Miracle-dz/UniFAR-FTUs huggingface dataset:
Servo Motor Fault Diagnosis Based on Data Fusion

Servo motor is the core part of automatic production line. Its performance and reliability have great influence on the normal operation of the whole automatic production line. In this paper, a servo motor fault diagnosis method based on data fusion is proposed aiming at the problem that single sensor's diagnosis result is uncertain, the fault degree diagnosis and multi-fault diagnosis are difficult. Adaptive weighted fusion algorithm is used to fuse the vibration signals of two channels at the data layer to enhance the signals. The weighted fusion of fault data with different fault degree is carried out to generate new data with various faults. EWT is improved to denoise the new data generated by fusion and the envelope spectrum diagram of the de-noised signal is obtained. The envelope spectrum diagram is input into the YOLOV3 network as a training set for training, so as to realize fault diagnosis. The results show that the proposed method does not rely on human experience and can be accurately identified in multi-fault state. The experimental results show that the proposed method has high recognition rate and stability.
Energy-Age Tradeoff in Status Update Communication Systems Based on HARQ

The development of the Internet of Things (IoT) has given birth to many real-time applications, which have strict requirements for fresh data. Therefore, a new indicator, age-of-information (AoI), is defined to measure data freshness. On the other hand, the IoT devices such as remote sensors are usually energy-constrained. In this paper, we study the energy-age tradeoff in a state update system. In this system, a source node senses the status data and transmits to a receiver through an error-prone channel. When the transmission fails, the source may either retransmits the existing data to save energy, or sense and transmits new data to reduce the age-of-information. Hybrid Automatic Repeat request (HARQ) and threshold-based retransmission strategy are adopted. Then, we derive the expressions of the average information age and average energy consumption of the system. Finally, the theoretical results are verified via simulations, and the energy-age tradeoff relationship is analyzed. In addition, it is shown that HARQ strategy is more efficient than Automatic Repeat request (ARQ) when the channel condition is very poor.
Computing the Rate-Distortion Function of Bivariate Gaussian Gray-Wyner Networks

In this paper, the rate-distortion (RD) theory of Gray-Wyner lossy source coding system under linear strategies is investigated. An analytical expression of the RD function under quadratic distortion constraints with jointly Gaussian distributed sources is proposed. The proof is provided briefly using convex optimization theory and Lagrangian minimization approach to get an alternative characterization for Lagrangian minimization. Then RD function can be computed using the property of Gaussian random variables.
Improved EEPROM tunnel- and gate-oxide quality by integration of a low-temperature pre-tunnel-oxide RCA SC-1 clean

The effects of integration of a low-temperature RCA standard clean-1 (SC1) on the tunnel- and gate-oxide charge-to-breakdown (Q/sub BD/) and voltage ramped dielectric breakdown (VRDB) distribution in a 0.7 /spl mu/m CMOS EEPROM process technology were studied. A low-temperature ( 80/spl deg/C) SC1. The reduced silicon diode etchrate of the low-temperature SC1 allowed for additional gate-oxide annealing during the gate oxidation cycle, while keeping the overall thermal budget (Dt)/sup 1/2/ for the technology equivalent to that with the higher temperature SC1. This resulted in improved gate-oxide VRDB distributions and QED values on large capacitor structures. The tunnel-oxide QBD improvement was most likely due to reduced surface roughness in the tunnel-oxide window regions with the lower temperature SC1. The process including the low-temperature SC1 was also proven to provide equivalent yield to the process with the high temperature SC1 on a 0.7 /spl mu/m, 7 nS 128 macrocell EEPROM programmable logic device.
Curriculum Reinforcement Learning-Based Computation Offloading Approach in Space-Air-Ground Integrated Network

Space-air-ground integrated network (SAGIN) is emerging as a prominent framework supporting the ever-growing Internet of Things (IoT) applications in the areas without infrastructures. In this paper, we investigate the problem of IoT task offloading under the SAGIN scenario where multiple IoT devices cooperatively use computing resources. We formulate the task offloading problem of minimizing the processing delay of all tasks, taking into account the dynamics of tasks generated by each IoT device, the mobility of unmanned aerial vehicle (UAV), and the difference in computing power between the UAV and the low earth orbit (LEO) satellite. Then the problem is formulated as a Markov decision process (MDP). To cope with the dynamics and complexity of the system, as well as the training difficulties caused by the large number of agents, we propose a curriculum learning-multi-agent deep deterministic policy gradient (CL-MADDPG) approach to learn the near-optimal offloading strategy. Simulation results show that the proposed method has satisfactory convergency and can significantly reduce the average task processing delay.
An investigation of corporate approaches to sustainability in the US engineering and construction industry

Sustainability involves focusing on a project’s impact in three areas: (1) environmental; (2) economical; and (3) societal. In reality, companies’ approaches in the three areas are not evenly distributed. A study of current trends in the US construction industry related to the emerging focus on sustainable construction will be of great value for the engineering and construction industry to recognize the differences in corporate approaches to sustainability, for the sake of team alignment. A random cross‐section of 300 companies, consisting of 150 owner companies, 75 contractor companies and 75 design firms representing a variety of industries, was selected from annual company data assembled by the weekly construction news publication Engineering News Record (ENR). To identify sustainability‐related concepts within the context of these company documents, content analyses and chi‐square statistical analyses were performed on the selected companies’ publicly available annual reports and mission/vision/values statements. The primary objectives are to: (1) identify concepts of sustainability that are currently being emphasized within the US engineering and construction industry, possibly at the expense of other concepts; and (2) develop an understanding of the relationships between various company and industry groups related to the fundamental concepts of sustainable construction.
Extracting Potential Innovators Using Innovator Scores and Naive Bayes Classifier

It is important to effectively penetrate a market with products in marketing of convenience products such as foods with short life cycles. It can be said that as products penetrate innovators, they will also penetrate late market segment. This study proposes a method to extract whether users are potential innovators or not based on the users' text data. We used users' innovator scores as the correct answer data and the morphological analysis of the text as the feature data, and applied a naive bayes classifier to extract innovators. As a result of validation using a beverage brand as the application object, we confirmed that a proposed method can extract innovators with high accuracy in method of classifying users into innovators and imitators. A proposed method is characterized by its ability to extract potential innovators on a large scale using a single free-form question item that is easy to input. In the future, we plan to improve the accuracy of the five cluster classifications of innovators, early adopters, early majority, late majority, and laggards, which are more effective for marketing analysis, and to apply a proposed method to many products to continuously verify the generality of a method.
AI environment for predictive maintenance in a manufacturing scenario

Industries generate and collect a huge amount of data about their processes. Usually such data contain relevant information, which can be used to monitor and analyze the processes, but also improve them, applying optimization techniques that allow to enhance different aspects, such as machine maintenance scheduling, product quality, use of resources and so on and so forth. The Digital Twin (DT) concept has been recently applied in the context of Industry 4.0, to exploit this data, leveraging advanced physical modelling, data analysis, Artificial Intelligence (AI) algorithms for optimization and prediction, which are two key concepts in the Industry 4.0 paradigm. Currently, the major challenge in this field is to make these techniques available for the industries and ensure their ease of use for the final users. This paper presents an holistic solution that combining two open-source software - i.e., REclaim oPtimization and simuLatIon Cooperation in digitAl twin (REPLICA) and Optimization Platform for Refurbishment and Re-manufacturing (OPR2) - provides a flexible, open and easy-to-use AI environment that allows data scientists to create, test, connect and deploy their algorithms and to optimize them. This work presents a first prototype of this solution, then describes how it has been tested and validated with real industry data and finally provides the results obtained with these tests.
A Study on Light Trespass of Dynamic LED Advertising Sign Flickering on Adjacent Residents at Night

Nowadays, with the growing popularity of dynamic LED advertising signs, the light trespass caused by these signs is being taken more and more seriously. This study analyzes experimentally how various flickering cycles affect the visual fatigue of residents who read in the room adjacent to these signs. According to the results of the research, an increase in the flickering cycle of the sign corresponds with a decrease in the degree of disturbance to adjacent residents. Moreover this study suggests that the flickering cycle of a dynamic LED advertising sign should be set above four seconds to ensure minimal discomfort.
A Low-Profile Differential-Fed Microstrip Patch Antenna with Wide-Beamwidth and High-Gain under Dual-Band by Multi-Resonant Modes

A low-profile differential-fed microstrip patch antenna (MPA) with high-gain and wide-beamwidth under dual-band by multi-resonant modes is proposed in this paper. Firstly, the dielectric constant of the substrate is properly selected to achieve wide HPBW at TM10 mode. Then, the gain is improved via a linear slot on the patch on upper band at TM30 mode and two ring slots are etched around the coaxial inner to achieve impedance matching in dual bands. Finally, the simulated results reveal that the HPBW of the proposed MPA is increased to around 134 deg at lower band and the gain of 9.63 dBi is acquired at upper band simultaneously. Meanwhile, it keeps a low profile of around 0. 027λ0.
AUDIO FFT FILTER BANKS

FFT-based nonuniform filter banks are proposed based on channelsized inverse FFTs applied to nonuniform frequency-partitions (or overlap-add decompositions) of the Short Time Fourier Transform (STFT). Audio filter banks (particularly octave filter banks) are considered as application examples. Trade-offs discussed include perfect reconstruction, aliasing cancellation, flexibility of filterchannel band edges, use of the FFT for speed, multirate timedomain channel signals, time-varying filtering, and associated issues.
An Implantable Antenna for Wireless Body Area Network Application

In this paper, an implantable planar inverted-F antenna (PIFA) for an artificial cardiac pacemaker is proposed. The antenna has a simple structure with a low profile and is placed on the top side of the pacemaker. The dimensions of the pacemaker system, including the antenna element, are 42×43.6×11 mm. When the antenna is embedded in pig tissue, its S11 value is 10.94 dB at 403 MHz and the 10 dB impedance bandwidth of the antenna is 6 MHz (399 406 MHz). The proposed PIFA in tissue has a peak gain of 20.19 dBi and a radiation efficiency of 1.12 % at 403 MHz. When the proposed antenna is placed in a flat phantom, its specific absorption ratio (SAR) value is 0.038 W/kg (1 g tissue). Performances of the proposed PIFA is sufficient to operate at the MICS band (402 ~ 405 MHz).
Finite time synchronization of fractional-order brushless DC motor systems by soft variable structure control

In this paper, finite-time synchronization of two fractional order brushless DC motor (BLDC) systems with control constraints is investigated. First, a fractional-order BLDC model is proposed, a soft variable structure control scheme is introduced for the finite time synchronization of two fractional order chaotic systems with control constraints. Considering the constraints of the controller, the control parameters of the controller are limited by the soft variable structure method. The finite-time stability of the error systems is rigorously proven. Then, the controller is proposed for the finite-time synchronization of fractional-order brushless BLDC model systems with disturbances. Finally, numerical simulation results are presented to demonstrate the effectiveness and feasibility of the proposed strategy.
Increasing the capacity of GSM cellular radio using adaptive antennas

The capacity of mobile cellular radio networks can be considerably increased if a higher level of cochannel interference can be tolerated. An adaptive antenna combines the outputs of an array of elements to maximise the wanted signal and suppress interfering signals. The author proposes an adaptive antenna algorithm suitable for GSM and the urban multipath environment. The algorithm is based on iterative reference signal reconstruction from the GSM training sequence. The average error rate is obtained by simulation for four equal power interfering signals and an urban GSM multipath model. An adaptive antenna can only practically be deployed at the base station. Realising its gains in a real network on both the up and downlinks requires, for the downlink, a number of techniques. This is discussed and a concept of how the full capacity gains can be achieved in an evolutionary manner on both links is proposed. The concept is equally applicable to the DCS 1800 and DCS 1900 systems based on GSM.
Machine-Learning-based Aortic Stenosis Detection for Electronic Stethoscope

Analyzing the phonocardiogram (PCG) collected by a electronic stethoscope can help to quickly diagnose structural heart diseases. In this paper, we aim at automatic detection of aortic stenosis (AS) based on PCG, with the aid of two proposed machine-learning based methods. The first method is a model-dependent method, a Gaussian mixture model - hidden Markov model (GMM-HMM) method, which exploits the temporal relationship among states in cardiac sounds. The second one is a data-dependent method, implemented by a 1D/2D-fused-feature-based convolutional neural network. The results of comparative experiments showed that both methods can fulfill the AS automatic diagnosis task to a certain extent, and in most cases CNN scored higher than GMM-HMM, which indicated the importance of automatically learning an unknown model from data in this problem, although the GMM-HMM method with fewer parameters also have potential advantages in practice.
LoRa Performance Analysis for LEO Satellite IoT Networks

Due to the wide coverage of satellite communication systems, the Satellite Internet of Things (S-IoT) has become a new hot spot in the field of Internet of Things. Long Range (LoRa) signal is one of the main directions of terrestrial Internet of Things because of the low power wide area network (LPWAN) characteristic. In this paper, we present a low earth orbit (LEO) IoT model, based on the capture effect and successive interference cancellation (SIC) demodulation technology, to analyze the uplink performance of the LoRa. Furthermore, we derive a connection probability equation for LoRa signal from the viewpoint of received signal strength indicator (RSSI) in Rice channel. Simulation results can be used to guide the Initial transmission power setting for satisfying the average access success probability (ASP) requirement of LEO satellite IoT networks.
A Compressive Sensing Based Channel Prediction Scheme with Uneven Pilot Design in Mobile Massive MIMO Systems

Massive Multiple Input Multiple Output (MIMO) is a widely used technique that can provide tremendous gain in spectral efficiency. However, the degradation of beamforming performance due to outdated Channel State Information at the Transmitter side (CSIT) induced by mobility of users, both in Time Division Duplex (TDD) and Frequency Division Duplex (FDD) modes, has been a severe problem awaiting to be solved. Many channel prediction schemes have been proposed to address this problem. Most of them focus on the prediction of future CSI on pilot symbols, then reconstruct the non-pilot symbols through conventional interpolation methods. However, the CSI cannot be simply reconstructed through interpolation in high mobility scenario because of the limitation of even pilot design—the phenomenon is named as “Doppler aliasing” in this paper. To address this, we propose a novel uneven pilot pattern that can provide more Doppler spectrum resolution compared with even pilot which is currently used in most of communication systems. Based on the novel pilot setting, we then propose a channel prediction scheme based on the compressive sensing technique. Numerical results show our scheme can outperform the state-of-the-art algorithms, including deep neural networks and autoregression models, for about 15 percents in terms of average throughput in simulated channel generated by the COST2100 channel model.
An Improved Bayesian Zero-Velocity Detection Algorithm for Pedestrian Navigation Based on MIMU

In the pedestrian navigation the zero-velocity detection is the prerequisite for zero velocity update, and its accuracy will greatly affect the navigation accuracy. Aiming at the problem that the Bayesian zero-velocity detection algorithm (SHOE) can't provide the accurate zero-velocity interval and that it may judge a zero-velocity interval as multiple zero-velocity intervals, this paper proposes an improved Bayesian zero-velocity detection algorithm with adaptive threshold(ISHOE). In the original algorithm, the threshold is reset when a zero-velocity interval begins. But in the improved algorithm, the threshold won't be reset untill the zero-velocity ends. Considering that the attenuation threshold will cause the acquired zero-velocity interval to last too long, this paper takes the threshold at the time when the non- zero-velocity interval enters the zero-speed interval as a fixed threshold, and collects the zero-velocity interval to obtain a more reasonable zero-velocity interval. The algorithm proposed in this paper can obtain zero-velocity intervals, and effectively avoids judging a zero-velocity interval as multiple zero-velocity intervals. In order to test the detection accuracy of the improved Bayesian zero-velocity detection algorithm with adaptive threshold, this paper compares and evaluates the new algorithm with the original algorithm (SHOE), acceleration variance detection algorithm (MV), acceleration amplitude detection algorithm (MAG) and angular velocity energy detection algorithm (ARE) under two different pace conditions. The results show that, without adjusting the threshold of the improved Bayesian detection algorithm with adaptive threshold, the algorithm can still achieve high accuracy and is suitable for applications in multiple sports modes.
Satisfying real client requirements through student-centred coursework.

This paper outlines a rationale and course design strategy used for creating a course that was student-centred and focused on satisfying the needs of industry clients through project work. The pedagogical underpinnings of the course are based on authentic assessment, which is used to promote motivation and interest of students in a higher education institution. Students were given the opportunity in a multimedia development course to develop real E-commerce business solutions. This is the third evolutionary development of the unit. The design of the unit now reflects contemporary pedagogy, taking into consideration the needs of students and the changing requirements demanded by multimedia-centric ebusiness, as promoted by industry and government. The objectives were to develop a unit that was oriented towards authentic and student centred learning, as well as providing a motivating environment with a high level of interaction from professionals in industry.
Observer-Based Fault-Tolerant Control for Nonlinear Multi-agent Systems in Finite Frequency Domain

In this paper, a fault-tolerant controller is designed for a class of nonlinear multi-agent systems with actuator faults in the finite frequency domain. First, the observer is designed to estimate the value of the state variables. Then, based on the estimated results of the state observer, a robust consensus control protocol is designed. The protocol ensures the external disturbances suppression performance index and the state consensus in the case of fault occurs. The effectiveness of the control protocol is proved in the finite frequency domain. Finally, the simulation results are shown to demonstrate the effectiveness of the consensus control protocol.
Research on Zinc Layer Thickness Prediction Based on LSTM Neural Network

Hot-dip galvanizing is a widely used steel anti-rust method. By immersing clean steel products in molten zinc solution, a uniform zinc layer is attached to the surface of steel products, which can slow down the corrosion of steel products to a certain extent. In the products produced by the hot-dip galvanizing process, the thickness, uniformity and firmness of the zinc layer are important technical performance indicators to measure product quality. In this paper, using the advantages of LSTM neural network in processing long sequence information, a zinc layer thickness prediction model based on LSTM neural network is proposed, and the zinc layer thickness prediction model is established based on the data of hot-dip galvanizing production line. The experimental results show that the mean square error of the test set prediction results is 2.790, the average absolute error is 1.359g/m2, and the average absolute percentage error is 1.824%, which achieves effective prediction of zinc layer thickness and proves the feasibility of applying LSTM neural network to zinc layer thickness prediction Sex. Compared with the multiple linear regression model, the MSE, MAE, and MAPE of the LSTM neural network model are better than the multiple linear regression model, and have higher prediction accuracy.
A new lumped-elements power-combining amplifier based on an extended resonance technique

A technique for combining power FETs in the output stage of a power amplifier is presented. The active devices are combined with simple inductor/capacitor networks and can be laid out across a single die while still allowing each device to be independently accessed for biasing. The inductors can range from fully integrated spirals to simple wire bonds, making this technique applicable over a broad range of frequencies. For linear RF power applications this is an effective technique for spreading more heat, while at high frequencies the junction parasitics are easily absorbed into this type of design. DC losses are minimized since each device can be biased individually, furthermore, it is possible to adjust the bias separately for each device to account for device nonuniformity across the die.