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A regional industry intelligence business platform based on adaptive clustering

International Journal of Circuits, Systems and Signal processing 2020年 14卷 656-660页

作者： Liu, Junjie Cai, Danlin Zhu, Daxin Huang, Siyu Network Information Center Quanzhou Normal University Quanzhou China Fujian University Laboratory of Intelligent Computing and Information Processing Quanzhou China Fujian Provincial Key Laboratory of Data-Intensive Computing Quanzhou China College of Mathematics and Computer Science Quanzhou Normal University Quanzhou China

How to grasp comprehensive, timely, effective and accurate business competitive intelligence has become an urgent and critical issue for regional industrial clusters. Therefore, a Industrial Internet Platform for Regional Economic based on Adaptive Clustering called IIPRE is developed. The multi thread oriented extraction technology is used to collect the business intelligence data of specific industries. The data is clustered by rule-based machine learning, and the business information data model is used for analysis. Finally, the visualization report is generated by big data visualization software. The system uses intelligent Retrieval technology to automatically complete the functions from acquisition to processing automatically, to generate data information to meet the application requirements, uses Automatic Classification Technology to provide automatic classification function combining machine-based automatic learning and rule-based information, and uses Personalized Display Technology to provide customizable personalized display pages for individuals, to organize and adjust the hot information, thematic information, clustering, related words, early warning, statistics and other information released by the system. The construction of the platform provides enterprises with comprehensive, timely, effective and accurate business competitive intelligence services, improves the strategic planning, competitive intelligence acquisition and industrial information sharing capabilities of regional industrial clusters, and will achieve tremendous economic and social benefits. © 2020, North Atlantic University Union NAUN. All rights reserved.

关键词： Data visualization

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No-Reference Image Quality Assessment Based on Multi-scale Convolutional Neural networks

No-Reference Image Quality Assessment Based on Multi-scale C...

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computing Conference, 2019

作者： Chen, Peikun Niu, Yuzhen Huang, Dong College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

ISBN: (纸本)9783030228675

Big data brings more challenges to image quality assessment (IQA). First of all, reference images are not always available, so full-reference IQA (FR-IQA) and reduced-reference IQA (RR-IQA) can be problematic for big data. Because of no requirement for any pristine knowledge, no-reference IQA (NR-IQA) has recently received a great deal of attention. In recent years, deep convolution neural network (CNN) has achieved many successes in visual recognition tasks. Researches in literature have shown that NR-IQA metrics can be learned from features extracted using deep CNN. But existing CNN-based IQA models focus on local spatial information but ignore global spatial structure. In this paper, we propose a NR-IQA model learns from multi-scale CNN (MSCNN). Multi-scale mechanism has boosted the performances of many singe-scale methods in a variety of applications. MSCNN is a multi-scale CNN model which uses fully connected layers on top of multiple single-scale CNN models which responsible for feature extraction at different scales. In this way, the features extracted at multiple scales work together to assess image quality. Extensive experiments are conducted on the LIVE database and the experimental results validate the effectiveness of our MSCNN index compared to typical FR-IQA and state-of-the-art NR-IQA metrics. Furthermore, the results of cross database experiments show that MSCNN has good generalization ability. © 2019, Springer Nature Switzerland AG.

关键词： Big data

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Electric Load Forecasting Based on Sparse Representation Model 13th

Electric Load Forecasting Based on Sparse Representation Mod...

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13th International Conference on Green, Pervasive, and Cloud computing, GPC 2018

作者： Huang, Fangwan Zheng, Xiangping Yu, Zhiyong Yang, Guanyi Guo, Wenzhong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030150921

Accurate electric load forecasting can prevent the waste of power resources and plays a crucial role in smart grid. The time series of electric load collected by smart meters are non-linear and non-stationary, which poses a great challenge to the traditional forecasting methods. In this paper, sparse representation model (SRM) is proposed as a novel approach to tackle this challenge. The main idea of SRM is to obtain sparse representation coefficients by the training set and the part of over-complete dictionary, and the rest part of over-complete dictionary multiplied with sparse representation coefficients can be used to predict the future load value. Experimental results demonstrate that SRM is capable of forecasting the complex electric load time series effectively. It outperforms some popular machine learning methods such as Neural network, SVM, and Random Forest. © 2019, Springer Nature Switzerland AG.

关键词： Electric load forecasting

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Self-learning and self-adaptive resource allocation for cloud-based software services

Self-learning and self-adaptive resource allocation for clou...

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作者： Chen, Xing Lin, Junxin Lin, Bing Xiang, Tao Zhang, Ying Huang, Gang College of Mathematics and Computer Science Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China College of Physics and Energy Fujian Normal University Fuzhou China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China

In the presence of scale, dynamism, uncertainty, and elasticity, cloud engineers face several challenges when allocating resources for cloud-based software services. They should allocate appropriate resources in order to guarantee good quality of services as well as low cost of resources. Self-adaptive ability is needed in this process because engineers' intervention is difficult. Traditional self-adaptive resource allocation methods are policy-driven. Thus, cloud engineers usually have to develop separate sets of rules for each systems in order to allocate resources effectively, which leads to high administrative cost and implementation complexity. Machine learning has made great achievements in many fields, and it can be also applied to resource allocation. In this paper, we present a self-learning and self-adaptive approach to resource allocation for cloud-based software services. For a given cloud-based software service, its QoS model is firstly trained on history data, which is capable to predict the QoS value as output by using the information on workload and allocated resources as inputs. Then, on-line decision-making on resource allocation can be carried out automatically based on genetic algorithm, which is aimed to search reasonable resource allocation plan by using the QoS model. We evaluate our approach on RUBiS benchmark, demonstrating the accuracy of the QoS model over 90% and the improvement of resource utilization by 10%-30%. © 2018 John Wiley & Sons, Ltd.

关键词： Resource allocation

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Image Classification Based on BP Neural network and Sine Cosine Algorithm

Image Classification Based on BP Neural Network and Sine Cos...

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IEEE International Conference on intelligent Data Acquisition and Advanced computing Systems

作者： Haoqiu Song Zhiwei Ye Chunzhi Wang Lingyu Yan School of Computer Science Hubei University of Technology China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University China

Image classification is one of the most important tasks in image analysis and computer vision. BP neural network is a successful classifier for the task. However, with regard to the low study efficiency and the slow convergence speed in BP algorithm, some optimization algorithms have been proposed for achieving better results. Among all these methods, BP neural network improved by particle swarm optimization (PSO) and genetic algorithm (GA) may be the most successful and classical ones. Nevertheless, both GA and PSO are easy to fall into the local optimal solution, which has a great impact on the precision of classification. As a result, a novel optimization algorithm called sine cosine algorithm (SCA) is presented to improve the classification performance. The experimental results manifest that the proposed method has good performances, and the classification accuracy is better than BP neural network optimized by GA, PSO or other algorithms.

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Research On Short-term Electric Load Forecast Based On Grey Neural network and Snap-drift Cuckoo Search Algorithm

Research On Short-term Electric Load Forecast Based On Grey ...

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IEEE International Conference on intelligent Data Acquisition and Advanced computing Systems

作者： Feng Chen Zhiwei Ye Jun Su Haofeng Lang Xiaoxiao Shi Shuqing Wang School of computer Science Hubei university of Technology Wuhan China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

In order to provide users with reliable and qualified power, it becomes an indispensable task to enhance the forecasting capability of the short-term power load. However, the existing approaches of short-term electric load forecasting are not proper enough. A short-term electric load forecasting method based on grey neural network based on snap-drift cuckoo search optimization algorithm (SDCS-GNN) is proposed in this paper. Parameters of gray neural network (GNN) are selected randomly which is similar to the initial spatial position of birds' eggs in the parasitic nest of cuckoo. The SDCS is utilized to search the better weight and threshold of the conventional gray neural network (GNN), which improves the stability and accuracy of the prediction model. To validate the superior performance of the proposed method, several well-known evolutionary algorithms such as particle swarm optimization (PSO), grey wolf optimization (GWO), moth-fire suppression optimization (MFO) and cuckoo search optimization (CS) are employed to constitute the contrast experiment of the prediction of short-term power load. The mean squared error predicted by the SDCS-GNN model is the smallest, which compared with GNN, PSO-GNN, GWO-GNN, MFO-GNN, and CS-GNN is 0.36, 1.79, 15.23, 4.53, 2.93, respectively. The Average prediction accuracy of SDCS-GNN model is better than other models which is 7.1592, 1.427, 15.1516, 11.5438, 10.5202, respectively. The simulation results show that the SDCS-GNN model has better approximation ability and higher prediction accuracy than the conventional GNN and other evolutionary algorithms in the short-term electric load forecasting. The experiments above indicates that the prediction method is effective and feasible.

关键词： Neural networks Prediction algorithms Predictive models Optimization Load modeling Load forecasting

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Cost Efficient Offloading Strategy for DNN-Based Applications in Edge-Cloud Environment

Cost Efficient Offloading Strategy for DNN-Based Application...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yinhao Huang Bing Lin Yongjie Zheng Junqin Hu Yuchang Mo Xing Chen College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China College of Physics and Energy Fujian Normal University Fuzhou China Fujian Province University Key Laboratory of Computational Science Huaqiao University Quanzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Deep Neural networks (DNNs) are pervasively used in a large number of applications, such as Microsoft Cortana, Apple Siri, and Google Now. The traditional method is to offload them in the cloud, which causes serious data transmission delay and high network resource cost, as well as user privacy leakage. The emergence of Mobile Edge computing(MEC) provides a new solution for the execution of DNN-based applications. With the increase of the computing power of mobile devices and the appearance of edge nodes, offloading partial layers of the DNN-based application to the edge will speed up system response, reduce the burden of cloud center and protect privacy of users. Most of existing researches focus on the optimization of response delay for DNN-based applications in edge computing system, but there is no mature solution to the cost optimization of edge computing system. To effectively reduce the cost of edge computing for offloading DNN-based applications, we propose a offloading strategy based on Discrete Particle Swarm Optimization with Genetic Operators(DPSO-GO). By introducing crossover operator and mutation operator of genetic algorithm, it overcomes the defect that the traditional PSO algorithm is easy to fall into local optimum. The results show that our algorithm can reduce the cost of edge computing system effectively while satisfying the delay requirements of all the DNN-based applications.

关键词： Mobile handsets Task analysis Cloud computing Edge computing Servers Optimization Bandwidth

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Agricultural Product Traceability System Based on Blockchain Technology

Agricultural Product Traceability System Based on Blockchain...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Weipeng Xie Xianghan Zheng Xiaoliang Lu Xiaowei Lin Xionghui Fan Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China MingByte Technology (Qingdao) Co. Ltd Qingdao China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

The quantity of agricultural products produced in China is huge. How to ensure the quality of agricultural products from farmland to table is a problem that has been studied all the time. From the perspective of decentralization, this paper uses the blockchain technology to combine the characteristics of agricultural products traceability, and the alliance chain hyperledge recology. As the underlying technology of the blockchain, the Fabric1.4.2 project under the application, the factory, quality inspection, logistics, distribution and supervision as the alliance nodes in the alliance chain, combined with the Internet of Things technology to automatically collect data to achieve agricultural products from planting, processing, quality Traceability of inspection, logistics, distribution and other processes. Through the scanning of the two-dimensional code, the matching of the traceable data and the data uplink are realized, and the products produced are guaranteed to be one yard and one code. Finally, the trace data of the production and circulation process of the product is realized through the scanning of the code of the e-commerce platform of the WeChat mini program.

关键词： Blockchain Organizations Peer-to-peer computing Agricultural products Inspection Production facilities

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Research on network Intrusion Detection Based on Support Vector Machine Optimized with Grasshopper Optimization Algorithm

Research on Network Intrusion Detection Based on Support Vec...

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IEEE International Conference on intelligent Data Acquisition and Advanced computing Systems

作者： Zhiwei Ye Yiheng Sun Shuang Sun Sikai Zhan Han Yu Quanfeng Yao Key Laboratory of Intelligent Computing and Information Processing Fujian Province University School of computer Science Hubei university of Technology Wuhan China Wuhan FiberHome Technical Services Co. Ltd.

As one of the most important parts of network security, more significance is attached to intrusion detection system (IDS). Numerous techniques including support vector machine (SVM) have been applied to the intrusion detection. However, many methods are utilized to improve the original SVM whose performance is markedly depended on its kernel parameters. Evolutionary algorithms such as genetic algorithm (GA) and particle swarm algorithm (PSO) are also employed to search better kernel parameters while the traditional optimization methods are vulnerable to fall into local minima with slow speed of convergence. In order to improve the precision of SVM in intrusion detection, the support vector machine based on grasshopper optimization algorithm (GOA-SVM) is proposed in the paper. To verify the practicality of the proposed method, several contrast experiments have been carried out using tool of Matlab. The experimental results finally demonstrates the superior performance of the proposed method on intrusion detection.

关键词： Support vector machines Optimization Intrusion detection Kernel Genetic algorithms Particle swarm optimization Artificial neural networks

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Self-Evolutionary Neuron Model for Fast-Response Spiking Neural networks

TechRxiv

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TechRxiv 2019年

作者： Zhang, Anguo Han, Ying Hu, Jing Niu, Yuzhen Gao, Yueming Chen, Zhizhang Zhao, Kai College of Physics and Information Engineering Fuzhou University Fujian350108 China The Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fuzhou350116 China Research Institute of Ruijie Ruijie Networks Co. Ltd Fujian350002 China School of Public Health Xiamen University Xiamen361102 China College of Information and Intelligent Transportation Fujian Chuanzheng Communications College Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University The Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian350108 China College of Physics and information Engineering Fuzhou University the Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fujian350108 China College of Physics and Information Engineering Fuzhou University 350108 China Faculty of Science and Technology University of Macau 999078 China

We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal. Experiment codes are available at https://***/anvien/Evol-SNN. © 2019, CC0.

关键词： Neural networks

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