scholarly journals Building a Real-Time 2D Lidar Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Nadim Arubai ◽  
Omar Hamdoun ◽  
Assef Jafar
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Tilt Angle ◽  
State Of The Art ◽  
The State ◽  
Prediction Problem ◽  
Learning Methods ◽  
Depth Prediction ◽  
Avoidance Problem

Applying deep learning methods, this paper addresses depth prediction problem resulting from single monocular images. A vector of distances is predicted instead of a whole image matrix. A vector-only prediction decreases training overhead and prediction periods and requires less resources (memory, CPU). We propose a module which is more time efficient than the state-of-the-art modules ResNet, VGG, FCRN, and DORN. We enhanced the network results by training it on depth vectors from other levels (we get a new level by changing the Lidar tilt angle). The predicted results give a vector of distances around the robot, which is sufficient for the obstacle avoidance problem and many other applications.

scholarly journals ​Behavior Recognition of Group-ranched Cattle from Video Sequences using Deep Learning

2021 ◽  
Author(s):  
Rotimi-Williams Bello ◽  
Ahmad Sufril Azlan Mohamed ◽  
Abdullah Zawawi Talib ◽  
Salisu Sani ◽  
Mohd Nadhir Ab Wahab
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
State Of The Art ◽  
The State ◽  
Video Sequences ◽  
Detection Accuracy ◽  
Behavior Recognition ◽  
Learning Methods ◽  
Important Indicator ◽  
Daily Behavior

Background: One important indicator for the wellbeing status of livestock is their daily behavior. More often than not, daily behavior recognition involves detecting the heads or body gestures of the livestock using conventional methods or tools. To prevail over such limitations, an effective approach using deep learning is proposed in this study for cattle behavior recognition. Methods: The approach for detecting the behavior of individual cows was designed in terms of their eating, drinking, active, and inactive behaviors captured from video sequences and based on the investigation of the attributes and practicality of the state-of-the-art deep learning methods. Result: Among the four models employed, Mask R-CNN achieved average recognition accuracies of 93.34%, 88.03%, 93.51% and 93.38% for eating, drinking, active and inactive behaviors. This implied that Mask R-CNN achieved higher cow detection accuracy and speed than the remaining models with 20 fps, making the proposed approach competes favorably well with other approaches and suitable for behavior recognition of group-ranched cattle in real-time.

scholarly journals Antimicrobial peptide identification using multi-scale convolutional network

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Xin Su ◽  
Jing Xu ◽  
Yanbin Yin ◽  
Xiongwen Quan ◽  
Han Zhang
Keyword(s):  
Deep Learning ◽  
Antimicrobial Peptide ◽  
State Of The Art ◽  
Peptide Identification ◽  
The State ◽  
Fusion Model ◽  
Learning Methods ◽  
Convolutional Network ◽  
Multi Scale ◽  
Peptide Database

Abstract Background Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN.

scholarly journals Improving deep learning method for biomedical named entity recognition by using entity definition information

2021 ◽  
Vol 22 (S1) ◽  
Author(s):  
Ying Xiong ◽  
Shuai Chen ◽  
Buzhou Tang ◽  
Qingcai Chen ◽  
Xiaolong Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
The State ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Learning Methods ◽  
Named Entity ◽  
Sequence Labeling ◽  
Biomedical Named Entity Recognition

Abstract Background Biomedical named entity recognition (NER) is a fundamental task of biomedical text mining that finds the boundaries of entity mentions in biomedical text and determines their entity type. To accelerate the development of biomedical NER techniques in Spanish, the PharmaCoNER organizers launched a competition to recognize pharmacological substances, compounds, and proteins. Biomedical NER is usually recognized as a sequence labeling task, and almost all state-of-the-art sequence labeling methods ignore the meaning of different entity types. In this paper, we investigate some methods to introduce the meaning of entity types in deep learning methods for biomedical NER and apply them to the PharmaCoNER 2019 challenge. The meaning of each entity type is represented by its definition information. Material and method We investigate how to use entity definition information in the following two methods: (1) SQuad-style machine reading comprehension (MRC) methods that treat entity definition information as query and biomedical text as context and predict answer spans as entities. (2) Span-level one-pass (SOne) methods that predict entity spans of one type by one type and introduce entity type meaning, which is represented by entity definition information. All models are trained and tested on the PharmaCoNER 2019 corpus, and their performance is evaluated by strict micro-average precision, recall, and F1-score. Results Entity definition information brings improvements to both SQuad-style MRC and SOne methods by about 0.003 in micro-averaged F1-score. The SQuad-style MRC model using entity definition information as query achieves the best performance with a micro-averaged precision of 0.9225, a recall of 0.9050, and an F1-score of 0.9137, respectively. It outperforms the best model of the PharmaCoNER 2019 challenge by 0.0032 in F1-score. Compared with the state-of-the-art model without using manually-crafted features, our model obtains a 1% improvement in F1-score, which is significant. These results indicate that entity definition information is useful for deep learning methods on biomedical NER. Conclusion Our entity definition information enhanced models achieve the state-of-the-art micro-average F1 score of 0.9137, which implies that entity definition information has a positive impact on biomedical NER detection. In the future, we will explore more entity definition information from knowledge graph.

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

Real-time multiple object tracking using deep learning methods

2021 ◽  
Author(s):  
Dimitrios Meimetis ◽  
Ioannis Daramouskas ◽  
Isidoros Perikos ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Real Time ◽  
Multiple Object Tracking ◽  
Learning Methods ◽  
Multiple Object

Named Entity Recognition and Relation Extraction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-39
Author(s):  
Zara Nasar ◽  
Syed Waqar Jaffry ◽  
Muhammad Kamran Malik
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
The State ◽  
Entity Recognition ◽  
Joint Models ◽  
Named Entity ◽  
Textual Data ◽  
Benchmark Datasets

With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense importance to extract information nuggets from this data. One approach towards effective harnessing of this unstructured textual data could be its transformation into structured text. Hence, this study aims to present an overview of approaches that can be applied to extract key insights from textual data in a structured way. For this, Named Entity Recognition and Relation Extraction are being majorly addressed in this review study. The former deals with identification of named entities, and the latter deals with problem of extracting relation between set of entities. This study covers early approaches as well as the developments made up till now using machine learning models. Survey findings conclude that deep-learning-based hybrid and joint models are currently governing the state-of-the-art. It is also observed that annotated benchmark datasets for various textual-data generators such as Twitter and other social forums are not available. This scarcity of dataset has resulted into relatively less progress in these domains. Additionally, the majority of the state-of-the-art techniques are offline and computationally expensive. Last, with increasing focus on deep-learning frameworks, there is need to understand and explain the under-going processes in deep architectures.

A Survey of Real-Time Industrial Wireless LAN Research

2015 ◽  
Vol 738-739 ◽  
pp. 1105-1110 ◽  
Author(s):  
Yuan Qing Qin ◽  
Ying Jie Cheng ◽  
Chun Jie Zhou
Keyword(s):  
Real Time ◽  
Future Development ◽  
Wireless Lan ◽  
State Of The Art ◽  
The State ◽  
Local Networks ◽  
The Real ◽  
Time Requirements ◽  
And Performance ◽  
Fundamental Mechanism

This paper mainly surveys the state-of-the-art on real-time communicaton in industrial wireless local networks(WLANs), and also identifys the suitable approaches to deal with the real-time requirements in future. Firstly, this paper summarizes the features of industrial WLANs and the challenges it encounters. Then according to the real-time problems of industrial WLAN, the fundamental mechanism of each recent representative resolution is analyzed in detail. Meanwhile, the characteristics and performance of these resolutions are adequately compared. Finally, this paper concludes the current of the research and discusses the future development of industrial WLANs.

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