This paper presents short term load forecasting using multi-variable linear regression (MLR) for big data. Load forecasting is very important for planning, operation, resource scheduling and so on in power system. Tot...
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ISBN:
(纸本)9781538627266
This paper presents short term load forecasting using multi-variable linear regression (MLR) for big data. Load forecasting is very important for planning, operation, resource scheduling and so on in power system. Total electric demand dynamically changes in a power system and mainly depends on temperature, humidity, wind speed, human nature, regular activities, events, etc. input variables. For the help of sensors and data science, enough historical and future input data with good accuracy are easily available. On the other hand, linear regression is a proven method, widely used in industries for forecasting. It is deterministic and robust. However, it is slow for big data because it needs large size matrix operations. In this paper, linear regression is formulated for small number of variables with big data and multi-core parallel processing is applied in all matrix operations that allow unlimited historical big data and unlimited scenarios in acceptable execution time limit. Mean absolute percent error is 3.99% of real field recorded data shown in Simulation and Result section.
With the rapid development of Internet and the continuous rise of network users, the network traffic in various regions is increasing rapidly. In the face of a large number of high speed and high throughput of the net...
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ISBN:
(纸本)9781538694039
With the rapid development of Internet and the continuous rise of network users, the network traffic in various regions is increasing rapidly. In the face of a large number of high speed and high throughput of the network environment, traditional packet capture methods and processing capabilities cannot reach the corresponding speed, which results in severe packet loss. This paper focuses on a high-performance packet acquisition and distribution method to break through the performance bottleneck of universal servers and network cards. This paper studies a packet capture method based on DPDK platform, and uses the processing of hash value in RSS to improve the efficiency of data packet distribution, which realizes the process from performance acquisition to efficiently multi-core parallel processing. This method can effectively reduce packet loss and improve the data packet processing rate. It can also reduce resource waste and network overhead for traffic capture and distribution. Preliminary experiments show that DPDK-based traffic processing has obvious advantages over PF-RING and Netmap in data processing speed.
In an Adaptive Optics system, the Real Time Processor is as important as the human brain. processing latency is a key index of Real Time Proceesors. In this paper, we propose a new processing method that significantly...
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ISBN:
(纸本)9781628413571
In an Adaptive Optics system, the Real Time Processor is as important as the human brain. processing latency is a key index of Real Time Proceesors. In this paper, we propose a new processing method that significantly reduce the processing latency, which combined the design idea of multi-core parallel processing on space and time. In addition, by comparing the operating speed of CPU and the I/O speed of memory, we propose a reasonable memory allocation scheme. The experimental results show that the processing latency is 59.7us per frame using multi-core DSP TMS320C6678 as processing platform. The experiment is conducted on a system with 968 sub-apertures and 913 actuators.
A background subtraction algorithm based on the codebook approach was implemented on a multi-core processor in a parallel form, using the OpenMP system. The aim of the experiments was to evaluate performance of the mu...
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ISBN:
(纸本)9783642215117
A background subtraction algorithm based on the codebook approach was implemented on a multi-core processor in a parallel form, using the OpenMP system. The aim of the experiments was to evaluate performance of the multithreaded algorithm in processing video streams recorded from monitoring cameras, depending on a number of computer cores used, method of task scheduling, image resolution and degree of image content variability. The results of the tests are presented and discussed. The main purpose of the research is application of the tested algorithm in a real-time video content analysis system, e.g. for automatic detection of important security threats.
Compared to the traditional SAR imaging algorithm, Back Projection(BP) algorithm is an accurate point-by-point imaging radar algorithm based on time-domain, with simple principle and without any approximation error in...
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ISBN:
(纸本)9781510822023
Compared to the traditional SAR imaging algorithm, Back Projection(BP) algorithm is an accurate point-by-point imaging radar algorithm based on time-domain, with simple principle and without any approximation error in the imaging process. However, because of intensive computation and low efficiency, it's a new challenge to storage to capacity, throughput and processing ability of DSPs, a single DSP is not enough to meet these demands. So a parallel implementation method of BP algorithm based on TMS320C6678 DSP is proposed in this *** put forward a large point FFT multi-core parallel processing method on 2/4/8 cores what is frequently used in BP algorithm, and a multi-core synchronization method based on distributed memory. Finally using the measured data, we verify the parallel method can greatly enhance the multi-coreparallelism, and the real-time performance of BP algorithm has been significantly improved.
This paper presents short term load forecasting using multi-variable linear regression (MLR) for big data. Load forecasting is very important for planning, operation, resource scheduling and so on in power system. Tot...
详细信息
This paper presents short term load forecasting using multi-variable linear regression (MLR) for big data. Load forecasting is very important for planning, operation, resource scheduling and so on in power system. Total electric demand dynamically changes in a power system and mainly depends on temperature, humidity, wind speed, human nature, regular activities, events, etc. input variables. For the help of sensors and data science, enough historical and future input data with good accuracy are easily available. On the other hand, linear regression is a proven method, widely used in industries for forecasting. It is deterministic and robust. However, it is slow for big data because it needs large size matrix operations. In this paper, linear regression is formulated for small number of variables with big data and multi-core parallel processing is applied in all matrix operations that allow unlimited historical big data and unlimited scenarios in acceptable execution time limit. Mean absolute percent error is 3.99% of real field recorded data shown in Simulation and Result section.
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