A Novel Energy-Efficient Approach for Human Activity Recognition

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper

IoT and Industry 4.0 technologies in Digital Manufacturing Transformation

The evolution of internet of things, cyber physical system, digital twin and artificial intelligence is stimulating the transformation of the product-centric processes toward smart control digital service-oriented ones. With the implementation of artificial intelligence and machine learning algorithms, IoT has accelerated the movement from connecting devices to the Internet to collecting and analyzing data by using sensors to extract data throughout the lifecycle of the product, in order to create value and knowledge from the huge amount of the collected data, such as the knowledge of the product performance and conditions. The importance of internet of things technology in manufacturing comes from its ability to collect real time data and extract valuable knowledge from these huge amount of data which can be supported through the implementation of smart IoT-based servitization framework which was presented in this research together with a 10-steps approach diagram. Moreover, literature review has been carried out to develop the research and deepen the knowledge in the field of IoT, CPS, DT and Artificial Intelligence, and then interviews with experts have been conducted to validate the contents, since DT is a quite new technology, so there are different points of view about certain concepts of this technology. The main scope and objective of this research is to allow organizational processes and companies to benefit form the value added information that can be achieved through the right implementation of advanced technologies such as IoT, DT, CPS, and artificial intelligence which can provide financial benefits to the manufacturing companies and competitive advantages to make them stand among the other competitors in the market. The effectiveness of such technologies can not only improve the financial benefits of the companies, but the workers\u2019 safety and health through the real time monitoring of the work environment. Here in this research the main aim is to present the right frameworks that can be used in the literature through companies and researchers to allow them to implement these technologies correctly in the boundaries of their businesses. In addition to that, the Smart factory concept, as introduced in the context of Industry 4.0, promotes the development of a new interconnected manufacturing environment where human operators cooperate with machines. While the role of the operator in the smart factory is substantially being rediscussed, the industrial approach towards safety and ergonomics still appears frequently outdated and inadequate. This research approaches such topic referring to the vibration risk, a well-known cause of work-related pathologies, and proposes an original methodology for mapping the risk exposure related to the performed activities. A miniaturized wearable device is employed to collect vibration data, and the obtained signals are segmented and processed in order to extract the significant features. An original machine learning classifier is then employed to recognize the worker\u2019s activity and evaluate the related exposure to vibration risks. Finally, the results obtained from the experimental analysis demonstrate feasibility and the effectiveness of the proposed methodology

Réseau de communication à haut niveau d'intégrité pour des systèmes de commande-contrôle critiques intégrant des nappes de microsystèmes

Vu le développement important des micro-systèmes, leur utilisation sous forme de nappes dans les systèmes de commande-contrôle critiques est incontournable. Cela soulève néanmoins des défis, parmi lesquels la définition d'un système de communication à haut niveau d'intégrité. L'étude que nous avons effectuée sur des réseaux standard montre que les protections classiques à base de codes CRC ne permettent pas d'obtenir le niveau d'intégrité visé. Pour l'atteindre, nous avons proposé une solution originale - fonction de contrôle évolutive - qui tire profit du fait que, pour les systèmes de commande-contrôle envisagés (systèmes à dynamique lente), l'intégrité est à considérer sur un lot de messages et non un seul message. La solution proposée a ensuite été validée via des simulations Matlab-Simulink. Le cas d'étude utilisé est celui de systèmes de commande de vol du futur, en vue de pouvoir commander des nappes de milliers de micro-surfaces tels que des micro-spoilers. ABSTRACT : With the wide development of microsystems, it is likely that they will be massively used in critical control systems. This raises many new challenges, among which the definition of a dependable communication network with high integrity level. The study of standard communication networks and their basic commonly used transmission errors detection techniques based on CRC codes shows that they cannott ensure the required integrity level. To reach this integrity level, we propose an original solution - an evolutive error control function - based on the slowly evolution characteristic of the control systems considered, for which the integrity is considered for a set of consecutive messages and not for a single message. The validation of the proposed solution is based on MATLAB simulation models we have developed. The study case is about the integration of thousands of micro-surfaces such as micro-spoilers in future flight control systems