Design and analysis of an ultrasonic tactile sensor using electro-mechanical analogy

Qian, Yanjun; Salehian, Armaghan; Han, Sang-Wook; Kwon, Hyock-Ju

doi:10.1016/j.ultras.2020.106129

Cited by 12 publications

(5 citation statements)

References 24 publications

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“…Finally, the test on an ex vivo porcine liver verifies that the sensor can distinguish real biological tissues of different hardness and identify the shape of the lump. The sensing time of the sensor is about 0.5 s for a single detection point, which is much shorter than that of the dynamic piezoelectric tactile sensor in [13] which takes 1 s, and much shorter than another piezoelectric ultrasonic sensor in [24] which takes 1 min.…”

Section: Discussionmentioning

confidence: 95%

A dual-mode tactile hardness sensor for intraoperative tumor detection and tactile imaging in robot-assisted minimally invasive surgery

Zhang¹,

Wei²,

Yue³

et al. 2021

Smart Mater. Struct.

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Intraoperative tumor detection and shape identification through manual palpation are routinely performed in traditional open surgeries to ensure complete tumor resection. However, most existing robot-assisted minimally invasive surgery (RMIS) systems lack tactile feedback and rely on vision heavily. Traditional tactile sensing methods require the sensor to be placed normal to the tissue surface. But this requirement cannot always be met due to the limited degrees of freedom and the complexity of the environment in confined spaces. This paper proposes a miniaturized piezoelectric tactile sensor for tissue hardness detection by measuring its electrical impedance spectrum. It has two unique detection modes in two orthogonal directions—transverse and longitudinal, and can detect hardness even when the contact angle is large. It is verified by simulations and experiments that both detection modes can be used to detect hardness in the normal contact condition. However, in the case of hardness detection at a large contact angle, the sensitivity of the sensor in the transverse detection mode is significantly higher than that in the longitudinal mode, implying that this mode is more suitable for the large-angle detection. The sensor is then tested on silicone phantoms with hard inclusions and also on an ex vivo porcine liver. In addition, a tactile imaging algorithm based on Gaussian process regression is used to generate the complete hardness distribution of the test sample, which is further processed to extract the shape and boundary of the hard inclusion. The results show that the accuracy of shape detection is high (recall ⩾ 95%, specificity ⩾ 97%), and the smallest feature size it could detect is 1.3 mm. This proves that the proposed tactile sensor has the potential to perform high-accuracy tumor detection in RMIS.

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Section: Discussionmentioning

confidence: 95%

A dual-mode tactile hardness sensor for intraoperative tumor detection and tactile imaging in robot-assisted minimally invasive surgery

Zhang¹,

Wei²,

Yue³

et al. 2021

Smart Mater. Struct.

View full text Add to dashboard Cite

show abstract

“…The analogy has been implemented in many applications such as suspension modeling and control [25]- [28], vehicle drive trains [29], structural dynamics [30], in designing and controlling of flexible structures [31], optimization and design of inductive power transfer systems [32], piezoelectric vibration energy harvesters [33]. While other studies aimed for vibration applications as the their main of interest [34]- [40]. The variety of applications which benefit from idea of analogy, is another substantial reason for conducting this study.…”

Section: Related Workmentioning

confidence: 99%

Electric Circuit-Based Modeling and Analysis of the Translational, Rotational Mechanical and Electromechanical Systems Dynamics

et al. 2022

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To cope with the rapid development in technology, engineers are dealing with complex and heterogenous systems which composed of blocks that belong to different engineering fields such as electrical, mechanical, chemical, electromechanical, fluid, thermal etc. Mechanical and electrical systems are more often go hand to hand in many industrial systems. For system analyzing and designing purpose, engineers must model and simulate the systems to investigate problems and aim for the best performance before proceeding to the manufacturing stage. In the presence of complex mechanical system blocks, electrical and electronics engineers are often facing difficulties in modeling the mechanical blocks. Although similarity between individual mechanical and electrical elements is recognized since long time, but it has not drawn deserved attention for the use in the system level. In this paper, we investigate in great details how enabling electrical and electronics engineers to easily model and analyze complex mechanical and electromechanical systems through systematic approach. For this objective, thirteen rules are set, established, and elaborated on how to find the electrical circuit equivalent of a given mechanical or electromechanical system in order to be modelled and analyzed. The proposed approach is tested on both complex translational mechanical and electromechanical systems which includes a rotational mechanical system. Findings demonstrate that models generated by the equivalent of electric circuits are matching the models of existing mechanical and electromechanical system by 100%. The proposed systematic approach is promising and can be widely implemented in several industrial fields.INDEX TERMS Modeling and simulation; a set of rules; complex mechanical systems; electrical system; analogy.

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“…Construction machinery based on common frequency AC motors, such as vibrators, will produce a lot of noise, affecting the work and life of the surrounding people [1][2]. From the perspective of environmental protection, China has revised the technical standards of the noise level of construction machinery products and has begun to implement them in some major cities [3][4].…”

Section: Introductionmentioning

confidence: 99%

Visualization and analysis of electrical parameter design based on digital sensors

Wan,

Wang

2024

Applied Mathematics and Nonlinear Sciences

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This paper provides an in-depth study on the application of digital sensor technology in electrical fire detection. It firstly focuses on the design of electrical parameters to improve the accuracy of fire detection. Based on the research results of recurrent neural networks, the article combines the Long Short-Term Memory (LSTM) network and the Gated Recurrent Unit (GRU) neural network. It innovatively proposes an electrical fire feature recognition method based on the LSTMGRU network. The study also incorporates fuzzy inference techniques to optimize the fire alarm decision-making process, which achieves a hierarchical output for electrical fire detection recognition and alarm decision-making. The experimental results show that the LSTM-GRU network achieves an accuracy of 98.83% in electrical fire classification and recognition, significantly better than the results of using LSTM network or GRU network alone. Regarding electrical fire distance recognition, the relative error of the method only ranges from 0.32% to 2.10%, and its output of fire alarm decision is entirely correct. The study not only verifies the high accuracy and reasonableness of the electrical fire feature recognition method based on digital sensing technology, but also provides a brand new idea for recognizing electrical fire detection and alarm decision-making.

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Design and analysis of an ultrasonic tactile sensor using electro-mechanical analogy

Cited by 12 publications

References 24 publications

A dual-mode tactile hardness sensor for intraoperative tumor detection and tactile imaging in robot-assisted minimally invasive surgery

A dual-mode tactile hardness sensor for intraoperative tumor detection and tactile imaging in robot-assisted minimally invasive surgery

Electric Circuit-Based Modeling and Analysis of the Translational, Rotational Mechanical and Electromechanical Systems Dynamics

Visualization and analysis of electrical parameter design based on digital sensors

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