Miracle-dz/UniFAR-FTUs huggingface dataset:
A Few-Shot Learning Method Using Feature Reparameterization and Dual-Distance Metric Learning for Object Re-Identification

Many object re-identification (Re-ID) methods that depend on large-scale training datasets have been proposed in recent years. However, the performance of these methods degrades dramatically when insufficient training data are available. To address this challenging problem, we propose a few-shot object re-identification (FSOR) method that enhances the generalization and discrimination abilities of object Re-ID models trained on small datasets. This method applies two novel techniques: reparameterization for feature vectors and dual-distance metric learning. The reparameterization mechanism transforms the primary feature vector of each input image into a Gaussian distribution to enhance the robustness of the FSOR method when performing object Re-ID tasks. The dual-distance metric learning technique, called H&C learning, considers both the hard mining distance and the center-point distance between each query sample and each support set of different object identities. H&C learning extracts the characteristics of the entire training dataset more precisely than other approaches and thus improves the discriminative abilities of object Re-ID models. Extensive experiments on both person and vehicle Re-ID datasets, such as Market-1501, DukeMTMC-ReID, CUHK03, and VeRi-776, show that the FSOR method has improved performance and outperforms state-of-the-art methods when the amount of labeled training data is small.
INT-probe: Lightweight In-band Network-Wide Telemetry with Stationary Probes

Visibility is essential for operating and troubleshooting intricate networks. In-band Network Telemetry (INT) has been embedded in the latest merchant silicons to offer high-precision device and traffic state visibility. INT is actually an underlying technique and each INT instance covers only one monitoring path. The network-wide measurement coverage therefore requires a high-level orchestration to provision multiple INT paths. An optimal path planning is expected to produce a minimum number of paths with a minimum number of overlapping links. Eulerian trail has been used to solve the general problem. However, in production networks, the vantage points where one can deploy probes to start and terminate INT paths are constrained. In this work, we propose an optimal path planning algorithm, INT-probe, which achieves the network-wide telemetry coverage under the constraint of stationary probes. INT-probe formulates the constrained path planning into an extended multi-depot k-Chinese postman problem (MDCPP-set) and then reduces it to a solvable minimum weight perfect matching problem. We analyze algorithm's theoretical bound and the complexity. Extensive evaluation on both wide area networks and data center networks with different scales and topologies are conducted. We show INT-probe is efficient, high-performance, and practical for real-world deployment. For a large-scale data center networks with 1125 switches, INT-probe can generate 112 monitoring paths (reduced by 50.4 %) by allowing only 1.79% increase of the total path length, promptly resolving link failures within 744.71ms.
RealCaPP: Real-time capable Plug & Produce communication platform with OPC UA over TSN for distributed industrial robot control

The industry of tomorrow is changing from central hierarchical industrial and robot controls to distributed controls on the industrial shop floor. These fundamental changes in network structure make it possible to implement technologies such as Plug & Produce. In other words, to integrate, change and remove devices without much effort at runtime. In order to achieve this goal, a uniform architecture with defined interfaces is necessary to establish real-time communication between the varying devices. Therefore, we propose an approach to use the combination of OPC UA and TSN to automatically configure real-time capable communication paths between robots and other cyber-physical components and execute real-time critical tasks in the distributed control system.
Robust Visual SLAM Algorithm for Dynamic Indoor Environments

In real scenes, traditional visual SLAM algorithm is limited by the assumption of static environment. Due to the influence of moving objects, the traditional visual odometry makes a large number of mismatches and makes the system unable to run statically in real scenes. In this paper, a robust visual SLAM algorithm for indoor dynamic environment is proposed based on deep learning. Firstly, the object detection network is used to detect the dynamic objects and determine the moving objects in the surrounding environment. Secondly, a semantic data association method and key-frame selection strategy are proposed for the tracking thread and the local graph thread to reduce the influence of moving objects on the algorithm accuracy. Finally, the TUM open datasets is validated. Experimental results show that the proposed method reduces the mean root mean square error (RMSE) of ORB-SLAM2 by more than 94% in dynamic scenarios compared to the previous improved ORB-SLAM2. In addition, compared to other deep learning based SLAM systems, the proposed method has a better balance of speed and accuracy in dynamic environments.
Cartoon and Texture Image Decomposition Driven by Weighted Curvature

In this paper, we propose a curvature-guided model with divergence-free constraints to facilitate image decomposition. Since basic TV regularization has difficulty in processing edge geometry information of cartoon image, in order to preserve the edge features, we introduce a level set curvature term to smooth the uniform area, and use the edge indicator function as the weighted regularization to preserve the edge. In addition, to control the smoothness, a gradient function is proposed to balance the edge indicator function and the curvature term. On the other hand, we find that the existing partial decomposition models measure the oscillation function with the $H^{-1}$ functional, but this functional ignores the divergence-free vector field during the Hodge decomposition process, then the texture will lose some vector direction information, and then affects the decomposition results. By analyzing the theory of divergence-free vector field, a new decomposition model with the constraint of divergence-free vector field is proposed in this paper. Numerical experiments show that the proposed model can well preserves the edges of cartoon and protects the texture of repetitive patterns.
Quantized Set-membership Filtering for Automatic Guided Vehicles with Unknown-but-bounded Noises

This paper investigates the quantized set-membership filtering (SMF) problem for network-based automatic guided vehicle (AGV) systems in the presence of unknown-but-bounded (UBB) process and measurement noises. Considering the bandwidth constraints of the communication networks, the measurement outputs of the AGV are quantized before transmitting to the remote filter. Two independent quantizers are designed to quantize each variable in the two-dimensional output vector separately. Then, an SMF scheme is presented to solve the problem of the remote state estimation for the AGV with UBB noises and the quantization error. Next, a recursive convex optimization algorithm is provided to obtain the state estimation ellipsoid sets for the considered AGV. Simulation results demonstrate the effectiveness of the proposed method.
Biomimetic SLAM Algorithm Based on Growing Self-Organizing Map

A Biomimetic SLAM Algorithm Based on Growing Self-Organizing Map (GSOM-BSLAM), inspired by spatial cognitive mechanism of mammalian hippocampus, is proposed to resolve uncertainty problems in location identification and lack of real-time performance in simultaneous localization and mapping. The algorithm connects activation characteristics of the place cell and neurons in the output layer of the neural network to construct a topological map of space using a self-organizing growable mapping neural network. It utilizes self-motion-aware information to obtain activation response of the place cell to estimate the robot position information, improving the localization accuracy and real-time performance of the system. Meanwhile, an accurate environmental cognitive map is finally created by incorporating color-depth images for closed-loop detection and error correction for spatial cell path integration. The proposed algorithm is validated using publicly available KITTI and St. Lucia datasets. The experimental results demonstrate that the proposed algorithm outperforms RatSALM by 37.8% and 36.5% in terms of localization accuracy and real-time performance, respectively, indicating good mapping capabilities.
Temporal Relations Extraction and Analysis of Log Events for Micro-service Framework

Micro-service is a prevalent framework for Internet services. Its loosing coupling design has gained popularity recently. Loosing coupling services offer convenience, scalability and flexibility, however bring unexpected maintenance problems. This paper focuses on the problem where service administrations attempt to monitor the runtime services performance and status with mined service logs. To achieve it, we propose to extract unstructured logs as log patterns and visualise the services temporal relations using temporal graphs. Our analysis and data mining method can help to give new insights in system management area and non-trivial analysis process. The early experiments and analysis can clearly reveal inner structures of system and help to gain deep understanding.
Research on UWB/IMU location fusion algorithm based on GA-BP neural network

In order to solve the problem of large errors in single positioning technology in complex indoor environments, a positioning fusion method based on GA-BP neural network is proposed. The internal calibration of the IMU is used to reduce or eliminate the coordinate axis deviation and fixed error of the IMU, and the obtained acceleration, angular velocity, attitude Angle information and the PDR positioning mechanism are used to carry out inertial navigation. UWB positioning technology uses an improved TWR ranging algorithm for ranging, and uses the trilateral ranging method to obtain the corresponding coordinates. The BP neural network is used to fuse the data of the two positioning technologies, and the genetic algorithm (GA) is used to optimize the initial weights and thresholds of the BP neural network to improve the performance and prediction accuracy of the network. The experimental results show that the GA-BP neural network integrates the two positioning data and calculates the positioning results. The average positioning error of the GA-BP neural network is 9.6cm, which is about 80% higher than the positioning accuracy of the single positioning method and 43.9% higher than the prediction error of the BP neural network.
A comparison of Logit and Probit models using Monte Carlo simulation

The Logit model and the Probit model are statistical models of Logistic regression and probability-based pattern recognition algorithms. The cases in which the Logit model and the Probit model fit better, respectively, are assessed to provide selection suggestions. Criteria, such as the AIC, Deviance, and Pseudo R-square, are selected to assess the goodness-of-fit of the two models. Using Monte Carlo simulation in different data scenarios found that some criteria have limited applicability in assessing the goodness of fit and two models are not sensitive to whether or not the independent variables are independent of each other. The difference between the two models is mainly in the sample size and the kurtosis of the data. The Probit model fits well to the smaller data set generated for all kurtosis cases, while the Logit model fits better when the sample size from the Leptokurtic distribution is larger.
Intelligent Fault Diagnosis of Vibrating Screen in Flow Supply Chain Based on Convolutional Neural Network

This paper proposes an intelligent fault diagnosis method for vibrating screen of the coal preparation plant by using a convolutional neural network. Firstly, a wavelet packet decomposition algorithm is used to decompose the vibration data of the vibrating screen into several frequency bands, and the mean value of each frequency band is extracted as the eigenvalue. Secondly, an one-dimensional convolutional neural network is constructed to classify the eigenvalues to diagnose the vibrating screen fault. Finally, compared with other methods, the proposed diagnostic algorithm has higher accuracy. Experiments show that the proposed diagnosis algorithm has high accuracy and speed and greatly improves the production efficiency of coal preparation plant.
PSA-GRU: Modeling Person-Social Twin-Attention Based on GRU for Pedestrian Trajectory Prediction

This paper addresses the path prediction problem of multiple pedestrians interacting in dynamic scene, which is a great challenge because of the complexity and subjectivity of pedestrian movements. To forecast the future trajectory of a pedestrian, it is necessary to consider pedestrian subjective intention and social interaction information. Previous methods ignore that important position nodes of historical trajectories can reflect the subjective intention of pedestrians in complex trajectories. In this work, we present Person-Social Twin-Attention network based on Gated Recurrent Unit (PSA-GRU), which can fully utilize important position nodes of personal historical trajectory and social interaction information between pedestrians. In our approach, the person-attention encoding module extracts the most salient parts of personal history trajectory and helps model learn where and how pedestrians will go. Meanwhile, the social-attention pool module contributes to keep distance among different pedestrians and simulates the dynamic interaction of all pedestrians in real scenes. PSA-GRU also takes advantage of Gated Recurrent Unit (GRU) to enable the model to improve the computational efficiency. Experimental results demonstrate that the prediction accuracy and efficiency of our proposed model are both greatly improved on UCY and ETH datasets.
ECC-Aware Fast and Reliable Pattern Matching Redundancy Analysis for Highly Reliable Memory

Advanced capacity and density of memory have resulted in an increase in the probability of memory faults. The in-memory Error Correction Code (ECC), which solves this problem, is a widely used technology to improve the yield of highly integrated memory. However, the use of in-memory ECC causes problems that have not been considered in memory repair algorithms. Redundancy analysis is effective for repairing memory with redundant memory and in-memory ECC. In this paper, an ECC-aware fast and reliable pattern matching redundancy analysis algorithm for memory using both spare memory and in-memory ECC is proposed. This algorithm simplifies large-scale fault groups using in-memory ECC and includes an early termination method that can determine whether a memory that cannot be repaired with line spares can be repaired considering in-memory ECC. Experimental results show that the proposed pattern matching redundancy analysis algorithm achieves a similar yield but 14.6% less RA time and 8.6 times higher reliability compared to the existing redundancy analysis algorithms.
Study on the Effect of High Temperature and High-Current Rate on Fast Charging of Lithium-ion Batteries

A convenient and fast charging method is key to promote the development of electric vehicles (EVs). High current rate can improve the charging speed, nevertheless leading to more lithium plating. Increasing battery temperature can reduce the lithium plating caused by high rate charging, which benefits cell life. This paper delineates the behavior of lithium-ion batteries at high temperature and high current rate through the model analysis and experiments verification. The first-order equivalent circuit model of battery is used to analyze the variables that affect the charging process. The effect of temperatures and current rates on internal resistance is discussed. The variation law of internal resistance with the temperature and current rate is revealed. Experiments are applied against the 2.2Ah cylindrical ternary lithium batteries in different temperature ranges (25°C-55°C) and charging rates (1C-6C). Charging results demonstrate that high temperature and high current rate have dramatic effects on the fast charging performance of batteries. Charging the battery at 55°C and 6C can significantly improve the charging speed.
A Real-Time Visual-Inertial Monocular Odometry by Fusing Point and Line Features

In this paper, a monocular visual-inertial odometry that utilize both point and line features is deduced. Compared with point features, line features provide more geometric information of the environment, which are more reliable in textureless scenes. However, extracting line segment features from the image are very time consuming, which will affect the real-time performance of the system. To deal with this problem, EDLines line segment detector is introduced to replace the LSD algorithm. Geometric properties of lines are utilized to reject the mismatching of line segment feature. Plücker coordinates and orthonormal representation of lines are used to represent 3D lines. Afterwards, we optimize the state by minimizing a cost function consists of pre-integrated IMU residuals and visual feature re-projection residuals in a sliding window optimization framework. The proposed odometry was tested on the public datasets. The results demonstrate that the presented system can operate in real time with high accuracy.
Secure Consensus Control for Multi-Agent Systems with Multi-sensor Multi-rate Sampling Under DoS Attacks

This paper studies the secure control problem of linear multi-agent systems (MASs) with a multi-sensor multi-rate (MSMR) sampling mechanism under denial-of-service (DoS) attacks, where DoS attacks prevent the transmission of information among agents. A matching mechanism is introduced in order to solve the problem that the time sequences of the transmitted information cannot be synchronized caused by MSMR sampling. A switching approach is used to characterize different modes of the MASs. Based on Lyapunov stability theory, a sufficient condition of secure consensus is given in term of linear matrix inequalities. Finally, a simulation example is presented to verify the effectiveness of the proposed result.
Evaluation Study of Different Integration Methods of LCC Compensation Network for Various Types of Magnetic Structures of Wireless Power Transfer

Nowadays, wireless power transfer (WPT) plays an essential function in the power electronics systems, due to its significant reliability and efficiency. Majority of electrical devices which using batteries can have a chance of charge wirelessly, for instance, most of the electric home devices and electric vehicles (EVs). However, owing to the size and structure of the devices as well as applications, which want to use the wireless power transfer system, compactness is an important part for designers. Some magnetic structures use the large size of coils, or some compensation methods utilize more inductive and capacitive elements, which are affected the size of the whole WPT system. One solution for the size issue is integrated the compensation elements such as placing the compensation coils into the main coils of the WPT. Therefore, the system reaches more compactness and requires lesser spaces compared to the non-integrated one. In this paper, the various types of magnetic structures for WPT applications are magnetically analyzed, and different methods for integrating the compensation coils are evaluated with 3D finite element analysis. Hence, this paper assists the engineers in a way that they can use these integration methods for each type of magnetic structures as well as the compensation networks directly and accurately, to have a compact WPT system.
Synthesis of Controllers for Co-Safe Linear Temporal Logic Specifications using Reinforcement Learning

Recently, the interest in controller synthesis for complex tasks is rapidly growing [1], [2], and in most cases, environments are unknown, which limits applications of traditional control methods. In this paper, we use reinforcement learning method to learn to optimally achieve complex tasks under unknown environments. Specifically, we model the uncertain environments using the Markov Decision Processes (MDPs). The high-level control objective is described by the syntactically co-safe Linear Temporal Logics (scLTLs). Under such settings, we propose a new method for the reward design procedure. The proposed new reward function utilizes the information of automata which are induced from scLTL tasks. Furthermore, we compare the proposed reward function with existing approaches in the standard grid world environments. We show that, by using our reward function, the learning process converges faster and finally optimally achieves scLTL tasks.
Rotary Shaft Encoder Inaccuracy Compensation for PMA-SynRM Drives Application Using Augmented State UKF

The use of permanent magnet-assisted synchronous reluctance motor (PMA-SynRM) is increasing due to its advantages and superiority over induction motors. The undesirable effects of the encoder measurement error on the drive systems especially during low-range speeds may cause disorder in drive performance and sometimes its instability. For the low-cost application that requires a low-resolution encoder, this problem is felt obviously. This article presents a mathematical solution in order to compensate for the instability problem of a PMA-SynRM control at low speeds based on an augmented state unscented Kalman filter (ASUKF). First, a mathematical analysis for the effect of encoder error on the speed control instability is performed, and then an augmented nonlinear dynamical model for the PMA-SynRM is derived. Using an ASUKF on the presented augmented model of the PMA-SynRM, the error of the encoder is detected and accurately estimated. Using the estimated errors and pure motor state variables, the accuracy and robustness of the control are increased and its stability is guaranteed. The experiments performed for a 0.35 kW PMA-SynRM confirm the performance validity of the proposed method.
Delay and Peak-Age-of-Information of ALOHA Networks With Limited Retransmissions

This letter analyzes the impact of packet retransmissions on the performance of wireless random access protocols. In particular, we focus on the effects of two controllable parameters, namely the rate at which retransmissions occur and the number of allowed retransmissions. In a scenario where packets are generated according to a Poisson process and new packets substitute old ones upon arrival, we determine the conditions in which a given throughput or packet success probability constraint is achievable. We also prove, for a general packet success probability function of the traffic, that selecting the smallest feasible value of the number of allowed retransmissions along with an appropriate retransmission rate is optimal in terms of mean delay to transmit packets and mean Peak-Age-of-Information. We illustrate this result with numerical examples.