Adaptive Space-Aware Infotaxis II as a Strategy for Odor Source Localization.

Mobile robot olfaction of toxic and hazardous odor sources is of great significance in anti-terrorism, disaster prevention, and control scenarios. Aiming at the problems of low search efficiency and easily falling into a local optimum of the current odor source localization strategies, the paper proposes the adaptive space-aware Infotaxis II algorithm. To improve the tracking efficiency of robots, a new reward function is designed by considering the space information and emphasizing the exploration behavior of robots. Considering the enhancement in exploratory behavior, an adaptive navigation-updated mechanism is proposed to adjust the movement range of robots in real time through information entropy to avoid an excessive exploration behavior during the search process, which may lead the robot to fall into a local optimum. Subsequently, an improved adaptive cosine salp swarm algorithm is applied to confirm the optimal information adaptive parameter. Comparative simulation experiments between ASAInfotaxis II and the classical search strategies are carried out in 2D and 3D scenarios regarding the search efficiency and search behavior, which show that ASAInfotaxis II is competent to improve the search efficiency to a larger extent and achieves a better balance between exploration and exploitation behaviors.

Design of Gas Monitoring Terminal Based on Quadrotor UAV.

The problem of air pollution is an increasingly serious worldwide. Therefore, in order to better monitor the gas components in the atmosphere, the design of a gas monitoring terminal based on a quadrotor UAV, including software and hardware design, is hereby carried out. Besides, a pump-suction series cavity is designed to reduce the influence of airflow disturbance on the UAV, which is verified to possess a certain anti-interference ability through Computational Fluid Dynamics(CFD) simulation experiments. In addition, a linear regression algorithm is used for sensor calibration and a polynomial piecewise regression method is used for temperature compensation. The experimental results show that the R2 of the model reaches 0.9981, the fitting degree is rather high, and the output is closer to the real gas concentration value after calibration. At the same time, the temperature compensation parameters are determined, which considerably improves the accuracy of the entire hardware terminal. Finally, the vehicle exhaust monitoring experiment is conducted, and the experimental results show that this scheme can successfully detect the exhaust position of the vehicle exhaust under the interference of the downwash flow of the UAV, thereby proving the reliability and accuracy of the monitoring terminal.

Development of a Low-Cost Portable Electronic Nose for Cigarette Brands Identification.

In China, the government and the cigarette industry yearly lose millions in sales and tax revenue because of imitation cigarettes. Usually, visual observation is not enough to identify counterfeiting. An auxiliary analytical method is needed for cigarette brands identification. To this end, we developed a portable, low-cost electronic nose (e-nose) system for brand recognition of cigarettes. A gas sampling device was designed to reduce the influence caused by humidity fluctuation and the volatile organic compounds (VOCs) in the environment. To ensure the uniformity of airflow distribution, the structure of the sensing chamber was optimized by computational fluid dynamics (CFD) simulations. The e-nose system is compact, portable, and lightweight with only 15 cm in side length and the cost of the whole device is less than $100. Results from the machine learning algorithm showed that there were significant differences between 5 kinds of cigarettes we tested. Random Forest (RF) has the best performance with accuracy of 91.67% and K Nearest Neighbor (KNN) has the accuracy of 86.98%, which indicated that the e-nose was able to discriminate samples. We believe this portable, cheap, reliable e-nose system could be used as an auxiliary screen technique for counterfeit cigarettes.

Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array.

The gas sensor array has long been a major tool for measuring gas due to its high sensitivity, quick response, and low power consumption. This goal, however, faces a difficult challenge because of the cross-sensitivity of the gas sensor. This paper presents a novel gas mixture analysis method for gas sensor array applications. The features extracted from the raw data utilizing principal component analysis (PCA) were used to complete random forest (RF) modeling, which enabled qualitative identification. Support vector regression (SVR), optimized by the particle swarm optimization (PSO) algorithm, was used to select hyperparameters C and γ to establish the optimal regression model for the purpose of quantitative analysis. Utilizing the dataset, we evaluated the effectiveness of our approach. Compared with logistic regression (LR) and support vector machine (SVM), the average recognition rate of PCA combined with RF was the highest (97%). The fitting effect of SVR optimized by PSO for gas concentration was better than that of SVR and solved the problem of hyperparameters selection.

Discriminative Detection of Different Cigarette Brands Using a Fast-Response Electronic Nose.

Electronic noses are an artificial olfactory system that mimics the animal olfactory function. Currently, metal oxide semiconductor (MOS) gas sensors play a vital role in the development of high-performance electronic noses and have widespread applications across various fields. They are particularly valuable in ensuring food safety, monitoring air quality, and even detecting explosives for antiterrorism purposes. However, there is an increasing demand for electronic noses to exhibit faster response times in large-scale commercial applications. To address this challenge, we developed a novel MOS gas sensor with a porous ceramic substrate, specifically designed to facilitate rapid gas diffusion. The sensing performance of the sensor array was evaluated and the result showed that the T90 time of porous ceramic-assisted MOS sensor was significantly (57%) shorter than sensors with a normal substrate. Moreover, the electronic nose system had demonstrated remarkable capability in accurately distinguishing between five distinct types of hazardous gases, including VOCs as well as ammonia. Furthermore, a low-cost electronic system was developed and applied to cigarette brand identification; 2490 groups of data were collected for each individual test at only a cost of 20 s. By employing a machine learning algorithm to analyze the data, an accuracy higher than 95% was achieved (96.29% for K nearest neighbor and 96.32% for random forest). We found that our system can resolve the onset time of electronic nose measurement with enough precision, and it was expected that this special approach by using porous ceramic as an insulating substrate can provide a simple and reliable method to manufacture a fast-response electronic nose.

Equalization Optimizer-Based LSTM Application in Reservoir Identification.

In recent years, the use of long short-term memory (LSTM) has made significant contributions to various fields and the use of intelligent optimization algorithms combined with LSTM is also one of the best ways to improve model shortcomings and increase classification accuracy. Reservoir identification is a key and difficult point in the process of logging, so using LSTM to identify the reservoir is very important. To improve the logging reservoir identification accuracy of LSTM, an improved equalization optimizer algorithm (TAFEO) is proposed in this paper to optimize the number of neurons and various parameters of LSTM. The TAFEO algorithm mainly employs tent chaotic mapping to enhance the population diversity of the algorithm, convergence factor is introduced to better balance the local and global search, and then, a premature disturbance strategy is employed to overcome the shortcomings of local minima. The optimization performance of the TAFEO algorithm is tested with 16 benchmark test functions and Wilcoxon rank-sum test for optimization results. The improved algorithm is superior to many intelligent optimization algorithms in accuracy and convergence speed and has good robustness. The receiver operating characteristic (ROC) curve is used to evaluate the performance of the optimized LSTM model. Through the simulation and comparison of UCI datasets, the results show that the performance of the LSTM model based on TAFEO has been significantly improved, and the maximum area under the ROC curve value can get 99.43%. In practical logging applications, LSTM based on an equalization optimizer is effective in well-logging reservoir identification, the highest recognition accuracy can get 95.01%, and the accuracy of reservoir identification is better than other existing identification methods.

Amperometric Hydrogen Sensor Based on Solid Polymer Electrolyte and Titanium Foam Electrode.

Trace hydrogen detection plays an important role in the safety detection of lithium-ion batteries (LIBs) due to the generation and leakage of trace hydrogen in the early stage of LIBs damage. In this work, an amperometric hydrogen sensor based on solid polymer electrolyte was reported. The sandwich device structure was realized, which could directly diffuse the gas from both sides to the three-phase interface (gas/electrode/electrolyte) to participate in the reaction through the optimal design of the gas diffusion path. Then, platinum nanoparticles (Pt-NPs) were loaded on the metal foam by electroplating, and the porous electrode was filled with solid polymer electrolyte. A sensor with high specific surface area, high catalytic activity, and high sensitivity was obtained. Finally, the hydrogen oxidation reaction (HOR) mechanism of the platinum-loaded (Pt-loaded) titanium foam (Ti foam) electrode under both anaerobic and aerobic conditions was verified, and the properties of the sensor was evaluated. The hydrogen sensor with a "sandwich" structure has the advantages of high sensitivity, good stability, low detection limit and low cost, which provides a technical solution for the safety and real-time monitoring of LIBs.

Photocatalytic dehydrogenative C-C coupling of acetonitrile to succinonitrile.

The coupling of acetonitrile into succinonitrile, an important terminal dinitrile for value-added nylon production, via a dehydrogenative route is highly attractive, as it combines the valuable chemical synthesis with the production of green hydrogen energy. Here, we demonstrate that it is possible to achieve a highly selective light driven dehydrogenative coupling of acetonitrile molecules to synthesize succinonitrile using anatase TiO2 based photocatalysts in aqueous medium under mild conditions. Under optimized conditions, the formation rate of succinonitrile reaches 6.55 mmol/(gcat*h), with over 97.5% selectivity to target product. Mechanism studies reveal that water acts as cocatalyst in the reaction. The excited hole of anatase semiconductor oxidizes water forming hydroxyl radical, which subsequently assists the cleavage of sp3 C-H bond of acetonitrile to generate ·CH2CN radical for further C-C coupling. The synergy between TiO2 and Pt cocatalyst is important to enhance the succinonitrile selectivity and prevent undesirable over-oxidation and hydrolysis. This work offers an alternative route to prepare succinonitrile based on renewable energy under mild conditions and avoid the use of toxic reagents and stoichiometric oxidative radical initiators.

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model.

Bird swarm algorithm is one of the swarm intelligence algorithms proposed recently. However, the original bird swarm algorithm has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these short-comings, a dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies was established. First, establishing a dynamic multi-swarm bird swarm algorithm and the differential evolution strategy was adopted to enhance the randomness of the foraging behavior's movement, which can make the bird swarm algorithm have a stronger global exploration capability. Next, quantum behavior was introduced into the bird swarm algorithm for more efficient search solution space. Then, the improved bird swarm algorithm is used to optimize the number of decision trees and the number of predictor variables on the random forest classification model. In the experiment, the 18 benchmark functions, 30 CEC2014 functions, and the 8 UCI datasets are tested to show that the improved algorithm and model are very competitive and outperform the other algorithms and models. Finally, the effective random forest classification model was applied to actual oil logging prediction. As the experimental results show, the three strategies can significantly boost the performance of the bird swarm algorithm and the proposed learning scheme can guarantee a more stable random forest classification model with higher accuracy and efficiency compared to others.

A Study of Modified Infotaxis Algorithms in 2D and 3D Turbulent Environments.

Emergency response to hazardous gases in the environment is an important research field in environmental monitoring. In recent years, with the rapid development of sensor technology and mobile device technology, more autonomous search algorithms for hazardous gas emission sources are proposed in uncertain environment, which can avoid emergency personnel from contacting hazardous gas in a short distance. Infotaxis is an autonomous search strategy without a concentration gradient, which uses scattered sensor data to track the location of the release source in turbulent environment. This paper optimizes the imbalance of exploitation and exploration in the reward function of Infotaxis algorithm and proposes a mobile strategy for the three-dimensional scene. In two-dimensional and three-dimensional scenes, the average steps of search tasks are used as the evaluation criteria to analyze the information trend algorithm combined with different reward functions and mobile strategies. The results show that the balance between the exploitation item and exploration item of the reward function proposed in this paper is better than that of the reward function in the Infotaxis algorithm, no matter in the two-dimensional scenes or in the three-dimensional scenes.

Efficient NH3 Detection Based on MOS Sensors Coupled with Catalytic Conversion.

A solution of NH3 detection based on catalytic conversion of NH3 into NOx was proposed by using MOS gas detectors and Pt-supported catalysts. The catalysts convert NH3 into NOx, which is a very sensitive analyte for MOS detectors. Catalysts based on Pt-loaded HZSM-5 and Al2O3 were prepared by wet impregnation. MOS detectors were fabricated from nanosized In2O3 and WO3 using screen-printing techniques. As expected, MOS sensors based on In2O3 and WO3 have an extremely high sensitivity to NO2; nevertheless, they have a relatively low response to NH3 and a large cross-sensitivity to typical interfering gases such as CO and ethanol. By the present solution, MOS sensors could very sensitively respond to NH3, even down to 0.25 ppm. In addition, it was also found that the catalysis also combusts the reducing gases into CO2 and water and consequently significantly improves the selectivity of NH3. Lastly, we would to like to stress that the proposed concept of the catalytic conversion method suggests the potential utility for broader measurements by using different catalysts and gas detectors and that only a part of the usage for NH3 was presented here.