, and the integration of multi-stage configurations with deflectors. Our findings reveal significant efficiency improvements through strategic modifications. Specifically, by reducing the height-to-diameter ratio from 1.2 to 0.8 and maintaining a Tip Speed Ratio (TSR) of 0.6, the power coefficient increased by 15%, from 0.33 to 0.38. Further optimization was achieved by adjusting the blade twist angle, with an increase in power coefficient up to an optimal angle of 45°, beyond which efficiency declined. Implementing a two-stage turbine setup with a 90-degree phase difference between stages further improved the power coefficient to 0.51 at the same TSR. Additionally, the use of deflectors, particularly at a 90° angle, significantly boosted the power coefficient, highlighting their effectiveness in optimizing water flow impact on the turbine. This comprehensive study not only advances the understanding of Savonius turbine optimization but also contributes to broader renewable energy applications. The research offers critical insights into sustainable hydroelectric power generation, providing practical solutions to enhance turbine performance for real-world applications. © 2024

关键词： Clean energy

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A Review on Role of IoT and Machine Learning in Agriculture 7

A Review on Role of IoT and Machine Learning in Agriculture

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kumar, Gulshan Choudhary, Vishal Kumar Kumar, Shravan Kumar, Rajan Kumar, Mukesh Gourshettiwar, Palash M. Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798350359299

The agricultural area has undergone a significant transformation owing to the progress made in IoT. It is imperative to have a dependable remote monitoring solution right now. This study aims to accomplish two goals. First, a solution based on an app is offered, which effectively assists in remotely administering the field by displaying the current sensor values. In an Internet of Things based prototype surveillance system, a multi-class categorization method utilizing machine learning and deep learning is incorporated to classify labels such as clear farm, cow, horse, wild boar, and wild elephant. The analysis involved Support Vector Machines and Convolutional Neural Networks, with the accuracy measure being employed to determine the optimal model. The advancements in the Internet of Things (IoT) have significantly transformed the daily lives of individuals. A comprehensive understanding of soil properties gives a variety of soil-related information that can be used to improve crop yields. Machine learning is a cutting-edge technology that aids with agricultural precision and provides answers to crop yield issues. Significant informative advances and Machine Learning (ML) open up new opportunities for deciphering, quantifying, and comprehending data-intensive prediction in agricultural settings. This study thoroughly examines a number of machine learning topics for Internet of Things-based smart agriculture systems. © 2024 IEEE.

关键词： Support vector machines

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