scholarly journals Evaluating the Potential of WorldView-3 Data to Classify Different Shoot Damage Ratios of Pinus yunnanensis

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 417
Author(s):  
Linfeng Yu ◽  
Zhongyi Zhan ◽  
Lili Ren ◽  
Shixiang Zong ◽  
Youqing Luo ◽  
...  
Keyword(s):  
Multinomial Logistic Regression ◽  
Satellite Image ◽  
Classification Model ◽  
Linear Discriminant ◽  
Pinus Yunnanensis ◽  
Study Results ◽  
Shoot Damage ◽  
Beetle Damage ◽  
Individual Trees ◽  
Different Levels

Tomicus yunnanensis Kirkendall and Faccoli and Tomicus minor Hartig have caused serious shoot damage in Yunnan pine (Pinus yunnanensis Faranch) forests in the Yunnan province of China. However, very few remote sensing studies have been conducted to detect the different shoot damage ratios of individual trees. The aim of the study was to evaluate the suitability of eight-band WorldView-3 satellite image for detecting different shoot damage ratios (e.g., “healthy”, “slightly”, “moderately”, and “severely”). An object-based supervised classification method was used in this study. The tree crowns were delineated on a 0.3 m pan-sharpened worldview-3 image as reference data. Besides the original eight bands, normalized two-band indices were derived as spectral variables. For classifying individual trees, three classifiers—multinomial logistic regression (MLR), a stepwise linear discriminant analysis (SDA), and random forest (RF)—were evaluated and compared in this study. Results showed that SDA classifier based on all spectral variables had the highest classification accuracy (78.33%, Kappa = 0.712). Compared to original eight bands of Worldview-3, normalized two-band indices could improve the overall accuracy. Furthermore, the shoot damage ratio was a good indicator for detecting different levels of individual damaged trees. We concluded that the Worldview-3 satellite data were suitable to classify different levels of damaged trees; therefore, the best mapping results of damaged trees was predicted based on the best classification model which is very useful for forest managers to take the appropriate measures to decrease shoot beetle damage in Yunnan pine forests.

scholarly journals 49 Effects of in-vitro digestibility in cannulated steers when supplemented different levels of glycerol

2020 ◽  
Vol 98 (Supplement_2) ◽  
pp. 55-56
Author(s):  
Noheli Gutierrez ◽  
Jamie A Boyd
Keyword(s):  
Dry Matter ◽  
Cold Water ◽  
Rumen Fluid ◽  
Latin Square ◽  
Optimal Level ◽  
Nutrient Digestibility ◽  
In Vitro Digestibility ◽  
Study Results ◽  
Different Levels

Abstract A study was conducted to evaluate effects of increasing concentration of food grade glycerol on rumen environment and nutrient digestibility. Three ruminally cannulated Jersey steers were used in this study. The study was conducted from March to May 2019. Experimental design was a 3x3 Latin square with a 2wk adjustment period followed by a 1wk collection period. Diet was coastal bermudagrass hay based. Different forage types were introduced in the incubation process to evaluate digestibility. Glycerol was administered once a day at 0, 15, or 20% of DMI (dry matter intake). dNDF (digestible NDF) and dDM (digestible dry matter) was determined using an ANKOM Daisy II incubator inoculated with 200g fresh rumen fluid and incubated for 12, 24, 48 and 72 h at 39°C. Each vessel contained ground forage samples in filter bags in triplicate. After incubation, filter bags were rinsed with cold water and dried for 24h in a 55°C forced air oven. Data were analyzed using the Proc MIXED procedure of SAS version 9.4. There was no difference dNDF in effect of different levels of glycerol between forage types by diet. But a numerical tendency was observed that dNDF was decreased at 20% inclusion rates in comparison to 0 and 15% inclusion of glycerol in the diet. Neither steer nor run was significantly different in the study. However as expected digestibility over time was significantly different (P < 0.001). A significant increase was observed in DMI with the increased levels of glycerol in the diet (P = 0.003), both the 15% and 20% levels of glycerol increased in DMI in comparison to the control (0%). It appears based on these study results that digestibility may be inhibited, as levels of dietary glycerol increase in the diet and more work needs to be done to find the optimal level of glycerol supplementation.

scholarly journals Excessive Crossed Disparity Detection by Visual Evoked Potentials to Reduce Visual Discomfort in 3D Viewing

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Xiao Wang ◽  
Liuye Yao ◽  
Zhiyu Qian ◽  
Lidong Xing ◽  
Weitao Li ◽  
...  
Keyword(s):  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Experimental System ◽  
Occipital Lobe ◽  
Cost Effective ◽  
Subjective Assessment ◽  
Classification Model ◽  
Visual Discomfort ◽  
Linear Discriminant ◽  
Visual Evoked

As excessive crossed disparity is known to cause visual discomfort, this study aims to establish a classification model to discriminate excessive crossed disparity in stereoscopic viewing in combination with subjective assessment of visual discomfort. A stereo-visual evoked potentials (VEPs) experimental system was built up to obtain the VEPs evoked by stereoscopic stimulus with different disparities. Ten volunteers participated in this experiment, and forty VEP datasets in total were extracted when the viewers were under comfortable viewing conditions. Six features of VEPs from three electrodes at the occipital lobe were chosen, and the classification was established using the Fisher’s linear discriminant (FLD). Based on FLD results, the correct rate for determining the excessive crossed disparity was 70%, and it reached 80% for other stimuli. The study demonstrated cost-effective discriminant classification modelling to distinguish the stimulus with excessive crossed disparity which inclines to cause visual discomfort.

scholarly journals EEG-Based Emotion Classification for Alzheimer’s Disease Patients Using Conventional Machine Learning and Recurrent Neural Network Models

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7212
Author(s):  
Jungryul Seo ◽  
Teemu H. Laine ◽  
Gyuhwan Oh ◽  
Kyung-Ah Sohn
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Recurrent Neural Network ◽  
Classification Model ◽  
Support Vector ◽  
Number Of Patients ◽  
Study Results ◽  
Conventional Machine

As the number of patients with Alzheimer’s disease (AD) increases, the effort needed to care for these patients increases as well. At the same time, advances in information and sensor technologies have reduced caring costs, providing a potential pathway for developing healthcare services for AD patients. For instance, if a virtual reality (VR) system can provide emotion-adaptive content, the time that AD patients spend interacting with VR content is expected to be extended, allowing caregivers to focus on other tasks. As the first step towards this goal, in this study, we develop a classification model that detects AD patients’ emotions (e.g., happy, peaceful, or bored). We first collected electroencephalography (EEG) data from 30 Korean female AD patients who watched emotion-evoking videos at a medical rehabilitation center. We applied conventional machine learning algorithms, such as a multilayer perceptron (MLP) and support vector machine, along with deep learning models of recurrent neural network (RNN) architectures. The best performance was obtained from MLP, which achieved an average accuracy of 70.97%; the RNN model’s accuracy reached only 48.18%. Our study results open a new stream of research in the field of EEG-based emotion detection for patients with neurological disorders.

scholarly journals Using GIS and Machine Learning to Classify Residential Status of Urban Buildings in Low and Middle Income Settings

2020 ◽  
Vol 12 (23) ◽  
pp. 3847
Author(s):  
Christopher T. Lloyd ◽  
Hugh J. W. Sturrock ◽  
Douglas R. Leasure ◽  
Warren C. Jochem ◽  
Attila N. Lázár ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Image ◽  
Classification Model ◽  
Middle Income ◽  
Residential Status ◽  
Urban Buildings ◽  
Ensemble Machine Learning ◽  
Republic Of The Congo ◽  
Building Footprint ◽  
Low And Middle Income

Utilising satellite images for planning and development is becoming a common practice as computational power and machine learning capabilities expand. In this paper, we explore the use of satellite image derived building footprint data to classify the residential status of urban buildings in low and middle income countries. A recently developed ensemble machine learning building classification model is applied for the first time to the Democratic Republic of the Congo, and to Nigeria. The model is informed by building footprint and label data of greater completeness and attribute consistency than have previously been available for these countries. A GIS workflow is described that semiautomates the preparation of data for input to the model. The workflow is designed to be particularly useful to those who apply the model to additional countries and use input data from diverse sources. Results show that the ensemble model correctly classifies between 85% and 93% of structures as residential and nonresidential across both countries. The classification outputs are likely to be valuable in the modelling of human population distributions, as well as in a range of related applications such as urban planning, resource allocation, and service delivery.

scholarly journals A Multilevel Analysis of Neighbourhood, School, Friend and Individual-Level Variation in Primary School Children’s Physical Activity

2019 ◽  
Vol 16 (24) ◽  
pp. 4889
Author(s):  
Ruth Salway ◽  
Lydia Emm-Collison ◽  
Simon J. Sebire ◽  
Janice L. Thompson ◽  
Deborah A. Lawlor ◽  
...  
Keyword(s):  
Physical Activity ◽  
Vigorous Physical Activity ◽  
Classification Model ◽  
Accelerometer Data ◽  
Individual Level ◽  
Friendship Groups ◽  
Multiple Classification ◽  
Multiple Membership ◽  
Children’S Physical Activity ◽  
Different Levels

Physical activity is influenced by individual, inter-personal and environmental factors. In this paper, we explore the variability in children’s moderate-to-vigorous physical activity (MVPA) at different individual, parent, friend, school and neighbourhood levels. Valid accelerometer data were collected for 1077 children aged 9, and 1129 at age 11, and the average minutes of MVPA were derived for weekdays and weekends. We used a multiple-membership, multiple-classification model (MMMC) multilevel model to compare the variation in physical activity outcomes at each of the different levels. There were differences in the proportion of variance attributable to the different levels between genders, for weekdays and weekends, at ages 9 and 11. The largest proportion of variability in MVPA was attributable to individual variation, accounting for half of the total residual variability for boys, and two thirds of the variability for girls. MVPA clustered within friendship groups, with friends influencing peer MVPA. Including covariates at the different levels explained only small amounts (3%–13%) of variability. There is a need to enhance our understanding of individual level influences on children’s physical activity.

scholarly journals PSYCHOLOGICAL WELL-BEING AND THE ONSET OF COGNITIVE IMPAIRMENT AMONG CHINESE OLDER ADULTS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S114-S115
Author(s):  
Jiaan Zhang
Keyword(s):  
Older Adults ◽  
Cognitive Impairment ◽  
Multinomial Logistic Regression ◽  
Well Being ◽  
Functional Health ◽  
Survey Study ◽  
Psychological Well Being ◽  
Chinese Older Adults ◽  
Study Results ◽  
Over Time

Abstract Previous research has shown the beneficial effects of positive psychological assets on health, but more research is needed to confirm the prospective effects on cognitive function. The purpose of this study is to examine the relationship between psychological well-being and the earliest onset of cognitive impairment among Chinese older adults. Data came from 2000 to 2014 waves of the Chinese Longitudinal Healthy Longevity Survey. Study sample consisted of 6,225 older adults who were free from cognitive impairment in 2000. Psychological well-being was measured based on seven items that assessed optimism, conscientiousness, self-determination, happiness, self-esteem, pessimism, and loneliness, with responses ranging from “always (1)” to never (5)”. Negative feelings items were reverse coded. Higher score indicated more positive psychological well-being. Cognitive impairment was measured by a Chinese version of the Mini-Mental State Examination. Respondents scored at or above 24 were regarded as having no cognitive impairment. A multi-category time-varying variable was used to capture four potential outcomes: (1) persistently free of cognitive impairment between waves, (2) onset of cognitive impairment, (3) death between waves, and (4) attrition. Socio-demographics, chronical diseases conditions, functional health status were served as controls. Multilevel multinomial logistic regression models that account for clustering of observations within a subject over time were employed for the study. Results show that more positive psychological well-being is significantly associated with reduced risk of cognitive impairment onset and death over time. Results suggest that developing more psychological resilience-based intervention programs among older adults may help them delay the onset of cognitive impairment.

scholarly journals Adversarial Training for Community Question Answer Selection Based on Multi-Scale Matching

2019 ◽  
Vol 33 ◽  
pp. 395-402
Author(s):  
Xiao Yang ◽  
Madian Khabsa ◽  
Miaosen Wang ◽  
Wei Wang ◽  
Ahmed Hassan Awadallah ◽  
...  
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Classification Problem ◽  
Classification Model ◽  
Training Set ◽  
Community Based ◽  
Multi Scale ◽  
Adversarial Training ◽  
Source Of Information ◽  
Different Levels

Community-based question answering (CQA) websites represent an important source of information. As a result, the problem of matching the most valuable answers to their corresponding questions has become an increasingly popular research topic. We frame this task as a binary (relevant/irrelevant) classification problem, and present an adversarial training framework to alleviate label imbalance issue. We employ a generative model to iteratively sample a subset of challenging negative samples to fool our classification model. Both models are alternatively optimized using REINFORCE algorithm. The proposed method is completely different from previous ones, where negative samples in training set are directly used or uniformly down-sampled. Further, we propose using Multi-scale Matching which explicitly inspects the correlation between words and ngrams of different levels of granularity. We evaluate the proposed method on SemEval 2016 and SemEval 2017 datasets and achieves state-of-the-art or similar performance.

scholarly journals Detection of Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in Black Oat Seeds (Avena strigosa Schreb) Using Multispectral Imaging

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3343 ◽  
Author(s):  
Fabiano França-Silva ◽  
Carlos Henrique Queiroz Rego ◽  
Francisco Guilhien Gomes-Junior ◽  
Maria Heloisa Duarte de Moraes ◽  
André Dantas de Medeiros ◽  
...  
Keyword(s):  
High Performance ◽  
Multispectral Imaging ◽  
Texture Features ◽  
Pathogenic Fungi ◽  
Classification Model ◽  
Linear Discriminant ◽  
Avena Strigosa ◽  
Oat Seeds ◽  
Conventional Methods ◽  
Color And Texture Features

Conventional methods for detecting seed-borne fungi are laborious and time-consuming, requiring specialized analysts for characterization of pathogenic fungi on seed. Multispectral imaging (MSI) combined with machine vision was used as an alternative method to detect Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in black oat seeds (Avena strigosa Schreb). The seeds were inoculated with Drechslera avenae (D. avenae) and then incubated for 24, 72 and 120 h. Multispectral images of non-infested and infested seeds were acquired at 19 wavelengths within the spectral range of 365 to 970 nm. A classification model based on linear discriminant analysis (LDA) was created using reflectance, color, and texture features of the seed images. The model developed showed high performance of MSI in detecting D. avenae in black oat seeds, particularly using color and texture features from seeds incubated for 120 h, with an accuracy of 0.86 in independent validation. The high precision of the classifier showed that the method using images captured in the Ultraviolet A region (365 nm) could be easily used to classify black oat seeds according to their health status, and results can be achieved more rapidly and effectively compared to conventional methods.

scholarly journals Multifidelity Multidisciplinary Design Optimization of Integral Solid Propellant Ramjet Supersonic Cruise Vehicles

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Xiaojian Sun ◽  
Jianquan Ge ◽  
Tao Yang ◽  
Qiangqiang Xu ◽  
Bin Zhang
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Solid Propellant ◽  
Multidisciplinary Design ◽  
Preliminary Design ◽  
Flight Trajectory ◽  
Traditional Design ◽  
Study Results ◽  
Optimization Parameters ◽  
Different Levels

Integral solid propellant ramjet (ISPR) supersonic cruise vehicles share the characteristic that they are highly integrated configurations. The traditional design of vehicles cannot achieve a balance between computational expense and accuracy. A multifidelity multidisciplinary design optimization (MDO) platform has been developed in this study. The focus of the platform is on ISPR supersonic cruise vehicles. Firstly, codes of discipline with different levels of fidelity (LoF) were established, such as geometry, aerodynamics, radar cross-section calculations, propulsion, mass, and trajectory discipline codes. Secondly, two MDO frameworks were constructed through discipline codes. A low LoF MDO framework is suitable for conceptual design, and a medium LoF MDO framework is suitable for preliminary design. Finally, taking the optimization problem with the minimum overall detection probability of flight trajectory as an example, the low LoF framework first explores the entire design space to achieve the mission requirements, and then, the medium LoF MDO framework accepts the low LoF framework optimization parameters. Hence, the optimization target is reached with more detailed parameters and higher fidelity. Additionally, an example for a solid propellant missile with minimum total mass is tested by the platform. The study results show that the multifidelity MDO framework not only exploits interactions between the disciplines but also improves the accuracy of optimization results and reduces the iteration time.

scholarly journals Tracing the Geographical Origin of Durum Wheat by FT-NIR Spectroscopy

Foods ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 450 ◽  
Author(s):  
Annalisa De Girolamo ◽  
Marina Cortese ◽  
Salvatore Cervellieri ◽  
Vincenzo Lippolis ◽  
Michelangelo Pascale ◽  
...  
Keyword(s):  
Durum Wheat ◽  
Near Infrared ◽  
Southern Italy ◽  
Geographical Origin ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
North Central ◽  
Linear Discriminant ◽  
The World

Fourier transform near infrared (FT-NIR) spectroscopy, in combination with principal component-linear discriminant analysis (PC-LDA), was used for tracing the geographical origin of durum wheat samples. The classification model PC-LDA was applied to discriminate durum wheat samples originating from Northern, Central, and Southern Italy (n = 181), and to differentiate Italian durum wheat samples from those cultivated in other countries across the world (n = 134). Developed models were validated on a separated set of wheat samples. Different pre-treatments of spectral data and different spectral regions were selected and compared in terms of overall discrimination (OD) rates obtained in validation. The LDA models were able to correctly discriminate durum Italian wheat samples according to their geographical origin (i.e., North, Central, and South) with OD rates of up of 96.7%. Better results were obtained when LDA models were applied to the discrimination of Italian durum wheat samples from those originating from other countries across the world, having OD rates of up to 100%. The excellent results obtained herein clearly indicate the potential of FT-NIR spectroscopy to be used for the discrimination of durum wheat samples according to their geographical origin.

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