Miracle-dz/UniFAR-FTUs huggingface dataset:
Mass-property Identification System for Three-axis Air Bearing Table Based on Simulated annealing Algorithm and Fading Memory Filter

The mass property of the three-axis air bearing table is unknown when simulating the attitude and orbital motion of a satellite. This paper builds a mathematical model for the identification of the quality characteristics of the three-axis air bearing table. An identification method for the quality characteristics of a three-axis air bearing table based on the simulated annealing algorithm (SA) is proposed. To improve the identification accuracy, a method for identifying the quality characteristics of a three-axis air bearing table based on the fading memory filter (FM) is proposed. The mass-property identification system uses flywheels as the excitation source of the three-axis air bearing and uses the inertial measurement unit (IMU) to measure the angular velocity. The results show that the moment of inertia identification error of the SA algorithm is about 3.48%, and it is capable of getting away from local optimum. The moment of inertia identification error based on the FM filter is less than 2% with faster convergence speed and higher identification accuracy.
A Lagrangian Column Generation Approach for the Probabilistic Crowdsourced Logistics Planning

In recent years we have increasingly seen the movement for the retail industry to move their operations online. Along the process, it has created brand new patterns for the fulfillment service, and the logistics service providers serving these retailers have no choice but to adapt. The most challenging issues faced by all logistics service providers are the highly fluctuating demands and the shortening response times. All these challenges imply that maintaining a fixed fleet will either be too costly or insufficient. One potential solution is to tap into the crowdsourced workforce. However, existing industry practices of relying on human planners or worker's self-planning have been shown to be inefficient and laborious. In this paper, we introduce a centralized planning model for the crowdsourced logistics delivery paradigm, considering individual worker's spatio-temporal preferences. Considering worker's spatio-temporal preferences is important for the planner as it could significantly improve crowdsourced worker's productivity. Our major contributions are in the formulation of the problem as a mixed-integer program and the proposal of an efficient algorithm that is based on the column generation and the Lagrangian relaxation frameworks. Such a hybrid approach allows us to overcome the difficulty encountered separately by the classical column generation and Lagrangian relaxation approaches. By using a series of real-world-inspired numerical instances, we have demonstrated the effectiveness of our approach against classical column generation and Lagrangian relaxation approaches, and a decentralized, agent-centric greedy approach. Our proposed hybrid approach is scalable to large problem instances, with reasonable solution quality, and achieves better allocation fairness.
Entropy-Based Extraction of Useful Content from Spectrograms of Noisy Speech Signals

In the paper, we extend and apply the 2D local entropy method (2DLEM) used for signal time-frequency repre-sentation to useful content extraction from noisy speech signals. In our previous work, we presented the 2D local entropy method (2DLEM) and tested it on synthetic signals and a small number of real-world signals. We now extend the application of our method by applying it to recorded speech signals combined with noise from different sources. The database we used is commonly used in speech recognition, where tested methods usually have the best result achieved on clean signals without added noise or on denoised signals. The 2DLEM method is used for the extraction of useful content, and in this paper, we test it in real-world scenarios. Our results show promising results for all tested signals regardless of the noise source or signal-to-noise ratios (SNRs). Combining the 2DLEM method with speech recognition methods could improve the performance of speech recognition and understanding systems.
Location Selection of Electric Vehicle Charging Station Based on Improved Immune Algorithm

This paper is devoted to the location selection method for electric vehicle charging stations utilizing the improved immune algorithm. Firstly, the user satisfaction evaluation function of electric vehicles is proposed based on the satisfaction optimization theory. Then, mathematical model of users’ satisfaction is established. Based on the model, a novel optimal algorithm is designed. Because of the improvement of cross and mutation, the proposed strategy is more accurate and convergent than the conventional immune algorithm. Furthermore, the proposed algorithm is implemented based on the immune algorithm, which offers the best site selection for EVs. Finally, Jinan City was used as an example to conduct a simulation experiment.
The 2020 Power Blackout in the Brazilian State of Amapá: Lessons Learned and Opportunities

The Brazilian state of Amapa=-notable for having 70% of its area consisting of unexplored Amazonian forests-experienced a massive power blackout in November 2020. An internal short-circuit in a transformer led to an explosion followed by a fire at the Macapa substation. This event left about 90% of the Amapa's population without power for several days in the middle of the COVID-19 pandemic, disrupting the lives of hundreds of thousands of people and raising concerns from several electricity sector stakeholders. This paper discusses select lessons learned from the power blackout and presents future opportunities to improve power system reliability and resilience for the grids in Amapá, and other similar Brazilian states, with a special focus on the increasingly important role of of distributed energy resources and smart grid technologies.
Low-Profile, Broadband, Dual-Linearly Polarized, and Wide-Angle Millimeter-Wave Antenna Arrays for Ka-Band 5G Applications

A low-profile, broadband, dual-linearly polarized (dual-LP), and wide-angle millimeter-wave antenna array with beam-steerable capabilities operating in the Ka-band is proposed for 5G applications. By jointly exploiting miniaturized vertically stacked elliptical patches, orthogonal aperture-coupled stripline feeding structures, and groups of shorting pins loaded close to the coupling apertures, the proposed dual-LP element has a small footprint of 4.5 × 4.5 mm with a total thickness of 1.57 mm, which exhibits a measured −10 dB impedance bandwidth of 21.8% and a mutual coupling below −41 dB. Wideband stripline-based Butler matrix circuits are designed and integrated with the 1 × 4 element array for verifying its broadband dual-LP multibeam radiation performance. Experimentally, the integrated array generates four beams with an angular coverage of about ±61° and ±75° for x- and y-polarizations, revealing that the proposed array is a promising candidate for 5G applications.
COVID-19 Fake News Prediction On Social Media Data

It is, to tell the truth, that the COVID-19 pandemic has put the whole world in a tough time, and sensitive information concerning COVID-19 has grown tremendously online. Most importantly, the gradual spread of fake news and misleading information during these hard times can have dire consequences, causing widespread panic and exacerbating the apparent threat of a pandemic that we cannot ignore. Because of the time-consuming nature of evidence gathering and careful truth-checking, people get confused between fallacious and trustworthy statement. So, we need a way to keep track of misinformation on social media. Most people think that all social media information is real information though, at the same time, it is a shame that some people misuse this social media platform for their own benefit by spreading misinformation. Many individuals take advantage by playing with the weaknesses of others. As a result, people around the world not only are facing COVID-19, they are also facing infodemics. To get rid of this kind of fake news, we have proposed a research model that can predict fake news related to the COVID-19 issue on social media data using classical classification methods such as multinomial naïve bayes classifier, logistic regression classifier, and support vector machine classifier. Moreover, we have applied a deep learning based algorithm named distil BERT to accurately predict fake COVID-19 news. These approaches have been used in this paper to compare which technique is much more convenient for accurately predicting fake news about COVID-19 on social media posts. In addition, we have used a data-set that included 6424 social media posts.
Fault Diagnosis Strategy of Electro-Mechanical Flight Actuator Using Fuzzy Disturbance Observer

In this paper, we study the fault diagnosis problem of electro-mechanical flight actuator (EMFA) in the presence of nonlinear terms and unknown disturbances, which leads to a new fault diagnosis method based on fuzzy disturbance observer. Firstly, the model combined with nonlinear terms and synthesized disturbances is established for EMFA. Secondly, the fuzzy logic system is employed to develop a disturbance observer thereby generating the disturbance estimations for the EMFA. After that, the stability analysis illustrates that the disturbance observer error can converge to a small range around zero. The estimation errors can be used in the fault diagnosis process and then applied to detect whether the fault has occurred. Finally, the numerical simulations and discussions are carried out to demonstrate that the effectiveness of the proposed method, which shows that the designed detect strategy can reduce the false alarm rate of fault diagnosis.
Comparison of deep learning methods for image deblurring on light optical materials microscopy data

The robustness of image processing and machine learning algorithms for object and anomaly detection, image segmentation or failure analysis tasks is strongly influenced by the image quality. Image blur is still a problem in microscopy because high quality images of non-planar samples with high resolution are not feasible even with high technical and manual effort. In this paper we evaluate deep neural network models for image deblurring on light optical microscopy data. We present a new image deblurring dataset with sharp ground truth images and a variation of different out-of-focus blur and vibration blur images. We show that image quality enhancement using deep learning methods has great potential in microscopy-based failure analysis. The best method achieved an improvement for the PSNR metric from 31.24 to 35.19, for the SSIM metric from 0.7981 to 0.9472 and for the IoU score from 0.845 to 0.944 on the given test dataset.
Fault diagnosis and fault-tolerant control for non-linear UAV system subject to sensor and actuator faults

The problem of fault diagnosis (FD) and fault tolerant control (FTC) for nonlinear unmanned aerial vehicle (UAV) systems subject to sensor and actuator faults is discussed in this paper. Firstly, the nonlinear UAV systems model with disturbances is shown in the paper. Then, a novel radial basis function (RBF) adaptive observer is proposed to estimate sensor and actuator faults simultaneously, while the parameters of the RBF observer can be obtained by solving the proposed linear matrix inequality. After that, a robust output feedback fault-tolerant controller is carried out so that the system trajectory can still track the system expected trajectory after the failure occurs. Finally, a simulation example is presented to further illustrate the effectiveness of the algorithm proposed in this paper.
Fault diagnosis of liquid rocket engine thrust chamber based on improved augmented particle filter

The fault diagnosis algorithm proposed in this paper firstly establishes the nonlinear thermodynamic model of the thrust chamber of the liquid rocket engine under normal mode and four common fault modes. Secondly, the particle filter is designed to estimate the output and residuals of the thrust chamber and complete the fault detection task by using the generalized likelihood ratio test method. Thirdly, an extended fault estimation particle filter is established by merging the fault estimation vector into the extended state estimation vector to complete the task of fault estimation. Finally, a fault recognition algorithm is designed according to the fault estimation result to complete the fault recognition task in the case of a single fault, at the same time, the recognition result is used to improve the accuracy of the enhanced particle filter. The proposed method is verified to realize real-time and accurate fault diagnosis of thrust chamber under the influence of uncertain factors such as process and measurement noise.
Asynchronous Event-Trigger Consensus of Leaderless Homogeneous Nonlinear Networked Lagrangian Systems

This paper studies the asynchronous event-trigger control of networked Lagrangian system collaboration. The main control goal is to make the velocity and position status of each subsystem converge. In this paper, the non-linearity existing in the actual system, the limitation of communication and computing resources are considered. It is difficult for general algorithms to meet the requirements of general distributed algorithms with continuous update actions. Therefore, this paper introduces the event-trigger mechanism with asynchronous update rules into the distributed control problem of the second-order nonlinear networked Euler-Lagrange system. Aiming at the strongly connected Lagrange network, an event-triggered distributed control algorithm is proposed, and a suitable Lyapunov function is selected to prove its stability. It is shown that based on the proposed algorithm, all agents can achieve consensus. Finally, the theoretical results are verified by numerical simulation.
SgWalk: Location Recommendation by User Subgraph-Based Graph Embedding

Popularity of Location-based Social Networks (LBSNs) provides an opportunity to collect massive multi-modal datasets that contain geographical information, as well as time and social interactions. Such data is a useful resource for generating personalized location recommendations. Such heterogeneous data can be further extended with notions of trust between users, the popularity of locations, and the expertise of users. Recently the use of Heterogeneous Information Network (HIN) models and graph neural architectures have proven successful for recommendation problems. One limitation of such a solution is capturing the contextual relationships between the nodes in the heterogeneous network. In location recommendation, spatial context is a frequently used consideration such that users prefer to get recommendations within their spatial vicinity. To solve this challenging problem, we propose a novel Heterogeneous Information Network (HIN) embedding technique, SgWalk, which explores the proximity between users and locations and generates location recommendations via subgraph-based node embedding. SgWalk follows four steps: building users subgraphs according to location context, generating random walk sequences over user subgraphs, learning embeddings of nodes in LBSN graph, and generating location recommendations using vector representation of the nodes. SgWalk is differentiated from existing techniques relying on meta-path or bi-partite graphs by means of utilizing the contextual user subgraph. In this way, it is aimed to capture contextual relationships among heterogeneous nodes more effectively. The recommendation accuracy of SgWalk is analyzed through extensive experiments conducted on benchmark datasets in terms of top-n location recommendations. The accuracy evaluation results indicate minimum 23% (@5 recommendation) average improvement in accuracy compared to baseline techniques and the state-of-the-art heterogeneous graph embedding techniques in the literature.
Missing Value Imputation and Prediction of River Water Quality Based on GRU–Autoencoder with Input-decay

With the development of human beings, the pollution of freshwater resources occurs very frequently, which directly leads to the reduction of available water sources and the increase of maintenance costs. In order to manage and protect water resources better, a large number of sensors are used to monitor water quality. However, the problem of missing data has a negative impact on our data analysis work. In this paper, we propose a new missing value imputation model which based on GRU autoencoder with input-decay(GRUD-AE). The prediction module is added to the output of the autoencoder to realize the short-term prediction. The model is trained with datasets which contain missing value. The input-decay module is applied to learning the time correlation of sequences which could help improve imputation accuracy. Experiments show that our model performs better than the traditional missing value imputation and prediction methods.
Intelligent Robotic System for Solving Dissection Puzzle Combining K-Nearest Neighbors, Decision Tree and Deep Q Network

The autonomous learning of an intelligent robotic system has enormous potential to solve assembly tasks. Deep reinforcement learning algorithms generally apply to robotics and their application level is still limited to relatively simple tasks due to the complexity and variability of the industrial environment. This paper presents an intelligent robotic system for composing a tangram puzzle with a set of polygons through an industrial robot, a suction device and a camera. A camera system is used to detect and estimate the 3D position and orientation of the polygons. An intelligent robotic system combines K-nearest neighbors, decision tree and deep Q network to learn different target photo puzzles and aims at assembling in industry.
Development of Speech Dialogue Systems for Social AI in Cooperative Game Environments

There is increasing interest in developing more human-like artificial intelligence (AI) capable of natural social interaction. Previous research has suggested ideas about what it means to be "life-like" AI, and some studies have attempted to test these hypotheses using game environments. In this paper, we introduce the development of the Speech Dialogue System for a "Social AI", which communicates and interacts autonomously with a human player in cooperative game environments (in this case a social survival game called "Don’t Starve Together"). Based on our hypothesis that the AI should contain specific components to be perceived as more human-like, we conducted a series of pilot tests to develop the Social AI using a data-driven approach. After finishing the pilot tests, we identified six components to add or revise, based on participant interactions and feedback. These components mainly include features of the Speech Dialogue System that pertain to the interplay of AI behavior with contextual factors of the social environment ("the game state"). In future work, we intend to improve the Social AI based on these findings. The research here highlights the use of cooperative game environments for data-driven development of speech dialogue systems for artificial agents.
A Feature Extraction Method Using Multiscale CLAE for Intelligent Fault Diagnosis

In order to avoid the occurrence of large-scale accidents to the greatest extent, it is necessary to find out the faults in the continuous operation of mechanical equipment and make corresponding decisions. With the development of artificial intelligence technology, autoencoder(AE) has been widely used in fault diagnosis, among which class level autoencoder(CLAE) effectively overcome the intra-class variations of detection data under different loads. However, the CLAE model just uses a single scale method to extract features from the data, and when the data is mixed with noise, the classification performance will decrease. In this work, we proposed a multiscale class level autoencoder model(MSCLAE) which aims at learning robust and discriminative features. Specifically, we extract single-scale features from each CLAE model with different input and ouput dimensions respectively and combine these features for fault pattern recognition. The experimental results on a motor bearing dataset have demonstrated that the proposed method can extract more robust features and obtain better anti-noise ability.
On-Line Determination of Salient-Pole Hydro Generator Parameters by Neural Network Estimator Using Operating Data (PEANN)

A novel application of Artificial Neural Network (ANN) to estimate and track Hydro Generator Dynamic Parameters using online disturbance measurements is presented within this paper. The data for training ANN are obtained through off-line simulation of the generators modelled in a one-machine-infinite-bus environment using the parameters sets that are representative of practical data. The Levenberg-Marquardt algorithm has been adopted and assimilated into the back-propagation learning algorithm for training feed-forward neural networks. The inputs of ANN are organized in coordination with the results obtained from the observability analysis of synchronous generator dynamic parameters in its dynamic behaviour. A collection of 10 ANNs with similar input patterns and different outputs are developed to determine a set of dynamic parameters. The trained ANNs are employed in a real-time operational environment for estimating generator parameters using online measurements acquired during disturbance conditions. The ANNs are employed and tested to identify generator parameters using online measurements obtained during different disturbances. Simulation studies demonstrate the ability of the ANNs to accurately estimate dynamic parameters of hydro-generators. The results also show the impact of test conditions on the accuracy degree of estimation for these parameters. The optimal structure of ANNs is also determined to minimize the error in estimating each dynamic parameter.
Associate Semantic-Instance Segmentation of 3D Point Clouds Based on Local Feature Extraction

Segmentation of 3D point cloud which can express the information of complex scene more accurately is an important basis for 3D scene understanding. However, how to effectively use 3D point cloud information for complex scenes is rarely discussed. This work proposes a two-stage network to achieve semantic segmentation and instance segmentation of point clouds. Specifically, a simple multitasking network is firstly developed by extracting the multi-category features of local point cloud, which can also achieve superior segmentation results. Then, a learnable network is established to make semantic segmentation and instance segmentation mutually promote each other, so as to segment complex scenes more accurately. The validity of this network is proved by experiments and evaluation on S3DIS dataset. Compared to other well-known networks, the proposed two-stage network shows its superiority.
Electrical Resistance Tomography Image Reconstruction Based on Res2net4 Network

Electrical resistance tomography (ERT) is a non-invasive process measurement technology, which has the advantages of high speed, low cost and strong robustness. It has a broad application prospect in multiphase flow monitoring and measurement. Image reconstruction is the key part of electrical resistance tomography. Because it is a nonlinear and ill posed mathematical problem, the traditional image reconstruction methods cannot achieve high accuracy. Therefore, this paper proposes a deep learning method based on Res2net4 network for ERT image reconstruction. The network is composed of two large residual modules, each of which is grouped and then connected by residual within the group to fuse more feature scales. The network loss function is composed of cross entropy and L2 regularization term in the last layer, which is used to constrain and monitor the imaging process. The simulation and experimental results show that the proposed network can reconstruct complex medium distribution with a higher imaging quality than LBP, Landweber, Tikhonov and other traditional image reconstruction algorithms.