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modeling of a Highly Sensitive Lorentz Force-Based CMOS-MEMS Magnetometer for E-Compass applications 8

Modeling of a Highly Sensitive Lorentz Force-Based CMOS-MEMS...

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8th international conference on Intelligent and Advanced Systems (ICIAS) / World Engineering, Science and Technology Congress

作者： Al-Mandi, Osamah Lutf Qaid Ahmed, A. Y. Dennis, J. O. Khir, M. H. Md Univ Teknol Petronas Dept Elect & Elect Bandar Seri Iskandar 32610 Perak Darul Rid Malaysia Univ Teknol Petronas Dept Fundamental & Appl Sci Bandar Seri Iskandar 32610 Perak Darul Rid Malaysia

ISBN: (纸本)9781728176666

Magnetometers are devices used to measure the magnetic field, however, most commercialized magnetometers are facing several disadvantages. If not being of low sensitivity, the device attains a high cost and high-power consumption. Thus, it is a necessity to mathematically design and model CMOS-MEMS magnetometer which will be able to detect low magnetic fields. A fine simulation using ConventorWare software is applied to validate the designed magnetometer model. In this paper, Lorentz force and an integration of CMOS and MEMS technologies were implemented. The designed magnetometer is made in one mode (out-of-plane) to function in one axis (z-axis), and the CMOS-MEMS magnetometer output signal is determined by piezoresistive sensing technique as piezoresistors are connected in full Wheatstone bridge circuit. A 3-D solid model was created and meshed based on the theoretical calculations and data. simulation results shows that theoretical and simulation results are almost the same, except that resonance frequency is of 11% difference and 11.6% for quality factor. The average percentage difference between calculated and simulated displacement when magnetic field is detected to be of 2.801%.

关键词： Lorentz Force MEMS Resonator CoventorWare CMOS technology

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Congestion Control of Multi-layer Cellular Networks based on modeling of Transmit Power 3

Congestion Control of Multi-layer Cellular Networks based on...

引用

2020 3rd international conference on modeling, simulation and Optimization technologies and applications, MSOTA 2020

作者： Dai, Jiahao Chen, Yongqun Chen, Yucheng Meng, Aodong Science and Technology on Communication Information Security Control Laboratory China China Electronic Group Corporation NO.36 Institute Zhejiang Jiaxing China

The explosive growth of network traffic and the limited storage node resources make the existing network architecture bear great pressure, and network congestion appears in work and life from time to time. In order to ensure the timeliness and effectiveness of the network, we propose a congestion control method based on transmission power. Different from previous work, we built a multi-layer cellular network consisting of multiple macro stations, small base stations and mobile terminals. On this basis, we establish a congestion control model based on the mixed queuing model of service window and calculate the input/output rate and congestion. Finally, we use simulation to verify the effectiveness of the congestion model. The network builder can use our model to set the appropriate power of macro stations and small base stations to ensure network stability. © Published under licence by IOP Publishing Ltd.

关键词： Base stations

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simulation of the Worst Failure Probability of Sharing Platform under Unknown Demand Distribution 3

Simulation of the Worst Failure Probability of Sharing Platf...

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2020 3rd international conference on modeling, simulation and Optimization technologies and applications, MSOTA 2020

作者： Wang, Changjun Gong, Xueling Glorious Sun School of Business and Management Donghua University Shanghai200051 China

The sharing economy integrates the idle resources to match the fragmented market demand via the Internet platform. Different from the supply side, the demand is highly uncertain, in which, significant spikes and valleys exist. Therefore, it is assumed that only the statistical characteristics, instead of precise distributions, are known. The matching efficiency of supply and demand in sharing platform is studied. Firstly, a linear programming model under the moment constraints is proposed to describe the worst failure possibility in demand satisfaction. Then, the equivalent semi-infinite program is developed by Lagrange duality, and the analytical expression of the worst failure probability can be given. Finally, simulation study is implemented. This work not only riches the studies of moment-based bound, but also provides the managerial insights to the demand management of the sharing platform in the uncertain setting. © Published under licence by IOP Publishing Ltd.

关键词： Probability distributions

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simulation Study on Intelligent Bolt Monitoring System Based on Cloud Service 3

Simulation Study on Intelligent Bolt Monitoring System Based...

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2020 3rd international conference on modeling, simulation and Optimization technologies and applications, MSOTA 2020

作者： Xu, Lichao Zhang, Jimin Luo, Jinnan Institute of Rail Transit Tongji University Shanghai201804 China

As a common connector in the industrial field, bolts play a key role in many fields. However, it is hard to obtain the stress value of a bolt directly in traditional ways. In order to achieve that, an intelligent bolt monitoring system based on Cloud Service was studied in this paper. An ultrasonic measurement technology based on the Sono-elasticity Principle was presented first, which laid a foundation for measuring the displacement of a bolt when it was stressed. Then, we did some experiments on emulating the whole process of the system through software simulation. Finally, Cloud Service was introduced to restore simulated data of a bolt, which made it possible for professionals to monitor online. The result shows that the system can work well. In conclusion, an intelligent bolt monitoring system based on Cloud Service can greatly improve work efficiency and bring many benefits to related fields and industries. © Published under licence by IOP Publishing Ltd.

关键词： Bolts

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Deep learning for sleep disorder diagnosis: Enhancing EEG Analysis with Machine Learning Techniques

Deep learning for sleep disorder diagnosis: Enhancing EEG An...

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Learning and Technology conference (L&T)

作者： Baher Mohamed Enfel Barkat Dept. of Electrical & Computer Engineering Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The rising prevalence of sleep disorders highlights their disastrous effects on health, well-being, cognitive performance, memory, and learning outcomes. Sleep disorders such as insomnia and sleep apnea normally require extensive instrumentation and expertise for diagnosis, which is not always possible or even available in most cases. This work, therefore, proposes a state-of-the-art use of Machine Learning (ML) to develop a model for the classification and detection of sleep patterns from electroencephalogram (EEG) signals. The model is trained on a synthetic dataset, developed using mathematical simulations of real-life EEG data. This is a novelty in this area, which allows this model to be precise yet robust without relying too much on real-world *** application bridges advanced AI techniques with real-world applications and smooths out the experience for healthcare professionals and researchers alike. This research highlights how ML might transform sleep medicine through efficient, extended solutions for EEG data analysis and sleep disorder detection. We provide a realistic EEG simulation combined with an intuitive application, a powerful platform that makes advanced sleep diagnostics more accessible and impactful, which supports the development of educational strategies or technologies.

关键词： Deep learning Training Sleep Transforms Brain modeling Electroencephalography Mathematical models Data models Medical diagnostic imaging Synthetic data

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simulation and Analysis of Microparticles Acoustophoretic Separation in Multi-Channel Lab-Chip Device 16

Simulation and Analysis of Microparticles Acoustophoretic Se...

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IEEE 16th international conference on the Experience of Designing and Application of CAD Systems (CADSM)

作者： Klymkovych, Tamara Bokla, Nataliia Matviykiv, Oleh Stakhiv, Volodymyr Lviv Polytech Natl Univ Dept Comp Aided Design Lvov Ukraine

ISBN: (纸本)9781665438940

The separation of Suspended Microparticles based on physical and chemical properties is an essential step in numerous microfluidic applications. Recent research of acoustofluidic effects proves that acoustophoresis is an effective and flexible method for continuous sorting and separation of multiple beads, living cells, and suspended microparticles. The following research dedicated to the novel design of a microparticle sorter consisted of two pairs of IDT electrodes and provided precise sorting capabilities for multi-channel microfluidic structures. modeling and FEM simulation of the soot and fly ash microparticles separation in CAD/CAE tools allow visualizing the multiphysical interactions between fluid flow, standing acoustic waves, and suspended microparticles in microfluidic Lab-chip devices.

关键词： microfluidics Lab-Chip sorting separation microparticles acoustofluidics

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modeling Performance and Power on Disparate Platforms Using Transfer Learning with Machine Learning Models 1

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international conference on modeling, simulation and Optimization, CoMSO 2020

作者： Mankodi, Amit Bhatt, Amit Chaudhury, Bhaskar Kumar, Rajat Amrutiya, Aditya Dhirubhai Ambani Institute of Information and Communication Technology Gandhinagar India

ISBN: (数字)9789811598296

ISBN: (纸本)9789811598289

Cross prediction is an active research area. Many research works have used cross prediction to predict the target system’s performance and power from the machine learning model trained on the source system. The source and target systems differ either in terms of instruction-set or hardware features. A widely used transfer learning technique utilizes the knowledge from a trained machine learning from one problem to predict targets in similar problems. In this work, we use transfer learning to achieve cross-system and cross-platform predictions. In cross-system prediction, we predict the physical system’s performance (runtime) and power from the simulation systems dataset while predicting performance and the power for target system from source system both having different instruction-set in cross-platform prediction. We achieve runtime prediction accuracy of 90% and 80% and power prediction accuracy of 75% and 80% in cross-system and cross-platform predictions, respectively, for the best performing deep neural network model. Furthermore, we have evaluated the accuracy of univariate and multivariate machine learning models, the accuracy of compute-intensive and data-intensive applications, and the accuracy of the simulation and physical systems. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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CiThruS2: Open-source Photorealistic 3D Framework for Driving and Traffic simulation in Real Time

CiThruS2: Open-source Photorealistic 3D Framework for Drivin...

引用

IEEE Intelligent Transportation Systems conference (ITSC)

作者： Galazka, Emilian Niemirepo, Teo T. Vanne, Jarno Tampere Univ Ultra Video Grp FI-33014 Tampere Finland

ISBN: (纸本)9781728191423

The automotive and transport sector is undergoing a paradigm shift from manual to highly automated driving. This transition is driven by a proliferation of advanced driver assistance systems (ADAS) that seek to provide vehicle occupants with a safe, efficient, and comfortable driving experience. However, increasing the level of automation makes exhaustive physical testing of ADAS technologies impractical. Therefore, the automotive industry is increasingly turning to virtual simulation platforms to speed up time-to-market. This paper introduces the second version of our open-source See-Through Sight (CiThruS) simulation framework that provides a novel photorealistic virtual environment for vision-based ADAS development. Our 3D urban scene supports realistic traffic infrastructure and driving conditions with a plurality of time-of-day, weather, and lighting effects. Different traffic scenarios can be generated with practically any number of autonomous vehicles and pedestrians that can be made to comply with dedicated traffic regulations. All implemented features have been carefully optimized and the performance of our lightweight simulator exceeds 4K (3840 x 2160) rendering speed of 60 frames per second when run on NVIDIA GTX 1060 graphics card or equivalent consumer-grade hardware. Photorealistic graphics rendering and real-time simulation speed make our proposal suitable for a broad range of applications, including interactive driving simulators, visual traffic data collection, virtual prototyping, and traffic flow management.

关键词： Solid modeling Visualization Three-dimensional displays Virtual environments Lighting Rendering (computer graphics) Real-time systems

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Deep Learning with Wireless Sensor Network Platform for Multimedia Data modeling

Deep Learning with Wireless Sensor Network Platform for Mult...

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IEEE international conference on Engineering Education: Innovative Practices and Future Trends (AICERA)

作者： Marwa Saad Mahdi Hussin Mazin Riyadh Al-Hameed Mustafa Al-Tahee Sajad Ali Zearah Hussein Alaa Diame Ahmed Raed Al-Tameemi Computer Technologies Engineering Al-Turath University College Baghdad Iraq Department of Medical Devices Engineering Technologies National University of Science and Technology Dhi Qar Nasiriyah Iraq Medical instruments engineering techniques Al-farahidi University Baghdad Iraq Scientific Research Center Al-ayen University Thi-Qar Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq Computer Engineering Techniques Al-Nisour University College Baghdad Iraq

Recent years have seen a meteoric rise in the volume of multimedia data generated by industries like e-commerce, finance, transportation, and social networks. Furthermore, the purpose of wireless multimedia sensor networks (WMSN) is to offer real-time multimedia applications using wireless sensors. Furthermore, Deep learning has an enormous impact on many applications involving multimedia data for tasks, including picture clustering, image classification, natural language processing, and speech recognition. Multimedia data accuracy, WMSN noise, and unattended situations are all problems for traditional methods. There are many moving parts in a successful organization, but network effectiveness is the backbone of an online business. To combat the problem of data noise in wireless sensor networks, the authors of this article propose using Deep Learning to aid in the development of Effective Multimedia Data Models (DLA-EMDM). In particular, no previous work has tried to model multimedia data cyclically using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). Based on the simulation findings, it is clear that the proposed DLA-EMDM approach improves the precision, efficiency ratio, cost-effectiveness, and silence of multimedia data processing in a WSN. By enhancing the aforementioned wireless network, we may furnish enhanced technical support and platform services to businesses and set the stage for enhancing business performance.

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Planet Optimization with Machine Learning Enabled Power Usage Forecasting modeling in Smart Grid Environment

Planet Optimization with Machine Learning Enabled Power Usag...

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international conference on Engineering Technology and their applications (IICETA)

作者： Zainab abed Almoussawi Waleed Hadi Madhloom Kurdi Basma Mohammed Khaleel Kassem AL-Attabi Hawraa Ali Sabah Waleed Khalid Alazzai College of Islamic Science Ahl Al Bayt University karbala Iraq Altoosi University College Najaf Iraq Computer Technology Engineering Department Al Rafidain University College Bagdad Iraq The Islamic university Najaf Iraq National University of Science and Technology Dhi Qar Iraq Medical Technical College Al-Farahidi University Baghdad Iraq

The smart grid (SG) significantly enhances the conventional power grids with information and communication technologies, control decision-making systems, simulation analysis, advanced measurement and sensing. In contrast to conventional power grids, the SG has further benefits in stability, situational awareness, self-healing, information interaction, and renewable energy consumption. But the precise prediction of long-term electric energy consumption remains a challenge. Recently, machine learning (ML), particularly deep learning, has rapidly advanced and has demonstrated outstanding performance in various tasks of the SG fields. The representation capability of ML approaches is considerably enhanced. The SG is a curial framework area, hence ML algorithms including it should be interpretable for improving reliability of the system and increasing user trust. This study develops a new Planet Optimization with Machine Learning Enabled Power Usage Forecasting modeling (POML-PUFM) in SG environment. The presented POML-PUFM technique forecast the utilization of power in the SGs for smart city applications. In the presented POML-PUFM technique, data pre-processing takes place to transform them into compatible format. For forecasting purposes, the POML-PUFM technique employs twin-support vector machine (TWSVM) model. In addition, the study used PO model to adjust the parameter related to the TWSVM method. The prediction performance of the presented POML-PUFM algorithm is examined and the outcomes are examined under several measures. The study verified the improvements of the POML-PUFM technique over other ML models.

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