Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning-Based Link Fault Identification and Localization in Complex Networks

With the proliferation of network devices and rapid development in information technology, networks such as Internet of Things are increasing in size and becoming more complex with heterogeneous wired and wireless links. In such networks, link faults may result in a link disconnection without immediate replacement or a link reconnection, e.g., a wireless node changes its access point. Identifying whether a link disconnection or a link reconnection has occurred and localizing the failed link become a challenging problem. An active probing approach requires a long time to probe the network by sending signaling messages on different paths, thus incurring significant communication delay and overhead. In this paper, we adopt a passive approach and develop a three-stage machine learning-based technique for link fault identification and localization (ML-LFIL) by analyzing the measurements captured from the normal traffic flows, including aggregate flow rate, end-to-end delay, and packet loss. ML-LFIL learns the traffic behavior in normal working conditions and different link fault scenarios. We train the learning model using support vector machine, multilayer perceptron, and random forest. We implement ML-LFIL and carry out extensive experiments using Mininet platform. Performance studies show that ML-LFIL achieves high accuracy while requiring much lower fault localization time compared to the active probing approach.
Revisiting Perspective Information for Efficient Crowd Counting

Crowd counting is the task of estimating people numbers in crowd images. Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regressions. A major challenge of this task lies in the perspective distortion, which results in drastic person scale change in an image. Density regression on the small person area is in general very hard. In this work, we propose a perspective-aware convolutional neural network (PACNN) for efficient crowd counting, which integrates the perspective information into density regression to provide additional knowledge of the person scale change in an image. Ground truth perspective maps are firstly generated for training; PACNN is then specifically designed to predict multi-scale perspective maps and encode them as perspective-aware weighting layers in the network to adaptively combine the outputs of multi-scale density maps. The weights are learned at every pixel of the maps such that the final density combination is robust to the perspective distortion. We conduct extensive experiments on the ShanghaiTech, WorldExpo’10, UCF_CC_50, and UCSD datasets, and demonstrate the effectiveness and efficiency of PACNN over the state-of-the-art.
Effects of Design Variables on Demagnetization Fault in Permanent Magnet Synchronous Motor

This paper studies the effects of design variables on the Permanent Magnet (PM) demagnetization of a Permanent Magnet Synchronous Motor (PMSM) using a two-dimensional (2D) finite element analysis (FEA) software package. As a result, it is shown that pole embrace and magnet thickness are the most important geometrical dimensions of the PMSM in terms of demagnetization fault. The effect of demagnetization fault on the magnetic field distribution, the back-electromotive force (EMF) induced and the electromagnetic torque of PMSM are examined.
A Note on NSRLMS, NSRLMF, and NSRLMMN Adaptive Algorithms

In this paper, we compare the expressions for the steady-state mean-square error (MSE), the optimum step-size, and the corresponding minimum tracking MSE of the normalized sign regressor least mean square (NSRLMS), the normalized sign regressor least mean fourth (NSRLMF), and the normalized sign regressor least mean mixed-norm (NSRLMMN) algorithms for the case of real-valued data. The expressions for the steady-state MSE, the optimum step-size, and the corresponding minimum tracking MSE of the NSRLMF and NSRLMMN algorithms based on energy conservation relation approach are available in the literature for the case of real-valued data. Thus, in order to compare these three algorithms, we have derived the expressions for the steady-state MSE, the optimum stepsize, and the corresponding minimum tracking MSE of the NSRLMS algorithm based on energy conservation relation approach for the case of real-valued data. Finally, simulation results to substantiate the analytical results of the NSRLMS algorithm are also presented for the case of real-valued data.
KECA for Identifying the Habitats of Chinese Mitten Crab (Eriocheir Sinensis) Based on Aroma Analysis

Chinese mitten crabs (Eriocheir Sinensis) are rich in resources, wide varieties, and extensive origins. Crabs in Yangcheng Lake are most famous. However, some businessmen are driven by profits to subvert the market. In this paper, the aroma of 12 crabs of three producer areas was described by using electronic nose, and the difference in crabs' flavor profile between different producing areas was explored. 7 sensors, response data of the electronic nose were analyzed by using kernel entropy component analysis (KECA), principal component analysis (PCA), and linear discriminant analysis (LDA). Results showed that KECA had a better accuracy than PCA and LDA in locality identification. The distinguish effect of electronic nose on the crabs from different areas was overlapped, such as weeping Tai Lake and Gucheng Lake, but Yangcheng Lake crab could be distinguished dramatically from other localities in KECA. Therefore, electronic nose can be used as an important method to identify the crab habitat based on the different flavor profile of crabs.
Arterial Coordination Control Signal Transition Optimization Based on Volatility Analysis

In order to reduce the adverse effects of the arterial intersections signal control strategy dynamic changes during arterial coordination control signal transition. This paper analyzes the fluctuation characteristics during the transition of control signals. The phase offset variation volatility model, signal cycle fluctuation volatility model, green ratio volatility model are constructed, and the volatility model analysis is conducted on the two aspects of the multi-period transition and multi-intersection coordination cycle transitions. The particle swarm optimization algorithm was used to solve the multi-objective optimization problem in the arterial coordination transition, and the best transition control strategy was finally obtained. The simulation results show that the proposed algorithm is optimized compared with the traditional Add algorithm and the Subtract algorithm in terms of vehicle average delay, vehicle average stop times, and average queue length, which can make the signal control effect better.
Optimal Controller Design for Virtual Sensing With Independent Noise Source Measurement

In certain applications, when the placement of sensors at the target location is not feasible, a common practice is to obtain the target information indirectly via sensors at other locations. This technique is known as virtual sensing (or soft sensing), and involves various signal processing techniques. Virtual sensing is applied to such systems to estimate the error signal for feedback control. However, issues regarding both stability and performance degradation become significant because the estimation is not precise. In this brief, we propose a virtual sensing control (VSC) method using a feedforward controller to compensate the mismatch between an error sensor and virtual sensor under the feedback control. The results show that when an independent noise source measurement is available, a feedforward controller is more appropriate than estimation as it maintains the stability of the feedback system while achieving a good performance enhancement. For such a VSC system, the design of both feedforward and feedback controllers is coupled. In this brief, we derive an optimal controller design method to maximize the noise reduction capability. We show the advantages of the proposed VSC method by applying it to the implementation of an active noise control system.
ATLAS Robot:A Teaching Tool for Autonomous Agricultural Mobile Robotics

Robotics is heralded as an integral part of precision farming, the application of technology to aid the solution of the burdening food shortage. However, robotics is quite intimidating to some students and a challenging subject to handle for professors, particularly as the vehicles used in agriculture can be rather large and powerful. The objective of this research is to develop an interactive teaching tool that can be used to learn the fundamentals of robotics and autonomous navigation as a springboard to the larger agricultural robots. This paper discusses the results of using a robot, named ATLAS due to its use of GPS navigation. Various behaviors that the robot can accomplish were included, such as following a wall surface, and navigating to a target waypoint or set of waypoints. Some real projects developed by students who used the ATLAS robot in their study are also presented. Furthermore, this paper can be used by different educational institutions as an alternative teaching approach in handling robotics subject.
Study of Spatial Asynchrony Analysis for Solar Irradiance

For the rapid growth of the photovoltaic power generation system, the change in the solar irradiance has more influence on the power system than ever before. The fluctuation of the electrical power generation derived from the renewable energy causes the instability of the voltage and the frequency on the power transmission system, and the power system dispersively introduced photovoltaic systems is affected by the spatial change in the solar irradiance along with the time variation. Hence, the spatiotemporal analysis for the solar irradiance is important to design the energy management system placed a large number of photovoltaic power generation systems. In particular, the spatial asynchrony of the irradiance is relevant to the smoothing effect of the photovoltaic power fluctuation. For measuring the asynchrony among the different time series data, cross-sample entropy has been used as the indicator in the field of econophysics and biophysics. Applying cross-sample entropy approach to spatial time series data, the quantification and visualization for the spatial asynchrony of solar irradiance can be expected. However, conventional cross-sample entropy analysis did not consider the time variation of the asynchrony. Therefore, we introduce the time window analysis to cross-sample entropy approach and show the time shift of the spatial asynchrony of solar irradiance. In this paper, we introduce time window analysis to the cross-sample entropy approach and apply our method to spatial time series of satellite estimation of solar irradiance data for showing the time variation of the asynchrony.
A Theoretical Framework for Quality Estimation and Optimization of DSP Applications Using Low-Power Approximate Adders

In this paper, we present a framework for analytically estimating the output quality of common digital signal processing (DSP) blocks that utilize approximate adders. The framework is based on considering the error of approximate adders as an additive noise (approximation noise) that disturbs the output of the DSP block in question. A signal processing theoretical modeling approach for describing the power of the approximation noise which is the integral of error spectral density over the bandwidth, is developed. The output qualities of DSP blocks, such as finite impulse response filter, discrete cosine transform, and fast Fourier transform, which utilize approximate adders, are thus estimated. The accuracy of the proposed framework is evaluated by comparing mathematical model predictions to simulation results by using the signal-to-noise ratio (SNR) metric. The inaccuracy of the SNRs predicted by the framework was, on average, less than 2.5dB compared with that obtained from simulations. Therefore, a mathematical optimization approach based on Lagrange Multipliers for optimizing design parameters is also presented. The optimization is realized by choosing a proper configuration of the target block, such as determining the data width of the inexact computation part for each approximate adder in the design.
A Dempster-Shafer Sensor Fusion Approach for Traffic Incident Detection and Localization

Traffic incident detection and localization is an important application in traffic management systems. The ability to detect and localize traffic incidents enables a timely response to accidents and facilitates effective and efficient traffic flow management. This paper presents a sensor-network based approach for tackling the problem of incident localization. Traffic count sensors, which tend to be an element of the road infrastructure, are used as the source of traffic sensory data. Such sensors come in a variety of types and capabilities, providing the potential for complementary and redundant information gathering. Thus, it is conceivable to fuse such sensory information to achieve insightful and accurate incident detection and localization. In this context, the Dempster-Shafer (DS) theory of evidence is used as the foundation for fusing traffic sensory data. In this paper, a traffic model generator and two traffic-counting sensory systems are employed for acquiring traffic data pertinent to the distribution of cars on a given road segment. Experimental analysis on the performance of the proposed approach is provided.
Modeling and Control of a Rotating Flexible Spacecraft: A Port-Hamiltonian Approach

In this brief, we develop a mathematical model of a flexible spacecraft system composed of a hub and two symmetrical beams using the port-Hamiltonian framework. This class of system has favorable properties, such as passivity for controller synthesis and stability analysis, where the global Hamiltonian plays the role of a Lyapunov function candidate. The spacecraft model is viewed as a power-conserving interconnection between an infinite (beam) and finite (hub) dimensional system. We show that the interconnection result has a port-Hamiltonian structure and is passive. The introduction of a nonlinear feedback term, which takes into account the beam’s flexibility, is developed using the control by an interconnection approach. The closed-loop stability is proven; then, through explicitly solving the partial differential equations of the system, asymptotic stability is obtained. Finally, the experimental results are carried out to assess the validity of the proposed design methodology.
The TUBS Road User Dataset: A New LiDAR Dataset and its Application to CNN-based Road User Classification for Automated Vehicles

We present a novel approach for classifying pre-segmented laser scans of road users with consideration of real-time capability for applications in automated vehicles. Our classification approach uses 2.5D Convolutional Neural Networks (CNNs) to process range data as well as intensity information retrieved from reflected beams. We do not solely rely on publicly available laser scan datasets, which lack several features, but we provide an additional dataset from real-world sensor recordings, annotated by a tracking-based automatic labeling process. We evaluate the classification performance of our CNN regarding different feature configurations. For training, we use automatically and manually labeled data as well as mixtures with other public datasets. The results show promising classification capabilities. Training with automated labels shows similar results, providing a possibility to avoid the need for manual editing expense.
Content-based Video Copy Detection using Binary Object Fingerprints

Content-based video copy detection in large-scale databases is still an open issue. One of the main reasons is that geometrical attack can easily surpass the global features of the frame while local features are inefficient in terms of compact representation and computational complexity. In this paper, we propose to use binary object fingerprints to represent video frame for improving the robustness of the video copy detection system. It is because salient object can be robustly detected using advanced convolutional neural network (CNN) based object detector. We proposed to use the well-known RetinaNet for generating object regions from the input frame and then these regions are used to generate binary fingerprints for fast copy detection in the database. This approach can maintain compact representation of video frame and high searching speed by binary fingerprint searching scheme. Experimental results show that the proposed approach can achieve about 10% higher recall rate with only sacrificing 1% prediction rate on VCDB dataset.
Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems Approaches to Forecast the Electricity Data for Load Demand, an Analysis of Dinar District Case

Short-term load forecasting is an important issue for the electric power system in efficiently managing the network and reducing operating costs. In addition, with the recent improvements in distributed generation and storage systems, this has become even more important. Access to the high-resolution dataset derived from smart counters allows new forecasting strategies to evolve to match distributed load on the demand side. In this study, short-term load forecasting (STF) of a small region was performed using Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) methods. For load forecasting, the electricity consumption and temperature data for the year 2017 were used as input to the network and next hour demand was predicted. The smallest forecasting error is investigated with the Mean Absolute Percent Error (MAPE) and Root Mean Square Error (RMSE) performance criteria. It showed that RMSE is better than ANFIS with 616.2753 and MAPE 8.8688 prediction error.
Hermaphroditism in a violet snail, Janthina pallida (Gastropoda, Caenogastropoda): a contribution

The first data are provided on the gonad development of the violet snail Janthina pallida Thompson, 1840. Histological investigations were carried out on twenty-two specimens cast ashore alive in the Straits of Messina (central Mediterranean). All specimens had an ovotestis with distinct male and female gonads. In large individuals, germinative cells in different stages of spermatogenesis and flagellate spermatozoa were detected, together with oocytes in different stages of oogenesis and mature oocytes; in small specimens only immature male and female gonads were observed. No specimens of intermediate size were found. Simultaneous rather than size-related hermaphroditism in J. pallida contrasts with the current view of protandric hermaphroditism as a common trait in Janthina genus, instead suggesting a changing reproductive pattern, driven by population density and food availability.
Efficient transfer learning and online adaptation with latent variable models for continuous control

Traditional model-based RL relies on hand-specified or learned models of transition dynamics of the environment. These methods are sample efficient and facilitate learning in the real world but fail to generalize to subtle variations in the underlying dynamics, e.g., due to differences in mass, friction, or actuators across robotic agents or across time. We propose using variational inference to learn an explicit latent representation of unknown environment properties that accelerates learning and facilitates generalization on novel environments at test time. We use Online Bayesian Inference of these learned latents to rapidly adapt online to changes in environments without retaining large replay buffers of recent data. Combined with a neural network ensemble that models dynamics and captures uncertainty over dynamics, our approach demonstrates positive transfer during training and online adaptation on the continuous control task HalfCheetah.
Document Embedding Based Supervised Methods for Turkish Text Classification

Following the recent increase in the amount of available data, Deep Learning has become the most popular branch of Machine Learning. This trend can also be seen in Natural Language Processing (NLP) especially since textual data can now be scraped from in World Wide Web in vast quantities and used in an unsupervised or semi-supervised manner. For this reason, Deep Learning methods are being used more frequently. In this work we devise several classification methods based on the Paragraph Vector model (a.k.a. Doc2Vec) which represents documents as vectors. These include k-Nearest Neighborhood classifier (k-NN), Support Vector Machines (SVM), Centroid Classifier (CC) that works on paragraph vectors of documents and a custom made method which uses pairwise cosine similarities between documents and class centroids as features in Doc2Vec space. Our experiments use a number of representations and classifiers combined in various ways. On the representation side the Paragraph Vector model is compared with Term Frequency (tf) and Term Frequency-Inverse Document Frequency (tf-idf) using SVM, k-NN, CC and Centroid Features Support Vector Machine (CFSVM) as classifiers.
Leveraging Online Word of Mouth for Personalized App Recommendation

The popularity and development of mobile devices and mobile apps have dramatically changed human life. Due to the tremendous and still rapidly growing number of mobile apps, helping users find apps that satisfy their demands remains a difficult task. To address this problem, we propose a personalized mobile app recommender system based on the textual data of user reviews on the app store. Topic modeling techniques are applied to extract hidden topics of user reviews, and the probability distributions of the topics are utilized to represent the features of the apps. Then, the user profile is constructed based on the user’s installed apps to capture user preferences. Both the topic distributions of the apps and user preferences are taken into account to produce recommendation scores to generate recommendation lists for target users. We crawl real-world data sets from app stores to evaluate the performance. The experimental results show that user reviews are effective for deriving the features of apps, and the proposed user-review-based app recommender system improves the performance of existing approaches. We conclude that the user reviews on the app store effectively represent the features of apps and play a significant role in personalized app recommender systems.
A Novel Image Super-Resolution Technology Based on the Wavelet Coefficients Prediction Scheme

The super-resolution technologies can be roughly categorized into the sparse coding based methods and learning-based methods. In the sparse coding based methods, the image patches or wavelets coefficients are often utilized to establish the image patch database. However, the time-consuming searching process makes the real-time applications difficult. In the learning-based methods, the DFT or DWT coefficients are used to train the machine learning system. In this paper, we propose a novel learning-based super-resolution method with wavelet coefficients prediction scheme to rebuild the high-resolution images with high PSNR and SSIM scores. The license plate and ecological duck images are used to analyze the performance of the proposed method. The experimental results show that the reconstructed high-resolution license plate images can have PSNR 48 dB and SSIM 0.99 and the reconstructed high-resolution ecological duck images (with high texture distribution) can have PSNR 33 dB and SSIM 0.98. The proposed method outperforms the conventional methods in terms of PSNR and SSIM. Furthermore, the efficiency of the proposed method is fast enough for the real-time applications.