Harmony Search (HS) is a metaheuristic optimisation algorithm inspired by musical improvisation. So far it has been applied to various optimisation problems, and there are several application-oriented review papers. H...
详细信息
Harmony Search (HS) is a metaheuristic optimisation algorithm inspired by musical improvisation. So far it has been applied to various optimisation problems, and there are several application-oriented review papers. However, this review paper tries to focus on the historical development of algorithm structure instead of applications. This paper explains the original HS algorithm along with a selection of modified and hybrid HS methods: adaption of original operators of the basic harmony search, parameter adaption, hybrid methods, handling multi objective optimisation problems and constraint handling.
Frequent subgraph mining(FSM) is a subset of the graph mining domain that is extensively used for graph classification and clustering. Over the past decade, many efficient FSM algorithms have been developed with impro...
详细信息
Frequent subgraph mining(FSM) is a subset of the graph mining domain that is extensively used for graph classification and clustering. Over the past decade, many efficient FSM algorithms have been developed with improvements generally focused on reducing the time complexity by changing the algorithm structure or using parallel programming techniques. FSM algorithms also require high memory consumption, which is another problem that should be solved. In this paper, we propose a new approach called Predictive dynamic sized structure packing(PDSSP) to minimize the memory needs of FSM algorithms. Our approach redesigns the internal data structures of FSM algorithms without making algorithmic modifications. PDSSP offers two contributions. The first is the Dynamic Sized Integer Type, a newly designed unsigned integer data type, and the second is a data structure packing technique to change the behavior of the compiler. We examined the effectiveness and efficiency of the PDSSP approach by experimentally embedding it into two state-of-the-art algorithms, g Span and *** compared our implementations to the performance of the originals. Nearly all results show that our proposed implementation consumes less memory at each support level, suggesting that PDSSP extensions could save memory, with peak memory usage decreasing up to 38% depending on the dataset.
With the development of Internet of Things technology,more and more devices are connected to the Internet,including not only traditional computers,mobile phones and other smart terminal devices,but also various sensor...
详细信息
With the development of Internet of Things technology,more and more devices are connected to the Internet,including not only traditional computers,mobile phones and other smart terminal devices,but also various sensor *** sensor devices can collect a variety of environmental information and physical quantities,such as temperature,humidity,air pressure,light intensity,vibration,*** data have the characteristics of real-time,scale and diversity,and need to be processed and analyzed by appropriate *** the basis of previous studies,this project summarized the application of various machine learning algorithms in device state detection,compared the differences of various machine learning algorithms in sensor device detection and made comparative analysis,calculated the evaluation parameters of MSE,RMSE,MAE,MAPE,R and other aspects of the machine learning regression *** the effects of various regression models for better monitoring and prediction of equipment *** the analysis of a large number of historical data,different equipment state models can be established,and these models can be used to monitor and predict the current equipment *** can effectively avoid production line downtime or other losses caused by equipment failures or *** the same time,through the in-depth analysis of historical data,we can find some potential problems and take corresponding measures to prevent *** project aims to summarize the application of various machine learning algorithms in device status detection,compare and contrast the differences of various machine learning algorithms in sensor device detection,realize efficient processing and analysis of sensor data,calculate MSE,RMSE,MAE,MAPE,R and other evaluation parameters,and evaluate and compare each *** provide more accurate,reliable and efficient equipment condition monitoring and forecasting services for enterprises and individuals.
Nano, pico, and the so-called CubeSat satellites are taking place due to the emergent improvements in both high-performance nano and pico electronics and computational technologies. More than 1600 nanosats and CubeSat...
详细信息
Nano, pico, and the so-called CubeSat satellites are taking place due to the emergent improvements in both high-performance nano and pico electronics and computational technologies. More than 1600 nanosats and CubeSats exist nowadays (i.e. 685 nanosats launched, 613 CubeSats launched, 405 nanosatellites in orbit, 321 operational nanosatellites, 71 nanosats destroyed on launch, etc.), with an incredible panoply of different constellations, governmental and non-governmental, high and easy to reach technologies, instruments in miniatures and missions from the military to universities and schools. This paper describes an approach to the implementation of the land surface temperature split-window (LST-SW) (Sobrino and Raissouni, IJRS 2000) algorithm structure based on the field programmable gate array (FPGA) technology. Due to the ever-increasing integrated circuit fabrication capabilities, the future of FPGA technology promises both higher densities and higher speeds for CubeSats on-board computations purposes. The research application shows the advantages of the used Xilinx Virtex-5 LX50 series FPGA approach in the LST-SW implementation with higher sampling rates than what is available from existing digital signal processing (DSP) chips, lower costs than an application specific integrated circuits (ASIC) for moderate volume applications and more flexibility than the alternate approaches. Since many current FPGA architectures are in-system programmable, the configuration of the device may be changed to implement different functionalities if required depending on the LST-SW parameters for each corresponding author. Finally, preliminary results show that the proposed LST-SW Xilinx Virtex-5 LX50 FPGA implementation approach is exceedingly flexible. Moreover, this implementation provides a considerable and promising performance that is suitable for future CubeSats on board LST-SW computations purposes.
An approach to formation and training of an artificial neural network (ANN) based on thin-film memristive metal-oxide-metal nanostructures, which exhibit the effect of bipolar resistive switching, has been proposed. A...
详细信息
An approach to formation and training of an artificial neural network (ANN) based on thin-film memristive metal-oxide-metal nanostructures, which exhibit the effect of bipolar resistive switching, has been proposed. An experimental electric circuit of a small-sized ANN (a two-layer perceptron with 32 memristive elements) has been constructed. An algorithm for formation of weighting coefficients (ANN training), which takes into account probable spread of technological parameters of memristive structures has been developed.
Finding network communities (i.e. community detection) is a famous topic in network science. By far, many widely concerned community detection approaches are designed by using evolutionary computation methods. Recent ...
详细信息
ISBN:
(数字)9781728158570
ISBN:
(纸本)9781728158587
Finding network communities (i.e. community detection) is a famous topic in network science. By far, many widely concerned community detection approaches are designed by using evolutionary computation methods. Recent years, a new evolutionary algorithm called state transition algorithm (STA) was created and developed. In our previous work, a population-based discrete STA (MDSTA) has been put forwarded to settle network community detection task. Similar to most population-based evolutionary algorithms, MDSTA has a relatively complex algorithm structure which may limit the application of the algorithm. To address this problem, a backtracking-based discrete STA (BDSTA) is designed in this study. BDSTA is an individual-based method, and two kinds of substitute operators based on label-based representation strategy and locus-based representation strategy are used in BDSTA for global search and local search, respectively. Owing to that the individual-based algorithms often fall into a stagnation solution, we employ a backtracking search strategy in the global search procedure. Finally, five real-world networks and the extended GN artificial networks are used to test BDSTA and some state-of-art algorithms. Experimental results prove that BDSTA often get high-quality community partitions and it is more efficient than these state-of-art algorithms.
Convolutional neural network(CNN) was a widely used algorithm for image classification in the field of computer *** present,in terms of identification of illegal web pages,the main application methods rely on manpower...
详细信息
ISBN:
(纸本)9781510871076
Convolutional neural network(CNN) was a widely used algorithm for image classification in the field of computer *** present,in terms of identification of illegal web pages,the main application methods rely on manpower too much,which is a costly and time-consuming *** paper will apply the CNN algorithm to the identification of illegal networks,build a CNN algorithm framework on the server side of browsers and web *** can also use CNN's outstanding performance in image classification to classify images of illegal websites and conducts realtime data on illegal *** paper will introduce the CNN algorithm characteristics from the algorithm structure and function.
The fractal art graphic is one of the main manifestations of fractal art, which can be produced through mathematical models and programming on a computer. The paper investigates the designing concept of fractal art ba...
详细信息
ISBN:
(纸本)9783037850978
The fractal art graphic is one of the main manifestations of fractal art, which can be produced through mathematical models and programming on a computer. The paper investigates the designing concept of fractal art based on its self similarity and the iterative method, elaborates in detail the algorithms and steps of several kind of typical fractal graphics, and by properly inserting some controlling variables, has generated a large number of exquisite and inspiring fractal graphics using the JAVA programming language, confirming the validity and usability of presented algorithm's.
The fractal art graphic is one of the main manifestations of fractal art,which can be produced through mathematical models and programming on a computerThe paper investigates the designing concept of fractal art based...
详细信息
The fractal art graphic is one of the main manifestations of fractal art,which can be produced through mathematical models and programming on a computerThe paper investigates the designing concept of fractal art based on its self similarity and the iterative method,elaborates in detail the algorithms and steps of several kind of typical fractal graphics,and by properly inserting some controlling variables,has generated a large number of exquisite and inspiring fractal graphics using the JAVA programming language,confirming the validity and usability of presented algorithm's
Diversity is deemed to be a key issue in classifier combination. For this reason, not every classifier is an expert for every query pattern. Thus, many researchers have focused on dynamic ensemble selection. Most work...
详细信息
ISBN:
(纸本)9781479919611
Diversity is deemed to be a key issue in classifier combination. For this reason, not every classifier is an expert for every query pattern. Thus, many researchers have focused on dynamic ensemble selection. Most works, however, use only one criterion to perform the dynamic selection. Hence, multiple criteria can provide a decision more effective than the one produced by any of the criteria. Another important issue is accuracy of the classifiers, which strongly depends on the adequate choice of its parameters, including, for example, learning algorithm, structure and input feature vector. Therefore, we present a hybrid intelligent system to generate automatically a pool of classifiers, and choose dynamically an ensemble to predict each query pattern. The method evolves simultaneously the classifier parameters and trains, via a learning algorithm, the candidate solutions. Meta-features are extracted and used to build meta-classifiers to predict whether a base classifier is competent enough to classify the query pattern. Experimental results show that the proposed method improves classification accuracy when compared against current state-of-the-art techniques.
暂无评论