Miracle-dz/UniFAR-FTUs huggingface dataset:
Content-Based Image Retrieval System Via Deep Learning Method

Faced with the huge image data in the context of big data era, how to effectively manage, describe, and retrieve them has become a hotspot issue in academic circles. In this paper, we propose an end-to-end image retrieval system based on deep convolutional neural network and differential learning method. We first build an image matching dataset based on the gravitational field model, that is to add a similarity score label for each image in the dataset production stage. Then we train the improved deep learning model and verify the effectiveness of the algorithm on three common image matching dataset (i.e., Caltech-101, Holidays and Oxford Paris). Finally, the experimental results show that our improved deep learning model with differential learning method that used for image retrieval system has state-of-the-art image matching performance. The overall retrieval accuracy in Caltech-101, Holidays and Oxford Paris datasets are 88.5%, 94.1 % and 96.2%, respectively. As the number of returned images increases, the image retrieval accuracy of the system decreases slightly and eventually becomes stable at a high value. And the differential learning based retrieval method is superior to many traditional algorithms in terms of image matching accuracy and single image processing speed.
Automatic Indexer for Polish Agricultural Texts

Today, the majority of resources are available in d igital forms to acquire information. We have to search through collections f documents. In this paper text indexing which can improve searching is de cribed. Next, indexing tool, the Agrotagger, which is useful for documents i the field of agriculture, is presented. Two available versions of the Agrotag ger are tested and discussed. The Agrotagger is useful only for the En glish language despite the fact that it uses multilingual thesaurus Agrovoc. B ecause of the Agrotagger is not useful for texts in Polish, it is important to create similar tool appropriate for the Polish language. The problems connected wit h ex ensive inflection in languages such as Polish language in the process of indexing were discussed. In the final part of the paper, it is presented des ign and implementation of a system, based on the Polish language dictionary and the Agrovoc. Additionally some tests of implemented system are d iscussed.
An intelligent value chain model with internet enterprises based on blockchain

Information asymmetry and false information are disturbing and have been damaging investors' interests in investment management. The current studies usually only aim to evaluate the value of enterprises to reduce the degree of information falsehood, but they can't solve the problem from the origin. In this paper, we use the blockchain technologies to restructure the enterprise value system to lower the impacts of these problems, including improving value chain of enterprises with smart-contracts, defining token as an incentive method and using token to evaluate the value of enterprises. Then implementing the new value system into the internet industry to accomplish industrialization. We find that the novel value system not only saves the costs of time and wages, but also ensures the authenticity and reliability of enterprises data.
Kinematics Ayalysis for a Heavy-load Redundant Manipulator Arm Based on Gradient Projection Method

Inverse kinematics is quite an important issue for redundant manipulator arms. This paper presents the kinematics analysis including forward kinematics and inverse kinematics for a large-scale heavy-load redundant manipulator arm. Firstly, the overview of the heavy-load redundant manipulator arm which is developed for a multi-purpose remote maintenance system for tokamak is introduced. After that, the forward kinematics of the manipulator arm is analyzed using D-H method, and simulated in Matlab. Finally, the inverse kinematics of the manipulator arm is investigated by combining the gradient projection method and obstacle avoidance optimization in order to solve the redundancy of the manipulator arm. The work in this paper is the basis for path planning of redundant manipulator arms.
Customer segmentation through multiple correspondence analysis

The aim of the paper is to highlight the categorical methods’ potential of application which could be useful when drawing companies marketing strategies. Multivariate statistical techniques have always been used for quantitative analysis in marketing decision support systems (MDSS), nevertheless, Big data now provide numerous qualitative variables whose processing requires the use of categorical statistical methods. Dealing with Big data in marketing research, we carried out a Multiple Correspondences Analysis to show how to investigate the intricate relations among the categorical variables observed and their categories with the specific regard to consumers purchasing behavior data. These analyses allow to detect the essential aspects as a rational basis for adopting more effective marketing strategies.
Design Optimization of Axial-Flux Eddy Current Coupler

Ahstract-A procedure for design optimization of the axial-flux eddy current coupler is proposed in this paper. The equivalent circuit model of eddy current coupler is employed to study the influence of the geometrical parameters on the performance of eddy current coupler, then the ranges and constraints of the design parameters are developed. The use of eddy current coupler can prevent the damage caused by the vibration or overload from the load side but bring a drop in the transmitted speed and additional moment of inertia. To make a minimum influence on the original drive system, eddy current coupler is designed to operate at the rated torque of the drive motor as well as with high efficiency and low moment of inertia. The temperature rise in the copper plate is also taken into account to avoid the irreversible demagnetization of the permanent magnets. The performance of the designed coupler is verified through 3-D finite element method (FEM).
A Series Voltage Regulator for the Radial DC Microgrid

The concept of a novel series voltage regulator (SVR) for controlling the dc-bus voltage of a radial dc microgrid is presented in this paper. The proposed SVR uses a dual-active-bridge dc–dc converter followed by a full-bridge dc–dc converter. It injects dynamic voltage in series with the dc grid to compensate resistive drop over the network. As a result, the voltage level at the different points of the grid becomes independent of load variation and stays within the specified limit. Note that the required power rating of the SVR is very low (say 2.7%) compared to the load demand considering 5% voltage regulation. In this paper, the voltage regulator is connected at the midpoint of the grid, but it may be connected in some other locations to get optimal rating of the same. The proposed configuration is simulated in MATLAB/SIMULINK at a 380-V level to check the dynamic performance under various operating conditions. A scaled-down version (at 30-V level) of the proposed system is developed in the laboratory to experimentally validate the concept. The results show the effectiveness of such a voltage regulator for the radial dc microgrid, especially under critical load condition.
Experimental Investigation of Multi-GNSS in Static Mode

The results of multi-GNSS, including Global Positioning System, GLONASS, GALILEO and BEIDOU, experimental investigation are presented in a paper. Accuracy characteristics, mean square deviations for various cases of multi-GNSS and type of approach are provided with analysis regarding possibility of global navigation satellite system use at the considered moment of time.
A Generalized Discontinuous PWM Scheme for Three-Level NPC Traction Inverter With Minimum Switching Loss for Electric Vehicles

A discontinuous pulsewidth modulation (DPWM) strategy for three-level neutral-point-clamped (NPC) traction inverter drive for electric vehicles (EVs) with minimum switching loss for the full power factor (PF) range is developed and presented in this paper. Considering the dynamic loading conditions in an EV propelled by an induction motor (IM), the discontinuous modulation strategy is generalized to obtain minimum switching loss for a wide variations in load PF and modulation depth. The dynamic operating conditions in EV may render frequent unbalances in dc-link capacitor voltages in NPC inverter. It is demonstrated that the addition of an appropriate offset signal with the reference discontinuous signals generates compensating neutral current in the right direction, which can be used to mitigate existing unbalance in two dc-link capacitor voltages. Minimum switching loss at variable PF is attained by positioning the nonswitching clamped regions of the reference auxiliary signals according to the load PF. The effectiveness of the proposed scheme for a wide range of torque/speed variations is studied through simulation by developing a MATLAB/SIMULINK model of an IM traction inverter drive with field-oriented control strategy for closed loop control. Finally, the modulation scheme is validated through experimentation in a small-scale prototype NPC inverter with motor loads.
A Multiple Improved Notch Filter-Based Control for a Single-Stage PV System Tied to a Weak Grid

In this paper, a control scheme based on improvement in a generalized integrator is implemented on a three-phase single-stage grid-tied solar photovoltaic system with the distribution static compensator capabilities under grid abnormal conditions of voltage distortion and voltage unbalance. The photovoltaic voltage-source converter system compensating for the reactive power consumed by nonlinear load at point of common coupling provides load balancing and mitigates harmonics. The proposed multiple-improved-notch-filter-based quadrature signal generator control approach extracts the load current fundamental component, independent of the grid voltage. This control has better dc offset and harmonics component rejection capability in comparison to a conventional second-order generalized integrator algorithm. The perturb and observe-based maximum power point tracking algorithm is applied for the extraction of maximum power from the photovoltaic array. The system is analyzed under different abnormal conditions of voltage distortions, voltages unbalance, voltage swell, voltage sag, load currents unbalance, and insolation change on a prototype developed in the laboratory. The system performance is found to be satisfactory, within limits as described in an IEEE-519 standard while feeding active power to distribution network and connected loads.
A Dynamic Parameter Identification Method for Flexible Joints Based on Adaptive Control

This paper investigates the dynamic parameter identification for flexible joints without additional sensors. The estimation accuracy of traditional methods based on the original dynamic model is limited by measurement of angular acceleration. Recently, approaches using integral operations are raised to solve this problem. However, the integral operations easily result in error accumulation and deteriorate the parameter estimation accuracy. This study proposes a new dynamic parameter identification method for flexible joints. By constructing an alternative system function and estimating its outputs through nonmodel-based adaptive control, we avoid the measurement of angular acceleration and integral operations. On this basis, dynamic parameters of flexible joints are estimated using the recursive least squares (RLS) method. The experimental results indicate that the proposed method possesses better estimation accuracy than the general RLS- and integral-based methods. It is also validated that the proposed method can provide accurate estimation even under a relatively low-resolution system. Applications to model-based motion control and physical human-robot interaction are also verified.
Wind Power Curve Modeling and Wind Power Forecasting With Inconsistent Data

Wind power curve modeling is a challenging task due to the existence of inconsistent data, in which the recorded wind power is far away from the theoretical wind power at a given wind speed. In this case, confronted with these samples, the estimated errors of wind power will become large. Thus, the estimated errors will present two properties: heteroscedasticity and error distribution with a long tail. In this paper, according to the above-mentioned error characteristics, the heteroscedastic spline regression model (HSRM) and robust spline regression model (RSRM) are proposed to obtain more accurate power curves even in the presence of the inconsistent samples. The results of power curve modeling on the real-world data show the effectiveness of HSRM and RSRM in different seasons. As HSRM and RSRM are optimized by variational Bayesian, except the deterministic power curves, probabilistic power curves, which can be used to detect the inconsistent samples, can also be obtained. Additionally, with the data processed by replacing the wind power in the detected inconsistent samples with the wind power on the estimated power curve, the forecasting results show that more accurate wind power forecasts can be obtained using the above-mentioned data processing method.
Parallel and Distributed State Estimation

In this chapter, we discuss parallel and distributed techniques for state estimation. We cover the design of parallel state estimation, which will run on a parallel mainframe; then we design an asynchronous distributed state estimation method by taking the unpredictability of communication delays into account. The mathematical proofs and analyses for the convergence of asynchronous distributed state estimation algorithms are included. The proposed distributed state estimation algorithms will run on a distributed system that consists of computers at the ISO and RTO sites, and their satellite control centers.
Fusion Techniques Comparison of GeoEye-1 Imagery

Many satellite image fusion techniques have been developed in order to produce a high resolution multispectral (MS) image by combining a high resolution panchromatic (PAN) image and a low resolution MS image. Heretofore, most high resolution image fusion techniques have used IKONOS and QuickBird images. Recently, GeoEye-1, offering the highest resolution of any commercial imaging system, was launched. In this study, we have experimented with GeoEye-1 images in order to evaluate which fusion algorithms are suitable for these images. This paper presents compares and evaluates the efficiency of five image fusion techniques, the trous algorithm based additive wavelet transformation (AWT) fusion techniques, the Principal Component analysis (PCA) fusion technique, Gram-Schmidt (GS) spectral sharpening, Pansharp, and the Smoothing Filter based Intensity Modulation (SFIM) fusion technique, for the fusion of a GeoEye-1 image. The results of the experiment show that the AWT fusion techniques maintain more spatial detail of the PAN image and spectral information of the MS image than other image fusion techniques. Also, the Pansharp technique maintains information of the original PAN and MS images as well as the AWT fusion technique.
Multiplying Channel Capacity: Aggregation of Fragmented Spectral Resources

The explosive growth of mobile Internet-based applications and ecosystems has been driving insatiable demands for data transmission speed. From third generation to fourth generation, the typical data rate of cellular networks has been significantly enhanced from a megabits-per-second to almost a gigabits-per-second level [1]. According to Shannon? s communication theory, an increase of frequency bandwidth plays the key role in expanding channel capacity. However, the highly scattered allocation of long-term evolution (LTE) bands does not support contiguous expansion of bandwidth and acts as the major barrier to further channel capacity enhancement. Therefore, the LTE Advanced standard proposed the concept of carrier aggregation (CA) for the first time, as illustrated in Figure 1(a) [2]. This enables concurrent operation of multiple spectral resources such that the channel bandwidth can be effectively multiplied without increasing the signal-to-noise ratio across the entire channel [3].
Closed-Loop in Neuroscience: Can a Brain be Controlled?

An experimental setup capable of stimulating, inhibiting, sensing, processing and analyzing a biological system is presented. The experimental setup is based on open-source, open hardware commercial devices. It can be applied to record and control neuronal activity, for example to establish causal links between neurons and behavior in a closed-loop fashion. Cells or group of cells can be triggered by acoustical, electrical or light impulses. This last case is based on a technique defined as optogenetics. The latter is used in an example that reads and controls neuronal activity of a population of neurons in laboratory rats forcing them to transition between neuronal states, illustrates the system developed in this work.
Hybrid Encryption Model of AES and ElGamal Cryptosystems for Flight Control Systems

Computer networks and internet application are growing fast, so data security is the challenging issue of today that touches many areas (particularly in aviation). To prevent unauthorized access to the user data or database, any transmitting process should be securely encrypted. Aviation is critical area and its criticality amplifies by strong communication between both on ground and in motion objects. The goals of this paper are: 1) To introduce new hybrid cryptographic algorithm model using combination of two cryptographic algorithms AES and ElGamal; 2) Provide comparison between two symmetric, asymmetric algorithms and new hybrid model; 3) To show an effectiveness and security of new hybrid model which makes the algorithm strong against vulnerabilities. Currently many encryption algorithms are available to secure the data but some algorithms consume lot of computing resources such as memory and CPU time. This paper shows comparative analysis experimental results on those encryption algorithms. The objective of this research is to evaluate the performance of AES, ElGamal cryptography algorithms and AES&ElGamal hybrid cryptography algorithm. The performance of the implemented encryption algorithms is evaluated by means of encryption and decryption time and memory usage. To make comparison experiments, for those algorithms special software was created. The programming language Java is used for implementing the encryption algorithms. As a result, paper shows that new hybrid encryption model is more secure and strong compared to previous cryptography models and it can be used for flight control systems security.
Dealing with Imbalanceness in Hierarchical Multi-Label Datasets Using Multi-Label Resampling Techniques

The task of learning from imbalanced datasets has been widely investigated in the binary, multi-class and multilabel scenarios. Although this problem also affects hierarchical datasets, to the best of our knowledge, there are no works in the literature that deal with imbalanceness in hierarchical contexts. In this paper we propose metrics to measure "how imbalanced" is a Hierarchical Multi-Label Dataset, in addition to an approach to deal with this imbalanceness using Multi-Label resampling techniques. The proposed technique is based on the conversion of the dataset labels to a strictly multi-label format, applying wellknown multi-label resampling techniques and then converting the dataset back to its hierarchical taxonomy. The experimental evaluation over a highly imbalanced Music Genre Recognition dataset achieved promising results, with an increase of 0.2337 in the Avg-AUROC metric in relation to the original dataset.
Professional Nursing Concepts: Competencies for Quality Leadership

Professional Nursing Concepts: Competencies for Quality Leadership takes a patient-centered, traditional approach to the topic of nursing education and professional development. This dynamic text engages students in recognizing the critical role that nurses play in healthcare delivery. Divided into four sections, this text has a unique framework and exclusively covers the Institute of Medicine's (IOM) five core competencies on quality and healthcare for all healthcare professionals. The first section introduces students to the history of the nursing profession, and covers such topics as nursing education, regulation, and accreditation. Section two explores the healthcare context in which nursing is practiced including continuum of care, health promotion, disease prevention, and illness, nursing ethics, and the nurse's role in health policy. The third section focuses on each of the five core competencies set forth by the IOM, and the final section focuses on the practice of nursing today and in the future particularly exploring the critical issue of the nursing shortage and the transformation of nursing practice.
Self-balancing car based on ARDUINO UNO R3

This paper uses a simple two-wheel self-balancing car as the design platform. , the gesture sensing system with MPU6050 serves as the main sensor. The data of two sensors are filtered and fused by discrete Kalman Filter. ARDUINO is a nuclear control processor to complete the processes of data collection, analysis, processing and body control. PID control algorithm is adopted to realize two wheels which are self-balancing and use mobile APP to connect with Bluetooth in order to send commands to control the car's movement. The experimental results show that the performance of the system meets requirements of this design.
Markov Modeling Methods for Performance Analysis of IEEE 802.11 Protocol

As the mainstream standard of WLANs, analyzing the performance of IEEE 802.11 protocol has been a research hotspot in the field of communication. In many research methods, the mathematical model based on Markov chain is one of the most popular methods for performance analysis of IEEE 802.11 DCF. This paper classifies Markov chains as one-dimension, two-dimension and three-dimension, introduces their application in analyzing the performance of 802.11 DCF respectively and summarizes their characteristic. The results proposed in this paper show that the appropriate dimension of Markov chain needs to be selected according to the complexity of the improved MAC protocol and considered communication scenarios.
An improved SVM classifier based on multi-verse optimizer for fault diagnosis of autopilot

With regard to the lack of the sample of faults in the test of autopilot, a model of fault diagnosis based on support vector machine (SVM) optimized by multi-verse optimizer (MVO) is put forward. SVM does well in solving the few samples and nonlinear problem, which is suitable for the fault diagnosis of autopilot. To solve the overfitting and underfitting resulted from the improper parameters of SVM, multi-verse optimizer was applied to optimizing the parameters of SVM. By this way, a model of fault diagnosis with better performance was built. The simulation experiment results show that the accuracy of SVM based on MVO can achieve 98.3673% using 50 training samples. However, the accuracy of genetic algorithm (GA)-SVM achieves 91.0204% and the accuracy of SVM based on gravitational search algorithm (GSA) achieves 91.6327%. The simulation experiment results shows that SVM based on MVO has much better performance than others.
Design and Development of Injection Current Control On Inverter-Based Proportional Resonant Method

A current controller in PV on-grid systems have a significant effect on the quality of the current injected to the grid. In this case, the current controller should provide a high quality sinusoidal current waveform with the low appearance of harmonic distortion. One popular current control method that could be found in literatures is proportional-resonant control system. This paper is preliminary work on the implementation of the current injection control system for on-grid PV system which have been built in our Lab. The PR control system have been simulated in Matlab-Simulink software environment with good result and will be embedded in a low cost dsPIC microcontroller.