scholarly journals The Application of Genetic Algorithm in Motion Detection for Data Storage Optimization

2014 ◽  
Vol 3 (3) ◽  
pp. 199-202
Author(s):  
John Carlo M. Butil ◽  
Ma Lei Frances Magsisi ◽  
John Hart Pua ◽  
Prince Kevin Se ◽  
Ria Sagum
Keyword(s):  
Genetic Algorithm ◽  
Data Storage ◽  
Motion Detection ◽  
Storage Optimization

scholarly journals Storage Optimization using Adaptive Thresholding Motion Detection

2021 ◽  
Vol 11 (2) ◽  
pp. 6869-6872
Author(s):  
M. Atif ◽  
Z. H. Khand ◽  
S. Khan ◽  
F. Akhtar ◽  
A. Rajput
Keyword(s):  
Data Storage ◽  
Motion Detection ◽  
Detection Method ◽  
Adaptive Threshold ◽  
Video Data ◽  
Adaptive Thresholding ◽  
Security Personnel ◽  
Storage Optimization ◽  
Cctv Camera

Data storage is always an issue, especially for video data from CCTV cameras that require huge amounts of storage. Moreover, monitoring past events is a laborious task. This paper proposes a motion detection method that requires fewer calculations and reduces the required data storage up to 70%, as it stores only the informative frames, enabling the security personnel to retrieve the required information more quickly. The proposed method utilized a histogram-based adaptive threshold for motion detection, and therefore it can work in variable luminance conditions. The proposed method can be applied to streamed frames of any CCTV camera to efficiently store and retrieve informative frames.

scholarly journals Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time

2018 ◽  
Vol 5 (1) ◽  
pp. 27 ◽  
Author(s):  
Kukuh Triyuliarno Hidayat ◽  
Riza Arifudin ◽  
Alamsyah Alamsyah
Keyword(s):  
Genetic Algorithm ◽  
Data Storage ◽  
Relational Database ◽  
Execution Time ◽  
Data Retrieval ◽  
Data Conversion ◽  
Query Execution ◽  
Algorithm Optimization ◽  
Fitness Value ◽  
Best Execution

The relational database is defined as the database by connecting between tables. Each table has a collection of information. The information is processed in the database by using queries, such as data retrieval, data storage, and data conversion. If the information in the table or data has a large size, then the query process to process the database becomes slow. In this paper, Genetic Algorithm is used to process queries in order to optimize and reduce query execution time. The results obtained are query execution with genetic algorithm optimization to show the best execution time. The genetic algorithm processes the query by changing the structure of the relation and rearranging it. The fitness value generated from the genetic algorithm becomes the best solution. The fitness used is the highest fitness of each experiment results. In this experiment, the database used is  MySQL sample database which is named as employees. The database has a total of over 3,000,000 rows in 6 tables. Queries are designed by using 5 relations in the form of a left deep tree. The execution time of the query is 8.14247 seconds and the execution time after the optimization of the genetic algorithm is 6.08535 seconds with the fitness value of 0.90509. The time generated after optimization of the genetic algorithm is reduced by 25.3%. It shows that genetic algorithm can reduce query execution time by optimizing query in the part of relation. Therefore, query optimization with genetic algorithm can be an alternative solution and can be used to maximize query performance.

scholarly journals Cloud Storage Strategy of Blockchain Based on Genetic Prediction Dynamic Files

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 398
Author(s):  
Jiali Tang ◽  
Chenrong Huang ◽  
Huangxiaolie Liu ◽  
Najla Al-Nabhan
Keyword(s):  
Genetic Algorithm ◽  
Data Storage ◽  
Cloud Storage ◽  
Rapid Expansion ◽  
Load Prediction ◽  
Analysis Model ◽  
Periodic Load ◽  
Complex Load ◽  
Storage Method ◽  
Allocation Process

With the rapid expansion of data volume, traditional data storage methods have been unable to meet the practical application requirements of blockchain cloud storage. Aiming for the cloud storage problem of blockchain, a new storage access method for predicting dynamic file load is proposed. By predicting the load status of cloud storage files in advance, the load of each blockchain data node at the next moment is first estimated. A hierarchical genetic algorithm is used to construct the connection weights between the hidden layer and the output layer, which makes the data network converge faster and more accurate, thereby effectively predicting the node load. In addition, based on the file allocation, an evaluation analysis model is constructed to obtain the time response capability of each file during the allocation process. The node’s periodic load prediction value is used to calculate the corresponding weight of the node and it is continuously updated, retaining the advantages of the static weighted polling algorithm. Combined with the genetic algorithm to help predict the file assignment access strategy of the later load of each node, it can meet the system requirements under complex load conditions and provide a reasonable and effective cloud storage method. The experimental evaluation of the proposed new strategy and new algorithm verifies that the new storage method has a faster response time, more balanced load, and greatly reduced energy consumption.

Optimization of Sensor Placement in SHM Based on the Dual Coded Genetic Algorithm

2012 ◽  
Vol 151 ◽  
pp. 139-144
Author(s):  
Jian Xi Yang ◽  
Li Wen Zhang
Keyword(s):  
Genetic Algorithm ◽  
Data Storage ◽  
Large Scale ◽  
Sensor Placement ◽  
Optimal Solution ◽  
Continuous Rigid Frame Bridge ◽  
Rigid Frame ◽  
Rigid Frame Bridge ◽  
Efficiency And Reliability ◽  
Search Capability

This paper uses of the dual structure of coded genetic algorithm to optimize the sensor placement methods. The method using the optimal preservation strategy using the adaptive part of the cross, overcomes deficiencies of computer applying to the lengthy large-scale structure data, storage space, and to ensure that the optimal solution search. Finally, through the analysis of a continuous rigid frame bridge Project, proved that the method superior to the effective independent method in the search capability, computational efficiency and reliability, but still need to further improve the speed of convergence.

Improved Genetic Algorithm to Calibration of the Pulse Coupled Neural Network Parameter

2013 ◽  
Vol 694-697 ◽  
pp. 1993-1997
Author(s):  
Rui Cheng Feng ◽  
Hai Yan Li ◽  
Zhi Yuan Rui ◽  
Rui Sheng Chen ◽  
Bao Cheng Zhou
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Image Recognition ◽  
Motion Detection ◽  
Recognition Accuracy ◽  
Improved Genetic Algorithm ◽  
Network Parameter ◽  
Edge Information ◽  
Quality Of Motion

Based on the research of PCNN algorithm,details the basic idea of the genetic algorithm, introduction of genetic algorithm for optimal family.In order to verify the effectiveness of the algorithm,implements the algorithm and comparison algorithm on the PC with the VS2008, CUDA, OpenCV 2.2.The experimental results showed that:Improved PCNN algorithm to better deal with the multi-modal regional background,improve the accuracy of moving object segmentation.Effectively filter interference prospects at the same time,complete retention of the shape, the edge information of the moving object.In this paper, the improved genetic algorithm can guarantee the quality of motion detection, and provide a guarantee for the the subsequent image recognition accuracy.

scholarly journals CAD Based Electric Transporter Path Planning and Production Storage Optimization Using Genetic Algorithm – Industrial Case Study

2020 ◽  
Vol 14 (2) ◽  
pp. 174-179
Author(s):  
Miha Kovačič ◽  
Goran Đukić ◽  
Brigita Gajšek ◽  
Klemen Stopar
Keyword(s):  
Heat Treatment ◽  
Genetic Algorithm ◽  
Path Planning ◽  
Spring Steel ◽  
The Other ◽  
Control Line ◽  
Industrial Case Study ◽  
Universal System ◽  
Storage Optimization ◽  
Transport Passage

Štore Steel Ltd. is one of the largest flat spring steel producers in Europe. There are two production lines after rolling – one for flat bars and the other for round bars. The flat bars production generally consists of visual examination, straightening and cutting operation. In addition, heat treatment or magnetic particle testing could be conducted. On the other hand, the round bars production consists generally of straightening, automatic control line control, chamfering and cutting. In addition, heat treatment is possible. For manipulation of the material in the rolling plant, the electric transporter and several cassettes are used. In the paper path planning and production storage optimization (i.e. storage spaces for cassettes) were conducted using genetic algorithm. The production storage is actually the space between main transport passage and individual operations. In the research the universal system using CAD geometry is presented where AutoCAD environment and in-house developed AutoLISP system were used. The production storage – storage spaces for cassettes (location and orientation) with corresponding electric transporter trajectories are represented as CAD objects and thus form individual solution/organism. The organisms undergo simulated evolution. The results of the evolution are compared with actual production storage in the steel plant.

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