Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Recurrent Neural Networks Based Fault Diagnosis Model for Multi-mode Process

The process operation of industry units generally has multiple modes, and the previous methods of fault diagnosis often need to first divide the data and then determine the specific mode situation. A multi-mode fault diagnosis model is proposed by composing of multiple recurrent neural networks (MRNN) in parallel, in which only one model is built for the complex modes without identifying mode information in advance. Parallel RNN modules are combined appropriately into the model to judge whether the current state of process operation is faulty from different perspectives, so as to achieve the similar effect of modeling a class of working modes for each module. Several fault prediction and training strategies are also studied for this model and the suitable strategy is proposed. The simulation data of the multi-mode process are generated through the Tennessee Eastman process to verify the effect of the proposed model.
A Review on Electromagnetic and Chemical Energy Storage System

Power production is the support that helps for the betterment of the industries and functioning of the community around the world. Generally, the power production is one of the bases of power systems, the other being transmission and its consumption. The paper analyses electromagnetic and chemical energy storage systems and its applications for consideration of likely problems in the future for the development in power systems. In addition to this, the limitations for application and challenges of energy storage system are extensively analyzed so to have a better picture about the technology and there may be an effective solution to that in near future and we can have a reliability of power supply and sustainable development of the resources also goes hand in hand.
Experiments and Observations of 5G NSA Reliability and Latency Performance in Metro Train Environment

Recently, commercial Fifth-Generation (5G) networks have been widely deployed, enabling data traffic to have higher data rates. 5G will be useful in vertical markets, such as railway environments. In such a case, reliability and latency are key performance metrics. This study aims to understand the performance factors related to packet loss and excessive latency in the rail environment. We analyzed measurement data under the metro rail environment and verified that both location and handovers are associated with unsatisfactory performance, such as packet losses and excessive latency. We also observed that some handover events are unnecessary. With these insights, we will better understand 5G metro rail communications and design a better network system in the future.
Study on Array Electrode System for Visualization Measurement in Air-Solid Two-Phase Flow

In power, chemical, pharmaceutical, metallurgical, and other industrial production, the visual information of gas-solid two-phase flow in closed pipelines is of great significance to the safety, economy, and efficiency of the production process. According to the basic principle of electrical capacitance tomography (ECT), we analyzed and designed a 12-electrode capacitance sensor, realized the data acquisition circuit with capacitance digital conversion (CDC) chip as the core and ARM chip as the control platform, and developed the upper computer software based on QT. We have tested the visualization system, and the results show that the system has significant stability, and the measurement results are clear and accurate.
Global-Local Similarity Function for Automatic Playlist Generation

This paper proposes the Global-Local Similarity Function (GLSF) to exploit the multi-scale cues in track sequences for automatic playlist generation (APG). Unlike previous neighborhood-based methods only looking on local similarities for a given playlist, GLTS is constructed by first modeling the fine-grained audio features of each track, then capturing the long-term relations among consecutive tracks. Specifically, the fine-grained audio features are captured beat-by-beat to represent the rhythmic variation of music. The long- term relations are modeled by a designed track distance constraint (TD-constraint) to alleviate the incoherences and un- smooth transition in track sequences. The fine-grained audio features and TD-constraint are aggregated as the final GLSF by a simple distance function. Objective and subjective evaluations show that GLSF-based APG achieves better smooth transition and ensure the long-term content consistency among the tracks. Furthermore, GLSF yields a better understanding of the sequential relationship between tracks and propose a promising way to improve APG algorithms. 1
An Empirical Analysis of Naïve Bayes, SVM, Logistic Regression and Random Forest to Spot False Information in Real-World Networks

Fake news has become ingrained in our daily lives as a result of its rapid dissemination across various social media platforms. Because of the massive propagation of false news on social and news media, identifying false information has emerged as an essential study topic. To analyse the authenticity of the information to find out whether it is original or false, current false news detection methods mostly depend on idiomatic expression and different models inspired by artificial intelligence. Many traditional methodologies have yet to be seen to identify false news, but machine learning approaches attained a lot of admiration in the current times. This inspired us to try a comparative analysis among ML classifiers to address the challenge of detecting fake news. In this paper, an empirical analysis of different machine learning classifiers is provided to spot false news using real-world networks. In our experiment, the support vector machine attained the highest fidelity in real-world networks.
Majority Logic Based In-Memory Comparator

To conquer the von Neumann bottleneck, it is necessary to shift computation to the place where data resides. In this work, we present a methodology to move the comparator circuit to the memory array. A binary comparator is implemented in a transistor-accessed ReRAM (Resistive Random Access Mem-ory) array using majority logic. The majority primitive is a better logic primitive than NAND/NOR/IMPLY primitives for arithmetic intensive operations and also can be realized in memory array efficiently as a READ operation. Using Majority gates and vast gate-level parallelism in the array, comparator is implemented optimally, latency and energy-wise. Using the methodology presented for 1-bit and 8-bit, any n-bit comparator can be realized in 6 + 4log2n cycles. Moreover, a general transistor-accessed memory array can be used to implement the proposed comparator without the need for any supreme modifications to the memory peripherals and is efficient as well in terms of energy.
Multi-User Position Estimation and Performance Trade-offs in IEEE 802.11az WLANs

Location-Based Services (LBSs) using wireless communication systems have witnessed a dramatic increase in both demand and applicability. In particular, there is a need for high-accuracy position estimation in dense indoor environments, while sustaining data communication services. In this paper, analytical expressions are formulated to characterize the trade-off between the position estimation accuracy and the latency/throughput performance in a multi-user IEEE 802.11az scenario. Furthermore, optimization frameworks are developed using which the proposed enhancements can tune the degree of trade-off, in terms of the number of preamble symbol repetitions. The impact of the desired position accuracy, Signal-to-Noise Ratio (SNR), bandwidth, and user-grouping strategies are evaluated as well.
Oil derrick damage identification based on wavelet packet analysis and deep learning algorithm

Aiming at the disadvantages of traditional oil derrick damage identification, such as troublesome operation and cumbersome calculation process, combined with the advantages of wavelet packet analysis for non-stationary and nonlinear signal processing and the ability of bidirectional long short-term memory (BLSTM) network to model time series, a damage identification method of oil derrick based on wavelet packet analysis and bidirectional long short-term memory network is proposed. Firstly, use wavelet packet analysis to extract the energy features of the acceleration response signal of the oil derrick after damage; Secondly, combined with the BLSTM deep learning algorithm for supervised learning of the damage location; Finally, the feature information is mapped to the corresponding damage location through the fully connected layer and the softmax classification layer, and the recognition results are obtained. The method is applied to the damage identification of ZJ70 derrick model in the laboratory. The results show that the method can quickly and accurately identify the damage location, and has high damage classification accuracy and anti-interference ability.
Novel ARMF Integrated with Improved LSDA and Its Application in Fault Diagnosis

The industrial process is developing to be intelligent and complex, thus the process data presents high dimension, nonlinearity and highly coupled features. Facing these features, this paper proposes a signal pattern-matching fault diagnosis method based on the adaptive rank-order morphological filter (ARMF) integrated with improved locality sensitive discriminant analysis (LSDA) named ILSDA-ARMF. This proposed methodology first fully extracts the variable features related to the fault using the improved LSDA; then the data after dimensionality reduction (DR) is used for signal pattern matching by using ARMF to achieve fault classification. The main advantage of the improved LSDA is that the Mahalanobis distance considers the correlation between samples and their nearest neighbor points. Meanwhile, the Tennessee Eastman (TE) chemical process is experimented with to verify the performance of the proposed ILSDA-ARMF. The simulation results show that the method proposed in this paper achieves more satisfactory results compared with other related methods.
A Scalable System Model for Discrete Muscle-Like Actuators

A general system modeling and simulation technique is described for discrete muscle-like actuators. These actuators are composed of smaller, modular contractile units connected together, each of which may be controlled individually. Units are activated (recruited) in a manner similar to muscle cell recruitment, whereby the contractions of each individual smaller unit sum to produce a contraction of the entire actuator. Each unit has a lumped-parameter dynamic model, and a mathematical technique is described for automatically constructing a state-space realization of the entire actuator from the individual lumped-parameter models of the units, based on the manner in which they are connected. The method is demonstrated by simulating four different configurations of a particular type of muscle-like actuator driven by the contractions of individual miniature solenoids, demonstrating the flexibility of the approach, and how the system dynamics vary with the activation of the individual units.
Development and Analysis of In GaAs Nanowire Junctionless MOSFET with 10 nm gate length

The device performance of nanowire junctionless MOSFET (NW-JL-MOSFET) with InGaAs as core material in the sub-10 nm regime is examined by a device simulator, namely ATLAS. The focus is on gate-all-around nanowires with InGaAs core. These devices are further examined by adding gate-stack (high-k dielectric + oxide) method. It is observed that optimal selection of structure parameters of InGaAs NW-JL-MOSFET attains higher drain current and optimal performance. This proposed architecture also provides better switching speed of the structure. The gate dielectric material optimization of the structure is attained via broad device simulation. In this manuscript, a literature is carried for the short channel effects like subthreshold swing, Ion/Ioff ratio, Threshold voltage and drain induced barrier lowering (DIBL). The InGaAs device has a better threshold voltage and Ion/Ioff ratio, according to simulation data.
Homotopy properties of endpoint maps and a theorem of Serre in subriemannian geometry

We discuss homotopy properties of endpoint maps for affine control systems. We prove that these maps are Hurewicz fibrations with respect to some $W^{1,p}$ topology on the space of trajectories, for a certain $p>1$. We study critical points of geometric costs for these affine control systems, proving that if the base manifold is compact then the number of their critical points is infinite (we use Lusternik-Schnirelmann category combined with the Hurewicz property). In the special case where the control system is subriemannian this result can be read as the corresponding version of Serre's theorem, on the existence of infinitely many geodesics between two points on a compact riemannian manifold. In the subriemannian case we show that the Hurewicz property holds for all $p\geq1$ and the horizontal-loop space with the $W^{1,2}$ topology has the homotopy type of a CW-complex (as long as the endpoint map has at least one regular value); in particular the inclusion of the horizontal-loop space in the ordinary one is a homotopy equivalence.
Design of Low-power Quad-edge-triggered Flip-flop at Switch Level

This paper presents a novel approach to design quad-edge-triggered flip-flop (QETFF) based on ternary clock. At first, the previous QETFF consisting of 3-to-1 multiplexers is reviewed and analyzed; the property of ternary clock is pointed out. By using it, a novel multiplexer is proposed, following which a novel design of QETFF named after uQETFF is proposed, of which the circuit is simpler than the previous one, saving 8 transistors. Then, HSPICE simulations with TSMC 180 nm CMOS technology are carried out on the proposed uQETFFs, their ideal logic functionality being shown. At last, performance comparisons between the proposed uQETFFs and its equivalent flip-flops are made. From the comparisons, it can be seen that the proposed binary uQETFF consumes 13.6% less energy than the equivalent double-edge-triggered flip-flop (DETFF) based on binary clock; the proposed ternary uQETFF optimizes the overall circuit performance with a reduction of 43.5% in PDP (power-delay product) compared to the previous ternary QETFF. Therefore, the proposed uQETFFs are simpler and their characteristics are more desirable, which are more close to the requirements of the practical applications.
Nine Switch Multifunctional Converter Configuration for Integrating Dynamic Voltage Restorer and Solar Photovoltaic

This paper presents a new way to integrate Dynamic Voltage Restorer (DVR) and grid-connected Photovoltaic (PV) structure using Nine Switch Converter (NSC) to mitigate voltage sag and supply power to critical voltage sensitive load. NSC configuration provides two different three-phase outputs. One output of the NSC is arranged in a series connection near the point of common coupling (PCC), and the other is arranged in a shunt connection near the load end. The series connection helps to mitigate supply voltage sag and distortion problems while the shunt output provides PV power in the connected network. A PV-fed boost converter powers the DC side of the NSC. Here the Synchronous Reference Frame (SRF) theory generates the control signals for NSC, and to track the maximum power from the PV panel, an incremental conductance algorithm (INC) is used. At different operating conditions, the validity of the proposed system is verified using MATLAB/Simulink.
Surface and mechanical stress effects in AlGaN/GaN high electron mobility transistors

Gallium Nitride (GaN) belongs to a class of materials called wide band-gap semiconductors. In recent years, the versatile nature of this material has been exploited for a wide range of applications from solid state lighting to RF and microwave communication, as well as high power switching. The first part of this thesis discusses planar AlGaN/GaN transistors. GaN is a piezoelectric material, and changes in mechanical stress result in a change in the charge density which in turn affects the maximum current in AlGaN/GaN transistors. Finite element modelling techniques were applied to quantify the mechanical stress distribution in planar AlGaN/GaN RF transistors resulting from device fabrication, and operation in the onand off-state. Thereafter, two important surface and interface effects were studied in this thesis. In the first one, the impact of surface cleanings, surface treatments and plasma-based dry etch conditions on two different types of ohmic contact technologies was investigated. Contact resistance measurements were correlated with surface characterization results. The second was that of interface positive charges at the A12 03-GaN interface and the increase in electron density in the device resulting from them. In both these problems, a combination of device electrical measurements and material characterization techniques was used to establish direct correlations between device behavior and material properties. The second part of the thesis deals exclusively with nano-ribbon (NR) or fin-like AlGaN/GaN transistors. Fundamental transport properties of charge density and mobility in NR devices were studied in order to understand the difference in behavior of these devices from planar devices. The influence of passivation films on the charge density in these structures was investigated, using A1203 passivation as a specific example. Electron mobility degradation due to sidewall-scattering in NR devices was quantified using different mobility extraction methods based on device measurements. The thesis concludes with a potential application of NR AlGaN/GaN transistors for high linearity power amplification. A new kind of transistor with varying threshold voltages along the gate width is proposed to improve the DC and RF linearity of GaN-based devices. Thesis Supervisor: Toma's Palacios Title: Professor, Electrical Engineering and Computer Science
Space-time coding design for multiple source nodes full-duplex cooperative communication

Cooperative communication is a promising technology to improve the performance of resource-limited or cost-sensitive single antenna wireless equipment. Moreover, full duplex (FD) could theoretically double the spectral efficiency of a cooperative communication system comparing to the conventional half duplex (HD) communication. Although researchers have studied FD cooperative communication with two source nodes, and proposed an Alamouti-style space-time code, there is not much works on multiple source nodes scenarios yet. This article presents our results on the FD cooperative communication with multiple source nodes. We provide two orthogonal space-time block codes (OSTBC) for both three and four source nodes scenarios. The proposed codes skillfully share data in as less time slots as possible by taking advantages of the FD communication. Consequently, spectral efficiency of the FD cooperation is improved. Furthermore, the proposed codes provide full diversity in addition to a simple ML decoding. Finally, computer simulations verify the performance of the proposed OSTBC design.
Error-State Kalman Filter for Online Evaluation of Ankle Angle

This paper presents an Error-State Kalman Filter (ESKF) for state estimation in a 2-DOF robotic prosthetic ankle. The filter estimates the ankle angle in inversion-eversion (IE), external-internal (EI), and dorsiflexion-plantarflexion (DP), using measurements from two low-cost magnetic, angular rate, and gravity sensor modules (MARGs), also known as 9-axis Inertial Measurement Units (IMUs). To this end, we transformed raw MARG measurements into body frames and modeled the states and constraints of the 2-DOF robotic prosthesis in an Error State Kalman Filter (ESKF). Experimental tests showed the proposed ESKF provided better results than the Madgwick filter, a commonly used attitude estimator. The proposed filter is developed for ankle prostheses requiring direct angle measurement and can be expanded to an online evaluation of ankle angle in humans.
Research on Vibration Signal Denoising of Non-contact Optical Fiber Sensing System

The detection of cavitation-induced vibration and noise in centrifugal pumps has been widely investigated due to its significant impact on their steady and reliable operation. In this paper, a non-contact optical fiber sensing system based on wavelength division multiplexing technology is proposed and demonstrated. By the application of a wavelength division multiplexing scheme, two interference subsystems, which can work simultaneously at different wavelengths, are developed. One of the subsystems detects the signal of the noise-induced phase distortion and the other measures the vibration velocity of the pump. Real-time reduction of noise is realized utilizing processing the signals from both subsystems. Experimental results show that broadband disturbance and environmental noise can be suppressed. The working frequency can be accurately and stably obtained.
An Automatic Approach for Aircraft Landing Process Based on Iterative Learning Control

The aircraft landing process is the most complicated flight phase, and it is also the most prone to flight accidents. Completing a safe landing process puts high demands on the pilot's skills, which makes the cost of training an outstanding pilot rather expensive. The aircraft dynamics and kinematics model consist of nonlinear, highly coupled, multi-complexity equations. A large number of state data transformations will be generated during the flight, and there are very strict requirements on the timeliness and accuracy of data analysis and processing. In this paper, the authors take the advantage of iterative learning control methodology to propose a optimization algorithm based on gradient to solve the tracking problem of a given aircraft landing trajectory. The simulation results indicate that applying this new algorithm is able to gradually make the error of flight path converge to zero. Compared with traditional control methods, this iterative learning control algorithm has more excellent performance in terms of accuracy.
User Scheduling in Massive MIMO: A Joint Deep Learning and Genetic Algorithm Approach

Due to the limited number of radio frequency (RF) chains in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) receivers using analog beamforming/hybrid beamforming, there is a restriction in scheduling the number of users in each transmission time interval. Therefore, fast and low-complexity user scheduling methods based on the instantaneous channel state information (CSI) are needed. In this paper, we propose novel user scheduling methods based on deep learning (DL) to reduce the size of the search space by using the learning capability of a deep neural network (DNN). We formulate the user scheduling combinatorial optimization problem as a regression problem followed by a user separation procedure through decision boundaries that are learned by a trained DNN. The decision boundaries are used to separate the users into two subsets. Then, one of the subsets is selected to be searched to find the users that maximize the sum-rate capacity. The proposed method can achieve a very low outage probability with a few number of searches. In order to achieve ergodic capacity with lower computation complexity, the proposed method is employed in combination with the genetic algorithm (GA) algorithm to take advantage of intelligent initial population selection. Our simulation results show that the proposed user scheduling methods can offer remarkably low complexity.