Predicting Surface Roughness and Moisture of Bare Soils Using Multi- band Spectral Reflectance Under Field Conditions

Soil surface roughness, denoted by the root mean square height(RMSH), and soil moisture(SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spectral reflectance(SR). In regards to this issue, three SM levels and four RMSH levels were artificially designed in this study; a total of 12 plots was used, each plot had a size of 3 m × 3 m. Eight spectral observations were conducted from 14 to 30 October 2017 to investigate the correlation between RMSH, SM, and SR. On this basis, 6 commonly used bands of optical satellite sensors were selected in this study, which are red(675 nm), green(555 nm), blue(485 nm), near infrared(845 nm), shortwave infrared 1(1600 nm), and shortwave infrared 2(2200 nm). A negative correlation was found between SR and RMSH, and between SR and SM. The bands with higher coefficient of determination R^2 values were selected for stepwise multiple nonlinear regression analysis. Four characterized bands(i.e., blue, green, near infrared, and shortwave infrared 2) were chosen as the independent variables to estimate SM with R^2 and root mean square error(RMSE) values equal to 0.62 and 2.6%, respectively. Similarly, the four bands(green, red, near infrared, and shortwave infrared 1) were used to estimate RMSH with R^2 and RMSE values equal to 0.48 and 0.69 cm, respectively. These results indicate that the method used is not only suitable for estimating SM but can also be extended to the prediction of RMSH. Finally, the evaluation approach presented in this paper highly restores the real situation of the natural farmland surface on the one hand, and obtains high precision values of SM and RMSH on the other. The method can be further applied to the prediction of farmland SM and RMSH based on satellite and unmanned aerial vehicle(UAV) optical imagery.

Impairment of the Regulation of Gonadal Function in Channa punctatus by Metacid-50 and Carbaryl under Laboratory and Field Conditions

Regulation of gonadal function by gonadotropic hormone (GtH) and gonadotropin-releasing hormone (GnRH) in Channa punctatus was significantly affected by nonlethal levels of Metacid-50 and Carbaryl. Under laboratory conditions, the time-dependent decrease in serum GtH level was higher in Carbaryl-treated fish than in Metacid-50-treated fish. The situation was reversed in the field, with a higher inhibitory effect of Metacid-50 being recorded. On the other hand, pituitary GtH content and GnRH activity were inhibited to a greater extent by Metacid-50 than by Carbaryl under both field and laboratory conditions. The present findings highlight that even low doses of Metacid-50 and Carbaryl are effective enough to cause reproductive damage, as evidenced by homeostatic unbalance of the reproductive regulatory system. 1990 Academic Press. Inc.

Different Growth Characteristics of Grewia mollis, Grewia tenax and Grewia villosa Under Nursery and Field Conditions

This study was carried out to assess the growth characteristics of Grewia moll＆, Grewia tenax and Grewia villosa under the nursery and field conditions. Two experiments were conducted at the farm of the College of Natural Resources and Environmental Studies, University of Juba, Khartoum, Sudan. Randomized complete block design with three replications was used. Morphological and physiological factors were measured. Seedlings height, number of leaves, number of branches and sub-branches were different （P 〈 0.05） among the three species at the nursery stage and under field conditions. Collar diameter showed significant difference among the species under field conditions. Physiological factors exhibited more significant variations in the field than at the nursery stage. Variations in growth characteristics were attributed to genetics differences and different growth habit, while variations in physiological factors （photosynthesis and transpiration rate） were attributed to differences in leaf structure, size and number of stomatal pores.

Characterizing large-scale weak interlayer shear zones using conditional random field theory

The shear behavior of large-scale weak intercalation shear zones(WISZs)often governs the stability of foundations,rock slopes,and underground structures.However,due to their wide distribution,undulating morphology,complex fabrics,and varying degrees of contact states,characterizing the shear behavior of natural and complex large-scale WISZs precisely is challenging.This study proposes an analytical method to address this issue,based on geological fieldwork and relevant experimental results.The analytical method utilizes the random field theory and Kriging interpolation technique to simplify the spatial uncertainties of the structural and fabric features for WISZs into the spatial correlation and variability of their mechanical parameters.The Kriging conditional random field of the friction angle of WISZs is embedded in the discrete element software 3DEC,enabling activation analysis of WISZ C2 in the underground caverns of the Baihetan hydropower station.The results indicate that the activation scope of WISZ C2 induced by the excavation of underground caverns is approximately 0.5e1 times the main powerhouse span,showing local activation.Furthermore,the overall safety factor of WISZ C2 follows a normal distribution with an average value of 3.697.

Semantic role labeling based on conditional random fields

Due to the fact that semantic role labeling （SRL） is very necessary for deep natural language processing, a method based on conditional random fields （CRFs） is proposed for the SRL task. This method takes shallow syntactic parsing as the foundation, phrases or named entities as the labeled units, and the CRFs model is trained to label the predicates＇ semantic roles in a sentence. The key of the method is parameter estimation and feature selection for the CRFs model. The L-BFGS algorithm was employed for parameter estimation, and three category features： features based on sentence constituents, features based on predicate, and predicate-constituent features as a set of features for the model were selected. Evaluation on the datasets of CoNLL-2005 SRL shared task shows that the method can obtain better performance than the maximum entropy model, and can achieve 80. 43 % precision and 63. 55 % recall for semantic role labeling.

TONE MODELING BASED ON HIDDEN CONDITIONAL RANDOM FIELDS AND DISCRIMINATIVE MODEL WEIGHT TRAINING

The use of hidden conditional random fields （HCRFs） for tone modeling is explored. The tone recognition performance is improved using HCRFs by taking advantage of intra-syllable dynamic, inter-syllable dynamic and duration features. When the tone model is integrated into continuous speech recognition, the discriminative model weight training （DMWT） is proposed. Acoustic and tone scores are scaled by model weights discriminatively trained by the minimum phone error （MPE） criterion. Two schemes of weight training are evaluated and a smoothing technique is used to make training robust to overtraining problem. Experiments show that the accuracies of tone recognition and large vocabulary continuous speech recognition （LVCSR） can be improved by the HCRFs based tone model. Compared with the global weight scheme, continuous speech recognition can be improved by the discriminative trained weight combinations.

Rockhead profile simulation using an improved generation method of conditional random field

Rockhead profile is an important part of geological profiles and can have significant impacts on some geotechnical engineering practice,and thus,it is necessary to establish a useful method to reverse the rockhead profile using site investigation results.As a general method to reflect the spatial distribution of geo-material properties based on field measurements,the conditional random field(CRF)was improved in this paper to simulate rockhead profiles.Besides,in geotechnical engineering practice,measurements are generally limited due to the limitations of budget and time so that the estimation of the mean value can have uncertainty to some extent.As the Bayesian theory can effectively combine the measurements and prior information to deal with uncertainty,CRF was implemented with the aid of the Bayesian framework in this study.More importantly,this simulation procedure is achieved as an analytical solution to avoid the time-consuming sampling work.The results show that the proposed method can provide a reasonable estimation about the rockhead depth at various locations against measurement data and as a result,the subjectivity in determining prior mean can be minimized.Finally,both the measurement data and selection of hyper-parameters in the proposed method can affect the simulated rockhead profiles,while the influence of the latter is less significant than that of the former.

Power entity recognition based on bidirectional long short-term memory and conditional random fields

With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service response provision.Knowledge graphs are usually constructed based on entity recognition.Specifically,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge fusion.In this work,the entities and characteristics of power entity recognition are analyzed,the mechanism of entity recognition is clarified,and entity recognition techniques are analyzed in the context of the power domain.Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated,and the two methods are comparatively analyzed.The results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the BLSTM.The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.

A CONDITIONAL RANDOM FIELDS APPROACH TO BIOMEDICAL NAMED ENTITY RECOGNITION

Named entity recognition is a fundamental task in biomedical data mining. In this letter, a named entity recognition system based on CRFs （Conditional Random Fields） for biomedical texts is presented. The system makes extensive use of a diverse set of features, including local features, full text features and external resource features. All features incorporated in this system are described in detail, and the impacts of different feature sets on the performance of the system are evaluated. In order to improve the performance of system, post-processing modules are exploited to deal with the abbreviation phenomena, cascaded named entity and boundary errors identification. Evaluation on this system proved that the feature selection has important impact on the system performance, and the post-processing explored has an important contribution on system performance to achieve better resuits.

Conditional Random Field Tracking Model Based on a Visual Long Short Term Memory Network

In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is established by using a visual long short term memory network in the three-dimensional(3D)space and the motion estimations jointly performed on object trajectory segments.Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation.To address the uncertainty of the length and interval of trajectory segments,a multimode long short term memory network is proposed for the object motion estimation.The tracking performance is evaluated using the PETS2009 dataset.The experimental results show that the proposed method achieves better performance than the tracking methods based on the independent motion estimation.

Adaptive foreground and shadow segmentation using hidden conditional random fields

Video object segmentation is important for video surveillance, object tracking, video object recognition and video editing. An adaptive video segmentation algorithm based on hidden conditional random fields (HCRFs) is proposed, which models spatio-temporal constraints of video sequence. In order to improve the segmentation quality, the weights of spatio-temporal con- straints are adaptively updated by on-line learning for HCRFs. Shadows are the factors affecting segmentation quality. To separate foreground objects from the shadows they cast, linear transform for Gaussian distribution of the background is adopted to model the shadow. The experimental results demonstrated that the error ratio of our algorithm is reduced by 23% and 19% respectively, compared with the Gaussian mixture model (GMM) and spatio-temporal Markov random fields (MRFs).

A Remote Sensing Image Semantic Segmentation Method by Combining Deformable Convolution with Conditional Random Fields

Currently,deep convolutional neural networks have made great progress in the field of semantic segmentation.Because of the fixed convolution kernel geometry,standard convolution neural networks have been limited the ability to simulate geometric transformations.Therefore,a deformable convolution is introduced to enhance the adaptability of convolutional networks to spatial transformation.Considering that the deep convolutional neural networks cannot adequately segment the local objects at the output layer due to using the pooling layers in neural network architecture.To overcome this shortcoming,the rough prediction segmentation results of the neural network output layer will be processed by fully connected conditional random fields to improve the ability of image segmentation.The proposed method can easily be trained by end-to-end using standard backpropagation algorithms.Finally,the proposed method is tested on the ISPRS dataset.The results show that the proposed method can effectively overcome the influence of the complex structure of the segmentation object and obtain state-of-the-art accuracy on the ISPRS Vaihingen 2D semantic labeling dataset.

Exploiting PLSA model and conditional random field for refining image annotation

This paper presents a new method for refining image annotation by integrating probabilistic la- tent semantic analysis （PLSA） with conditional random field （CRF）. First a PLSA model with asymmetric modalities is constructed to predict a candidate set of annotations with confidence scores, and then model semantic relationship among the candidate annotations by leveraging conditional ran- dom field. In CRF, the confidence scores generated lay the PLSA model and the Fliekr distance be- tween pairwise candidate annotations are considered as local evidences and contextual potentials re- spectively. The novelty of our method mainly lies in two aspects ： exploiting PLSA to predict a candi- date set of annotations with confidence scores as well as CRF to further explore the semantic context among candidate annotations for precise image annotation. To demonstrate the effectiveness of the method proposed in this paper, an experiment is conducted on the standard Corel dataset and its re- sults are ＇compared favorably with several state-of-the-art approaches.

Fast Chinese syntactic parsing method based on conditional random fields

A fast method for phrase structure grammar analysis is proposed based on conditional ran- dom fields （CRF）. The method trains several CRF classifiers for recognizing the phrase nodes at dif- ferent levels, and uses the bottom-up to connect the recognized phrase nodes to construct the syn- tactic tree. On the basis of Beijing forest studio Chinese tagged corpus, two experiments are de- signed to select the training parameters and verify the validity of the method. The result shows that the method costs 78. 98 ms and 4. 63 ms to train and test a Chinese sentence of 17. 9 words. The method is a new way to parse the phrase structure grammar for Chinese, and has good generalization ability and fast speed.

An Image Segmentation Algorithm Based on a Local Region Conditional Random Field Model

To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.

Enhanced Identifying Gene Names from Biomedical Literature with Conditional Random Fields

Identifying gene names is an attractive research area of biology computing. However, accurate extraction of gene names is a challenging task with the lack of conventions for describing gene names. We devise a systematical architecture and apply the model using conditional random fields （CRFs） for extracting gene names from Medline. In order to improve the performance, biomedical ontology features are inserted into the model and post processing including boundary adjusting and word filter is presented to solve name overlapping problem and remove false positive single words. Pure string match method, baseline CRFs, and CRFs with our methods are applied to human gene names and HIV gene names extraction respectively in 1100 abstracts of Medline and their performances are contrasted. Results show that CRFs are robust for unseen gene names. Furthermore, CRFs with our methods outperforms other methods with precision 0.818 and recall 0.812.

Reservoir Conditions of Central Gas Field in Shaan-Gan-Ning Basin

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不同矿物调理剂对Cd污染水田土壤性质及水稻Cd含量的影响

【目的】探究不同矿物调理剂对广西镉(Cd)污染水田土壤性质及水稻Cd含量的影响,为利用水田生产安全稻米提供参考依据。【方法】选用钙镁硅调理剂A和B、钙硅调理剂C和钙镁调理剂D(分别设为A、B、C和D处理),在广西某受Cd污染水田开展Cd污染大田修复治理试验,以不施用调理剂为对照(CK)。处理后于水稻成熟期测定土壤的pH、阳离子交换量(CEC)、总Cd和土壤有效态Cd含量及水稻根、茎、叶和籽粒的Cd含量,分析不同矿物调理剂对Cd污染水田土壤的修复治理效果及Cd在水稻各部位的残留状况,并对土壤总Cd含量、CEC和pH与有效态Cd含量进行相关分析。【结果】施用矿物调理剂可显著提高Cd污染水田的土壤pH和CEC(P<0.05,下同),二者分别较CK提高15.0%~16.6%和20.4%~23.8%;可显著降低Cd污染水田的土壤有效态Cd含量,较CK下降14.0%~19.3%。施用矿物调理剂后水田的土壤有效态Cd含量与pH呈显著负相关,pH的对数ln(pH)值与土壤有效态Cd含量的相关系数为0.4053;ln(pH)值与CEC呈极显著负相关(P<0.01),相关系数为0.6326;随着土壤pH和CEC的上升,土壤有效态Cd含量持续下降。施用矿物调理剂均可有效降低水稻根、茎、叶和籽粒的Cd含量及水稻地上部Cd的富集系数,其中,水稻籽粒的总Cd含量下降35.9%~50.2%,水稻地上部(茎、叶和籽粒)Cd的富集系数分别下降19.5%~33.5%、32.1%~44.9%和38.6%~52.6%。【结论】施用不同矿物调理剂均可显著提升水田土壤pH和CEC,从而降低土壤有效态Cd含量,抑制Cd从土壤向水稻根、茎、叶和籽粒迁移,有效降低水稻Cd含量,减少Cd在水稻地上部(茎、叶和籽粒)富集。

爆炸场强电磁辐射测试系统设计

针对高能战斗部爆炸电磁辐射信号频段宽、变化幅度大、持续时间长、测试条件复杂等特点,设计基于混频天线的爆炸场强电磁辐射测试系统.设计4种测试天线:短波天线(1.5~30 MHz)、超宽带天线(30~1000 MHz)、微带天线(5.9~6.0 GHz、8.4~8.5 GHz),通过优化天线结构、增加匹配电路提高测试效能,能够覆盖军械装备所有电磁敏感频段.采用模块化思路设计具有合路器、放大器、限幅器、滤波器等功能的信号调理器,放大器模块采取“固定增益放大器+步进式衰减器”结构,不仅实现全频段0~30 dB调节,同时将调节系统精度提升至0.5dB,限幅器模块将采样信号功率限制在数采仪器最大承受功率的65%以下,极大提高测试系统对爆炸场强电磁信号的适应能力.通过采取混频段天线合路、高精度系数调节、多功能信号调理等技术手段,使测试系统满足高能战斗部爆炸电磁辐射测试试验所有指标.

建筑群风场环境下空调室外机周围热环境分析

空调运行时,外部室外机的散热会受到建筑群内复杂风场环境的干扰,继而影响其运行效率。以布置在凹槽内部的空调室外机为研究对象,考虑建筑群内部的流场变化,研究了正面风、后面风向下室外机运行时的热环境,分析了室外机随楼高、位置的热环境差异,并着重对比了处于迎风建筑和下风向建筑中室外机散热的差异。研究表明,空调室外机的运行效能受建筑群内风场环境的影响较大。正面风向下,风速越大,室外机效率越差;下风向建筑B处室外机运行时的效率更差。后面风向下,处于下风向建筑A处室外机的平均进风温度相对来说更高。