Remote Measurement System Research Articles

MOVEMENT-CONTROLLED STREET LIGHTING SYSTEM WITH MINIMAL POWER CONSUMPTION

Energy consumption in the public lighting system represents the largest part of the energy usage of a community, while the maintenance and operation of the system are a considerable expense for every city. This paper focuses on products and innovative components for street lighting, proposing a consumption-reducing solution based on an intelligent system for remote measurements and control with dimming technologies for high-intensity discharge (HID) lamps. The results indicate that considerable energy savings are achieved and the service life of the lamps is extended. A movement-controlled street light system is controlled according to the specific mode. The modes are controlled by two sensors, which are the light-dependent resistor (LDR) and the infrared (IR) sensor. This system can automatically turn on and off the lights according to traffic flow. It operates at night, and the focus is only on the one-way road at a junction. The street light will be on when there is at least one road user; otherwise, it will turn off. This design can save a great deal of energy compared to conventional street lights that keep a light throughout the night. Moreover, the maintenance cost can be reduced, and the lifespan of the system will increase. As a result, the system has been successfully designed and implemented as a model system.

Multipoint temperature measurement system composed of fiber-optic Fabry–Perot interference sensors and a wavelength-division multiplexing filter

A multipoint optical-fiber remote temperature measurement system was developed using reflection-type sensors consisting of a Fabry–Perot interference (FPI) structure with good temperature characteristics combined with a wavelength-division multiplexing (WDM) filter. The FPI sensor was fabricated using a short temperature-sensing region sandwiched between single-mode fibers. FPI optical fibers and a WDM filter functioned as the temperature sensors and wavelength-selective optical source using an amplified spontaneous emission light source, respectively. This system was operated in a dense WDM configuration using an arrayed waveguide wavelength filter.

Design of single-laser remote methane gas measurement system with distance measurement based on time-division multiplexing technology

Limited to the principle of Tunable Diode Laser Absorption Spectroscopy (TDLAS) technology, the existing open-path laser remote methane measurement system can only measure the integral concentration, and it needs to be matched with a laser rangefinder to complete the calculation of the average concentration, which increases the complexity of the system. To solve this problem, by driving the laser diode (LD) in time-division multiplexing mode, we complete the integral concentration of gas and distance measurement with the use of a single LD. We complete the design of the laser signal transceiver structure and finish the corresponding hardware circuit design, including the LD driving circuit, frequency generation, mixer circuit, and optoelectronic signal receiving circuit. The experiment shows the measurement error of the system is less than 2.5% while measuring the average concentration in the case of different concentrations and distances, and the system greatly reduces the system complexity while satisfying the performance requirements.

Autonomous Sensor System for Low-Capacity Wind Turbine Blade Vibration Measurement.

This paper presents the design, implementation, and validation of an on-blade sensor system for remote vibration measurement for low-capacity wind turbines. The autonomous sensor system was deployed on three wind turbines, with one of them operating in harsh weather conditions in the far south of Chile. The system recorded the acceleration response of the blades in the flapwise and edgewise directions, data that could be used for extracting the dynamic characteristics of the blades, information useful for damage diagnosis and prognosis. The proposed sensor system demonstrated reliable data acquisition and transmission from wind turbines in remote locations, proving the ability to create a fully autonomous system capable of recording data for monitoring and evaluating the state of health of wind turbine blades for extended periods without human intervention. The data collected by the sensor system presented in this study can serve as a foundation for developing vibration-based strategies for real-time structural health monitoring.

Remote sensor measurement system for sub-centimeter structural change detection

This work presents an autonomous remote sensor measurement system to monitor structural change detection. We utilized a portable network analyzer as a core component of a ground-based synthetic aperture radar to achieve structural monitoring. First, we realized a 2-meter rail apparatus, where antennas and radio frequency components of the radar can move, to obtain a synthetic radar aperture. Then, we develop signal processing algorithms to obtain radar images and to perform change detection. Although the range resolution of our system is 15 cm due to the frequency bandwidth of the radar, we have successfully developed an interferometric change detection technique to achieve up to 440μm change detection of the monitored structure.

Integration of Multiple Sensors into an ROV for Remote Measurement in the Fukushima Daiichi Nuclear Power Station

To measure and analyze the distribution and criticality of submerged fuel debris “in situ” in the decommissioning process of the Fukushima Daiichi Nuclear Power Station (1F), we developed a system for remote measurement using multiple sensors consisting of the neutron sensor, the sonar, and the sub-bottom profiler (SBP) mounted on a small remotely operated underwater vehicle (ROV). First, the locations presumed to contain fuel debris were explored based on the information of the water bottom shape obtained using the sonar and the bottom surface substructure obtained using the SBP. Second, the system identified the location of fuel debris by detecting neutrons emitted by spontaneous fissions of fuel debris using the neutron sensor with diamond elements. Simulated fuel debris was placed at the bottom of a test water tank that simulated the pedestal. In the primary containment vessel (PCV) of 1F, and a verification test of remote measurement was conducted using the ROV mounted on the multiple sensors. The desired results were obtained.

Development of contamination detection system combined with various remote devices

The detection of alpha and beta contamination locations is important for decontaminating nuclear facilities. In the high radiation dose rate environment at the decommissioning sites, the contamination measurement by the workers is not effective. Thus, we developed a remote automatic contamination measurement system using a new scintillator-based detector. A 50 mmφ × 100-μm-thick YAlO3(Ce)(YAP:Ce) scintillator was coupled with a flat panel-type multianode photomultiplier tube. The detector was installed downwards at the bottom of a robot. It has an energy measurement capability, and the energy measurement could discriminate the alpha particles from the beta and 222Rn alpha particles. With the energy information, alpha and beta particles could be identified and mapped simultaneously. In addition, a slow-moving robot could be used to obtain statistically sufficient counts in a single run measurement, allowing the evaluation of surface contamination density using only alpha particles. The remote automatic contamination measurement system will be useful in visualizing the contamination distribution in environments that are inaccessible to workers.

Построение системы измерения параметров подвесных ОПН на базе микроконтроллера с беспроводной передачей данных

The article is aimed at analyzing the possibility of constructing a system for remote measurement of leakage currents, measuring the pulses of current flowing through the surge arrester with the registration of the time of the event with subsequent accumulation and transmission to service personnel. A system for transmitting measured parameters via the ZigBee protocol is constructed. A calculation of the required power supply for continuous operation of the device for more than 20 years is made.

Cloud-based pipe corrosion monitoring using electromechanical impedance instrumented piezoelectric ring sensor

The corrosion of small-diameter pipes has resulted in numerous significant accidents and substantial economic losses in both industrial and civil sectors. This paper presents a wireless smart corrosion monitoring system based on electromechanical impedance (EMI) technology. This system comprises a smart corrosion sensing node (SCSN) designed using EMI principles and a wireless impedance monitoring system (WIMS) for remote impedance measurement. A finite element simulation was conducted to assess the performance of the SCSN under uniform corrosion conditions. Additionally, a comparative test, with and without fluid inside the pipe, was performed to determine the impact of the fluid on test results. To validate the effectiveness of the proposed wireless smart corrosion monitoring node, corrosion monitoring experiments were conducted on pipe specimens equipped with the sensing node. These findings were complemented by separate corrosion experiments, and it was revealed that as corrosion increased, the peak frequency decreased, indicating a strong linear relationship. The study demonstrates that the wireless smart corrosion monitoring node is highly capable of monitoring pipe corrosion in an experimental setting.

A Prediction Model for Remote Lab Courses Designed upon the Principles of Education for Sustainable Development

The COVID-19 pandemic brought about a cease to the physical-presence operation of many laboratory-based university courses. As a response, higher education courses turned into distance learning. Distance education can foster sustainability through resource savings offered by the benefits of technology use. Therefore, there is a necessity to establish a pathway for sustainability practices concerning the increasing distance education enrollment and technological progress. Under the previous concept, this research paper presents a remote lab for the “Data Acquisition Systems” course, delivered during the pandemic as the digital twin of its respective conventional lab. This remote lab was designed on the Education for Sustainable Development (ESD) principles to help students develop critical thinking, problem-solving, and collaboration competencies. This paper aims to develop a concrete framework for identifying factors that critically affect students’ performance during remote lab courses. The analysis is based on students’ engagement data collected by the NI-ELVIS remote lab measurement system during the spring academic semester of 2020 at the University of West Attica, Greece. Furthermore, the paper develops a competent prediction model for students at risk of failing the lab. The findings indicate that content comprehension and theory-exercise familiarization were the main risk factors in the case of the specific remote lab. In detail, a unit increase in content comprehension led to a 2.7 unit decrease in the probability of the risk occurrence. In parallel, a unit increase in theory familiarization through exercises led to a 3.2 unit decrease in the probability of the risk occurrence. The findings also underlined that risk factors such as critical thinking were associated with ESD competencies. Besides this, the benefits of delivering distance-learning labs according to the proposed methodology include environmental benefits by contributing to resource and energy savings since students who are about to fail can be located early and assisted.

Applying Novel Techniques from Physical and Biological Sciences to Life Detection

One of the primary goals of the space exploration community is to unambiguously detect past or present life outside of Earth. As such, a number of so-called life detection technologies, instruments, and approaches have been applied as part of past, current, and future space missions. As astrobiology is a truly interdisciplinary field within the realm of space exploration with major contributions from physical and biological sciences (among others), recently there has been development of a number of relevant techniques from scientific fields that have yet to be fully applied to extraterrestrial life detection. As a culmination of the 2021 Blue Marble Space Institute of Science (BMSIS) Young Scientist Program (YSP), we present a number of techniques drawn from various fields (including, but not limited to, chemistry, materials science, biology, nanotechnology, medical science, astrophysics, and more) that either have been or have the potential to be applied to life detection research. These techniques broadly fall under three categories: instrumentation for in situ measurements of biosignatures within the solar system, calculations or observational techniques for remote measurements of exoplanet biosignatures, and technosignatures. We hope that this primer serves to inspire the field to consider applying more potential technologies from adjacent fields into any of these three categories of life detection.

Concept of a system for remote vibration measurement in electric vehicles driven by rotaring body motors

The article proposes a vibration measurement system for electric vehicles powered by motors with rotating housing. The motors were designed and patented by the author. The presented solution is characterized by low cost, high reliability and minimal power consumption. Block diagrams of the designed system were presented and described in detail.

Photonic remote microwave frequency measurement with a tunable measurement range based on dispersion compensation technology

In this paper, a novel and efficient photonic-assisted remote frequency measurement (RFM) system with a significantly simplified structure and flexible operation range is proposed. In this system, the remote antenna unit (RAU) and the the central station (CS) are separated to ensure the concealment and safety of the signal processing unit. At the RAU, the unknown radio frequency (RF) signal is modulated by a dual-polarization dual-drive Mach–Zehnder modulator (DPol-DMZM). In the CS, changing the dispersion coefficient or length of the dispersion medium, the microwave frequency measurement range of the RFM system can be easily tuned without system reconstruction, and different RAUs located at different places can be controlled to work at the same measurement range. The simulation results show that a frequency measurement over the high frequency range (> 18 GHz) can be achieved with a measurement error better than ± 0.2 GHz. Noteworthy, the impact of the non-ideal factors such as bias drift, intensity noise, phase noise, the equivalent deviation of the polarization beam splitter (PBS), and the dispersion value of the single mode fiber (SMF) are also discussed. It has been proved that they have little influence on the system performance over the high frequency range.

Machine Learning-Based Classification of Human Behaviors and Falls in Restroom via Dual Doppler Radar Measurements.

This study presents a radar-based remote measurement system for classification of human behaviors and falls in restrooms without privacy invasion. Our system uses a dual Doppler radar mounted onto a restroom ceiling and wall. Machine learning methods, including the convolutional neural network (CNN), long short-term memory, support vector machine, and random forest methods, are applied to the Doppler radar data to verify the model’s efficiency and features. Experimental results from 21 participants demonstrated the accurate classification of eight realistic behaviors, including falling. Using the Doppler spectrograms (time–velocity distribution) as the inputs, CNN showed the best results with an overall classification accuracy of 95.6% and 100% fall classification accuracy. We confirmed that these accuracies were better than those achieved by conventional restroom monitoring techniques using thermal sensors and radars. Furthermore, the comparison results of various machine learning methods and cases using each radar’s data show that the higher-order derivative parameters of acceleration and jerk, and the motion information in the horizontal direction are the efficient features for behavior classification in a restroom. These findings indicate that daily restroom monitoring using the proposed radar system accurately recognizes human behaviors and allows early detection of fall accidents.

Remote quantification of methane leaks in the laboratory and in biogas plants

ABSTRACT Gas leaks can occur not only in industrial environments such as refineries but also in biogas plants. The main compound of the emitted biogas is methane, which is a greenhouse gas with a higher global warming potential than carbon dioxide (CO). Considering that the climate change and the reduction of greenhouse gases are becoming more and more important, it is crucial to be able to not only localize but also to quantify methane leakages. Based on the quantification of methane leakages, not only the effect of the effluent gases to the atmosphere but also the economic loss can be estimated. Another aspect is safety: biogas consists of different gases, mainly methane which can create an explosive mixture with air, but also hydrogen sulphide, which is toxic. This motivates remotely measuring the gas emissions. In this paper a remote measurement system and model is presented to estimate the methane volume flow due to a leak based on infrared optical sensors and sensor data fusion. The sensors are calibrated and the system is tested in the laboratory as well as in biogas plants. The laboratory results show that the measurement model allows an estimation of the methane volume flow if the assumptions of the measurement model are met. The highest relative deviations occur for small reference methane volume flows while the absolute deviations are similar. In contrast to the laboratory measurements the results of the measurements in biogas plants show partially high deviations from the reference. These deviations can have multiple reasons which cannot be determined for each single case because many of them occur at the same time. Future work includes improvements in order to reduce the deviations of the methane volume flow estimation. This could be done not only by improving the processing steps of the velocity estimation but also by using more accurate sensors.

Strain Sensing by Electrical Capacitive Variation: From Stretchable Materials to Electronic Interfaces

AbstractSensing the motion of objects and humans is essential for various applications, including human‐machine interfaces. Over the past few years, motion sensing has been extensively studied using capacitive strain sensors that can be utilized for wireless communications. The performance and functionality of capacitive strain sensors have been improved by achieving high sensitivity, large‐area sensing, ultra‐stretchability, and simplicity in design, measurement methods, and wireless integration. This review article highlights recent developments in capacitive strain sensors, including their characteristics and applications. First, the mechanisms and techniques employed to enhance the sensitivity of capacitive strain sensors are reviewed. Then, the notable features of this sensing strategy are highlighted, such as its ability to cover a wide area, properties of the required electronic interface, and sensor implementation inside a remote measurement system via an oscillating circuit. Therefore, a global review of the capacitive strain sensor that has been previously attempted can participate in developing an effective sensing method to meet the market need.

Research on Actual Road Emission Prediction Model of Heavy-Duty Diesel Vehicles Based on OBD Remote Method and Artificial Neural Network

In order to establish a prediction model of PM and NOx emission factors for heavy-duty diesel vehicles under actual road conditions based on OBD remote monitoring and big data, this paper carried out actual road tests on two China V heavy-duty diesel vehicles to obtain transient OBD and emission data by a Portable Emission Measurement System (PEMS) and self-developed On-board Remote Emission Measurement System (OREMS). According to the degree of influence of different parameters in the engine OBD on PM and NOx emissions, the principal component analysis method is used to extract the principal component parameters used to predict the model input, and the construction of a "Heavy-duty Diesel Vehicle Predictive Model based on Remote Monitoring Data and Neural Network Technology". Finally, the predictive model is trained and verified by PEMS test data. The prediction model provides new means and methods for the future development of large-scale heavy-duty diesel vehicle NOx and PM emission predictions under actual road operation.

Development of a walking aid with remote electrocardiogram measurement and global positioning system function

ABSTRACT The aging population has become one of the major issues in the world and in Taiwan. Among many aging diseases, heart disease and dementia are the main problems that affect the daily lives of the elderly and their family members. In this study, we have developed a smart walking aid with remote electrocardiogram (ECG) measurement and global positioning system (GPS) functions. The hardware of the walking aid consists of a brass column sensor with a self-developed ECG sensing circuit, a location module comprising a GPS and antenna, and a wireless transmission module with a microprocessor control unit and a Wi-Fi module. The software includes two parts: a self-developed ECG program for identifying arrhythmia with a human–machine interface to display the results and a self-developed location program integrated with Google Maps. The smart walking aid we have developed in this study was aiming to help family members or medical staff to monitor the physical conditions of their elderly family or patients who suffer from heart disease or dementia and identify their location when they experience disorientation.

The Stability Analysis of Potentiometric Urea Biosensor Under Microfluidic System and Remote Measurement

Stability is one of the most important characteristics of the potentiometric urea biosensors; however, it has rarely been discussed in previous literature. In this article, the stability of the potentiometric urea biosensor based on the urease-Ag NPs/RuO2 sensing film was studied. Using the voltage–time ( $V$ – $T$ ) measurement system, the stability was verified by analyzing the biosensor’s reproducibility, repeatability, long-term measurement, and effect of temperature. Based on the results of this study, the proposed potentiometric urea biosensor had good reproducibility [relative standard deviation (RSD) was 2.23%] and repeatability (RSD was 1.16%). The urease-Ag NPs/RuO2 sensing film still has 90% initial sensitivity after 15 days of storage at room temperature. Moreover, the proposed urea biosensor was integrated into the microfluidic system and the remote measurement system. The sensing characteristics of the potentiometric urea biosensor under different flow rates (1.4–7.0 $\mu \text{L}$ /min, 0–30 $\mu \text{L}$ /min) were studied. In the remote measurement, the average sensitivity was 45.75 mV/decade and the linearity was 0.986.

Power-saving smart energy grid meter for blackout management

ABSTRACT Power generation and transmission effectiveness of energy systems have always been impacted around the globe. An imbalanced load of different devices on whole grids have factually conceived disastrous events across history. Traditionally, to resolve imbalances, blackouts are conducted to stabilize the supply to the remainder of the grid network. This paper presents the development of a remote energy measurement system using Arduino and sensors (C.T. & P.T.), to measure and protect user-end components from potential failure. Arduino records individual sensor data and transmits the relative data through Wi-Fi via a local internet connection to the base station. The unit also displays all related data such as energy consumption (Power), current, and voltage on an integrated display panel. This paper also proposes an improved energy meter design, capable of monitoring load prioritized electrical circuits for load management to avoid complete blackouts. The model is simulated, and energy meter hardware is developed, assessing the load shedding scheme using the proposed prioritization of load circuits.