Journal of Intelligent & Fuzzy Systems - Volume 37, issue 1

Authors: Sun, Baisheng | Qian, Jinping | Qu, Kaiyue | Draper, Geoffrey M.

Article Type: Research Article

Abstract:  Cities are the products of human social and economic development, and urbanization is the inevitable result of productivity improvement and social progress. Urban green space network is the only living infrastructure in the city, which provides essential space for the protection of urban biodiversity. Therefore, this paper proposes a heuristic decision tree model to construct the ecological urban green space network. The construction of ecological urban green space ecological network is studied based on the Geographical Information Systems (GIS) concepts. GIS and data mining technology are combined, to build the proposed decision tree model with monitoring and evaluation as the …core, exploring its texture scale and characteristics, with green space types classified. Finally, the experimental results show that the proposed model can classify the types of green space in the natural geographical environment and play a decisive role in guiding the judgment of environmental monitoring and assessment. Therefore, the proposed algorithm has a positive effect on the monitoring and evaluation of the natural environment. Show more

Keywords: Decision tree, eco-city, ecological network

DOI: 10.3233/JIFS-179066

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 79-86, 2019

Authors: Haixia, Liu

Article Type: Research Article

Abstract:  To further explore the state monitoring of rotating machinery, the state monitoring and fault diagnosis functions have been presented by using vibration signal feature extraction and expression technology. After describing the development status of vibration signal extraction and expression, the state monitoring and fault diagnosis model of rotating machinery are constructed by using this technology. Based on the algorithm flow and evaluation model, an optimization and update scheme is proposed, and an adequate evaluation model is established by evaluating the fault detection status effectively. A genetic algorithm is added to the model to assist optimization to achieve timely analysis and …processing of data. In the testing of mechanical fault detection model, the efficiency of data processing and the number of fault detection are tested for vibration signal feature extraction. The test results show that the vibration signal feature extraction technology speeds up the state detection effect and the processing results are more precise. Show more

Keywords: Vibration signal, rotating machinery state, monitoring fault diagnosis, genetic algorithm

DOI: 10.3233/JIFS-179067

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 87-94, 2019

Authors: Yang, Xiaomin

Article Type: Research Article

Abstract:  To promote the development of English-Chinese bilingual context, a CBR-based bilingual contextual intelligent model is constructed. This paper first expounds the development history of the bilingual context in English and Chinese. Concerning contextual knowledge, a CBR-based bilingual context system is designed. Using machine learning theory, the bilingual context of English and Chinese is analyzed, which provides a good environment for language learning. In this paper, a data mining algorithm is applied to optimize and update the algorithm flow and evaluation model. By evaluating the development pattern of bilingual context effectively, an effective recognition model is established. In the bilingual context …intelligent model test, the practicability and efficiency test of the model shows that CBR has good practicability and is worth further promotion. Show more

Keywords: Bilingual context, artificial intelligence, CBR, context model

DOI: 10.3233/JIFS-179068

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 95-101, 2019

Authors: Kong, Fei | Li, Jiangbo | Wang, Yumin

Article Type: Research Article

Abstract:  As the main channel of human-computer communication, multimedia education technology can realize the virtualization of education and multi-dimensional information. In this paper, the authors design a human-computer interactive English experience teaching based on fuzzy set and BP neural network. With the development of intelligent education, the improvement of English teaching quality is particularly important, especially the effectiveness of classroom teaching. The construction of interactive education system has changed the traditional classroom teaching mode and made it possible for individual autonomous learning. This English teaching system is an E-learning platform based on network updating. The E-learning system has the following advantages: …(1) E-learning media is easy to update, can provide more teaching content, and provide convenience for teaching. (2) E-learning interaction can stimulate learning interest; (3) E-learning is rich in educational materials and easy to set teaching objectives. (4) Using E-learning’s human-computer interaction function, we can strengthen the supervision of education and teaching process. Show more

Keywords: Fuzzy set, human-computer interaction, English teaching, evaluation model

DOI: 10.3233/JIFS-179069

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 103-113, 2019

Authors: Mabrok, Mohamed A. | Abdel-Aty, Abdel-Haleem

Article Type: Research Article

Abstract: Extracting repeated unknown maneuvers or patterns preformed by a human operator in a cyber-physical systems can lead to better understanding of the behavior of the human operator who is controlling or sharing tasks with dynamical systems. These repeated maneuvers can be extracted by analyzing the inputs and outputs of the human operator using control theory and data mining tools. In this paper, we introduce geometrical shape-based pattern detection approach for input-output data. A pattern or a maneuver is defined as the maximum repeated behavior in time series trajectory data that is generated from the operator’s inputs and outputs. A two-phase …algorithm is developed in this paper, the first phase consists of trajectory segmentation, creating segment fingerprint, clustering, and symbolic representations. The second phase of the proposed algorithm is pattern extraction phase, which is inherited from the motif finding algorithms in time series data and DNA sequences. Show more

Keywords: Pattern recognition, human in the loop, trajectory segmentation

DOI: 10.3233/JIFS-179070

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 115-123, 2019

Authors: Huang, Sizhe | Xu, Huosheng | Xia, Xuezhi | Yang, Fan | Zou, Fuhao

Article Type: Research Article

Abstract:  Fine-Grained ship classification is quite challenging because the visual differences between the subcategories are small. Due to the large intra-class similarity, it is very difficult to classify the ship objects without bounding box/part annotations. In this paper, we propose a model that combines multiple deep CNN features and use fusion strategies to explore of multi-scale features relationship. Because different levels/depths CNN features have different properties, so we combine multiple low-level local CNN features with high-level global CNN feature for object classification. The model shows a good way of tailoring pre-trained CNN models to fine-grained ship classification, which have lower cost …in computation and storage compared with some state-of-the-art CNN methods and achieves the significant classification performances in FGVC-Aircraft and Stanford Cars datasets. Show more

Keywords: Convolutional neural networks, Fine-grained classification, ship recognition

DOI: 10.3233/JIFS-179071

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 125-135, 2019

Authors: Peng, Hua | Liu, Liang | Liu, Jiayong | Lewis, Johnwb R.

Article Type: Research Article

Abstract:  Now, the classifier network anomaly traffic detection method is generally considered to be a general method of good detection effect and high detection precision. In order to detect abnormal network traffic more efficiently, so as to ensure the security of Internet users, a network traffic anomaly detection algorithm based on Mahout Classifier is studied. Aiming at the problem of time correlation and the influence of abnormal samples on accuracy of detection statistics, and the first detection point is eliminated and the applicable detection point is added. The BP neural network algorithm and Bayesian network model are used to predict the …abnormal probability of the anomaly node, the detection precision is optimized, and the exception points of the training set are reorganized. In view of the anomalies detected by anomaly detection models, an emergency response method is proposed, which not only detects anomalies, but also handles anomalies. Show more

Keywords: Mahout classifier, network anomaly, flow rate, detection algorithm

DOI: 10.3233/JIFS-179072

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 137-144, 2019

Authors: Si, Chen | Xiaomei, Zhang

Article Type: Research Article

Abstract:  Selecting and developing superior industries for regional adjustment can optimize industrial structure and promote regional coordinated development, which is an inevitable choice to promote regional economic development. Forestry industry is an important ecological resource and advantageous economic industry in China. Optimizing regional forestry industry structure is conducive to promoting the formation of forestry green supply chain and forestry green circular economy. At the same time, forestry development is highly dependent on forest resources endowment and natural conditions. The eastern part of China is the southern forest region. The congenital ecological location advantage is better, which makes the industrial structure advantage …and location advantage better. The central region has the advantage of national strategic policy support, but its forestry resource endowment is not strong. This paper adopts the dynamic deviation-share method to study the total amount, structure and location competitiveness of forestry industry in 16 provinces. By comparing the forestry economic structure and regional competitive advantages of provinces, the author analyzed the comprehensive competitiveness and differences of forestry development and screened out the forestry dominant industries. The results show that the northeast forest region is rich in forest resources and has a good industrial structure, but its geographical location is not good at economic development level, its development conditions are not superior, and its competitive potential is weak. The industrial structure of the western region is very poor, but the forest resources are abundant, and the ecological advantages are good, which can offset the impact caused by the inadequate industrial structure. The forestry development in the eastern region mainly relies on the development of economic forest product planting and collecting industry, flower and ornamental plant planting. It has strong competitiveness and rapid development in the field of forestry economy. This study is of great significance for improving the level of provincial forestry economic development, building green forestry economy and narrowing the gap of provincial forestry economic development. Show more

Keywords: Deviation-share, forestry industry, fuzzy comprehensive evaluation, green circular economy

DOI: 10.3233/JIFS-179073

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 145-157, 2019

Authors: Shehata, A.M.

Article Type: Research Article

Abstract:  In this paper we formulate a dynamic economic emission and spinning reserve dispatch (DEESRD) problem which determines the optimal power and spinning reserve schedule by simultaneously minimizing the power and spinning reserve costs, and the amount of emission under some constraints. Demand response (DR) can improve the reliability and reduce the energy price. In this paper, we focus on Game Theory DR program which is one of the incentive-based DR programs. We incorporate DR into the DEESRD problem by formulating dynamic economic emission and spinning reserve dispatch with demand response (DEESRD-DR) problem. The objective of the DEESRD-DR is to minimize …the energy and reserve costs, minimize the amount of emission and maximize the benefit of the generation company (GENCO). The optimal solutions of the DEESRD and DEESRD-DR problems can be obtained using artificial intelligent-based optimization techniques such as differential evolution, particle swarm optimization, artificial immune system, artificial bee colony, etc., however these methods give an open-loop optimal solution. The open-loop nature cannot deals with inaccuracies, modeling uncertainties and unexpected external disturbances where the power system components suffer from, therefore we have designed closed-loop solutions by model predictive control (MPC). The performance of the MPC has been investigated by applying the MPC strategy to the DEESRD and DEESRD-DR problems with test system consisting of five generating units and five customers. Show more

Keywords: Computational intelligent technique, dynamic dispatch, emission, optimization, feedback control, spinning reserve, demand response

DOI: 10.3233/JIFS-179074

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 159-170, 2019

Authors: Wang, Lejie

Article Type: Research Article

Abstract: This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218 .

DOI: 10.3233/JIFS-179075

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 171-184, 2019