N-Net: 3D Fully Convolution Network-Based Vertebrae Segmentation from CT Spinal Images

2019 ◽  
Vol 33 (06) ◽  
pp. 1957003 ◽  
Author(s):  
Wenhui Zhou ◽  
Lili Lin ◽  
Guangtao Ge
Keyword(s):  
Surgical Planning ◽  
Global Structure ◽  
Experimental Results ◽  
Semantic Features ◽  
Structure Information ◽  
Residual Structure ◽  
Vertebrae Segmentation ◽  
High Level ◽  
Operative Assessment ◽  
Better Than

Accurate vertebrae segmentation from CT spinal images is crucial for the clinical tasks of diagnosis, surgical planning, and post-operative assessment. This paper describes an [Formula: see text]-shaped 3D fully convolution network (FCN) for vertebrae segmentation: [Formula: see text]-net. In this network, a global structure guidance pathway is designed for fusing the high-level semantic features with the global structure information. Moreover, the residual structure and the skip connection are introduced into traditional 3D FCN framework. These schemes can significantly improve the accuracy of vertebrae segmentation. Experimental results demonstrate the effectiveness and robustness of our method. A high average DICE score of 0.9499 [Formula: see text] 0.02 can be obtained, which is better than those of existing methods.

scholarly journals RDF-to-Text Generation with Graph-augmented Structural Neural Encoders

2020 ◽  
Author(s):  
Hanning Gao ◽  
Lingfei Wu ◽  
Po Hu ◽  
Fangli Xu
Keyword(s):  
State Of The Art ◽  
Global Structure ◽  
The State ◽  
Experimental Results ◽  
Text Generation ◽  
Structure Information ◽  
Text Quality ◽  
Human Evaluation ◽  
Proposed Model

The task of RDF-to-text generation is to generate a corresponding descriptive text given a set of RDF triples. Most of the previous approaches either cast this task as a sequence-to-sequence problem or employ graph-based encoder for modeling RDF triples and decode a text sequence. However, none of these methods can explicitly model both local and global structure information between and within the triples. To address these issues, we propose to jointly learn local and global structure information via combining two new graph-augmented structural neural encoders (i.e., a bidirectional graph encoder and a bidirectional graph-based meta-paths encoder) for the input triples. Experimental results on two different WebNLG datasets show that our proposed model outperforms the state-of-the-art baselines. Furthermore, we perform a human evaluation that demonstrates the effectiveness of the proposed method by evaluating generated text quality using various subjective metrics.

scholarly journals Chinese Emergency Event Recognition Using Conv-RDBiGRU Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Haoran Yin ◽  
Jinxuan Cao ◽  
Luzhe Cao ◽  
Guodong Wang
Keyword(s):  
Neural Network ◽  
Event Recognition ◽  
Semantic Features ◽  
Residual Structure ◽  
Convolution Operation ◽  
The Neural Network ◽  
Emergency Event ◽  
The Matrix ◽  
Learned Features ◽  
Better Than

In view of the weak generalization of traditional event recognition methods, the limitation of dependence on field knowledge of expert, the longer train time of deep neural network, and the problem of gradient dispersion, the neural network joint model, Conv-RDBiGRU, integrated residual structure was proposed. Firstly, text corpus is preprocessed by word segmentation and stop words processing and uses word embedding to form the matrix of word vectors. Then, local semantic features are extracted through convolution operation, and deep context semantic features are extracted through RDBiGRU. Finally, the learned features are activated by softmax function and the recognition results are output. The novelty of work is that we integrate residual structure into recurrent neural network and combine these methods and field of application. The simulation results show that this method improves precision and recall of Chinese emergency event recognition, and the F-value is better than other methods.

Big Data Summarization Using Novel Clustering Algorithm and Semantic Feature Approach

2017 ◽  
Vol 4 (3) ◽  
pp. 108-117
Author(s):  
Shilpa G. Kolte ◽  
Jagdish W. Bakal
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
Federal Court ◽  
Semantic Feature ◽  
Experimental Results ◽  
Semantic Features ◽  
Legal Cases ◽  
Data Summarization ◽  
Summarization Method ◽  
Better Than

This paper proposes a big data (i.e., documents, texts) summarization method using proposed clustering and semantic features. This paper proposes a novel clustering algorithm which is used for big data summarization. The proposed system works in four phases and provides a modular implementation of multiple documents summarization. The experimental results using Iris dataset show that the proposed clustering algorithm performs better than K-means and K-medodis algorithm. The performance of big data (i.e., documents, texts) summarization is evaluated using Australian legal cases from the Federal Court of Australia (FCA) database. The experimental results demonstrate that the proposed method can summarize big data document superior as compared with existing systems.

scholarly journals Image Cationing with Visual-Semantic LSTM

2018 ◽  
Author(s):  
Nannan Li ◽  
Zhenzhong Chen
Keyword(s):  
Visual Processing ◽  
State Of The Art ◽  
Sampling Strategy ◽  
Experimental Results ◽  
Visual Cell ◽  
Semantic Features ◽  
Training Process ◽  
Image Captioning ◽  
Low Level ◽  
High Level

In this paper, a novel image captioning approach is proposed to describe the content of images. Inspired by the visual processing of our cognitive system, we propose a visual-semantic LSTM model to locate the attention objects with their low-level features in the visual cell, and then successively extract high-level semantic features in the semantic cell. In addition, a state perturbation term is introduced to the word sampling strategy in the REINFORCE based method to explore proper vocabularies in the training process. Experimental results on MS COCO and Flickr30K validate the effectiveness of our approach when compared to the state-of-the-art methods.

scholarly journals Robust Switched Tracking Control for Wheeled Mobile Robots Considering the Actuators and Drivers

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4316 ◽  
Author(s):  
José García-Sánchez ◽  
Salvador Tavera-Mosqueda ◽  
Ramón Silva-Ortigoza ◽  
Victor Hernández-Guzmán ◽  
Jacobo Sandoval-Gutiérrez ◽  
...  
Keyword(s):  
Experimental Results ◽  
Sliding Modes ◽  
Wheeled Mobile Robots ◽  
Differential Drive ◽  
Mechanical Structure ◽  
Dc Motors ◽  
Medium Level ◽  
Power Stage ◽  
High Level ◽  
Better Than

By using the hierarchical controller approach, a new solution for the control problem related to trajectory tracking in a differential drive wheeled mobile robot (DDWMR) is presented in this paper. For this aim, the dynamics of the three subsystems composing a DDWMR, i.e., the mechanical structure (differential drive type), the actuators (DC motors), and the power stage (DC/DC Buck power converters), are taken into account. The proposed hierarchical switched controller has three levels: the high level corresponds to a kinematic control for the mechanical structure; the medium level includes two controls based on differential flatness for the actuators; and the low level is linked to two cascade switched controls based on sliding modes and PI control for the power stage. The hierarchical switched controller was experimentally implemented on a DDWMR prototype via MATLAB-Simulink along with a DS1104 board. With the intention of assessing the performance of the switched controller, experimental results associated with a hierarchical average controller recently reported in literature are also presented here. The experimental results show the robustness of both controllers when parametric uncertainties are applied. However, the performance achieved with the switched controller introduced in the present paper is better than, or at least similar to, performance achieved with the average controller reported in literature.

scholarly journals Research on Fast Face Retrieval Optimization Algorithm Based on Fuzzy Clustering

2022 ◽  
Vol 2022 ◽  
pp. 1-7
Author(s):  
Xiajun Dong ◽  
Bin Huang ◽  
Yuncai Zhou
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Recognition Accuracy ◽  
Retrieval Algorithm ◽  
Semantic Features ◽  
Database Retrieval ◽  
Face Images ◽  
The Face ◽  
High Level ◽  
Better Than

Aiming at the problem of long retrieval time for massive face image databases under a given threshold, a fast retrieval algorithm for massive face images based on fuzzy clustering is proposed. The algorithm builds a deep convolutional neural network model. The model can be used to extract features from face photos to obtain a high-dimensional vector to represent the high-level semantic features of face photos. On this basis, the fuzzy clustering algorithm is used to perform fuzzy clustering on the feature vectors of the face database to construct a retrieval pedigree map. When the threshold is passed in for database retrieval of the target face photos, the pedigree map can be quickly retrieved. Experiments on the LFW face dataset and self-collected face dataset show that the model is better than the commonly used K-means model in face recognition accuracy, clustering effect, and retrieval speed and has certain commercial value.

THE SOURCES OF AUXIN HORMONE TO GROWTH OF SHOOT PINEAPPLE STEM CUTTING (Ananascomosus.L. Merr.)

2016 ◽  
Vol 1 (1) ◽  
pp. 45-52
Author(s):  
Palupi Puspitorini
Keyword(s):  
Water Concentration ◽  
Urine Concentration ◽  
Experimental Results ◽  
Stem Cuttings ◽  
Randomized Design ◽  
Natural Hormone ◽  
Cow Urine ◽  
Urine Concentrations ◽  
Completely Randomized Design ◽  
Better Than

The aim of this study was to select the best sources of auxin of which it can stimulate the growth of shoots Pineapple plant cuttings. This research is compiled in a completely randomized design (CRD) with 4 treatments and 6 replications. The Data were statistically Analyzed by the DMRT. Level of treatment given proves that no treatment 0%, cow urine concentration of 25%, young coconut water concentration of 25% and Rootone F 100 mg / cuttings. The results showed that cow urine concentrations of 25% and Rootone F 100 mg give the best results in stimulating the growth of shoots pineapple stem cuttings. Experimental results concluded that the effect of this natural hormone were better than the shoots without given hormone.           

Visible Laser Probing (VLP) with GaP Solid Immersion Lens Demonstrating 110 nm Resolution in Common Laser Probing Applications

2016 ◽  
Author(s):  
Travis Eiles ◽  
Patrick Pardy
Keyword(s):  
Gallium Phosphide ◽  
Fault Isolation ◽  
Solid Immersion Lens ◽  
Laser Probing ◽  
Isolation Methods ◽  
Visible Laser ◽  
High Level ◽  
Industry Needs ◽  
Diameter Reduction ◽  
Better Than

Abstract This paper demonstrates a breakthrough method of visible laser probing (VLP), including an optimized 577 nm laser microscope, visible-sensitive detector, and an ultimate-resolution gallium phosphide-based solid immersion lens on the 10 nm node, showing a 110 nm resolution. This is 2x better than what is achieved with the standard suite of probing systems using typical infrared (IR) wavelengths today. Since VLP provides a spot diameter reduction of 0.5x over IR methods, it is reasonable, based simply on geometry, to project that VLP using the 577 nm laser will meet the industry needs for laser probing for both the 10 nm and 7 nm process nodes. Based on its high level of optimization, including high resolution and specialized solid immersion lens, it is highly likely that this VLP technology will be one of the last optically-based fault isolation methods successfully used.

A Computational Method for the Identification of Endolysins and Autolysins

2020 ◽  
Vol 27 (4) ◽  
pp. 329-336 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Baowen Chen ◽  
Xu Tan ◽  
Huaikun Xiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cell Wall ◽  
Experimental Results ◽  
Computational Method ◽  
Lytic Enzyme ◽  
Support Vector ◽  
Lytic Enzymes ◽  
Data Set ◽  
Optimal Feature ◽  
Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

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