Dynamic analysis of auger driller during luffing motion by bond graph

To investigate the inherent complex dynamic characteristics of luffing mechanism of auger driller, the rigid body motion of structures and the dynamic behavior of the drive system should be studied in an integrated model. The working principle and structural characteristics of the luffing mechanism is firstly analyzed, then the bond graph model of revolute joint, cylinder and boom are proposed based multi-body theory, and the bond graph model of hydraulic system is also constructed. Through the analysis of the dynamic characteristics and interaction rules of each sub model, the transmission path of power flow is described. Coupling the boom structure and hydraulic actuator, the complete bond graph model of luffing mechafnism have been developed in a unified way. The total governing equations of the system have been derived from the model. Numerical results of chamber pressure of luffing cylinder implies to the good accuracy of the bond graph study, while comparing with experimental results. Meanwhile, the effects of the installation position parameters of the joints on system response have been studied through simulation, which provides a theoretical basis for improving the dynamic performance of the luffing mechanism

Science Communication in Context of China: Reducing the Regional Imbalance

Science communication is influenced by various factors of social context, such as politics, economy, culture and history, and these factors have different impact on science communication in different time and space domain. China is a country with appreciable regional diversity, where exist huge gap in economic, social, educational and science and technology development. The imbalance in development makes social context of science communication a prominent feature in the country. In China, the demand of citizens on science communication presents diversified and complex features, which were intensified by the leapfrog development of science communication pattern. Based upon above considerations, China is taking public science communication strategy in a localized way by government and the society as well. This paper discusses the functions which science communication played in reducing the regional imbalance in China

Understanding the Connotation, Impact and Measurement Concerning Informatization of Science Popularization in China

Informatization is the new direction of the effort in the field of science popularization in China. It refers to the change not only in the technological approach and the channels of science popularization, but also to the idea and the model of science popularization. This paper shed light on the connotation concerning informatization of science popularization (ISP) in three dimensions. It illustrates that ISP produces profound impact on the science popularization practice, the individuals, and society. Regarding practice, ISP in China includes the digitalization period, networking period and intelligentializing period. ISP makes the practice more powerful and efficient. It is of importance for ISP to promote the construction of the knowledge society and create the atmosphere of scientific culture in society. Finally, the measurement framework of ISP is proposed, which aims at promoting the working process and achieving the ultimate goal through a monitoring method

Network Traffic Classification Based on External Attention by IP Packet Header

As the emerging services have increasingly strict requirements on quality of service (QoS), such as millisecond network service latency ect., network traffic classification technology is required to assist more advanced network management and monitoring capabilities. So far as we know, the delays of flow-granularity classification methods are difficult to meet the real-time requirements for too long packet-waiting time, whereas the present packet-granularity classification methods may have problems related to privacy protection due to using excessive user payloads. To solve the above problems, we proposed a network traffic classification method only by the IP packet header, which satisfies the requirements of both user's privacy protection and classification performances. We opted to remove the IP address from the header information of the network layer and utilized the remaining 12-byte IP packet header information as input for the model. Additionally, we examined the variations in header value distributions among different categories of network traffic samples. And, the external attention is also introduced to form the online classification framework, which performs well for its low time complexity and strong ability to enhance high-dimensional classification features. The experiments on three open-source datasets show that our average accuracy can reach upon 94.57%, and the classification time is shortened to meet the real-time requirements (0.35ms for a single packet).Comment: 12 pages, 5 figure

TransMUSE: Transferable Traffic Prediction in MUlti-Service Edge Networks

The Covid-19 pandemic has forced the workforce to switch to working from home, which has put significant burdens on the management of broadband networks and called for intelligent service-by-service resource optimization at the network edge. In this context, network traffic prediction is crucial for operators to provide reliable connectivity across large geographic regions. Although recent advances in neural network design have demonstrated potential to effectively tackle forecasting, in this work we reveal based on real-world measurements that network traffic across different regions differs widely. As a result, models trained on historical traffic data observed in one region can hardly serve in making accurate predictions in other areas. Training bespoke models for different regions is tempting, but that approach bears significant measurement overhead, is computationally expensive, and does not scale. Therefore, in this paper we propose TransMUSE, a novel deep learning framework that clusters similar services, groups edge-nodes into cohorts by traffic feature similarity, and employs a Transformer-based Multi-service Traffic Prediction Network (TMTPN), which can be directly transferred within a cohort without any customization. We demonstrate that TransMUSE exhibits imperceptible performance degradation in terms of mean absolute error (MAE) when forecasting traffic, compared with settings where a model is trained for each individual edge node. Moreover, our proposed TMTPN architecture outperforms the state-of-the-art, achieving up to 43.21% lower MAE in the multi-service traffic prediction task. To the best of our knowledge, this is the first work that jointly employs model transfer and multi-service traffic prediction to reduce measurement overhead, while providing fine-grained accurate demand forecasts for edge services provisioning

Water-containing i-propanol-n-butanol-ethanol (IBE) as a next-generation biofuel of n-butanol for diesel engine

The high emission level of diesel engines has been an issue of global concern and the sophisticated means of controlling the emissions were not cost-effective. In this work, effects of water addition in a bio-derived fuel to mitigate engine emissions and enhances the brake thermal efficiency have been investigated. Four test samples including IBE10, IBE30, IBE29.5W0.5 and IBE29W1 have been prepared and tested in a diesel engine. The engine combustion characteristics, performance and emissions have been observed. It has been established that the water containing blends improve the BTE, BSFC and further reduces emissions at varying loads. In comparison with IBE30, IBE29W1 (29 vol. % IBE, 1 vol. % water and 90 vol. % diesel) has shown decreasing peak in-cylinder pressure and increases ignition delay and combustion duration by 0.13% – 4.8 %, 0.5% – 12.4 % and 0.26% – 3.8 % respectively. As for the engine performance, BTE has been increased by 2.6 % – 14.1% and BSFC decreased by 0.1% – 15 %, respectively, and the emissions of UHC, smoke, CO and NOx emissions was decreased by 21% – 42.6%, 0% – 21.7%, 5.4% –11%, and 0.64% – 9%, at varying loading conditions respectively

Water-containing i-propanol-n-butanol-ethanol (IBE) as a next-generation biofuel of n-butanol for diesel engine

223-233The high emission level of diesel engines has been an issue of global concern and the sophisticated means of controlling the emissions were not cost-effective. In this work, effects of water addition in a bio-derived fuel to mitigate engine emissions and enhances the brake thermal efficiency have been investigated. Four test samples including IBE10, IBE30, IBE29.5W0.5 and IBE29W1 have been prepared and tested in a diesel engine. The engine combustion characteristics, performance and emissions have been observed. It has been established that the water containing blends improve the BTE, BSFC and further reduces emissions at varying loads. In comparison with IBE30, IBE29W1 (29 vol. % IBE, 1 vol. % water and 90 vol. % diesel) has shown decreasing peak in-cylinder pressure and increases ignition delay and combustion duration by 0.13% – 4.8 %, 0.5% – 12.4 % and 0.26% – 3.8 % respectively. As for the engine performance, BTE has been increased by 2.6 % – 14.1% and BSFC decreased by 0.1% – 15 %, respectively, and the emissions of UHC, smoke, CO and NOx emissions was decreased by 21% – 42.6%, 0% – 21.7%, 5.4% –11%, and 0.64% – 9%, at varying loading conditions respectively