Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Fault Diagnosis Algorithm Based on Convolutional Neural Network

Most of the traditional fault diagnosis methods rely on the expert knowledge of artificial extraction features and related fields, and these algorithms are not accurate, and the robustness and generalization ability are poor. Convolutional neural network is one of the most widely used deep learning models. Based on its unique convolution-pooling network structure, convolutional neural network has powerful feature extraction and expression capabilities. In this paper, based on the characteristics of one-dimensional vibration signals, a fault diagnosis algorithm model based on one-dimensional convolutional neural network is proposed. Through the experiment of the bearing fault public data set, the proposed algorithm has more than 99% fault recognition rate.
SAE Level 3 Autonomous Driving Technology of the ETRI

We, the electronics and telecommunications research institute (ETRI), have been developing autonomous driving technology of society of automotive engineers (SAE) level 3. Our technology is at a demonstration stage and includes a variety of technologies. This paper introduces our technology for autonomous driving – core s/w, machine learning, mapping and simulation.
A Comparison Study on Legal Document Classification Using Deep Neural Networks

Despite the rapid development of artificial intelligence technology in legal services around the world, little research work is being performed in the area of legal document classification in Korean language. In this paper, we propose and compare three different legal document classification approaches based on two deep neural network models, i.e., Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), and two word embedding schemes. Based on nearly 60,000 precedent case data, we obtained the highest classification accuracy (up to 86 percent) with the RNN model with Word2Vec embedding.
Privacy Protection with Pseudonymization and Anonymization In a Health IoT System: Results from OCARIoT

This paper presents the implementation of a users' privacy protection approach in a health Internet of Things (IoT) system. It is composed of a set of security layers based on cryptography, pseudonymization and anonymization techniques applied to processed (Data-In-Use, DIU), stored (Data-At-Rest, DAR) and transmitted (Data-In-Motion, DIM) data. Regarding security and privacy in IoT systems, especially in digital health systems, it is necessary to guarantee that the user rights are respected. This requires a security-in-depth strategy established based on risk-based results, every interconnecting actors, their security and privacy requirements and the specific aspects of the entire ecosystem, including the applications and platform. The presented privacy protection approach was developed and applied in a digital health platform, OCARIoT.
Research on Multi-modal Mandarin Speech Emotion Recognition Based on SVM

In this paper, based on speech multi-modal fusion emotion recognition, the key issues of speech signal preprocessing, feature extraction, fusion strategy, fusion method and emotion recognition classification are studied in depth. In this paper, the construction of emotional recognition theory model and the research of feature fusion and classification recognition algorithms are carried out. Speech-based emotion recognition research processes the speech signal by SVM, and calculates their maximum and minimum values. The specific methods and processes of emotional feature extraction are introduced in detail, and the extracted features are analyzed by emotion classification and recognition. The obtained recognition results verify the validity of the extracted features.
Creation of Necessary Technical and Expert- Analytical Conditions for Development of the Information System of Evaluating Open Text Information Sources’ Influence on Society

In this paper, we trained distributional models (patterns) for text preprocessing in Word2vec and Glove. Three variants of text preprocessing were used to train distributional patterns. Based on the implemented distribution model Word2Vec, a vector representation was obtained for a cluster-separated test sample of 30 news items. All variants of the weighted average calculation of the vector representation of texts were considered. Two-stage clustering was carried out. After training the Doc2Vec model on normalized documents, a vector representation was obtained for each document. The following news about the same event was selected for the test, but from different sources. A 2-dimensional “factual cube” was analyzed.
Segmentation of Ocular Pathologies Using Deep Convolutional Neural Network

Diabetes Mellitus (DM) is a chronic, progressive and life-threatening disease. The ocular manifestations of DM, Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME), are the leading causes of blindness in the adult population throughout the world. Early diagnosis of DR and DM through screening tests and successive treatments can reduce the threat to visual acuity. In this context, we propose an encoder decoder based semantic segmentation network SOP-Net (Segmentation of Ocular Pathologies Using Deep Convolutional Neural Network) for simultaneous delineation of retinal pathologies (hard exudates, soft exudates, hemorrhages, microaneurysms). The proposed semantic segmentation framework is capable of providing segmentation results at pixel-level with good localization of object boundaries. SOP-Net has been trained and tested on IDRiD dataset which is publicly available with pixel level annotations of retinal pathologies. The network achieved average accuracies of 98.98%, 90.46%, 96.79%, and 96.70% for segmentation of hard exudates, soft exudates, hemorrhages, and microaneurysms. The proposed methodology has the capability to be used in developing a diagnostic system for organizing large scale ophthalmic screening programs.
Feature Extraction of Seafarers’ Workload Based on EEG Signals

Most of maritime accidents have been proved to be caused by human errors. In order to quantitatively analyze human factor of seafarers, we carried out the simulation test of ship operation. During the test, a brain instrument was used to collect Electroencephalogram (EEG) signals of the subjects. In the current research stage, EEG signals have been denoised by wavelet transform, and EEG features are extracted by finite impulse response(FIR) digital filter. The feature can be used as the feature vector of Support Vector Machine(SVM) classifier for training, and finally a classifier for workload recognition is established.
Reforming Warning and Obstacle Detection Assisting Visually Impaired People on mHealth

Obstacle detection and warning application is to implement for assisting visually impaired people to reduce a harm causing injury. In detail, we study, plan, analyze, design, and develop the obstacle detection module for preventing the visually impaired people crashing the obstructions and send them the alert message for preventing an accidence. Improving the quality of life making them safe and live without anxiety. Regarding methodology, we select an object detection system using on real-time camera or VDO processing named “You Only Look Once” or YOLO. There are two versions; YOLOv2 and YOLO9000, which have tested in this development. YOLOv2 is higher performance than YOLO9000 in 20:7 frames per second (FPS), and higher accuracy. After that, we integrate the detection and notification modules together. Finally, this system can detect and send the alert message to the visually impaired people immediately before they crash the obstructions.
Graph-Based Analysis of Housing Needs: Towards a Citizen-Oriented Urban Planning Approach

Given that nowadays learning occurs in diverse ways (with the participation of learning communities, social and personal networks, among others), the individuals and their generated collective knowledge are considered in the creation of computer-mediated collaborative solutions. However, in the field of E-governance and E-participation there is a lack of authorities that see their citizens as active components of urban planning. Involving people towards the change of their cities and improve their resilience is a new challenge. The present work provides a preliminary study to analyze the views of 1606 citizens of Quito and 1248 citizens of Portoviejo regarding their perception about what a suitable environment to live means. Once we did a graph-based analysis and comparison of this information, we found that in both Quito and Portoviejo, the needs for living near to the family and workplaces are prevalent. Nevertheless, while in Quito citizens expect to live in a secured neighbourhood, people living in Portoviejo look for environments where the basic services are implemented. Our findings are thought to be used in a Collaborative Urban Planning platform where citizens may be able to create, acquire, and transfer knowledge.
Sentiment Analysis of Reviews Based on Deep Learning Model

As known as opinion mining, sentiment analysis is a work by using the "natural language processing" method to find out the author’s attitude, emotion or evaluation on certain topics. This paper using a dataset by Mass et al from its original Stanford AI Repository, and a commonly pre-processing method——word embedding, and establish a deep learning model for sentiment analysis. From the perspective of data analysis, learn about the movie preferences and cultural characteristics of audiences from domestic and foreign. In the experiment, we compared the performance of RNN, LSTM and GRU in natural language processing, and improve the efficiency and accuracy of sentiment analysis by a fusion model which integrating recurrent neural networks variant at the output of convolutional neural network.
Drug-Drug Interactions Prediction Based on Drug Embedding and Graph Auto-Encoder

Identification of potential Drug-Drug Interactions (DDI) for newly developed drugs is essential in public healthcare. Computational methods of DDI prediction rely on known interactions to learn possible interaction between drug pairs whose interactions are unknown. Past work has used various similarity measures of drugs to predict DDIs. In this paper, we propose an effective approach to DDI Prediction using rich drug representations utilizing multiple knowledge sources. We have used the Drug-Target Interaction (DTI) Network to learn an embedding of drugs by using the metapath2vec algorithm. We have also used drug representation gained from the rich chemical structure representation of drugs using Variational Auto-Encoder. The DDI prediction problem is modeled as a link prediction problem in the DDI network containing known interactions. We represent the nodes in the DDI network as their embeddings. We apply a link prediction algorithm based on Graph Auto-Encoders to predict additional edges in this network, which are potential interactions. We have evaluated our approach on three benchmark DDI datasets, namely DrugBank, SemMedDB, and BioSNAP. Experimental results demonstrate that the proposed method outperforms the prior methods in terms of several performance metrics (AUC, AUPR, and F1-score) on all the datasets. Furthermore, we have also evaluated the role of the individual type of drug representation embeddings in boosting up the performance of DDI Prediction.
Landing of Aircraft Using Integral State Feedback Sliding Mode Control

Landing is daunting control design problem because it demands high accuracy and precision by pilot. Among the various states to control for stability and landing in an Aircraft, one is its altitude to handle during landing. Since the linearized model of aircraft is considered and is used for altitude control design. For best tracking performance during glide and flare phase, Integral State feedback Sliding Mode Control (ISSMC) is applied, which is based on unit vector approach. To eliminate error between responded altitude and referenced altitude, a dynamic demand first order filter is used. There has been noted a significant improvement in performance as compared with PID control. When comparing the Integral State feedback Sliding Mode Control with PID during landing phase, the ISSMC yields the best response of glide and flare maneuverability for smooth touch down during landing on runway.
A Research on Thermal Properties of Brushless Motor Drives for Aerospace Use

Connected to an electric motor via the switching circuit through the direct current (DC) power supply, a brushless motor drive controls the rotating speed of the motor under the condition of input signals with different duty cycles. This paper focuses on the problem resulting in the burning of the overheating drive. For that, the paper relies on numerical simulation to offer thermal distribution data about the internal components of the device when being used in the extreme condition for satellite models, i.e. with the rated voltage and maximum load current, and then to analyze the reasonableness of their layout; at the same time, the paper gives the minimum dimensions of the heat sink mounted to the brushless motor drive used in the worst condition, followed by a simulation and testing of the temperature distribution in relation to the drive's case. The simulation and testing results indicate that when used with high temperature, the drive with a built-in heat sink is able to meet the demand for using the device with the case temperature lower than 125°C and the junction temperature lower than 150°C. The research findings of this paper can provide technical support for the design and use of brushless motor drives.
Representation Learning for Background Music Identification in Television Shows

Although audio fingerprinting has been widely used in various applications, the performances of audio fingerprinting methods are extremely decreased in case of identifying the background music mixed with speech in TV shows. To solve this, we present an approach to represent embeddings for background music identification using deep convolutional networks. We construct triplet dataset including the original songs, the same songs mixed with voices, and different songs. Then, we train the network with triplet loss function with adaptive margin. By nearest neighbor classifier, the closest embedding is found among the ones of original songs. As comparing top-1 accuracy of music identification, it is shown that our representation learning of the embedding from each music segment mixed with speech has meaningful information for music identification.
Phytotoxicity of silver nanoparticles (AgNPs) prepared by green synthesis using sage leaves (Salvia officinalis)

Silver nanoparticles (AgNPs) are widely investigated with regard to their physical, chemical, but also biological properties. Antibacterial and antitumor properties of AgNPs have been intensively studied. In addition, the synthesis using a green approach brings further significant biological properties. However, it is also necessary to monitor the potential toxicity of such nanoparticles in different ecosystems. In this study, the effect of AgNO3 and AgNPs on germinated plants of Zea mays was studied. Effects on basic growth and physiological parameters were observed. There was a statistically significant difference between the variants tested.
Hashtag Recommender System Based on LSTM Neural Reccurent Network

The successfulness reached by numerous neural network models for encoding word embedding has conducted approaches for encoding vector representation for sentences, paragraphs or even micro-blogs. Meanwhile, the hashtag is a keyword that denotes the topic of a tweet. Hashtags supply significant information for several text mining tasks, for instance sentiment classification, news analysis, etc. Hence, an appropriate hashtag recommender system is needed to assist users in choosing relevant hashtags for their tweets. Therefore, we present a hashtags recommender method to encode the tweet vector-based representation by using a long short-term memory recurrent neural network. Specifically, we first learn words vector-based representation by training the Skip gram model to generate embeddings of tweets. Then we apply the density-based spatial clustering to gather similar tweets into homogeneous clusters. Subsequently, we recommend the k top highest results of tweets to the user, via calculating their co-occurrence with the nearest clusters centers to a given tweet. Our experiments on a real data set show appropriate results.
Detection of Prostate Cancer's Antigen in Sub-pico Range of Concentration using the Faradaic-mode Electrochemical Impedance Spectroscopy

A biosensing platform for detection of the prostate cancer's ‘gold standard’ marker, the prostate-specific antigen (PSA) is reported. The developed assay combines the application of gold interdigitated microelectrode as the sensing device, together with the electrochemical impedance spectroscopy (EIS) in Faradaic mode of detection for a highly sensitive quantification of PSA binding on the sensor's surface. The binding test revealed the developed assay's capability in detecting the target in sub-pico range of concentration of 10 ng/ml down to 100 pg/ml of PSA in 100 mM phosphate buffered saline (PBS) solution (pH 7.4), covering the clinically accepted PSA threshold value for the ‘screening’ of prostate cancer at 4 ng/ml of PSA.
Multi-Task Compressive Sensing of Vibration Signal using GMM Clustering for Wireless Transmission

Wireless sensor network has been widely used in the prognostic and health management system. Signal compression is often required during wireless transmission because of the high sampling frequency. In recent years, compressive sensing has great potential to increase the efficiency of wireless transmission. In this paper, we use the multi-task compressive sensing within a multi-task learning setting. Under this modeling, data from all tasks contribute toward inferring a posterior on the parameters. Moreover, a GMM clustering method is used for finding the best classification among signal blocks. Simulation results show that more signal blocks may not necessarily lead to higher reconstruction accuracy. Conversely, reconstruction error may be reduced by concentrating signal blocks with similar distribution together. The performance of proposed method is validated by gearbox data.
SCinet DTN-as-a-Service Framework

Transferring big data over Wide Area Networks (WANs) is challenging because optimization is dependent on the specifics of multiple parameters. Network services, paths, and technologies have different characteristics, including loss rate, latency, and available capacity. Yet, frameworks currently used to configure and orchestrate transfer systems, measure performance, and analyze results have limited capabilities. We propose a framework, DTN-as-a-Service (DaaS), for high-performance network data transfers using and integration of techniques, including virtualization, network provisioning, and performance data analysis. This framework has a modular design for supporting multiple transfer tools, optimizers and orchestrators for the data transfer environment, including \textit{Docker} and \textit{Kubernetes}. We present a \textit{Jupyter} based workflow for high-speed network data transfer in data-intensive science and evaluate the performance of the transfer with a simple programmable visualizer implemented in the framework. This framework has been implemented as a prototype at two recent SC supercomputing conferences. With the increase in the number and the capacity of WAN links at the conferences (multiple 100 Gbps WAN circuits), the challenges involved in setting up, testing, debugging, verifying and running applications on high-performance systems connecting to the conference SCinet WAN circuits also increase. The SCinet implementation of the DaaS framework for the conference community allowed users to control hardware, software, and network infrastructure for high-speed network data transfer, primarily for large scale applications. Through the evaluation of the framework in our test setup, we demonstrated that NVMe over Fabrics with TCP is twice as efficient compared to using conventional TCP in high-speed NVMe-to-NVMe transfers. We also implemented a 400 Gbps LAN experiment to evaluate the DaaS framework.
Cross-Domain Machinery Fault Diagnosis Using Adversarial Network with Conditional Alignments

The rolling element bearing is essential for rotating machines in real industries. The rolling element bearing faults are supposed to be detected and identified in time to reduce maintenance cost and enhance reliability. The traditional way of fault diagnosis depends on experts’ knowledge and experience on the collected machinery vibration data. However, it is becoming more and more difficult to diagnose the health states of the complicated modern machines Using the conventional methods. Currently, with the rapid development of data-driven methods such as deep learning, more accurate diagnosis results have been obtained. While the data-driven methods have been widely used in the basic tasks where the concerned data are from the same distribution, the cross-domain diagnostic tasks are still challenging where distribution discrepancy exists between two different kinds of data. In this paper, a novel network for diagnostics is designed based on domain adversarial neural network, where the diagnostic knowledge learned from the domain for source data is transferred to the domain for the target data. Conditional domain adaptation is proposed to improve data alignments between domains in the learned sub-space. The experimental results on the popular CWRU dataset validate the effectiveness and efficiency of the cross-domain fault diagnosis network in this paper.
Fast and accurate iris segmentation methods using LoG filters

For visible wavelengths, a new method is introduced using truncated LoG filter with specific preprocessing to deal with challenges of eyebrows, illumination variation, eyelid masking, perspective, and movements. Here, the iris boundary is detected first and then pupil is marked with maximally stable extremal regions. The experiments conducted on challenging database UBIRIS v2 demonstrate the improved segmentation accuracy of the proposed methods. The comparison with other popular methods provides promise in terms of accuracy and speed.
An efficient graph structure inference strategy based on random walk model on graph: Application to functional brain networks

A new strategy to solve the graph structure estimation problem from a noisy observed dependency matrix is proposed in this paper. It relies on an efficient inversion of the random walk model on graph employed to model the latter matrix. To this end, the sparsity assumption of the graph Laplacian associated to the target graph structure is optimally dealt with during the optimization process. The efficiency of the proposed algorithm is confirmed using both synthetic data and also using real electroencephalographic (EEG) signals in the context of inferring functional brain network.
Actor-centric Short-clip Generation System

This paper proposes a system which recognizes faces in videos and automatically generates an actor-centric short clip. First, deep learning based face recognition is performed with movies and dramas, which recognizes 1,000 Korean actors. Secondly, temporal unit segments about actors’ appearance time in a video are made. Next neighboring unit segments in which the same actors appeared are merged. Finally, each actor’s shortclips are generated based on the merged segment information.