Miracle-dz/UniFAR-FTUs huggingface dataset:
Nowhere to Hide Methodology: Application of Clustering Fault Diagnosis in the Nuclear Power Industry

When a system crashes, fast and accurate log-based fault diagnosis can remarkably reduce the recovery time of the system and avoid further economic losses. Especially for the nuclear power industry, recovery time will lead not only to economic losses but also to international repercussions. Nevertheless, the massive quantity of obscure log information and the existence of hidden nodes pose major challenges to fault diagnosis and root cause determination. To overcome these obstacles, we propose the nowhere to hide (NTH) methodology, an efficient method to diagnose faults and locate root causes. We implement log-node and node-log mapping to avoid vital data loss in collecting fault logs and hidden nodes; furthermore, we utilize the logic of the nuclear power unit process system to reveal the crucial information in fault logs and hidden nodes and their causality to determine the root cause. We evaluate the methodology in a real nuclear industrial environment. The results show that system administrators can efficiently determine the root cause with the proposed methodology. Finally, we discuss the enhancements that are underway to improve the methodology.
Adjustment of Digital Screens to Compensate the Eye Refractive Errors via Deconvolution

Eye glasses, contact lenses and medical operations can correct some refractive errors in the eye. However, some type of aberrations cannot be cured with current technology. In this case, people with refractive errors in their eyes encounter difficulties to interact with digital screens, one of the most important interaction medium between human and technological devices. In this work, a method of adjustment on digital screens is proposed for these people. By the point spread function of human eye being obtained, a digital screen or image can be changed by deconvolution method so that the patient can be given a clear vision. For the contrast problem arising due to the deconvolution, two new enhancement approaches, namely double-sided contrast enhancement and sigmoid mapping, are proposed. The proposed methods and the current technique to enhance contrast for the visually impaired people are compared both qualitatively and quantitatively.
Data Fusion and Industrial Equipment Diagnostics Based on Information Technology

Heterogeneous data fusion obtained in real time from several sources is a key objective when diagnosing equipment in all industries and transport. The paper proposes a heterogeneous data fusion model and a model for equipment diagnostics which are based on the levels of the Joint Directors of Laboratories (JDL) model using Data Mining technology; ontology storage containing clear, fuzzy and fuzzy-functional cognitive models; soft computing methods and probabilistic-static methods. The models proposed consider the heterogeneous data fusion in terms of a systemic approach and are not tied to any industry. A detailed description of the forecasting model of electrical equipment (EE) at an oil company is considered. The models developed will improve the accuracy and completeness of EE diagnostics through joint processing by expanding the volume of data received, taking into account the knowledge of staff on duty which will in turn lead to the timely identification of EE faulty states and failures, the equipment repair, reduction of the time spent by a chief engineer for decision-making to repair or replace the EE.
User-Centric Intelligent UAV Swarm Networks: Performance Analysis and Design Insight

To enhance the network coverage and capacity, unmanned aerial vehicle (UAV) communication has become a promising technology thanks to the high mobility and the flexible deployment of UAVs. However, the consistency of the service is not guaranteed due to the limitation single UAV base station (BS), leading to the research for the user-centric UAV swarm networks. This paper derives the semi-closed-form expressions of coverage probability and average achievable rate for the user-centric UAV swarm networks to characterize the performance gain obtained from the increased UAV swarm diversity. Especially, the user-centric UAV swarm networks organize a dynamic UAV swarm for each user consisting of potential serving UAVs in user’s vicinity, which gives the design of boundaryless cells to serve each user seamlessly without user’s involvement. By modeling the locations of the UAVs as Poisson point process (PPP) and modeling the channel fading as Nakagami- $m$ distribution, the analytical results are calculated under two transmission modes, depending on whether cell coordination is considered or not. The results show that the coverage performance can be improved by 30% without cell coordination and 50% with cell coordination in comparison with traditional cell-centric networks when the UAV altitude is 100 m and SINR threshold is 0 dB.
Equivalence of Joint ML-Decoding and Separate MMSE-ML Decoding for Training-Based MIMO Systems

In order to gain insights into the potential and behavior of training-based MIMO systems, the relationship of joint decoding scheme and separate decoding scheme is considered, and the equivalence between these two decoding schemes is proved. For the considered joint decoding scheme, receiver decodes out data by joint processing of received signals of both training symbols and data symbols in the maximum likelihood (ML) sense. We refer it as the joint ML-decoder. By contrast, in the considered separate decoding scheme, receiver first estimates channels in the minimum mean-square-errors (MMSE) sense, and then, based on the estimated channel information and the received signals of data symbols, the receiver decodes out data in the ML sense. We refer it as the separate MMSE-ML decoder. Notice that this separate MMSE-ML decoder is different from the decoder appeared in the most of existing works, where a kind of mismatched ML decoding is used after the phase of channel estimation. Although the above-mentioned decoding schemes have different decoding procedures, we prove that the joint ML-decoder and the separate MMSE-ML decoder are equivalent, while they outperform the mismatched ML decoder. With this equivalence, it is implied that the MMSE channel estimator is optimal when the overall system performance is considered, and the separate MMSE-ML decoding scheme can achieve the same performance with that of joint ML decoding scheme. Furthermore, this equivalence also provides us another way to analyze the system performance of the considered separate or joint decoding scheme. Namely, the results obtained from the considered joint decoding scheme can be directly applied to the considered separate decoding scheme, and vice versa.
A New Control Strategy for SR Generation System Based on Modified PT Control

The Switched Reluctance generator (SRG) has been widely used as a constant voltage source due to its advantages of simple structure, low cost and high control flexibility. However, the output voltage ripples cannot be ignored due to its unique operating principle with phase commutation. In this paper, the influence of the control parameters on the output voltage ripples are analyzed, and a fly-wheeling pulse train (FW-PT) control strategy is proposed to suppress the voltage ripple. The output voltage can be regulated by the FW-PT control strategy by using two or more sets of preset control pulse combinations, therefore it has the advantages of simple circuit implementation, no Network compensation and fast response speed. The characteristics of steady-state and dynamic behaviors of the switched reluctance power generation system by using different control strategies are simulated and compared, and a platform of 200W 8/6 SRG is built for experimental verification. Simulation and experimental results confirm that compared with the traditional PID control strategy, the FW-PT control strategy can be used to not only suppress the output voltage ripple, but also achieve faster response and dynamic characteristics.
Characterization of Supercapacitor for the Development of Energy Storage Units

Starch derived activated carbons are prepared at different carbonization temperatures of 700°C, 800 °C and 900°C. Good porous morphology of the prepared activated carbon was found in scanning electron microscopy study. Three supercapacitor cells were fabricated by using the prepared activated carbon as an electrode material and 6M KOH as an electrolyte. Electrochemical testing of the supercapacitor were made by using the well known three techniques as; cyclic voltammetry, Impedance Spectroscopy and charge-discharge. Outcomes of these studies suggested that the activated carbon prepared at 800°C exhibits much better capacitive assets than the other two. Analyzing electrochemical properties of fabricated supercapacitor cells by Impedance Spectroscopy, a lowest bulk resistance 0.231 ohm cm2 and charge transfer resistance 1.44 ohm cm2 was found for activated carbon 800°C among all of three. The experimental impedance data were fitted successfully by using theoretical electrical equivalent circuit model of the supercapacitor. For the optimized supercapacitor, the calculated specific capacitance from the cyclic voltammetry method is found 261.3 F g−1 at a voltage scan rate of 20 mV s−1. By analyzing charge-discharge data the observed specific capacitance was 230.5 F g−1 and it was correspond to the specific energy and specific power of 17.2 Wh Kg−1 and 983 W Kg−1, respectively. These outcomes represent the potentiality of the supercapacitor to be used in various microelectronic circuits.
Pilot Based Orthogonal BS Identity Code Channel Estimation for LTE Downlink MISO Systems

This paper proposes adding orthogonal BS identity code with pilot symbol on coherent RB for LTE downlink FDD MISO-OFDM systems. The suboptimal linear LS criterion is applied to MS receiver to estimate channel state information and mitigates the effect of pilot contamination in multi-cell scenarios. The simulation results in case of full pilot reuse show that SINR performance obtained from the proposed technique is higher than that of the conventional receiver without BS identity code in multipath fading channel and high Doppler speed.
Enhancement of Sensitivity for Surface Plasmon Resonance Biosensor with Higher Detection Accuracy and Quality Factor

In this paper, we demonstrate a highly sensitive Kretschmann configuration based surface plasmon resonance (SPR) biosensor with high detection accuracy (DA) and quality factor (QF). Five layers of biosensor model is proposed using numerical simulation and graphical analysis. In this model, graphene and heterostructures of black phosphorus improve the sensitivity of biosensor. Silver (Ag) is coupled with prism CaF2 for better reflectivity, since CaF2 has the less refractive index (RI). The mentioned model gives the highest sensitivity for 633 nm wavelength p-polarized light is 263.51 º/RIU with higher detection accuracy (DA) 2.14 and quality factor (QF) 57.915 RIU-1.
Collaborative Deep Learning Techniques for Sentiment Analysis on IMDb Dataset

Sentiment analysis is the most widely used approach to predict the user reviews. Many machine learning techniques have been performed to gain proper predictions about the data. These classifiers do not consider long term dependency and max pooling. To improve predictions using these features, in this paper we classify reviews using Deep learning technologies. We also collaborate Convolution Neural Network and Long Short Term Memory Recurrent Neural Network to obtain higher accuracy with less loss and in a lesser computing time.
On Preventing Symbolic Execution Attacks by Low Cost Obfuscation

While various software obfuscation techniques have been proposed to protect software, new types of threats keep emerging such as the symbolic execution attacks. Such attacks automatically analyze programs and are not accounted for by many of the existing obfuscation methods. Nevertheless, several methods against symbolic execution attacks exist such as linear obfuscation methods relying on Collatz conjuncture or obfuscation methods based on one-way hash functions. However, these methods bear several issues. Namely, linear obfuscation is weak against manual analysis due to its deterministic output. On the other hand, SHA-1 requires significant computational cost; and thus, it can be applied to only a limited number of targets. Therefore, in this research, we propose to employ a combination of several computationally cheap (arithmetic) obfuscating operations for preventing symbolic execution attacks. Through an experiment using angr and KLEE as symbolic execution tools, we demonstrate that obfuscation operation using array reference, bit rotation and XOR effectively prevents symbolic execution attacks at a low computational cost.
Analysis on Digital Elevation Model Data for 3D Modeling

Three-dimensional (3D) modeling process continues to develop rapidly. In its implementation, there are several 3D models that require elevation information from the model to be made. In this study, an experiment was carried out in the form of a test of Digital Elevation Model (DEM) data analysis which made it possible to serve as the basis for making 3D plains. The DEM data has a color depth that contains an elevation value that is described in a gray or grayscale level. This information will be used to create plain 3D modeling with the help of 3D modeling software. The object of the DEM data used as a sample is Seram Island, Maluku. The results showed that the DEM data provided by Earth Explorer was feasible to be used as the basis for 3D modeling of terrain that had many fields. The data can be seen by knowing the pixel value of each image detail based on the gray level in testing using 1 to 8 bits. Each bit increment will give a different level of detail. The height of the DEM data used can also be analyzed by converting it to contour information. To get better modeling results, it is necessary to take the level of resolution of the DEM data processing into account.
Design of a Service Robot Cloud Service Platform

With the development of Cloud Computing, more and more cloud platforms are designed for robots. However, the problem of low efficiency of service call becomes serious when large scale robots access the cloud platform based on Service Oriented Architecture (SOA). This paper proposes a service robot cloud platform to overcome this problem. Firstly, the service robot cloud platform is designed. Secondly, the robot service scheduling algorithm based on Genetic Algorithm (GA) is proposed to make a more efficient service scheduling. Lastly, the voice recognition service is used to verify the availability and high services accuracy of the platform. Cloudsim is used to test a number of performances on the cloud platform comparing with the traditional Service Oriented Architecture. The results show that the proposed cloud platform architecture has higher service invocation efficiency and availability.
Blockchain Based Variability Management Solutions for Fog Native Open Source Software

The complexity of Cloud-Fog-Edge computing systems is high due to the high variability which can be expected to grow further in the future with more IoT devices, computing architecture and specific requirements from security, regulation and users. One way to manage this complexity is to consider the Cloud-Fog-Edge Continuum as a single infrastructure of devices and components to be used in a seamless way to deliver new advanced services. Such a framework would integrate the handling of all connected devices, requirements and manage their variability. This paper proposes a framework called VarOps which provides an intuitive Workbench, enabling the design of software considering variability, making possible the re-use of dockerized components to significantly increase efficiency in development of new solutions. VarOps exploits Blockchain as a distributed ledger technology, variability formal modelling approaches, Smart Contracts, Trustful and Trustless Smart Oracles and knowledge management solutions as the underlying technologies and presents some example use cases.
Identifying Event Bursts Using Log-Normal Distribution of Tweet Arrival Rate in Twitter Stream

Online social streams has become a prominent channel for facilitating new sources of information and quick communication links, specifically during mass emergencies. The task of event burst detection focuses on monitoring Twitter streams, to identify emergent patterns based on tweet arrival rate. The vital features of rising sharply in frequency for modelling an event in text streams leads to burst activity. In this paper, a statistical approach is framed for Twitter streaming channel with a sudden change in the frequency of tweet showing a log-normal distribution of data arrival $\gamma$. The burstiness of event is framed as the Z-score of the tweet arrival rate $\lambda$ which corresponds to the frequency of tweets delimited in the time window w. Moreover, the proposed approach identify events which generates a stratified representation of the set of keyword bursts. The experimental outcome determines a hierarchical composition of event detection on the diversified stream by choosing optimal parameters.
Performance Analysis of Fifth Order Low-pass Chebyshev Filter using Planar RF MOSFETs

The process of designing higher order filters using a cascade of lower order filters only increases power consumption and area occupied by the filter circuit. A solution to this problem is to design planar filters using FET technology. In this paper, the implementation of a 5th order Chebyshev low-pass filter using MOSFETs is presented, as a proof of concept, to achieve optimization in terms of power dissipation and area occupied by the circuit. The performance of the filter is compared with 5th order Chebyshev low-pass filters, designed using discrete LC components and stepped-impedance respectively. The design analysis, estimation of power dissipation and area occupied, presented in this paper can be easily extended to nth order low-pass Chebyshev filters for n>2. The comparative analysis highlights the limitations associated with the various existing filter technologies.
Challenges in multimodal notifications for monitoring cyberphysical systems

In this paper, we discuss the main challenges for human monitoring of complex cyberphysical systems, with the use of mobile technology such as smartphones, tablets and head-mounted augmented (or virtual) reality headsets. We discuss the limitations of human cognition in terms of attention management, and provide an overview of the state of the art in mobile notification systems. Concluding, we describe the role of machine learning and artificial intelligence in the assistance of mobile CPS monitoring engineers.
Evolution and Analysis of Dielectric Properties of Typical Materials Under Strong Microwave Field

The complex dielectric constant is the intrinsic parameter of microwave material, and the accurate acquisition of its value is critical for its application. Generally, the dielectric properties of microwave materials are obtained under weak electromagnetic field. However, with the continuous development of high power microwave technology, the microwave field intensity around materials gradually increases, and the test results of the dielectric properties of materials under the weak electromagnetic field will not be able to characterize the test results under the high power environment. Therefore, a novel testing technology based on the resonator method is proposed, which provides an effective solution for the measurement and characterization of microwave dielectric properties of materials under strong microwave field. Based on the constructed system, the evolution rules of the microwave characteristics of four typical materials are obtained. In the experiment, the temperature of the samples is precisely monitored, and the evolution rules of the temperature characteristics of the dielectric properties of the samples under strong and weak microwave fields are compared. The comparison results demonstrate that the dielectric properties of materials under strong fields are related not only to the microwave heating effect but also to the mechanism of the non-thermal microwave effect.
Performance analysis of Super Twisting Sliding Mode Controller in Lower Extremity Exoskeleton

Lower extremity exoskeleton is a kind of wearable robot and is used both in medial and industrial applications for different purposes. In medical field it is used as a therapeutic equipment and in industry especially in defence, to carry heavy loads. This paper deals with how effectively the load carrying exoskeleton can track the motion of the wearer and how much load it can carry over from the wearer. Non Linear model for Hip, Knee and the ankle of the exoskeleton have been modeled by including uncertainties. Super twisting control algorithm is designed and simulated for Multi Input Multi Output (MIMO) exoskeleton system model. The Controller performance is analyzed by introducing external disturbances and parameter variations.
Concept Detection using Multiple Feature Set and Classifiers

Visual concept detection is the task of determining concept present in image or video by extracting low level features and training of classifiers in general. Researchers have used various features and classifiers for concept detection. In this paper performance evaluation of fusion of features and classifier is presented. Color moment, HSV histogram, wavelet transform and combination of these features have been used in proposed system. Artificial Neural Network (ANN) and Support Vector Machine (SVM) are employed for classification. The proposed system is implemented on Corel 1K image dataset and Trecvid 2007 benchmark video dataset. The system performance is evaluated using predictive measures of precision, recall and f score. Using simple fusion of features average precision of SVM classifier is better than ANN. The proposed global feature fusion based method is simple yet effective in concept detection task.
Customer Churn Analysis and Prediction Using Data Mining Models in Banking Industry

A new method for customer churn analysis and prediction has been proposed. The method uses data mining model in banking industries. This has been inspired by the fact that there are around 1,5 million churn customers in a year which is increasing every year. Churn customer prediction is an activity carried out to predict whether the customer will leave the company or not. One way to predict this customer churn is to use a classification technique from data mining that produces a machine learning model. This study tested 5 different classification methods with a dataset consisting of 57 attributes. Experiments were carried out several times using comparisons between different classes. Support Vector Machine (SVM) with a comparison of 50:50 Class sampling data is the best method for predicting churn customers at a private bank in Indonesia. The results of this modeling can be utilized by company who will apply strategic action to prevent customer churn.