Health Management Decision of Sensor System Based on Health Reliability Degree and Grey Group Decision-Making

Self-Assembly 3D Porous Crumpled MXene Spheres as Efficient Gas and Pressure Sensing Material for Transient All-MXene Sensors

Environmentally friendly degradable sensors with both hazardous gases and pressure efficient sensing capabilities are highly desired for various promising applications, including environmental pollution monitoring/prevention, wisdom medical, wearable smart devices, and artificial intelligence. However, the transient gas and pressure sensors based on only identical sensing material that concurrently meets the above detection needs have not been reported. Here, we present transient all-MXene NO₂ and pressure sensors employing three-dimensional porous crumpled MXene spheres prepared by ultrasonic spray pyrolysis technology as the sensing layer, accompanied with water-soluble polyvinyl alcohol substrates embedded with patterned MXene electrodes. The gas sensor achieves a ppb-level of highly selective NO₂ sensing, with a response of up to 12.11% at 5 ppm NO₂ and a detection range of 50 ppb-5 ppm, while the pressure sensor has an extremely wide linear pressure detection range of 0.14-22.22 kPa and fast response time of 34 ms. In parallel, all-MXene NO₂ and pressure sensors can be rapidly degraded in medical H₂O₂ within 6 h. This work provides a new avenue toward environmental monitoring, human physiological signal monitoring, and recyclable transient electronics.

Balanced Distribution Adaptation for Metal Oxide Semiconductor Gas Sensor Array Drift Compensation

Drift compensation is an important issue for metal oxide semiconductor (MOS) gas sensor arrays. General machine learning methods require constant calibration and a large amount of label gas data. At the same time, recalibration will cause a lot of costs, and label gas is difficult to obtain in practice. In this paper, a novel drift compensation method based on balanced distribution adaptation (BDA) is proposed. First, the BDA drift compensation method can adjust the conditional distribution and marginal distribution between the two domains through the weight balance factor, thereby more effectively reducing the mismatch between the two domains. When the BDA method performs classification tasks through machine learning, no labeled data is required in the target domain. Then, the particle swarm optimization algorithm is used to improve the accuracy of drift compensation. Individuals in the population are initialized randomly, and their fitness values are calculated. Iterative optimization of the population individuals is conducted until the optimal weight balance factor parameters are calculated. Finally, the BDA method is experimentally verified on the public gas sensor drift data set. Experimental results showed that the BDA method was significantly better than the existing joint distribution adaptation (JDA) method and other standard drift compensation methods such as K-Nearest Neighbor (KNN). In the two setting groups, the recognition accuracy was 4.54% and 1.62% ahead of the JDA method, and 12.23% and 15.83% ahead of the KNN method.

Measuring the Efficiency of Economic Growth towards Sustainable Growth with Grey System Theory

In the paper, a new indicator exemplifying the conversion efficiency of expenditures towards economic growth into results pertaining to sustainable development, dubbed the “Synthetic Efficiency Indicator for Economic Growth” (hereinafter: “SEI-EG”) has been proposed. The inspiration for proposing such an indicator was the identification of the lack of connections between research on economic convergence and the research area connected with sustainable growth category. It was assumed that, in the first place, outcomes of the proposed convergence will be visible in developed economies, represented by EU15 member states. The set goal was to provide an answer to the question of difference between EU15 member states with respect to efficiency of converging expenditures exemplifying economic growth into results pertaining to sustainable growth. The research was conducted for 2016–2018 using Grey System Theory. With the use of the elaborated indicator, the authors created a ranking list of countries based on the efficiency of economic growth towards sustainable growth criterion. The conducted research proved that, in general, the smaller EU member states are characterized by significantly higher efficiency of converging expenditures exemplifying economic growth into results pertaining to sustainable development in the researched area. Among the countries with large economies, only Germany showed efficiency comparable to smaller ones.

Grey Fuzzy Multiple Attribute Group Decision-Making Methods Based on Interval Grey Triangular Fuzzy Numbers Partitioned Bonferroni Mean

Considering the characteristics such as fuzziness and greyness in real decision-making, the interval grey triangular fuzzy number is easy to express fuzzy and grey information simultaneously. And the partition Bonferroni mean (PBM) operator has the ability to calculate the interrelationship among the attributes. In this study, we combine the PBM operator into the interval grey triangular fuzzy numbers to increase the applicable scope of PBM operators. First of all, we introduced the definition, properties, expectation, and distance of the interval grey triangular fuzzy numbers, and then we proposed the interval grey triangular fuzzy numbers partitioned Bonferroni mean (IGTFPBM) and the interval grey triangular fuzzy numbers weighted partitioned Bonferroni mean (IGTFWPBM), the adjusting of parameters in the operator can bring symmetry effect to the evaluation results. After that, a novel method based on IGTFWPBM is developed for solving the grey fuzzy multiple attribute group decision-making (GFMAGDM) problems. Finally, we give an example to expound the practicability and superiority of this method.

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.