Relevant bibliographies by topics / Intelligent pressure sensor

Academic literature on the topic 'Intelligent pressure sensor'

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Published: 6 September 2023

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Journal articles on the topic "Intelligent pressure sensor"

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Wang, Hao, Meng Nie, and Qing An Huang. "Design of Intelligent Meteorological System Based on MEMS." Key Engineering Materials 609-610 (April 2014): 801–6. http://dx.doi.org/10.4028/www.scientific.net/kem.609-610.801.

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Intelligent weather station system based on MEMS sensors is designed. The automatic meteorological system includes a MEMS temperature sensor, MEMS humidity sensor, MEMS pressure sensor, MEMS wind speed sensor and the sensor intelligent control system, etc. The intelligent control system has functions such as precise timing, multiple sensor data automatic acquisition, storage and uploading, which realizes the intelligent control of this weather station system.
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Lu, Xiaozhou, Xi Xie, Qiaobo Gao, Hanlun Hu, Jiayi Yang, Hui Wang, Songlin Wang, and Renjie Chen. "Design of biomimetic human-skin-like tactile flexible sensor." Sensor Review 39, no. 3 (May 20, 2019): 397–406. http://dx.doi.org/10.1108/sr-01-2018-0007.

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Purpose The hands of intelligent robots perceive external stimuli and respond effectively according to tactile or pressure sensors. However, the traditional tactile and pressure sensors cannot perform human-skin-like intelligent properties of high sensitivity, large measurement range, multi-function and flexibility simultaneously. The purpose of this paper is to present a flexible tactile-pressure sensor based on hyper-elastics polydimethylsiloxane and plate capacitance. Design/methodology/approach With regard to this problem, this paper presents a flexible tactile-pressure sensor based on hyper-elastics PDMS and plate capacitance. The sensor has a size of 10 mm × 10 mm × 1.3 mm and is composed of four upper electrodes, one middle driving electrode and one lower electrode. The authors first analyzed the structure and the tactile-pressure sensing principle of human skin to obtain the design parameters of the sensor. Then they presented the working principle, material selection and mechanical structure design and fabrication process of the sensor. The authors also fabricated several sample devices of the sensor and carried out experiments to establish the relationship between the sensor output and the pressure. Findings The results show that the tactile part of the sensor can measure a range of 0.05-1N/mm2 micro pressure with a sensitivity of 2.93 per cent/N and a linearity of 0.03 per cent. The pressure part of the sensor can measure a range of 1-30N/mm2 pressure with a sensitivity of 0.08 per cent/N and a linearity of 0.07 per cent. Originality/value This paper analyzes the tactile and pressure sensing principles of human skin and develop an intelligent sensitive human-skin-like tactile-pressure sensor for intelligent robot perception systems. The sensor can achieve to imitate the tactile and pressure function simultaneously with a measurement resolution of 0.01 N and a spatial resolution of 2 mm.
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Martins, Leonardo, Rui Lucena, Rui Almeida, João Belo, Cláudia Quaresma, Adelaide Jesus, and Pedro Vieira. "Intelligent Chair Sensor." International Journal of System Dynamics Applications 3, no. 2 (April 2014): 65–80. http://dx.doi.org/10.4018/ijsda.2014040105.

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In order to develop an intelligent system capable of posture classification and correction the authors developed a chair prototype equipped with air bladders in the chair's seat pad and backrest, which can in turn detect the user posture based on the pressure inside said bladders and change their conformation by inflation or deflation. Pressure maps for eleven standardized postures were gathered in order to automatically detect the user's posture, with resource to neural networks classifiers. First the authors tried to find the best parameters for the neural network classification of our data, obtaining an overall classification of around 80% for eleven standardized postures. Those neural networks were then exported to a mobile application to achieve a real-time classification of the standardized postures. Results showed a real-time classification of 93.4% for eight standardized postures, even for users that experimented for the first-time our intelligent chair. Using the same mobile application they devised and implemented two correction algorithms, acting due to conformation change of the bladders in the chair's seat when a poor seating posture is detected for certain periods of time.
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Luo, Yongsong, Xiaoliang Chen, Hongmiao Tian, Xiangming Li, Yangtianyu Lu, Yang Liu, and Jinyou Shao. "Gecko-Inspired Slant Hierarchical Microstructure-Based Ultrasensitive Iontronic Pressure Sensor for Intelligent Interaction." Research 2022 (June 14, 2022): 1–13. http://dx.doi.org/10.34133/2022/9852138.

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Highly sensitive flexible pressure sensors play an important role to ensure the safety and friendliness during the human-robot interaction process. Microengineering the active layer has been shown to improve performance of pressure sensors. However, the current structural strategy almost relying on axial compression deformation suffers structural stiffening, and together with the limited area growth efficiency of conformal interface, essentially limiting the maximum sensitivity. Here, inspired by the interface contact behavior of gecko’s feet, we design a slant hierarchical microstructure to act as an electrode contacting with an ionic gel layer, fundamentally eliminating the pressure resistance and maximizing functional interface expansion to achieving ultrasensitive sensitivity. Such a structuring strategy dramatically improves the relative capacitance change both in the low- and high-pressure region, thereby boosting the sensitivity up to 36000 kPa-1 and effective measurement range up to 300 kPa. To verify the advantages of high sensitivity, the sensor is integrated with a soft magnetic robot to demonstrate a biomimetic Venus flytrap. The ability to perceive weak stimuli allows the sensor to be used as a sensory and feedback window, realizing the capture of small live insects and the transportation of fragile objects.
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Guo, Zhenxin, Lixin Mo, Yu Ding, Qingqing Zhang, Xiangyou Meng, Zhengtan Wu, Yinjie Chen, Meijuan Cao, Wei Wang, and Luhai Li. "Printed and Flexible Capacitive Pressure Sensor with Carbon Nanotubes based Composite Dielectric Layer." Micromachines 10, no. 11 (October 23, 2019): 715. http://dx.doi.org/10.3390/mi10110715.

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Flexible pressure sensors have attracted tremendous attention from researchers for their widely applications in tactile artificial intelligence, electric skin, disease diagnosis, and healthcare monitoring. Obtaining flexible pressure sensors with high sensitivity in a low cost and convenient way remains a huge challenge. In this paper, the composite dielectric layer based on the mixture of carbon nanotubes (CNTs) with different aspect ratios and polydimethylsiloxane (PDMS) was employed in flexible capacitive pressure sensor to increase its sensitivity. In addition, the screen printing instead of traditional etching based methods was used to prepare the electrodes array of the sensor. The results showed that the aspect ratio and weight fraction of the CNTs play an important role in improving the sensitivity of the printed capacitive pressure sensor. The prepared capacitive sensor with the CNTs/PDMS composite dielectric layer demonstrated a maximum sensitivity of 2.9 kPa−1 in the pressure range of 0–450 Pa, by using the CNTs with an aspect ratio of 1250–3750 and the weight fraction of 3.75%. The mechanism study revealed that the increase of the sensitivity of the pressure sensor should be attributed to the relative permittivity increase of the composite dielectric layer under pressure. Meanwhile, the printed 3 × 3 and 10 × 10 sensor arrays showed excellent spatial resolution and uniformity when they were applied to measure the pressure distribution. For further applications, the flexible pressure sensor was integrated on an adhesive bandage to detect the finger bending, as well as used to create Morse code by knocking the sensor to change their capacitance curves. The printed and flexible pressure sensor in this study might be a good candidate for the development of tactile artificial intelligence, intelligent medical diagnosis systems and wearable electronics.
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Patra, J. C., A. C. Kot, and G. Panda. "An intelligent pressure sensor using neural networks." IEEE Transactions on Instrumentation and Measurement 49, no. 4 (2000): 829–34. http://dx.doi.org/10.1109/19.863933.

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Yu, Qingyang, and Jian Zhang. "Flexible Capacitive Pressure Sensor Based on a Double-Sided Microstructure Porous Dielectric Layer." Micromachines 14, no. 1 (December 30, 2022): 111. http://dx.doi.org/10.3390/mi14010111.

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In the era of intelligent sensing, there is a huge demand for flexible pressure sensors. High sensitivity is the primary requirement for flexible pressure sensors, whereas pressure response range and resolution, which are also key parameters of sensors, are often ignored, resulting in limited applications of flexible pressure sensors. This paper reports a flexible capacitive pressure sensor based on a double-sided microstructure porous dielectric layer. First, a porous structure was developed in the polymer dielectric layer consisting of silicon rubber (SR)/NaCl/carbon black (CB) using the dissolution method, and then hemisphere microstructures were developed on both sides of the layer by adopting the template method. The synergistic effect of the hemispheric surface microstructure and porous internal structure improves the deformability of the dielectric layer, thus achieving high sensitivity (3.15 kPa−1), wide response range (0–200 kPa), and high resolution (i.e., the minimum pressure detected was 27 Pa). The proposed sensing unit and its array have been demonstrated to be effective in large-area pressure sensing and object recognition. The flexible capacitive pressure sensor developed in this paper is highly promising in applications of robot skin and intelligent prosthetic hands.
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Zhu, Lingfeng, Yancheng Wang, Deqing Mei, and Chengpeng Jiang. "Development of Fully Flexible Tactile Pressure Sensor with Bilayer Interlaced Bumps for Robotic Grasping Applications." Micromachines 11, no. 8 (August 12, 2020): 770. http://dx.doi.org/10.3390/mi11080770.

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Flexible tactile sensors have been utilized in intelligent robotics for human-machine interaction and healthcare monitoring. The relatively low flexibility, unbalanced sensitivity and sensing range of the tactile sensors are hindering the accurate tactile information perception during robotic hand grasping of different objects. This paper developed a fully flexible tactile pressure sensor, using the flexible graphene and silver composites as the sensing element and stretchable electrodes, respectively. As for the structural design of the tactile sensor, the proposed bilayer interlaced bumps can be used to convert external pressure into the stretching of graphene composites. The fabricated tactile sensor exhibits a high sensing performance, including relatively high sensitivity (up to 3.40% kPa−1), wide sensing range (200 kPa), good dynamic response, and considerable repeatability. Then, the tactile sensor has been integrated with the robotic hand finger, and the grasping results have indicated the capability of using the tactile sensor to detect the distributed pressure during grasping applications. The grasping motions, properties of the objects can be further analyzed through the acquired tactile information in time and spatial domains, demonstrating the potential applications of the tactile sensor in intelligent robotics and human-machine interfaces.
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Gao, Jinxia, Longjun Liu, Peng Gao, Yihuan Zheng, Wenxuan Hou, and Junhui Wang. "Intelligent Occlusion Stabilization Splint with Stress-Sensor System for Bruxism Diagnosis and Treatment." Sensors 20, no. 1 (December 22, 2019): 89. http://dx.doi.org/10.3390/s20010089.

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Bruxism is a masticatory muscle activity characterized by high prevalence, widespread complications, and serious consequences but without specific guidelines for its diagnosis and treatment. Although occlusal force-based biofeedback therapy is proven to be safe, effective, and with few side effects in improving bruxism, its mechanism and key technologies remain unclear. The purpose of this study was to research a real-time, quantitative, intelligent, and precise force-based biofeedback detection device based on artificial intelligence (AI) algorithms for the diagnosis and treatment of bruxism. Stress sensors were integrated and embedded into a resin-based occlusion stabilization splint by using a layering technique (sandwich method). The sensor system mainly consisted of a pressure signal acquisition module, a main control module, and a server terminal. A machine learning algorithm was leveraged for occlusal force data processing and parameter configuration. This study implemented a sensor prototype system from scratch to fully evaluate each component of the intelligent splint. Experiment results showed reasonable parameter metrics for the sensors system and demonstrated the feasibility of the proposed scheme for bruxism treatment. The intelligent occlusion stabilization splint with a stress sensor system is a promising approach to bruxism diagnosis and treatment.
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Raj, Deepak S., and Ramesh H. S. Babu. "IFAA: An Intelligent Framework Aware Algorithm to Determine the Boundary of Area under Attack in Military Surveillance and Reconnaissance WSN." Revue d'Intelligence Artificielle 36, no. 4 (August 31, 2022): 635–40. http://dx.doi.org/10.18280/ria.360417.

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Wireless sensor networks (WSNs) have proven effective in military applications of surveillance and reconnaissance. Sensors capable of detecting pressure, temperature, movement and presence of specific chemicals are deployed in such applications. Traditionally, sensor data is collected and transferred to a centralized high-capacity node or control station. Analysis of data is carried out at such centralized facilities. Information or intelligence gathered from sensor data after analysis is used to generate control and management commands that are relayed back to sensor nodes. The situation is analogous to an actual wartime scenario where soldiers who are on the field are equivalent to the sensors. Soldiers observe and sense the situation and communicate their observations to the decision maker who is stationed in the control tent. On gathering field information, the decision maker analyses the data and arrives at his decision which is again communicated to the soldiers on the field. Soldiers as well as sensors are not placed illogically or randomly but intentionally and strategically. Observations made on the field ultimately affect how the soldiers or sensors continue to function. Intelligence gained on the field ultimately gets used on the field itself. Our attempt is to observe, analyze and apply intelligence on the field itself. This work proposes an intelligent algorithm that is aware of the sensor network topology, analyses sensor data within the network and uses the network framework to arrive at usable intelligence. Locally generated intelligence avoids communication to and from the command/control and adds value to military surveillance and reconnaissance applications of WSN. Intelligent sensor management allows us to use just the necessary number of sensors while saving resources on otherwise redundant expenditure. In the present work we have designed and applied a dynamic boundary computation algorithm to determine the boundary of the area under attack. We have compared the results of simulation experiments incorporating the proposed algorithm against a control experiment without the algorithm.
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Dissertations / Theses on the topic "Intelligent pressure sensor"

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Rathore, Pradeep Kumar. "Cmos compatible mems structures for pressure sensing applications." Thesis, IIT Delhi, 2015. http://localhost:8080/iit/handle/2074/6894.

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De, Clerck Albrey Paul. "Modeling the Thermal Performance of an Intelligent MEMS Pressure Sensor with Self-Calibration Capabilities." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/100688.

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Recent industry trends toward more complex and interconnected systems have increased the demand for more reliable pressure sensors. One of the best methods to ensure reliability is by regularly calibrating the sensor, checking its functionality and accuracy. By integrating a micro-actuator with a pressure sensor, the sensor can self-calibrate, eliminating the complexities and costs associated with traditional sensor calibration methods. The present work is focused on furthering understanding and improving the thermal performance of a thermopneumatic actuated self-calibrating pressure sensor. A transient numerical model was developed in ANSYS and was calibrated using experimental testing data. The model provided insights into the sensor's performance not previously observed in experimental testing, such as the temperature gradient within the sensor and its implications. Furthermore, the model was utilized for two design studies. First, the sensor's inefficiencies were studied, and it was found that a substrate with low thermal conductivity and high thermal diffusivity is ideal for both the sensor's efficiency and a faster transient response time. The second design study showed that decreasing the size of the sealed reference cavity, decreases power consumption and transient response time. The study also showed that decreasing the cavity base dimension has a larger effect on decreasing power consumption and response time. Overall, the present work increases understanding of the self-calibrating pressure sensor and provides insight into potential design improvements, moving closer to true self-calibrating pressure sensors.
Master of Science
Pressure sensors are used in most engineering applications, and the demand is ever increasing due to emerging fields such as the Internet of things (IOT), automations, and autonomy. One drawback of current pressures sensor technology is their need to be calibrated, ensuring accuracy and function. Sensor calibration requires equipment, trained personnel, and must be done regularly, resulting in significate costs. Borrowing technology, methods, and materials from the integrated circuit industry, the costs of sensor calibration can be addressed by the development of an intelligent MEMS (micro-electromechanical system) pressure sensor with self-calibration capabilities. The self-calibrating capability is achieved by combining a micro-actuator and a micro- pressures sensor into one system. This work focuses on complementing previously obtained experimental testing data with a thermal finite element model to provide a deeper understanding and insight. The model is implemented in the commercial software ANSYS and model uncertainties were addressed via model calibration. The model revealed a temperature gradient within the sensor, and insight into its potential effects. The model is also used as a design tool to reduce energy inefficiencies, decrease the time it takes the sensor to respond, and to study the effects of reducing the sensor size. The studies showed that the power consumption can potentially be decreased up to 92% and the response time can be decreased up to 99% by changing the sensor's substrate material. Furthermore, by halving the sensor reference cavity size, the cavity temperature can be increased by 45% and the time for the sensor to respond can be decrease by 59%.
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Liang, Fang-Cheng. "Nouvelle application multifonctionnelle pour textiles intelligents dans les dispositifs optoélectroniques." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAV020.

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A ce jour, le développement de textiles intelligents, de peaux artificielles, de capteurs de paramètres environnementaux et de composés optoélectroniques souples ; qui nécessite des innovations à la fois dans la synthèse des matériaux, leur conception mécanique mais aussi, à l’échelle industrielle, en stratégie de production ; présente un intérêt majeur dans le domaine du prêt-à-porter connecté. D’un point de vue mécanique, l’obtention des propriétés de flexibilité et d’étirabilité à faible coût, via un procédé simple, au sein d’un matériau léger et capable de s’expandre sur de grandes surfaces est un prérequis essentiel pour incorporer des dispositifs optoélectroniques au sein des objets connectés portables. Parmi les différents procédés couramment utilisés, l’electrospinning est une technique simple, facilement adaptable et peu onéreuse qui permet un ajustement fin et flexible des morphologies de fibres, l’assemblage de plusieurs nanofibres fonctionnelles et une production en continue à haut débit. Ces multiples avantages sont à l’origine des nombreux travaux concernant l’utilisation de l’electrospinning dans le domaine du prêt-à-porter électronique et/ou connecté. Cependant, il est nécessaire de développer des projets innovants pour ce secteur qui incluent des capteurs détectant des paramètres environnementaux (pH, température), des chemo-capteurs colorimétriques à large spectre (full-color), des composantes électroniques étirables et capteurs tactiles
To date, the development of smart textiles, artificial skins, environmental sensory devices, and flexible/stretchable optoelectronics involve the innovation of material synthesis, mechanical design, and fabrication strategies have attracted considerable attention in wearable displays. The mechanically flexible and stretchable functions with cost-effective, facile, lightweight, and large-area expandability are essential modules to fabricate the optoelectronic devices in various wearable display applications. Among them, electrospinning is an easy, versatile, and inexpensive technique enables flexible morphology tuning, assembling various functional nanofibers, and high-throughput continuous production has motivated extensive studies on wearable electronics applications. Therefore, it is necessary to develop innovative projects including the environment-sensing elements with pH-sensing dependency, temperature-sensitive, full-color switchable chemosensors, stretchable electronics, and tactile sensors for various wearable electronics applications
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Pešl, Jiří. "Implementace rozhraní IO-Link do snímačů tlaku." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-220337.

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This thesis is concerned with the measurement of physical quantities. Thesis describes sensors in general terms and requirements, which delivers latest trends in the field of automation. The main theme of this thesis is sensors for meassuring pressure, and their connection to the automation chain. In the next part thesis describes the design of the converter for connecting SMART sensors for pressure measuring, that use HART communication protocol, to the automation systems managed by a communication protocol IO-link. First will be described in detail various communication standards by their ISO OSI model, and on this theoretical basis will be based design of converter between communication protocol HART and communication protocol IO-Link. The last part will be Designed software for this converter.
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Singh, K. "Ann based intelligent pressure sensor in noisy environment." Thesis, 2014. http://ethesis.nitrkl.ac.in/5594/1/E-51.pdf.

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There are so many problems that arise due to nonlinearity, direct digital readout is one of them i.e. with the help of such devices taking the direct digital readout is not possible. Therefore we are bound to operate the instrument in their linear range of the characteristics only ,in other words we can say that the usable range of the instrument is getting restricted due to this problem. not only the usable range ,but also the accuracy of the instrument is affected if we are not able to use the full range of the instrument. One more factor important to mention is the variation of nonlinearity from instrument to instrument place to place and time to time ,sometimes it depends on some uncertain factors which are not possible to predict. Here capacitive pressure sensor(CPS) is the topic of discussion for adaptive linearization. We can introduce an intelligent inverse model in series with the nonlinear instrument or a sensor to reduce the nonlinearity present there. A switched capacitor circuit (SCC) is used to convert the change in capacitance of the CPS. Because of the change in applied pressure the capacitance of the CPS changes, this change in capacitance of the CPS due to applied pressure is converted into proportional voltage which can then be applied to an ANN model to estimate the pressure applied. This model gives satisfactory performance for wide temperature range (-20 to 70 ) and signal to noise ratio of 40 dB and above.
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WEI, NG CHOON, and 黃俊衛. "An Intelligent Sensor with Vibrations and Pressures Measurement for Automated Polishing Machine Monitoring." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9vh4rk.

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碩士
南臺科技大學
電機工程系
107
The surface grinding and polishing process in traditional industries mainly rely on manual operation, which is not only inefficient, but also harmful to the labors’ health with the noise and particle pollution generated. With the development of Industry 4.0, robotic arms are widely used in all kinds of factories. In order to improve the accuracy and reproducibility of automated polishing machines in production process, functions like real-time monitoring and anomaly detection are added, and it is expected to apply robotic arms to polishing and grinding process, at the lowest manufacturing cost. This study proposes an intelligent sensing device for automated polishing machine with vibration and pressure sensor which can senses and responds instantly during grinding process. In this study, we integrate high sensitivity inertial measurement unit (IMU) sensor, pressure sensor, and micro-chip to implement the sense detection of the robotic arm in this study. When the vibration is over the setting limit or getting physical impact, the robot arm will stop working immediately and return to the safe point, waiting for inspection of the problem by staff. Through the Bluetooth 4.0 wireless transmission, the feedback of vibration signal will be transmitted to the main control system instantly. In addition, during the grinding process, the pressure between the processed product and the grinder is very important. The proposed system uses a pressure sensing method to precisely control the touch pressure between the processed product and the grinder within a range of 10 Newton (N), thereby improving the production process and the yield rate. The pressure sensor had a high correlation with the value of standard pressure measuring instruments (R2=0.9946). During polishing, the device can be measured on the actual robotic arm by using 3 different work pieces. The average values of the force at 20N, 15N, 10N and 6N were 20±0.04, 15±0.10, 10±0.04, 6±0.07. In order to verify the stability of the IMU measurement, we set the IMU device on the vibration platform and simulate a frequency from 5Hz to 30 Hz. The experiment results show that the device of the study can indeed detect the frequency on the vibration platform. When in actual used in polishing process of the robotic arm, the contact pressure during the polishing compared with the experimental data of the measuring instruments, and the error rate is within 1%. In the abnormal word pieces experiment, the robotic arm does sound an alarm every time after 10 polishing. In order to facilitate the operator do the correction, this study also designed a separate battery, through the battery device can continuously use up to 6 hours of endurance. We hope that through the integration of the proposed device and robot arms, we can decrease manual operation and be helpful with the vision of robotic arm monitoring and smart manufacturing.
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Books on the topic "Intelligent pressure sensor"

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Healy, Susan D. Adaptation and the Brain. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780199546756.001.0001.

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The rationale for this work is to make some sort of sense of the seeming myriad of adaptive explanations for why vertebrate brains vary in size. The role that natural selection has played in brain size has been addressed using the comparative method, which allows identification of evolutionary patterns across species. One starting assumption is that brain size is a useful proxy for intelligence and therefore that large-brained animals are more intelligent than smaller-brained animals. Five classes of selection pressure form the majority of explanations: ecology, technology, innovation, sex, and sociality. After chapters in which I describe the difficulties of measuring both brain size and intelligence (cognition), I address the evidence for each of the five factors in turn, reaching the conclusion that although ecology provides the best explanations for variation in the size of brain regions, none of the factors yet offers a robust and compelling explanation for variation in whole brain size. I end by providing the steps I consider necessary to reach such an explanation, steps that I suggest are feasible, if challenging.
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Sethna, Razeshta. The Cost of Free Speech. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190656546.003.0009.

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This chapter by Razeshta Sethna examines her experiences as a print journalist for the Dawn Media Group, and a presenter for Geo TV and Dawn TV. Sethna reveals professional tensions between levels of editorial control and the failure of newspaper owners and editors to protect journalists, and she illuminates ways that fear works to prevent journalists from protesting against the murder of colleagues. She unravels connections between violent politics, state violence, and the media. These involve the Muttahida Qaumi Movement party (MQM), whose militants have burnt the city’s newspaper offices and threatened journalists—and the military and Inter-Services Intelligence (ISI) agencies who pressure senior editors to censor views considered too liberal, keep silence around the state’s repression of democratic freedoms and human rights, and the ‘disappearances’ of activists in Balochistan. Notwithstanding, the proliferation of Karachi’s television media since 2007 has positioned journalists at the forefront of open criticism against violence.
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Olivér, Gábor. CRITIQUE OF THE ASILOMAR AI PRINCIPLES = AZ ASILOMARI ELVEK KRITIKÁJA. GeniaNet Bt., 2022. http://dx.doi.org/10.15170/cotaap-2022.

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The intelligent man with consciousness is the pinnacle of the evolution of matter that we know. So far. We know, however, that evolution will not stop. Although with sections and dead ends of varying lengths, it is moving towards the increasing complexity of organizations, so it is probably not the human in today's sense is its end point. The development of artificial intelligences that we are planning, for example, may meet both our intentions and the criteria of evolution, but as a novelty it also holds the possibility of a future without us. The latter, in turn, creates tension between the species-maintainer desires of homo sapiens and the unknown future course of evolution. The Asilomar principles seek to alleviate this tension by limiting the development of artificial intelligences. However, as technological advances lead to an increase in autonomy, this is at most a plan for time-gaining. In addition to the Asilomar program, then, there is a need for a “Second Foundation” that can reconcile the future of man not only with artificial intelligences but also with evolution. If we want to survive, the evolutionary adaptation of homo sapiens could really ease the pressure of technological determinism on us. At the 2017 International Conference on Artificial Intelligence Safety Technology in Asilomar, participants signed an agreement.[1] They were of the opinion that the development of artificial intelligences should be controlled. More specifically, to limit the future development of algorithms in a way that suits for homo sapiens. In doing so, they sought to meet the future challenges posed by autonomous technologies.[2] The question is whether the Asilomar goal is a real possibility or just a formulation of desires? In the following, after a brief introduction to the ability or get know and formability of the future, I examine the truthfulness of the Asilomar program.
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Clarke, Steve, Hazem Zohny, and Julian Savulescu, eds. Rethinking Moral Status. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192894076.001.0001.

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Common-sense morality implicitly assumes that reasonably clear distinctions can be drawn between the ‘full’ moral status usually attributed to ordinary adult humans, the partial moral status attributed to non-human animals, and the absence of moral status, usually ascribed to machines and other artefacts. These assumptions were always subject to challenge; but they now come under renewed pressure because there are beings we are now able to create, and beings we may soon be able to create, which blur traditional distinctions between humans, non-human animals, and non-biological beings. Examples are human non-human chimeras, cyborgs, human brain organoids, post-humans, human minds that have been uploaded into computers and onto the internet, and artificial intelligence. It is far from clear what moral status we should attribute to any of these beings. While commonsensical views of moral status have always been questioned, the latest technological developments recast many of the questions and raise additional objections. There are a number of ways we could respond, such as revising our ordinary suppositions about the prerequisites for full moral status. We might also reject the assumption that there is a sharp distinction between full and partial moral status. The present volume provides a forum for philosophical reflection about the usual presuppositions and intuitions about moral status, especially in light of the aforementioned recent and emerging technological advances.
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Book chapters on the topic "Intelligent pressure sensor"

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Lata, Anamika, and Nirupama Mandal. "Design and Development of Bending Sensor-Based Pressure Transducer." In Algorithms for Intelligent Systems, 139–44. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3368-3_14.

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Gu, Yumao, Yuanzhen Dai, Yang Liu, and Xiaoping Chen. "Electronic Artificial Skin for Application in Pressure Sensor." In Advances in Intelligent Systems and Computing, 433–39. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16841-8_40.

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Shao, Guoyou, Meng Yuan, and Ping Liu. "Performance Analysis of Pressure Sensor and Finite Element Simulation." In Advances in Intelligent and Soft Computing, 203–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25194-8_24.

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Zhao, Lin, Jiqiang Wang, Long Jiang, and Lianqing Li. "Optical Fiber Pressure Sensor Based on Corrugated Diaphragm Structure." In Advances in Intelligent Systems and Computing, 741–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34387-3_91.

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Wang, Huabing, and Changyuan Wan. "Research on Sleeping Posture Recognition Method Based on Pressure Sensor." In Advances in Intelligent Systems and Computing, 235–44. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20451-8_23.

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Liu, Ping, Guoyou Shao, Meng Yuan, and Ying Chen. "Electrical Properties and Mechanics Performance Analysis of MEMS Pressure Sensor." In Advances in Intelligent and Soft Computing, 217–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25194-8_26.

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Jasiulek, Dariusz. "Concept of Sensor for Mining Machines Powered by Pressure Changes." In Advances in Intelligent Systems and Computing, 175–83. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15857-6_18.

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Anakal, Sudhir, and P. Sandhya. "Low-Cost IoT Based Spirometer Device with Silicon Pressure Sensor." In Advances in Intelligent Systems and Computing, 153–61. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2475-2_14.

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Chen, Xiao, Cong Zhang, Chuang Ma, Haixiao Liu, Yanling Zheng, Yi Jiang, Yuanyuan Zu, and Jianwei Niu. "Evaluation of Helmet Comfort Based on Flexible Pressure Sensor Matrix." In Advances in Intelligent Systems and Computing, 833–39. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11051-2_127.

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Upadhyay, Shivam, Vijay Laxmi Kalyani, and Chandraprabha Charan. "Designing and Optimization of Nano-ring Resonator-Based Photonic Pressure Sensor." In Advances in Intelligent Systems and Computing, 269–78. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0129-1_29.

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Conference papers on the topic "Intelligent pressure sensor"

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Khaleghian, Seyedmeysam, and Saied Taheri. "Intelligent Tire Based Pressure Monitoring Algorithm." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71048.

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Intelligent tire is a relatively new technology that provides useful tire-road contact information by directly monitoring the interaction between the tire and the road. Different types of sensors are attached to the tire inner-liner for this purpose; the sensor data then will be used to estimate the tire-road contact parameters as well as to monitor the tire conditions. In this study, a tri-axial accelerometer was used and a two-steps intelligent tire based pressure monitoring algorithm was developed in this study. First, the angular velocity of the wheel was estimated based on the parameters extracted from the acceleration components through a trained neural network. Then the estimated wheel angular velocity from the first step was used along with the acceleration components to estimate the power of radial acceleration. The estimated power was compared to the actual one and the tire pressure condition was judged to be “normal” or “low”. To train the neural networks, the experimental data collected using an instrumented vehicle was used. A VW Jetta 2003 was used for this purpose and instrumented with appropriate sensors; intelligent tires, steering wheel sensor to measure the steering angle, steering velocity and steering torque, encoders to measure the angular speed of the wheels and an Inertial Measurement Unit (IMU) to measure the vehicle linear and angular acceleration. Another set of experimental data with different tire pressures and different vehicle velocity was then used to validate the algorithm; good agreements were observed between the estimated tire pressures and the actual ones.
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Yang Chuan and Li Chen. "The intelligent pressure sensor system based on DSP." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579148.

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Huang, Ruey-Shing S., Hsien-Chung Lee, Mark Gross, and C. M. Horwitz. "Novel cantilever-beam field-emission pressure sensor." In Measurement Technology and Intelligent Instruments, edited by Li Zhu. SPIE, 1993. http://dx.doi.org/10.1117/12.156490.

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Mahmood, Usman, Adel Al-Jumaily, and Moha'med Al-Jaafreh. "Type-2 Fuzzy Classification of Blood Pressure Parameters." In 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information. IEEE, 2007. http://dx.doi.org/10.1109/issnip.2007.4496910.

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Buzi, Erjola, Huseyin Rahmi Seren, Max Deffenbaugh, Ahmed Bukhamseen, and Mohamed Larbi Zeghlache. "Sensor Ball: Autonomous, Intelligent Logging Platform." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/31149-ms.

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Abstract Recent developments in automation and electronics have enabled modernization and miniaturization of oilfield instruments. One product of these trends is our autonomous logging platform called "Sensor Ball". The Sensor Ball is a handheld, untethered logging tool that one person can deploy and recover from a pressurized well with no special equipment and crews (Deffenbaugh, 2016). The only tool needed is a wrench to open the cap of the wellhead. The operator puts the sensor ball in through the cap, then sequentially opens and closes the crown and master valves. This process takes only a few minutes. Once clear of the well head, the Sensor Ball falls down the well, logging data as it travels downhole. During this time, all the wellhead valves are closed and there is no need for the field crew to stay at the well site. We present data from recent Sensor Ball deployments to log pressure and temperature profiles and bottom-hole pressures. Depth information is provided by a novel onboard sensor that detects the connections between casing or tubing joints like a casing collar locator. A small dissolvable metal weight is magnetically attached to the housing and is sized to make the Sensor Ball descend at about 1 foot per second. At the desired depth, Sensor Ball drops the weight to become buoyant in the wellbore fluids and return to the surface. As it returns, it repeats the logging measurements, storing temperature and pressure data in its internal memory. After a typical four-to-eight hour mission, the operator returns to the well, opens and closes the well head valves in reverse order, removes the cap and takes out the Sensor Ball. The logged data are downloaded wirelessly to a laptop or cell phone. A lightweight, syntactic foam housing provides buoyancy and protects the electronics from the well fluids. The small thermal mass of the housing minimizes the temperature distortion in the downhole environment. This miniaturized technology simplified logging to a one-person job and shortened the time at the well from multiple hours to a few minutes. This work describes a novel method of retrieving downhole data, which is a practical and inexpensive alternative to wireline or slickline logging and permanently-installed sensors (Deffenbaugh, 2017). In this paper we present the system design and our recent field results from vertical and deviated wells. We also describe a new application of the Sensor Ball where we perform extended bottom-hole pressure measurements in addition to logging temperature and pressure along the wellbore.
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Krall, Christoph, and Pascal Nicolay. "A completely wireless and passive low-pressure sensor." In 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2015. http://dx.doi.org/10.1109/issnip.2015.7106919.

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Salibindla, S., B. Ripoche, D. T. H. Lai, and S. Maas. "Characterization of a new flexible pressure sensor for body sensor networks." In 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2013. http://dx.doi.org/10.1109/issnip.2013.6529758.

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Adelsberger, R., and G. Troster. "PIMU: A wireless pressure-sensing IMU." In 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2013. http://dx.doi.org/10.1109/issnip.2013.6529801.

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Wang, Zhongming. "Two-Point Calibration Method for Intelligent Ceramic Pressure Sensor." In 2022 2nd International Conference on Networking, Communications and Information Technology (NetCIT). IEEE, 2022. http://dx.doi.org/10.1109/netcit57419.2022.00041.

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Ji, Tao, Qingle Pang, and Xinyun Liu. "An Intelligent Pressure Sensor Using Rough Set Neural Networks." In 2006 IEEE International Conference on Information Acquisition. IEEE, 2006. http://dx.doi.org/10.1109/icia.2006.305816.

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Reports on the topic "Intelligent pressure sensor"

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Delwiche, Michael, Boaz Zion, Robert BonDurant, Judith Rishpon, Ephraim Maltz, and Miriam Rosenberg. Biosensors for On-Line Measurement of Reproductive Hormones and Milk Proteins to Improve Dairy Herd Management. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7573998.bard.

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The original objectives of this research project were to: (1) develop immunoassays, photometric sensors, and electrochemical sensors for real-time measurement of progesterone and estradiol in milk, (2) develop biosensors for measurement of caseins in milk, and (3) integrate and adapt these sensor technologies to create an automated electronic sensing system for operation in dairy parlors during milking. The overall direction of research was not changed, although the work was expanded to include other milk components such as urea and lactose. A second generation biosensor for on-line measurement of bovine progesterone was designed and tested. Anti-progesterone antibody was coated on small disks of nitrocellulose membrane, which were inserted in the reaction chamber prior to testing, and a real-time assay was developed. The biosensor was designed using micropumps and valves under computer control, and assayed fluid volumes on the order of 1 ml. An automated sampler was designed to draw a test volume of milk from the long milk tube using a 4-way pinch valve. The system could execute a measurement cycle in about 10 min. Progesterone could be measured at concentrations low enough to distinguish luteal-phase from follicular-phase cows. The potential of the sensor to detect actual ovulatory events was compared with standard methods of estrus detection, including human observation and an activity monitor. The biosensor correctly identified all ovulatory events during its testperiod, but the variability at low progesterone concentrations triggered some false positives. Direct on-line measurement and intelligent interpretation of reproductive hormone profiles offers the potential for substantial improvement in reproductive management. A simple potentiometric method for measurement of milk protein was developed and tested. The method was based on the fact that proteins bind iodine. When proteins are added to a solution of the redox couple iodine/iodide (I-I2), the concentration of free iodine is changed and, as a consequence, the potential between two electrodes immersed in the solution is changed. The method worked well with analytical casein solutions and accurately measured concentrations of analytical caseins added to fresh milk. When tested with actual milk samples, the correlation between the sensor readings and the reference lab results (of both total proteins and casein content) was inferior to that of analytical casein. A number of different technologies were explored for the analysis of milk urea, and a manometric technique was selected for the final design. In the new sensor, urea in the sample was hydrolyzed to ammonium and carbonate by the enzyme urease, and subsequent shaking of the sample with citric acid in a sealed cell allowed urea to be estimated as a change in partial pressure of carbon dioxide. The pressure change in the cell was measured with a miniature piezoresistive pressure sensor, and effects of background dissolved gases and vapor pressures were corrected for by repeating the measurement of pressure developed in the sample without the addition of urease. Results were accurate in the physiological range of milk, the assay was faster than the typical milking period, and no toxic reagents were required. A sampling device was designed and built to passively draw milk from the long milk tube in the parlor. An electrochemical sensor for lactose was developed starting with a three-cascaded-enzyme sensor, evolving into two enzymes and CO2[Fe (CN)6] as a mediator, and then into a microflow injection system using poly-osmium modified screen-printed electrodes. The sensor was designed to serve multiple milking positions, using a manifold valve, a sampling valve, and two pumps. Disposable screen-printed electrodes with enzymatic membranes were used. The sensor was optimized for electrode coating components, flow rate, pH, and sample size, and the results correlated well (r2= 0.967) with known lactose concentrations.
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