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Computing open 2024年第2期computing-open卷

作者： Guanghong Deng Liangpei Lin Baihao Lin Wenlong Jing Xiaodan Zhao Yong Li Rui Yang Shidun Xie Pengfeng Wei GuangDong Engineering Technology Research Center of UAV Remote Sensing Network Guangzhou iMapCloud Intelligent Technology Co. Ltd. Guangzhou 510095 P. R. China Guangdong Province Engineering Laboratory for Geographic Spatiotemporal Big Data Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou 510070 P. R. China Foshan iMapCloud Intelligent Technology Co. Ltd. Foshan 528251 P. R. China

The application of the combination of unmanned aerial vehicles (UAVs) and artificial intelligence is a hot topic in the intelligent inspection of substations, and meter reading is a very challenging task. This paper proposes a method based on the combination of YOLOv6n object detection and Deeplabv3 + image segmentation and performs post-processing on the segmented images to obtain meter readings. First, YOLOv6n is used to detect the meter area of the aerial image and classify the meters. Second, the detected meter images are fed into the image segmentation model. The backbone network of the Deeplabv3 + algorithm is improved by using the MobileNetv3 network, which not only effectively extracts pointers and scales, but also makes the model more lightweight. Third, License Plate Recognition Network (LPRNet) is used to recognize digital meter images. In order to solve the problem of inaccurate pointer meter readings, to begin with, the segmented image is corroded; in addition, the circular dial area is flattened into a rectangular area by concentric circle sampling method. Finally, the meter reading is calculated by the position of the pointer, the scale and the total range of the meter. The post-processing part uses numba to optimize the inference speed. The experimental results show that in two datasets, The mean average precision of 50 (mAP50) accuracy of the YOLOv6n model using this method reached 99.71% and 98.60%, respectively, and the inference speed of a single image was 17.1 ms and 13.2 ms, respectively. The mean intersection over union (mIoU) of the image segmentation model reached 82.00%, 74.73%, 73.50%, 82.26% and 73.20%, respectively, and the single segmentation speed reached 33.7 ms. The LPRNet model has a recognition accuracy of 99.17% and a single image inference speed of 14.7 ms. At the same time, several mainstream object detection and semantic segmentation algorithms are compared. The experimental results show that the method in this paper greatly im

关键词： Object detection image segmentation YOLOv6n Deeplabv3 + meter reading

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Probabilistic neural networks for fluid flow model-order reduction and data recovery

arXiv

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arXiv 2020年

作者： Maulik, Romit Fukami, Kai Ramachandra, Nesar Fukagata, Koji Taira, Kunihiko Argonne Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States School of Science for Open and Environmental System Keio University Yokohama223-8522 Japan High Energy Physics Division Argonne National Laboratory LemontIL60439 United States Mechanical Engineering Keio University Yokohama223-8522 Japan Mechanical and Aerospace Engineering University of California Los AngelesCA90095 United States

We consider the use of probabilistic neural networks for fluid flow model-order reduction and data recovery. This framework is constructed by assuming that the target variables are sampled from a Gaussian distribution conditioned on the inputs. Consequently, the overall formulation sets up a procedure to predict the hyperparameters of this distribution which are then used to compute an objective function given training data. We demonstrate that this framework has the ability to provide for prediction confidence intervals based on the assumption of a probabilistic posterior, given an appropriate model architecture and adequate training data. The applicability of the present framework to cases with noisy measurements and limited observations is also assessed. To demonstrate the capabilities of this framework, we consider canonical regression problems of fluid dynamics from the viewpoint of reduced-order modeling and spatial data recovery for four canonical data sets. The examples considered in this study arise from (1) the shallow water equations, (2) a two-dimensional cylinder flow, (3) the wake of NACA0012 airfoil with a Gurney flap, and (4) the NOAA sea surface temperature data set. The present results indicate that the probabilistic neural network not only produces a machine-learning-based fluid flow model but also systematically quantifies the uncertainty therein to assist with model interpretability. Copyright © 2020, The Authors. All rights reserved.

关键词： Surface waters

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Experimental Evaluation of a Novel Personal Assistant in Greek Language for Ambient Assisted Living Environments employing home robots

Experimental Evaluation of a Novel Personal Assistant in Gre...

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2018 South-East Europe Design Automation, Computer engineering, Computer Networks and Social Media Conference, SEEDA_CECNSM 2018

作者： Spournias, Alexandros Christopoulos, Konstantinos Antonopoulos, Konstantinos Panagiotou, Christos Antonopoulos, Christos Orfanoudakis, Theofanis Voros, Nikolaos Computer and Informatics Engineering Department Embedded System Design and Application Laboratory TEI of Western Greece Antirio Greece Embedded System Design and Application Laboratory School of Science and Technology Digital Systems and Media Computing Laboratory TEI of Western Greece Hellenic Open University Patras Greece Embedded System Design and Application Laboratory TEI of Western Greece Antirio Greece School of Science and Technology Digital Systems and Media Computing Laboratory Hellenic Open University Patras Greece

ISBN: (纸本)9786188331419

A personal assistance, is a software component offering advanced Human Machine Interface (HMI) features by initiating and making a "conversations" between humans and machines. Its main objective is to identify the sentences and the context from human's question and respectively give an adequate response to the question posed. The proper response is given after analyzing the words of the question with those registered in its database. This type of application is called a personal assistant - chatbot. In this work, such an application is presented where it can be installed in any smart home as well as assisted living environment. Through the application, all automations, as well as the home robot also, are controlled by human voice in Greek language. Also, a number of auxiliary applications can be performed in the direction of better human service. © 2018 Tei of Western Macedonia.

关键词： Assisted living

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Study on the influence of international oil price on domestic oil price by APT-ECM

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Journal of Physics: Conference Series 2021年第1期1978卷

作者： Ziyuan Liu Yu Wang Shiwei Xu Shengwei Wang Denghua Li Han Zhou Agricultural Information Institute of CAAS Beijing 100081 Beijing Agricultural Monitoring & Early Warning Engineering Technology Research Center Beijing 100081 Key Open Laboratory of Intelligent Agricultural Early Warning Technology & System of CAAS Beijing 100081 Key Laboratory of Agricultural Information Service Technology of the Ministry of Agricultural and Rural Affairs Beijing 100081

Based on the monthly data of soybean, soybean oil and soybean meal prices in China and the United States from 2006 to 2020, this paper applied the asymmetric error correction model (APT-ECM) to analyze the influence of international oil price on domestic oil price before and after Sino-US trade friction. The results show that: 1) the influence of international domestic soybean price on domestic soybean price is significant before and after Sino-US trade friction, while the influence of domestic price on international price is insignificant; 2) the impact of international soybean oil price on domestic soybean price is stable in the early stage but fluctuates evidently in the latter stage; 3) domestic soybean oil price is exposed to insignificant impact of international soybean oil price.

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EEG Based Depression Recognition by Combining Functional Brain Network and Traditional Biomarkers

EEG Based Depression Recognition by Combining Functional Bra...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuting Sun Huayu Chen Xuexiao Shao Liangliang Liu Xiaowei Li Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in BrainScience and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Beijing China Ministry of Education Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Lanzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Resting-state EEG data were collected from 24 major depressive patients (MDD) and 29 normal controls using 128-electrode geodesic sensor net. To better identify depression, we extracted multi-type of EEG features including linear features (L), nonlinear features (NL), functional connectivity features phase lagging index (PLI) and network measures (NM) to comprehensively characterize the EEG signals in patients with MDD. And machine learning algorithms and statistical analysis were used to evaluate the EEG features. Combined multi-types features (All: L+ NL + PLI + NM) outperformed single-type features for classifying depression. Analyzing the optimal features set we found that compared to other type features, PLI occupied the largest proportion of which functional connections in intra-hemisphere were much more than that of in inter-hemisphere. In addition, when using PLI features and All features, high frequency bands (alpha, beta) could achieve obviously higher classification accuracy than low frequency bands (delta, theta). Parietal-occipital lobe in the high frequency bands had great effect in depression identification. In conclusion, combined multi-types EEG features along with a robust classifier can better distinguish depressive patients from normal controls. And intra-hemispheric functional connections might be an effective biomarker to detect depression. Hence, this paper may provide objective and potential electrophysiological characteristics in depression recognition.

关键词： Electroencephalography Depression Feature extraction Electrodes Biomarkers Support vector machines Entropy

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A multiple-proxy stalagmite record reveals historical deforestation in central Shandong, northern China

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Science China Earth Sciences 2020年第11期63卷 1622-1632页

作者： Liangcheng TAN Wen LIU Tianli WANG Peng CHENG Jingjie ZANG Xiqian WANG Le MA Dong LI Jianghu LAN R.Lawrence EDWARDS Hai CHENG Hai XU Li AI Yongli GAO Yanjun CAI State Key Laboratory of Loess and Quaternary Geology Institute of Earth EnvironmentChinese Academy of SciencesXi’an 710061China Center for Excellence in Quaternary Science and Global Change Chinese Academy of SciencesXi’an 710061China Institute of Global Environmental Change Xi’an Jiaotong UniversityXi’an 710049China Open Studio for Oceanic-Continental Climate and Environment Changes Pilot National Laboratory for Marine Science and TechnologyQingdao 266061China 801 Institute of Hydrogeology and Engineering Geology Shandong Provincial Bureau of Geology&Mineral ResourcesJinan 250014China Shandong Engineering Research Center for Environmental Protection and Remediation on Groundwater Jinan 250014China Library of Chang’an University Xi’an 710064China Department of Earth Sciences University of MinnesotaMinneapolis MN 55455USA School of Geography Nanjing Normal UniversityNanjing 210097China Institute of Surface-Earth System Science Tianjin UniversityTianjin 300072China Department of Geological Sciences University of Texas at San AntonioSan Antonio TX 78249USA

Evaluating anthropogenic impacts on regional vegetation changes during historical time is not only important for a better understanding of the Anthropocene but also valuable in improving the vegetation-climate *** this study,we analyzed stable isotopes(δ^18O,δ^13C)and trace elements(Mg/Ca,Sr/Ca)of a stalagmite from Huangchao Cave in central Shandong,northern China.230Th and AMS14C dating results indicate the stalagmite deposited during 174BC and AD1810,with a hiatus between AD638 and *** similarities of theδ^18O and trace elements in the stalagmite suggest they are reliable precipitation ***δ^13C of the stalagmite,a proxy of vegetation change,was generally consistent with local precipitation and temperature variations on a centennial-scale before the 15th *** typically varied from–9.6‰to–6.3‰,indicating climate controlled C3 type vegetation during this ***,a persistent and marked increasing trend in theδ^13C record was observed since the 15th century,resulting inδ^13C values from–7.7‰to–1.6‰in the next four *** unprecedentedδ^13C change caused by vegetation deterioration cannot be explained by climate change but is fairly consistent with the dramatically increasing population and farmland in *** suggest that the increasing deforestation and reclamation in central Shandong began to affect vegetation in the mountain region of central Shandong since the 15th century and severely destroyed or even cleared the forest during the 16th–18th century.

关键词： Speleothem Stable isotopes Trace elements Deforestation Human activity 16th century

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Designing and Application of Wearable Fatigue Detection system Based on Multimodal Physiological Signals

Designing and Application of Wearable Fatigue Detection Syst...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoxuan Qiu Fuze Tian Qiuxia Shi Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Lanzhou China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Ministry of Education Lanzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Judging mental fatigue can be guided by acquiring and analyzing various physiological data. However, the existing equipment focuses on single physiological signals, such as electroencephalogram (EEG) and electrocardiogram (ECG), which ignores the preponderance of multimodal physiological signals in fatigue states. Alternatively, some equipment is difficult to operate and thus unsuitable for portable applications. To this end, this paper details the design of a miniaturized multi-physiological signal acquisition system. The system not only acquires EEG and ECG based on combining wavelet transform with Kalman filter, but also uses precise temperature sensors to synchronously acquire proximal skin temperature signals which are not easily interfered with by environmental noise or other physiological signals of human body. Through a fatigue assessment experiment on ten healthy subjects, it was verified that our equipment reliably detects mental fatigue states by monitoring and analyzing EEG, ECG, and proximal skin temperature data. In actual applications, this system appears to the traits of easy operation and good stability. It provides better hardware support for the pervasive applications and concrete implementation of mental health in multi-scene, and can popularize use.

关键词： Fatigue Electrocardiography Electroencephalography Skin Temperature measurement Temperature distribution Temperature sensors

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MODMA dataset: a Multi-modal open Dataset for Mental-disorder Analysis

arXiv

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arXiv 2020年

作者： Cai, Hanshu Gao, Yiwen Sun, Shuting Li, Na Tian, Fuze Xiao, Han Li, Jianxiu Yang, Zhengwu Li, Xiaowei Zhao, Qinglin Liu, Zhenyu Yao, Zhijun Yang, Minqiang Peng, Hong Zhu, Jing Zhang, Xiaowei Gao, Guoping Zheng, Fang Li, Rui Guo, Zhihua Ma, Rong Yang, Jing Zhang, Lan Hu, Xiping Li, Yumin Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Open Source Software and Real-Time System Lanzhou University Ministry of Education China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Lanzhou University Second Hospital China

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation;the 3-electrode EEG signals of 55 subjects were recorded in resting state;the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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Investigating forest photosynthetic response to elevated CO2 using UAV-based measurements of solar induced fluorescence 38

Investigating forest photosynthetic response to elevated CO2...

引用

38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018

作者： Maseyk, Kadmiel Atherton, Jon Thomas, Rick Wood, Kieran Tausz-Posch, Sabine Arthur, Alasdair Mac Porcar-Castell, Albert Tausz, Michael School of Environment Earth and Ecosystem Sciences Open University United States Optics of Photosynthesis Laboratory Institute for Atmospheric and Earth System Research/Forest Sciences University of Helsinki Finland School of Geography Earth and Environmental Sciences University of Birmingham United Kingdom School of Civil Aerospace and Mechanical Engineering University of Bristol United Kingdom School of BioSciences University of Birmingham United Kingdom School of Geosciences University of Edinburgh United Kingdom

ISBN: (纸本)9781538671504

The response of ecosystems to increasing atmospheric CO2 will have significant, but still uncertain, impacts on the global carbon and water cycles. A lot of information has been gained from Free Air CO2 Enrichment (FACE) experiments, but the response of mature forest ecosystems remains a significant knowledge gap. One of the challenges in FACE studies is obtaining an integrated measure of canopy photosynthesis at the scale of the treatment ring. A new remote sensing approach for measuring photosynthetic activity is based on Solar Induced Fluorescence (SIF), which is emitted by plants during photosynthesis, and is closely linked to the rates and regulation of photosynthesis. We proposed that UAV-based SIF measurements, that enable the spectrometer field of view to be targeted to the treatment ring, provide a unique opportunity for investigating the dynamics of photosynthetic responses to elevated CO2. We have successfully tested this approach in a new FACE site, located in a mature oak forest in the UK. We flew a series of flights across the experiment arrays, collecting a number of spectra. We combined these with ground-based physiological and optical measurements, and see great promise in the use of UAV-based SIF measurements in FACE and other global change experiments. © 2018 Crown

关键词： Face Forestry Calibration Temperature measurement Fluorescence Ecosystems Physiology

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Giant optical nonlinearity in single silicon nanostructure: Ultrasmall all-optical switch and super-resolution imaging

arXiv

引用

arXiv 2020年

作者： Duh, Yi-Shiou Nagasaki, Yusuke Tang, Yu-Lung Wu, Pang-Han Cheng, Hao-Yu Yen, Te-Hsin Ding, Hou-Xian Nishida, Kentaro Hotta, Ikuto Yang, Jhen-Hong Lo, Yu-Ping Chen, Kuo-Ping Fujita, Katsumasa Chang, Chih-Wei Lin, Kung-Hsuan Takahara, Junichi Chu, Shi-Wei Department of Physics National Taiwan University 1 Sec 4 Roosevelt Rd. Taipei10617 Taiwan Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka565-0871 Japan Institute of Physics Academia Sinica 128 Sec. 2 Academia Rd. Taipei11529 Taiwan AIST-Osaka University Advanced Photonics and Biosensing Open Innovation Laboratory AIST 2-1 Yamadaoka Suita Osaka565-0851 Japan Institute of Photonic System National Chiao Tung University 301 Gaofa 3rd Road Tainan711 Taiwan Institute of Imaging and Biomedical Photonics National Chiao Tung University 301 Gaofa 3rd Road Tainan711 Taiwan Center for Condensed Matter Sciences National Taiwan University 1 Sec 4 Roosevelt Rd. Taipei10617 Taiwan Photonics Center Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka565-0871 Japan Molecular Imaging Center National Taiwan University 1 Sec 4 Roosevelt Rd. Taipei10617 Taiwan

Silicon photonics has attracted significant interest in recent years due to its potential in integrated photonics components1,2 as well as all-dielectric meta-optics elements.3 Strong photon-photon interactions, aka optical nonlinearity, realizes active control of aforementioned photonic devices.4,5 However, intrinsic nonlinearity of Si is too weak to envision practical applications. To boost the nonlinear response, long interaction-length structures such as waveguides, or resonant structures such as microring resonators or photonic crystals have been adopted.6,7 Nevertheless, their feature sizes are typically larger than 10 μm, much larger than their electronic counterparts. Here we discover, when reducing the size of Si resonator down to ~100 nm, a giant photothermal nonlinearity that yields 400% reversible and repeatable deviation from linear scattering response at low excitation intensity (mW/μm2). The equivalent nonlinear index n2 at nanoscale is five-order larger than that of bulk, due to Mie resonance enhanced absorption and high-efficiency heating in the thermally isolated nanostructure. In addition, the nanoscale thermal relaxation time reaches nanosecond, implying GHz modulation speed. This large and fast nonlinearity enables applications toward all-optical control in nanoscale, as well as super-resolution imaging of silicon. Copyright © 2020, The Authors. All rights reserved.

关键词： Photonic devices

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