The interleaving/multiplexing technique was used to realize a 200?MHz real time data acquisition system. Two 100?MHz ADC modules worked parallelly and every ADC plays out data in ping pang fashion. The design improv...The interleaving/multiplexing technique was used to realize a 200?MHz real time data acquisition system. Two 100?MHz ADC modules worked parallelly and every ADC plays out data in ping pang fashion. The design improved the system conversion rata to 200?MHz and reduced the speed of data transporting and storing to 50?MHz. The high speed HDPLD and ECL logic parts were used to control system timing and the memory address. The multi layer print board and the shield were used to decrease interference produced by the high speed circuit. The system timing was designed carefully. The interleaving/multiplexing technique could improve the system conversion rata greatly while reducing the speed of external digital interfaces greatly. The design resolved the difficulties in high speed system effectively. The experiment proved the data acquisition system is stable and accurate.展开更多
A new system called alternate data acquisition and real-time monitoring system has been developed for long-time discharge in tokamak operation. It can support continuous on-line data acquisition at a high sampling rat...A new system called alternate data acquisition and real-time monitoring system has been developed for long-time discharge in tokamak operation. It can support continuous on-line data acquisition at a high sampling rate and a graphic display of the plasma parameters during the discharge. Thus operators can monitor and control the plasma state in real time. An application of this system has been demonstrated on the HT-7 tokamak.展开更多
Data mining (also known as Knowledge Discovery in Databases - KDD) is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The aims and objectives of data...Data mining (also known as Knowledge Discovery in Databases - KDD) is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The aims and objectives of data mining are to discover knowledge of interest to user needs.Data mining is really a useful tool in many domains such as marketing, decision making, etc. However, some basic issues of data mining are ignored. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? Is there any rule we should obey in a data mining process? In order to discover patterns and knowledge really interesting and actionable to the real world Zhang et al proposed a domain-driven human-machine-cooperated data mining process.Zhao and Yao proposed an interactive user-driven classification method using the granule network. In our work, we find that data mining is a kind of knowledge transforming process to transform knowledge from data format into symbol format. Thus, no new knowledge could be generated (born) in a data mining process. In a data mining process, knowledge is just transformed from data format, which is not understandable for human, into symbol format,which is understandable for human and easy to be used.It is similar to the process of translating a book from Chinese into English.In this translating process,the knowledge itself in the book should remain unchanged. What will be changed is the format of the knowledge only. That is, the knowledge in the English book should be kept the same as the knowledge in the Chinese one.Otherwise, there must be some mistakes in the translating proces, that is, we are transforming knowledge from one format into another format while not producing new knowledge in a data mining process. The knowledge is originally stored in data (data is a representation format of knowledge). Unfortunately, we can not read, understand, or use it, since we can not understand data. With this understanding of data mining, we proposed a data-driven knowledge acquisition method based on rough sets. It also improved the performance of classical knowledge acquisition methods. In fact, we also find that the domain-driven data mining and user-driven data mining do not conflict with our data-driven data mining. They could be integrated into domain-oriented data-driven data mining. It is just like the views of data base. Users with different views could look at different partial data of a data base. Thus, users with different tasks or objectives wish, or could discover different knowledge (partial knowledge) from the same data base. However, all these partial knowledge should be originally existed in the data base. So, a domain-oriented data-driven data mining method would help us to extract the knowledge which is really existed in a data base, and really interesting and actionable to the real world.展开更多
Because of the complex nature of the changes in the current and movement of the riverbeds by bridge scouring, it is impossible to understand or predict these changes. In order to have a reliable data, it is critical t...Because of the complex nature of the changes in the current and movement of the riverbeds by bridge scouring, it is impossible to understand or predict these changes. In order to have a reliable data, it is critical to have the current methods and equipment for measuring bridge scouring replaced with technology that could acquire real-time bridge scouring data. Despite the critical need for real-time data acquisition, the harsh environmental conditions have prevented the scientific community from acquiring real-time data. Harsh environmental conditions were addressed by the developmental of an automated, remote data collection system, allowing real-time data such as scour movement, scour depth, and scour trend to be viewed in a safe location. As a result, accurate sea-floor movements were seen for the first time, aiding the direction and future of bridge scour research, ultimately contributing greatly to the safety of bridges.展开更多
The advent of the Internet of Things(IoT)is transforming traditional agriculture into a more efficient,sustainable,and economically viable sector.This paper delves into the integration of IoT technology in agriculture...The advent of the Internet of Things(IoT)is transforming traditional agriculture into a more efficient,sustainable,and economically viable sector.This paper delves into the integration of IoT technology in agriculture,emphasizing its role in enhancing crop monitoring,irrigation management,and pest control through real-time data acquisition and automated systems.We discuss the core components of IoT which facilitate the seamless collection and transmission of agricultural data,enabling precise farming operations.The paper highlights three major applications of IoT in agriculture:precision agriculture,smart irrigation systems and advanced pest management techniques.The transformative impacts of IoT are analyzed across economic,environmental,and social dimensions,illustrating how IoT not only boosts agricultural productivity and market competitiveness but also fosters sustainable practices and supports community resilience.Through this comprehensive exploration,the paper aims to provide valuable insights for producers,policymakers,and developers,positioning IoT as a cornerstone of modern agricultural strategies that are both effective and sustainable.展开更多
文摘The interleaving/multiplexing technique was used to realize a 200?MHz real time data acquisition system. Two 100?MHz ADC modules worked parallelly and every ADC plays out data in ping pang fashion. The design improved the system conversion rata to 200?MHz and reduced the speed of data transporting and storing to 50?MHz. The high speed HDPLD and ECL logic parts were used to control system timing and the memory address. The multi layer print board and the shield were used to decrease interference produced by the high speed circuit. The system timing was designed carefully. The interleaving/multiplexing technique could improve the system conversion rata greatly while reducing the speed of external digital interfaces greatly. The design resolved the difficulties in high speed system effectively. The experiment proved the data acquisition system is stable and accurate.
基金Meg-science Program of the Chinese Academy of Sciences (No. 19981303)
文摘A new system called alternate data acquisition and real-time monitoring system has been developed for long-time discharge in tokamak operation. It can support continuous on-line data acquisition at a high sampling rate and a graphic display of the plasma parameters during the discharge. Thus operators can monitor and control the plasma state in real time. An application of this system has been demonstrated on the HT-7 tokamak.
文摘Data mining (also known as Knowledge Discovery in Databases - KDD) is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The aims and objectives of data mining are to discover knowledge of interest to user needs.Data mining is really a useful tool in many domains such as marketing, decision making, etc. However, some basic issues of data mining are ignored. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? Is there any rule we should obey in a data mining process? In order to discover patterns and knowledge really interesting and actionable to the real world Zhang et al proposed a domain-driven human-machine-cooperated data mining process.Zhao and Yao proposed an interactive user-driven classification method using the granule network. In our work, we find that data mining is a kind of knowledge transforming process to transform knowledge from data format into symbol format. Thus, no new knowledge could be generated (born) in a data mining process. In a data mining process, knowledge is just transformed from data format, which is not understandable for human, into symbol format,which is understandable for human and easy to be used.It is similar to the process of translating a book from Chinese into English.In this translating process,the knowledge itself in the book should remain unchanged. What will be changed is the format of the knowledge only. That is, the knowledge in the English book should be kept the same as the knowledge in the Chinese one.Otherwise, there must be some mistakes in the translating proces, that is, we are transforming knowledge from one format into another format while not producing new knowledge in a data mining process. The knowledge is originally stored in data (data is a representation format of knowledge). Unfortunately, we can not read, understand, or use it, since we can not understand data. With this understanding of data mining, we proposed a data-driven knowledge acquisition method based on rough sets. It also improved the performance of classical knowledge acquisition methods. In fact, we also find that the domain-driven data mining and user-driven data mining do not conflict with our data-driven data mining. They could be integrated into domain-oriented data-driven data mining. It is just like the views of data base. Users with different views could look at different partial data of a data base. Thus, users with different tasks or objectives wish, or could discover different knowledge (partial knowledge) from the same data base. However, all these partial knowledge should be originally existed in the data base. So, a domain-oriented data-driven data mining method would help us to extract the knowledge which is really existed in a data base, and really interesting and actionable to the real world.
文摘Because of the complex nature of the changes in the current and movement of the riverbeds by bridge scouring, it is impossible to understand or predict these changes. In order to have a reliable data, it is critical to have the current methods and equipment for measuring bridge scouring replaced with technology that could acquire real-time bridge scouring data. Despite the critical need for real-time data acquisition, the harsh environmental conditions have prevented the scientific community from acquiring real-time data. Harsh environmental conditions were addressed by the developmental of an automated, remote data collection system, allowing real-time data such as scour movement, scour depth, and scour trend to be viewed in a safe location. As a result, accurate sea-floor movements were seen for the first time, aiding the direction and future of bridge scour research, ultimately contributing greatly to the safety of bridges.
文摘The advent of the Internet of Things(IoT)is transforming traditional agriculture into a more efficient,sustainable,and economically viable sector.This paper delves into the integration of IoT technology in agriculture,emphasizing its role in enhancing crop monitoring,irrigation management,and pest control through real-time data acquisition and automated systems.We discuss the core components of IoT which facilitate the seamless collection and transmission of agricultural data,enabling precise farming operations.The paper highlights three major applications of IoT in agriculture:precision agriculture,smart irrigation systems and advanced pest management techniques.The transformative impacts of IoT are analyzed across economic,environmental,and social dimensions,illustrating how IoT not only boosts agricultural productivity and market competitiveness but also fosters sustainable practices and supports community resilience.Through this comprehensive exploration,the paper aims to provide valuable insights for producers,policymakers,and developers,positioning IoT as a cornerstone of modern agricultural strategies that are both effective and sustainable.
