Miracle-dz/UniFAR-FTUs huggingface dataset:
Modulated Model-Free Predictive Control With Minimum Switching Losses for PMSM Drive System

A modulated model-free predictive control with minimum switching losses (MSL-MMFPC) is proposed to improve the steady-state performance and reduce the switching losses for a permanent magnet synchronous motor (PMSM) drive system. Firstly, two adjacent current vectors are determined based on the predefined first-level cost function, and then, make the current vector at the next control period equal to the reference current vector by modulating the selected current vectors properly. Additionally, in order to keep optimal control performance also in the over-modulation region, a new rotating coordinate frame is used to adjust the optimal voltage vector. Then, the second-level cost function is designed to select the optimal voltage vector sequence, so that the switching of a VSI leg does not happen during the phase-current maximum, which can reduce the switching losses of the inverter. The simulation and experimental results verify the effectiveness of the proposed control method.
A New Feature Selection Method for Intrusion Detection

Intrusion detection is an effective measure to prevent host and network attacks. The use of an intrusion detection system makes up for the shortcomings of traditional firewall technology, signature authentication technology, and access control technology in security protection. However, due to the imbalance of the distribution of intrusion detection data samples, the mutual redundancy between sample features seriously affects the accuracy and efficiency of specific attack detection. In this paper, a proposal was forward, the appropriate feature subsets are selected respectively based on mutual information and the firefly algorithm, and then proposed a feature selection strategy combining two feature subsets. Experiment outcomes show that the accuracy and efficiency of detection are improved by using the best feature set, compared to the use of all features.
The Lateral Control of Autonomous Vehicles: A Review

Human need safety, comfort, and speed in driving-requirements that can be fulfiled by autonomous vehicles that enable drivers to avoid obstacles and maintain a safe distance from other motorists. These function are executed through lateral vehicle control, which has been the subject of considerable research. The current research was aimed at providing a comprehensive review and description of previous investigations that implemented both conventional and innovative lateral control methods, such as proportional- integral-derivative control, fuzzy logic, artificial intelligence, neural networks, genetic algorithms, and combined approaches. The evaluated studies were also classified into two categories, namely simulation and experimental research that used real-world tools. The paper concludes with a recomendation to use an alternative method called direct inverse control. Which is a modification of neural network- based control. This method is advantageous because it uses output/input feedback, thereby effectively functioning in unpredicable terrain. This feature is highly suitable because autonomous vehicles are non-linear system.
Weakly Supervised Vitiligo Segmentation in Skin Image through Saliency Propagation

Vitiligo is a skin disorder where pale or white patches develop due to the lack or absence of melanocytes. Vitiligo affects around 0.5% to 1% of the world's population, and it may have a profound psychological impact on patients' quality of life. In this paper, we present a novel weakly supervised framework to segment vitiligo regions with high quality, which is a fundamental task for the assessment of vitiligo. The proposed framework starts with pre-training a classification network using only image-level labels. Then we observed that the activation map obtained from the image classification network could be further exploited and introduced into the saliency propagation process as useful information. Finally, the saliency propagation process is performed on the graph built on superpixels to obtain a meaningful saliency map. These three steps lead to a compelling yet elegant method. Moreover, we propose a new large vitiligo image dataset named Vit2019. To the best of our knowledge, this is currently the first dataset for image segmentation of vitiligo diseases. Experimental results demonstrate the superiority of the proposed model over state-of-the-arts.
Multi-Surface Admittance Control Approach applied on Robotic Assembly of Large-Scale parts in Aerospace Manufacturing

The robotization of assembly operations is one of the requests of aircraft manufacturers. During the assembly of large-scale sub-assemblies, contact forces between parts must be controlled. Exceeding some limits can damage the aircraft parts. This can happen because of the poor accuracy of industrial robots and uncertainties of parts' positions, and orientations. This paper proposes a new approach to control the movement of the robot end-effector, taking into account the contact forces between the parts during assembly. The suggested approach can be used to assemble complex shape and large-scale parts. Based on the admittance control, the proposed approach is used to ensure multi-surface contact. It allows to control the interaction forces at each contact surface. Each contact is modeled by a mass-spring-damper system. This approach was tested on the assembly of two large-scale airplane's parts using a KUKA robot (KR340), equipped with a Force/Torque (F/T) sensor. The performance of this multi-surface approach was compared to one surface admittance control.
Home Automation System Ontology for Digital Building Twin

The paper dwells upon creating the ontologies for a digital building twin. The topic is home automation system ontology design. This is a modular ontology comprising set basic ontologies. The design uses the existing home automation, smart home, and Internet of things ontologies. The paper describes the classes of this subject area and their interclass relations. Ontologies greatly facilitate data exchange between the embedded models and the service programs of the digital twin, as well as between the digital twin and external agents, users or other software.
Developing a Student Feedback System using a Design-Based Research Approach

We have developed a web-based feedback system that students can use to report difficult, easy, engaging, and boring sections of a lecture in real-time. Such feedback can identify potentially problematic lecture content, track cognitive-affective dynamics in a classroom, and assist instructors in retrospective self-evaluation. We use a mixed-method approach within a design-based research (DBR) framework. In this paper, we discuss initial development and implementation of the feedback system, which constitutes the first cycle of DBR in the project. We discuss potential use-cases of such data and follow the DBR framework to identify strengths and weaknesses in the current prototype and its implementation. Based on such an analysis we come up with a list of modifications for the next cycle of DBR.
Interpretable Prediction of Protein-Ligand Interaction by Convolutional Neural Network

Evaluation of protein-ligand interaction is a crucial step in the process of drug discovery. Recently, several methods based on deep learning have gained impressive binary classification performance on protein-ligand binding prediction. However, lack of three-dimensional complex data still limits the accuracy and robustness of evaluation of protein-ligand binding affinity, as well as the prediction of their binding sites. In this paper, we propose a novel convolutional neural network based method for estimating the binding affinity between protein and ligand using only 1D sequence data. Even with the same amount of sample size, this model outperforms other structure-dependent traditional and machine learning based methods in terms of both binary classification and regression task. Furthermore, we use this model to identify the key amino acid residues of protein that are vital for binding interaction, which provides biological interpretation.
Vector Control of Modular Multilevel Converter Based Unipolar HVDC System

Multi-levels converters are useful for medium voltage and high power applications. Among the several multi-level converter topologies, the modular multilevel converters (MMC) are increasingly used for HVDC systems due to high reliability and simple power circuit owing to their modular structure. This paper presents a unipolar HVDC system for bulk power transfer between two grids. The closed loop vector control algorithm based on rotating d-q reference frame is used for the control and operation of above HVDC system. The simulation results of proposed HVDC system is verified and presented.
Automatic Generation Control of Multi-Area Hydro Power System using Moth Flame Optimization Technique

The novel moth-flame optimization (MFO) algorithm has been employed for the automatic generation control (AGC) mechanism of a three-area hydro power system model. This work is to suppress the damped oscillations, subjected to perturbation of load, for the studied model. The present aspect investigates the responses, which is dynamic in nature, in respect of area frequency and power of tie-line oscillations (the two main AGC indices). The constrained optimization problems that play key roles in the design of controller gains are formulated by implementing the propounded MFO algorithm. System AGC responses with the designed controller parameters are studied with two different natures of load profiles. The proposed MFO algorithm based fast acting Sugeno fuzzy logic (SFL) technique is applied for the investigated model of power system to obtain AGC responses for on-line, off-nominal operating condition. In both the off-line as well as the on-line normal operating conditions, robustness of the proposed MFO-SFL controllers is checked by varying some important physical parameters that deviate by ±25% from their rated values. The simulation based outcomes reveal that the propounded MFO based PID controller exhibit more dynamic control with robust performance as juxtaposed to the studied genetic algorithm based approach.
Spatial–Spectral Relation Network for Hyperspectral Image Classification With Limited Training Samples

Due to the data-hungry nature of deep neural networks and the high dimensionality of hyperspectral imagery (HSI) data, the scarcity of training samples remains a significant challenge for HSI classification based on deep learning. Recently, the pairing or recombining of samples has proven to be an efficient approach to increase the amount of input training data, and accordingly, the architecture of a network trained by these samples has to be redesigned, since the input is no longer a single sample. Following this strategy, in this article, we propose a spatial–spectral relation network (SS-RN) for HSI classification with limited training samples. The SS-RN takes advantage of the relation network architecture to precisely capture the profound similarity between samples. Additionally, to make SS-RN more suitable for HSI classification, an entire three-dimensional (3-D) neighborhood instead of the isolated pixel is considered to explore both spectral and spatial information thoroughly. Multiple support samples are also selected for each class to make sure that the extracted features are stable enough to avoid the intraclass similarity and interclass dissimilarity problem of HSI. The network is composed on the basis of 3-D convolutional neural network blocks with the aim of extracting spatial–spectral features. Extensive experiments on three widely used HSI datasets demonstrate that the proposed method can achieve better classification accuracy than conventional deep learning methods with limited labeled training samples.
Object Distance Measurement System Using Monocular Camera on Vehicle

To support autonomous vehicles that are currently often studied by various parties, the authors propose to make a system of predicting the distance of objects using monocular cameras on vehicles. Distance prediction uses four methods and the input parameter was obtained from images processed with MobileNets SSD. Calculations using linear regression are the simplest calculations among the four methods but have an error of 1 % with a standard deviation of 1.65 meters. While using the first method, the average error value is 9% with a standard deviation of 0.43 meters. By using the second calculation, the average error resulted in 6% with a standard deviation of 0.35 meters. The experimental method had an average error of 1 % with a standard deviation of 0.26 meters, so the experimental method was used.
MalJPEG: Machine Learning Based Solution for the Detection of Malicious JPEG Images

In recent years, cyber-attacks against individuals, businesses, and organizations have increased. Cyber criminals are always looking for effective vectors to deliver malware to victims in order to launch an attack. Images are used on a daily basis by millions of people around the world, and most users consider images to be safe for use; however, some types of images can contain a malicious payload and perform harmful actions. JPEG is the most popular image format, primarily due to its lossy compression. It is used by almost everyone, from individuals to large organizations, and can be found on almost every device (on digital cameras and smartphones, websites, social media, etc.). Because of their harmless reputation, massive use, and high potential for misuse, JPEG images are used by cyber criminals as an attack vector. While machine learning methods have been shown to be effective at detecting known and unknown malware in various domains, to the best of our knowledge, machine learning methods have not been used particularly for the detection of malicious JPEG images. In this paper, we present MalJPEG, the first machine learning-based solution tailored specifically at the efficient detection of unknown malicious JPEG images. MalJPEG statically extracts 10 simple yet discriminative features from the JPEG file structure and leverages them with a machine learning classifier, in order to discriminate between benign and malicious JPEG images. We evaluated MalJPEG extensively on a real-world representative collection of 156,818 images which contains 155,013 (98.85%) benign and 1,805 (1.15%) malicious images. The results show that MalJPEG, when used with the LightGBM classifier, demonstrates the highest detection capabilities, with an area under the receiver operating characteristic curve (AUC) of 0.997, true positive rate (TPR) of 0.951, and a very low false positive rate (FPR) of 0.004.
High-Throughput and Low-Latency Distributed Management Proxy for Key-Value Store Over 100Gbps Ethernet on FPGA

Key-value store (KVS) is one of the most important NoSQL databases. To satisfy the demand for growing data in the era of big data, distributed KVS systems that enhance the scalability of storage systems are widely used in recent years. Hence, high-performance distributed management that implements functions such as data partitioning and message distribution is indispensable. In this paper, we present a high-throughput and low-latency distributed management proxy (DMP) for key-value store over 100Gbps Ethernet (100GbE). The proxy-based design which has flexibility and generality is fully implemented on FPGA. In the DMP, the consistent hashing algorithm is designed to partition KVS packets in pipeline. Then a hardware network offload engine is proposed to transmit network messages over 100GbE on FPGA to reduce the latency penalties caused by consistent hashing. Besides, in order to utilize the 100Gbps Ethernet bandwidth, four fully parallel and pipelined data paths are implemented in the DMP on an FPGA. The experimental results show that the peak throughput of the proposed DMP can reach 77.4 million queries per second (QPS), and the latency can achieve as low as 0. 8µs for the small-size packets, which is 7.4x higher and 23x faster respectively than the software proxy implementation accelerated by Intel's DPDK.
Integrated Assessment of the Effectiveness and Structuring of Objects in the Internet of Things System

The paper deals with the construction of Internet of things systems. The solution of two problems is considered. The first problem is to build an integrated assessment of system objects effectiveness, based on analysis the information of indicators achieved by objects characteristics values. Choice of rational variant of objects structuring and their grouping in the appropriate way depends on the choice of integral estimation model variant. To solve the grouping problem the approach based on neural networks is used. The parameters of the first neural network are learning rate, normalization parameter, and similarity criterion. In second neural network, this network uses a reset mechanism-temporary deactivation of the group if the similarity condition is not met. Ordering the numbering of Internet of things objects system by value of integral assessment of their functioning efficiency and grouping them into a given number of ordered groups leads to a certain variant of system structuring. Improving the efficiency of an integrated structured system is achieved through the use of transformational mechanisms. The first of them is objects absorption of the leading groups of objects from the groups of outsiders and affects the structural efficiency of the Internet of things, as it leads of the original set of objects reduction. The second of them ensures movement of objects in the leading groups through the rational use of the the resource allocated by the management center and affects the resource efficiency of the entire system. The optimization of mechanisms modeling leads to a number of discrete programming problems. Numerical methods for finding the best option for the transformation of the original system of the Internet of things, represented as a set of groups of objects requires on the one hand orientation to known approaches, and on the other taking into account the features of mathematical descriptions of transformation conditions and the possibility of constructing combined algorithms.
A multimodal approach for localization of Ackerman steering micro ground vehicles in bad GPS reception environments

A closed-loop control system requires a state estimator for feedback. In the domain of mobile robots, this is called localization. The problem of localization for micro ground vehicles in outdoor environments with no modifications to the environment is a challenging one as the tolerances for micro ground vehicles are lower. The error in the data from the GPS is higher for ground vehicles than it is for aerial vehicles in urban environments. Different approaches such as the use of optical flow and non-holonomic constraints have been explored in the past on an individual basis but have their disadvantages. In this work, these methods, along with a mathematical model of the physical system were studied individually and then combined together to eliminate their individual pitfalls. This significantly increased the reliability of the system across different scenarios.
A New High-Frequency Characterization for Coupled Transmission Lines Fabricated on Lossy Organic Substrates

In this article, a novel experimental characterization technique for coupled transmission lines fabricated on organic substrates is presented. The high-frequency characterizations of transmission lines fabricated on organic substrates are extremely difficult due to parasitic resonances. We eliminated the parasitic resonance effects by combining physical circuit models with experimental characterizations. The reconstructed coupled transmission-line parameters obtained using the proposed technique are compared from 10 MHz to 40 GHz with the measurement data using a vector network analyzer (VNA). The transmission-line parameters using S-parameter measurement data are too contaminated with external noise, whereas those using the proposed technique can effectively eliminate the parasitic effects.
Improved Sensitivity and Selectivity of Direct Localized Surface Plasmon Resonance Sensor Using Gold Nanobipyramids for Glyphosate Detection

Direct localized surface plasmon resonance (LSPR) sensor using anisotropic gold nanoparticles with bipyramidal structure namely as gold nanobipyramids (GNBPs) has been proposed. The high curvature at the tips of GNBPs enhanced LSPR effect owing to electron-rich sites thus improved sensor performance. In this study, we demonstrated that GNBPs-based LSPR sensor is sensitive and can detect the presence of glyphosate as low as 0.1 % (w/w). Our experimental studies show that the GNBPs-based LSPR sensor exhibits improved sensitivity compared to the gold bone nanorods (GBNRs) and gold nanorods (GNRs)-based LSPR sensor. For t-SPR band, the GNBPs provided sensitivity factor as high as 4.76 and 5.17 times larger than the sensitivity factor of GBNRs and GNRs, while for l-SPR band, the sensitivity factor of GNBPs are 2.87 times larger than GBNRs and 1.57 times larger than GNRs. The selectivity of GNBPs-based LSPR sensor was evaluated by comparing its response to four different analytes and the response of LSPR sensor towards glyphosate is much higher than the responses towards chlorpyrifos, acetic acid and acetone. Hence, it is confirmed that GNBPs-based LSPR sensor has a good selectivity towards glyphosate. In addition, the proposed sensor system shows a good stability and repeatability for a period of 600 seconds and during five cycling tests, respectively.
Analysis and Control of AC and Hybrid AC-DC Microgrid: A Review

The concept of smart grid and microgrid is a vast area of research now a days. In this paper, various control strategies are presented for ac and hybrid ac-dc microgrid. Based on hierarchical control levels of microgrid, the different control strategies are discussed in local control, secondary control and global control. Several research surveys are made for these methods. A proper control technique is required for constant generation of all units. Comparisons are done between different control techniques.
Modeling the Formation of User Replying Network on Government Microblogs: An Exponential Random Graph Model

With the rapid development of social media, people get increasingly involved in public affairs through social networks, e.g., microblogs. Although social network plays a significant role in public affairs, some online government communities lack sufficient response, triggering an negative environment for policy decision process. In order to foster a healthy online government community, understanding the mechanism and drivers of user interaction becomes critical, especially for user to user interaction. We build a theoretical model to explain the formation of user to user network and understanding the underlying mechanism of why a user would be replied by others in government microblogging. We collect the user information, posted microblogs and the replies from government microblogs in 10 different areas to construct user replying networks. By using exponential random graph model (GRGM), we delve into the mechanism of how the network structures and node attributes affect the formation of user replying networks. Our experimental results validate that the mutual, homophily and emotional effect are the main drivers of users replying behavior.