Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Minimum Gain Lemma

The computation of the minimum sensitivity of uncertain Linear Time Invariant (LTI) systems is presented in the paper. The system interconnection is given by a generic Linear Fractional Transformation (LFT) of a nominal model and an uncertain block, where the input-output behavior of the latter is described by Integral Quadratic Constraints (IQC). The extension of the Minimum Gain Lemma is presented for such interconnections, resulting in a convex optimization problem subject to Linear Matrix Inequality (LMI) constraints. With the aim of the Generalized-KYP (GKYP) lemma the minimum gain/sensitivity is computed over a certain finite frequency range. Connection with the already existing literature is highlighted, providing an insight on the obtained results. A numerical example is given to illustrate and validate the proposed methodology.
Fault Diagnosis and Fault-Tolerant Control for Leader-Follower Multi-Agent Systems with Time Delay

Under the guidance of the directed graph, this paper studies the fault diagnosis (FD) and fault-tolerant control (FTC) of the leader-follower multi-agent systems (MASs) with time delay. Actuator faults and disturbances are considered in this study, which make the work of this paper more challenging. Firstly, a robust distributed observer on the basis of the relative estimation error is put forward to obtain the fault estimation information. Then, after getting the fault information obtained by the observer, a dynamic compensation FTC protocol on the basis of the relative output tracking error is developed to make the output of followers can track the leader's output. Finally, the proposed algorithm is verified by an example and satisfactory results are obtained.
Proactive 3C Resource Allocation for Wireless Virtual Reality Using Deep Reinforcement Learning

Virtual reality (VR) over wireless has emerged as an important application in future mobile networks. However, it is difficult for the existing mobile networks to meet the requirements of massive data transmissions and ultra-low latency for wireless VR. Multi-access edge computing (MEC) network, providing caching and computing capacities at network edge, emerges as a promising method to support wireless VR. However, mobile VR users' quality of experience (QoE) may be degraded by frequent handoffs. In this paper, we propose a proactive caching, computing and communication (3C) resource allocation method to provide smooth VR videos to handoff users. Specifically, the expected 3-dimensional (3D) video or 2D video for rendering is cached at a target base station (BS) ahead of time, and the size and quality of the video file are decided according to the 3C resources at the BS. Then, we model the the proactive 3C resource allocation as a Markov decision process and an effective allocation policy is obtained by a model-free algorithm based on deep reinforcement learning. Numerical results show that the proposed method can provide VR users with high QoE when they are moving between BSs.
Achieving consensus in spite of stubbornness: time-varying concatenated Friedkin-Johnsen models

A concatenated Friedkin-Johnsen (FJ) model is a two time-scale opinion dynamics model in which stubborn agents discuss a sequence of issues. For each issue, a FJ model is adopted, and concatenation refers to the fact that the final opinion of the agents at issue s becomes the initial condition at issue s + 1. In this paper we deal with the case in which the system is open, i.e., the group of interacting agents changes at each issue, and so does their stubbornness. A concatenated FJ model can in this case be represented as an infinite product of stochastic matrices. For such system, we obtain sufficient conditions under which the opinions of the agents converge to consensus.
Offset Learning based Channel Estimation for IRS-Assisted Indoor Communication

The system capacity can be remarkably enhanced with the help of intelligent reflecting surface (IRS) which has been recognized as a advanced breaking point for the beyond fifth-generation (B5G) communications. However, the accuracy of IRS channel estimation restricts the potential of IRS-assisted multiple input multiple output (MIMO) systems. Especially, for the resource-limited indoor applications which typically contains lots of parameters estimation calculation and is limited by the rare pilots, the practical applications encountered severe obstacles. Previous works takes the advantages of mathematical-based statistical approaches to associate the optimization issue, but the increasing of scatterers number reduces the practicality of statistical approaches in more complex situations. To obtain the accurate estimation of indoor channels with appropriate piloting overhead, an offset learning (OL)-based neural network method is proposed. The proposed estimation method can trace the channel state information (CSI) dynamically with non-prior information, which get rid of the IRS-assisted channel structure as well as indoor statistics. Moreover, a convolution neural network (CNN)-based inversion is investigated. The CNN, which owns powerful information extraction capability, is deployed to estimate the offset, it works as an offset estimation operator. Numerical results show that the proposed OL-based estimator can achieve more accurate indoor CSI with a lower complexity as compared to the benchmark schemes.
Mal-LSGAN: An Effective Adversarial Malware Example Generation Model

Various Machine Learning (ML) models have been developed for malware detection. But their widespread application is challenged by adversarial attacks using adversarial malware examples. Generative Adversarial Networks (GAN) is one of the effective approaches to help build possible unknown attacks and expose the vulnerability of targeted systems. The existing GAN-based ML models have the weaknesses of unstable training and low-quality adversarial examples. In this paper, we propose a novel Mal-LSGAN model to tackle these weaknesses. By using a Least Square (LS) loss function and new activation function combinations, Mal-LSGAN achieves a higher Attack Success Rate (ASR) and a lower True Positive Rate (TPR) in 6 ML detectors, compared with the existing MalGAN and Imp-MalGAN. In Multi-Layer Perceptron (MLP), Mal-LSGAN can even decrease TPR from 97.81% of original examples to 2.92% of adversarial examples. The experimental results also demonstrate that Mal-Lsgangets the preferable transferability of adversarial malware examples.
State estimation of non-linear systems over random access wireless networks*

We study emulation-based state estimation for non-linear plants that communicate with a remote observer over a shared wireless network subject to packet losses. To reduce bandwidth usage, a stochastic communication protocol is employed to determine which node should be given access to the network. We describe the overall wireless system as a hybrid model, which allows us to capture the behaviour both between and at transmission instants, whilst covering network features such as random transmission instants, packet losses, and stochastic scheduling. Under this setting, we provide sufficient conditions on the transmission rate that guarantee an input-to-state stability property for the corresponding estimation error system. We illustrate our results with an example of Lipschitz non-linear plants.
Deep learning-based defense and detection scheme against eavesdropping and typical cyber-physical attacks

Cyber-physical systems (CPSs) play an increasingly important role in critical industrial infrastructure. However, the use of communication networks leads to vulnerabilities in systems. Considering the negative effects caused by the recent attack events, guaranteeing safe and reliable operation of infrastructures is necessary. This paper proposes a dual denoising auto-encoder (DDAE) and based on which proposes a unified scheme (in the sense of sharing the DDAE models) to protect cyber-physical systems (CPS) from being eavesdropped, and to detect the typical attacks, i.e., false data injection (FDI) attacks, denial-of-service (DoS) attacks, and replay attacks. The industrial data are encrypted by the encoder of the offline-trained DDAE before transmission, and at the receiving end, the transmitted data are analyzed by a residual generator-based detection module. If the received data are trustworthy, they will be decrypted and used in the subsequent industrial processes. Otherwise, attack warnings will be triggered. The simulation results on the IEEE-57 bus system show that the proposed scheme can effectively prevent the transmitted data from being eavesdropped and can accurately detect typical cyber-physical attacks.
ECHO: Enhanced Conditional Handover boosted by Trajectory Prediction

Conditional handover (CHO) has been introduced in 5G to improve mobility robustness, namely, to reduce the number of handover failures by preparing target Base Stations (BSs) in advance and allowing the user to decide when to make a handover. This algorithm constantly prepares and releases BSs, thereby adapting to the fast changing radio condition. A user might make a handover to a distant BS that has a favorable channel only for a short time due to signal fluctuations. This increases the handover rate and might result in a Radio Link Failure (RLF) afterwards. Moreover, the constant preparation and release of BSs leads to an increased exchange of control messages between the user, the serving BS and all target BSs. Hence, there is a need to carefully select the target BSs. Therefore, we propose the Enhanced CHO (ECHO) scheme that uses trajectory prediction to prepare the BSs along the user's path. To achieve this, we also propose a Sequence to Sequence (Seq2Seq) mobility prediction model. ECHO with only one prepared BS (ECHO-1) outperforms CHO with three prepared BSs. ECHO-1 reduces the handover rate by 23 percent and the RLF rate by 77 percent, while also reducing the number of control messages in the network by 69 percent.
Accurate and Fast Detection of DDoS Attacks in High-Speed Network with Asymmetric Routing

The existing DDoS attack detection methods based on a single monitoring point only consider symmetric routing scenarios, which may not be practical. Such schemes will produce high false positives when facing the asymmetric routing scenarios. Besides, few of them are applicable in high-speed networks. The paper designs a DDoS detection scheme customized for high-speed networks and takes asymmetric routing scenarios into account. Systematic sampling is applied to high-speed incoming traffic, and a proposed Double Composite Structure Sketch (DCSS) is utilized for fast recording and extraction of features based on the characteristics of DDoS attacks in both symmetric and asymmetric routing scenarios. Then classifiers are trained for online DDoS detection. Our experimental results using the public dataset show that in a 10Gbps network with asymmetric routing, our approach can accurately detect UDP Flood and SYN Flood attacks within 20 seconds when the sampling rate is set to 1/2048.
User-Side RIS: Realizing Large-Scale Array at User Side

Massive multiple-input multiple-output (MIMO) with a large-scale antenna array at the base station (BS) side is one of the most essential techniques for 5G wireless communications. However, due to the forbidden hardware cost and mismatched size, it is physically limited to deploy massive MIMO at the user side. To break this limitation, inspired by the promising technique called reconfigurable intelligent surface (RIS), we firstly propose the concept of user-side RIS (US-RIS) which is a cost- and energy-efficient realization for large-scale array at the user side. Different from the existing RISs that work as base-station-side RISs (BSS-RISs), US-RIS is the first usage of RIS at the user side. Then, we propose a novel architecture of user with the aid of US-RIS with a multi-layer structure for compact implementation. Based on the proposed multi-layer US-RIS, we formulate the signal-to-noise ratio (SNR) maximization problem in the US-RIS-aided communications. To tackle the challenge of solving this non-convex problem, we propose a multi-layer precoding design that can obtain the optimal parameters at transceivers and US-RIS by iterative optimization. Finally, simulation results are shown to verify the practicability and superiorities of the proposed multi-layer US-RIS as a realization of the large-scale array at the user side.
Ultra-Reliable and Low Latency Wireless Communications with Burst Traffics: A Large Deviation Method

Ultra-reliable and low latency communications (URLLC) has recently attracted much attention because it holds the promise of supporting mission-critical applications in task-driven radio access networks. In URLLC, finite-blocklength coding plays an important role. In this paper, we are interested in the performance limit of finite-blocklength coded wireless URLLC with burst traffics, which may induce severe delay in practice. A large deviation technique is exploited to derive the delay violation probability given a hard delay constraint. Specifi-cally, an approximate QoS exponent is conceived to characterize the reliability-latency tradeoff, i.e., tradeoff between the error probability and delay violation probability. Further, we present closed-form upper and lower bounds of the QoS exponent, which are shown to become tight in the high signal-to-noise ratio (SNR) regime. Our theoretical analysis is also validated by numerical results.
Research on the Process and Cost Analysis Software for DFP of Fuzes

The process and cost analysis software is designed for producibility of fuzes. According to the process course, the data required is worked out, the factory database is established. After the process course and machining parameter is established, the process files is prigrammed. OLE of software and drawing file of CAD is established. The calculation and analysis report for cost is worked out.
The Application of ANN Realized by MATLAB to Underground Water Quality Assessment

The realizing function of RBF network is provided in the toolbox of MATLAB 6.5 and the method of calculating this function possesses properties such as adaptation for determining the construction network and independence of initial weight value on person. A favorable outcome appeared after we apply this function to evaluating the quality of the underground water environment in FuZhou City, attempting to use the RAND function in MATLAB to construct enough training samples, checking samples and outputs of their targets through interpolation between grades of the water quality evaluation standard, use the PREMNMX function to pretreat the original data,determine the limits of water quality grades . The methods in this paper is meaningful in improving the precision and objectivity of underground water environment quality evaluation.
Research on an efficient splitting plane-wave FDTD method

Aiming at the two-dimensional (2-D) TMz model,this paper presents a new and efficient method for introducing plane wave sources based on the concept of split-field,namely the splitting plane-wave FDTD (SP-FDTD) method. This method fundamentally eliminates the error caused by the mismatch between interpolation and phase velocity. The leakage test with different incident angles of the Sine plane wave source and Gaussian pulse wave source shows that SP-FDTD can achieve a perfect total-field/scattered-field separation,and the leakage falls below -340 dB. In addition,SP-FDTD method can be extended to 3-D and higher-order FDTD methods,which is another advantage of the method described in the paper.
Research on forging method of marine long-shaft heaving forging

As the most important components of marine power installation,the quality of the long-axis heavy forgings has a direct influence on the hydrodynamic characteristics and normal voyage of marine.The squaring process of the marine intermediate shaft forged by flat anvil,FM method and the unequal flat anvil was simulated.The finite element model was established based on numerical simulation software Deform-3D,and the temperature field,stress distribution,forging force and forging efficiency were analyzed.The results show that comparing with the FM method and unequal flat anvil forging,when forging by flat anvil,the forming efficiency is the highest,the forging force is the smallest and the deceasing speed of the surface temperature of forging billet is the slowest;when forging by FM method,the ideal compressive stress distribution state can be obtained along the billet cross section.
Research on Rolled-compactness Performance of Multilevel Interlocked-dense Type Asphalt Mixture

Based on the structure,compaction,material characters and the compaction request of multilevel interlocked-dense type asphalt mixture,it is found that through the lab testing and theory analyzing,the asphalt mixture with skeleton structure is difficult to be roll-compaceted which would lead to poor permeability.Compaction of rutting tests reflect the rolled-compactness performance of multilevel interlocked-dense type asphalt mixture preferably.The results show that it can not take the interlocking more seriously.And the construction technology of rolled-compactness is improved.
Modelling and stability analysis of a flexible rotating beam in collision

This paper considers the problem of a rotating flexible beam in collision with an external object. The model’s equations for the flexible beam in collision exhibits instant changes during impact times, therefore the model is cast in the class of switched infinite dimensional operator systems. The aim is to study the stability of the closed loop system with a PD control law, making use of the semigroup formalism together with Lyapunov stability theory. To this end, we present a new stability result making use of multiple Lyapunov functions obtained as an adaptation of a theorem from finite dimensional hybrid systems theory. We show the port-Hamiltonian modelling procedure for the colliding rotating flexible beam, using distributed parameter equations to describe the beam’s dynamic. Then, we compute the equilibrium position of the closed loop systems, and using the shifted variables with respect to the equilibrium position, we cast the system in the class of switched infinite operator systems. Finally we select the Lyapunov functions for the contact and noncontact phases and we show, through numerical simulations, that they respect the assumptions of the proposed stability theorem.
Odyssey and Other Code Science Success Stories

®, an agile software development methodology based on eXtreme Programming (XP), we 1 recently delivered an application (code-named Odyssey) consisting of 400,000-plus executable source lines of code (ESLOC) to one of the world’s premier industrial automation companies. The application was written in C++ by as many as 17 developers (including some of our customer’s staff) in approximately 15 months. We are geographically remote from this customer. Odyssey was delivered a month and a half ahead of schedule with a productivity rate of 43 ESLOC per coding hour. During the project duration, approximately 2,400 defects were found and fixed, yielding a captured defect density of six defects per thousand lines of code (KLOC). During a thorough, more than six-week customer-conducted acceptance test, only about 200 defects were found (none severe), yielding a delivered defect density of 0.5/KLOC. The customer is delighted with the product and is confident of the competitive edge achieved.
Agarose and Wax Tissue-Mimicking Phantom for Dynamic Magnetic Resonance Imaging of the Liver

Objectives: Polymer phantoms can be used to simulate the properties of tissues or organs such as the liver during magnetic resonance imaging (MRI). Although there are reports of simulated liver phantoms with hepatocellular carcinoma (HCC); no studies have documented the simulating of the typical dynamic pattern. Aim: The present study aimed to investigate properties of developed phantoms with respect to the stability, including chemical, mechanical, electrical, as well as MRI properties (T1- and T2- signal intensities), in addition to simulating HCC in a dynamic way. Methods: In this study, liver parenchyma of size 23 ×18 ×13 cm was simulated using three different agarose-wax samples (agarose at concentrations of 2.5 wt%, 4.5 wt%, and 6.0 wt%) mixed with a fixed wax concentration of 2.6 wt%. HCC samples were fabricated using polyurethane and glycerol of various diameters (0.5, 1.0, and 2.0 cm). Results: The results showed that the agarose-wax sample with a concentration of 2.5 wt% was the most stable sample among the other samples (P-value, 0.468) in mechanical and T1 and T2 intensities. It was also noted that the sample at the concentration of 4.5 wt% had the closest density value to human liver with a difference of 7.88%. Moreover, the agarose-wax sample at a concentration of 2.5 wt% had the closest compressibility and conductivity values, and T1-relaxation time compared to the human liver. However, the largest changes in relaxation times were observed in the fifth week of all samples (P-value = 0.047). The typical enhancement pattern for a simulated HCC of a minimum size, which can be measured with MRI, was 1.0 cm, using a body coil, and 0.5 cm using a head coil. Conclusions: The chemical, electrical density, compressive strength, density, and MR imaging properties of the phantom were measured and compared to those of the human body.
Reducing Synchronization Overhead for Persistent RNNs

Recurrent Neural Networks (RNNs) are an important category of Deep Neural Network (DNN) that are widely used in speech recognition, sentiment analysis, and many other important applications. RNNs often use GPUs to harness the available parallelism to improve performance. However, their efficiency is hindered by sequential data dependencies in each timestep on outputs from previous timesteps, which makes it difficult to efficiently utilize GPUs and other accelerators for RNNs. In an effort to overcome this inefficiency, previous work introduced Persistent Recurrent Neural Networks (PRNNs), which improve efficiency by exploiting kernel fusion and the GPU’s memory hierarchy to reuse network weights over multiple timesteps. Thus, PRNNs have been shown to outperform regular RNNs. However, since PRNN condense the entire RNN, which normally spans hundreds of GPU kernels, into a single large kernel with multiple phases, they require explicit synchronization at the end of each phase within the kernel. Unfortunately, synchronization in GPUs, such as global barriers, is extremely inefficient. This problem worsens for more complex RNNs such as Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) which require additional global synchronization to synchronize their additional sub-computations. Moreover, prior open-source work only added persistency Vanilla RNNs, whose usage is very limited in the real world. To extend the benefits of persistency to more applications, we implement Persistent Gated Recurrent Units (PGRU) to enable further research opportunities on PRNNs, including reducing synchronization overhead for both PRNN and PGRU.