Miracle-dz/UniFAR-FTUs huggingface dataset:
Modélisation du transfert thermique des câbles en galerie

Une analyse critique de la modelisation des echanges thermiques des câbles d’energie en galerie est realisee, avec l’etude de facteurs d’influences propres aux modes de pose des câbles dans ces ouvrages. Les câbles, de plusieurs kilometres, chauffent du fait du courant transmis dans leurs âmes. Ils sont alors refroidis par une convection forcee turbulente, dans l’axe des câbles, et echangent par rayonnement avec les parois. Pour etudier ce refroidissement, un banc d’essais experimental a ete concu, en similitude de Reynolds, modelisant differents câbles soumis a un ecoulement turbulent developpe. Les resultats sont compares et etayes par des simulations numeriques 3D, developpees sous l’outil OpenFOAM. Les etudes realisees ont permis de mettre en evidence la mesestimation importante de l’echange convectif par la norme existante. Celle-ci propose un refroidissement qui se compose en realite pour un tiers de l’effet de supports maintenant les câbles et d’un autre tiers d’un ecoulement encore en debut d’etablissement. Le dernier tiers etant le refroidissement effectif des câbles. L’impact des groupements des câbles est aussi etudie, avec un resultat etonnant ou un groupe de deux câbles en nappe est trouve mieux refroidis qu’un seul, lorsque ceux-ci sont places en proche paroi de la galerie. A l’issue des travaux, une unique loi de refroidissement conservative est proposee, permettant de modeliser tous les cas traites. Cette loi a un impact important sur l’intensite maximale admissible des câbles, qui est degradee de 5% a 9% par rapport a precedemment pour un cas idealise.
Effects of Extended Stochastic Gradient Descent Algorithms on Improving Latent Factor-Based Recommender Systems

High-dimensional and sparse (HiDS) matrices from recommender systems contain various useful patterns. A latent factor (LF) analysis is highly efficient in grasping these patterns. Stochastic gradient descent (SGD) is a widely adopted algorithm to train an LF model. Can its extensions be capable of further improving an LF models’ convergence rate and prediction accuracy for missing data? To answer this question, this work selects two of representative extended SGD algorithms to propose two novel LF models. Experimental results from two HiDS matrices generated by real recommender systems show that compared standard SGD, extended SGD algorithms enable an LF model to achieve a higher prediction accuracy for missing data of an HiDS matrix, a faster convergence rate, and a larger model diversity.
Adaptively Biased Output Cap-Less NMOS LDO With 19 ns Settling Time

This paper presents an output external capacitor-less, fully integrated, fast settling NMOS low-dropout (LDO) regulator with adaptively biased error amplifier (EA) for system-on-chip core applications. The adaptive biasing technique increases both loop bandwidth and slew-rate of the LDO by 100% at full load condition without changing no-load quiescent current. Direct feedback to gate-to-source voltage of the NMOS regulation FET provides fast load transient response. The proposed LDO employs a cross-coupled common-gate input based EA, with transconductance boosting, achieving twice unity-gain bandwidth in comparison to a typical folded-cascode common-source input stage. Low output impedance of NMOS regulation stage and low input impedance of the EA reduce load dependent stability issue. The proposed regulator is designed and fabricated in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS technology with die-area of 0.21 mm<sup>2</sup>. The LDO generates a regulated output voltage of 1.4–1.6 V from an input voltage of 1.6–1.8 V, consumes 133 <inline-formula> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula> quiescent current, and supports 0 pF to 50 pF load capacitance. Measured results show 166 mV undershoot with 19 ns settling time for a load step from 9 mA to 40 mA in 350 ps edge-time for zero-load capacitance. After using adaptive biasing, the settling time is reduced by 37% and 36% for 0 pF and 50 pF load, respectively.
Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger

Researches on Indonesian named entity (NE)tagger have been conducted since years ago but without using deep learning. Most researches employed traditional machine learning algorithms such as association rule, support vector machine, random forest, naïve bayes, etc. In those researches, the word lists as gazetteers or clue words are provided to enhance the accuracy. Here, we attempt to employ deep learning in our Indonesian NE tagger. We use long short-term memory (LSTM)as the topology since it is the state-of-the-art of NE tagger. By using LSTM, we don't need a word list in order to enhance the accuracy. Basically, there are two main things that we investigate. First is the output layers of the network: Softmax vs conditional random field (CRF). Second is the usage of part of speech (POS)tag embedding input layer. Using 8400 sentences as the training data and 97 sentences as the evaluation data, we found that POS tag embedding as the input layer improved the performance of our Indonesian NE tagger. As for the comparison between Softmax and CRF, we found that both architectures have a weakness in classifying an NE tag.
Improved 2-norm Based Fuzzy Least Squares Twin Support Vector Machine

In order to reduce the higher training cost of support vector machine (SVM) and its sensitivity towards noise and outliers, two fuzzy based approaches are proposed in this paper. The proposed approaches are based on least squares twin support vector machine (LSTWSVM) and fuzzy support vector machine (FSVM). The effects of noise and outliers are reduced by assigning lower membership values to the data points which are away from the class centers. Further, 2-norm of the slack vectors of the LSTWSVM formulation is taken after multiplying to their respective diagonal matrices of the membership values to effectively utilize the fuzzy membership principle and to make the optimization problem strongly convex. Moreover, the proposed approaches solve linear equations instead of quadratic programming problems which help in training faster. The effectiveness of the proposed approaches are established by comparing the classification accuracies and training time with support vector machine, fuzzy support vector machine, twin support vector machine and least squares twin support vector machine.
Singlish SenticNet: A Concept-Based Sentiment Resource for Singapore English

Singlish (or Singapore Colloquial English) is markedly distinct from Standard English due to extensive influence from other languages in Singapore. There is thus a need to construct Singlish-specific resources and tools to improve the sentiment analysis performance of online texts in Singlish. This paper leverages sentic computing techniques to develop Singlish SenticNet, a concept-level resource for sentiment analysis that provides the semantics and sentics associated with 10,000 words and multi-word expressions in Singlish. It is semi-automatically constructed by applying graph-mining and multi-dimensional scaling techniques on the affective commonsense knowledge collected from different sources. The knowledge is represented redundantly at three levels (semantic network, matrix, and vector space), each useful for a certain reasoning. A preliminary evaluation revealed a higher accuracy for Singlish SenticNet than SenticNet in the polarity assessment of Singlish tweets.
Design and Implementation of Task-oriented Rotor-flying Manipulator*

Rotor-flying manipulator system is a new type of unmanned aerial vehicle which consists of a multirotor platform and a multi-degree-of-freedom arm attached underneath. The high performance system with advanced manipulation capability can physically interact with the environment and achieve aerial operation autonomously in complex environment. To resolve the autonomous operation ability of the system in unstructured environment and deal with the strong coupling characteristics between the flight platform and the manipulator, This paper designed and developed a rotor-flying multi-joint manipulator system, including hardware design and assembly, the design of control system and establishment of experimental platform. The dynamic compensation for gravity offset control scheme to ensure the stable flight of system based on the backstepping algorithm. Then guided by onboard vision, an experiment for autonomous grasping is conducted in outdoor environment based on visual servo control scheme.
Energy Efficient Adder for Bio-Medical Applications

Recent developments in sensors and novel device structures have opened up many new possibilities in medical field, especially in implantable medical devices. The battery recharge timing places stringent requirements on power consumption by these devices. The design of energy-efficient circuits and systems thus becomes more crucial. Adder is the basic building block for all these devices. In this paper, a 4-bit Brent-Kung adder is implemented with a new adiabatic logic family derived from the PFAL, denoted as modified PFAL or MPFAL and using FinFET device. The performance of the adder circuit is analyzed by comparing the power dissipation and delay with that of static CMOS and PFAL designs. The simulation results indicate reduction in power dissipation of 98% and 96% over static CMOS and PFAL designs. The delay improvement is 24% for MPFAL circuit.
A Price Based Demand Side Management Strategy for Residential Sector in Bangladesh

The perpetual increment of electricity demand in residential sector for a middle-income country like Bangladesh indicates the adoption of avant-garde electrical home appliances in large number by the consumers to lead a more comfortable and complacent life. The burgeoning industrialization in recent years is the indication of progression towards prosperity and economic stability which also requires enormous amount of electricity. However, instead of only augmenting the power generation capacity, the existing energy could be utilized in a much smarter and efficacious way in order to create a world with lower level of noxious carbon emission through an ingenious strategy called the Demand Side Management (DSM). In this paper, we have mainly highlighted DSM strategies for judiciously regulating the electricity consumption pattern of residential sector which is the prime factor of immense energy requirement. Moreover, for encouraging the unaccustomed people of Bangladesh about the bright sides of DSM, we have proposed a time-of-use (ToU) based pricing scheme, and through the MATLAB simulation results it has been shown that not only the peak to average ratio (PAR) of the energy consumption curve but also the electricity cost of the consumers can be scaled down which can depreciate the massive infrastructural and operational cost of peaking power plants.
Performance Analysis of a HMM based Automatic Patient's Case History Generator in Bangla

This paper looks at the feasibility of a Hidden Markov Model (HMM) based speech recognition system to serve as a Bangla transcription device for doctors, who will dictate the case history of patients. The experiments are performed using Hidden Markov Toolkit (HTK). The features used are the Mel Frequency Cepstral Coefficients (MFCC) of the audio signal, which 39 features. The audio data is collected from ten male speakers and the train-test split is 50–50. The system consists of a word parser program, followed by an isolated word recognizer. The word parser takes discretely spoken sentences and outputs word audios. Each word audio is inputted to the word recognizer and the output words are concatenated. Five experiments were repeated twice, due to some words performing poorly in the first run. So, in the second run, more training data was added for the low accuracy words. The final sentence recognition accuracy was 80% and for most words, the recognition accuracy is above 90%. In conclusion, HMM-based recognition systems are feasible for transcription devices.
A novel parameter free approach for link prediction

-Social networks have become the most common way to connect people and to diffuse information, thus many efforts have been devoted to analyze their mining and evolution. The link prediction is one of the most important issues in the computational analysis of social networks. The main goal is to predict links that may appear in the future. To this purpose, a new similarity based method is proposed whose main strategies rely on the path depth from a source node to a destination node and their degrees. The experimental results on five instances of social networks show the impact of the path length on the method performance. Based on the values of the area under curve (AUC), the proposed method predicts links with high accuracy when compared with the existing ones.
Application of Fisher-PCA in Fault Diagnosis of TE Process

For the current plant big data, high-dimensional production environment, and the need for quick diagnosis of plant failures, this paper proposes an improved Fisher-PCA dimension reduction method for quick fault diagnosis of the entire system. This method is based on principal component analysis. Using the advantage of PCA dimensionality reduction on data loss, the Fisher criteria function is rapidly diagnosed in high-dimensional data. This method is applied to the TE process which is a real simulation of the strong coupling chemical production process. Simulation experiments show that this method can effectively distinguish the high-dimensional coupled fault data and normal data, and the method is practical and effective.
Word Segmentation for Javanese Character Using Dictionary, SVM, and CRF

Word segmentation method based on dictionary for Javanese character still cannot overcome the problem of ambiguity, derivational word segmentation, and unknown words identification. We propose a new approach for Javanese character segmentation, i.e. a machine learning based method using CRF and SVM with a set feature of Javanese character's categorization and its combination with dictionary-based method. Through several experiments, it is proven that combination of dictionary-based method and CRF performs best than word segmentation using CRF, SVM, and traditional dictionary-based word segmentation.
Hybrid Multiplexing (OTDM/WDM) Technique for Fiber Optic Communication

Today’s world needs high data transmission and huge bandwidth systems through optical fiber communication networks. For achieving that, passive optical networks (PON) are much more in practice. In this work, we have presented a hybrid multiplexed system, containing 4 channel optical time domain multiplexing (OTDM) and 2 channel wavelength division multiplexing (WDM) schemes. To utilize the available optical bandwidth for multiplexed system, carrier-suppressed return to zero (CSRZ) modulation technique is used. To mitigate the dispersion effect in fiber, symmetrical dispersion compensation scheme is used in the proposed system. Erbium-doped fiber amplifier (EDFA) is used to compensate the linear loss. The system is simulated for data rate of 40 Gbps. A link distance of 1,150 km is achieved with the proposed system. The main purpose of hybrid multiplexing of OTDM and WDM is to get higher spectral efficiency and cost reduction. Due to the use of hybrid multiplexing, it is possible to design a 8 × 40 Gbps system with the help of two optical sources. The performance of the proposed system is analyzed in terms of bit error rate, received power, optical time domain, frequency domain signal and Q-factor. The system will be highly useful for next generation high-speed fiber optic communication applications.
JPEG Implementation Forensics Based on Eigen-Algorithms

Due to the widespread diffusion of JPEG compression standard, image forensic researchers have developed a series of techniques to reverse engineer specific JPEG-based information (e.g., the used quantization matrix, presence of double JPEG compression, JPEG grid alignment, etc.). In this paper, we focus on the detection of traces left on images by the use of different JPEG implementations (e.g., characteristic of proprietary software suites). Specifically, given a JPEG image under analysis, we propose a compact descriptor that enables to distinguish which JPEG implementation has been used. This is done considering the challenging scenario in which the same quantization matrix is used. Results show that it is possible to distinguish between two popular JPEG implementations, as well as to adapt the proposed methodology to additional JPEG-based forensic problems.
Generalized Lagrange Multiplier Method and KKT Conditions With an Application to Distributed Optimization

The Lagrange multiplier method is widely used for solving constrained optimization problems. In this brief, the classic Lagrangians are generalized to a wider class of functions that satisfies the strong duality between primal and dual problems. Then the generalized Karush–Kuhn–Tucker conditions for this generalized Lagrange multiplier method are derived. This useful method has applications in optimization problems and designs of consensus protocols, which is demonstrated by proposing a new continuous-time algorithm and its distributed version for optimization. The convergence advantages of the distributed algorithm are shown in a simulation example.
Shape-Memory Alloy Actuated Low Frequency Astable Electromechanical Multivibrator

This paper presents a design of an astable electromechanical multivibrator using Shape Memory Alloy (SMA) spring. The device has been designed by assembling a nitinol spring, an elastic spring and a bistable mechanical toggle switch, and driving the assembly by a DC voltage source. The same design can be operated by AC voltage also. The proposed SMA-actuated astable electromechanical multivibrator can generate an electromechanical oscillation of 33 mHz directly from a DC voltage source. This astable electromechanical multivibrator can be used as an oscillatory translational or reciprocating curvilinear motion actuator of large deflection and large block force. The design has been prototyped and tested with bench-top experimental set-up.
An Inception-Based Data-Driven Ensemble Approach to Camera Model Identification

Proposing digital image forensic approaches for linking a suspected image to its source is of particular interest nowadays, as they can be useful to link the ownership of an investigated image with sensitive content and even help to spot image manipulations. In this paper, we propose a data-driven based solution composed of processing the output of two Convolutional Neural Networks based on the Inception philosophy. We show the effectiveness of the proposed model for the difficult problem of camera model identification, considering high-resolution images and also small patches.
A Deep Neural Network Approach for Crop Selection and Yield Prediction in Bangladesh

Agriculture is the essential ingredients to mankind which is a major source of livelihood. Agriculture work in Bangladesh is mostly done in old ways which directly affects our economy. In addition, institutions of agriculture are working with manual data which cannot provide a proper solution for crop selection and yield prediction. This paper shows the best way of crop selection and yield prediction in minimum cost and effort. Artificial Neural Network is considered robust tools for modeling and prediction. This algorithm aims to get better output and prediction, as well as, support vector machine, Logistic Regression, and random forest algorithm is also considered in this study for comparing the accuracy and error rate. Moreover, all of these algorithms used here are just to see how well they performed for a dataset which is over 0.3 million. We have collected 46 parameters such as - maximum and minimum temperature, average rainfall, humidity, climate, weather, and types of land, types of chemical fertilizer, types of soil, soil structure, soil composition, soil moisture, soil consistency, soil reaction and soil texture for applying into this prediction process. In this paper, we have suggested using the deep neural network for agricultural crop selection and yield prediction.
Design and Simulation Analysis of a Novel Outer Rotor IPM Motor Used in Electric Vehicles

Aiming at the distributed drive system of electric vehicles, this paper presents an outer-rotor permanent-magnet synchronous in-wheel motor with a novel U-shape rotor structure. Instead of the traditional surface-mounted permanent-magnet structure, adopting the U-shape interior permanent magnet (IPM) structure is beneficial to improving the performance of the in-wheel motor of electric vehicles (EVs). The finite element analysis (FEA) models of the U-shape interior permanent magnet motor and the surface-mounted permanent-magnet motor are both established to compare. The results show that the novel U-shape interior permanent-magnet motor not only reduces the amount of permanent magnet, but also improves the back electromotive force (EMF), torque output, flux weakening capability and reduces the cogging torque. The research shows that the U-shape interior permanent magnet motor is better suited to the requirements of the motor used in electric vehicles, and provides some reference for the design of the in-wheel motor of electric vehicles.
A Linguistically Interpretable ELANFIS for Classification Problems

In this paper, a clustering based extreme learning adaptive neuro-fuzzy inference system (CELANFIS) is proposed to improve the interpretability of the neuro-fuzzy model. Sub-clustering of input-output data is done to obtain the cluster centers which are used to obtain the membership function parameters of the CELANFIS, such that it satisfies a novel distinguishability constraint, for improving the interpretability of the network. The consequent parameters are obtained using the Moore-Penrose pseudo inverse thus resulting in faster training. Benchmark real world classification problems are used to evaluate the performance of the proposed network. Performance comparison of the proposed network with the Least Square Support Vector Machine (LS-SVM) and ELANFIS shows a satisfactory tradeoff between model accuracy and interpretability.
Autonomous Pole Balancing Design In Quadcopter Using Behaviour-Based Intelligent Fuzzy Control

Quadcopter is a helicopter VAV that uses four rotors of propeller to maneuver [1]. Some Quadcopter development uses the concept of artificial intelligence behavior-based intelligent fuzzy control (BBIFC). Some quadcopter maneuver movements that are currently being developed include cooperative movement between quadcopter, rotating and reversing movements, ball throwing movements, obstacle avoidance movements, ball capture movements, and automation of balancing motion on quadcopter. The balancing motion includes balancing when flying at a certain position, balancing for landing, and balancing when carrying loads [2]. Autonomous Pole Balancing on Quadcopter has been designed and implemented using Behavior-Based Intelligent Fuzzy Control, the design of automation of quadcopter motion in balancing with carrying loads namely a pole placed on a quadcopter. Quadcopter is used to balance the pole, so that in one calculation cycle must be completed before the pole falls. With an angle of 800 tolerance, the pole will arrive at the slope angle within 1 second, so that, in one calculation cycle must be completed in less than 1 second.