Miracle-dz/UniFAR-FTUs huggingface dataset:
Static Anti-windup Control for Unstable Linear Systems with the Actuator Saturation

We use the method of state feedback $H_{\infty}$ regulation to construct a controller for unstable linear systems, so that a common closed-loop system (CLS) meets the requirements of performance and robustness for the asymptotical stability. However, it affects the control effect and even causes instability of the CLS, since the amplitude of the system's control input is saturated due to practical constraint. Therefore, an anti-windup (AW) strategy is proposed to design a compensator without or with exogenous input, making the CLS asymptotically stabe or stabe by means of L2 gain, and maintains good dynamic characteristics. Furthermore, an intuitive evaluation index of AW is given. Finally, the effectiveness of the AW controller design is verified via a case study.
Calibration Method of Mecanum Wheeled Mobile Robot Odometer

This paper described a novel method for intrinsic calibration to reduce positioning errors dynamically. The odometry errors generated by movement of mobile robot had bad effects on the function of robot, such as the path planning and SLAM. We constructed the kinematic and dynamic of mecanum wheels. For static calibration, corrective indices were designed to compensate for the longitudinal, lateral and angular errors of mobile robot. For dynamic calibration, the dynamic parameters calculated by the least squared method was used to estimate the maximum acceleration index. The effectiveness of this method was proved by experiments of square and curved trajectories on mobile robot. Comparing to others, the Results showed that our method could improve the position errors by at least 80%. This method could be considered as a higher priority calibration method for odometer errors of mecanum wheel mobile robot.
Slot-coupled perforated dielectric resonator ESPAR for X-band applications

In this paper, an Electronically Steerable Passive Array Radiators (ESPAR) of perforated dielectric resonator antennas (PDRAs) that excited in a higher-order mode is presented. The perforation does not change the size and resonance mode of the dielectric resonator but reduces the relative dielectric constant of the dielectric resonator and increases the impedance bandwidth. The PDRA-ESPAR antenna mentioned enables continuous beam scanning from −32 degrees to 32 degrees at 11 GHz and maintains impedance matching during beam scanning. In addition, the PDRA-ESPAR mentioned has a bandwidth of approximately 2 GHz over the entire scan range with a relative bandwidth of approximately 18.1%.
Ka-Band Single Layer Wideband Microstrip Patch Antenna

In this paper, a Ka-band wideband single layer microstrip patch antenna with a 180° phase shifter is proposed. Compared with the conventional patch antenna, this patch antenna has a few advantageous features such as simple structure, wider bandwidth and low cost. The wideband property can be obtained by combining the wideband patch antenna and the wideband 180° phase shifter. Differential feeding is adopted in order to increase the symmetry of the radiation patterns. The geometry structure, working principle, design method and simulated results are described in detail. The designed antenna has an impedance bandwidth of 18.2%, and its peak gain can reach about 7.43 dB.
Speed Control For Indirect Vector Control Of Induction Motor Drives At Low Speeds

Use of induction motor (IM) at it's highest efficiency is a big demanding task because of their complex mathematical model and non linear features during saturation. These elements make the control of IM strenuous and call for the use of high performance control algorithm such as field orientation control. This paper introduces a speed controller, particularly at minimum speeds by utilizing Fuzzy logic controller (FLC) for indirect field-oriented control (IFOC) of induction motor (IM) drives fed by a four-switch three-phase (FSTP) inverter. From suggested viewpoint, the IM drive is used with a FSTP inverter as an alternative for the conventional six-switch three-phase (SSTP) inverter. Utter IFOC programme includes the FLC of IM drives with the suggested FSTP inverter is raised in MATLAB/Simulink. By considering the good dynamic performance, cost minimization and low total harmonic distortion (THD), FSTP inverters are comparable with SSTP inverters. The dynamic performance and robustness of proposed FLC with the FSTP inverter fed IM drive is verified with PI controller. It is established that the suggested FLC is further powerful than the PI controller under speed tracking and load disturbances
Research on non-intrusive load event detection algorithm

Event detection is an important step of non-intrusive load disaggregation. The accuracy of event detection directly affects the correct rate of load classification identification. Aiming at the shortcomings of existing event detection and feature extraction algorithms, using the steady-state active power as the feature, three improved event detection algorithms are proposed: improved CUSUM bilateral accumulation algorithm, improved sliding chi-square-GOF algorithm, and improved sliding Cepstrum analysis. The algorithm compares and analyzes the event detection accuracy of the three event detection methods. The experimental results show that the accuracy is better than most of the literature results, and the improved sliding chi-square-GOF algorithm can effectively detect large reference power events, avoiding missed detection of this situation.
Waveguide Switchable Bandstop and Bandpass Filters Using RF MEMS Switches

Waveguide switchable bandstop and bandpass filter designs are demonstrated in this paper. The designs are based on the design of rectangular waveguide that has switching planar bandstop resonators inserted on the sidewall. These switchable bandstop resonators consist of two identical metal patches separated by switching elements. The switching elements can be of RF MEMS or semiconductor transistor type. These resonators allow electrically switching of the waveguide while providing close to ideal on-state insertion loss and narrow stop band during off-state. These switches permit the switchable bandstop and bandpass filters design. The concept is demonstrated for a switchable five-pole bandstop filter and a switchable three-pole bandpass filter, both at the centre frequency of 14.25 GHz and for 500 MHz bandwidth.
Full Reference Image Quality Assessment from IQA Datasets: A Review

In the past few dacades, images are widely used as a medium of description and exchanging of information. There are number of challenges, that need to be overcome in terms of degradation sensitivity and modeling is human visual system (HVS). Thus performing accurate image quality assessment (IQA), is one of the main activities. There has been an exponential growth in research activities related to IQA. A large number of techniques have been proposed in this area, primarily related to subjective IQA (use human observerations for IQA) and objective IQA (use mathematical metrics for IQA), have been proposed. Earlier techniques were mainly depended upon extracting features and using these features to perceive the quality of images. Latest techniques now are more depended upon extracting texture information, image structure or statistical features. In this paper, a detailed survey of various quality assessment mechanisms is explored.
Online Management of Hybrid DRAM-NVMM Memory for HPC

Non-volatile main memories (NVMMs) offer a comparable performance to DRAM, while requiring lower static power consumption and enabling higher densities. NVMM therefore can provide opportunities for improving both energy efficiency and costs of main memory. Previous hybrid main memory management approaches for HPC either do not consider the unique characteristics of NVMMs, depend on high profiling costs, or need source code modifications. In this paper, we investigate HPC applications' behaviors in the presence of NVMM as part of the main memory. By performing a comprehensive study of HPC applications and based on several key observations, we propose an online hybrid memory architecture for HPC. It only requires low-overhead sampling of memory accesses for its page placement decisions. The experimental results obtained through running a wide range of HPC applications show that the proposed architecture can service up to 88% of accesses from DRAM if 90% of the main memory is built from NVMM. The NVMM lifetime can also be extended by up to 90% compared to all-NVMM.
Statistical Qos Guarantees for Licensed-Unlicensed Spectrum Interoperable D2D Communication

In this paper, we propose network-assisted device-to-device (D2D) communication in licensed and unlicensed spectrum interoperable networks, to improve D2D users’ throughput while alleviating the spectrum scarcity issue of cellular networks. The idea of licensed and unlicensed spectrum interoperability is based on the findings of the IEEE 1932.1 working group. Conventionally, D2D users were only able to communicate by using either cellular or non-cellular networks and no interoperability mechanism was available. The proposed scheme brings in many benefits including but not limited to higher D2D users’ throughput, alleviation in spectrum scarcity issue of cellular networks, and better network management. However, ensuring quality-of-service (QoS) in this dynamic environment is a challenging task. To this end, we analyze the QoS using a well-known analytical tool “Effective Capacity (EC)” for eNodeB-assisted as well as WiFi-assisted D2D communication. Moreover, we also see the impact of neighboring cells’ load and full-duplex transceiver at eNodeB and WiFi access point on the EC of D2D users. Simulation results show that EC increases with a decrease in neighboring cell’s load and decreases when more stringent QoS constraints are imposed. Results also show that the maximum sustainable source rate at the transmitter’s queue increases with an increase in maximum allowed packet delay but converges to a maximum value soon after that.
Hybrid-Residual-Based Odometry for Camera-LiDAR Systems

In this paper, we present a hybrid-residual-based odometry approach using observations from a monocular camera and sparse depth information from LiDAR. Our approach takes full advantage of different types of complementary information from both, the image and the laser scan, to build an accurate odometry. The proposed approach utilizes both reprojected and photometric features, where the reprojected residuals and the photometric residuals are jointly minimized. To enhance the accuracy and robustness, the occluded points are explicitly filtered during the odometry process. Experiments are conducted both on real world data and open source datasets to show accuracy and robustness for odometry algorithm. The results suggest that our odometry approach can achieve competitive localization accuracy.
Double Compressed AMR Audio Detection Using Long-Term Features and Deep Neural Networks

Detecting double compressed audio files is an important problem for audio forensics applications such as audio forgery detection and determining the authenticity of an audio file appearing as an evidence. In this paper, we focus on detecting double compressed adaptive multi-rate (AMR) audio using deep neural network (DNN) classifier and long-term average spectrum (LTAS) and long-term average cepstrum (LTAC) features. Experiments conducted on TIMIT database show that compression rate has a significant impact on the performance. LTAS and LTAC features yield similar performance with slight differences. Removing unvoiced audio frames is found to reduce the detection accuracy and multi-condition training does not bring any performance improvement.
Coordinated Intersection Signal Design for Mixed Traffic Flow of Human-Driven and Connected and Autonomous Vehicles

This manuscript investigated coordinated intersection signal design problem for mixed traffic flow of Human-Driven Vehicles (HDVs) and Connected and Autonomous Vehicles (CAVs). Two main macroscopic impact of the mixed flow on signal setting are considered: saturation flow rate and the platoon dispersion. In order to capture the traffic flow operational characteristics on coordinated intersections, three locations, namely entrance location where the loop detector was located at, and upstream intersection and downstream intersection were defined. Two types of vehicle cumulative curves, namely cumulative arrival profile and cumulative departure profile were constructed. The mixed-flow traffic dynamics were analyzed, and the arrival-departure curves relationship was derived using a combination of Newell car-following and Akçelik acceleration model. A mixed-flow platoon dispersion model was proposed to describe the vehicle’s progression between two locations. Due to the nonlinear nature of the problem, a particle swarm optimization (PSO) method was employed to obtain the optimal signal parameters, including the cycle length, green duration, and optimal offset. The algorithm was implemented and validated in a case study involving two intersections, with the demand formulated and simulated by the Markov chain. The results showed that the proposed model could effectively decrease delays when compared with current signal control methods.
Terahertz Spectrum Recognition of Pathogens Based on PCA-Siamese Neural Network

In the terahertz timedomain spectroscopy technique , 16 c ommon pathogens were experimentally studied and their characteri stics absorption spectra in the frequency range of 0.1 to 2.2THz wer e obtained . The terahertz absorption spectra of 16 common pathog ens were trained and identified by Siamese neural network method . First , the terahertz absorption spectra of the 16 pathogens were re duced by PCA to construct training data . Then , the constructed Si amese neural network model was trained by back propagation . Fin ally , the pathogen measured at different times was used as the targ et spectrum to evaluate the model , after comparing with the trainin g data , the matching absorption spectrum was obtained , and the re cognition rate reached 97.34% . The recognition results fully indica te that the identification of different kinds of pathogens can be reco gnized by Siamese neural network , which provides an effective met hod of the detection and identification of pathogens by terahertz spe ctroscopy .
A Study of Energy Optimization for MANET

The routing protocol on which the MANET works, or the technic of MANET has a basic standard which can control many nodes on it’s network that makes a proper way to route a packet between all the nodes on it’s network. Network stability, Security, Performance, Efficiency, and Energy efficiency are some provocations to mobile adhoc networks and here we are discussing about the energy optimization of MANET. As there is no fix structure for MANET and it is proper self-configuring network with a number of nodes. As the mobiles are useless without good batteries, so the energy optimization is very big issue of concern in MANET because it has the limited source of energy. Enormous traffics demands for the omnipresent access and developing multimedia applications crucially increases the energy consumption of battery-powered mobile devices.
Relationship Between Management Policies and Profitability for Second Section-Listed Manufacturing Companies of the Tokyo Stock Exchange (2016 Results)

In this paper, we try to analyze the relationship between companies' management policies and profitability. Management policies are very important guidelines for companies, because they include matters regarded as important to the business, its founding spirit, company motto, and so on. Therefore, we consider that management policies affect profitability. Data on management policies and profitability (measured by return on assets (ROA)) were gathered from annual securities reports because their descriptions are accurate and the reports are easy to obtain. As management policies were written in natural language and the data to be analyzed becomes complex, a neural computational method known as "potential learning" which can interpret internal representations, was used. As a result, we found that the generalization performance of the model was 0.5938 (accuracy) and management policies included the words related to "Place - institution" and "Number", which may affect ROA.
Comparison of Maximum Power Point Tracking techniques used for solar photovoltaic system

The renewable sources of energy have become a good alternative for conventional sources of energy resources. Hence, the utilization of renewable energy resources like solar, wind etc. has increased the need of research for improving the efficiency of such renewable energy resources so that the maximum energy can be extracted from such renewable sources. This paper presents a comparison between the two Maximum Power Point Tracking (MPPT) algorithms used for the solar renewable system, namely Perturb and Observe (P&O) method and Incremental Conductance (IC) method. Limitation of P&O method and advantages of IC method is discussed in this paper. This comparison study will be helpful for future solar photovoltaic (P-V) applications.
A Method to Estimate Request Sentences using LSTM with Self-Attention Mechanism

Recently, many opinions are accumulated on the review sites. These reviews are very useful for the service providers and users. Since it requires much cost to extract the useful information manually, there is a need for processing the information automatically. Sentiment analysis is a representative example of the processes. In the sentiment analysis, opinions are classified into positive opinions and negative opinions. The general sentiment analysis have focused on sentiment sentences (positive / negative), however, have not focused on request sentences such as "Please set an electric pot in the room". In this research, we aimed to estimate request sentences using deep learning, especially Recurrent Neural, Network (RNN) with multiple Self-Attention mechanisms. We proposed a request estimation method using LSTM with four Self-Attention mechanisms to represent the sentences from multiple perspectives. As the result, we confirmed the effectiveness of the proposed method.
Optimization on Unified Theory of Acceptance and Use of Technology for Driverless Car Test Behavior

To demonstrate that the integrated technology acceptance model based on the keras multilayer perceptrons (MLP) has good optimization effects, and predicts the ability of the public to test the behavior of unmanned vehicles more accurately than traditional statistical methods, The training and testing of MLP and SPSS linear regression were compared under 96 valid questionnaires. The results show that when MLP modeling is completed, its MSE=1.191 is significantly better than SPSS MSE=1.316, which shows that the model has good prediction effect after optimization training through deep learning.
A Broadband and Low Sidelobe Monopulse Slot Antenna Array by Using Cavities and Multilayer Waveguide Feed-Networks

In this paper, a monopulse slot antenna array is designed to exhibit the performances of broad bandwidth and low side lobe. The antenna structure is consisted by corporate adjustment cavities and multilayer amplitude tapering feed-networks. Each adjustment cavity below the four radiation slots, which operates high radiation efficiency and great reflection suppression in a broad bandwidth. A multilayer amplitude-tapering feed-network with taylor synthesis is proposed for realize low side lobe characteristic. With detailed analysis, the prototype monopulse slot array antenna is presented with relative operation bandwidth is 15.6%, as well as the VSWR is less than 2.0. At center frequency, the gain of the sum beam is 32.4dB with the first side lobe is −23dB in E plane and −22dB in H plane respectively, as well as cross polarization is less than −40dB. Also, the depth of the difference beams in E-plane and H-plane are lower than −60dB and −50dB, respectively.
A Multi-Sensor Fusion System for Improving Indoor Mobility of the Visually Impaired

Independent movement in an unknown indoor environment is a challenging task for the visually impaired. By considering the connectivity of the corridor (room doors and stairs are all connected by the corridor), we propose an assistive navigation system to help visually impaired users navigate in corridor environments in this paper. Based on semantic simultaneous localization and mapping (SLAM), the corridor area is determined and mapped to a semantic map. Semantic path planning is then performed according to the lowest energy cost principle by taking the safety into consideration. The YOLO neural network is employed to detect and identify common indoor landmarks such as Toilet, EXIT, Staircase et at. , and the system is able to give voice feedback about objects along its line of sight during the movement. This interaction helps to enhance perception about objects and places to improve travel decisions. A TurtleBot2 robot with a laptop, a RPLIDAR A2, and a Microsoft Kinect V1 are utilized to validate the localization, mapping and navigation module, while the perception module uses a ZED stereo camera to capture the objects and landmarks along its line of sight. The software modules of this system are implemented in Robot Operating System (ROS) and tested in our lab building.
Battery Thermal Characteristics Estimation Using Finite Element Method

In this study, an investigation of thermal characteristics was carried out at two stages namely, the experimental stage and the simulation and modeling stage. In the experimental stage, the battery consists of 1 cell with a capacity of 14Ah. At the experimental stage, the battery under investigation works at the discharge currents C1, C2, and C4, with natural convection studies in insulation and non-insulation systems. The assumptions used in this study are the battery used have experienced more than 10 cycles, the heat radiation from the battery is ignored, the parameters and thermal constants are considered constant. The ambient temperature range for operation is at 24°C – 28,5°C. Experimental results show that the battery system under insulation conditions has more stable thermal characteristics compared to non-insulation systems. As well as the results of the simulation stage 1 battery cell under conditions of insulation and non-insulation. In addition, estiomation were also made for the 10 battery insulation system. The temperature rise characteristic shows an exponential graph on all simulations performed. By evaluating the measurement values in the experimental and simulation stages, the results of the non-insulation conditions show an error for the C1 discharging current of 1,77%, C2 of 1,97%, and C4 of 0,38%. The results of insulation conditions show an error for the C1 emptying current of 1,10%, C2 of 0,53%, and C4 of 0,05%.
Research on Deep Learning Model Construction Based on Virtual Reality Technology

Deep learning is a process in which the learner studies new knowledge critically based on the existing knowledge and integrate existing knowledge with new knowledge to achieve deep understanding. Deep learning reflects the concept of active learning, collaborative learning and inquiry learning. As a new simulation technology, VR technology has such characteristics as immersion, interactivity and imagination, which provides learners experience, emotion and situational support for deep learning. Therefore, learners immerse themselves in the virtual reality space created by VR technology and explore new knowledge independently. Based on the support of VR technology for deep learning, this paper explored the elements of VR - based deep learning model, basic model construction method and design of deep learning route, which would offer model reference for institutions and personnel engaged in the field of deep learning.