Research on cloud-edge-terminal collaborative intelligent control of coal shearer based on 5G communication

The current shearer control system is difficult to deal with the complex working conditions of the underground working face. The realization of the intelligent height and speed regulation control of the shearer is faced with three major technical problems:the lack of data communication capacity of traditional 4G, WiFi and other wireless communication technologies, the low data processing synergy of the centralized cloud computing platform, and the need for high manual intervention assistance for coal cutting control. In order to promote the intelligent development of the shearer and the whole fully mechanized mining face, starting from solving the key technologies of the shearer such as 5G communication technology, cloud-side-end collaboration technology, intelligent height and speed regulation control technology, a shearer intelligent height and speed regulation control system based on 5G+cloud-side-end collaboration technology was developed, and the 5G communication technology and cloud-side-end collaboration technology used in the system were described in detail, the hardware platform and communication network of the shearer intelligent height adjustment and speed regulation control system were introduced. The shearer intelligent height and speed regulation control system adopts a multimodal fusion coal rock distribution identification model based on feature level fusion and decision level complementarity, and on the basis of coal rock distribution identification technology, the shearer intelligent height adjustment and speed regulation control strategy of "memory cutting coal+coal rock distribution identification intervention" is used for cutting control. The 81309 Working Face in Shendong Baode Coal Mine conducted a 2-month application test of the control system, the test results showed that the packet loss rate of the real-time communication of the shearer was about 0.003%, the end-to-end average communication delay between the shearer and the centralized control center had decreased by 75%, the failure rate of the shearer drum had decreased by 42%, and the coal gangue mixing rate of the front scraper conveyor had decreased by 42.9%, which verified the practicality and feasibility of the shearer intelligent control system and the hardware platform. The work improved the intelligent degree and reliability level of shearer control, which can be popularized and applied to promote the intelligent development of fully mechanized mining face

Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory

In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed method performs with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully mechanized coal mining face was demonstrated to specify the effect of the proposed system

Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory

In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed method performs with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully mechanized coal mining face was demonstrated to specify the effect of the proposed system

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia

11th International Coral Reef Symposium Abstracts

https://nsuworks.nova.edu/occ_icrs/1001/thumbnail.jp