Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Brittle Fracture Rate Estimator for Steel Property Evaluation Using Deep Learning After Drop-Weight Tear Test

This study proposes an automated brittle fracture rate (BFR) estimator using deep learning. As the demand for line-pipes increases in various industries, the need for BFR estimation through drop-weight tear test (DWTT) increases to evaluate steel’s property. Conventional BFR or ductile fracture rate (DFR) estimation methods require an expensive 3D scanner. Alternatively, a rule-based approach is used with a single charge-coupled device (CCD) camera. However, it is sensitive to the hyper-parameter. To solve these problems, we propose an approach based on deep learning that has recently been successful in the fields of computer vision and image processing. The method proposed in this study is the first to use deep learning approach for BFR estimation. The proposed method consists of a VGG-based U-Net (VU-Net) which is inspired by U-Net and fully convolutional network (FCN). VU-Net includes a deep encoder and a decoder. The encoder is adopted from VGG19 and transferred with a pre-trained model with ImageNet. In addition, the structure of the decoder is the same as that of the encoder, and the decoder uses the feature maps of the encoder through concatenation operation to compensate for the reduced spatial information. To analyze the proposed VU-Net, we experimented with different depths of networks and various transfer learning approaches. In terms of accuracy used in real industrial application, we compared the proposed VU-Net with U-Net and FCN to evaluate the performance. The experiments showed that VU-Net was the accuracy of approximately 94.9 %, and was better than the other two, which had the accuracies of about 91.8 % and 93.7 %, respectively.
Cooperative Autonomy in the CMRE ASW Multistatic Robotic Network: Results From LCAS18 Trial

Robotics networks for underwater surveillance applications offer the promise for the desired monitoring at lower costs than traditional approaches based on manned assets. Autonomy and networking are crucial aspects to consider in order to overcome the challenges typical of the marine environment, such as the limitations in underwater communications. This paper addresses both these aspects, describing the cooperative autonomy architecture and the strategies of the AUVs of the CMRE Anti-Submarine Warfare (ASW) network. We report tests from the LCAS18 trial. In this trial, cooperative strategies were supported by data sharing, facilitating track classification and the creation of a common tactical picture. The CMRE intelligent Cooperative Autonomous Decision Making Engine (iCADME) controlled the various phases of the robot mission. The produced tracks were classified in real-time and the ones likely to be generated by a target were selected to trigger a cooperative track prosecution strategy. The final objective was to facilitate track-to-track associations for the final target confirmation. To support cooperative autonomy, in LCAS18, the novel CMRE Cognitive Communication Architecture (CCA) was used in the CMRE ASW network for the first time. CCA managed the communications, with the aim of improving the use of the available bandwidth.The results from LCAS18 are one of the first examples in literature demonstrating the potentials of cooperative autonomy strategies and data fusion in realistic surveillance scenarios. Only autonomous decisions can in fact improve the mission performance when a reliable communication channel with the Command and Control centre cannot be guaranteed. Furthermore, cooperative decisions and data sharing allow to exploit the spatial diversity that robotic networks can provide.
The Effect of Embedding Dimension Reduction on Increasing LSTM Performance for Sentiment Analysis

Increasing the amount of data in sentiment analysis research become a problem that requires a different approach. Traditional machine learning has limitations in handling large amounts of data (big data era). Deep learning approach appears to be one of the approaches that can be used. Deep learning in some research can provide good results, especially in NLP research. This research will use the LSTM approach to do sentiment analysis. The input data is modeled into sequence form. This modeling input data into sequence form is expected to improve better accuracy in sentiment classifying. In addition, an analysis of changing in dimension reduction of embedding data with keras framework was experimented to determine its effect on LSTM performance.
High level strategy for revisiting objects with low cost AUVs

This paper describes a high level strategy to guarantee the revisit of a target in underwater environment by an Autonomous Underwater Vehicle (AUV) subject to high drift in position and equipped with specific exteroceptive sensors such as sonars or cameras. It assumes the availability of a certain a priori map of the environment. Firstly, based on the Field Of View (FOV) of the exteroceptive sensor and the characteristics of the different landmarks in the map, a registration map is computed. This indicates the sets of the robot configurations able to detect the different landmark considered. All these sets called relocation areas enable to relocate the robot position and reduce its uncertainty by detecting the geolocalized landmarks. These relocation areas are the nodes of a hyper-graph. Between two nodes, the vehicle navigates by dead-reckoning. Based on the backward reach set of a registration map, the links between the different registration maps can be created according to a motion model taking into account uncertainties. Finally, a backward graph search minimizes a cost function to propose a strategy of revisit of intermediate landmarks to connect the starting position and the relocation area(s) of the target you would like to revisit.
Assessment of Intelligent Decision Support Systems Effectiveness in Technological Processes of big Data Processing

The article defines criteria and their indicators used to assess the effectiveness of intelligent decision support systems (IDSS) in technological processes of educational sphere big data processing. These criteria and indicators are given as part of a three-level hierarchical structure, which comprises information process (upper level), IDSS (middle level), and IDSS data store (lower level). Approaches to identification of IDSS functional responsiveness and resource capacity as well as performance of such IDSS on data analysis tasks using machine learning models are given. The results of the work done were tested while developing IDSS components for The Institute of Digital Education of Moscow City University.
Exploring Data Quantity Requirements for Domain Adaptation in the Classification of Satellite Image Time Series

Land cover maps are a vital input variable in all types of environmental research and management. However the modern state-of-the-art machine learning techniques used to create them require substantial training data to produce optimal accuracy. Domain Adaptation is one technique researchers might use when labelled training data are unavailable or scarce. This paper looks at the result of training a convolutional neural network model on a region where data are available (source domain), and then adapting this model to another region (target domain) by retraining it on the available labelled data, and in particular how these results change with increasing data availability. Our experiments performing domain adaptation on satellite image time series, draw three interesting conclusions: (1) a model trained only on data from the source domain delivers 73.0% test accuracy on the target domain; (2) when all of the weights are retrained on the target data, over 16,000 instances were required to improve upon the accuracy of the source-only model; and (3) even if sufficient data is available in the target domain, using a model pretrained on a source domain will result in better overall test accuracy compared to a model trained on target domain data only—88.9% versus 84.7%.
A Fuzzy base Approach to Reduce the Domain of Ambiguities in Software Requirement

The role of fuzzy logic in the field of software engineering has grown exponentially bringing some new theoretical advances and many practical applications. It has been applied to various areas like requirement engineering, software reusability, software maintainability, etc. In this paper, we will focus on requirement elicitation, which is the first and important stage of requirement engineering, because if anything goes wrong in this stage the final product will be different from what the user actually wanted. Moreover, the cost of changes that are discovered in the later stages of software development may grow exponentially proportional to the stage in which it is discovered. In most of the elicitation techniques, the requirements collected are ambiguous, which means there is a fair chance of requirement getting misinterpreted.In our paper, we have proposed a fuzzy-based approach to reduce the domain of ambiguous requirements collected during the elicitation phase. The reason for applying fuzzy is clear as it can be viewed as an excellent tool for dealing with ambiguities.Our proposed approach is very effective and it can reduce the overall time and cost involved in software development. Further, it increases the reliability of the requirement document which leads to product satisfaction.
Social-aware navigation in crowds with static and dynamic groups

Serious games with virtual characters heavily depend on character properties related to social interactions and awareness. This paper presents social-aware navigation for crowds in serious games. It enables a virtual character to join a group or navigate to a goal position through a crowd with static and dynamic group formations in a safe and socially acceptable way. The social-aware space is modeled in order to produce a speed map used by a fast marching method to find the fastest path to the goal position. We evaluate the method on a simulated scenario and public crowd datasets. Initial results show that social-aware navigation is capable of driving a virtual character to join a group or navigate to the goal in a realistic way while considering social norms and interactions.
Supervised classification approach dedicated to proton exchange membrane fuel cell diagnostic

This paper deals with a supervised classification approach dedicated to diagnose a PEMFC system. The purpose is to detect and isolate a fault occurring on a fuel cell system based on electrochemical impedance measurements. The air compressor failure, fuel poisoning and stack overheating are the faults considered in this study. Some experimental tests are performed on two similar stacks (only the power changes). The k-nearest neighbors is the supervised classification method that is used as it is well known for its efficiency and simplicity of implementation.
A Time-reversal based CP-free OFDM system for underwater acoustic communication

In practical applications such as transmissions between nodes in underwater acoustic networks, underwater acoustic communication is limited by the underwater acoustic channel and the hardware. In order to avoid inter-block interference (IBI) and to formulate circular convolution, cyclic-prefixes have been widely used in underwater acoustic OFDM systems, which induces severe spectrum efficiency reduction. In this paper, we introduced a CP-free OFDM system with the help of time-reversal. Simulation results under Rayleigh channel and real underwater channels measured in Danjiangkou Lake of China are presented to show the advantage of the proposed system.
Development of a Novel Deep-Sea in situ Bubble Sampling Instrument: SeaBAT

Methane (CH4) is a greenhouse gas that is often found in a solid hydrate clathrate form in marine sediments along continental margins. Under certain conditions, CH4 can escape from the seafloor as free gas bubbles and rise in the water column. Traditional methods of determining CH4 flux from the seafloor into the shallow ocean often rely on determining the rise height of the bubble plume, but cannot identify how much CH4 is left in the bubbles at the terminus of their rise. Here we describe the development of the Deep-Sea Bubble Acquisition Tool (SeaBAT), a novel bubble capture system for determining the composition of these bubbles in situ. The SeaBAT was deployed at the Cascadia Margin - an area well known for its active CH4 bubble seeps - where over 90 bubble samples were collected and analyzed for composition. Most bubbles contained over 90% CH4 with trace amounts of Nitrogen (N2), Oxygen (O2), and Carbon Dioxide (CO2).
BATS Coding for Underwater Acoustic Communication Networks

Communication networks formed by Autonomous Un- derwater Vehicles (AUVs) have recently been employed for a variety of applications in oceanographic exploration and environmental protection, and military uses. The promise of such networks is very high, but their performance is limited by the low reliability of communications over the underwater acoustic channel. In order to increase the end-to-end throughput in multi-hop network configurations, chains of relay nodes can be employed. Unfortunately, the usual techniques utilized to address channel errors and packet erasures in multi-hop terrestrial networks such as Automatic Repeat Request (ARQ) and Forward Error Correction (FEC) coding are ineffective in the underwater channel due to adverse multipath propagation and long delays. Solutions based on network coding have, therefore, been investigated in the literature. We propose and evaluate utilization of Batched Sparse (BATS) coding, which is a combination of fountain coding and network coding, in order to improve the resilience of multi-hop underwater acoustic communication networks (UWACNets) consisting of multiple underwater transceiver nodes. BATS coding allows to overcome the limitations of channel coding in terms of robustness and those of network coding in terms of computational complexity, while noticeably improving throughput performance.
A X-band Coplanar-coupled-fed Microstrip Array with Enhanced Bandwidth for Coastal Radar

A coplanar-coupled-fed microstrip antenna array is proposed on a single-layer substrate with enhanced bandwidth to provide a low-cost solution for coastal radar systems. Coplanar coupled feeding technology is applied to enhance the impedance bandwidth as well as lowering manufacturing costs. Two slots are etched on the non-radiating edges of antenna element to broad the bandwidth further. And the feed network is designed based on the Taylor distribution of the current to obtain low side lobe. The simulated results show that the proposed microstrip array achieves 4.06% impedance bandwidth from 9.15 GHz to 9.53 GHz. The gain is better than 19.3 dBi, and the side lobe level is lower than −26 dB.
Method for Long-term Estimating Ice Algae Biomass Based on Spectral Attenuation Characteristics of the Sea Ice

Ice algae plays an important role in the polar marine ecosystem, so it is essential to estimate the biomass of polar ice algae. In this paper, we measured the internal irradiance of sea ice and calculated the spectral attenuation characteristics of sea ice media. We used Empirical Orthogonal Function(EOF) analysis to reduce the dimension of data, extract the characteristics of spectral attenuation coefficient, and established the relationship between chlorophyll a concentration and spectral attenuation coefficient through Generalized Linear Model. A method for estimating the biomass of polar ice algae represented by chlorophyll a concentration was proposed.
Unsupervised deep learning based change detection in Sentinel-2 images

Change Detection (CD) is an important application of remote sensing. Recent technological evolution resulted in the availability of optical multispectral sensors that provide High spatial Resolution (HR) images with many spectral bands. Such characteristics allow for new applications of CD, however present new challenges on the proper exploitation of the information. HR multitemporal data processing is challenging due to spatial correlation of pixels and spatial context information needs to be exploited to benefit from multitemporal HR images. Moreover most of the state-of-the-art CD methods exploit single or couple of spectral channels from the optical sensors to derive CD map. To overcome these challenges, this paper presents a novel unsupervised deep-learning based method that can effectively model contextual information and handle all the bands in multispectral images. In particular, we focus on the Sentinel-2 images provided by the European Space Agency (ESA) that provides both higher spatial and temporal resolution optical images with 13 spectral bands with respect to previous generation sensors. Experimental results on the urban Onera satellite CD (OSCD) dataset and on agricultural multitemporal images from Barrax, Spain confirms the effectiveness of the proposed method.
Discontinuous Control Synthesis for the Buck Voltage Converter

Voltage converters are widely used in engineering as power supplies and voltage stabilizers. The construction of each voltage converter consist of reactive energy storage (inductive and capacitive elements) and a switching device. With the development of semiconductor technology, it is possible to abandon mechanical switching devices, and to use semiconductor diodes, transistors, and thyristors as switching elements, which switching frequency can reach several hundred kilohertz. The main problem associated with the control of semiconductor voltage converters is to stabilize the output voltage depending on the input voltage and a variable power consumption load. The article deals with the control problem of a voltage buck-converter under the specified conditions. The non-linear discontinuous control law is developed in the article, which allows to stabilize the output voltage under conditions of unknown input voltage and output load current. The basic idea is based on the base of the so-called “vortex algorithms”, which provides invariance with respect to external unmatched perturbations. Theoretical results can be implemented with using modern pulse-width modulation converters. Simulation results shows the efficiency of the developed algorithms.
The Role of the Mechanical Speed Frequency on the Induction Motor Fault Detection via the Stray Flux

Lately, the monitoring and analysis of the induction motor stray flux has been a modern trend and significant research work has been accomplished. Most papers have focused on the monitoring of rotor electrical faults around the fundamental stray flux signature, imitating in this way the traditional Motor Current Signature Analysis (MCSA). However, more significant fault related harmonics exist at other frequencies and most significantly around the mechanical frequency. The existence of the mechanical frequency in the stator current is still the best signature for detection of the mixed rotor eccentricity fault. Even healthy motors present this harmonic due to some low level inherent eccentricity. Despite that, it will be shown for the first time in this paper, with extensive Finite Element Analysis (FEA) and experimental testing, that the mechanical frequency associated harmonics in the stray flux can be purely rotor electrical fault related and completely independent from any rotor eccentricity and rotor imbalance. This makes this specific harmonic unreliable for any rotor fault diagnosis although can be a good indicator of rotor electrical faults at low slip operation. Finally, the sidebands of the mechanical frequency harmonics appear to be very sensitive to the broken rotor bar fault while quite immune to the number of the rotor bars.
Designing Educational Games on E-learning SMANAS Based Learning Experience Design

E-learning has been increasingly promoted in education as alternative for learning method and teaching. There are many platform open source used for applying e-learning in the school such as chamilo. Chamilo is type of learning management system who support multimedia for learning process, but some user used just for creating course, quiz, and upload material. Eventhough educational game being popular multimedia for facilitating learning process. Learning Experience Design (LXD) offer solution for difficulting development learning experience by apply educational game on e-learning. Purpose of the research are develop educational game on e-learning SMANAS based learning experience design case study System of Linear Equation in Three Variables (SPLTV) and evaluate user experience of the educational game using User Experience Questionaire (UEQ). The result of our study confirm that first, educational game suitable for e-learning environment. Second, educational game give excellent impressions of the group attractiveness, perspective, efficiency, dependability, stimulation and novelty. It means educational game based Learning Experience Design engaged student to get learning experience.
Motor Failure Tolerant Control System With Self Diagnostics for Unmanned Multirotors

The paper presents a method for rotor failure detection and control system reconfiguration in case of unmanned multirotor propulsion system breakdown. The proposed algorithm uses onboard IMU measurements for failure detection and isolation. An adaptive fault tolerant control is designed for detecting and correcting partial or complete rotor failures. In case of a complete failure, the center of mass is reconfigured and control system is switched to a tri-rotor configuration. It has been demonstrated through simulations that the adaptive parameters converge to the true values of rotor efficiency. Further, in-flight experiments were conducted to demonstrate the failure detection and fault tolerant controller on a MAV quadrotor. Obtained results validate the effectiveness of the proposed motor failure tolerant control method to effectively compensate for complete loss of one rotor, leading to a resilient flight control mechanism.
Gaussian Forensic Detection using Blur Quantity of Forgery Image

For a design of the Gaussian forensic detection (GFD) in the altered digital images, this paper presents a feature vector that is defined as a blurring quantity by the window size of the Gaussian filtering. The window size is prepared ten types, and their blur quantity is computed by the Gaussian filtering, respectively. In the proposed scheme of the GFD, the defined 10-dim. feature vector of the image is trained in a SVM (Support Vector Machine) classifier for the Gaussian forensic detection of the forged images. In the experiment, the measured area under the curves (AUC) are above 0.9 from a classification point of view between the altered and Gaussian filtered image. Thus, the grade evaluation of the proposed method is rated as "Excellent (A). "
Role of Sonic hedgehog signaling in cell cycle, oxidative stress, and autophagy of temozolomide resistant glioblastoma

The first‐line chemotherapy treatment for Glioblastoma (GBM) ‐ the most aggressive and frequent brain tumor ‐ is temozolomide (TMZ). The Sonic hedgehog (SHH) pathway is involved with GBM tumorigenesis and TMZ chemoresistance. The role of SHH pathway inhibition in the potentiation of TMZ's effects using T98G, U251, and GBM11 cell lines is investigated herein. The combination of GANT‐61 and TMZ over 72 hr suggested a synergistic effect. All TMZ‐resistant cell lines displayed a significant decrease in cell viability, increased DNA fragmentation and loss of membrane integrity. For T98G cells, G2/M arrest was observed, while U251 cells presented a significant increase in reactive oxygen species production and catalase activity. All the cell lines presented acidic vesicles formation correlated to Beclin‐1 overexpression. The combined treatment also enhanced GLI1 expression, indicating the presence of select resistant cells. The selective inhibition of the SHH pathway potentiated the cytotoxic effect of TMZ, thus becoming a promising in vitro strategy for GBM treatment.