Miracle-dz/UniFAR-FTUs huggingface dataset:
Branch Current based Distribution System State Estimation

For last some decades there has been significant changes in distribution systems. These changes have a substantial impact on the system configuration thereby creating new opportunities of monitoring. In particular, the smart grids, perhaps the next stage of the distribution systems. Such developments need a comprehensive knowledge of the system. State Estimation is a procedure that provides a dependable estimation of system states on all system buses. It was originally developed for transmission systems but for distribution systems, due to different topology the state estimation approach must be reorganized in a way so that it can be applied to the system. In this paper, an approach using branch currents as state variables is proposed, and an explanation to address weakly meshed distribution system is proposed using IEEE feeders to formulate the algorithm for monitoring. The proposed estimator allows the processing of all type of measurements including conventional and phasor measurements.
Simulation Model of the Computer's Units and Peripherial Equipment Interaction

The paper deals with the principles of constructing a simulation model for studying the basic processes and devices in the computer I / O subsystem. The procedure for selecting the composition of the studied characteristics of input-output devices, as well as the structure and level of detail of the parameters of the objects under study is considered. Typical computer hardware with keyboard and monitor is offered as a peripheral device. When modeling, probabilistic characteristics of data processing in the input-output subsystem are set. The modeling program is developed in Java in NetBeans 8.1. A user interface based on the animation of the processes in I / O subsystem is described. This provides obtaining a visual representation of interrupts, generation of control signals, and transfer of codes from peripheral devices to the memory and the central processor. The user interface provides the setting of the necessary modes and simulation parameters. The simulation results are presented in the form of time charts of devices and the numerical values of these characteristics. The input / output system under study can operate in one of three modes: only with information input, with data input and output to a monitor, and also only with data processing and output to a monitor. The paper presents the study results of the simulated system in various modes, confirming the adequacy of the developed model.
Telecom Big Data Assisted BS Resource Analysis for LTE/5G Systems

Due to the diverse services and high rate requirements of telecom users, mobile cellular systems envisage the challenges of high resource usage, especially the base station (BS) in the hot-spot area. This paper investigates on telecom big data of LTE/5G systems, and then we design a telecom big data assisted comprehensive BS resource analysis (CBSRA) algorithm. CBSRA algorithm considers six categories of resource data/indicators. Then, CBSRA algorithm judges the usage level of each indicator. Finally, CBSRA algorithm takes the comprehensive analysis and outputs the results. We implement the CBSRA algorithm in a realistic cellular system in China, and the proposed algorithm can assist operator to effectively detect BS with high resource usage.
Multifunctional Filtering Circuits: 3D Multifunctional Filtering Circuits Based on High-Q Dielectric Resonators and Coaxial Resonators

The rapid development of wireless technologies has created high demand for microwave circuits with compact size, low loss, and high efficiency. Bandpass filters (BPFs) are essential in wireless systems, which are cascaded with many circuits in RF front ends, i.e., power dividers [1], couplers [2], switches [3], and power amplifiers (PAs) [4]. They can be codesigned as multifunctional filtering circuits, including passive and active circuits, to reduce the loss and size of the whole system.
An Intelligent Resource Selection Framework for Cloud Computing Environments

Cloud computing provides computing resources as utility services. It offers clients with several usage scenarios. These scenarios fulfill the services requested by the clients with a lower cost as compared with deploying them locally. This paper introduces an intelligent resource selection framework that provides the client with the best set of resources in terms of computing cost and the fulfillment of client's requests. It is assumed that the requested resources can be provided to the client from multiple data centers. Additionally, cloud resources are allocated in units determined according to client's request. The client submits his resource requirements in terms of units of computing power, storage, networking, software … etc. Then, the proposed framework finds the best set of resources that satisfies the client's request. Moreover, it takes into consideration the inter-dependencies among resources that lead to mutually allocating certain resources together. Additionally, it considers the overhead incurred in acquiring and using the requested resources. This problem is formulated as a constrained optimization problem and has been tackled in this paper using genetic algorithms. Simulation studies show that the proposed framework provides a near-optimal solution for the studied cases.
Comparison of Linearity and Intermodulation Distortion Metrics for T - and Pi - Gate HEMT

This paper presents a comprehensive analysis of a Pi ($\pi$) - Gate AlGaN/GaN HEMT based on TCAD Simulations. For model validation, the simulation deck of T - Gate AlGaN/GaN HEMT has been calibrated with respect to an experimental data. The calibrated structure is further used for optimizing $\pi$ - Gate HEMT architecture. Comparisons demonstrate an improvement in terms of the lower Intermodulation Distortion by 24% and higher Linearity metric by almost 10 times in case of $\pi$ - Gate HEMT while maintaining similar ION /IOFF, device power and slightly lower cut - off frequency. This enforces the suitability of $\pi$ Gate HEMT architecture for milli - meter wave applications and low noise amplifiers (LNA) and emphasizes that a suitably deigned and fabricated $\pi$ Gate HEMT can substantially improve the device linearity along with lower intermodulation distortion. All the performance analysis has been carried out using the Atlas TCAD simulator available from Silvaco.
Sinusoidal Oscillator using 32-nm CNTFET-OTA

In this work, a design and simulation study of voltage controlled sinusoidal oscillator is presented using Carbon nanotubes field effect transistor (CNTFET) based operational transconductance amplifier (CNTFET-OTA) using minimum number of active and passive components. A comparative analysis of low power CNTFET-OTA and traditional complementary metal oxide semiconductor (CMOS) based operational transconductance amplifier (CMOS-OTA) at 32-nm technology node carried out by altering various carbon nanotubes (CNTs) parameters shows that CNTFET-OTA offers 280%, 262% and 256% improvement in the DC gain, output resistance and transconductance respectively. A lesser total harmonic distortion (THD) with 136 times lesser power consumption is achieved when compared to CMOS-OTA. Further, a quadrature model based sinusoidal oscillator design is verified with the simulated results. The simulation results have confirmed excellent temperature stability for wide oscillation frequency range.
The lipid composition of the blood serum in certain forms of ovarian amenorrhea.

: A study of serum lipids in 68 women with ovarian forms of amenorrhea has documented prominent lipid abnormalities in most women with ovarian dystrophy and gonadal dysgenesis. It presented as elevation of cholesterol, triglyceride, low density lipoprotein cholesterol levels and a decrease in high density lipoprotein cholesterol levels, with Types IV and IIb dyslipoproteinemia. These findings suggest that the women with ovarian dystrophy and gonadal dysgenesis are at a high risk of early onset of atherosclerosis and cardiovascular diseases.
Loop Closure Detection Based on Improved Hybrid Deep Learning Architecture

Loop closure detection plays a vital role in the visual Simultaneous Localization and Mapping (SLAM) system. Traditional Bag-of-Visual-Word (BoVW) methods for loop closure detection has been successfully applied to various SLAM systems. The Convolutional Neural Networks (ConvNets) based methods can automatically learn feature representation from original image, and it is more robust to illumination changes. However, these two methods all have some limitations. In order to overcome the shortcomings of the traditional methods extract insufficient features, and the ConvNets based methods unable to adapt complex environment changes, in this paper, we propose an improved hybrid deep learning architecture (HDLA) to generate high-level semantic image features for loop closure detection specifically. This network is based on hybrid ConvNet modified by us to especially cope with robust and real-time feature extraction. We present extensive experiments on three real world datasets to evaluate each of the specific challenges in loop closure detection. The results demonstrate that the proposed method achieves superior performances and provides a feasible solution for loop closure detection.
Node Importance Ranking Algorithm Based on Grey Relational Degree

With the continuous evolution of power grid towards smart grid, the scale of power communication network is expanding day by day, with the services carried by it more complex. Thus, power communication network is more vulnerable. In order to solve this problem, it is necessary to accurately identify the important nodes of the network in advance. Based on the topological characteristics of the power communication network and the running services, three indexes are selected in this paper. They are close centrality, betweenness centrality and the importance of the services carried by the nodes. What’s more, the degree of correlation between each index and the ideal solution is obtained by using the gray correlation algorithm, and then the node importance is determined. Finally, the simulation is carried out in a small power communication network to verify the rationality of the algorithm.
Customer Satisfaction Estimation in Contact Center Calls Based on a Hierarchical Multi-Task Model

This article presents a novel customer satisfaction (CS) estimation method that outputs both turn-level and call-level estimations simultaneously. Our key idea is to directly apply turn-level estimation results to call-level estimation and optimize them jointly; previous works treat both as being independent. Our proposal applies long short-term memory recurrent neural networks (LSTM-RNNs) to turn-level and call-level CS estimation to capture long-range sequential context in contact center calls. In addition, both networks are hierarchically stacked so as to use turn-level estimation results for call-level estimation directly. In order to learn the relationship between the two tasks, we also introduce joint optimization training to the stacked model. Several analyses of turn-level and call-level CS are provided on acted and real calls to support the proposed method. Experiments show that the proposed framework outperforms the conventional methods in both turn-level and call-level estimations.
Fake News Detection on Social Media: A Systematic Survey

These days there are instabilities in many societies in the world, either because of political, economic, and other societal issues. The advance in mobile technology has enabled social media to play a vital role in organizing activities in favour or against certain parties or countries. Many researchers see the need to develop automated systems that are capable of detecting and tracking fake news on social media. In this paper, we introduce a systematic survey on the process of fake news detection on social media. The types of data and the categories of features used in the detection model, as well as benchmark datasets are discussed.
Prediction of Wind Speed and Power at Kottamia Astronomical Observatory based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Wind power is the energy generated through the flow of air on the wind turbines and convert their mechanical power into electrical power. Wind energy is a plentiful renewable energy source and is widely distributed with low cost of electricity generation from available resources. Wind speed prediction has a significant role in wind energy production. The presented work provides a new approach to predict and compute wind speed and forecasts stochastic wind power in an astronomical site based on the model of Adaptive Neuro-Fuzzy Inference System (ANFIS). A computed prediction output (wind speed) based on (ANFIS) algorithm is compared with original data obtained from the same location. The predicted wind speed and the power produced from the wind energy have been calculated and evaluated to assess the proposed model. This model has been used to predict the weather for availability of green energy in future times. We introduce the simulation results that show the performance of the proposed model in predicting wind speed and power generated in renewable energy systems.
Decarbonization of the power generation system in Central America

Global warming is considered the most important cumulative environmental impact of contemporary times. One of the strategies to reduce greenhouse gas emissions is the decarbonization of anthropogenic activities. One of the routes marked for this purpose is to decarbonize the energy sector, specifically the generation of electricity. Biomass is one of the renewable sources that, in addition to contributing to this purpose, allows stabilizing the electricity distribution network, given its firm power characteristics.
Auto-Tuning of GPC weights based on Particle Swarm Optimization applied to a Manipulator End-Effector Trajectory Tracking

Manipulators are becoming more and more common to perform many tasks, whose accomplishment is often related to the applied control law. Regarding to this issue, control theory is helpful as the proper controller choice may turn the manipulator into a handy tool. Within this context this work presents an automatic tuning method for the Generalized Predictive Controller (GPC) in order to tracking the trajectory of a manipulator end-effector. The strategy employs Particle Swarm Optimization (PSO) to properly determine GPC cost function weights at each iteration that lead to zero error tracking. The proposed controller is compared to classical approaches for three different trajectories with results showing a better performance and tracking accuracy for the proposed approach.
Tilt Angle Effect Analysis on Nodes of Inductive Resonance Wireless Power Transfer

Electricity is one of the primary needs in life. The increasing demand for electric consumption proportionally increases the application of cables. The excessive application of wires also increases the possibility of a short circuit and fire-sparks. Wireless power transfer is an excellent alternative for this problem that would eliminate cables and increase electrical safety. The current researches in wireless power transfer are combining the conformal strongly-coupled magnetic resonance and tilting the coil position to optimize the efficiency and increase the transferred power. The current challenge of wireless power transfer is distance; therefore, to reach more places, a node or amplifier circuit is needed to increase the range of this electric power transfer. This paper investigates the effect of nodes’ tilt angle application in wireless power transfer. The experiment results show that the use of 3 nodes is possible to extend the induction area to power more electronic devices. The angle between the transmitter and receiver significantly affects the efficiency of the system. The maximum efficiency of the system can reach 3.89% by using one node and three nodes when the node angle is 5o. The produced power is decreasing as the angle is increasing. However, when the angle reaches 40o, the produced power is increasing and similar to the initial angles. The maximum power value is obtained when the angle is 5o, which is 0.2496 watts.
A Novel Circuit Architecture for Generating Narrow Pulses via Spectrum Stitching

In this paper, a novel circuit architecture is proposed to generate narrow pulses with high flexibility and without high cost. The desired narrow pulse signal is obtained via combining multiple sub-signals. The bandwidth of individual sub-signals is much smaller than the bandwidth of the desired pulse signal, and thus the sub-signals can be generated by low-speed digital-to-analog converters (DACs). When the sub-signals are combined, their spectra are stitched into the wideband spectrum of narrow pulse signal. Theoretically, arbitrary pulse waveforms can be synthesized through the proposed “spectrum stitching” technique without resorting to high-speed DACs, as long as the sub-signals are synchronized among each other in time. A prototype circuit is implemented to verify the fundamental scheme of the proposed “spectrum stitching” technique. The experimental results demonstrate that, Dirichlet pulses with temporal width of approximately 4 ns are successfully synthesized through combining the outputs of a regular 80-MHz waveform generator. In addition, numerical and experimental results indicate that, the proposed “spectrum stitching” circuit architecture is quite insensitive to various practical errors.
Outdoor Particle Filter Localization with Sparse Observation

Nowadays, autonomous lawn mowers are widely used in Europe. The robust autonomous operation and the ease of installation has lead to a substantial market share. Most autonomous lawn mowers move in a random fashion or with simple patterns because their self-localization capabilities are very limited. In this work, we analyze the potential of using apriori information about the shape of the boundary wire in combination with electromagnetic wire sensor readings for a particle-filter-based localization. In our experiments, this approach enables us to completely compensate for odometry drift. We achieve an accuracy of 20cm to 30cm in position and 3° in orientation for common garden sizes.
Study Characteristics of Disturbance in Frequency 9-150 kHz Generated by Household Appliance at Different points in Building Installation System

Nowadays there are many household appliances use inverter technology in order to improve energy efficiency. However, after being examined by the researcher, the use of this technology can generate disturbances in the frequency range 9-150 kHz. The disturbance generated by appliances may either increase or decrease due to the interaction of many appliances in a shared network. This study focuses on observing the characteristics of the disturbance in the frequency range 9-150 kHz generated by household appliances at a different point in building installation system. A vacuum cleaner is used as a disturbance source appliance and hotel which has many rooms operating of some household appliances is chosen as an object of measurement. The measurement is conducted at three points room socket, room panels, and floor panels. The PC Based oscilloscope is used to obtained time-domain signal and by using FFT the time domain signal then transformed to the frequency domain to the frequency spectrum of disturbance in the frequency range 9-150 kHz. The measurement shows that the disturbance characteristics generated by vacuum cleaner at three-point of hotel installation system are different due to the interaction of other appliances in the shared network. the disturbance of vacuum cleaner decreases 39.45 mV between socket and room panel and 20.225 mV between room panel and floor panel.
A Novel Sensing Method to Detect Tissue Boundaries During Robotic Needle Insertion Based on Laser Doppler Flowmetry

This study investigates the use of Laser Doppler Flowmetry (LDF) as a method to detect tissue transitions during robotic needle insertions. Insertions were performed in gelatin tissue phantoms with different optical and mechanical properties and into ex-vivo sheep brain. The effect of changing the optical properties of gelatin tissue phantoms was first investigated and it was shown that using gelatin concentration to modify the stiffness of samples was suitable. Needle insertion experiments were conducted into both one-layer and two-layer gelatin phantoms. In both cases, three stages could be observed in the perfusion values: tissue loading, rupture and tissue cutting. These were correlated to force values measured from the tip of the needle during insertion. The insertions into ex-vivo sheep brain also clearly showed the time of rupture in both force and perfusion values, demonstrating that tissue puncture can be detected using an LDF sensor at the tip of a needle.
Classification and Spectral Mapping of Stationary and Moving Objects in Road Environments Using FMCW Radar

In order to establish a reliable map of the road environment, this paper aims to classify the stationary and moving objects unlike the previous researches which generally focus on object recognition. The characteristics of the radar signals of stationary and moving objects were analyzed and the relation between the slope of pattern in radar time-frequency spectrum and relative velocity of the object was described mathematically. To discriminate the stationary and moving objects, the difference between the measured velocity by the slope and the velocity of the ego-vehicle was proposed as a feature. The statistical characteristics of stationary and moving objects according to the proposed feature were modeled using Gaussian model. To investigate the performance of the proposed method, the similarity between modeling of stationary and moving objects was quantified. Additionally, the receiver operating characteristics (ROC) curve and the correlation coefficient between the proposed feature and the ground-truth feature map was applied to verify the performance.
Robust PID Control Design in CPS-based Batch Distillation Column

Interconnected system between computation and physical process (Cyber-Physical Systems) has been widely used in industrial processes. In CPS-based industrial process, sensors, controllers, and actuators are connected into a communication network. The communication network may introduce delay time uncertainties due to shared resources and load traffic in the network. Furthermore, the nonlinear time-varying characteristic of batch distillation column may causes another uncertainties to take into account in control system design. Parameter model and delay process uncertainty is introduced due to linearized system approximation that unmodeled high-frequency dynamics. The dynamic uncertainty on both I/O channel are also introduced to the system uncertainty. In this paper, robust PI and PID controller using AMIGO method with appropriate weighting function is designed to guarantee robust stability spesification of batch distillation column. The impact of system uncertainties to closed-loop system performances such as peak overshoot and integral error is investigated. MATLAB/Simulink simulation is used to validate the methods before its implementation in CPS-based batch distillation column. Based on simulation, the proposed robust PI/PID controller can guarantee robust stability of system compared to conventional PID controller. Furthermore, the robust PI/PID controller can improve closed-loop system performances compared to conventional PID.