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Application of a Nonparametric pattern recognition algorithm to the Problem of Testing the Hypothesis of the Independence of Variables of Multi-Valued Functions

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MEASUREMENT TECHNIQUES 2022年第1期65卷 17-23页

作者： Lapko, A., V Lapko, V. A. Bakhtina, A., V Russian Acad Sci Inst Computat Modeling Siberian Branch Krasnoyarsk Russia Reshetnev Siberian State Univ Sci & Technol Krasnoyarsk Russia

The problem of hypothesis testing for the independence of two-dimensional random variables in the analysis of variables of multi-valued functions is considered. To solve it, we used a technique based on a nonparametric kernel-type pattern recognition algorithm corresponding to the maximum likelihood criterion. The technique made it possible to bypass the problem of decomposing the random variable domain of values into intervals. Based on the results of computational experiments, the effectiveness of the applied technique was estimated depending on the type of multi-valued functions, the level of random noise and the amount of initial statistical data. The results obtained are relevant for solving the problem of detecting natural and technical objects from remote sensing data.

关键词： hypothesis testing independent random variables dependent random variables multi-valued functional dependencies two-dimensional random variables pattern recognition algorithm maximum likelihood test kernel estimation of probability density

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A pattern recognition algorithm for AETA Original Geoacoustic Data Anomaly Detection 4

A Pattern Recognition Algorithm for AETA Original Geoacousti...

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4th IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Lv, Mengxuan Yong, Shanshan Wang, Xinan Peking Univ Key Lab Integrated Microsyst Shenzhen Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9781728143903

We found a kind of anomalous waveform that appeared frequently before and after the earthquake based on AETA original geoacoustic data. To capture these abnormal geoacoustic signals, we designed a pattern recognition algorithm and transformed original geoacoustic data into anomalous eigenvalues. In this paper, we selected original geoacoustic data for five AETA stations in 2018 as the research object. Through the above-mentioned algorithm, we got the anomalous eigenvalues of the five AETA stations and then contrasted analysis with seismic events around the station that met the conditions of magnitude and shock time. Experiment results show, there is a clear correlation between surrounding seismic events and the anomalous eigenvalues extracted from the original geoacoustic data of the pattern recognition algorithm proposed in this paper.

关键词： AETA Original Geoacoustic Data pattern recognition algorithm Earthquake Precursor Anomaly Detection

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Statistical Analysis of the Capabilities of Various pattern recognition algorithms for Fracture Detection Based on Monitoring Drilling Parameters

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ROCK MECHANICS AND ROCK ENGINEERING 2020年第5期53卷 2265-2278页

作者： Liu, Wenpeng Rostami, Jamal Ray, Asok Elsworth, Derek Colorado Sch Mines Earth Mech Inst Dept Min Engn Golden CO 80401 USA Penn State Univ Dept Mech Engn University Pk PA 16802 USA Penn State Univ Dept Energy & Mineral Engn University Pk PA 16802 USA

Ground conditions, including characteristics of fractures, joints, bed separations, and strengths of rock layers, are critical factors for proper design of openings in underground mining and construction projects. Correct understanding of geologic conditions allows for improvement and optimization of ground support design and minimizing incidents of ground failure and instabilities in underground workings. Rock bolts have been widely accepted as the preferred method of ground support in almost all forms of rock excavation applications. The concept of monitoring drilling data to evaluate characteristics of geological features of interest in the rock surrounding the underground opening is a very attractive option for developing the geological model of the ground on real-time basis. This includes information on distributions of joints and bed separations, locations of voids, and strengths of rock layers, which enables the automated and rapid evaluation of ground conditions while drilling is in progress. Several smart drilling systems have been developed and proposed to detect joints;however, they offer limited capabilities and have exhibited difficulties in identifying joints with small apertures. The current study was focused on developing a more sensitive method to locate joints with smaller apertures along the hole being drilled with an instrumented roof bolter. A series of full-scale drilling tests were carried out in samples which contained simulated joints with different inclined angles in controlled laboratory conditions. New joint detection programs, with improved capabilities based on various pattern recognition algorithms, have been developed and used for analysis of data recorded in the full-scale drilling tests. To precisely locate joints, composite parameter was also used to offer more accurate detection. This paper reviews the laboratory testing program, data analysis, logic/algorithms used in the programs, statistical analysis of the detection results, a

关键词： Inclined joints Fracture detection Composite drilling parameters Field penetration index (FPI) Instrumented roof bolter pattern recognition algorithm

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Enhancing selectivity and sensitivity in gas sensors through noble metal-decorated ZnO and machine learning

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APPLIED SURFACE SCIENCE 2025年 693卷

作者： Kwon, Yeong Min Son, Yeseul Lee, Do Hyung Lim, Min Hyeok Han, Jin Kyu Jang, Moonjeong Park, Seoungwoong Kang, Saewon Yim, Soonmin Myung, Sung Lim, Jongsun Lee, Sun Sook Bae, Garam Kim, Soo-Hyun Song, Wooseok Korea Res Inst Chem Technol KRICT Thin Film Mat Res Ctr 141 Gajeong Ro Daejeon 34114 South Korea Ulsan Natl Inst Sci & Technol UNIST Sch Mat Sci & Engn Ulsan 44919 South Korea ASTAR Inst Mat Res & Engn Singapore 138634 Singapore Natl Nano Fab Ctr NNFC Daejeon 34141 South Korea Dankook Univ Dept Phys Cheonan 31116 South Korea Sungkyunkwan Univ Dept Elect & Comp Engn Suwon 16419 South Korea

The growing need for highly sensitive and selective gas sensors has spurred extensive research on enhancing metal-oxide-semiconductor-based sensors. In this study, we explored the gas-sensing performance of ZnO thin films functionalized with noble metals (Ir, Ru, and IrRu alloys) via atomic layer deposition for the detection of hazardous gases. The incorporation of noble metals led to significant improvements in the gas-sensing behavior driven by both electronic and chemical sensitization mechanisms. To further enhance gas selectivity, machine learning-based data analysis was employed, enabling precise classification of various gases with 100 % accuracy. These findings underscore the potential of noble metal-functionalized ZnO sensors for advanced gas detection, illustrating the effective combination of material engineering and cutting-edge data analysis techniques for the development of intelligent, selective, and stable gas sensor platforms.

关键词： Next-generation gas sensors Functionalization Sensor array pattern recognition algorithm

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Metal-decorated 3D tin oxide nanotubes in a monolithic sensor array chip for room-temperature gas identification

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JOURNAL OF ALLOYS AND COMPOUNDS 2024年 976卷

作者： Yan, Jia Song, Zhilong Jiangsu Univ Inst Energy Res Zhenjiang 212013 Jiangsu Peoples R China

Inadequate limit of detection at room temperature and unsatisfactory selectivity remain challenge for wide applications of the chemiresistive gas sensors. Nanostructured gas sensors ensure the desirable activation energy for the gas adsorption and chemical reactions, favorable for room-temperature parts-per-billion (ppb) level gas detection. In this work, we demonstrated a single-chip integrated gas sensor array (4 x4 pixels) based on kinds of metal (Pt, Pd, Au, Ag) decorated 3D SnO2 nanotubes for ultrasensitive room-temperature gas-sensing. The nanostructured sensor array has a high surface area-to-volume ratio, enabling room temperature sensing with high detection response toward H2 (minimum 5 ppm), formaldehyde (minimum 50 ppb), toluene (minimum 50 ppb) and NO2 (minimum 100 ppb) with the detection accuracy within 5% range, respectively. In addition, the effect of metals decoration on the gas identification features were systematically conducted, and these specific response features can be mainly attributed to metal activation capability toward the gases, which enables demonstrating differentiable response patterns for the target gases identification employing a pattern recognition algorithm. These results demonstrate that the proposed strategy helps to provide an excellent route for the future low-power-consumption smart sensing devices design and fabrication.

关键词： 3D nanostructured tin oxide Metal decoration Gas sensor array Room temperature pattern recognition algorithm Gas identification

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High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level

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SENSORS AND ACTUATORS B-CHEMICAL 2023年 375卷

作者： Kwon, Yeong Min Oh, Byungjoo Purbia, Rahul Chae, Hee Young Han, Gi Hyeon Kim, Sun-Woo Choi, Kyoung-Jin Lee, Yunsik Kim, Jae Joon Baik, Jeong Min Ulsan Natl Inst Sci & Technol UNIST Sch Mat Sci & Engn Ulsan 44919 South Korea Korea Res Inst Chem Technol KRICT Thin Film Mat Res Ctr 141 Gajeong Ro Daejeon 34114 South Korea Ulsan Natl Inst Sci & Technol Dept Elect Engn Ulsan 44919 South Korea Samsung Elect Memory Business Div 1-1 Samsungjeonja Ro Hwaseong Si Gyeonggi Do South Korea Sungkyunkwan Univ SKKU Sch Adv Mat Sci & Engn Suwon 16419 South Korea Sungkyunkwan Univ SKKU Inst Energy Sci & Technol SIEST Suwon 16419 South Korea

Developing a better understanding of gas-metal oxide interactions as well as improving the output performance with the aging of the gas sensing array represents a critical challenge. In this work, we have designed a surfacefunctionalized metal-oxide sensors array containing six units for detecting NO2, SO2, H2S, and CO hazardous gases. The responses of the sensor array to four target gases are enhanced by various catalytic effects on the surface, and they show distinctive responses to each gas. For improved selectivity, a pattern recognition algorithm is implemented under an edge computing-based environment to relieve the overload of a monitoring server. Moreover, a self-calibration method for long-term stability is designed to indirectly calibrate gas responses with aging. With its edge-computing device prototype, which includes a sensor array and its readout integrated circuit, four target gases are distinguished by an artificial neural network algorithm with 97% accuracy. The present work describes the effective platforms for emerging next-generation gas sensors with improved communication and monitoring systems.

关键词： Next -generation gas sensors Functionalization Sensor array Self -calibration pattern recognition algorithm

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Novel algorithm for Radon Real-Time Measurements with a Pixelated Detector

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SENSORS 2022年第2期22卷 516页

作者： Rizzo, Alessandro Cardellini, Francesco Poggi, Claudio Borra, Enrico Ciciani, Luca Narici, Livio Sperandio, Luciano Vilardi, Ignazio Italian Natl Agcy New Technol Energy & Sustainabl Radiat Protect Inst IRP Via Anguillarese 301 I-00123 Rome Italy Italian Natl Agcy New Technol Energy & Sustainabl Natl Inst Ionizing Radiat Metrol INMRI Via Anguillarese 301 I-00123 Rome Italy Italian Natl Agcy New Technol Energy & Sustainabl Radiat Protect Inst IRP Via Enrico Fermi 45 I-00044 Rome Italy Univ Roma Tor Vergata Phys Dept Via Ric Sci 1 I-00133 Rome Italy

Nowadays, radon gas exposure is considered one of the main health concerns for the population because, by carrying about half the total dose due to environmental radioactivity, it is the second cause of lung cancer after smoking. Due to a relatively long half-life of 3.82 days, the chemical inertia and since its parent Ra-226 is largely diffuse on the earth's crust and especially in the building materials, radon can diffuse and potentially saturate human habitats, with a concentration that can suddenly change during the 24 h day depending on temperature, pressure, and relative humidity. For such reasons, 'real-time' measurements performed by an active detector, possibly of small dimensions and a handy configuration, can play an important role in evaluating the risk and taking the appropriate countermeasures to mitigate it. In this work, a novel algorithm for pattern recognition was developed to exploit the potentialities of silicon active detectors with a pixel matrix structure to measure radon through the alpha emission, in a simple measurement configuration, where the device is placed directly in air with no holder, no collection filter or electrostatic field to drift the radon progenies towards the detector active area. This particular measurement configuration (dubbed as bare) requires an alpha/beta-discrimination method that is not based on spectroscopic analysis: as the gas surrounds the detector the alpha particles are emitted at different distances from it, so they lose variable energy amount in air depending on the traveled path-length which implies a variable deposited energy in the active area. The pixels matrix structure allows overcoming this issue because the interaction of alpha, beta and gamma particles generate in the active area of the detector clusters (group of pixels where a signal is read) of different shape and energy dispersion. The novel algorithm that exploits such a phenomenon was developed using a pixelated silicon detector of the TimePix

关键词： radon real-time measurements TimePix MediPix pattern recognition algorithm

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PERFORMANCE OF pattern recognition algorithmS IN IDENTIFYING BANKING NETWORKS 7

PERFORMANCE OF PATTERN RECOGNITION ALGORITHMS IN IDENTIFYING...

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7th International Conference on Computing, Communications and Informatics (ICACCI)

作者： Sasidharan, Arun Palakkeel, Prashobhan Amrita Vishwa Vidyapeetham Dept Management Bangalore Karnataka India

ISBN: (纸本)9781538653142

A review of literature shows that bank branching can be influenced by economic, geographic, legal, cultural and demographic characteristics apart from institutional decision making. Identifying the branching pattern can help in answering many questions like how a particular banking group is different from other? It can reveal secrets regarding strategic decision making about diversification and growth of banking networks. Present study apply machine learning algorithms to recognize the branching pattern of banking networks in India and compare the differences across groups and types. An evaluation of the performance of the algorithms shows that it is very useful employ a narrative approach for getting important insights from the data compared to traditional approaches. The study also provides a visual map evaluation to enrich the outcomes from the pattern recognition exercise.

关键词： pattern recognition algorithm Machine learning Classification algorithm Banking networks

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Applications of Hand Gestures recognition in Industrial Robots: A Review 11

Applications of Hand Gestures Recognition in Industrial Robo...

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11th International Conference on Machine Vision (ICMV)

作者： Lei, Qujang Zhang, Hongda Xia, Zanwu Yang, Yang He, Yue Liu, Shoubin Chinese Acad Sci Guangzhou Inst Adv Technol Beijing Peoples R China Harbin Inst Technol HIT Shenzhen Peoples R China

ISBN: (数字)9781510627499

ISBN: (纸本)9781510627499

Hand gesture recognition (HGR) is a natural way of Human Machine Interaction and has been applied on different areas. In this paper, we discuss works done in the area of applications of HGR in industrial robots where focus is on the processing steps and techniques in gesture-based Human Robot Interaction (HRI), which can provide useful information for other researchers. We review several related works in the area of HGR based on different approaches including sensor based approach and vision approach. After comparing the two approaches, we found that 3D vision-based HGR method is a challenging but promising researching area. Then, concerning works of implementation of HGR in industrial scenario are discussed in detail. pattern recognition algorithms that effectively used in HGR like k-means, DTW etc. are briefly introduced as well.

关键词： Hand gesture recognition human Robot Interaction industrial robot pattern recognition algorithm

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Adding new evidence to the attribution puzzle of the recent water shortage over Sao Paulo (Brazil)

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TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY 2018年第1期70卷 1-14页

作者： Pattnayak, K. C. Gloor, E. Tindall, J. C. Brienen, R. J. W. Barichivich, J. Baker, J. C. A. Spracklen, D. V. Cintra, B. B. L. Coelho, C. A. S. Univ Leeds Sch Geog Leeds W Yorkshire England Univ Leeds Sch Earth & Environm Leeds W Yorkshire England Univ Austral Chile Inst Conservac Biodiversidad & Terr Valdivia Chile Ctr Climate & Resilience Res CR 2 Santiago Chile INPE CPTEC Cachoeira Paulista SP Brazil

Sao Paulo, Brazil has experienced severe water shortages and record low levels of its water reservoirs in 2013-2014. We evaluate the contributions of Amazon deforestation and climate change to low precipitation levels using a modelling approach, and address whether similar precipitation anomalies might occur more frequently in a warming world. Precipitation records from INMET show that the dry anomaly extended over a fairly large region to the north of Sao Paulo. Unique features of this event were anomalous sea surface temperature (SST) patterns in the Southern Atlantic, an extension of the sub tropical high into the Sao Paulo region and moisture flux divergence over Sao Paulo. The SST anomalies were very similar in 2013/14 and 2014/15, suggesting they played a major role in forcing the dry conditions. The SST anomalies consisted of three zonal bands: a cold band in the tropics, a warm band to the south of Sao Paulo and another cold band poleward of 40 S. We performed ensemble climate simulations with observed SSTs prescribed, vegetation cover either fixed at 1870 levels or varying over time, and greenhouse gases (GHGs) either fixed at preindustrial levels (280 ppm CO2) or varying over time. These simulations exhibit similar precipitation deficits over the Sao Paulo region in 2013/14. From this, we infer that SST patterns and the associated large-scale state of the atmosphere were important factors in determining the precipitation anomalies, while deforestation and increased GHGs only weakly modulated the signal. Finally, analyses of future climate simulations from CMIP5 models indicate that the frequency of such precipitation anomalies is not likely to change in a warmer climate.

关键词： water shortages deforestation pattern recognition algorithm climate change sea surface temperature anomaly

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