Miracle-dz/UniFAR-FTUs huggingface dataset:
Contingency Control Capability of an Optimized HVDC-Based VSC Transmission System

Power network operations are increasingly becoming complex due to upsurge in power transactions, following the ongoing power system deregulation and restructuring. The alteration in dynamics of power operations based on this upsurge, usually results into contingency because power facilities are made to operate in region of their limits. In this regards, high voltage direct current (HVDC) based voltage source converter (VSC) and other flexible alternating current transmission systems (FACTS) imbedded with VSC have established competence for noticeable diverse power network characteristics improvements. Therefore, this study applies optimized VSC-HVDC transmission system for mitigation of bus voltage and line thermal limit violation as a result of n-1-line outage contingency during bilateral, simultaneous and multilateral transactions. Line contingency evaluation through real power performance index (RPPI) is adopted for severe contingency identification purposes. Optimization of VSC-HVDC system is achieved through statically developed sensitivity-based analysis of VSC control parameters, while its performance for both steady state and contingency conditions are verified with IEEE 6 and 30 bus networks. An improvement in power flow, voltage deviation minimization and automatic alleviation of violated thermal and voltage limits during contingency present optimized VSC-HVDC system as a solution for network performance optimization especially during various transactions occasioned by unbundling power processes. Besides, the superiority of the proposed optimization techniques is established through performance comparison with other sensitivity methods involving reactive power (RPSM), active power (APSM) and performance index (PISM), where the proposed sensitivity method (PSM) outperformed these other methods.
A Cloud-based Mobile Healthcare Monitoring Framework with Location Privacy Preservation

Nowadays, ubiquitous healthcare monitoring applications are becoming a necessity. In a pervasive smart healthcare system, the user’s location information is always transmitted periodically to healthcare providers to increase the quality of the service provided to the user. However, revealing the user’s location will affect the user’s privacy. This paper presents a novel cloud-based secure location privacy-preserving mobile healthcare framework with decision-making capabilities. A user’s vital signs are sensed possibly through a wearable healthcare device and transmitted to a cloud server for securely storing user’s data, processing, and decision making. The proposed framework integrates a number of features such as machine learning (ML) for classifying a user’s health state, and crowdsensing for collecting information about a person’s privacy preferences for possible locations and applying such information to a user who did not set his privacy preferences. In addition to location privacy preservation methods (LPPM) such as obfuscation, perturbation and encryption to protect the location of the user and provide a secure monitoring framework. The proposed framework detects clear emergency cases and quickly decides about sending a help message to a healthcare provider before sending data to the cloud server. To validate the efficiency of the proposed framework, a prototype is developed and tested. The obtained results from the proposed prototype prove its feasibility and utility. Compared to the state of art, the proposed framework offers an adaptive context-based decision for location sharing privacy and controlling the trade-off between location privacy and service utility.
Attack-free Protocol Design with Distributed Noncontinuous Appointed-time Unknown Input Observer

This paper considers the output-feedback consensus problem of linear multi-agent systems under directed communication topologies. To avoid the network attack from potential malicious attacker, the concept of attack-free protocol is proposed, wherein the information transmission via communication channel is forbidden. Under this circumstance, only relative output measurement can be utilized to generate the observer and the protocol. In this paper, by viewing the relative input of neighboring agents as the unknown input, novel distributed unknown input observer is presented for each agent to achieve appointed-time estimation of the consensus error, where only measured relative output information is used. The fully distributed adaptive protocol is then designed based on the proposed observer. The distributed observer takes a pulsative form at some time instants, which is noncontinuous; while such form takes the advantage of reducing computational cost, compared with the pairwise observer design structure. Simulation results are also conducted to illustrate the effectiveness of the proposed attack-free protocol.
Intrusion Detection System using Feature Selection With Clustering and Classification Machine Learning Algorithms on the UNSW-NB15 dataset

The identification of malicious network traffic through intrusion detection systems (IDS) becomes very challenging. This malicious network appears as a network protocols or normal access. In this paper, for the classification of cyberattacks, four different algorithms are used on UNSW-NB15 dataset, these methods are naive bays (NB), Random Forest (RF), J48, and ZeroR. Also, K-MEANS and Expectation Maximization (EM) clustering algorithms are used to cluster the UNSW-NB15 dataset into two clusters depending on the target attribute attack, or normal network traffic. To develop an optimal subset of features, Correlation-based Feature Selection (CFS) is used, then the mentioned classification and clustering techniques are used. The used methods gives an efficient tool for studying and analyzing intrusion detection in large networks. The result show that RF and J48 algorithms performed best results with 97.59%, and 93.78%, respectively.
Fully 3D-Printed Modular Pipe-Climbing Robot

In industrial production, current pipe-climbing robots can replace humans in real-time monitoring, leak inspection, and other tasks. However, most pipe-climbing robots are designed for specific applications with complex structures; this limits the adaptability to various conditions. To solve this issue, this letter presents a one-piece 3D-printed pipe-climbing robot composed of sequenced novel soft bending mechanisms. The mechanisms use the small strains of soft materials to achieve large bending deformation. The proposed robot is highly adaptable to pipes with various diameters, radii of curvature, inclinations, and even outer/inner walls because of modular grippers and an inchworm inspired middle section. The good strength and flexibility of the robot are simultaneously achieved by reasonable structural design using one material. In this letter, the design concept and processing methods are introduced. The finite element method is adopted to predict the maximum bending angle of modules to aid design, and a torque test bench is built to acquire output torque and restoring torque. Then, the model of the entire robot is built to facilitate specialized design. Finally, several experiments are conducted to verify the good adaptability of the robot to pipes of various diameters (18–36 mm), inclinations (0–90°, even horizontal pipes), and radii of curvature (minimum to 19 mm). The static bearing ability of the robot reaches 1000g (without climbing at 7 bar, 22 mm diameter vertical pipes), which is almost 80 times its weight. In addition, the load-bearing ability of the robot while climbing on pipes in various states is obtained.
A New Robust Copy Move Forgery Detection Method

— One of the passive image authentication methods, copy move forgery detection has become an important research area. In this study, a robust forgery detection method was proposed considering the attacks that can be applied to the image. In the method, suspicious source and target regions are detected by matching SIFT keypoints extracted after obtaining the texture information of the image. The feature vectors of the image sub-blocks are obtained by using DCT-phase to detect forged pixels within these regions. The fact that the corresponding blocks have similar feature vectors indicates that the pixels in the block are forged. In the performance analysis, online dataset named GRIP was used and compared with popular studies in the literature.
Direction of Arrival Estimation by Using Deep Autoencoder Network

— In this paper, an unique and innovative method which uses problem deep ecoder networks is proposed for the solution of arrival angle estimation problem. Also details of the proposed method are explained. Although there are various direction finding methods in the literatüre, no mature studies performing sirection finding using an autoencoder have been found. an encoder have been found. In the study shown in [1], the autoencoders designed for different spatial regions were used to filter the received signal into spatial subregions before the deep neural network making direction prediction. In the proposed study, direction estimation is made only by using deep ecoder networks over the reconstruction errors that result from encoding and recoding of signals reaching multiple antenna arrays. For every azimuth direction in the region of interest, there exists a unique otuencoder which has been trained by signals with constant SNR. Proposed direction finding method that looks at the reconstruction errors produced by these autoencoders for the impinging signal has been compared with the currently known and widely used MUSIC algorithm and the performances of both algorithms have been examined
An algorithm with reduced computational complexity for estimation of parameters of linear stationary discrete systems

The estimation of linear stationary discrete systems parameters (LSDS) by the measured input and output values is of major importance in control systems synthesis. Most of the methods for parameters estimation are based on the least squares method which implementation is related to matrices inversion.The present paper aims to propose an algorithm for estimation of parameters of LSDS, described in the state-space, in which, the size of the inverted matrices is always n×n. Proposed is a procedure for matrices inversion with reduced computational complexity, by using the least squares method (LSM).
LSTM and WaveNet Implementation for Predictive Maintenance of Turbofan Engines

With the development of technology, the condition analysis of industrial machines over sensor data has become more commonly used. These developments have made the processing and interpretation of sensor data a new problem to be solved. In this study, Long Short Term Memory (LSTM) and WaveNet are used to produce solutions for predicting remaining useful life. Experimental studies were performed on jet engine sensor data containing degradation and failures using subsets FD001 and FD003 in C-MAPSS Turbofan engine dataset. The results obtained were analyzed using various evaluation metrics and graphs. Mean square error (MSE) values obtained from LSTM tests are 11.02 for FD001 and 26.89 for FD003. MSE values obtained from WaveNet tests are 10.85, 20.71 for FD001 and FD003 respectively. Finally, tests are also evaluated by using decision level fusion and applying weighted sum rule to LSTM and WaveNet models. The results obtained clearly showed that the decision level fusion system have promising results for predictive maintenance.
Structural diversity and luminescent properties of lanthanide 2,2- and 2,3-dimethylsuccinate frameworks

The structures of fourteen new lanthanide frameworks (La, Ce, Eu, Tb, Y and Lu) containing the 2,2- or 2,3-dimethylsuccinate ligands are reported. While the majority of the known 2,2-dimethylsuccinate frameworks feature two dimensionally bonded layers, capped by hydrophobic methyl groups, several of these new frameworks adopt quite different architectures. These include one dimensional inorganically connected chains (La and Ce) with only non-covalent interactions in the other two dimensions, and three dimensional covalently bonded frameworks (Eu and Lu) with spaces in their structure to accommodate the bulky methyl groups. The new 2,3-dimethylsuccinate frameworks (La and Y) adopt three-dimensional covalently bonded frameworks. The factors affecting the formation of structures with different dimensionalities are examined and compared to previously reported transition metal frameworks. In addition, the sequence of phases formed with changing lanthanide size, concentrations and temperatures are rationalised. The luminescent properties of several 1- and 2-D frameworks doped with Eu and Tb are reported, with the Y host exhibiting the most intense emission.
Task-Space Cooperative Tracking of Manipulators via A Unified Inner/Outer-Loop Distributed Design

This paper addresses task-space adaptive coordinated tracking of networked manipulators with an inner/outer-loop closed control architecture, considering all the uncertain kinematics, dynamics, disturbances, and unavailable task-space velocities. One observation is that modern robotic applications may encounter situations that task-space controllers cannot be implemented on robots with the closed control architecture. In addition, it assumes that the combination of the inner/outer loop is stable and effects of unknown robotic dynamics are neglected. Existing papers in [9]–[12] provide task-space synchronization with an open control architecture and require each robot to fully access a desired global task, and to communicate via undirected or strongly connected digraphs. In contrast, this paper proposes a distributed framework over a directed graph so that a robust, distributed, outer-loop control scheme is developed to achieve task-space coordination with dynamic effects being considered and not modifying the inner control loop. Numerical simulations are presented to show the effectiveness of the design.
Integrated Path-tracking and Control Allocation Controller for Autonomous Electric Vehicle under Limit Handling Condition

In current literature, a number of studies have separately considered path-tracking (PT) control and control allocation (CA) method, but few of studies have integrated them together. This study proposes an integrated PT and CA method for autonomous electric vehicle with independent steering and driving actuators in the limit handling scenario. The high-level feedback PT controller can determine the desired total tire forces and yaw moment, and is designed to guarantee yaw angle error and lateral deviation converge to zero simultaneously. The low-level CA method is formulated as a compact quadratic programming (QP) optimization formulation to optimally allocate individual control actuator. This CA method is designed for a prototype experiment electric vehicle with particularly steering and driving actuator arrangement. The proposed integrated PT controller is validate through numerical simulation based on a high-fidelity CarMaker model on highspeed limit handling scenario.
Faster Wi-Fi Fingerprinting Using Feature Selection

Wi-Fi fingerprinting has been widely used for indoor positioning, as Wi-Fi technology is easily deployed and supported. In fingerprinting, a database is created using the received signal strength indicator (RSSI) values in the area of interest; position prediction is performed by finding the best match for a measured RSSI among the values in the database. As location positioning gains importance for continuous interactive (CI) applications in large indoor spaces such as malls and airports, the fingerprinting databases become larger, making it computationally more difficult to position targets in real-time. On the other hand, CI applications such as Augmented Reality (AR) require low-latency positioning for a good user experience. In this work, we propose to use feature selection methods along with the K-nearest neighbors (KNN) classification and regression algorithms in order to create a simple and swift location positioning system. Our evaluation of various feature selection methods shows that computation times for positioning can be reduced by 75% using feature selection.
A Novel Low-Energy CNTFET-Based Ternary Half-Adder Design using Unary Operators

Energy consumption is a critical factor to be reduced when designing embedded systems and IoT devices. By using Multiple-valued logic (MVL) circuits, interconnections complexity and energy consumption are decreased in comparison to binary systems. This paper uses MVL circuits to present a ternary half-adder (THA) with reduced energy consumption to maintain the battery usage in nano-scale embedded systems and IoT devices. The proposed CNTFET-based circuit uses a dualvoltage (Vdd and Vdd/2) and novel unary operators to improve the performance. Extensive HSPICE simulations show impressive improvements in reducing transistors count, decreasing energy consumption, increasing noise tolerance, and enhancing the robustness of process variations compared to previous circuits.
Accelerated Convergence of Time-Splitting Algorithm for MPC using Cross-Node Consensus

The splitting strategy over prediction horizon of model predictive control (MPC) has the potential to compute optimal action in a parallel way. However, such time-splitting algorithms often lead to very slow convergence speed because the state consensus only happens in each pair of adjacent nodes, i.e. a point-to-point topology. This paper proposes a generic cross-node consensus method to extend the shortcoming of limiting to point-to-point topology for the purpose of accelerating the convergence of time-splitting MPC. The cross-node consensus is realized by predicting the state transition from one node to another using plant prediction model, which can increase the information exchange efficiency in the prediction horizon. The time-splitting optimization algorithm is implemented by combing with alternating directions method of multipliers (ADMM). Simulations with autonomous driving show that this new algorithm significantly reduces the number of iterations in time-splitting MPC, averagely about 81% compared with classic time-splitting technique.
Delay Margin for Containment Control of Second-Order Multi-Agent Systems Over Directed Graphs*

In this paper, we mainly study the delay robustness on containment control of second-order multi-agent systems with uncertain communication delays over directed graphs. Our purpose is to find the maximal delay range that can be tolerated under which the multi-agent system can maintain containment robustly. We consider a second-order multi-agent system under position and velocity feedback protocols in which each agent has two unstable real poles. We derive the exact expression of the delay margin for multi-agent system containment. Furthermore, based on the analysis of the exact delay margin, we obtain its explicit upper bound. The results show that the position of poles and the network connectivity will affect the bound of delay margin. Finally, a numerical example is used to illustrate the effectiveness of the proposed theoretical results.
Outage Performance of Dual-Hop TAS/Alamouti-STBC NOMA System

In this study, outage performance of dual-hop non-orthogonal multiple access system, where the base station simultaneously communicates with two mobile users via a halfduplex amplify-and-forward relay by applying transmit antenna selection/Alamouti space-time block coding technique, is investigated over Raylegih fading channels. By defining fundamental analytical equations, the outage probability of both mobile users is investigated via Monte Carlo simulations. Simulation results demonstrate that outage probability performance of the user with lower power allocation can be improved by the proposed technique, but it does not change for the other user.
Optimization of magnetic core structure based on DD coils for electric vehicle wireless charging

Coupling system has great influence on the transmission performance of wireless charging system. Improving the mutual inductance between coupling coils can effectively improve the transmission efficiency. Using magnetic core is an effective way to enhance mutual inductance of coupling coils. The introduction of magnetic core also leads to core loss. Based on the DD coil structure and considering the magnetic core weight and coupling coefficient, a method for ferrite core design and optimization is proposed, which can reduce the core loss between transmitter and receiver and improve the average utilization ratio of the magnetic core. MAXWELL simulated and analyzed the coupling coil before and after optimization, and discussed its feasibility.
Efficient Feature Mapping in Classifying Proportional Data

In image classification, traditional kernels or feature mapping functions of Support Vector Machine(SVM) use discriminative features without considering the true nature of the data. Our work in this paper is motivated by the need to consider intrinsic distribution of L1 normalized histograms and develop a flexible feature mapping technique by combining histogram based features and distribution based density features. The proposed mapping technique contains prior knowledge about the the data which provides a flexible representation and thus increases the discriminative power of the classifier. Such flexibility is achieved due to the explanatory capabilities of Dirichlet, generalized Dirichlet and Beta-Liouville distributions to model proportional data. In addition to that, we present a general framework to estimate the parameters of these distributions by taking maximum likelihood (MLE) approach. Experimental results show that the proposed technique increases the effectiveness of SVM kernels for different computer vision tasks such as natural scene recognition, satellite image classification and human action recognition in videos.
LED Strip Based Robot Movement Intention Signs for Human-Robot Interactions

As a new kind of robots, called cooperative robots, are more commonly used in industry, a new way of communication is becoming more important due to the increasing number of closer cooperation between human and robots. This paper proposes the idea behind a novel method of using visual communication between cobots and humans focusing mainly on the field of industrial robotics. This device can decrease the mental stress experienced by the coworker and can increase the trust resulting in a closer to ergonomic workspace from the coworker viewpoint. Other possible usage is also discussed.
Optimization of WiFi Communication System using Low Power Ring Oscillator Delay Cell

Modern systems rely on wireless communication for data transmission and system control. Power dissipation and circuit area are crucial factors for such communication systems in portable devices. Ring oscillators are the popular choice for voltage-controlled oscillator (VCO) architecture for Phase locked loop (PLL). This is because ring oscillators exhibit wider tuning range and consume less area on chip. This paper presents a low power ring oscillator designed for wireless application at 5 GHz in the ISM band. A 3-stage starved current voltage-controlled ring oscillator was implemented with $\mathbf{0.13}\ \mu\mathbf{m}$ CMOS technology using ELDO Spice simulator from Mentor Graphics. With supply voltage of 1.2V, the power dissipation is 1.08 mW and the phase noise is -78 dBc/Hz at 1 MHz additionally, the proposed ring oscillator with new delay cell layout size occupied 7.1 by $\mathbf{11.7}\ \mu\mathbf{m}^{\mathbf{2}}$.