Perspective:Societally connected multimedia across cultures

The advance of the Internet in the past decade has radically changed the way people communicate and col- laborate with each other. Physical distance is no more a barrier in online social networks, but cultural differences (at the individual, community, as well as societal levels) still govern human-human interactions and must be con- sidered and leveraged in the online world. The rapid de-ployment of high-speed Internet allows humans to interact using a rich set of multimedia data such as texts, pictures, and videos. This position paper proposes to define a new research area called ’connected multimedia’, which is the study of a collection of research issues of the super-area social media that receive little attention in the literature. By connected multimedia, we mean the study of the social and technical interactions among users, multimedia data, and devices across cultures and explicitly exploiting the cultural differences. We justify why it is necessary to bring attention to this new research area and what benefits of this new research area may bring to the broader scientific research community and the humanity.     

基于像素拓展的图形分割与合成的方法研究

提出了一种新的图像分割与合成方法。对于图像的分割,提出了新的分割算法,先是手工将一些明显属于物体和明显属于背景的像素分割出来,然后创建一个小矩形区域进行像素拓展迭代,迭代到图像边界为止。对于图像的合成,采用平均法,设计了新的算法,使物体的基本颜色保持不变的同时,降低了合成图像中从源图像分割的物体与新背景的差异,并使得其饱和度、色度、强度更加自然真实。     

智能家居远程视频监控系统的设计与实现

针对传统模拟视频监控中图像质量差、无法联网、不可移植等不足,设计了一种基于嵌入式Linux的智能家居远程视频监控系统。远程视频监控系统以ARM（Advanced RISC Machines）处理器S3C2440和Linux操作系统为核心平台。在内核中添加了各种所需的驱动程序,基于V4L2的应用程序对USB摄像头进行图像视频采集,采用TCP/IP网络协议,进行视频数据传输。主机端采用跨平台开发工具Qt开发应用程序,接收图像视频数据并显示图像视频。实验结果显示,能在远程端看到清晰流畅的视频流,系统运行稳定可靠。     

An efficient and scalable parallel mapping of pulse-Doppler radar signal processing chain on a multi-core DSP

Digital Pulse-Doppler radar chain consists of signal processing algorithms that require high computing power. Multi-processor and multi-core parallel embedded machines are one of the solutions to meet real-time constraints of many radar applications. In this paper, we proposed efficient and scalable parallelization methods of the Pulse-Doppler radar signal processing chain. First, we evaluated Open Multi Processing (OpenMP) to identify its best scheduling technique in order to exploit efficiently the available computing cores. Then, we have proposed new parallel and scalable approaches based on direct memory access (DMA) and inter-processor communication (IPC) techniques, combined with the best OpenMP scheduling method to accelerate radar signal processing chain. To prove the scalability of our proposed parallel approaches, two radar use cases with different real-time and memory constraints have been experienced. We used the eight cores C6678 digital signal processor (DSP) as a target for all our implementations. The obtained results show an overall parallel efficiency of 95%, which is better than the best state-of-the-art implementations.     

Semi-automated user agents in distributed change management

Flexible integration of distributed design and manufacturing activities is one of the key issues in applying just-in-time principles along the logistics chain. The potential of AI based approaches in this field is clear, but the inherent asynchrony, concurrency, and inconsistency of distributed operations has reduced enthusiasm so far.This paper discusses coordination in the context of geographically distributed manufacturing of one-of-a-kind products. Cooperation of partners is based on semi-automated user agents which operate as an integrative layer between more dedicated tools and systems. The management of order changes in elevator manufacture is used as a case example.The work reported here is part of the DIMUN project (DIMUN, 1989) in the RACE program.     

New Paradigm of Data-Driven Smart Customisation through Digital Twin

Big data is one of the most important resources for the promotion of smart customisation. With access to data from multiple sources, manufacturers can provide on-demand and customised products. However, existing research of smart customisation has focused on data generated from the physical world, not virtual models. As physical data is constrained by what has already occurred, it is limited in the identification of new areas to improve customer satisfaction. A new technology called digital twin aims to achieve this integration of physical and virtual entities. Incorporation of digital twin into the paradigm of existing data-driven smart customisation will make the process more responsive, adaptable and predictive. This paper presents a new framework of data-driven smart customisation augmented by digital twin. The new framework aims to facilitate improved collaboration of all stakeholders in the customisation process. A case study of the elevator industry illustrates the efficacy of the proposed framework.     

Digital Twin-driven machining process for thin-walled part manufacturing

Thin-walled parts are widely used in the aerospace, shipbuilding, and automotive industry, but due to its unique structure and high accuracy requirements, which leads to an increase in scrapped parts, high cost in production, and a more extended period in the trial machining process. However, to adapt to fast production cycles and increase the efficiency of thin-walled parts machining, this paper presents a Digital Twin-driven thin-walled part manufacturing framework to allow the machine operator to manage the product changes, make the start-up phases faster and more accurate. The framework has three parts: preparation, machining, and measurement, driven by Digital Twin technologies in detail. By establishing and updating the workpiece Digital Twin under a different status, various manufacturing information and data can be integrated and available to machine operators and other Digital Twins. It can serve as a guideline for establishing the machine tool and workpiece Digital Twin and integrating them into the machining process. It provides the machine operator opportunities to interact with both the physical manufacturing process and its digital data in real-time. The digital representation of the physical process can support them to manage the trial machining from different aspects. In addition, a demonstrative case study is presented to explain the implementation of this framework in a real manufacturing environment.     

LearningADD: Machine learning based acoustic defect detection in factory automation

Defect inspection of glass bottles in the beverage industrial is of significance to prevent unexpected losses caused by the damage of bottles during manufacturing and transporting. The commonly used manual methods suffer from inefficiency, excessive space consumption, and beverage wastes after filling. To replace the manual operations in the pre-filling detection with improved efficiency and reduced costs, this paper proposes a machine learning based Acoustic Defect Detection (LearningADD) system. Moreover, to realize scalable deployment on edge and cloud computing platforms, deployment strategies especially partitioning and allocation of functionalities need to be compared and optimized under realistic constraints such as latency, complexity, and capacity of the platforms. In particular, to distinguish the defects in glass bottles efficiently, the improved Hilbert-Huang transform (HHT) is employed to extend the extracted feature sets, and then Shuffled Frog Leaping Algorithm (SFLA) based feature selection is applied to optimize the feature sets. Five deployment strategies are quantitatively compared to optimize real-time performances based on the constraints measured from a real edge and cloud environment. The LearningADD algorithms are validated by the datasets from a real-life beverage factory, and the F-measure of the system reaches 98.48 %. The proposed deployment strategies are verified by experiments on private cloud platforms, which shows that the Distributed Heavy Edge deployment outperforms other strategies, benefited from the parallel computing and edge computing, where the Defect Detection Time for one bottle is less than 2.061 s in 99 % probability.     

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.     

A Human-Robot Collaboration Framework for Improving Ergonomics During Dexterous Operation of Power Tools

In this work, we present a novel control approach to human-robot collaboration that takes into account ergonomic aspects of the human co-worker during power tool operations. The method is primarily based on estimating and reducing the overloading torques in the human joints that are induced by the manipulated external load. The human overloading joint torques are estimated and monitored using a whole-body dynamic state model. The appropriate robot motion that brings the human into the suitable ergonomic working configuration is obtained by an optimisation method that minimises the overloading joint torques. The proposed optimisation process includes several constraints, such as the human arm muscular manipulability and safety of the collaborative task, to achieve a task-relevant optimised configuration. We validated the proposed method by a user study that involved a human-robot collaboration task, where the subjects operated a polishing machine on a part that was brought to them by the collaborative robot. A statistical analysis of ten subjects as an experimental evaluation of the proposed control framework is provided to demonstrate the potential of the proposed control framework in enabling ergonomic and task-optimised human-robot collaboration.