Sensor Values Research Articles

Context-attentive robot reconfiguration for human-machine space missions

With humankind’s aspiration of extraterrestrial inhabitation, the need for robotic support is eminent. To master the emerging challenge of providing astronauts with assistive space robots possessing the required adaptability to new situations and tasks under the constraints of severely limited supply chains in extraterrestrial missions, the utilization of existing hardware to its fullest is required. We propose the combination of a concept with an architecture to jointly support context-aware collaboration between humans and robotic systems for implementing effective resource utilization in space missions. The underlying framework is an implementation of a flexible architecture that enables context awareness for robotic systems. It supports processing nodes for retrieval of context information from the raw sensor values as well as further inferences using the output of nodes. Context information spans across three entities and describes the current state of the environment, the astronaut, and the robot itself. This information then can be used to infer the human’s current intention as well as influence the behaviour to be executed next. Additionally, dynamic changes in the data processing chains are handled by the framework to facilitate an adequate adaptation of the system to situational events. We combine this architecture with the concept of space robots as a composition from building blocks, which are supported and made accessible by a software toolkit. The modular design approach enables online self-reconfiguration of robotic hardware and software components. In combination with dynamic mission planning, based on ontological descriptions of available resources and functionalities, robots are able to adapt their physical and computational appearance dynamically during a mission according to different tasks and goals. By incorporating these two developments in a joint deployment we envision raising the efficiency of robotic systems in human-machine interaction through the usage of self-reconfiguration as a reactive behavior in order to adapt to a specific task, recognized or derived from a human’s intention. Transfer of the proposed idea back to Earth may help to abate resource dissipation caused by deploying specialized monolithic systems with a narrow range of capabilities through utilizing the adaptivity of reconfigurable robots.

Robust design optimization of spin algorithm to reduce spinning time and vibration in washing machine

This study focuses on optimizing the spin algorithm of washing machines to reduce spin time and vibration, two crucial performance metrics often in a trade-off relationship due to structural limitations. We developed a simulator for the spin process that accounts for the uncertainty associated with the clothes’ position in the drum. Using this simulator, we designed a robust optimization algorithm to minimize spin time while maintaining acceptable vibration levels. The variation in the mass and position of laundry within the drum primarily affects vibration performance during the spin process. We formulated the unknown unbalance caused by the laundry as test-implementable unbalance parameters (UBP), represented as random variables. The uncertainties in the laundry’s unbalance for repeated experiments were estimated by the distribution of these UBPs. Experiments were conducted at parametrically designed points to measure vibrations and internal sensor values for control in specific revolutions per minute (RPM)ranges. These data were used to create approximate models. Monte Carlo simulation was then applied with UBP distributions obtained from the clothing experiments to meta-models, and the simulator was equipped with the spin algorithm. This process allowed us to predict the distribution of the maximum tub vibration displacement and spin time. A robust optimization algorithm was subsequently applied to the simulation model to derive the optimal spin algorithm. The proposed methodology yielded substantial improvements: a 25% reduction in the mean value of maximum displacement and a 90% reduction in the mean value of spin time compared to initial values.

Non-enzymatic electrochemical voltammetric sensors based on thulium:zinc oxide for dopamine detection

In this study, zinc oxide (ZnO) powders with dopant rates of 1 mol% thulium (Tm) (1Tm:ZnO), 2 mol% Tm (2Tm:ZnO), and 3 mol% Tm (3Tm:ZnO) nanoparticles were synthesized by co-precipitation method. Subsequently, Polyaniline (PANI)/1Tm:ZnO, PANI/2Tm:ZnO, and PANI/3Tm:ZnO nanocomposite sensors were prepared using a low-cost and simple sonication method. The results showed that the prepared PANI/1Tm:ZnO-based biosensor detected dopamine in the concentration range of (0.8–6.5) µM with a sensitivity of 0.2568 µAµM−1cm−2. Using these sensitivity values of the sensor, the LOD and LOQ values of the PANI/1Tm:ZnO-based sensor were calculated as 1.92 µM and 5.8 µM, respectively. The incorporation of 1% Tm into ZnO host (PANI/1Tm:ZnO) significantly enhanced the sensitivity of the biosensors.

Defect Detection in Grouting Sleeve Grouting Material by Piezoelectric Wave Method

The construction defects in grouting sleeves can jeopardize the safety of precast reinforced concrete structures. Thus, efficient and accurate defect detection is critical in engineering construction. In this paper, a defect detection method based on piezoelectric wave theory was proposed. Two piezoelectric ceramics were arranged within the grouting sleeve. One piezoelectric ceramic was affixed on the top of the steel bar, while the other was embedded in the grouting material, serving as the driver and sensor, respectively. The compactness defects, air cavity defects, steel anchoring defects, and water–binder ratio defects were set in the grouting sleeves, and the trends in time domain signals and signal evaluation indicators based on the wavelet packet total energy value (WPTEV) of different specimens were investigated. Based on the WPTEV, the evaluation index (EI) was proposed. In addition, the effect of the grouting material’s age on the piezoelectric wave signal was verified. Furthermore, the influence of grouting material defects on signals was simulated in ABAQUS, and the time domain signals and wavelet packet energy of sensor signals for specimens with varying defect dimensions were evaluated. The results showed that: (1) The defects in the grouting materials reduced the stress waves propagated through the grouting materials, resulting in a decrease in attenuation and an increasing trend in the signal; (2) The peak-to-peak values of piezoelectric ceramic sensors and the WPTEV of the signal increased with the degree of defects, and the WPTEV was more pronounced. For specimen M68, the WPTEV was 3.3 times that of the healthy specimen, however, the peak-to-peak value was only 2.3 times that of the healthy specimen; (3) The degree of defects was accurately determined by the defect evaluation index (EI) based on the WPTEV; (4) The signal was significantly attenuated with the increase in the age of the grouting material, especially in the first three days, and finally the signal achieved a stable value; (5) The numerical simulation indicated that the defects in the length and thickness of the air cavity in the grouting material were efficiently detected by the proposed piezoelectric arrangement in this study.

Mapping Acoustic Frictional Properties of Self-Lubricating Epoxy-Coated Bearing Steel with Acoustic Emissions during Friction Test

This work investigates the stick–slip phenomenon during sliding motion between solid lubricant-impregnated epoxy polymer-coated steel bars and AISI 52,100 steel balls. An acoustic sensor detected the stick–slip phenomenon during the tribo-pair interaction. The wear characteristics of the workpiece coated with different epoxy coatings were observed and scrutinized. The RMS values of the acoustic sensor were correlated with the frictional coefficient to develop a standard based on the acoustic sensor, leading to the detection of the stick–slip phenomenon. As per the findings, the acoustic waveform remained relatively similar to the friction coefficient observed during the study and can be used effectively in detecting the stick–slip phenomenon between steel and polymer interaction. This work will be highly beneficial in industrial and automotive applications with a significant interaction of polymer and steel surfaces.

From Sensors to Digital Twins toward an Iterative Approach for Existing Manufacturing Systems.

Digital twin technology is a highly valued asset in the manufacturing sector, with its unique capability to bridge the gap between the physical and virtual parts. The impact of the rapid increase in this technology is based on the collection of real-world data, its standardization, and its widespread deployment on an existing manufacturing system. This encompasses sensor values, PLC internal states, and IoT, as well as how the means of linking these data with their digital counterparts. It is challenging to implement digital twins on a large scale due to the heterogeneity of protocols and data structuring of subsystems. To facilitate the integration of the digital twin into existing manufacturing architectures, we propose in this paper a framework that enables the deployment of scalable digital twins from sensors to services of digital twins in an iterative manner.

Drowsy Driver Sleeping Device and Driver Alert System

Drowsiness is the main cause for major accidents which leads to the injuries, deaths and damages. To overcome this problem, we propose a system which uses various sensors. These sensors are used to detect the driver drowsy and monitor the health of the driver. The buzzer is used to alert the driver whenever the driver feels drowsy. Whenever the sensor values are not in the range of threshold value, the motor stops. In case of emergency, the GPS module determines the location and this information is sent through GSM to the particular person or in charge ward. All these sensor operations are controlled by Microcontroller. With the help of this system, the major road accidents canbe reduced by alerting the driver

Autoencoder-based detector for distinguishing process anomaly and sensor failure

Anomaly detection is a frequently discussed topic in manufacturing. However, the issues of anomaly detection are typically attributed to the manufacturing process or equipment itself. In practice, the sensor responsible for collecting data and monitoring values may fail, leading to a biased detection result – false alarm. In such cases, replacing the sensor is necessary instead of performing equipment maintenance. This study proposes an effective framework embedded with autoencoder-based control limits that can dynamically distinguish sensor anomaly from process anomaly in real-time. We conduct a simulation numerical study and an empirical study of semiconductor assembling manufacturers to validate the proposed framework. The results show that the proposed model outperforms other benchmark methods and can successfully identify sensor failures, even under conditions of (1) large variations in process values or sensor values and (2) heteroscedasticity effect. This is particularly beneficial in various practical applications where sensors are used for numerical measurements and support equipment maintenance.

Soil Moisture Sensor-Based Landslide Monitoring: A Laboratory-Based Approach for Guwahati City

Various techniques for landslide mapping, monitoring and modelling are being employed in a variety of studies to keep people safe from landslides. Guwahati, a city in Assam (India) is surrounded by hills, with varied slope angles, become prone to landslide during monsoon season. Relative increase in the moisture content of soil is a major parameter for determining the occurrence of landslides that are induced by rainfall. An experimental model with varying slope angles is demonstrated to witness some proportionality behaviour of soil moisture value for the collected soil sample from landslide prone areas. The soil moisture sensor value increases with increase in slope angle. The toe position of moisture value also shows a significant display of data during landslide. This early warning module can be incorporated with the help of Blynk Application to send messages to the residents of landslide prone areas. This study would be a cost effective alternative for landslide early warning hazard monitoring and fast emergency response process and the model may be considered as a miniature version of real-life slope conditions for the hills of Guwahati city, Assam, India.

Biomass carbon and Ti2C3MXene quantum dots as ratiometric fluorescent probes for sensitive detecting malachite green in fish sample

A visual detection method for malachite green (MG) in food was established based on ‘double-response-OFF’ ratiometric fluorescent paper-based sensor. Biomass carbon quantum dots (BCQDs) using broad bean shell, and Ti3C2MXene quantum (MQDs) dots modified by ethylenediamine were synthesized by solvothermal method. The MG and two kinds of quantum dots could undergo static quenching, and the fluorescence color of two kinds of quantum dots gradually changed from red to blue, eventually the fluorescence was quenched, and the pattern had a two-stage linear relationship using fluorescent spectrofluorometer in the range of 0.1–140.0 μM and the detection limit of 0.07 μM. On this basis, a BCQDs/MQDs ratiometric fluorescence paper-based sensor was constructed and applied to fish sample. Through mobile phone software-Color recognizer, RGB values of fluorescent paper-based sensor at various concentrations of MG were extracted. The results showed that MG concentration was linearly correlated with R’ value of RGB in the range of 20.0–140.0 μM with 16.5 μM detection limit. The method had been applied to the determination of canned fish and fresh basa fish samples, and the recovery rates were 97.33%–108.93% and 96.04%–117.97%, respectively. It proved that the ratiometric fluorescent paper-based sensor could be used for the rapid visual quantitative detecting MG in real samples.

River valley-inspired, high-sensitivity, and rapid-response capacitive three-dimensional force tactile sensor based on U-shaped groove structure

High-performance three-dimensional force (3D-force) tactile sensors with the capability of distinguishing normal and tangential forces in sync play a vital role in emerging wearable devices and smart electronics. And there is an urgent need for 3D-force tactile sensors with fast response and high flexibility. Herein, we design a capacitive 3D-force tactile sensors inspired by the U-shaped river valley surface morphology, which has satisfactory performance in terms of rapid response/recovery time (∼36 ms/∼ 36 ms), low hysteresis (4.2%), and high sensitivity (0.487 N−1). A theoretical model of general value for congener sensors is also proposed, obtaining a higher sensitivity through optimizing parameters. To verify the application potential of our device in actual scenarios, the robustness testing and gripping gamepad application were carried out. And it can recognize different motions in humans. Furthermore, principal component analysis is also conducted to demonstrate the distinct classification of different motions. Therefore, our work is eligible for the applications in wearable electronics, human–machine interaction, and soft intelligent robots.

Gated recurrent unit decision model for device argumentation in ambient assisted living

The increasing elderly population worldwide is facing a variety of social, physical, and cognitive issues, such as walking problems, falls, and difficulties in performing daily activities. To support elderly people, continuous monitoring and supervision are needed. Due to the busy modern lifestyle of caretakers, taking care of elderly people is difficult. As a result, many elderly people prefer to live independently at home without any assistance. To help such people, an ambient assisted living (AAL) environment is provided that monitors and evaluates the daily activities of elderly individuals. An AAL environment has heterogeneous devices that interact, and exchange information of the activities performed by the users. The devices can be involve in an argumentation about the occurrence of an activity thus leading to generate conflicts. To address this issue, the paper proposes a gated recurrent unit (GRU) learning techniques to facilitate decision-making for device argumentation during activity occurrences. The proposed model is used to initially classify user activities and each sensor value status. Then a novel method is used to identify argumentation among devices for activity occurrences in the classified user activities. Later, the GRU decision making model is used to resolve the argumentation and to identify the target activity that occurred. The result of the proposed model is compared with other existing techniques. The proposed model outperformed the other existing methods with an accuracy of 85.45%, precision of 72.32%, recall of 65.83%, and F1-Score of 60.22%.

Automated hydroponic nutrient control system for smart agriculture

<p><span>Hydroponics is a type of soil-free farming that uses less water and other resources than conventional soil-based farming methods. Hydroponic cultivation system has high yield per acre of land with minimal consumption of water and can be a possible to meet the growing food demand of the world. The hydroponic plants fertility must be preserved, proper nutrition, a environmental temperature, and nutrient stability are crucial. It will be simpler for a farmer to keep track of all hydroponic plants by automatically monitoring nutrient flow and ambient temperature stability. By implementing artificial intelligence-based regulating algorithms in the agriculture industry, recent technology advancements are highly helpful in resolving these issues. This paper presents, automated hydroponic nutrient control system (AHNCS) for smart agriculture. System architecture is consisting of sensors network, Raspberry pi 4 microcontroller and actuators. Raspberry pi 4 microcomputer read sensor values from sensors process and activates particular actuator. The automation of the hydroponic system helps to avoid human intervention. The utilization of sensors and actuators, promptly act for the needs of the plant without any delay. The AHNCS having high accuracy, high efficiency and less delay. Hence, automation of the existing hydroponic system can reduce human dependency, provide accurate results, constant monitoring of plant health.</span></p>

GA-BP based daylight prediction and sensor placement in residential areas

This paper conducts a comprehensive analysis of the light environment in different areas of residential houses located in Beijing’s Guangyuanli neighborhood. The main objective of this study is to assess the contribution of natural daylight to residential illuminance, with the aim of providing a foundation for reducing energy consumption and enhancing light comfort. While previous research predominantly focused on sensor deployment in single scenes such as offices and classrooms, these approaches are inadequate for multi-scene analysis of daylight in residential areas. Moreover, traditional deployment methods can negatively impact the quality of life and everyday living experience. To overcome these limitations, a prediction model is developed to estimate ceiling and window illuminance, as well as work surface illuminance in various scenes within residential buildings. The Dialux software is employed for data simulation, and a GA-optimized BP neural network is utilized to train the model, establishing the mapping relationship between input variables and corresponding illuminance outputs. By inputting sensor values from different scenarios into the model, the predicted daylight illuminance values can be obtained. This predictive approach facilitates the evaluation of light comfort and energy consumption reduction in different functional areas of residential buildings.

The room temperature highly sensitive ammonia gas sensor based on polyaniline and nitrogen doped graphene quantum dot coated flower‐like tungsten oxide composite

AbstractIn this study, the newly synthesized polyaniline (PANI)/polyethylenimine modified nitrogen doped graphene quantum dot (PN‐GQD)/tungsten oxide (WO3) ternary composites were used as ammonia (NH3) gas sensor detected the NH3 at room temperature. Fourier transform infrared spectrometer, field‐emission scanning electron microscopy, transmission electron microscopy, and x‐ray photoelectron spectroscopy were applied to illustrate the chemical structure and morphology of the fabricated ternary composites. The NH3 gas‐sensing performances of PANI/PN‐GQD/WO3 composite sensor were determined in the concentration between 0.6 and 2.0 ppm at room temperature and the response value of the ternary composite sensor with an exposure of 1.0 ppm NH3 was 19.9, which was much better than those of PANI and PANI/WO3 sensors. Because their highly sensitive detection of NH3 ranging from the concentration between 0.6 and 2.0 ppm at room temperature, the PANI/PN‐GQD/WO3 composite sensor was confirmed to be extremely effective to detect the kidney or hepatic disease from the human breath. This ternary composite sensor with an exposure of 1.0 and 2.0 ppm NH3 also displayed extraordinary repeatability and selectivity at room temperature. According to these findings, this fabricated PANI/PN‐GQD/WO3 composite sensor will be an excellent gas‐sensing material to detect the kidney or hepatic disease from the human breath.

Using synthetic data to benchmark correction methods for low-cost air quality sensor networks

A benchmark was performed, comparing the results of three different methodologies proposed by three institutions to calibrate a network of low-cost PM2.5 sensors, on an hourly basis, using synthetically generated real concentrations and sensor measurements. The objective of the network calibrations was to correct the 2000+ sensor measurements in the Netherlands for the sensitivity to (local) environmental conditions. The option to use real measurements was dropped because the number of low-cost sensors sufficiently close to the 40 reference measurement locations was assessed to be spatially insufficient to benchmark the proposed approaches. Instead, synthetic real concentrations were generated to enable validation at all sensor locations. Hourly actual sensor and actual fixed concentrations, as well as interpolated concentration maps, were used as underlying data to generate the synthetic data sets for the period of 1 month. The synthetic sensor measurement errors were constructed by sampling from a collection of differences between actual sensor values and actual measurements. Of the three tested calibration methods, two follow a similar approach, although having differences in, e.g., outlier analyses and method of grouping sensors, leading also to comparable corrections to the raw sensor measurements. A third method uses significantly stricter rules in outlier selection, discarding considerably more sensors because of insufficient quality. Differences between the methods become most apparent when analyzing data at a smaller time scale. It is shown that two network calibration methods are better at correcting the hourly/daily bias.

Forecasting Urban Sensory Values through Learning Attention-adjusted Graph Spatio-temporal Networks

Forecasting spatio-temporal correlated time series of sensor values is crucial in urban applications, such as air pollution alerts, biking resource management, and intelligent transportation systems. While recent advances exploit graph neural networks (GNN) to better learn spatial and temporal dependencies between sensors, they cannot model time-evolving spatio-temporal correlations (STC) between sensors and require pre-defined graphs, which are neither always available nor totally reliable, and target at only a specific type of sensor data at one time. Moreover, since the form of time-series fluctuation is varied across sensors, a model needs to learn fluctuation modulation. To tackle these issues, in this work, we propose a novel GNN-based model, Attention-adjusted Graph Spatio-Temporal Network (AGSTN). In AGSTN, multi-graph convolution with sequential learning is developed to learn time-evolving STC. Fluctuation modulation is realized by a proposed attention adjustment mechanism. Experiments on three sensor data, air quality, bike demand, and traffic flow, exhibit that AGSTN outperforms the state-of-the-art methods.

Suitability of performance adaptation methods for updating the thermodynamic cycle model of a turboprop engine

A model of the thermodynamic cycle can be used in various fields, such as engine operation and control optimization, diagnosis, and maintenance. It is therefore important to develop a thermodynamic cycle model with high accuracy, and for this purpose, studies have been conducted to increase the accuracy of the model using measurement data. This study was conducted to identify the most effective method of updating the thermodynamic cycle model based on measurement data for the turboprop engine. A performance adaptation approach was applied to update the thermodynamic cycle model using measured data, and adaptation factors were used to adjust the component performance and sensor values. We considered three methods of calculating the adaptation factors and generating functions to apply to an existing engine model, which was a non-adapted model. The first was based on the Newton-Raphson (NR) method, in which the factors were generated as a function through regression curve fitting (RCF). In the second method, adaptation factors were calculated using a genetic algorithm (GA)-based optimization technique, and were generated as a function using RCF. The third method involved optimizing the function of the adaptation factor through GA. The accuracy and calculation cost of each method were compared using steady-state data measured using an in-flight monitoring sensor for the engine condition. All three methods yielded better prediction accuracy than the existing engine model; particularly, the first approach, based on the NR and RCF, showed the highest improvement in accuracy and the lowest number of required calculations. The maximum absolute values of the relative errors for the fuel mass flow rate, low-pressure shaft power, compressor exit pressure, turbine exit temperature, and exhaust gas temperature for the adapted thermodynamic cycle engine model with the NR and RCF were 4.29%, 2.83%, 1.36%, 1.80%, and 3.49%, respectively. As a result, we believe that the NR and RCF can be effectively used to calculate new adaptation factors for added measurement data points and to create a new function including the added factors.

Hardware Realization of Long Range (LoRa) Based Telemetry System for Aquaculture Monitoring

Freshwater fish farming must pay attention to the condition of the water content in the pond and the water quality in the pond. Fish will feel comfortable if the efficiency of oxygen levels and pond water content is maintained. Freshwater fish farming needs to optimize crop yields so as not to get losses. The problem that is often encountered is that many fish die in the pond due to lack of water. Irrigation channels leading to fishponds experience blockages. The smoothness of the irrigation channel affects the volume of water in the pond. Blockage of irrigation channels is usually due to garbage trapped in irrigation, causing dirty water to settle and water sequestration to occur in the pond. This tool system aims to improve water quality and increase the efficiency of oxygen levels by using TDS and ultrasonic sensors. The parameter value on the sensor affects the work of the tool actuator. The tool actuator is a water pump that is used as a substitute for irrigation channels and an aerator motor to increase dissolved oxygen in water by moving the propeller on the surface of the pond water. Wireless technology is used for data communication. Because the land of freshwater fish farming ponds is a large area and is far from settlements, wireless communication is suitable for enabling easy and fast access to information and services. In this research, the testing methods used are functional testing and system performance testing. Functional testing is used to prove whether the system that has been implemented can meet the requirements of operational functions as planned. System performance testing is intended to monitor several parameters that can show the ability and reliability of the system in carrying out its operational functions. The result of the overall test is the LoRa communication distance that can communicate up to about 1000 meters, proving that LoRa technology has a strong enough ability in terms of range wireless communication. The PLE (Path Loss Exponent) of the LoRa module with 100 meters in LoS (indoor) conditions is 7.77, while in nLoS conditions in obstructed in-building, it is 10.13. The average error of the ultrasonic sensor type JSN SR-04T is 0.16% and has a difference of ± 1 cm. The TDS sensor with dissolved pool water content has an average value of 142.6 ppm with an error value of 1.05%. The PDAM water content has an average value of 112.1 ppm with a sensor value of 1.38%. The water content of the lime and detergent mixture has the highest observed ppm value, which is an average of 737.33 ppm, with a sensor error of 0.14%. The water pump activates when the pool water is low (when the ultrasonic distance reaches 60 cm), and the aerator activates when the water content is contaminated (when the TDS sensor value exceeds 500 ppm).

In situ synthesis of Zn-doped In2S3/In2O3 composites for the monitoring of trace ethanol at low temperature

In this paper, Zn-doped In2S3 flowers grow on the surface of the ceramic tube, and the surface was locally oxidized by injecting oxygen on the surface, so as to prepare Zn-doped In2S3/In2O3 composites with good sensing to ethanol gas. The characterization results showed that the composite has a hierarchical flower structure with uniform size (5–6 μm). Gas sensing tests exhibit that the sensor has outstanding response for ethanol gas. Zn doping and local oxidation significantly reduced the operating temperature of In2S3 from the original 240 °C–100 °C, and the gas sensing response of the sensor was also significantly improved. When the temperature is 100 °C, the response value of pure In2S3 sensor for 10 ppm ethanol gas is 3.55 and Zn/In2S3/In2O3 sensor's response value is increased to 22.82, which can be attributed to the increase of oxygen vacancy caused by Zn doping and the construction of In2S3/In2O3 heterojunction. Finally, density functional theory (DFT), as the theoretical basis of first-principle calculation, is used to calculate and analyze the gas-sensing enhancement mechanism of composite materials.