Introduction of Key Problems in Long-Distance Learning and Training

Liu, Shuai; Li, Zhaojun; Zhang, Yudong; Cheng, Xiaochun

doi:10.1007/s11036-018-1136-6

Cited by 266 publications

(17 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Two circuit control registers are used in the circuit design. One is used to record the status signal on the acquisition board for query, and one is used to write the control word to control the working state of the entire acquisition board [7,8].…”

Section: Hardware Design Of Electric Automation Big Data Control Systmentioning

confidence: 99%

Design of Big Data Control System for Electrical Automation

Gao

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

The traditional big data control system is limited by hardware and software resources, which leads to low efficiency of data access and calculation. Therefore, a big data control system for electrical automation is designed. The system is mainly designed from two aspects of hardware and software. The hardware mainly designs a digital acquisition circuit, selects the model of each part in the overall working module of the acquisition board, and designs a pseudo dual-port RAM to store data, which is convenient for the processing of massive data and Control; in software design, according to the needs of the system, light MySQL and distributed HBase are used to jointly design the ER diagram and database table of the database user database to make the database performance more optimized. In the control algorithm of the system, the butterfly operation is used to optimized calculate the speed, obtain the operation error through the operation and correct it to ensure the efficiency of the system operation. The experimental results show that the performance of the designed system is better than the traditional system in data insertion speed, access speed and calculation speed, which fully verifies the application value of the system.

show abstract

Section: Hardware Design Of Electric Automation Big Data Control Systmentioning

confidence: 99%

Design of Big Data Control System for Electrical Automation

Gao

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

show abstract

“…The multi-energy storage combined generation control mode includes multi-energy storage and distributed grid-connected fluctuation and fuzzy control, multi-energy storage island off-grid load control and fuzzy control. The multi-energy storage and distributed grid-connected control mode is to control the switch-on direction of the bidirectional DC-DC converter under the distributed grid-connected power supply mode, according to whether the output power fluctuation of the multi-energy power exceeds the national standard [11,12]. By controlling the start-up and stop time of energy storage or discharge, the energy storage can reduce the power wave momentum of multi-energy and reduce the impact of the power network.…”

Section: Control Generation Control Modementioning

confidence: 99%

Intelligent Control Method for Load of Multi-energy Complementary Power Generation System

Yang

Cao

Zhang

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

In order to reduce environmental pollution, the load of power generation system has become an important basis for the balance of power supply and demand. Therefore, an intelligent load control method for multi energy complementary power generation system is proposed. Firstly, the intelligent control scheme of multi energy complementary power generation system is formulated, the circuit principle of the control method is determined, and the frequency control parameters of power grid, frequency regulation of generator set and control mode of generator set are controlled. The experimental results show that the load intelligent control method of the multi energy complementary generation system can effectively control the load of the power generation system, and the multi energy complementary generation can smooth the randomness of single energy, the intermittent fluctuation of energy and the control effect of energy storage, and reduce the impact on the power grid, which is very suitable for distributed grid connected operation.

show abstract

“…Meanwhile, (9,5,15,4)•(9,5,15,4) mod 17 = (81,25,225,16) mod 17 = (13,8,4,16). We may verify that 3x 3 + 7x 2 + 3x + 6 can be represented by (13,8,4,16). Similarly, (x 3 + 2x 2 + 3x + 4) + (x 3 + 2x 2 + 3x + 4) mod x 4 + 1 = (2x 3 + 4x 2 + 6x + 8) ∼ = (1,10,13,8) = (9,5,15,4) + (9,5,15,4) mod 17.…”

Section: Plaintext Encodingmentioning

confidence: 99%

“…When compared with non-homomorphic encryptions, it removes the trust on the device and makes us ease to use the computing power of the device. Hence, homomorphic encryption has broad application prospects [5][6][7][8][9][10][11]. In a breakthrough work [12], Gentry demonstrated that fully homomorphic encryption was theoretically possible based on ideal lattices.…”

Section: Introductionmentioning

confidence: 99%

An Efficient BGV-type Encryption Scheme for IoT Systems

Yuan

Gao

2020

Applied Sciences

View full text Add to dashboard Cite

Internet of Thing (IoT) systems usually have less storage and computing power than desktop systems. This paper proposes an efficient BGV-type homomorphic encryption scheme in order fit for secure computing on IoT system. Our scheme reduces the storage space for switch keys and ciphertext evaluation time comparing with previous BGV-type cryptosystems. Specifically, the switch key in homomorphic computations can be a constant but no longer one for each level. Moreover, the product of two ciphertexts can be at the same sublayer as them and the multiplication operations can be repeated between two sublayers. As a result, the multiplication times will not be limited by L in an L-level circuit and, thus, the ciphertext evaluation time will decrease significantly. We implement the scheme with the C language. The performance test shows that the efficiency of the improved scheme is better than Helib in same configurations.

show abstract

Introduction of Key Problems in Long-Distance Learning and Training

Cited by 266 publications

References 6 publications

Design of Big Data Control System for Electrical Automation

Design of Big Data Control System for Electrical Automation

Intelligent Control Method for Load of Multi-energy Complementary Power Generation System

An Efficient BGV-type Encryption Scheme for IoT Systems

Contact Info

Product

Resources

About