Smarter systems/smarter diagnostics

With the advent of larger networks of smarter (more complex) subsystems, the task of keeping subsystems on-line and easily diagnosable becomes ever more important. This leads to the requirement to design more powerful diagnostic capability into the subsystem instead of adding it as an afterthought. The use of powerful microprocessors dedicated to individual control functions allows subsystems to have significant diagnostic capabilities. Distilling our experience in designing and maintaining microcomputer systems at Fermilab, we will examine the structure of a prototypical subsystem and discuss the use of hierarchical diagnostics for both hardware and software.     

Impact of customer impatience on a production service system

In this paper, we explore the impact of customer impatience on the performance of a production service system that consists of one production inventory subsystem and one service subsystem. It is inevitable that the un-storability of service makes customers wait in the production service system. Waiting for service easily leads to impatient behaviors. The impact of two kinds of impatient behaviors balk and reneging on the production service system performance is examined. The problem is formulated as a Markov system, then the stationary probability of the system is derived to conduct the performance evaluation. The results show that customer impatience has the different impact patterns on the two subsystems (i.e. production inventory subsystem and service subsystem) of the production service system. Specifically, customer impatience leads to the performance improvement of one subsystem along with the performance deterioration of the other subsystem. Meanwhile, service capacity plays an important role in the influence extent of customer impatience on the system performance. In addition, lower customer impatience does not necessarily improve system profit. Therefore, the management of the production service system with impatient customers will be more intractable. Some managerial insights and suggestions are proposed.     

基于分析的空调系统运行控制的多目标优化

针对空调系统运行效率低下,且运行过程中子系统之间相互影响、相互制约的问题,本文基于分析方法提出一种空调系统多目标运行优化方法,以提高空调系统整体的运行效率。以某机场航站楼空调系统为研究对象,采用分析方法建立了空调系统的生产结构图,并根据生产结构将空调系统划分为3个子系统。在建立子系统分析模型的基础上,以3个子系统的最小损为优化目标,采用多目标粒子群优化算法(MOPSO)对空调系统的运行参数进行优化。针对某一典型日的运行工况对空调系统进行多目标优化,结果表明:相对于空调系统的原始控制方式,采用多目标运行优化方法可以有效减小空调各个子系统以及整个系统的损,使空调系统的运行效率明显提高,达到了节能的目的。     

基于精细Runge-Kutta混合积分法的车桥耦合振动非迭代求解算法

针对结构非线性问题,采用4阶Runge-Kutta法展开精细积分法中响应状态方程的Duhamel项,构造了一种既可以避免迭代又具有较高精度的精细Runge-Kutta混合积分方法,在此基础上提出了适用于车桥耦合振动高效求解的分析框架。车桥耦合系统由车辆、桥梁子系统组成,均采用有限元建模,其中车辆子系统采用部件刚体假定,而桥梁子系统借助于振型叠加法缩减自由度数目;两个子系统内部非线性作用以及系统间的相互作用通过非线性的虚拟力表达。以一节4轴客车匀速通过32m简支梁为研究对象,分别采用分析框架法、Runge-Kutta法进行动力分析。数值结果对比表明：相对于Runge-Kutta法,精细Runge-Kutta混合法能够显著提高计算收敛的积分步长;分析框架可以应用到实际工程中。     

IoT-based networking for humanoid robots

Abstract

In this paper, a new network communication approach, named real-time network (RTNET), is designed and implemented for humanoid robots. The proposed five network objects – alarm, condition, message, mail, and file are used to represent the task and priority of the communication data. Compared to the existing protocols, the network scheduling mechanism of RTNET arranges, more efficiently, the priority and flow control of the five network communication objects to meet real-time requirements for the limited bandwidth of the local area network. RTNET can be further integrated with other protocols, such as EtherCAT or controller area networks (CAN Bus) for local control systems, e.g. robot arms, to improve the communication mechanism. The RTNET can also be used over Ethernet to connect each subsystem and to exchange information among those systems. Also, an Internet of things (IoT) network structure based on RTNET is proposed in this paper. The information of each subsystems is collected through RTNET and users can access all components in the IoT network. In this paper, the concept of RTNET is presented and RTNET has been implemented on a National Taiwan University (NTU) humanoid robot control system with CAN Bus.     

Understanding weld modelling processes using a combination of trained neural networks

In this paper, a set of neural networks has been trained for weld modelling processes with different architecture and training parameters. The set of neural networks is trained using actual weld data available in the literature. The performance of each neural network in this set is defined by two performance measures of interest, namely training error and generalization error. Instead of using one of the best networks from this set of trained networks, a method of combining the outputs of all the network from the set is proposed and is called the combined output (or output of the combined network). It is shown that the performance measures of interest obtained using this combined output is better than the performance measures of interest obtained by all the individual neural networks in the set.     

Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs

In recent years, progressive developments have been observed in recent technologies and the production cost has been continuously decreasing. In such scenario, Internet of Things (IoT) network which is comprised of a set of Unmanned Aerial Vehicles (UAV), has received more attention from civilian to military applications. But network security poses a serious challenge to UAV networks whereas the intrusion detection system (IDS) is found to be an effective process to secure the UAV networks. Classical IDSs are not adequate to handle the latest computer networks that possess maximum bandwidth and data traffic. In order to improve the detection performance and reduce the false alarms generated by IDS, several researchers have employed Machine Learning (ML) and Deep Learning (DL) algorithms to address the intrusion detection problem. In this view, the current research article presents a deep reinforcement learning technique, optimized by Black Widow Optimization (DRL-BWO) algorithm, for UAV networks. In addition, DRL involves an improved reinforcement learning-based Deep Belief Network (DBN) for intrusion detection. For parameter optimization of DRL technique, BWO algorithm is applied. It helps in improving the intrusion detection performance of UAV networks. An extensive set of experimental analysis was performed to highlight the supremacy of the proposed model. From the simulation values, it is evident that the proposed method is appropriate as it attained high precision, recall, F-measure, and accuracy values such as 0.985, 0.993, 0.988, and 0.989 respectively.     

Reliability Reallocation for Fuel Cell Vehicles Based on Genetic Algorithm

A fuel cell vehicle (FCV) is a type of alternative energy vehicle that could help resolve the energy crisis, mitigate environmental problems, and contribute to sustainable development. Developing an FCV with high reliability is an important goal for automobile factories and research institutions. Other key factors required by FCVs include mass production and customer approval. An FCV is a complex mechanism composed of many subsystems. During the development of the overall vehicle, steps should be taken to ensure that every subsystem is reliable. However, such development must also consider costs, which must be kept as low as possible. To ensure the reliability of FCV while operating under conditions that demand minimal cost, a genetic algorithm is employed to reallocate the reliability of the overall vehicle system. First, the growth factor of the reliability–feasibility of each subsystem is determined according to the complexity, importance, and technological level of the FCV subsystems. The FCV cost model is then established on the basis of such parameters as subsystem cost, reliability–feasibility growth factor, initial reliability, limit reliability, and so on. A genetic algorithm is then used to compute for the reliability of FCV subsystems. The rationality of reliability reallocation is verified according to the subsystem importance coefficient. This method considers the benefits for both enterprises and customers by applying principles of engineering and conducting a reliability study. Copyright © 2014 John Wiley & Sons, Ltd.     

Kernel Search-Framework for Dynamic Controller Placement in Software-Defined Network

In software-defined networking (SDN) networks, unlike traditional networks, the control plane is located separately in a device or program. One of the most critical problems in these networks is a controller placement problem, which has a significant impact on the network’s overall performance. This paper attempts to provide a solution to this problem aiming to reduce the operational cost of the network and improve their survivability and load balancing. The researchers have proposed a suitable framework called kernel search introducing integer programming formulations to address the controller placement problem. It demonstrates through careful computational studies that the formulations can design networks with much less installation cost while accepting a general connected topology among controllers and user-defined survivability parameters. The researchers used the proposed framework on six different topologies then analyzed and compared with Iterated Local Search (ILS) and Expansion model for the controller placement problem (EMCPP) along with considering several evaluation criteria. The results show that the proposed framework outperforms the ILS and EMCPP. Thus, the proposed framework has a 38.53% and 38.02% improvement in reducing network implementation costs than EMCPP and ILS, respectively.     

Modeling and analysis of fault tolerant multistage interconnection networks

Performance and reliability are two of the most crucial issues in today's high-performance instrumentation and measurement systems. High speed and compact density multistage interconnection networks (MINs) are widely-used subsystems in different applications. New performance models are proposed to evaluate a novel fault tolerant MIN arrangement, thereby assuring performance and reliability with high confidence level. A concurrent fault detection and recovery scheme for MINs is considered by rerouting over redundant interconnection links under stringent real-time constraints for digital instrumentation such as sensor networks. A switch architecture for concurrent testing and diagnosis is proposed. New performance models are developed and used to evaluate the compound effect of fault tolerant operation (inclusive of testing, diagnosis, and recovery) on the overall throughput and delay. Results are shown for single transient and permanent stuck-at faults on links and storage units in the switching elements. It is shown that performance degradation due to fault tolerance is graceful while performance degradation without fault recovery is unacceptable.     

Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks

Mobile broadband (MBB) networks are expanding rapidly to deliver higher data speeds. The fifth-generation cellular network promises enhanced-MBB with high-speed data rates, low power connectivity, and ultra-low latency video streaming. However, existing cellular networks are unable to perform well due to high latency and low bandwidth, which degrades the performance of various applications. As a result, monitoring and evaluation of the performance of these network-supported services is critical. Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users. This paper proposes a Bayesian model to estimate the minimum opinion score (MOS) of video streaming services for any particular cellular network. The MOS is the most commonly used metric to assess the quality of experience. The proposed Bayesian model consists of several input data, namely, round-trip time, stalling load, and bite rates. It was examined and evaluated using several test data sizes with various performance metrics. Simulation results show the proposed Bayesian network achieved higher accuracy overall test data sizes than a neural network. The proposed Bayesian network obtained a remarkable overall accuracy of 90.36% and outperformed the neural network.     

Performance analysis of a separate address/data bus multiprocessor system

Abstract

This paper presents a performance model of a special shared bus multiprocessor system, that features: (1) separate address‐bus and data‐bus with split transaction, pipelined cycle; (2) two‐level cache structure; and (3) multiple main memory and I/O modules. Accessing conflicts in these subsystems, maintaining shared data and DMA transfer between memory and I/O subsystems are also considered in the model. The representation for the complex behavior of a whole multiprocessor system distinguishes the model from others that present only one major subsystem. The performance model can be used not only to assist in evaluating the architectural design of aparticular system, but also directly utilized to identify subsystem bottlenecks and their causes in order to make performance improvements. Results show that: (1) the values of some key design parameters, such as cache line size and data‐bus width that yield the best throughput, are dependent on the performance of subsystems; (2) choosing the data‐bus width at one‐half of cache line size can achieve the lowest access time in the shared bus; (3) using cache‐to‐cache transfer protocol may prevent performance degradation caused by maintaining shared data; and (4) the activity of DMA transfer may significantly affect system throughput and should be included in a performance model of multiprocessor systems.     

Optimizing the product-based availability of a buffered industrial process

Many industrial processes for discrete consumable products consist of a series (or set) of sequential process operations (or subsystems) which are de-coupled by means of in-process storage buffers. Each subsystem of such a process contains one or more parallel coupled or uncoupled operating lanes. We describe the use of a discrete-event simulation model for determining the availability of such a process. We likewise define and use a genetic algorithm to determine process designs and operating rules that have high availability. A 65-variable example, consisting of four operating subsystems with at most four lanes per subsystem, is used to illustrate the method. The results for this and similar real-world applications indicate that, by applying this methodology, it is possible to design buffered industrial processes having high availability.     

A framework for operator – workstation interaction in Industry 4.0

We draw on cognitive and behavioural theories and on the artificial intelligence literature in order to propose a framework of future operator – workstation interaction in the ‘Industry 4.0’ era. We name the proposed framework ‘Operator – Workstation Interaction 4.0’. The latter’s capabilities permit an adaptive, ongoing interaction that aims to improve operator safety, performance, well-being, and satisfaction as well as the factory’s production measures. The framework is composed of three subsystems: (1) the observation subsystem which observes the operator and the processes occurring in the workstation, (2) the analysis subsystem which generates understanding and implications of the observations output, (3) the reaction subsystem which determines if and how to respond. The paper describes these elements and illustrate them using an example of a fatigued worker. The contributions, implications, and limitations of the proposed framework are discussed, and future research directions are presented.     

关于我国地铁建设降低投资问题

章本着技术先进,安全可靠,经济实用的原则,对降低地铁造价的可能性进行分析,提出了如下初步设想,充分作好前期准备工作,重视客流预测,考虑提高行车密度,选择合理的车站结构型式和埋深,合理确定车站设备用房和管理用户的面积,重视换乘站的设计,采用合理的车站间距,做好地下线与高架线的选择,隧道埋深,结构尺寸及施工方案的优化,切实贯彻设备国产化政策。     

On the performance of time division multiple access-based multihop fixed cellular networks with respect to available frequency carriers

In multihop cellular networks (MCN), the user nodes can act as relays and forward other nodes' traffic to/from base stations. There are several advantages of MCN such as the improved signal quality and higher coverage. However, it is known that multihop relaying networks require extra radio resources. Therefore the performance of MCN depends to a great extent on the availability of adequate radio resources. The performance of a time division multiple access (TDMA)-based multihop fixed cellular network is analysed with highlighting the dependence of the system performance on the amount of available radio resources, namely, the number of frequency carriers. Results show that in a fixed cellular network, the multihop architecture significantly outperforms the traditional single-hop architecture in terms of the outage probability and throughput if an adequate amount of frequency carriers is available in the network. Otherwise, the multihop fixed cellular networks architecture loses its superiority and might even lead to performance degradation, particularly at high loading levels.     

HELP-WSN-A Novel Adaptive Multi-Tier Hybrid Intelligent Framework for QoS Aware WSN-IoT Networks

Wireless Sensor Network is considered as the intermediate layer in the paradigm of Internet of things (IoT) and its effectiveness depends on the mode of deployment without sacrificing the performance and energy efficiency. WSN provides ubiquitous access to location, the status of different entities of the environment and data acquisition for long term IoT monitoring. Achieving the high performance of the WSN-IoT network remains to be a real challenge since the deployment of these networks in the large area consumes more power which in turn degrades the performance of the networks. So, developing the robust and QoS (quality of services) aware energy-efficient routing protocol for WSN assisted IoT devices needs its brighter light of research to enhance the network lifetime. This paper proposed a Hybrid Energy Efficient Learning Protocol (HELP). The proposed protocol leverages the multi-tier adaptive framework to minimize energy consumption. HELP works in a two-tier mechanism in which it integrates the powerful Extreme Learning Machines for clustering framework and employs the zonal based optimization technique which works on hybrid Whale-dragonfly algorithms to achieve high QoS parameters. The proposed framework uses the sub-area division algorithm to divide the network area into different zones. Extreme learning machines (ELM) which are employed in this framework categories the Zone's Cluster Head (ZCH) based on distance and energy. After categorizing the zone's cluster head, the optimal routing path for an energy-efficient data transfer will be selected based on the new hybrid whale-swarm algorithms. The extensive simulations were carried out using OMNET++-Python user-defined plugins by injecting the dynamic mobility models in networks to make it a more realistic environment. Furthermore, the effectiveness of the proposed HELP is examined against the existing protocols such as LEACH, M-LEACH, SEP, EACRP and SEEP and results show the proposed framework has outperformed other techniques in terms of QoS parameters such as network lifetime, energy, latency.     

Analysis of Wireless Backhaul Networks Based on Aerial Platform Technology for 6G Systems

As next generation communication technologies emerge, new high data rate applications and high-definition large-screen video streaming have become very popular. As a result, network traffic has been increasing so much that existing backhaul networks soon will not be able to support all traffic demands. To support these needs in future 6G mobile systems, the establishment of an additional backhaul wireless network is considered essential. As one of the solutions, a wireless backhaul network based on an aerial platform has been proposed. In order to explore the potential of aerial platforms as wireless backhaul networks, in this paper, the categories for wireless backhaul networks based on aerial platforms are investigated. This paper includes a survey of the definitions and characteristics of low altitude platforms (LAPs) and high altitude platforms (HAPs), as well as channel models according to the atmosphere. For wireless backhaul network designs based on aerial platforms, altitude and platform selection options, deployment options, energy issues, and security based on target location and performance were considered in the analysis and investigation.     

Recent Progress of HTS Microwave Applications in Satellite Receiver, Meteorological Radar, Mobile Communication and Radio Astronomy

The applications of high-temperature superconductor (HTS) filters or subsystems to radio astronomy, the third generation (3G) mobile communications, meteorological radar, and satellite are introduced. A wideband HTS filter for the Miyun 50-m radio telescope in Beijing was presented, and a novel resonator and a new style interleaved coupling structure were proposed for the design of the wideband filter. An HTS transceiver subsystem was constructed. A field trial in commercial TD-SCDMA base station network was carried out in Beijing and remarkable improvements were achieved for RF performance in both transmitting and receiving chains. A demonstration HTS meteorological radar station was set up in Beijing and it showed great enhancements in sensitivity and anti-interference ability in urban area wind profile measurements. An experimental HTS microwave receiver front end for satellite applications had been constructed and passed all the space qualification tests. As a payload of an experimental satellite, it was ready for launch.     

Reliability reallocation for cost uncertainty of fuel cell vehicles via improved differential evolution algorithm

A fuel cell vehicle (FCV) is composed of many subsystems; it is necessary to reallocate subsystem reliability to improve FCV system reliability. A comprehensive evaluation method considering uncertainty is proposed to obtain the interval value of feasibility factor. The FCV cost uncertainty model is established on the basic of parameters such as feasibility factor, initial reliability, limit reliability, and subsystem cost. To solve the optimization problem for cost uncertainty, the cost interval model is transformed into a deterministic model by interval order relation. An improved differential evolution (DE) algorithm is proposed to reallocate subsystem reliability for minimum cost.