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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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Improved Quality Aware Network Using Quality and Max Pooling Based Late Feature Fusion for Video-Based Person Re-Identification

Improved Quality Aware Network Using Quality and Max Pooling...

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International Conference on Computer science and Network Technology (ICCSNT)

作者： Yude Shi Xiao Ke Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Fuzhou China

ISBN: (纸本)9781728133003

Video-based person re-identification is an important research area in computer vision. Quality Aware Network (QAN) is a model that is robust to noise in the videos. It calculates the weighted average of all the frames' image features, with each frame's quality score as the weight, and generates a video feature. However, the QAN has poor ability to distinguish pedestrians with highly similar clothing. To address this problem, we analyze the advantages and shortcomings of Quality Pooling and Max Pooling in theory, and propose the Frame Quality and Max Pooling based Late Feature Fusion Method (QMFF). It aims at enhancing the QAN's ability to distinguish pedestrians with similar clothing. First, use Quality Pooling to get the initial ranking list. Then, for each Query, judge whether the top-N candidates in the ranking list are highly similar in clothing to one another. If the Query meets the similarity criterion, Max Pooling with Frame Quality Filter is used. Local salient features selected by Max Pooling help distinguish pedestrians more accurately. Experimental results on the iLIDS-VID dataset show that the proposed method is superior to the QAN and many recently proposed methods.

关键词： Videos Clothing Feature extraction Face recognition Computer science Databases Training

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Evolution analysis of information retrieval based on co-word network 3

Evolution analysis of information retrieval based on co-word...

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3rd IEEE International Conference on Electronic information Technology and Computer Engineering, EITCE 2019

作者： Zhang, Xinmeng Li, Xinguang Jiang, Shengyi Li, Xia Xie, Bolin Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou Guangdong China Non-universal Language Intelligent Processing Laboratory Guangdong University of Foreign Studies Guangzhou Guangdong China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou Guangdong China

ISBN: (纸本)9781728135847

With the development of natural language processing and artificial intelligence, remarkable progress has been made in the field of information retrieval In this paper, we selected papers on information retrieval from web of science in the past ten years, and constructed a Co-word network based on the co-occurrence relationship of keywords in the same paper. By analyzing the evolution of the Co-word network community, the core evolution and the number of keyword status, we discussed the evolution of research focus in the field of information retrieval in the past ten years. The results show that natural language processing, medical information retrieval, image retrieval and social media retrieval are the focus of information retrieval research in recent years. © 2019 IEEE.

关键词： information retrieval

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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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Flower Species Recognition System Combining Object Detection and Attention Mechanism 1

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15th International Conference on intelligent computing, ICIC 2019

作者： Qin, Wei Cui, Xue Yuan, Chang-An Qin, Xiao Shang, Li Huang, Zhi-Kai Wan, Si-Zhe Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University Jiangsu China College of Mechanical and Electrical Engineering Nanchang Institute of Technology NanchangJiangxi China

ISBN: (数字)9783030267667

ISBN: (纸本)9783030267650

In this paper, a flower species recognition system combining object detection and Attention Mechanism is proposed. In order to strengthen the ability of the model to process images under complex backgrounds, we apply the method of object detection to locate flowers that we want to recognize. For less time of training, object detection and classification are Integrate into an end-to-end network stacked attention modules to generate attention-aware features. Experiments are conducted on Flower 102, our method can recognize flower species against a complex background. With model owning attention module and transfer learning, we increase mAP from 73.8% to 74.7% and training time is reduced by about 15%. © 2019, Springer Nature Switzerland AG.

关键词： Object recognition

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TOP: Towards Better Toxicity Prediction by Deep Molecular Representation Learning

TOP: Towards Better Toxicity Prediction by Deep Molecular Re...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuzhong Peng Ziqiao Zhang Qizhi Jiang Jihong Guan Shuigeng Zhou Key Lab of Scientific Computing Intelligent Information Processing in Universities of Guangxi Nanning Normal University Nanning China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Tongji University Shanghai China

At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a corresponding deep learning-based framework called TOP (the abbreviation of TOxicity Prediction). TOP integrated a serial special data processing methods, a bidirectional gated recurrent unit-based RNN (BiGRU) and a fully connected neural network for end-to-end molecular representation learning and chemical toxicity prediction. TOP can automatically learn a mixed molecular representation from not only SMILES contextual information that describes the molecule structure, but also physiochemical properties. Therefore, TOP can overcome the drawbacks of existing methods that use either of them, thus greatly promotes toxicity prediction. We conducted extensive experiments over 14 classic toxicity prediction tasks on three different benchmark datasets, including balanced and imbalanced ones. The results show that, with the help of the novel molecular representation method, TOP significantly outperforms not only three baseline machine learning methods, but also five state-of-the-art methods.

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Plant Leaf Recognition Based on Conditional Generative Adversarial Nets 15th

Plant Leaf Recognition Based on Conditional Generative Adver...

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15th International Conference on intelligent computing, ICIC 2019

作者： Jiao, Zhihao Zhang, Lijun Yuan, Chang-An Qin, Xiao Shang, Li Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle Shanghai China School of Automotive Studies Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China

ISBN: (纸本)9783030267629

Plants play an important role in human life, Identifying and protecting plants has far-reaching implications for the sustainable development of the ecological environment. Plant leaves can often reflect important characteristics of plants, so it is scientific and feasible to effectively identify plant species through plant leaves. With the rapid development of deep learning in recent years, it has been widely applied in plant leaf recognition. Compared with the traditional method, deep learning based plant leaf recognition algorithm can extract plant leaf features more effectively and can greatly improve the performance. Based on the detailed analysis of the structural characteristics of three classical convolutional neural network models, this paper comprehensively compares the recognition performance of three convolutional neural network models on ICL plant leaf datasets. The experimental results show that the Conditional Generative Adversarial Net with optimized output layer has better recognition results on the plant leaf dataset than other convolutional neural network models. © 2019, Springer Nature Switzerland AG.

关键词： Neural network models

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Adaptively Extracting Structured Data from Web Pages

Adaptively Extracting Structured Data from Web Pages

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

作者： Yingnan Guo Jiajun Zhang Xing Chen College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Web pages contain a large amount of valuable information and resources, meanwhile may update at any time. However, the current Web-data extraction algorithms are generally targeted at specific web page structure. When web pages update, the problem which is caused by the changes of web pages may be encountered, leading to the inability to extract web page information or wrong information. In order to solve this problem, this paper proposes a new method to extract the feature values of each area in the web page through page rendering, and then combine the DOM tree structure of the page, semantic similarity and other information, so that it can still extract the target data correctly after the structure of the web page changes.

关键词： Data mining Web pages Feature extraction Layout Indexes Distributed processing Big Data

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Relay selections for cooperative underlay CR systems with energy harvesting

Relay selections for cooperative underlay CR systems with en...

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作者： Ye, Jia Liu, Zhedong Zhao, Hui Pan, Gaofeng Ni, Qiang Alouini, Mohamed-Slim Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom

In this paper, we consider a cooperative underlay cognitive radio system in which one secondary user (S) transmits its data to a secondary receiver with the assistance of multiple relays. In the considered underlay spectrum sharing scenario, the transmit power levers at S and forwarding relays are adjusted simultaneously according to a given threshold interference power that the primary user can tolerate. We also consider that relays adopt threshold decode-and-forward relay scheme and harvest energy from the primary transmitter with finite energy storage capacity under time switching scheme. Three best relay selection (RS) schemes, optimal source-relay link with wireless energy harvesting (WEH), optimal relay-destination link with WEH and optimal source-relay-destination link with WEH schemes, are proposed and studied. Closed-form analytical expressions for the outage probability of these three best RS schemes are derived, respectively. Finally, our analytical results are verified by Monte-Carlo simulations. © 2015 IEEE.

关键词： Energy harvesting

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Using Apriori Algorithm on Students' Performance Data for Association Rules Mining

Using Apriori Algorithm on Students' Performance Data for As...

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2nd International Seminar on Education Research and Social science(ISERSS 2019)

作者： Xiaodong Wu Yuzhu Zeng Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

With the development of information technology, many colleges and universities have established student information management system. The long-term operation of the student information management system will generate big data for colleges and universities. Moreover, there exists valuable information in the huge amount of data. Hence, it is necessary to use the data mining method to mine the massive data and get some valuable reference information so as to improve the teaching and management of students. In this paper, the Apriori algorithm is used to mine association rules of 34 courses of 100 students majoring in computer science and technology, so as to find out the correlation between courses and the factors that lead to the high or low grades of courses. R is used to conduct the experiment to discover the association rules, and the association rules are analyzed and discussed. The results of data mining on students' achievements in this work are expected to provide a reference for improving the teaching quality of computer science and technology courses.

关键词： data mining apriori algorithm association rules students' achievement analysis

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