Microstructure and properties of 35 kg large aluminum alloy flywheel housing components formed by squeeze casting with local pressure compensation

The squeeze casting method with local pressure compensation was proposed to form a flywheel housing component with a weight of 35 kg.The numerical simulation,microstructure observation and phase characterization were performed,and the influence of local pressure compensation on feeding of thick-wall position,microstructure and mechanical properties of the formed components were discussed.Results show that the molten metal keeps a good fluidity and the filling is complete during the filling process.Although the solidification at thick-wall positions of the mounting ports is slow,the local pressure compensation effectively realizes the local forced feeding,significantly eliminating the shrinkage cavity defects.In the microstructure of AlSi9Mg alloy,α-Al primarily consists of fragmented dendrites and rosette grains,while eutectic Si predominantly comprises needles and short rods.The impact of local pressure compensation on strength is relatively minimal,yet its influence on elongation is considerable.Following local pressure compensation,the average elongation at the compensated areas is 9.18%,which represents a 44.90%higher than that before compensation.The average tensile strength is 209.1 MPa,and the average yield strength is 100.6 MPa.The local pressure compensation can significantly reduce or even eliminate the internal defects in the 35 kg large-weight components formed by squeeze casting.

Detecting While Accessing:A Semi-Supervised Learning-Based Approach for Malicious Traffic Detection in Internet of Things

In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In this paper,we propose a semi-supervised learning-based approach to detect malicious traffic at the access side.It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side,and also is free of labeled traffic data in model training.Specifically,we design a coarse-grained behavior model of Io T devices by self-supervised learning with unlabeled traffic data.Then,we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data.Experimental results show that our method can achieve the accuracy of 99.52%and the F1-score of 99.52%with only 1%of the labeled training data based on the CICDDoS2019 dataset.Moreover,our method outperforms the stateof-the-art supervised learning-based methods in terms of accuracy,precision,recall and F1-score with 1%of the training data.

Robot-Oriented 6G Satellite-UAV Networks: Requirements, Paradigm Shifts, and Case Studies

Networked robots can perceive their surroundings, interact with each other or humans,and make decisions to accomplish specified tasks in remote/hazardous/complex environments. Satelliteunmanned aerial vehicle(UAV) networks can support such robots by providing on-demand communication services. However, under traditional open-loop communication paradigm, the network resources are usually divided into user-wise mostly-independent links,via ignoring the task-level dependency of robot collaboration. Thus, it is imperative to develop a new communication paradigm, taking into account the highlevel content and values behind, to facilitate multirobot operation. Inspired by Wiener’s Cybernetics theory, this article explores a closed-loop communication paradigm for the robot-oriented satellite-UAV network. This paradigm turns to handle group-wise structured links, so as to allocate resources in a taskoriented manner. It could also exploit the mobility of robots to liberate the network from full coverage,enabling new orchestration between network serving and positive mobility control of robots. Moreover,the integration of sensing, communications, computing and control would enlarge the benefit of this new paradigm. We present a case study for joint mobile edge computing(MEC) offloading and mobility control of robots, and finally outline potential challenges and open issues.

超采区浅层地下水数值模拟及预测分析

超采区的地下水变化趋势分析对于区域水资源可持续管理具有重要意义。本文以大同盆地浅层地下水超采区为研究对象,分析了含水层结构和地下水的补给、径流、排泄条件,明确了潜水含水层为影响地下水变化的主要区域。基于GMS与MODFLOW软件,建立了大同盆地潜水含水层各向同性非均质二维非稳定流模型,利用实测地下水流场对模型进行了验证,开展了未来不同场景条件下地下水位预测模拟与分析。结果表明,大同盆地2010—2019年平均地下水开采量(6.0861亿m^(3))大于2001—2016年平均地下水补给量(5.6254亿m^(3)),地下水位下降趋势明显。若地下水开采量下降至2019年开采量的85%,至2035年平均地下水位可回升11.6 m,恢复速度约0.68 m a;若综合考虑未来水源替换工程,至2035年地下水位可显著回升,恢复速率可达1.4 m a。本文研究成果可为大同盆地地下水管控提供科学依据,同时对于我国北方类似地下水超采区的水资源管理和评价具有一定参考价值。

大壁厚差复杂形状铝合金飞轮壳挤压铸造数值模拟

利用ProCAST软件对大壁厚差复杂形状ZL104铝合金飞轮壳挤压铸造过程进行数值模拟。结果表明:充型过程稳定且可分为4个阶段:流道连通、水平区域填充、竖直方向填充和难充填区填充。凝固过程中存在6个凝固滞后的特征区域,特征区域的缩孔缩松缺陷明显。缺陷位置预测准确,对预测的缺陷体积进行极差分析,确定最佳工艺参数为浇注温度650℃、比压48 MPa、模具温度220℃、补压比压800 MPa、补压延时10 s(A侧)和12 s(B侧)。最大铸造应力出现在凝固速度快、曲率大的薄壁结构中。仿真结果得到了实际工艺验证。

Fairness-Oriented Hybrid Precoding for Massive MIMO Maritime Downlink Systems with Large-Scale CSIT

Different from conventional cellular networks, a maritime communication base station(BS) has to cover a much wider area due to the limitation of available BS sites. Accordingly the performance of users far away from the BS is poor in general. This renders the fairness among users a challenging issue for maritime communications. In this paper, we consider a practical massive MIMO maritime BS with hybrid digital and analog precoding. Only the large-scale channel state information at the transmitter(CSIT) is considered so as to reduce the implementation complexity and overhead of the system. On this basis, we address the problem of fairness-oriented precoding design. A max-min optimization problem is formulated and solved in an iterative way. Simulation results demonstrate that the proposed scheme performs much better than conventional hybrid precoding algorithms in terms of minimum achievable rate of all the users, for the typical three-ray maritime channel model.

NOMA-Based UAV Communications for Maritime Coverage Enhancement

The lack of communication infrastructure in the ocean inevitably leads to coverage blind zones.In addition to high-throughput marine satellites,unmanned aerial vehicles(UAVs)can be used to provide coverage for these blind zones along with onshore base stations.In this paper,we consider the use of UAV for maritime coverage enhancement.Particularly,to serve more ships on the vast oceanic area with limited spectrum resources,we employ non-orthogonal multiple access(NOMA).A joint power and transmission duration allocation problem is formulated to maximize the minimum ship throughput,with the constraints on onboard communication energy.Different from previous works,we only assume the slowly time-varying large-scale channel state information(CSI)to reduce the system cost,as the large-scale CSI is locationdependent and can be obtained according to a priori radio map.To solve the non-convex problem,we decompose it into two subproblems and solve them in an iterative way.Simulation results show the effectiveness of the proposed solution.

Deep Learning-Based Symbol Detection for Time-Varying Nonstationary Channels

The highly dynamic channel(HDC)in an extremely dynamic environment mainly has fast timevarying nonstationary characteristics.In this article,we focus on the most difficult HDC case,where the channel coherence time is less than the symbol period.To this end,we propose a symbol detector based on a long short-term memory(LSTM)neural network.Taking the sampling sequence of each received symbol as the LSTM unit's input data has the advantage of making full use of received information to obtain better performance.In addition,using the basic expansion model(BEM)as the preprocessing unit significantly reduces the number of neural network parameters.Finally,the simulation part uses the highly dynamic plasma sheath channel(HDPSC)data measured from shock tube experiments.The results show that the proposed BEM-LSTM-based detector has better performance and does not require channel estimation or channel model information.

k-NN Based Bypass Entropy and Mutual Information Estimation for Incremental Remote-Sensing Image Compressibility Evaluation

Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.

Robustness Evaluation of Remote-Sensing Image Feature Detectors with TH Priori-Information Data Set

In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI)data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features.The TPI contains 1)raw and calibrated remote-sensing images with high spatial and temporal resolutions(up to 2 m and 7 days,respectively),and 2)a built-in 3-D target area model that supports view position,view angle,lighting,shadowing,and other transformations.Based on TPI,we further present a quantized approach,including the feature recurrence rate,the feature match score,and the weighted feature robustness score,to evaluate the robustness of remote-sensing image feature detectors.The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances.Three remote-sensing image feature detectors,including scale-invariant feature transform(SIFT),speeded up robust features(SURF),and priori information based robust features(PIRF),are evaluated using the proposed approach on the TPI data set.Experimental results show that the robustness of PIRF outperforms others by over 6.2%.

Secrecy Rate Maximization for 6G Cognitive Satellite-UAV Networks

To cover remote areas where terrestrial cellular networks may not be available,non-terrestrial infrastructures such as satellites and unmanned aerial vehicles(UAVs)can be utilized in the upcoming sixth-generation(6G)era.Considering the spectrum scarcity problem,satellites and UAVs need to share the spectrum to save costs,leading to a cognitive satellite-UAV network.Due to the openness of both satellite links and UAV links,communication security has become a major concern in cognitive satelliteUAV networks.In this paper,we safeguard a cognitive satellite-UAV network from a physical layer security(PLS)perspective.Using only the slowlyvarying large-scale channel state information(CSI),we jointly allocate the transmission power and subchannels to maximize the secrecy sum rate of UAV users.The optimization problem is a mixed integer nonlinear programming(MINLP)problem with coupling constraints.We propose a heuristic algorithm which relaxes the coupling constraints by the penalty method and obtains a sub-optimal low-complexity solution by utilizing random matrix theory,the max-min optimization tool,and the bipartite graph matching algorithm.The simulation results corroborate the superiority of our proposed scheme.

系统视角下的6G通信创新展望

The history of wireless communications can be divided into two eras.The first was a discovery-inspired era,initiated by the great physicist Maxwell’s discovery of the classical theory of electromagnetism,while the second was a technology-driven era that began with Marconi’s creation of the first practical world-wide radio-based wireless telegraph system,Shannon’s information theory,and the later invention of cellular mobile communications from the first generation(1G)to the fourth(4G).Nowadays,the fifth generation(5G)is regarded as a leading information and communication technology.

林业院校“蛋白质与酶工程”课程立体化教学研究

作为一门应用型较强的必修专业课——"蛋白质与酶工程"在中南林业科技大学多个专业中开展,包括生物技术、生物工程、生态学等。在该门课程的教学研究与革新过程中,应将教学资源作为基础,充分联动各个教学元素,建立多层次、全面的综合教学体系。在此目标上,本文总结了该门课程的改革措施,在实践中取得了满意的教学效果。同时将该模式推广至同类院校的同类型、相关联课程中以供参考。

Physical Layer Security for UAV Communications:A Comprehensive Survey

Due to its high mobility and flexible deployment,unmanned aerial vehicle(UAV)is drawing unprecedented interest in both military and civil applications to enable agile and ubiquitous connectivity.Mainly operating in an open environment,UAV communications benefit from dominant line-of-sight links;however,this on the other hand renders the communications more vulnerable to malicious attacks.Recently,physical layer security(PLS)has been introduced to UAV systems as an important complement to the conventional cryptography-based approaches.In this paper,a comprehensive survey on the current achievements of UAV-PLS is conducted.We first introduce the basic concepts including typical static/-mobile UAV deployment scenarios,the unique air-toground channel and aerial nodes distribution models,as well as various roles that a UAV may act when PLS is concerned.Then,we start by reviewing the secrecy performance analysis and enhancing techniques for statically deployed UAV systems,and extend the discussion to the more general scenario where the UAVs’mobility is further exploited.For both cases,respectively,we summarize the commonly adopted methodologies,then describe important works in the litera ture in detail.Finally,potential research directions and challenges are discussed to provide an outlook for future works in the area of UAV-PLS.

MEC-Empowered Non-Terrestrial Network for 6G Wide-Area Time-Sensitive Internet of Things

In the upcoming sixth-generation(6G)era,the demand for constructing a wide-area time-sensitive Internet of Things(IoT)continues to increase.As conventional cellular technologies are difficult to directly use for wide-area time-sensitive IoT,it is beneficial to use non-terrestrial infrastructures,including satellites and unmanned aerial vehicles(UAVs).Thus,we can build a non-terrestrial network(NTN)using a cell-free architecture.Driven by the time-sensitive requirements and uneven distribution of IoT devices,the NTN must be empowered using mobile edge computing(MEC)while providing oasisoriented on-demand coverage for devices.Nevertheless,communication and MEC systems are coupled with each other under the influence of a complex propagation environment in the MEC-empowered NTN,which makes it difficult to coordinate the resources.In this study,we propose a process-oriented framework to design communication and MEC systems in a time-division manner.In this framework,large-scale channel state information(CSI)is used to characterize the complex propagation environment at an affordable cost,where a nonconvex latency minimization problem is formulated.Subsequently,the approximated problem is provided,and it can be decomposed into sub-problems.These sub-problems are then solved iteratively.The simulation results demonstrated the superiority of the proposed process-oriented scheme over other algorithms,implied that the payload deployments of UAVs should be appropriately predesigned to improve the efficiency of using resources,and confirmed that it is advantageous to integrate NTN with MEC for wide-area time-sensitive IoT.

Two-Phase Rate Adaptation Strategy for Improving Real-Time Video QoE in Mobile Networks

With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors(video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.

DDoS Detection for 6G Internet of Things: Spatial-Temporal Trust Model and New Architecture

With the rapid development of the sixth generation(6G)network and Internet of Things(IoT),it has become extremely challenging to efficiently detect and prevent the distributed denial of service(DDoS)attacks originating from IoT devices.In this paper we propose an innovative trust model for IoT devices to prevent potential DDoS attacks by evaluating their trustworthiness,which can be deployed in the access network of 6G IoT.Based on historical communication behaviors,this model combines spatial trust and temporal trust values to comprehensively characterize the normal behavior patterns of IoT devices,thereby effectively distinguishing attack traffic.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method has advantages in terms of both accuracy and efficiency in identifying attack flows.

Resource-constrained maximum network throughput on space networks

This paper investigates the maximum network through- put for resource-constrained space networks based on the delay and disruption-tolerant networking （DTN） architecture. Specifically, this paper proposes a methodology for calculating the maximum network throughput of multiple transmission tasks under storage and delay constraints over a space network. A mixed-integer linear programming （MILP） is formulated to solve this problem. Simula- tions results show that the proposed methodology can successfully calculate the optimal throughput of a space network under storage and delay constraints, as well as a clear, monotonic relationship between end-to-end delay and the maximum network throughput under storage constraints. At the same time, the optimization re- sults shine light on the routing and transport protocol design in space communication, which can be used to obtain the optimal network throughput.

Characterization and Performance Evaluation of Turbulent Plasma Sheath Channel

Radio waves are highly attenuated and distorted by turbulent plasma sheath around hypersonic vehicles in near space, leading to communication blackout. The purpose of the paper is to investigate the plasma channel characteristics and the communication performances over the channel. We treat the turbulent plasma medium as a fast fading wireless channel. The coherence time and the spectrum spread of the plasma sheath channel are obtained in terms of root-meansquare(RMS). Baseband simulation scheme is proposed based on a stratified model of the plasma flow field. Results indicate that the coherence time is on the order of milliseconds and decreases rapidly with the increasing electron density turbulence. The spectrum spread due to plasma turbulence is also significant. Extensive simulations have been carried out to make communication performance evaluations. Quantitative results show that error floor takes place for PSK and QAM, while FSK with noncoherent detection is a promising method to mitigate the blackout problem.

基于微服务-工作流的市县协同创新管理模式研究

针对在创新管理过程中遇到创新氛围难以营造和评价、创新力量整合缺少渠道、课题实施计划性可控性不强、管理者无法实时管控课题研究过程以及创新成果推广应用缺少渠道等问题,本文提出了基于微服务-工作流的市县协同创新管理模式。该模式重点关注创新的课题孕育、课题发起、课题管理、推广应用四个阶段。从课题的孕育、发起、实施到推广,实现每个环节都可查、可控、可建议、可痕迹化,进而构建特色鲜明、要素集聚、活力迸发的企业高质量创新体系。