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A Direct-coupled Harmonic Rejection Mixer Employing IIP3 Improving Technology 2

A Direct-coupled Harmonic Rejection Mixer Employing IIP3 Imp...

引用

2nd international conference on computer Science, Electronic Information engineering and intelligent control Technology, CEI 2022

作者： Zhang, Hongyu Wu, Yiqiang International College of Chongqing University of Posts and Telecommunications Chongqing China Hangzhou Bluewave Inc. Rfic Department Shanghai China

ISBN: (数字)9781665476164

ISBN: (纸本)9781665476164

This paper introduces a high linearity, low power wideband harmonic rejection mixer (HRM). By fully utilizing the modified differential multiple gated transistor (DMGTR) technique, a novel direct-coupled high linearity HRM is proposed. Implemented in a 0.18-\mu\mathrm{m} CMOS process, and the current consumption is 6.20 mA under one 1.8-V supply. The measurements show that a 1.458-dB peak conversion gain, a 16.21-dBm IIP3, a 37.55-dBc 3rd harmonic rejection ratio (HRR) and a 67.52-dBc 5th HRR is obtained for a wide frequency range. © 2022 IEEE.

关键词： Mixers (machinery)

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Deep Learning Based Blood Flow Analysis in MRI Images 3

Deep Learning Based Blood Flow Analysis in MRI Images

引用

3rd international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2023

作者： Hiremath, Uma M Chickerur, Satyadhyan School of Computer Science and Engineering Kle Technological University Vidyanagar Karnataka Hubballi India

ISBN: (纸本)9798350306989

The study, deep learning algorithms, and compu-tational fluid dynamics (CFD) are combined to analyze blood flow patterns in MRI images. Due to complicated physiological structures and turbulent flows, traditional approaches for blood flow analysis in medical imaging frequently face challenges. In the current study, a deep learning architecture is suggested to accurately extract flow data from MRI scans, offering thorough insights into vascular behavior. Following the extraction, the data are merged into a CFD simulation, allowing for accurate hemodynamic modeling. The combination of deep learning and CFD improves the precision and effectiveness of blood flow analysis, paving up the possibility of potential developments in cardiovascular disease diagnosis and treatment planning. The suggested method employs deep learning-based techniques for flow analysis, ROI extraction, and image pre-processing. It's significant that the method presents a possible classification system for both normal and abnormal aortas. Fundamental flow characteristics, such as velocity, pressure, and turbulence, may be calculated using CFD and are crucial for understanding the underlying flow patterns. It is now possible to analyze and identify a variety of aortic issues with the aid of MRI images and the integration of CFD and deep learning, improving diagnosis and patient care. Experimental findings show the effectiveness with which the suggested method captures complex flow patterns and advances our knowledge of vascular dynamics. © 2023 IEEE.

关键词： Magnetic resonance imaging

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A lifting type intelligent digital stereo garage 2

A lifting type intelligent digital stereo garage

引用

2nd international conference on Mechanical engineering, intelligent Manufacturing and Automation Technology, MEMAT 2022

作者： Zhang, Wenqing Zhang, Qiang Zhang, Yishu Lanzhou Jiaotong University Lanzhou China Heilongjiang University of Science and Technology Harbin China

ISBN: (纸本)9783800757619

For the current problems of parking chaos, slow parking and difficult parking, it is necessary to design a set of intelligent digital stereo garage based on up and down computer. The garage system is structurally divided into an upper computer part and a lower computer part. The upper computer part takes the user system as the core, including database, information communication and face information recognition;the lower computer takes the controller as the core, including parking mechanical structure design, license plate information registration, security detection alarm and vehicle maintenance. In terms of hardware, the upper computer selects a more appropriate user system end, and the lower computer selects electronic devices that meet the actual requirements. In terms of software, the upper computer relies on Python's PyQt5 library to realize UI design, and the lower computer realizes signal processing and drive control of the controller through the program. The system uses image acquisition and combines various image processing means to accurately obtain license plate information. From the perspective of geometric model, the garage is divided into many small parking bins, which better improves the space utilization and management efficiency. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： License plates (automobile)

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Data-Driven Modeling and Working Condition Prediction in Process Industry Production 21

Data-Driven Modeling and Working Condition Prediction in Pro...

引用

21st international conference on Networking, Sensing and control, ICNSC 2024

作者： Zhang, Meifang Wang, Ziqi Bi, Jing Yuan, Haitao College of Computer Science Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798350365221

In the production process of the process industry, precise adjustment of working conditions presents a challenge due to the complexity of processes and unknown disturbances. Central control operators need to adjust setpoints based on deviations in process parameters and monitor target values to maintain system stability. However, many operating procedures excessively rely on human experience, increasing the uncertainty of the production process. In addition, the expert knowledge is not fully embedded in accumulated operations, limiting its potential in decision support. Therefore, data-driven modeling of production processes is essential for developing industrial expert systems to realize intelligent manufacturing. This work proposes a work condition prediction framework based on an Operation Mode Library (OML) to realize Working Condition Prediction (WCP), called for OML-WCP short. Taking the cement rotary kiln adjustment process as an example, a stable OML is constructed using Gaussian mixture clustering technology. Experimental results with real-life operation data of a cement plant reveal that the prediction accuracy of OML-WCP outperforms the existing methods. Moreover, the continuous accumulation of operating mode libraries can improve prediction accuracy in practical annlications. © 2024 IEEE.

关键词： Cements

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An Unified Approach for Traffic Sign Classificationand Flow Prediction using Deep Learning Technique 15

An Unified Approach for Traffic Sign Classificationand Flow ...

引用

15th international conference on advances in Computing, control, and Telecommunication Technologies, ACT 2024

作者： Kavitha, D. Lekhana, Kommareddy Rajeshri, Vigrahala Rushitha, Chennareddygari School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331300579

Efficient and safe traffic management is an important concern in modern urban environments. To address this challenge, we propose an integrated deep learning based solution that unifies traffic sign classification and traffic flow prediction using the LeNet architecture. The seamless fusion of these two critical tasks enables a holistic approach to traffic management, benefiting autonomous vehicles, traffic control systems, and road safety. In this unified approach, LeNet, a seminal convolutional neural network (CNN), serves as the backbone for traffic sign classification. Lenet Network is a very famous kind of configuration of convolutional neural networks that can be used to classify *** this paper, we have used Lenet to classify traffic signs, mainly used for selfdriving cars. Leveraging LeNet's capability to recognize traffic signs with high accuracy, we train it on a comprehensive dataset comprising 43 different classes of traffic signs. This dataset encompasses a wide range of shapes, colors, and conditions, allowing the LeNetbased classifier not only to identify individual traffic signs but also to provide valuable context for downstream traffic flow prediction. Extensive experiments on diverse datasets validate the effectiveness of our unified approach. We demonstrate superior traffic sign classification accuracy using LeNet, surpassing previous state-of- art methods. Additionally, our traffic flow prediction capabilities exhibit impressive accuracy and robustness across various traffic scenarios. Traffic flow prediction helps people for effective route planning so that the people can chose their routes to save and fuel and it helps to reduce traffic congestion. This research represents a significant step toward enhancing traffic management systems efficiency and safety by leveraging deep learning techniques. Our unified approach, combining LeNet- based traffic sign classification with traffic flow prediction, holds great promise for smarter and more r

关键词： Convolutional neural networks

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Predicting Blood Glucose Levels in Type 1 Diabetes Using Deep Learning and Regression Techniques

Predicting Blood Glucose Levels in Type 1 Diabetes Using Dee...

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2025 international conference on intelligent control, Computing and Communications, IC3 2025

作者： Singh, Jagendra Singh, Brijendra Pratap Dandotiya, Monika Ekvitayavetchanukul, Pongkit Rana, Manoj Singh, Bakshish School of Computer Science Engineering & Technology Bennett University Greater Noida India Poornima University Department of Computer Science & Engineering Jaipur India The Board of Khonkaen University Affaneirs Khonkaen University Thailand Teerthanker Mahaveer University Cdoe Moradabad India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India

ISBN: (纸本)9798331527495

Accurate blood glucose prediction is very critical for the effective management of Type 1 Diabetes as it allows timely interventions and prevents fluctuations in blood sugar levels. Due to such shortcomings, the traditional methods of monitoring that are followed are consistently unable to provide real-time insights and often cannot inform the patients about the stable levels of their glucose. This research studies advanced deep learning applications based on Long Short-Term Memory (LSTM), Gated recurrent unit (GRU), Autoregressive Integrated Moving Average (ARIMA), and linear regression models to predict blood glucose levels in terms of insulin intake dosage, consumption of carbohydrates, level of physical activity, and sleep patterns. The model performances with respect to Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean absolute percentage error (MAPE), and accuracy are deployed for training and performance evaluation. Results were obtained, which show that LSTM performed well in comparison to others with an accuracy of 97.68%, followed by ARIMA (92.23%), GRU (91.23%), and Linear Regression at an accuracy of 89.45%. Real-time testing also gave the results consistent with the above, showing accuracy at close par with the glucose values. Therefore, LSTM in diabetes management systems may allow for blood glucose monitoring in real-time and customised intervention. This research contributes towards developing intelligent systems with the capacity to predict problems and enhance patient outcomes and quality of life for those with Type 1 Diabetes by embedding deep learning models into predictive accuracy and timeliness. © 2025 IEEE.

关键词： Linear regression

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Design of temperature and humidity control device for substation terminal cabinet 6

Design of temperature and humidity control device for substa...

引用

6th IEEE international conference on Automation, Electronics and Electrical engineering, AUTEEE 2023

作者： Yumu, Chen Jingjing, Su Zihan, Zhang Chengpu, Song Xin, Chen Jiancheng, Sheng Minjiang University College of Computer and Control Engineering Fuzhou China

ISBN: (纸本)9798350305623

Based on the substation outdoor box cabinet in the operation process, which is prone to condensation and overheating, this paper designs a temperature and humidity control device for outdoor box cabinet of substation based on STM32 micro-controller. The device uses STM32 as the main control unit, and uses DHT 11 temperature and humidity sensor to detect the temperature and humidity of the box cabinet, and compare the threshold value to judge the temperature and humidity in the box cabinet and control the heater and fan to effectively drive moisture and heat. © 2023 IEEE.

关键词： intelligent control outdoor box cabinet STM32 single-chip microcomputer Temperature and humidity

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Research on intelligent PC component manufacturing control system based on environmental sensing technology 5

Research on intelligent PC component manufacturing control s...

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5th international conference on Mechatronics and computer Technology engineering, MCTE 2022

作者： Zhang, Ting Ting Wang, Li Wu, Ke Yang You, Ming Wang, Shuai Chongqing University-University of Cincinnati Joint Co-op Institute Chongqing University Chongqing400044 China Enterprise Technology Center Wuhan Construction Engineering Group Co. Hubei Wuhan430056 China

ISBN: (纸本)9781510661073

The production efficiency and cost of PC (precast concrete) component production line are determined by the degree of intelligence. Comparing domestic and foreign advanced PC component production lines and concepts, and analyzing the existing typical PC component manufacturing mode and characteristics, an intelligent PC component manufacturing control system is developed. Combined with the actual investigation of a domestic company's PC component production base, the system is used to intelligently monitor, control and schedule the mold table conveying system, production preparation system, concrete placing and vibrating system, maintenance system, mold dismantling and lifting system of the production line. The system can effectively improve the factory production organization efficiency, equipment safety and automation degree, optimize the manual operation environment, and provide useful reference for the intelligent PC component manufacturing. © 2022 SPIE.

关键词： control systems

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Advancing Wire Arc Additive Manufacturing: A New Era of Precision and Efficiency in Large Metal Component Production

Advancing Wire Arc Additive Manufacturing: A New Era of Prec...

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2024 OPJU international Technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Khan, Irfan Anjimoon, Shaik Asha, V. Singla, Atul Akhil, P. Alsultany, Nahed Mahmood Ahmed Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Applications Bangalore India Lovely Professional University Phagwara India St. Martin's Engineering College Dept. of Cse Telangana Secunderabad India National University of Science and Technology Collage of Dentist Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

A comprehensive Wire Arc Additive Manufacturing (WAAM) approach that employs five algorithms to improve massive metal components is described in this research. The adaptive layer thickness control algorithm adjusts layer thickness based on real-time complexity. This ensures additive manufacturing accuracy throughout. Also, the Material Versatility Optimization Algorithm simplifies working with multiple materials, making casting more efficient. The intelligent Path Planning Algorithm allocates the robotic arm's movement to traverse the shortest distance and uniformly distribute heat during deposition. The MultiMaterial-Materialon Strategy Algorithm improves material amounts, making WAAM more usable for additional materials. The Real-Time Excellent Assurance Algorithm constantly examines and adjusts parameters to ensure excellent production. A complete analysis of research reveals how algorithms are related and how vital they are for accuracy, speed, and flexibility. Testing shows that the recommended technique controls layer thickness, material variety, deposition velocity, accuracy, energy efficiency, and real-time tracking better than typical WAAM methods. Figures 6, 7, and 8 indicate that the proposed method delivers superior results across several criteria. The recommended WAAM approach can create more massive metal components by fundamentally changing how things are done. This research provides a comprehensive additive manufacturing approach to solve numerous large-scale metal production issues. © 2024 IEEE.

关键词： Quality control

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Decision-Making in Robotic Grasping with Large Language Models 16th

Decision-Making in Robotic Grasping with Large Language Mo...

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16th international conference on intelligent Robotics and Applications, ICIRA 2023

作者： Liao, Jianfeng Zhang, Haoyang Qian, Haofu Meng, Qiwei Sun, Yinan Sun, Yao Song, Wei Zhu, Shiqiang Gu, Jason Research Center for Intelligent Robotics Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964949

Recent advances in large language models have highlighted their potential to encode massive amounts of semantic knowledge for long-term autonomous decision-making, positioning them as a promising solution for powering the cognitive capabilities of future home-assistant robots. However, while large language models can provide high-level decision, there is still no unified paradigm for integrating them with robots’ perception and low-level action. In this paper, we propose a framework centered around a large language model, integrated with visual perception and motion planning modules, to investigate the robotic grasping task. Unlike traditional methods that only focus on generating stable grasps, our proposed approach can handle personalized user instructions and perform tasks more effectively in home scenarios. Our approach integrates existing state-of-the-art models in a simple and effective way, without requiring any fine-tuning, which makes it low-cost and easy to deploy. Experiments on a physical robot system demonstrate the feasibility of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Decision making

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