Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-Sway Control of Underactuated Cranes Using Linear Quadratic Regulator and Extended State Observer Techniques

The undesirable sway effects of the spreader of the underactuated cranes bring great challenges for control engineers. With traditional control methods, the spreader may spend much time to come to a standstill, which will significantly reduce the efficiency of port terminal. Linear quadratic regulator (LQR), which can carry out optimization by taking both position and angle into consideration, provides an adequate way for anti-sway control of the cranes. However, the performance of the LQR controller is generally poor in the presence of external disturbances and model uncertainties. To this end, an extended state observer (ESO) is further designed to compensate the undesirable effects of disturbances and uncertainties. A composite disturbance rejection controller by combining LQR and ESO is then developed for anti-sway control of the crane system subject to various uncertainties. Simulation results are finally provided to demonstrate the feasibility and efficiency of proposed controller.
PyFER: A Facial Expression Recognizer Based on Convolutional Neural Networks

Facial expression recognition (FER), one of the most trending research areas of the Human-Machine Interaction, is the task of detecting emotions by analyzing facial expressions and this analysis plays a critical role as it conveys the clearest information regarding the emotions of people. Despite the fact that the traditional machine learning algorithms produce high accuracies for similar tasks, they lack to detect emotions of faces, which are captured in a spontaneous manner (a.k.a. “in the wild”) or in different poses or environmental conditions. In this article, a novel convolutional neural network architecture, namely, PyFER, is proposed to address the FER problem, of which the efficiency was revealed thanks to the experiments conducted on a widely-used benchmark dataset. According to the experimental results, the accuracy of PyFER was calculated to be as high as 96.3% on a de-facto standard dataset, namely, CK +, and all facial expressions, except for $happiness$ , were correctly detected by PyFER, which is encouraging for future studies. 16.67% of the images that actually represented the facial expression happiness were misdetected as the facial expression fear. The experimental results confirmed that the proposed neural network architecture is fast enough to be integrated into real-time FER applications as it was able to complete the analysis of a given photo for an average of 12.8 milliseconds, which is in the tolerable limit to latency for real-time applications.
InBlock4: Blockchain-based Route Origin Validation

In this paper, we present InBlock4, a blockchain-based alternative to RPKI for the provision of Route Origin validation for BGP. InBlock4 embeds an alternative trust model to protect entities obtaining a resource allocations from errors and abuses from other entities in the allocation hierarchy. InBlock4 is compatible with BGPsec. Moreover, InBlock4 can be bootstrapped using the information in the RPKI and it can coexists with RPKI-based route origin validation. In the paper, we also present a working implementation of InBlock4 for Ethereum and we quantify its performance.
Experimental Study on Comparison Sound Quality Measurement of Thai fiddle Resonator

This work is a scientific experiment report. study how to measure and compare the sound quality of musical instruments. Especially the The Sx dwng or Thai fiddle This is an interesting process for development in term of multiciliary study of art and sciene and also expand Thai’s cultural capital in order to present a sustainable couture to be view on the world stage. This experiment hypothesized the finding of relations between the surface tension values of the python skin that tight-fitting on the resonator and the fundamental frequency values. This will be qualitative indicator in the production of The Sx dwng or Thai fiddle, that is economic and easy to use for the existing musical instrument maker.
FastScale: Fast Scaling Out of Network Functions

Scaling out network functions (NFs) offers great flexibility and elasticity to the management of service function chains (SFCs). However, existing solutions overlook the fact that scaling out an NF may influence downstream NFs. Their plans need several steps to scale out overloaded NFs, resulting in unpredictable SFC performance and slow convergence. In this paper, we propose FastScale, a novel framework that makes chain-wide scale-out plan within a single step. Once an overloaded NF is detected, it models the problem of scaling out NFs in consideration of both overloaded NFs and downstream NFs. It determines which NFs to scale and how many NF instances should be used by each NF within a single step so as to avoid unnecessary reconfiguration and reduce convergence time. After making scale-out plan, FastScale automatically updates the thresholds used to detect overloaded NFs to reduce user burdens. We have built a prototype of FastScale. Our evaluation demonstrates that FastScale reduces the convergence time of SFCs by up to 68% in comparison with existing solutions.
Participation of a DFIG Based Wind Energy Conversion System in Frequency Control

In this article, we study the participation of a DFIG-based wind turbine in frequency control in the event of a fault affecting the grid frequency. The method presented in this paper is based on the combination of primary control and the inertial response: The first controller is to produce less power than its maximum available. Therefore, we will create an active power reserve to exploit to support production in the grid frequency drop event. The second makes it possible to exploit part of the kinetic energy stored in the turbine at the start of the occurrence of the frequency fault to support the production.Therefore, this work presents the integration of these control structures as well as the study of their performance in the case of a wind turbine based on DFIG and controlled by the Backstepping technique. The results are obtained by a simulation in the MATLAB / SIMULINK environment.
Adaptive Video Streaming Using Dynamic NDN Multicast in WLAN

Video streaming takes a dominant role for rapidly-increasing mobile Internet traffic in Wireless Local Area Networks (WLAN). As consumers often request the same contents, such as popular videos and live videos, using multicast transmissions under Named Data Networking (NDN) architecture can significantly reduce the network cost. However, to guarantee the transmission reliability for wireless multicast, the existing multicast schemes generally adopt the lowest data rate (e.g., 1 Mbps for IEEE 802.11b, and 6 Mbps for IEEE 802.11a) to transmit the multicast data, inevitably reducing the best quality of experience (QoE) for those high-speed consumers. In this paper, under the Scalable Video Coding (SVC) mechanism, we propose an adaptive video streaming scheme by using the NDN multicast (named NM-ABR), where NDN multicast groups are dynamically formed according to the network conditions and SVC characteristics. In NM-ABR, after detecting a multicast group, we first devise a multicast data rate selection algorithm in order to maximize the receiving throughput of consumers within the group. Then, we integrate the transmission data rate into the adaptive bit rate (ABR) algorithm to request for video segments with the most suitable bitrate. At last, for those low-speed consumers, we propose an SVC-based multiple-layer control approach to reduce the retransmission of video segments in enhancement layers. We implement the proposed NM-ABR in NS-3 via the ndnSIM module, and conduct extensive experiments to demonstrate its efficacy in terms of video bitrate, startup delay, and stalling time.
A New Drainage Basin Delineation For The Southern Patagonian Ice Field Based On Terrasar-X Velocities And Tandem-X Elevations

The drainage basins or catchments for the Patagonian Ice Field are part of glacier inventories like the Randolph Glacier Inventory (RGI) or the Global Land Ice Measurements from Space (GLIMS). These catchments are used in many glaciological studies for integrating remote sensing measurements over the area of a single glacier. An accurate basin boundary delineation is therefore important for applications like mass balance measurements for individual glaciers in Patagonia. Here we investigate existing catchment delineations of the Southern Patagonian Ice Field (SPI) with a modified watershed algorithm that is capable of including ice velocity measurements from SAR offset tracking during the delineation process. The classical watershed delineation is performed using only a DEM. We show that apart from the basins of Bernardo, Greve, Tempano and Occidental there is no dependence of the basin boundary on the measured ice velocity direction and that the glaciers of SPI flow in the direction of the steepest surface slope of modern high resolution DEMs like the TDM global DEM or SRTM. Additionally, a map of basin probabilities has been produced, which highlights several locations on the ice field where the delineation of the exact basin boundary is difficult.
Dynamic Trip Pricing Considering Car Rebalances for Station-based Carsharing Services

This study proposes a dynamic trip pricing (DTP) problem considering the distribution imbalances of cars in one-way station- based carsharing systems. In the DTP problem, the trip prices are functions of the balancing degrees of both origin and destination stations. The DTP problem aims to maximize the expected profit of the carsharing system in a given planning horizon. A non-linear programming model is built for the DTP problem and solved by a particle swarm optimization algorithm. The proposed methods are validated and evaluated based on a larger number of randomly generated instances. The results indicate that the DTP scheme results in profit increment and car distribution improvement, which helps enhance user satisfactions. The sensitivity analysis shows that the scheme is also helpful when the user price elasticities are varied. Furthermore, the DTP scheme is applicable to different types of price-demand relationship.
Intelligent Control for Unmanned Flight Vehicles via Deep Reinforcement Learning

The problem of intelligent control for unmanned flight vehicles is studied in this paper. The linear model of unmanned flight vehicle is obtained by Jacobian linearization method according to the nonlinear model. The process of controller design can be divided into two steps. Firstly, to ensure the stability and prescribed performance of the closed loop system, the robust controller is given in terms of linear matrices inequalities based on robust control theory. Secondly, the intelligent controller is proposed to improve the transient performance via deep reinforcement learning. The parameters of controller can be tuned automatically in a neighborhood of robust controller's parameters. In the end, the simulation results are given to illustrated the effectiveness and superiority of the proposed method.
Wideband Vivaldi Microstrip Antenna for Rectenna Application

In this study, a wideband Vivaldi microstrip antenna is designed for rectenna application, which can utilize the excessively redundant electromagnetic energy emitted from radio frequency transmitter(s). The redundant energy will be harvested for many applications such as battery, wireless power transfer device, etc. The antenna is fabricated on FR4 substrate with the size of 230.36 mm x 230 mm. The measured -10 dB return loss bandwidth of the antenna is 3.42 GHz (0.82 – 4.24 GHz). Simulation results show the antenna gain ranges from 6.0 to 8.66 dB throughout commonly used frequency of applications such as cellular phones, WLANs, satellite communication, radars, and much more.
Adaptive dynamic surface control of nonlinear systems with dead zone

In this paper, we present control designs for a class of single-input and single-output (SISO) strict-feedback nonlinear systems with unknown dead-zone input. To consider the dead zone dynamics, the nonlinear dead zone is first represented as a linear time-varying system with a bounded disturbance. Then, a backstepping control method is proposed for adaptive controller design. In addition to using dynamic surface control (DSC) to solve the problem of the "explosion of complexity", a compensating signal is added to eliminate the effect of the dynamic surface filter error. Moreover, the prediction error between the system state and the serial-parallel estimation model is taken into consideration. Meanwhile, neural networks (NN) are used to deal with the unknown functions in the controller. Finally, simulations illustrate the effectiveness of the proposed approach.
Distributed Optimal Dispatching of Multi-Entity Distribution Network With Demand Response and Edge Computing

With large-scale penetrations of distributed generation (DG) and flexible loads, there will be multiple entities in traditional distribution network, such as distribution network operator (DNO), DG owner, and prosumers. Aiming at the collaborative optimization problem of distribution network among multiple entities, a distributed optimal scheduling approach of distribution network considering demand response and edge computing is proposed in this paper. Firstly, the virtual region decomposition method is proposed to divide the original distribution network into multiple regions according to different entities, and the bi-level optimization framework based on edge computing is constructed. Secondly, the optimal models of DNO, DG owner, and prosumers are established respectively, and the distributed optimal scheduling approach of distribution network with collaboration of control center and edge nodes is proposed. Then, the KKT conditions are adopted to realize the transformation of optimal models of DG owner and prosumers. Finally, the proposed distributed optimization scheduling approach is verified based on the modified IEEE33-node system. The results show that the proposed distributed optimal scheduling method can achieve better collaborative optimization among different entities in distribution network compared with the centralized optimization method.
Optimal Allocation of Distributed Generation Against Low Voltage in Distribution Network Based on Particle Swarm Optimization Algorithm

Aiming at the problem of low voltage in rural distribution network, this paper proposes an optimal allocation method of distributed generation against low voltage. Firstly, we establish a sitting and sizing optimization model for distributed generation, which takes the minimum system voltage, the average system voltage deviation and the network loss into consideration. Then the optimization problem is solved by a heuristic algorithm. Finally, the proposed method is simulated and tested on the IEEE-33 bus system, and the results demonstrate that the method proposed in this paper can solve the low voltage problem for each critical node in the distribution network and improve the voltage quality. Hence, the feasibility and effectiveness of the proposed method are verified.
Possibilities of Recognizing Depression with Convolutional Networks Applied in Correlation Structure

In the present study, we investigated the possibility of automatic detection of depression. We used convolutional neural networks applied on a special correlation structure that was extracted from speech signal. The quality of life of an individual with depression is greatly influenced by the severity of the depression, which in extreme cases can lead to suicide. On this basis, it is crucial that the disease is recognized at an early stage and that appropriate treatment is provided. This article describes a method that can detect depression through the use of convolutional neural networks by purely spectral features based on speech signal processing. We present how the accuracy of detecting depression changes based on the use of different acoustic-phonetic features and the correlation structure. Using this method, we were able to distinguish between healthy and depressed individuals based on their speech patterns with an accuracy of 84%.
LMI-based event-triggered control of discrete-time piecewise affine systems with norm-bounded uncertainties

This paper is concerned with the problem of designing event-triggered controllers for discrete-time piecewise affine (PWA) systems with norm-bounded uncertainties. Event-triggered scheme transmits signals only when the eventtriggering condition is violated. Due to its advantage in saving communication resources, it is utilized to design the state feedback controllers for piecewise affine systems. A quadratic cost function is introduced to quantifying the control performance. An optimization problem for minimizing the upper bound of the quadratic cost function is formulated in the form of linear matrix inequalities (LMIs). By solving the optimization problems a unique state feedback controller can be obtained. The resulting closed-loop system stability is guaranteed while the number of transmitted signals is reduced. A numerical example is given to show the effectiveness of our methods.
Relative Localization for UAVs Based on April-Tags

The formation flight of small UAV is one of the main directions of its development. However, relative positioning between UAVs is a key technology to ensure effective cooperative formation flight of UAV. At present, small UAVs mainly rely on GPS module for positioning, which is not high enough in positioning precision and is unstable in indoor environments. Therefore, their own sensors become particularly important in determining the relative position relationship among the formation members. To solve the above problems, this paper designed a UAV flight relative positioning algorithm based on cooperative marker under the robot operating system (ROS), and applied it to the actual control of quad-rotor UAV AR.Drone2. 0. The results showed that the UAV can follow the marker stably when the marker moves stably and slowly (1m/s).
Analysis of Affecting Technology Adoption Factors for Smart Home Services in Jabodetabek, Indonesia

Smart home services have been developed by several companies in Indonesia. However, the adoption of the use of smart home services in Indonesia is still relatively low. This study aims to analyze the adoption factors that influence people in the use of smart home services. The results of the questionnaire with respondents living in Jakarta, Bogor, Depok, Tangerang, and Bekasi (Jabodetabek) is analyzed by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) method. There are 11 hypotheses to be tested testing with Structural Equation Modeling (SEM). The analysis shows that there are seven accepted hypotheses. The factors that influence the Jabodetabek people in using smart home services are performance expectancy, effort expectancy, hedonic motivation, risk, trust, the attractiveness of alternatives, and behavioral intention.
The Evaluation Of The Influence Of Land Surface Temperature In Energy Balance Estimated By S-Sebi Model

Evapotranspiration (ET) is one of the least understood components of the hydrological cycle. Its application is varied, from agricultural, ecological and hydrological monitoring, to control of the evolution of climate change. The goal of this work was to analyze the influence that uncertainties in the estimate of Land Surface Temperature (LST) can cause on ET estimates by S-SEBI model in Pampa Biome area. The results indicate that the daily evapotranspiration is higher when the pixel LST is lower, which also shows the influence of land use on the variability of ET. They also demonstrated the importance of LST's accuracy in the selection of the driest and wettest pixels in applying S-SEBI model, because when there are uncertainties in estimates of LST, the errors in the estimates of the energy components multiply. The Pampa Biome native grass crops have lower Latent Heat Flux (LET) than other land uses, with higher values of LET during the spring-summer period when compared to autumn-winter.
A Pose Measurement Method of a Non-Cooperative Spacecraft Based on point cloud feature

This paper investigates the Attitude measurement for the Non-Cooperative Spacecraft problem. Considering there are no or few prior knowledge for the target, an autonomous measurement method for relative position and attitude of space non-cooperative spacecraft is proposed to solve the problem of relative position and attitude measurement of non-cooperative spacecraft in orbit capture in this paper. Specifically, a normal feature extraction method based on satellite plane is adopted, which can provide a good initial condition for solving the attitude matrix of the final target. The relative position and attitude of the satellite can be measured even if the rotation angle of the target is very large. Experiments show that the pose and motion method for measurement proposed in this paper has high accuracy and good stability.
Deep Learning Technology for Drunks Detection with Infrared Camera

This project manipulates for the purpose to solve the problem of alcohol measurement delays and reduces the issue of contamination that arises from the breath analyzer test. This project focuses on designing a novel infrared (IR) camera-based alcohol detection system with deep learning technology. This system consists of 2 parts. The first part is an infrared camera (FLIR) used for collecting both IR and normal images then the next part is an image processing system for alcohol detection based on deep learning technology operating on an iPhone operating system (iOS) mobile phone. Our handheld IR based detection system achieves an accuracy of 85.10% (135 population) accuracy with 4 levels of classification (sober, 1 glass, 2 glasses, or 3 glasses) and 74.07% with binary identification (Sober or Drunk). Each glass contains 200 ml of beer (5% vol).
Design and implementation of an unmanned boat visual target tracking system

This paper describes how to detect the water surface targets, obtain effective tracking information, and control the unmanned boat based on the information while the unmanned boat equipped with Raspberry Pi 3B. The structure of the image processing part and the control part of the unmanned boat is mainly designed. The image processing part adopts the Raspberry Pi CSI interface camera to collect the data, uses the CamShift (CS) algorithm to perform target tracking, obtains the angular deviation and distance between the target and the unmanned boat through conversion, and sends the information to the DSP processor. In the unmanned boat control part, the unmanned boat motion model is identified through experiments, and the PID controller is designed. Finally, the fixed target and the moving target are tracked through the pool experiment, and the rationality of the system is verified.
Optimal Scheduling of Multi-energy Hub System Based on Differential QPSO Algorithm

In order to solve the problems of strong coupling, complex constraints and high dimensions in optimal scheduling of multi-energy hub systems, differential evolution quantum particle swarm optimization (DEQPSO) algorithm is proposed. The algorithm combines the mutation, crossover and selection operations in the differential evolution algorithm with the particle position update formula in the quantum particle swarm optimization algorithm, which increases the diversity of the population in the quantum particle swarm optimization algorithm, solves the problem that the particles are easy to fall into the local optimum in the middle and later stages of the search, and improves the global search ability of the algorithm. The standard test function is used to test the algorithm. The test results show that the algorithm has good convergence and global search ability. In this paper, the algorithm is applied to the optimal scheduling of multi-energy hub system, and the calculation results show the effectiveness and applicability of the algorithm.