Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition

Handwritten mathematical expression recognition (HMER) has attracted extensive attention recently. However, current methods cannot explicitly study the interactions between different symbols, which may fail when faced similar symbols. To alleviate this issue, we propose a simple but efficient method to enhance semantic interaction learning (SIL). Specifically, we firstly construct a semantic graph based on the statistical symbol co-occurrence probabilities. Then we design a semantic aware module (SAM), which projects the visual and classification feature into semantic space. The cosine distance between different projected vectors indicates the correlation between symbols. And jointly optimizing HMER and SIL can explicitly enhances the model's understanding of symbol relationships. In addition, SAM can be easily plugged into existing attention-based models for HMER and consistently bring improvement. Extensive experiments on public benchmark datasets demonstrate that our proposed module can effectively enhance the recognition performance. Our method achieves better recognition performance than prior arts on both CROHME and HME100K datasets.Comment: 12 Page

Time-Space Relationship Analysis Model on the Bus Driving Characteristics of Different Drivers Based on the Traffic Performance Index System

With the extensive application of the concept of green traffic, the relationship between the driving characteristics of different drivers and energy consumption and traffic performance conditions, etc. is gradually becoming a research hotspot. Based on bus status data recorded by travel data recorders with a vehicle-mounted satellite positioning function and in view of external bus behaviours and driver’s performance, a bus driving characteristic model of drivers is established. A time-space analysis model of the driving characteristics of different drivers based on traffic performance index is also established through fuzzy association rules and a type-2 fuzzy set prediction algorithm. Test results show that the prediction algorithm can accurately describe the time-space relationship between the traffic congestion index and bus driving characteristic model and achieve relatively high prediction accuracy. The problem of the lagging release of traffic performance index caused by massive calculation for floating vehicle data can be effectively solved through this algorithm, which can serve as an important reference for analyzing traffic performance conditions, as well as the energy conservation and emission reduction of buses

Study on sensor fault instability prediction for the Internet of agricultural things based on largest Lyapunov exponent

U ovom se istraživanju primjenjuje algoritam najvećeg Lyapunovog eksponenta za predviđanje tipova greške u mreži bežičnog senzora "Interneta poljoprivrednih stvari". Podaci o greški u sustavu dobiveni su od Interneta poljoprivrednih stvari, koji se sastoji od mreže kalibriranog TDR senzora vlage tla u svrhu razvijanja modela za predviđanje nestabilnosti greške senzora na temelju algoritma najvećeg Lyapunovog eksponenta. U svrhu provjere primjenjivosti tog modela u predviđanju uzoraka za uvježbavanje pod različitim uvjetima, u ovom se istraživanju ispituje i uspoređuje takav algoritam s modelom C4.5 algoritma kao prikaza podataka o grešci za različite postotke uzoraka za uvježbavanje. Metoda najvećeg Lyapunovog eksponenta za predviđanje nestabilnosti primjenjuje se također na niz za uvježbavanje koji uglavnom uključuje normalne podatke. Algoritmom se postiže točnost predviđanja od 90,43 %, što je 5,55 % više nego kod algoritma C4.5 (84,88 %). Različiti algoritmi pokazuju određeni stupanj prilagodljivosti u različitim uvjetima primjene. Metodom najvećeg Lyapunovog eksponenta za predviđanje nestabilnosti postižu se bolji rezultati kad se koriste mnogi autentični primjeri. Rezultati testa prilagodljivosti primjene pokazuju da model predviđanja nestabilnosti greške senzora zasnovan na algoritmu najvećeg Lyapunovog eksponenta omogućuje pouzdan pristup za dobivanje informacija o greški senzora i predviđanje grešaka u Internetu Poljoprivrednih Stvari.This study uses the largest Lyapunov exponent algorithm to predict the fault types in the wireless sensor network of the Internet of Agricultural Things. System fault data are collected from the Internet of Agricultural Things, which is composed of a calibrated TDR soil moisture sensor network, to develop a sensor fault instability prediction model based on the largest Lyapunov exponent algorithm. To verify the applicability of this model in forecasting training samples under various conditions, this study tests and compares such algorithm with the C4.5 algorithm model as a fault data account for different percentages of training samples. The largest Lyapunov exponent instability prediction method is also applied on the training set that mostly comprises normal data. The algorithm achieves a prediction accuracy of 90,43 %, which is 5,55 % higher than that of the C4.5 algorithm (84,88 %). Different algorithms demonstrate a certain degree of adaptability in various application conditions. The largest Lyapunov exponent instability prediction method achieves better results when many accurate samples are used. The results from the application adaptability test show that the sensor fault instability prediction model based on the largest Lyapunov exponent algorithm provides a reliable approach for collecting sensor fault information collection and predicting faults in the Internet of Agricultural Things

A spatial-temporal estimation model of residual energy for pure electric buses based on traffic performance index

Odnos između potrošnje energije autobusa i prometnih uvjeta postupno je privukao istraživačku pozornost sa širenjem koncepta zelenog transporta i promocijom novih energetskih autobusa. U skladu s ovim razvojem, ova studija razvija prostorno-vremenski model procjene preostale energije za potpuno električne autobuse s algoritmima neizrazitog grupiranja i neizrazitim vremenskim nizovima. Ovi algoritmi se temelje na indeksu učinka prometa cestovnih dionica između najbližih autobusnih stanica. Nadalje, oni se uspostavljaju prema položajima vozila u prometu i autobusnim rutama u kombinaciji s podacima o potrošnji energije koji proizlaze iz sustava upravljanja baterijama potpuno električnih autobusa. Rezultati ispitivanja pokazuju da ti algoritmi procjene mogu točno opisati prostorno-vremenski odnos između indeksa učinka prometa i preostale energije u potpuno električnim autobusima. Tako se oni mogu primijeniti kao značajne reference u analizi prometnih uvjeta, očuvanja energije i smanjenja emisija za autobuse.The relationship between the energy consumption of buses and traffic conditions has gradually garnered research attention with the expansion of the green transportation concept and the promotion of new energy buses. In line with these developments, this study develops a spatial–temporal estimation model of residual energy for pure electric buses with fuzzy clustering and time-series algorithms. These algorithms are based on the traffic performance index of the road sections between the nearest bus stops. Furthermore, they are established according to the positions of floating vehicles and the bus routes in combination with the energy consumption data derived from the battery management system of pure electric buses. Test results show that these estimation algorithms can accurately describe the spatial–temporal relationship between traffic performance index and the residual energy in pure electric buses. Thus, they can be applied as significant references in the analysis of traffic conditions, energy conservation, and emission reduction for buses

From BASE-ASIA Toward 7-SEAS: A Satellite-Surface Perspective of Boreal Spring Biomass-Burning Aerosols and Clouds in Southeast Asia

In this paper, we present recent field studies conducted by NASA's SMART-COMMIT (and ACHIEVE, to be operated in 2013) mobile laboratories, jointly with distributed ground-based networks (e.g., AERONET, http://aeronet.gsfc.nasa.gov/ and MPLNET, http://mplnet.gsfc.nasa.gov/) and other contributing instruments over northern Southeast Asia. These three mobile laboratories, collectively called SMARTLabs (cf. http://smartlabs.gsfc.nasa.gov/, Surface-based Mobile Atmospheric Research & Testbed Laboratories) comprise a suite of surface remote sensing and in-situ instruments that are pivotal in providing high spectral and temporal measurements, complementing the collocated spatial observations from various Earth Observing System (EOS) satellites. A satellite-surface perspective and scientific findings, drawn from the BASE-ASIA (2006) field deployment as well as a series of ongoing 7-SEAS (2010-13) field activities over northern Southeast Asia are summarized, concerning (i) regional properties of aerosols from satellite and in situ measurements, (ii) cloud properties from remote sensing and surface observations, (iii) vertical distribution of aerosols and clouds, and (iv) regional aerosol radiative effects and impact assessment. The aerosol burden over Southeast Asia in boreal spring, attributed to biomass burning, exhibits highly consistent spatial and temporal distribution patterns, with major variability arising from changes in the magnitude of the aerosol loading mediated by processes ranging from large-scale climate factors to diurnal meteorological events. Downwind from the source regions, the tightly coupled-aerosolecloud system provides a unique, natural laboratory for further exploring the micro- and macro-scale relationships of the complex interactions. The climatic significance is presented through large-scale anti-correlations between aerosol and precipitation anomalies, showing spatial and seasonal variability, but their precise cause-and-effect relationships remain an open-ended question. To facilitate an improved understanding of the regional aerosol radiative effects, which continue to be one of the largest uncertainties in climate forcing, a joint international effort is required and anticipated to commence in springtime 2013 in northern Southeast Asia

Classical risk factors of cardiovascular disease among Chinese male steel workers: a prospective cohort study for 20 years

<p>Abstract</p> <p>Background</p> <p>Cardiovascular disease (CVD) constitutes a major public health problem in China and worldwide. We aimed to examine classical risk factors and their magnitudes for CVD in a Chinese cohort with over 20 years follow-up.</p> <p>Methods</p> <p>A cohort of 5092 male steelworkers recruited from 1974 to 1980 in Beijing of China was followed up for an average of 20.84 years. Cox proportional-hazards regression model were used to evaluate the risk of developing a first CVD event in the study participants who were free of CVD at the baseline.</p> <p>Results</p> <p>The multivariable-adjusted hazard ratio (HR) associated with every 20 mmHg rise in systolic blood pressure (SBP) was 1.63 in this Chinese male population, which was higher than in Caucasians. Compared to non-smokers, men who smoked not less than one-pack-a-day had a HR of 2.43 (95% confidence interval [CI], 1.75-3.38). The HR (95% CI) for every 20 mg/dl increase in total serum cholesterol (TC) and for every point rise in body mass index (BMI) was 1.13 (1.04-1.23) and 1.06 (1.02-1.09), respectively.</p> <p>Conclusions</p> <p>Our study documents that hypertension, smoking, overweight and hypercholesterolemia are major conventional risk factors of CVD in Chinese male adults. Continued strengthening programs for prevention and intervention on these risk factors are needed to reduce the incidence of CVD in China.</p

Angle steel tower bolt defect detection based on YOLO-V3

The bolts in the angle steel tower are seriously affected by corrosion and loss. This paper proposes a novel detection system based on YOLO-V3 to avoid the danger of traditional manual detection method for the bolt fault detection of the angle steel tower. A multi-scale convolution module is used to replace the ordinary convolution of original YOLO-V3 so as to obtain the spatial characteristics information of different scales in the image, and enhance the detection accuracy. The experimental results show that mAP of the proposed YOLO-SKIP network is 0.91. Our YOLO-SKIP model has achieved the best detection performance on the defective angle steel tower bolt data

DYNAMIC CHARACTERISTICS ANALYSIS OF SPHERICAL HYBRID SLIDING BEARINGS

Analyzing the influence of bearing parameters on the dynamic characteristics of spherical sliding bearings, which provided theoretical guidance for the operating stiffness, damping and stability of liquid spherical bearings. Taking spherical hybrid sliding bearings as the research object, established the mathematical model of lubrication analysis of spherical sliding bearings, the dynamic liquid lubrication equation under laminar flow was derived by the perturbation method, the partial differential equation of disturbance pressure was solved by the finite difference method, and the liquid was calculated numerically. The stiffness coefficient and damping coefficient of the spherical bearing were used to study the influence of the bearing speed, eccentricity, average oil film clearance and other parameters on the dynamic characteristic coefficient of the bearing through numerical analysis. The results show that the speed、 eccentricity and average oil film clearance have an important influence on the stiffness and damping of the oil film