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5th International Conference on Devices for Integrated Circuit, DevIC 2023

作者： Ganguly, Biswarup Ray, Radha Kumari Chatterjee, Arunava Paul, Subho Meghnad Saha Institute of Technology Electrical Engineering Department Kolkata India Application Development Associate Accenture Kolkata India Raghunathpur Govt. Polytechnic Electrical Engineering Department Purulia India Washington State University School of Electrical Engineering and Computer Science PullmanWA United States

ISBN: (纸本)9798350347265

Single-phase induction motors (SPIM) are fairly efficiently employed in household devices and possess wide applications in the industrial system. Typically, current signature-Analysis (CSA) based methods have been used for fault diagnosis, but not always been an efficient solution, especially for fractional kW SPIMs that may be replaced entirely iffaulty. This paper presents a deep learning (DL) based fault detection scheme of a SPIM which is in-service in household devices. The fault detection is done employing a wavelet based deep dense convolutional neural network (W-D2CNN), where several wavelets are used as convolutional filters for convolution operation. The convoluted feature maps are further passed through a proposed nonlinearity, i.e., SLReLU, for richer feature extraction. The uniqueness of the proposed method lies in the feature learning ability, where upper-And lower-layer features of the dense block fuse via concatenation operation. Also, the SL-ReLU has been designed to combine logarithmic function, softsign, and ReLU operation. The designed SL-ReLU minimizes the probability of convoluted output and gradient explosion, due to which training has been improved along with feature learning ability. Comprehensive results reveal the efficacy of the proposed work in terms of performance metrics with state-of-The-Art DL frameworks using the same dataset. The proposed work can also be extendable to multiphase-induction machines or any other electrical device working in any environment for fault identification. © 2023 IEEE.

关键词： Failure analysis

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Evaluating importance of AI in Robotics and Automation in Healthcare for Different age Groups 1

Evaluating importance of AI in Robotics and Automation in He...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Abu Eid, Aws Ismail Pethkar, Rajendra Huda, Bintul Patil, Jaykar Bhaskar Gatla, Ranjith Kumar Kumbhar, Ramchandra Arab Open University Faculty of Computing Studies Amman Jordan Jaywant College of Engineering and Polytechnic Department of Mechanical Engineering Karad India Sharda University Department of Computer Science & Technology Srinagar India Jaywant Institute of Management Karad India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India Department of Radio-Diagnosis Karad India

ISBN: (纸本)9798350360707

Nowadays robotics is mostly used in the world. There are many industries which used robots for reducing time and to have high precision of work. The use of artificial intelligence is now increasing which helps in automations and processing of machines. Thus, the focus of study is to find the different applications of automation of robotics (i.e. robotic process automation) in the field of healthcare and its impact on the employment of medical field. Four major categories can be used to group AI applications in healthcare: expressive, analytical, prognostic, and prescriptive. The only way to close the skills gap in the healthcare industry is to increase the application of artificial intelligence. Hospitals are developing mental commit robots to treat their patients. Through physical contact, these robots can have an impact on humans' psyche, physiology, and social interactions. Thus, in coming time most of the healthcare industry will be with the robots which will reduces the human interventions with the patients through one of the host servers. © 2024 IEEE.

关键词： Medical robotics

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Development of a Website-Based Financial Transaction Management System Using Agile with the Scrum Framework 10

Development of a Website-Based Financial Transaction Managem...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Latifah, Ayu Ramdan, Galih Muhammad Sidik, Muhamad Lutfhi Farid Faturrohman, Nadhif Nursyaban, Dzikri Ramelan, Agus Rahman, Fachrur Razy School of Electrical and Informatics Engineering Bandung Institute of Technology Bandung Indonesia Institut Teknologi Garut Department of Computer Science Garut Indonesia Universitas Sebelas Maret Electrical Engineering Departement Solo Indonesia Metal Industry Polytechnic of Morowali Department of Installation and Electrical Engineering Central Sulawesi Indonesia

ISBN: (纸本)9798350339543

The process of managing financial transactions, especially in the education sector, is still mostly carried out conventionally, so it has the potential to cause many risks, such as loss or damage to documents related to difficulties in searching for data as needed, transaction processes that take quite a long time, financial records that are not properly recorded. Well, it is prone to discrepancies between the nominal filing and the nominal disbursed conventional representations of financial statements that make the stakeholder evaluation process uncomfortable, and many other things. Therefore this research was conducted to build a website-based information system that can prevent this from happening in the future. The method used in the development of this information system is the Agile method with the Scrum framework. The Scrum framework consists of five stages, starting from Initiate, Plan, and Estimate, Implement, Review and Retrospect, and Release. This website was built using the Javascript programming language using the Vue JS framework for the Front-end. With this website-based application, it is hoped that transaction activities can be carried out better and correctly without being limited by space and time. © 2023 IEEE.

关键词： Websites

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Cross-Modality Registration using Bone Surface Pointcloud for Robotic Ultrasound-Guided Spine Surgery

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Journal of Medical Robotics Research 2025年第0期

作者： Ma, Xihan Zhang, Xiao Wang, Yang Nycz, Christopher J. Sungarian, Arno Ji, Songbai Huang, Xinming Zhang, Haichong K. Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Rd. Worcester 01609 MA United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute 100 Institute Rd. Worcester 01609 MA United States Memorial Medical Center University of Massachusetts 55 N Lake Ave. Worcester 01655 MA United States Department of Biomedical Engineering Worcester Polytechnic Institute 100 Institute Rd. Worcester 01609 MA United States

Image guidance using preoperative magnetic resonance imaging (MRI) and intraoperative ultrasound (US) can improve the outcome of spine surgery. Employing a robotic US system (RUSS) allows the automated acquisition of large 3D US volumes, facilitating accurate registration. However, such registration remains challenging due to the cross-modality discrepancy. To address this issue, we present a pipeline that extracts spine pointclouds from MRI and 3D US to perform per-vertebra registration. Experiments showed a registration accuracy of 1.82 mm in terms of residual root mean square error and 7.02 mm in terms of Chamfer distance. The pipeline exhibits superior robustness to suboptimal initial conditions compared with the two baseline methods. It also demonstrated good time efficiency under real-time conditions, demonstrating the potential applicability in RUSS-guided spine surgeries. © World Scientific Publishing Company.

关键词： cross-modality registration image-guided intervention robotic ultrasound Spine surgery ultrasound

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Single-Channel Super-Resolution SAR-GMTI via Atomic Norm Minimization

Single-Channel Super-Resolution SAR-GMTI via Atomic Norm Min...

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IEEE National Conference on Radar

作者： Bariscan Yonel Birsen Yazici Sean Thammakhoune Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

Synthetic aperture radar (SAR) imaging algorithms are designed to image stationary scatterers. A moving target is mis-positioned and results in artifacts in reconstructed images. In this paper, we present a novel approach to ground moving target imaging (GMTI) using a single channel SAR data based on atomic norm minimization (ANM) that is capable of localizing targets with sub-resolution cell precision. We use ANM to decompose the received SAR data into the echoes received from each scatterer in the scene, which facilitates processing echoes individually and isolating velocity related phase variations by cross-correlations across the slow-time samples. We use these estimates for motion compensation, and form the SAR image using the dual polynomial of the atomic decomposition. Motion parameters are then numerically estimated without a pre-specified grid. We derive the theoretical requirements on SAR imaging parameters, and detectable velocity limit; and demonstrate the off-grid, sub-resolution cell localization capability of our ANM-based method in SAR-GMTI setting by numerical simulations.

关键词： Location awareness Superresolution Imaging Radar imaging Minimization Radar polarimetry Motion compensation

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A Hybrid Model and Learning-Based Force Estimation Framework for Surgical Robots

A Hybrid Model and Learning-Based Force Estimation Framework...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Hao Yang Haoying Zhou Gregory S. Fischer Jie Ying Wu Department of Computer Science Vanderbilt University TN USA Department of Robotics Engineering Worcester Polytechnic Institute MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Haptic feedback to the surgeon during robotic surgery would enable safer and more immersive surgeries but estimating tissue interaction forces at the tips of robotically controlled surgical instruments has proven challenging. Few existing surgical robots can measure interaction forces directly and the additional sensor may limit the life of instruments. We present a hybrid model and learning-based framework for force estimation for the Patient Side Manipulators (PSM) of a da Vinci Research Kit (dVRK). The model-based component identifies the dynamic parameters of the robot and estimates free-space joint torque, while the learning-based component compensates for environmental factors, such as the additional torque caused by trocar interaction between the PSM instrument and the patient’s body wall. We evaluate our method in an abdominal phantom and achieve an error in force estimation of under 10% normalized root-mean-squared error. We show that by using a model-based method to perform dynamics identification, we reduce reliance on the training data covering the entire workspace. Although originally developed for the dVRK, the proposed method is a generalizable framework for other compliant surgical robots. The code is available at https://***/vu-maple-lab/dvrk_force_estimation.

关键词： Learning systems Torque Medical robotics Instruments Force Dynamics Estimation Surgery Robot sensing systems Trajectory optimization

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Real-Time Simulation of a Resilient Control Center for Inverter-Based Microgrids

Real-Time Simulation of a Resilient Control Center for Inver...

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Annual Conference of Industrial Electronics Society

作者： Milad Beikbabaei Ali Mehrizi-Sani The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University VA

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

The number of installed remote terminal units (RTU) is on the rise, increasing the observability and control of the power system. RTUs enable sending data to and receiving data from a control center in the power system. A distribution grid control center runs distribution management system (DMS) algorithms, where the DMS takes control actions during transients and outages, such as tripping a circuit breaker and disconnecting a controllable load to increase the resiliency of the grid. Relying on communication-based devices makes the control center vulnerable to cyberattacks, and attackers can send falsified data to the control center to cause disturbances or power outages. Previous work has conducted research on developing ways to detect a cyberattack and ways to mitigate the adverse effects of the attack. This work studies false data injection (FDI) attacks on the DMS algorithm of a fully inverter-based microgrid in real time. The fully inverter-based microgrid is simulated using an RTDS, an amplifier, an electronic load, a server, a network switch, and a router. The DMS is integrated into the server codes and exchanges data with RTDS through TCP/IP protocols. Moreover, a recurrent neural network (RNN) algorithm is used to detect and mitigate the cyberattack. The effectiveness of the detection and mitigation algorithm is tested under various scenarios using the real-time testbed.

关键词： Recurrent neural networks Microgrids Switches TCPIP Real-time systems Routing protocols Servers computer crime Transient analysis Resilience

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Memory-Based Set Point Modulation for Improved Transient Response of Distributed Energy Resources

Memory-Based Set Point Modulation for Improved Transient Res...

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Annual Conference of Industrial Electronics Society

作者： Milad Beikbabaei Brady Alexander Ashwin Venkataramanan Ali Mehrizi-Sani The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University VA

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

As the composition of the power grid evolves to integrate more renewable generation, its reliance on distributed energy resources (DER) is increasing. Existing DERs are often controlled with proportional integral (PI) controllers that, if not properly tuned or if system parameters change, exhibit sluggish performance or large overshoot. The use of set point automatic adjustment with correction-enabled (SPAACE) with a linear predictor improves the transient response of these DERs without the need to access the PI controller parameters. The limitation of the existing SPAACE method is the high sampling rate needed for improved performance, which is not always practical. This paper proposes the addition of a memory term to the SPAACE with a linear predictor. This memory term is the integral of the errors of previous samples, which adds another layer to the prediction to improve the response at lower sampling rates and further reduces the overshoot and settling time compared to the existing SPAACE method. Time-domain simulation studies are performed in PSCAD/EMTDC to show the effectiveness of the proposed controller.

关键词： Industrial electronics Transient response Time-frequency analysis Renewable energy sources PI control Frequency modulation Prediction algorithms Power grids Distributed power generation Time-domain analysis

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A Near Sensor Edge Computing System for Point Cloud Semantic Segmentation

arXiv

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arXiv 2022年

作者： Bai, Lin Zhao, Yiming Huang, Xinming Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

Point cloud semantic segmentation has attracted attentions due to its robustness to light condition. This makes it an ideal semantic solution for autonomous driving. However, considering the large computation burden and bandwidth demanding of neural networks, putting all the computing into vehicle Electronic Control Unit (ECU) is not efficient or practical. In this paper, we proposed a light weighted point cloud semantic segmentation network based on range view. Due to its simple pre-processing and standard convolution, it is efficient when running on deep learning accelerator like DPU. Furthermore, a near sensor computing system is built for autonomous vehicles. In this system, a FPGA-based deep learning accelerator core (DPU) is placed next to the LiDAR sensor, to perform point cloud pre-processing and segmentation neural network. By leaving only the post-processing step to ECU, this solution heavily alleviate the computation burden of ECU and consequently shortens the decision making and vehicles reaction latency. Our semantic segmentation network achieved 10 frame per second (fps) on Xilinx DPU with computation efficiency 42.5 GOP/W. Copyright © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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A Logical Circuit Optimization in Balancing Delay and Energy Consumption 23

A Logical Circuit Optimization in Balancing Delay and Energy...

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Proceedings of the 6th International Conference on Information Technologies and electrical engineering

作者： Qihang Shan The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University USA

ISBN: (纸本)9798400708299

The fast-developing chip manufacturing technique and scaling of transistors allow us to fit more transistors on a small chip. The scaling down process, however, is facing a challenge. The smaller transistors are, the more influential quantum channeling and silicon atom size limit become. To improve efficiency, the solution of scaling down is no longer an option. Therefore, to further improve the efficiency of a chip without scaling down transistors, this paper presents a combinational circuit and focuses on an optimization approach where energy consumption is reduced in exchange for increasing delay. By adjusting the size of transistors, energy is saved while maintaining delay to an acceptable range. This approach manages to reduce energy consumption by about 56% while increasing delay by 50%. This paper represents one of many possible approaches that researchers had and has been working on and this tradeoff can benefit some circuit designs depending on the circuit's purpose and hope to bring some insights on further optimization.

关键词： Delay Energy Consumption Optimization Tradeoff

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