다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

A strain sensor has been fabricated from a polymer nanocomposite with multiwalled carbon nanotube (MWNT) fillers. The piezoresistivity
of this nanocomposite strain sensor has been investigated based on an improved three-dimensional (3D) statistical resistor network
model incorporating the tunneling effect between the neighboring carbon nanotubes (CNTs), and a fiber reorientation model. The
numerical results agree very well with the experimental measurements. As compared with traditional strain gauges, much higher sensitivity
can be obtained in the nanocomposite sensors when the volume fraction of CNT is close to the percolation threshold. For a small
CNT volume fraction, weak nonlinear piezoresistivity is observed both experimentally and from numerical simulation. The tunneling
effect is considered to be the principal mechanism of the sensor under small strains.

Tunneling effect in a polymer/carbon nanotube nanocompositestrain sensor

Machine learning prediction of mechanical properties of concrete: Critical review

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

https://www.mdpi.com/1424-8220/20/11/3113/pdf

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates

Compressive strength prediction of recycled concrete based on deep learning

Novel soil environment monitoring system based on RFID sensor and LoRa

Numerous studies show that power semiconductors are significant contributors to the overall failure rate of pulsewidth modulation (PWM) voltage source rectifier (VSR) systems. Aimed at this problem, this paper presents an improved diagnosis system for open-circuit faults of a three-phase PWM VSR based on ac current distortion characteristics, which consists of a diagnosis module, a register module, and a decision module. The diagnostic module employs a hysteresis comparator to judge the change trend of the currents, and thereby diagnoses the single open-circuit (OC) faults. The register module is employed to achieve multi-switch OC fault diagnosis, and the decision module is used to output the final result. The proposed method extends the previous work to multi-switch OC fault conditions as well as improves the diagnosis accuracy. The experimental results show that the proposed method can correctly achieve fault diagnosis, including 6 kinds of single-switch faults and 15 kinds of different double-switch faults. Additionally, the proposed method also shows superior anti-interference performance and high robustness under abrupt load transient conditions, different power factor conditions, unbalanced and/or distorted grid voltage conditions, and different ac filter structure conditions. Furthermore, the average diagnostic time of this method is only 3.68 ms.

An Improved Open-Switch Fault Diagnosis Technique of a PWM Voltage Source Rectifier Based on Current Distortion

This paper presents a passive wireless humidity sensor for concrete monitoring. After discussing the transmission of electromagnetic wave in concrete, a novel architecture of wireless humidity sensor, based on Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) technology, is proposed for low-power application. The humidity sensor utilizes the top metal layer to form the interdigitated electrodes, which were then filled with polyimide as the humidity sensing layer. The sensor interface converts the humidity capacitance into a digital signal in the frequency domain. A two-stage rectifier adopts a dynamic bias-voltage generator to boost the effective gate-source voltage of the switches in differential-drive architecture. The clock generator employs a novel structure to reduce the internal voltage swing. The measurement results show that our proposed wireless humidity can achieve a high linearity with a normalized sensitivity of 0.55% %RH at 20 °C. Despite the high losses of concrete, the proposed wireless humidity sensor achieves reliable communication performances in passive mode. The maximum operating distance is 0.52 m when the proposed wireless sensor is embedded into the concrete at the depth of 8 cm. The measured results are highly consistent with the results measured by traditional methods.

A Novel Passive Wireless Sensor for Concrete Humidity Monitoring

This paper firstly introduces the importance of temperature control in concrete measurement, then a passive radio frequency identification (RFID) sensor tag embedded for concrete temperature monitoring is presented. In order to reduce the influences of concrete electromagnetic parameters during the drying process, a T-type antenna is proposed to measure the concrete temperature at the required depth. The proposed RFID sensor tag is based on the EPC generation-2 ultra-high frequency (UHF) communication protocol and operates in passive mode. The temperature sensor can convert the sensor signals to corresponding digital signals without an external reference clock due to the adoption of phase-locked loop (PLL)-based architecture. Laboratory experimentation and on-site testing demonstrate that our sensor tag embedded in concrete can provide reliable communication performance in passive mode. The maximum communicating distance between reader and tag is 7 m at the operating frequency of 915 MHz and the tested results show high consistency with the results tested by a thermocouple.

/pdf/novel-concrete-temperature-monitoring-method-based-on-an-1gk54ntayl.pdf

Fangming Deng

Papers

Compressive strength prediction of recycled concrete based on deep learning

Novel soil environment monitoring system based on RFID sensor and LoRa

An Improved Open-Switch Fault Diagnosis Technique of a PWM Voltage Source Rectifier Based on Current Distortion

A Novel Passive Wireless Sensor for Concrete Humidity Monitoring

Novel Concrete Temperature Monitoring Method Based on an Embedded Passive RFID Sensor Tag.