Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Parameters Identification of Lithium-ion Batteries Model to Improve the Hierarchical Model Predictive Control of Building MicroGrids

Energy storage systems are key elements for enabling the design of MicroGrids in buildings, specially to deal with stochastic renewable energy resources and to promote peak shifting. However, inaccuracies in the batteries' mathematical models due to temperature and ageing effects can reduce the performance of a MicroGrid system. To tackle these uncertainties, this article presents a two-level hierarchical model predictive controller empowered with a data-driven algorithm for real-time model identification of Lithium-ion batteries. The objective is to enhance their state of charge estimation and to make their maximum use without damaging them. The results demonstrate that it improves up to three times the accuracy of state-of-charge estimation and increases about 3% the annual building MicroGrid self-consumption rate. Furthermore, the division of the building MicroGrid energy management system into two hierarchical levels soften the drawbacks arise from the inaccuracies of day-ahead data prediction while reducing the computational cost. The proposed architecture guarantees higher energetic autonomy indexes than a conventional rule-based controller in all scenarios under study.
A Comprehensive Study on Convolutional Neural Networks for Chromosome Classification

Cytogenetics plays significant role in the diagnosis, prognosis and treatment evaluation of genetic disorders through chromosome image analysis technique called karyotyping. Karyotyping is the way by which chromosomes are classified into 24 classes. Digital image processing techniques and machine learning algorithms found its scope in automated karyotyping since they ease or eliminate manual efforts in chromosome classification and its analysis. Even though, researchers were putting great efforts in the design of Automated Karyotyping System (AKS), for the last three decades, a fully automated system is not yet routinely accepted in practice. These days, deepnets exhibit improved performance in computer vision tasks, they are progressively utilized for automating classification tasks as well. Here, two variants of deep Convolutional Neural Networks (CNNs) for chromosome classification are modelled. A preliminary study on the hyperparameters of these models has been conducted. Other state-of-the-art CNN models are experimented and analyzed for chromosome classification. Performance measures of all these CNN deep models are compared to formulate hypotheses on hyperparameters to classify chromosomes efficiently.
Remote Measurement of Dielectric Constants for Samples With Arbitrary Cross Sections

Measurements of dielectric constant are of significant importance. Existing methods, contact or noncontact, require the sample under test (SUT) with a specified dimension. It can be the cross section of a rectangular waveguide or a regular-shaped reference SUT. In this work, a new approach extended from microwave imaging is proposed to remotely measure the dielectric constants of 2-D SUTs with arbitrary cross sections. An algorithm based on scattering integral equations is derived for an efficient parameter retrieval. Experimental measurements based on a time-division bistatic multiantenna system were conducted. It is verified that by deriving an inverse scattering problem-based algorithm, the dielectric constants of such SUTs can be effectively retrieved from the scattering data detected by a measurement setup previously designed for microwave imaging, regardless of the complexity of their cross sections. The proposed approach can be readily applied to various 2-D samples such as dielectric section bars. It can also be conveniently extended to 3-D samples in the future.
A New Transfer Learning Fault Diagnosis Method Using TSC and JGSA Under Variable Condition

It is very difficult to obtain the label data of rolling bearings under the complicated and variable working conditions, which results in low diagnosis accuracy. Transfer sparse coding(TSC) is a new feature representation method, which can effectively extract features from data matrix. Joint geometric and statistical alignment (JGSA) is a domain adaptation method, which can reduce the distribution shift and geometric shift between domains. In order to make full use of the feature extraction ability of the TSC and the transfer classification ability of the JGSA, a new transfer learing fault diagnosis(TSC-JGSA) method based on combining the characteristics of the TSC and JGSA is proposed to realize the fault diagnosis of rolling bearings under variable working conditions in this paper. In the TSC-JGSA, the fast Fourier transform technology is used to transform the time-domain signals into frequency-domain amplitudes. Then the TSC is used to effectively extract the deep features from the obtained frequency-domain amplitudes in order to construct a sparse feature matrix, which is input into the JGSA in order to realize the fault diagnosis of rolling bearings. Finally, the vibration data of rolling bearings under variable working conditions is used to prove the effectiveness of the TSC-JGSA. The experiment results show that the TSC-JGSA can effecrively solve the problem of lacking label data in actual engineering by using label data in the laboratory, and obtan higher diagnosis accuracy than other compared methods. It provides a new diagnosis idea for rotating machinery.
A Novel Semantic Segmentation Model for Chinese Characters

Character segmentation plays an important role in optical character recognition (OCR). Due to the limitations of feature representation, traditional image analyzing based methods cannot well segment characters with connected or broken strokes, especially for the Chinese characters which usually have complex structures. To solve this issue, this paper proposes a novel segmentation model based on fully convolutional neural networks (FCN). The model first uses convolutional neural networks to extract spatial features, then shares them throughout the whole model. Two FCNs are used to extract character information to form a score map. Finally, character features are reused to adjust the accurate segmentation points in the score map. What’s more, to strengthen the ability of feature representation, a novel compound character feature which can well describe the characters’ outline is also proposed. The proposed method is validated on two datasets: GBSD and CASIA-HWDB-MT, against the methods proposed in the literature. Experimental results show that the proposed model outperforms state-of-the-art methods.
i5GAccess: Nash Q-learning Based Multi-Service Edge Users Access in 5G Heterogeneous Networks

In the heterogeneous wireless networks, it remains a significant challenge to achieve an efficient network selection strategy to satisfy the demands of a massive number of edge users and novel 5G services. In this paper, we formulate the network selection problem for edge users as a discrete-time Markov model, and propose a Nash Q-learning based intelligent network access algorithm for multi-agent system, named MAQNS. We consider the joint optimization of network selection strategies among different types of networks, aiming at maximizing the long-term performance of multi-agent system. Meanwhile, we use Analytic Hierarchy Process (AHP) and Grey Relation Analysis (GRA) to characterize the user preferences for networks. Experimental results show that comparing to the existing network selection algorithms, the proposed MAQNS has better performance in terms of system throughput, user blocking probability, average energy efficiency and average delay.
IL1B T-31C and VEGFA C+936T SNPs may be used as prognostic markers of rheumatoid arthritis treatment inefficiency

Objectives: The aim of our study was to analyze the association between some SNPs and treatment of rheumatoid arthritis with biological drugs.Patients and methods: We studied 368 Russian patients with rheumatoid arthritis. All of them were treated in accordance with the standard recommendations. Fifty seven patient received biological disease-modifying antirheumatic drugs. Single nucleotide polymorphisms (TNFA C-863A, TNFA G-308A, TNFA G-238A, IL1B T-31С, IL4 С-590T, IL6 G-174C, IL10 A-1082G, IL10 С-592А, VEGFA C+936T, VEGFA C-2578A) were determined by restriction fragment length polymorphism analysis of PCR-amplified fragments (PCR-RFLP) or RT-PCR.Results: The IL1B T-31C and VEGFA C+936T SNPs were association with bDMARDs treatment. Homozygotes IL1B -31CC and VEGFА +936 CC were more frequently in bDMARDs group compare with patients without bDMARDs (28.1% vs 17.1%, p=0.044 and 80.4% vs 66.6%, p=0.041). Other polymorphisms didn’t demonstrate any significant differences.Conclusions: Our data suggest, that IL1B T-31C and VEGFA C+936T SNPs can be used as part of a comprehensive assessment of the prognosis of the treatment effectiveness.
Wilderness Search and Rescue with Heterogeneous Multi-Robot Systems*

Recent developments in autonomous and communication technologies led the use of cooperative aerial and ground vehicles in Wilderness Search and Rescue missions (WiSAR). The use of a heterogeneous Multi-Robot System (MRS) improves the robustness and efficiency in achieving these tasks, comparing to the homogeneous systems with vehicles of the same characteristics. From this point, this paper proposed a Multi-Robot Task Allocation (MRTA) algorithm for heterogeneous vehicles (UAVs and UGVs) using the Market-based Approach to optimize the mission resources. The algorithm checks the availability of the vehicles, the characteristics of each task, and the payload required as inputs, then it provides each vehicle with a plan of tasks and charging commands. The proposed algorithm has been validated by performing several missions in simulations of mountains terrain with real dimensions, and the obtained results show the robustness and efficiency of the proposed system.
Dynamics of V-Shaped Electrothermal MEMS-Based Resonators

In this work, we study experimentally, analytically, and numerically the dynamics of electrothermally actuated micro-electro-mechanical V-shaped micro-beam resonators. Upon actuating the micro-beams electrothermally, we examine the various scenarios of natural frequencies crossing and avoided-crossing (veering) among the various vibration modes. The effect of various parameters is experimentally investigated on the veering and crossing including the micro-beam width and the initial rise at the mid-point. An analytical study is presented based on a reduced order model of a nonlinear Euler–Bernoulli shallow arch beam model. The dynamic behavior is also confirmed through a multi-physics finite-element model (FEM). The static and dynamic behaviors of the V-shaped micro-beams are compared against those of Arc-shaped micro-beams. [2020-0232]
A Descriptor System Approach for False Data Injection Attacks Toward Power System

This paper investigates the problem of false data injection attacks toward power system from the viewpoint of dynamic system. Firstly, by combining generator swing model and power flow equation, the power system is modeled as a descriptor linear system, which is monitored by a conventional observer. Then, a necessary and sufficient condition for perfect attack is given in terms of unstable eigenvector by eigen-decomposing the system matrices. Based on this, an attack scheme is proposed under which the attacker can destabilize the power system while passing the bad data detector. Compared with the existing static attack methods, the proposed scheme is more concealed and easier to implement. A simulation on IEEE 9-bus benchmark is finally presented to demonstrate the feasibility and effectiveness of our method.
Modelling and Analysis of Sensorless Current Sharing Approach

Sensorless current sharing enables even distribution of the currents in multiphase converters. This paper presents a detailed model of a proposed balancing controller based on sensorless current sharing, and a converter consisting of paralleled branches. Additionally, stability and sensitivity analysis are included. The model is verified with experiments by balancing the branch currents at different load currents.
Automatized Method of Moments to Estimate Process Model of Diesel Engine Dynamics

Step response-based identification is important topic related to modern process control and diagnostics. Although there are several adequate identification methods available, namely graphical and computational techniques, still there is increasing requirement to develop methods for the diagnostics of electric drives. Particular importance can be found in industrial applications where step input might be the only option for excitation-based identification. In this paper, parameter estimation of a diesel engine dynamics is carried out by considering automatized time continuous method of moments approach. The applicability of the proposed moment-based parameter estimation method is validated with experimental step response tests obtained from a hybrid bus operated in diesel engine only mode.
Cut-in Critical Level Prediction via Simulation Based Time-to-Collision Algorithm

Cut-in critical level prediction is of vital importance to Advanced Driving Assistance system (ADAS) to fulfil regional requirements and to increase safety. Time-to-Collision is the key component for critical levels of cut-in scenario. Hence, a new simulation based Time-to-Collision (TTC) calculation algorithm is firstly introduced in this paper. For the purpose of cut-in critical level prediction, the values of TTC in c.a 2000 cut-in cases are calculated, which are used to train a novel machine learning based cut-in critical level prediction method. The goal of ADAS functions development is to perform as a sophisticated driver, especially in dealing with risks. Thus, the correlation coefficient between ego vehicle deceleration and TTC could be used to evaluate the performance of different TTC calculation methods. In order to validate the superiority of simulation based TTC calculation algorithm, the Pearson correlation coefficient is calculated for the simulation based TTC and the TTC calculated by the traditional method, which are 0.7882 and 0.1357, respectively. Through enough valid regional cut-in samples trained prediction algorithm, TTC could be estimated accurately and effectively, i.e., the accuracy reaches 92%. To the best of the author’s knowledge the simulation based TTC calculation method and the cut-in critical level prediction learning algorithm are new contributions in ADAS field.
Cybersecurity of the Unmanned Aircraft System (UAS)

In recent years, UASs have become more and more popular. There are many reasons for this; one of the most popular is the enhancement of drones’ functionalities and improvement in battery life, stabilization, navigation, sensor technology, and much more. Many benefits drive growth. However, as the number of drones is expanding and their technological functionalities are evolving, drones’ use brings many concerns and challenges that should not be underestimated. This refers to issues in the area of cybersecurity, privacy, and public safety. UASs, under the international, regional, and national regime of aviation law, are considered aircraft. Since there is no existing cybersecurity framework specific to the UASs, the civil aviation cybersecurity framework should apply to their operations. This paper will focus on the potential cyber threats against UASs, providing examples of cyber-attacks from the past. Further, the aviation cybersecurity framework’s overview will follow to determine the current status of maturity at the international, regional (the European Union), and national (the Republic of Poland) levels. The paper’s conclusion will address the proposed recommendations for the UAS and aviation industries to be considered and potential solutions in terms of applying the aviation cybersecurity framework into the operation of UASs.
Bias Reduction in the Closed-Form Maximum Likelihood Estimator for the Nakagami-m Fading Parameter

In this letter, we discuss a corrective bias for the modified maximum likelihood estimator of the Nakagami-m fading parameter. The obtained corrected estimator has a closed-form expression, which plays an essential role in describing fading effects in wireless communication. Numerical results show that the bias-corrected estimator outperforms the existing methods and returns unbiased estimates for the fading parameter even for very small sample sizes.
Future grid stability, a cost comparison of Grid-Forming and Synchronous Condenser based solutions

Grid-Forming inverters have proven to be a key solution for future stability issues in the context of high penetration of power electronics in the grid. As synchronous condensers are presently used to locally solve such issue, this paper compares the costs of two deployment plan of the two solutions to ensure stability of a future realistic scenario in France for 2035. The needs will be based of on the latest work of MIGRATE for the Grid Forming (GFor) solution, and on ENTSOE requirement for inertia for the Synchronous Condenser (SC) scenario. The costs of the two solutions will be based on publicly available data of recent projects.
Simulated Study Of Maximum Power Point Tracking Methods (MPPT) By Simulink Realization

This paper realizes the Perturb and Observe (P&O) and Fuzzy logic controller (FLC) of maximum power point tracking (MPPT) methods. This is the trendy order to recognize the MPPT methods simulation by using a DC to DC boost converter penetration in PV panel simulation model is required which are also realized. The develop model and design with there proper accessible fulfil parameter and all component in simulink system used properly from MATLAB Simulink block assemblies. Since, shows the fluctuations of solar outcome irradiance and fever, production voltage current of solar panel be situated design replicated enhanced by means of this model, Or They can magnificently regularly tracking the maximum power point tracking method(MPP) extra exactly, accuratly.
Photovoltaic based Brushless DC Motor Using Cuckoo Algorithm as a Maximum Power Point Tracking

In this paper design and analysis of Solar energy based brushless dc motor circuit framework is discussed. The power and voltage curve of the system exhibit contains a large number of nearby maximun power points (MPPTs) and one maximum peak. The article introduces an audit for a Maximum Power Point Tracking (MPPT) based PV source focused on maximising the power output using cuckoo search optimisation technique. Cuckoo search gives a few points of interest, for example, the way toward tuning parameters is not many with high productivity next to quick union. Cuckoo search utilizes an arbitrary stroll as per demand trip in looking through procedure. MPPT is designed to utilize cuckoo search in contrast with other existing techniques which needs preparing for optimisation and the existing P&O. Zeta converter is directly used with driving pulse control of a PWM based PIC controller. The outcomes should predict that Cuckoo Search can follow MPPT under varying external working conditions with lower power misfortunes as compared to other techniques.
Multi-Agent Mean Field Predict Reinforcement Learning

The study of multi-agent reinforcement learning can solve many problems in real life. The current research can be divided into two aspects: one is adding the information of other agents into the critic-network to form a global critic-network, as MADDPG; the other is putting them into the actor-network, like CommNet, which takes the actions or observations from other agents into consideration. However, the two methods are faced with these problems: the action space is huge when the number of agents increases; In reality, due to the limitation of bandwidth and delay, communication often cannot perform well or even work normally. Inspired by MFRL, we design our algorithm MFPRL to solve this problem. The neighbors’ average action is predicted by a separate MFP network. The experiment shows that our method achieves better results than MFRL.
Smart Agricultural Knowledge Discovery System using IoT Technology and Fog Computing

The development of Internet of Things (IoT) and fog computing supports the agricultural field to increase crop productivity and reduce the usage of natural resources. The decision support system provides a wider knowledge to the farmers especially in the decision-making process to increase the crop productivity and profit. In the field of agriculture, different sensors and devices are incorporated to collect different data about the land and sent to the server in cloud environment. These data are processed further to leverage the required knowledge to the farmers. The farmers can communicate with the intelligent decision support system using mobile app and web application to obtain information about the land from domain experts. The proposed system is used as intelligent knowledge system that helps farmer to use natural resources effectively and reduce the effect of soil and water pollution by using pesticide and fertilizer based on the data stored in knowledge base. Precision agriculture with IoT and fog computing that allows people and device to be connected anywhere and anytime in a smart cloud environment in order to provide intelligent service to the farmers.
STANDAR KELENGKAPAN FITUR E-LEARNING SUPPLY CHAIN MANAGEMENT PADA PRODUK BACKLOG MENGGUNAKAN METODOLOGI SCRUM

ABSTRACT The development of E-Learning features is a concern among developers. Making E-Learning features that meet the standards and in accordance with the needs of students and lecturers is one of the requirements in its application. The effectiveness of learning that is expected to be effective using E-Learning is still difficult to achieve due to various factors, one of which is lack of features in the system. Supply Chain Management (SCM) is a supply chain learning that has several series of activities, so it is important to ensure all chapters in SCM are mastered by students on time. We will compare several features of the 3 Learning Management System (LMS) applications. Among them are SEVIMA EdLink, Coursera, and Blackboard CourseSites. The results of this study will produce a standard set of features specific to SCM E-Learning to support learning effectiveness. The standard setting for features completeness will use the SCRUM framework in the product backlog. The results of the analysis are Prototype using Website-based MOODLE Tools. Keywords : E-learning, MOODLE , LSM, SCM, SCRUM ABSTRAK Pengembangan fitur E-Learning menjadi salah satu perhatian di kalangan pengembang. Pembuatan fitur E-Learning yang memenuhi standar dan sesuai dengan kebutuhan mahasiswa maupun dosen menjadi salah satu syarat dalam penerapannya. Efektifitas dari pembelajaran yang diharapkan efektif menggunakan E-Learning saat ini masih sulit dicapai karna berbagai faktor, salah satunya kurang lengkapnya fitur dalam sistem. Supply Chain Management (SCM) merupakan pembelajaran supply chain yang memiliki beberapa rangkaian kegiatan, maka dari itu penting memastikan semua bab dalam SCM di kuasai mahasiswa tepat waktu. Kami akan membandingkan beberapa fitur dari 3 aplikasi Learning Manajemen System (LMS). Diantaranya adalah SEVIMA EdLink, Coursera, dan Blackboard CourseSites. Hasil dari penelitian ini akan menghasilkan sebuah standar kelengkapan fitur yang dikhususkan pada E-Learning SCM untuk menunjang efektifitas pembelajaran. Penentuan standar kelengkapan fitur akan menggunakan framework SCRUM pada backlog product. Hasil analisa berupa Prototype menggunakan Tools MOODLE berbasis Website. Kata Kunci: E-learning, MOODLE, LSM, SCM, SCRUM