Effective passenger flow control measures are essential for the safe operation of metro stations. Existing in-station control measures include adjusting the operation mode of escalators and setting up temporary fences. However, in practice, metro operators often adopt fixed operation modes during fixed periods, indicating that the current passenger flow control measures at metro stations are overly rigidified. Therefore, developing an adaptive control strategy to constantly balance the wildly fluctuating passenger flow and optimize the operation performance is a key issue in current research. In this study, transportation efficiency and congestion risk are selected as evaluation objectives for passenger transportation risk, and passenger flow feature, station structure, and passenger flow control measures are considered key influential factors. Subsequently, an adaptive optimization method integrating simulation and data interpolation is proposed. The software Legion is used to conduct 150 orthogonal simulations, and prediction models for passenger transportation risk are obtained by performing data interpolation on the simulation results. Finally, taking a certain metro station as a case study, the optimal passenger flow control strategy under any passenger flow composition is obtained by scenario acquisition, risk identification, and adaptive decision-making. The results show that setting up temporary fences can reduce the passenger density near the fare gates, while adjusting the running direction of escalators can reduce overcrowding on the platform. Under varying passenger flow composition, the optimal strategy for the current scenario can be obtained, controlling passenger transportation risk within an acceptable range and providing assistance for metro operators in decision-making.

Real-time and fast recognition of all kinds of traffic participants in intelligent driving has always been a major difficulty in the research of internet of vehicles. With the advent of edge computing, we try to deploy an image recognition algorithm directly to the intelligent vehicles. However, the original image recognition algorithm is difficult to be directly deployed on the vehicles due to limited edge device resources. Based on this, a fast recognition model of vulnerable traffic participants based on depthwise separable convolutional neural network (DSCYOLO) is proposed in this paper. The algorithm can significantly reduce the convolutional parameter quantity and computing load, making it suitable for deployment on the vehicle-mounted edge embedded devices. In order to validate the effectiveness of the proposed method, its simulation results are compared with the main target detection models Faster R-CNN, SSD and YOLOv3. The results show that the recognition time of the proposed model is reduced by 80.28%, 66.80% and 86.74%, respectively, on the basis of a relatively high recognition precision. The model can realize real-time detection and fast recognition of vulnerable traffic participants, so as to avoid a large number of traffic accidents. It has significant social and economic benefits.

With the continuous development of urbanization, metro systems have become an essential means of travel for urban residents. However, the increase of passengers using the metro system also increases the risk of occupant instability and stampede, which is especially significant on metro platforms considering the mixed passenger flow of boarding, waiting and getting off. How to grasp the overcrowding of passengers and gathering risk on the metro platform in time is the critical problem of the current research. To solve the difficulties in acquiring the number of passengers at metro platforms, a 7-phase model is constructed to estimate the risk of crowd gathering in each platform according to the Origin-Destination (OD) data. The proposed model divides a travel process into 7 components in detail and evaluates the crowd-gathering risk accurately to the platform. The travel time of each passenger is calculated by the station structure parameters and pedestrian parameters. Then, the travel stages of passengers are acquired by the travel time. Thirdly, the number of passengers on any platform and at any time is calculated. Finally, the temporal and spatial distribution of crowd-gathering risk on all metro platforms of a metro network is analyzed. The constructed passenger travel model and the crowd-gathering risk analysis method can effectively identify the high-risk platform and high-risk periods, which provides method and data support for metro operation safety improvement and crowd accident prevention.

Task scheduling is one of the key techniques for effective and reliable resource usage in cloud computing. In this paper, we designed a hybrid heuristic scheduling that employed particle swarm optimisation (PSO) and least accumulated slack time to respectively address the problem of assigning tasks to servers and the problem of the task scheduling for multi-core servers, to maximise the service level agreement (SLA) satisfaction for resource efficiency improvement and task execution in heterogeneous clouds with deadline constraints. Experimental results show that our method can complete up to 112.5% more tasks, compared with several classical and state-of-art task scheduling methods.

Extreme rainfall events, such as heavy rainfall and typhoon, can cause unexpected disruptions to the metro ridership and operating system, resulting in severe consequences such as infrastructure malfunctions, service termination and system paralysis. This paper focuses on the spatio-temporal impacts and resilience assessment of extreme rainfall events on metro ridership. The ridership data used in this paper are from the Automatic Fare Collection (AFC) system in Shenzhen Metro, and the time ranges from May to September in 2017 and 2018 with the 15-minute granularity. This paper not only utilizes big data to analyze the spatio-temporal characteristics of passenger flow under heavy rainfall and typhoon, but also innovatively introduces the meteorological warning signals and ridership resilience curve to analyze the resilience of ridership. The main results reveal that the general heavy rainfall affects passenger flow in the spatio-temporal imbalance. Especially for the spatial aspect, the imbalance of direction and section in peak hours significantly aggravates and the section passenger volume is even larger than usual. For typhoon events, extreme weather can strongly affect the distributions and recovery of metro ridership. Stronger typhoons can have a greater impact on resilience, but continuous rainfall can lead to a longer recovery time. The study results can help metro management agencies better understand the impacts of extreme weather on metro ridership to build a more weather-resilience metro system. (C) 2021 Elsevier B.V. All rights reserved.

The original pattern recognition and classification of crop diseases needs to collect a large amount of data in the field and send them next to a computer server through the network for recognition and classification. This method usually takes a long time, is expensive, and is difficult to carry out for timely monitoring of crop diseases, causing delays to diagnosis and treatment. With the emergence of edge computing, one can attempt to deploy the pattern recognition algorithm to the farmland environment and monitor the growth of crops promptly. However, due to the limited resources of the edge device, the original deep recognition model is challenging to apply. Due to this, in this article, a recognition model based on a depthwise separable convolutional neural network (DSCNN) is proposed, which operation particularities include a significant reduction in the number of parameters and the amount of computation, making the proposed design well suited for the edge. To show its effectiveness, simulation results are compared with the main convolution neural network (CNN) models LeNet and Visual Geometry Group Network (VGGNet) and show that, based on high recognition accuracy, the recognition time of the proposed model is reduced by 80.9% and 94.4%, respectively. Given its fast recognition speed and high recognition accuracy, the model is suitable for the real-time monitoring and recognition of crop diseases by provisioning remote embedded equipment and deploying the proposed model using edge computing.

With the development of complex networks in urban rail transit (URT), the topological structure changes accordingly and node importance also redistributes dynamically. However, many deficiencies exist in the single measure or unweighted network or static network when ranking node importance. Most importantly, the evolution mechanism of node importance with the network development is seldom studied. In view of this, in this paper, six unweighted and weighted complex networks are firstly modeled in the evolution of URT networks. One of Multiple Attribute Decision Making (MADM) methods is proposed, that is WTOPSIS (The Weighted Technique for Order of Preference by Similarity to Ideal Solution) algorithm combining Coefficient of Variation method and TOPSIS. Then four local and global centralities are aggregated and utilized in WTOPSIS to rank the node importance in those six networks. On the basis, the intersection degrees among the ranking sets are calculated to evaluate the similarities of ranking results. Furthermore, the factors contributing to the evolution of node importance are discussed quantitatively and qualitatively with examples. Finally, the feasibility of the method is verified by the Shenzhen Metro system in 2016. Results show that WTOPSIS algorithm outperforms the single attribute in ranking node importance, which makes up for the shortcomings in existing studies. Besides, for different stations in URT network development, node importance evolution is affected differently by the changes of topological structure and passenger flow. It is necessary to combine with the actual situations for the specific analysis. This study reveals the evolution mechanism of the node importance in the development of URT networks and it also has great theoretical and practical significance.

Metro systems, as major components of the transit system, have been significantly developed in recent years and an increasing number of new metro lines are operated in China. This paper not only analyses the short and long-term impact of new lines opening on passenger flow from passenger volume, section imbalance coefficient, direction imbalance coefficient and travel time, with the help of passenger flow data from September 2016 to October 2018 in Shenzhen but also proposes a new method to identify the growth period of the transfer flow in new lines. The result shows that the new metro lines cannot solve the problems of imbalance and congestion in Shenzhen Metro. it increased the travel time in the morning and evening peak, but largely decreased the travel time in the off-peak period. Additionally, there is a significant positive correlation between the length of the growth period and the number of transfer stations of the transfer line.

To study the topological complexity of urban rail transit (URT) networks with the multi-line transfer stations from different perspectives, Shenzhen Metro (SZM) is taken as an example and Space L & Space P models are established in this study. Then, based on multiple evaluation parameters and key nodes ranking, the differences of network topological complexity in two models are deeply explored and compared quantitatively. Some meaningful results have been obtained: (i) The characteristics of scale-free networks in Space L and Space P are proved through the eigenvector centrality distribution and truncated power-law distribution of cumulative degree. Scale-free networks show both robustness against random faults and vulnerability against deliberate attacks. The daily safety management at 16.87% of hub stations in Space P and 17.47% of hub stations in Space L should be taken seriously by metro managers in case of emergency events. (ii) Since the WS small-world effect in Space P model is more evident than that in Space L model, the connections among stations and OD accessibility of passenger are enhanced in Space P network. (iii) The important and risk nodes are concentrated in Space L and are more decentralized in Space P. P model has the stronger overall anti-attack capability than L model, which is more beneficial to the resilience of network. This study can realize the deeper understanding of URT system with different models and it can provide theoretical support for complex network analysis of URT system.

地铁车站类型识别和客流风险识别对地铁安全运营管理有着重要的作用.基于深圳地铁AFC(automatic fare collection)系统数据,采用无关值和异常值清理、聚合、均值滤波、标准化、主成分分析等数据清洗步骤,提取不同时段客流比例、不同天数客流比例和换乘客流比例等特征.运用Gauss混合模型(GMM)对工作日和周末客流进行聚类,分析客流出行规律,辨识车站类型及其对应的客流风险时段,提出车站客流风险分析方法,通过大数据分析对车站类型和客流风险进行识别.分析结果对掌握车站大客流风险情况,避免大客流冲击造成的拥挤踩踏等群体性事件的发生,保障乘客安全具有指导意义.

We have proposed a new evacuation model based on discrete time loss queuing method in order to effectively depict the queuing of pedestrians in an indoor space and its effect over evacuation performance. In this model, the calculation formula of pedestrian movement probability is given first based on field value and average queuing time; the average queuing time is depicted with the discrete time loss queuing method and the adopted evacuation strategy is set forth through defining cellular evolution process. Moreover, with the use of the established simulation platform for experiment, we have made a deep study of relations of parameters such as evacuation time, pedestrian density, exit number and average queuing time to obtain the pedestrian flow characteristic more in line with the reality. The result has shown that there is a great change in the evacuated population in the change of crowded state at the exit, and in the background of high population density, it is beneficial for reducing queuing time to prefer faraway exit to overcrowded exit for evacuation.

人工智能的快速发展,对教育的影响也越来越广泛而深入,其中深度神经网络是推动人工智能迅猛发展的关键技术.论文针对目前物联网虚拟仿真平台中存在的主要问题,采用基于深度神经网络算法的智能实验批改程序对学生的实验进行智能批改,大大减轻了教师的课后工作量,提高了教学效率,同时由于采取线上物联网项目仿真实验,大大降低了物联网传感器等器件地损坏和消耗,并且使学生在实验课前课后也可以进行仿真实验,提高了时间使用效率,大大减轻了物联网专业实验室的压力.

Given a graph G with n vertices and l edges, the load distribution of a coloring q: V → {red, blue} is defined as dq = (rq, bq), in which rq is the number of edges with at least one end-vertex colored red and bq is the number of edges with at least one end-vertex colored blue. The minimum load coloring problem (MLCP) is to find a coloring q such that the maximum load, lq = 1/l × max{rq, bq}, is minimized. This problem has been proved to be NP-complete. This paper proposes a memetic algorithm for MLCP based on an improved K-OPT local search and an evolutionary operation. Furthermore, a data splitting operation is executed to expand the data amount of global search, and a disturbance operation is employed to improve the search ability of the algorithm. Experiments are carried out on the benchmark DIMACS to compare the searching results from memetic algorithm and the proposed algorithms. The experimental results show that a greater number of best results for the graphs can be found by the memetic algorithm, which can improve the best known results of MLCP.

随着当前物联网边缘计算技术的发展,使得物联网数据安全访问的问题日益严峻,传统的基于用户权限的数据访问控制策略不能很好解决相关数据安全访问控制问题.为此,从用户身份认证和数据访问控制等方面研究物联网数据安全共享问题的解决方案.以物联网农田环境数据安全共享系统为应用背景,提出了一种基于用户属性和用户行为的数据安全访问控制模型,该模型以用户行为构建信任机制,并结合用户属性的设置与判定共同实现系统的数据安全访问控制.给出了模型的规则定义与构建、具体结构设计、数据处理控制流程以及用户信任度评价体系设计的详细过程及实例分析.通过分析结果表明,该模型设计具有较好的动态性和扩展性,能够实现物联网中用户对农田环境数据的安全访问,从而解决物联网数据安全共享问题.

For the realization of bioassay with complex fluidic manipulation and logic operation on lab-on-a-disc platform, we present an active integrated centrifugal microfluidic chip based on the on-board control of wax valves within a multilayer complex chip. The multilayer hybrid structure including a microfluidic layer and a printing circuit board (PCB) layer utilizes the digital logic of electronic system to control the logic of liquid flow in microfluidic layer. The coupling mechanism between both layers is based on heat transfer, namely, the heating resistors in PCB layer are used to melt and open the paraffin wax valves in microfluidic layer. Without the limitation of surface tension-dependent valves, the application of active valve could be freely designed, which can largely extend the ability of integration on microfluidic chip. Many complex functional units including liquid sequential loading and switching of liquid flow are demonstrated. As an application, we also present a multilayer complex chip for plasmid DNA extraction based on our platform. In a word, our active centrifugal microfluidic platform provides a solution for the integration of complex bioassay on rotating disc, which has great potential in the applications of point-of-care diagnostics (POC).

Application layer DDoS attack challenges web applications seriously. It launches attack by sending a large number of HTTP Get requests to a web server. The anomaly-based method is a promising method, which detects the DDoS attack by comparing the individual surfing behavior with a reference surfing-behavior profile. Yet due to the exist of noisy web logs caused by web-crawling, it is difficult to build robust reference profile for detection. This paper proposes a novel anomaly-based application DDoS detection scheme base on clustering method. Our method could tolerate the web-crawling traces in building reference surfing profile, and can detect different Application layer DDoS attack (e.g., repetitively getting several webpages, randomly getting webpages following hyper-links etc.). The simulation results show that our method can detect application layer DDoS attacks accurately.

So far, most ring signature schemes rely on hard number theory problems, such as discrete logarithm, bilinear pairings and so on. Unfortunately, the above underlying number theory problems will be solvable in the post quantum era. Lattice-based cryptography is a hotspot of research recently, due to its implementation simplicity and provable security reductions. When the hash-and-sign signature scheme was constructed based on the hardness of worst-case lattice problems, provably secure lattice-based ring signature schemes were finally constructed. However, the hash-and-sign ring signatures were rather inefficient (with megabytes long signatures). In this paper, we propose an alternative method for constructing lattice-based and identity-based ring signature scheme which does not use the hash-and-sign methodology. In the random oracle model, the proposed signature scheme based on the problem in general lattices is unforgeable and holds anonymity. Compared with the previous instantiations of the hash-and-sign ring signature schemes, the lengths of secret key, public key and signatures in the proposed scheme are much shorter. The signing algorithm is quite simple, with matrix-vector multiplications and rejection samplings.

In recent years, cloud storage has become an attractive solution due to its elasticity, availability and scalability. However, the security issue has started to prevent public clouds becoming more popular. Traditional encryption algorithms (both symmetric and asymmetric ones) fail to support achieving effective secure cloud storage due to severe issues such as complex key management and heavy redundancy. Ciphertext-policy attribute-based encryption (CP-ABE) scheme overcomes the aforementioned issues and provides fine-grained access control as well as deduplication features. CP-ABE has become a possible solution to cloud storage. However, its high complexity has prevented it from being widely adopted. This paper parallelises CP-ABE where issues to ensure secured cloud storage are considered and deployed in cloud storage environments. Major performance bottlenecks such as key management and encryption/decryption process are identified and accelerated, and a new AES encryption operation mode is adopted for further performance gains. Experimental results have demonstrated its effectiveness and such design is promising.