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Classification techniques of electronic nose: a review

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2024年第1期23卷 16-27页

作者： Jia, Pengfei Li, Xiaoyu Xu, Min Zhang, Lin Guangxi Univ Sch Elect Engn Nanning Peoples R China Guangxi Univ Guangxi Key Lab Intelligent Control & Maintenance Nanning Peoples R China Xihua Univ Coll Food Sci & Bioengn Chengdu Peoples R China Yangtze Normal Univ Sch Mech & Elect Engn Chongqing 408100 Peoples R China

Electronic nose (e-nose) is composed of a set of gas sensors combined with a series of algorithmic models. The practical application of the electronic nose system can prove that the electronic nose is more widely used in the classification problems, and always has a good performance. Moreover, it can be inferred that classification methods significantly influence e-nose. So far, the classification models proposed in e-nose can generally be divided into two categories. One is the linear classifier, representing the model of the Bayesian classifier, principal component analysis (PCA), and K-nearest neighbour (KNN), etc. The other is the nonlinear classifier, including support vector machine (SVM), random forest (RF), and extreme learning machine (ELM), etc. This review aims to supply a summary of the various classification methods used in e-nose, and provides a reference for the choice of an appropriate classification model used in e-nose in the specific application.

关键词： electronic nose e-nose classification techniques artificial intelligence algorithm

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AI augmentation in the field of digital image processing

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2021年第3期65卷 235-244页

作者： Malik, Rahul Lovely Profess Univ Sch Comp Sci & Engn Phagwara Punjab India

All of us are very well aware that identifying the information visually is more effective and efficient for human intelligence. In this aspect, one of the primary modes of interacting within and around for understanding the situation through images can act as crucial sources of information for the activities related to human intelligence. This paper's primary focus is to accomplish a better image processing impact using AI as part of image processing. Initially, this paper deals with introduction, elaboration, and mathematical representation on the essential theory of the ant colony algorithm. Furthermore, part of this paper deals with improvising the global search by introducing the crowding function of fish into the algorithm. Later, the improvised algorithm is utilised to segment the images to improve the impact of the segmentation process. Using this algorithm, the results demonstrate its feasibility, significant improvement in performance and the optimisation while segmenting the images.

关键词： computer vision image processing ant colony algorithm digital image image segmentation artificial intelligence algorithm

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Bounded Rationality, Group Formation and the Emergence of Trust: An Agent-Based Economic Model

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COMPUTATIONAL ECONOMICS 2022年第2期60卷 571-599页

作者： Kato, Jefferson Satoshi Sbicca, Adriana Univ Fed Parana Curitiba Parana Brazil

It is possible to model trust as an investment game, where a player in order to receive a reward or a better outcome, accepts a certain risk of defection by another player. Despite having achieved interesting insights and conclusions, traditional game theory does not predict the existence of trust between players who are selfish and exhibit maximizing behavior. However, experiments with these games reveal the presence of trust in player decision making. The purpose of this paper is twofold. First, it aims to build an agent-based economic model to show that trust revealed in these experiments can emerge from a simple set of dynamics. Using the generative methodology proposed by Epstein, we introduce natural selection, learning and group formation to the model to verify their impact on the emergence of trust between agents. Second, since the experiments reveal that the participants present bounded rational behavior, the paper aims to show that in an agent-based model, bounded rationality can be modelled through an artificial intelligence algorithm, the learning classifier system (LCS). As a result, we have observed that natural selection favors more selfish behavior. In addition, learning and the forming of groups increased trust in our simulations and they were able to reverse selfish behavior when introduced along with natural selection. The level of trust that emerged from the model with these three dynamics was similar to that observed in these experiments. Finally, it is possible to verify that the LCS was able to model bounded rational behavior in agents.

关键词： Trust Investment games Bounded rationality Agent-based simulations Learning artificial intelligence algorithm

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Uncertainty analysis-forecasting system based on decomposition-ensemble framework for PM2.5 concentration forecasting in China

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JOURNAL OF FORECASTING 2023年第8期42卷 2027-2044页

作者： Qu, Zongxi Hao, Xiaogang Zhao, Fazhen Niu, Chunhua Lanzhou Univ Sch Management Lanzhou 730000 Peoples R China Gansu Univ Chinese Med Clin Coll Tradit Chinese Med Lanzhou Peoples R China

Practical analysis and forecasting of PM2.5 concentrations is complex and challenging owing to the volatility and non-stationarity of PM2.5 series. Most previous studies mainly focused on deterministic predictions, whereas the uncertainty in the prediction is not considered. In this study, a novel uncertainty analysis-forecasting system comprising distribution function analysis, intelligent deterministic prediction, and interval prediction is designed. Based on the characteristics of PM2.5 series, 16 hybrid models composed of various distribution functions and swarm optimization algorithms are selected to determine the exact PM2.5 distribution. Subsequently, a hybrid deterministic forecasting model based on a novel decomposition-ensemble framework is established for PM2.5 prediction. Regarding uncertainty analysis, interval prediction is established to provide uncertain information required for decision-making based on the optimal distribution functions and deterministic prediction results. PM2.5 concentration series obtained from three cities in China are used to conduct an empirical study. The empirical results show that the proposed system can achieve better prediction results than other comparable models as well as provide meaningful and practical quantification of future PM trends. Hence, the system can provide more constructive suggestions for government administrators and the public.

关键词： artificial intelligence algorithm interval prediction PM2 5 prediction swarm optimization algorithm uncertainty analysis

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A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting

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APPLIED ENERGY 2024年 360卷

作者： Liu, Zhi-Feng Liu, You-Yuan Chen, Xiao-Rui Zhang, Shu-Rui Luo, Xing-Fu Li, Ling-Ling Yang, Yi-Zhou You, Guo-Dong Tianjin Univ Sci & Technol Coll Elect Informat & Automat Tianjin 300222 Peoples R China Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Key Lab Electromagnet Field & Elect Apparat Reliab Tianjin 300130 Peoples R China

Wind power generation plays a crucial role in promoting the transformation and advancement of the power industry and fostering sustainable development in society. However, wind power generation is susceptible to external factors, exhibiting significant volatility and randomness, which can adversely affect the stable operation of the power grid. The uncertainty associated with wind power generation can be effectively addressed through wind power prediction technology. While numerous wind power prediction methods have been developed, challenges remain, including imperfect data processing mechanisms and the optimization of model parameters, which hinder the effective utilization of wind power generation. To overcome these challenges, this research proposes a novel deep learning-based evolutionary model with a potential attention mechanism and memory decay-enhancement strategy for short-term wind power point-interval forecasting. The proposed model demonstrates high prediction stability and accuracy for both point values and intervals of short-term wind power, even under complex environmental and multi-seasonal conditions. The effectiveness of the proposed methods and strategies are validated using a measured dataset from the La Haute Borne wind farm in France. The results consistently show that, in complex environmental scenarios, the point prediction evaluation index of determination coefficient exceeds 90% and the interval prediction evaluation index of PI coverage probability exceeds 80%. Accurate short-term wind power point-interval forecasting contributes to enhancing the stable operation of the power system and improving the efficiency of wind energy utilization.

关键词： New power application Short-term wind power Point-interval forecasting Potential attention artificial intelligence algorithm

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Multi-perspective option price forecasting combining parametric and non-parametric pricing models with a new dynamic ensemble framework

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TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2024年 204卷

作者： Guo, Jingjun Kang, Weiyi Wang, Yubing Lanzhou Univ Finance & Econ Sch Stat & Data Sci Lanzhou 730020 Peoples R China Lanzhou Univ Finance & Econ Ctr Quantitat Anal Gansu Econ Dev Lanzhou 730020 Peoples R China

This article introduces a dynamic ensemble framework that integrates parametric and non-parametric pricing models. Within this framework, we propose a time-varying parametric pricing model optimized using artificial intelligence algorithms. Additionally, we construct a non-parametric pricing model using a 2-dimensional convolutional neural network (2D-CNN) to capture the interactions among options, enhancing the existing nonparametric pricing model. Validation using China's SSE 50 ETF options trading data reveals several key findings: Firstly, the dynamic integration method proposed in this study not only improves prediction accuracy but also enhances stability. Secondly, previous parametric pricing models do not effectively utilize their pricing performance, while our proposed time-varying parametric pricing model significantly enhances accuracy. Lastly, the 2D-CNN model, which considers interactions among options trades, proves to be reasonable and effective, outperforming common non-parametric pricing models. The dynamic ensemble framework proposed in this study effectively combines the strengths of both parametric and non-parametric pricing models. This research serves as an important reference for risk managers, institutional investors, and other stakeholders. Furthermore, it provides valuable research ideas for future scholars in the field.

关键词： Options price forecasting Options pricing Parameter optimization Deep learning Dynamic ensemble artificial intelligence algorithm

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A review of closed-loop reservoir management

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Petroleum Science 2015年第1期12卷 114-128页

作者： Hou, Jian Zhou, Kang Zhang, Xian-Song Kang, Xiao-Dong Xie, Hai State Key Laboratory of Heavy Oil Processing China University of Petroleum Qingdao 266580 Shandong China School of Petroleum Engineering China University of Petroleum Qingdao 266580 Shandong China CNOOC Research Institute Beijing 100027 China ZTE Corporation Shenzhen 518055 Guangdong China

The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. This is one of the most effective ways to exploit limited oil reserves more economically and efficiently. There are two steps in closed-loop reservoir management: automatic history matching and reservoir production optimization. Both of the steps are large-scale complicated optimization problems. This paper gives a general review of the two basic techniques in closed-loop reservoir management;summarizes the applications of gradient-based algorithms, gradient-free algorithms, and artificial intelligence algorithms;analyzes the characteristics and application conditions of these optimization methods;and finally discusses the emphases and directions of future research on both automatic history matching and reservoir production optimization. © 2015, The Author(s).

关键词： artificial intelligence algorithm Automatic history matching Closed-loop reservoir management Gradient-based algorithm Gradient-free algorithm Reservoir production optimization

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Microcontroller based Maximum Power Tracking of PV using Stimulated Annealing algorithm

Microcontroller based Maximum Power Tracking of PV using Sti...

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IEEE/OSA/IAPR International Conference on Informatics, Electronics and Vision (ICIEV)

作者： Azam, Md. Ali Abdullah-Al-Nahid, Syed Kabir, Md. Ashfanoor Chowdhury, Shah Mahmud Hasan Bangladesh Univ Engn & Technol Dept Elect & Elect Engn Dhaka 1000 Bangladesh AIUB Dept Elect & Elect Engn Dhaka 1000 Bangladesh

ISBN: (纸本)9781467311526

In this paper, a completely new algorithm based on Stimulated Annealing technique for MPPT is proposed to optimize standalone PV panel operating point even under rapidly and significantly changing weather pattern. The proposed algorithm is as simple as traditional P&O and Hill-climbing algorithm, yet it can successfully encounter the oscillation problem and steady state error problem even under worst case scenario with efficiency more than 99.99%. The algorithm eliminates hardware complexity and can be implemented using programmable microcontrollers. This paper presents performance comparison of stimulated annealing algorithm with three of the most popular artificially intelligent algorithms such as fuzzy logic algorithm, particle swarm optimization algorithm and Genetic algorithm. Step by step procedure for practical implementation of the proposed algorithm is included at the later part of this paper.

关键词： MPPT maximum power point tracking Stimulated Annealing High efficiency MPPT artificial intelligence algorithm

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Identification of multiple myeloma resistant cells using machine learning and laser tweezers Raman spectroscopy 13

Identification of multiple myeloma resistant cells using mac...

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Conference on Optics in Health Care and Biomedical Optics XIII

作者： Xie, Xingfei Wu, Ziqing Yuan, Hang Zhou, Zhehai Zhang, Pengfei Beijing Informat Sci & Technol Univ Key Lab Minist Educ Optoelect Measurement Technol & Instr Beijing 100192 Peoples R China Tianjin Univ Sch Precis Instrument & Optoelect Engn Tianjin 300072 Peoples R China

ISBN: (纸本)9781510667891;9781510667907

Multiple myeloma may develop resistance to certain drugs during chemotherapy, which have a fatal impact on treatment efficacy. At present, the drug resistance detection methods for multiple myeloma, such as proteomic identification and clone formation analysis, are relatively complex, and the accuracy and detection time are not ideal. In our work, laser tweezers Raman spectroscopy was used to collect 412 groups of spectra of two kinds of cells, namely, MM.1R and MM.1S, which were respectively resistant to dexamethasone and sensitive to dexamethasone. We selected support vector machine, random forest, linear discriminant analysis and other algorithms to train the pretreated Raman spectra, and the recognition accuracy on the test set was above 95%. This result shows that the combination of laser tweezers Raman spectroscopy and artificial intelligence algorithm can quickly detect drug resistance of cancer cells.

关键词： Multiple Myeloma drug resistance detection laser tweezers Raman spectroscopy artificial intelligence algorithm

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Real-time Bearing fault detection using Intelligent algorithm combined with Wavelet Transform

Real-time Bearing fault detection using Intelligent Algorith...

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Workshop on Microwave Theory and Techniques in Wireless Communications (MTTW)

作者： Dore, Pascal Chakkor, Saad El Oualkadi, Ahmed Univ Abdelmaalek Essaadi Ecole Natl Sci Appl Tanger Lab Commun & Informat Technol LabTIC Tanger Morocco

ISBN: (数字)9781665464390

ISBN: (纸本)9781665464390

The monitoring of bearings in electromechanical induction machines has become in the last decades a field where a lot of research is invested. We can understand this because of their responsibility for the defects of these machines and the enormous losses and expenses that they generate. However, if it is true that many methods have been proposed, it must be said that a large part of them, although based on the stator current, does not focus on the only defect of the bearing as we propose through this work. In this study, our goal is to analyze the current induced in a copper coil placed side by side with magnets placed in the inner ring of the bearing, by the Machine Current Signature Analysis method in order to detect from the genesis any defect of the bearing. To do this we will apply algorithms such as Cuckoo Search-Support Vector machine, Convolutional Neural Network, Kernel-Principal Component Analysis, Recurrent Neural Network, and Support Vector Machine on the data extracted from this induced current using the Wavelet Transform technique in order to determine among these algorithms, which one would allow to detect in real time and with a good precision the defect of bearing when it occurs during the operation of the machine that incorporates it. The whole with an aim of developing, in combination with the embedded electronics an autonomous electronic system of detection of the defect of bearing in real time in an effective way.

关键词： MCSA Bearing fault detection Real-time monitoring Wavelet transform artificial intelligence algorithm

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