Cloud based cyber-physical systems: Network evaluation study

In recent years, the Industry 4.0 concept brings new demands and trends in different areas; one of them is distributing computational power to the cloud. This concept also introduced the Reference Architectural Model for Industry 4.0 (RAMI 4.0). The efficiency of data communications within the RAMI 4.0 model is a critical issue. Aiming to evaluate the efficiency of data communication in the Cloud Based Cyber-Physical Systems (CB-CPS), this study analyzes the periods and data amount required to communicate with individual hierarchy levels of the RAMI 4.0 model. The evaluation of the network properties of the communication protocols eligible for CB-CPS is presented. The network properties to different cloud providers and data centers’ locations have been measured and interpreted. To test the findings, an architecture for cloud control of laboratory model was proposed. It was found that the time of the day; the day of the week; and data center utilization have a negligible impact on latency. The most significant impact lies in the data center distance and the speed of the communication channel. Moreover, the communication protocol also has impact on the latency. The feasibility of controlling each level of RAMI 4.0 through cloud services was investigated. Experimental results showed that control is possible in many solutions, but these solutions mostly cannot depend just on cloud services. The intelligence on the edge of the network will play a significant role. The main contribution is a thorough evaluation of different cloud providers, locations, and communication protocols to provide recommendations sufficient for different levels of the RAMI 4.0 architecture.     

Predictive maintenance using cox proportional hazard deep learning

Predictive maintenance (PdM) has become prevalent in the industry in order to reduce maintenance cost and to achieve sustainable operational management. The core of PdM is to predict the next failure so corresponding maintenance can be scheduled before it happens. The purpose of this study is to establish a Time-Between-Failure (TBF) prediction model through a data-driven approach. For PdM, data sparsity is regarded as a critical issue which can jeopardize algorithm performance for the modelling based on maintenance data. Meanwhile, data censoring has imposed another challenge for handling maintenance data because the censored data is only partially labelled. Furthermore, data sparsity may affect algorithm performance of existing approaches when addressing the data censoring issue. In this study, a new approach called Cox proportional hazard deep learning (CoxPHDL) is proposed to tackle the aforementioned issues of data sparsity and data censoring that are common in the analysis of operational maintenance data. The idea is to offer an integrated solution by taking advantage of deep learning and reliability analysis. To start with, an autoencoder is adopted to convert the nominal data into a robust representation. Secondly, a Cox proportional hazard model (Cox PHM) is researched to estimate the TBF of the censored data. A long-short-term memory (LSTM) network is then established to train the TBF prediction model based on the pre-processed maintenance data. Experimental studies using a sizable real-world fleet maintenance data set provided by a UK fleet company have demonstrated the merits of the proposed approach where the algorithm performance based on the proposed LSTM network has been improved respectively in terms of MCC and RMSE.     

A smart process controller framework for Industry 4.0 settings

This paper presents a smart supervisory framework for a single process controller, designed for Industry 4.0 shop floors. This digitization of a full supervisory suite for a single process controller enables self-awareness, self-diagnosis, self-prognosis, and self-healing (by definition, these "self" elements are missing from other supervisory frameworks diagnosing numerous controllers in parallel). The proposed framework is aligned with the concept of a Cyber Physical System (CPS), since its implementation generates a rich cyber physical entity of the controlled process. This CPS entity can either be considered as the process digital twin, or can provide a solid basis for generating it. Finally, the framework includes the main characteristics of Industry 4.0, such as advanced use of Artificial Intelligence (AI) and big data analysis. The framework is based on four modules: (1) Control and Awareness module—performing both continuous process control and adjustments, as well as machine learning (ML) and statistical process control (SPC) for identifying abnormalities that require further diagnosis; (2) Process -diagnosis module—performing continual (recurrent) analysis of the process state and trends; (3) Prognosis and Healing module—performing prognosis and automated intervention via parameter changes, re-configurations, and automated maintenance; (4) External Interaction Platform—an interactive module for interfacing with experts, presenting them with the process analysis information and obtaining feedback from them as part of a learning process. Using an implementation showcase to illustrate the methodological framework’s applicability, we demonstrate its real-world potential. The proposed framework could serve as a guide for implementing smart process control and maintenance systems in Industry 4.0 shop floors. It could also provide a firm basis for comparison with future suggested frameworks. Future research directions could include pursuing improvements to the proposed process control framework and validating the framework by case studies of its implementation.

     

Multimodal concept detection in broadcast media: KavTan

Concept detection stands as an important problem for efficient indexing and retrieval in large video archives. In this work, the KavTan System, which performs high-level semantic classification in one of the largest TV archives of Turkey, is presented. In this system, concept detection is performed using generalized visual and audio concept detection modules that are supported by video text detection, audio keyword spotting and specialized audio-visual semantic detection components. The performance of the presented framework was assessed objectively over a wide range of semantic concepts (5 high-level, 14 visual, 9 audio, 2 supplementary) by using a significant amount of precisely labeled ground truth data. KavTan System achieves successful high-level concept detection performance in unconstrained TV broadcast by efficiently utilizing multimodal information that is systematically extracted from both spatial and temporal extent of multimedia data.     

基于AVS的嵌入式音视频同步方法

音视频同步是数字电视广播和多媒体通信等应用的关键技术。该文提出一种基于AVS并结合嵌入技术的音视频同步方法。将压缩音频数据嵌入AVS视频编码系统,保证传输或存储、接收端解码与播放过程中的音视频始终同步。实验结果表明,该方法实现了音视频完全同步,能减小用于同步的开销。     

A per-object-granularity tracking mechanism and systemfor interactive TV viewership estimation and program rating in real time

TV program ratings directly affect the decisions of advertisers and TV stations program offerings. TV program providers use television ratings reports to offer appropriate programs as well as attract advertisers. In this paper we propose a framework for TV audience estimates and program ratings using a per-object-granularity tracking mechanism for interactive TV content tracking in real time. It can be applied to live TV broadcasting environments as well as recorded broadcasting environments. The major components of the proposed tracking architecture are an interactive TV content creation tool powered by hotlinked video technology, a tracking information delivery framework with live interactive content insertion capability, an interactive TV content receiver, and tracking servers. The proposed mechanism and system in this paper enables a broadcast station or a rating agency to automatically perform tracking functions in real time based on viewer actions as well as on the content of audiovisual programming.     

Audiovisual diarization of people in video content

Audio-Visual People Diarization (AVPD) is an original framework that simultaneously improves audio, video, and audiovisual diarization results. Following a literature review of people diarization for both audio and video content and their limitations, which includes our own contributions, we describe a proposed method for associating both audio and video information by using co-occurrence matrices and present experiments which were conducted on a corpus containing TV news, TV debates, and movies. Results show the effectiveness of the overall diarization system and confirm the gains audio information can bring to video indexing and vice versa.     

Mass personalization: social and interactive applications using sound-track identification

This paper describes mass personalization, a framework for combining mass media with a highly personalized Web-based experience. We introduce four applications for mass personalization: personalized content layers, ad hoc social communities, real-time popularity ratings and virtual media library services. Using the ambient audio originating from a television, the four applications are available with no more effort than simple television channel surfing. Our audio identification system does not use dedicated interactive TV hardware and does not compromise the user’s privacy. Feasibility tests of the proposed applications are provided both with controlled conversational interference and with “living-room” evaluations.     

井下数字安全广播系统的设计

针对传统的定压广播、局部扩音电话及小灵通等矿用广播系统的缺点,提出了一种井下数字安全广播系统的设计方案,介绍了该系统的整体结构及软、硬件设计方法。该系统采用Ogg Vorbis音频编码方式,以CAN总线为数据传输方式,可实现远程低速率音频传输。应用结果表明,该系统具有较好的音频还原能力。     

Deep learning models for predictive maintenance: a survey,comparison, challenges and prospects

Applied Intelligence - Given the growing amount of industrial data in the 4th industrial revolution, deep learning solutions have become popular for predictive maintenance (PdM) tasks, which...     

复杂系统维护策略最新研究进展: 从视情维护到预测性维护

对于复杂、可修复的工程系统, 设备维护是确保系统安全性、可靠性、可用性的重要手段之一. 系统维护策略已经历修复性维护、定时维护、视情维护等多种维护策略. 其中, 视情维护是目前最受关注的维护策略, 它通过收集和评估系统的实时状态信息进行维护决策, 具有全寿命周期内系统可靠性高、运营维护成本低等优点. 近年来, 随着物联网技术、信息技术和人工智能的快速发展, 一种更新颖的视情维护策略——预测性维护逐渐成为领域研究热点. 本文首先简要回顾了系统维护策略的发展历程; 然后, 重点介绍了视情维护的研究进展, 根据决策支持技术的不同, 将视情维护划分为基于随机退化模型的视情维护和基于数据驱动的预测性维护, 对每类技术的发展分支与研究现状进行了疏理、分析和总结; 最后, 探讨了当前复杂系统维护策略面临的挑战性问题和可能的未来研究方向.     

矿用数字广播系统设计

针对煤矿井下广播系统模拟音频广播方式存在灵活性差、语音通信范围小等问题,提出了一种新型的矿用数字广播系统设计方案,介绍了系统的总体结构、音频终端及网关程序的设计。该系统将音频终端接入工业以太网,实现了分组和定点广播,解决了井下的单播、组播问题。     

基于人机共融的包装机故障诊断系统的设计与实现

随着工业4.0和物联网时代的来临,基于经验和手册的设备维修方式已不能满足复杂设备维修的要求。而传统的设备诊断系统往往只注重从物理传感器采集数据,缺少引入人的经验,难以拥有自学习能力。本文以ZB45烟草包装机为例,提出一种具有自学习能力的人机共融新型故障诊断系统。系统采用贝叶斯网络,实现对传感器数据的自动推理。通过自然语言处理模块与用户交互,学习用户的维修经验,并用来改进诊断效果。提出了基于凸优化的标签选择方法,根据观察到的现象推荐合适的标签,以快速确定最可能的故障,实现快速找到报警号码对应的故障源。生产现场的实测数据表明,本系统可以有效降低万箱故障次数,有效提高故障诊断精度,降低故障排查时间。     

A target advertisement system based on TV viewer’s profile reasoning

The traditional broadcasting services such as terrestrial, satellite and cable broadcasting have been unidirectional mass media regardless of TV viewer’s preferences. Recently rich media streaming has become possible via the broadband networks. Furthermore, since bidirectional communication is possible, personalcasting such as personalized streaming service has been emerging by taking into account the user’s preference on content genres, viewing times and actors/actresses etc. Accordingly personal media becomes an important means for content provision service in addition to the traditional broadcasting service as mass media. In this paper, we introduce a user profile reasoning method for TV viewers. The user profile reasoning is made in terms of genre preference and TV viewing times for TV viewer’s groups in different genders and ages. For user profiling reasoning, the TV viewing history data is used to train the proposed user profiling reasoning algorithm which allows for target advertisement for different age/gender groups. To show the effectiveness of our proposed user profile reasoning method, we present plenty of the experimental results by using real TV usage history.     

CMMB标准中数据业务系统的设计与实现

中国移动多媒体广播系统目前以提供视音频节目为主,数据业务广播的实现与应用需要进一步发展.提出一种基于中国移动多媒体广播标准体系的数据业务系统并加以实现,阐述了系统架构与各功能模块设计,并对系统所涉关键技术的实现进行了详细说明.经实验证明该系统能够依据标准完成数据业务信息的封装、复用、发送、接收与还原,为用户提供种类丰富的数据广播节目,扩展了移动多媒体广播业务类型,满足人们对多元化信息的需求.     

The orchestration of Machine Learning frameworks with data streams and GPU acceleration in Kafka-ML: A deep-learning performance comparative

Machine Learning (ML) applications need large volumes of data to train their models so that they can make high-quality predictions. Given digital revolution enablers such as the Internet of Things (IoT) and the Industry 4.0, this information is generated in large quantities in terms of continuous data streams and not in terms of static datasets as it is the case with most AI (Artificial Intelligence) frameworks. Kafka-ML is a novel open-source framework that allows the complete management of ML/AI pipelines through data streams. In this article, we present new features for the Kafka-ML framework, such as the support for the well-known ML/AI framework PyTorch, as well as for GPU acceleration at different points along the pipeline. This pipeline will be described by taking a real Industry 4.0 use case in the Petrochemical Industry. Finally, a comprehensive evaluation with state-of-the-art deep learning models will be carried out to demonstrate the feasibility of the platform.     

Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case

Industry 4.0 is considered to be the fourth industrial revolution introducing a new paradigm of digital, autonomous, and decentralized control for manufacturing systems. Two key objectives for Industry 4.0 applications are to guarantee maximum uptime throughout the production chain and to increase productivity while reducing production cost. As the data-driven economy evolves, enterprises have started to utilize big data techniques to achieve these objectives. Big data and IoT technologies are playing a pivotal role in building data-oriented applications such as predictive maintenance.In this paper, we use a systematic methodology to review the strengths and weaknesses of existing open-source technologies for big data and stream processing to establish their usage for Industry 4.0 use cases. We identified a set of requirements for the two selected use cases of predictive maintenance in the areas of rail transportation and wind energy. We conducted a breadth-first mapping of predictive maintenance use-case requirements to the capabilities of big data streaming technologies focusing on open-source tools. Based on our research, we propose some optimal combinations of open-source big data technologies for our selected use cases.     

Data embedding in audio signal using multiple bit marking layers method

One of the biggest challenges in data embedding is that the confidential data need to be in the ‘transparency’ after being embedded into the audio signal. Therefore, embedding methods must reduce the influence of embedded data onto the original audio signal. In this paper, the multiple bit marking layers (MBML) method has been proposed to fulfill this requirement. This method reuses the results from the previous embedding time (layer) as the input data to continue embedding it into audio signals (i.e. the next layer). The quality of the proposed method is evaluated through embedding error (EE), signal-to-noise ratio (SNR), embedded capacity (EC) and contribution error (CE). Experimental results have shown that the proposed method provides better quality of EE, and SNR than any other proposed embedding methods such as: LSB (Least Significant Bit), ELS (Embedding Large Sample.), BM (Bit Marking), and the BM/SW (Sliding Window) method with a single layer.     

Enabling predictive maintenance integrated production scheduling by operation-specific health prognostics with generative deep learning

Predictive Maintenance (PdM) is one of the core innovations in recent years that sparks interest in both research and industry. While researchers develop more and more complex machine learning (ML) models to predict the remaining useful life (RUL), most models are not designed with regard to actual industrial practice and are not validated with industrial data. To overcome this gap between research and industry and to create added value, we propose a holistic framework that aims at directly integrating PdM models with production scheduling. To enable PdM-integrated production scheduling (PdM-IPS), an operation-specific health prognostics model is required. Therefore, we propose a generative deep learning model based on the conditional variational autoencoder (CVAE) that can derive an operation-specific health indicator (HI) from large-scale industrial condition monitoring (CM) data. We choose this unsupervised learning approach to cope with one of the biggest challenges of applying PdM in industry: the lack of labelled failure data. The health prognostics model provides a quantitative measure of degradation given a specific production sequence and thus enables PdM-IPS. The framework is validated both on NASA’s C-MAPSS data set as well as real industrial data from machining centers for automotive component manufacturing. The results indicate that the approach can both capture and quantify changes in machine condition such that PdM-IPS can be subsequently realized.     

Design and integration of the OpenCore-based mobile TV framework for DVB-H/T wireless network

In the array of mobile communication techniques, the application of a mobile phone combined with television is a new technique under development. As TV program is a real-time video/audio service, in comparison with either traditional video/audio file downloads or network video/audio streams, there are more technical difficulties to be overcome, in particular, how to satisfy the playback functions of TV programs in hand-held device. OpenCore is a multimedia framework, which has recently been widely applied in hand-held devices, but it does not offer functions of mobile TV. To solve this problem, this study incorporates the function of mobile TV into the OpenCore framework, in order to support both formats of TV signals, i.e. DVB-H and DVB-T. The incorporated function, DVB-H/T, has different characteristics, so that users can select TV signals according to their receiving environments and fulfill their needs in TV programs selection.