Journal of Intelligent & Fuzzy Systems - Volume 41, issue 1

Authors: Gao, Yan | Liu, Chenchen | Zhao, Liangyu | Zhang, Kun

Article Type: Research Article

Abstract: The q-rung orthopair fuzzy set is a powerful and useful tool to deal with uncertainty, but in actual decision-making process, decision-makers are usually required to analyze the actual problem dynamically. Therefore in this paper, we consider the time-series q-rung orthopair fuzzy decision making. First, we introduce the new cosine similarity measure of q-ROFS which combines the cosine similarity measure and the Euclidean distance measure. Then, we combine the advantages of projection method and grey correlation degree, establishing the nonlinear programming model to calculate the weights of attributes. Furthermore, we use the exponential decay model to get the weights formulas of …q-ROFS at different times. Then we replace the distance function with grey relational projection and extend TOPSIS method. Based on these, we propose a new MAGDM approach to deal with time-series q-rung orthopair fuzzy problem not only from the point of view of geometry but also from the point of view of algebra. Finally, we give a practical example to illustrate effectiveness and feasibility of the new method. Show more

Keywords: q-rung orthopair fuzzy set, time-series, grey correlation degree, cosine distance measure

DOI: 10.3233/JIFS-210841

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2161-2170, 2021

Authors: Zhao, Tingting | Yi, Xiaoli | Zeng, Zhiyong | Feng, Tao

Article Type: Research Article

Abstract: YTNR (Yunnan Tongbiguan Nature Reserve) is located in the westernmost part of China’s tropical regions and is the only area in China with the tropical biota of the Irrawaddy River system. The reserve has abundant tropical flora and fauna resources. In order to realize the real-time detection of wild animals in this area, this paper proposes an improved YOLO (You only look once) network. The original YOLO model can achieve higher detection accuracy, but due to the complex model structure, it cannot achieve a faster detection speed on the CPU detection platform. Therefore, the lightweight network MobileNet is introduced to …replace the backbone feature extraction network in YOLO, which realizes real-time detection on the CPU platform. In response to the difficulty in collecting wild animal image data, the research team deployed 50 high-definition cameras in the study area and conducted continuous observations for more than 1,000 hours. In the end, this research uses 1410 images of wildlife collected in the field and 1577 wildlife images from the internet to construct a research data set combined with the manual annotation of domain experts. At the same time, transfer learning is introduced to solve the problem of insufficient training data and the network is difficult to fit. The experimental results show that our model trained on a training set containing 2419 animal images has a mean average precision of 93.6% and an FPS (Frame Per Second) of 3.8 under the CPU. Compared with YOLO, the mean average precision is increased by 7.7%, and the FPS value is increased by 3. Show more

Keywords: Wildlife detection, YOLO, transfer learning, MobileNet, PANet

DOI: 10.3233/JIFS-210859

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2171-2181, 2021

Authors: Wang, Qian | Zhao, Wenfang | Ren, Jiadong

Article Type: Research Article

Abstract: Intrusion Detection System (IDS) can reduce the losses caused by intrusion behaviors and protect users’ information security. The effectiveness of IDS depends on the performance of the algorithm used in identifying intrusions. And traditional machine learning algorithms are limited to deal with the intrusion data with the characteristics of high-dimensionality, nonlinearity and imbalance. Therefore, this paper proposes an I ntrusion D etection algorithm based on I mage E nhanced C onvolutional N eural N etwork (ID-IE-CNN ). Firstly, based on the image processing technology of deep learning, oversampling method is used to increase the amount of original data to achieve …data balance. Secondly, the one-dimensional data is converted into two-dimensional image data, the convolutional layer and the pooling layer are used to extract the main features of the image to reduce the data dimensionality. Thirdly, the Tanh function is introduced as an activation function to fit nonlinear data, a fully connected layer is used to integrate local information, and the generalization ability of the prediction model is improved by the Dropout method. Finally, the Softmax classifier is used to predict the behavior of intrusion detection. This paper uses the KDDCup99 data set and compares with other competitive algorithms. Both in the performance of binary classification and multi-classification, ID-IE-CNN is better than the compared algorithms, which verifies its superiority. Show more

Keywords: Intrusion detection, convolutional neural network, image enhancement

DOI: 10.3233/JIFS-210863

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2183-2194, 2021

Authors: Niu, Guo | Ma, Zhengming

Article Type: Research Article

Abstract: Locally Linear Embedding (LLE) is honored as the first algorithm of manifold learning. Generally speaking, the relation between a data and its nearest neighbors is nonlinear and LLE only extracts its linear part. Therefore, local nonlinear embedding is an important direction of improvement to LLE. However, any attempt in this direction may lead to a significant increase in computational complexity. In this paper, a novel algorithm called local quasi-linear embedding (LQLE) is proposed. In our LQLE, each high-dimensional data vector is first expanded by using Kronecker product. The expanded vector contains not only the components of the original vector, but …also the polynomials of its components. Then, each expanded vector of high dimensional data is linearly approximated with the expanded vectors of its nearest neighbors. In this way, the proposed LQLE achieves a certain degree of local nonlinearity and learns the data dimensionality reduction results under the principle of keeping local nonlinearity unchanged. More importantly, LQLE does not increase computation complexity by only replacing the data vectors with their Kronecker product expansions in the original LLE program. Experimental results between our proposed methods and four comparison algorithms on various datasets demonstrate the well performance of the proposed methods. Show more

Keywords: Dimensionality reduction, locally linear embedding, local quasi-linear

DOI: 10.3233/JIFS-210891

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2195-2205, 2021

Authors: Ramalingeswar, J.T. | Subramanian, K.

Article Type: Research Article

Abstract: The effective coordination of solar photovoltaic (solar PV) with Electrical Vehicles (EV) can substantially improve the micro grid(MG) stability and economic benefits. This paper presents a novel Energy Management System (EMS) that synchronizes EV storage with Solar PV and load variability. Reducing grid dependency and energy cost of the MGs are the key objectives of the proposed EMS. A smart EV prioritization based control strategy is developed using fuzzy controller. Probabilistic approach is designed to estimate the EV usage expectancy in the near time zone that helps smart decision on choosing EVs. Minimizing battery degradation and maximizing EV storage exploitation …are the key objectives of EV prioritization. On the other hand, Water Filling Algorithm (WFA) is used for Optimal Storage Distribution (OSD) in each zone of energy need for load flattening. The proposed EMS is implemented in a real time on-grid MG scenario and different case studies have been investigated to realize the impact of proposed EMS. A comprehensive cost analysis has been conducted and the efficacy of the proposed EMS is analysed. Show more

Keywords: Solar PV, EV storage, WFA, load flattening, EV ranking

DOI: 10.3233/JIFS-210930

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2207-2223, 2021

Authors: Tian, Jinghui | Han, Dongying | Xiao, Lifeng | Shi, Peiming

Article Type: Research Article

Abstract: With the innovation and development of detection technology, various types of sensors are installed to monitor the operating status of equipment in modern industry. Compared with the same type of sensors for monitoring, heterogeneous sensors can collect more comprehensive complementary fault information. Due to the large distribution differences and serious noise pollution of heterogeneous sensor data collected in industrial sites, this brings certain challenges to the development of heterogeneous data fusion strategies. In view of the large distribution difference in the feature spatial of heterogeneous data and the difficulty of effective fusion of fault information, this paper presents a multi-scale …deep coupling convolutional neural network (MDCN), which is used to map the heterogeneous fault information from different feature spaces to the common spaces for full fusion. Specifically, a multi-scale convolution module (MSC) with multiple filters of different sizes is adopted to extract multi-scale fault features of heterogeneous sensor data. Then, the maximum mean discrepancy (MMD) is applied to measure the distance between different spatial features in the coupling layer, and the common failure information in the heterogeneous data is mined by minimizing MMD to fuse effectively in order to identify the failure state of the device. The validity of this method is verified by the data collected on a first-level parallel gearbox mixed fault experiment platform. Show more

Keywords: Fault diagnosis, information fusion, maximum mean difference, convolutional neural network

DOI: 10.3233/JIFS-210932

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2225-2238, 2021

Authors: Yan, Wei | Ding, Yuhan

Article Type: Research Article

Abstract: With the rapid development of Semantic Web, the retrieval of RDF data has become a research hotspot. As the main method of data retrieval, keyword search has attracted much attention because of its simple operation. The existing RDF keyword search methods mainly search directly on RDF graph, which is no longer applicable to RDF knowledge graph. Firstly, we propose to transform RDF knowledge graph data into type graph to prune the search space. Then based on type graph, we extract frequent search patterns and establish a list from frequent search patterns to pattern instances. Finally, we propose a method of …the Bloom coding, which can be used to quickly judge whether the information our need is in frequent search patterns. The experiments show that our approach outperforms the state-of-the-art methods on both accuracy and response time. Show more

Keywords: RDF knowledge graph, keyword, type graph, frequent search pattern, Bloom coding

DOI: 10.3233/JIFS-210950

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2239-2253, 2021

Authors: Chen, Lei | Han, Jun | Tian, Feng

Article Type: Research Article

Abstract: Fusing the infrared (IR) and visible images has many advantages and can be applied to applications such as target detection and recognition. Colors can give more accurate and distinct features, but the low resolution and low contrast of fused images make this a challenge task. In this paper, we proposed a method based on parallel generative adversarial networks (GANs) to address the challenge. We used IR image, visible image and fusion image as ground truth of ‘L’, ‘a’ and ‘b’ of the Lab model. Through the parallel GANs, we can gain the Lab data which can be converted to RGB …image. We adopt TNO and RoadScene data sets to verify our method, and compare with five objective evaluation parameters obtained by other three methods based on deep learning (DL). It is demonstrated that the proposed approach is able to achieve better performance against state-of-arts methods. Show more

Keywords: IR and visible images, image fusion, generative adversarial network, lab

DOI: 10.3233/JIFS-210987

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2255-2264, 2021

Authors: Firouzkouhi, Narjes | Amini, Abbas | Cheng, Chun | Soleymani, Mehdi | Davvaz, Bijan

Article Type: Research Article

Abstract: Inspired by fuzzy hyperalgebras and fuzzy polynomial function (term function), some homomorphism properties of fundamental relation on fuzzy hyperalgebras are conveyed. The obtained relations of fuzzy hyperalgebra are utilized for certain applications, i.e., biological phenomena and genetics along with some elucidatory examples presenting various aspects of fuzzy hyperalgebras. Then, by considering the definition of identities (weak and strong) as a class of fuzzy polynomial function, the smallest equivalence relation (fundamental relation) is obtained which is an important tool for fuzzy hyperalgebraic systems. Through the characterization of these equivalence relations of a fuzzy hyperalgebra, we assign the smallest equivalence relation …α i 1 i 2 ∗ on a fuzzy hyperalgebra via identities where the factor hyperalgebra is a universal algebra. We extend and improve the identities on fuzzy hyperalgebras and characterize the smallest equivalence relation α J ∗ on the set of strong identities. Show more

Keywords: Fuzzy hyperalgebra, fuzzy polynomial function, identity, fundamental relation, universal algebra, homomorphism

DOI: 10.3233/JIFS-210994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2265-2274, 2021

Authors: Jia, Zhifu | Liu, Xinsheng

Article Type: Research Article

Abstract: In this paper, we propose complex uncertain differential equations (CUDEs) based on uncertainty theory. In order to describe the evolution of complex uncertain phenomenon related to belief degrees, we apply the complex Liu process to CUDEs. Firstly, we pose a concept of a linear CUDE and prove that homogeneous linear CUDE and general linear CUDE have solutions. Then, we prove existence and uniqueness theorem of a special CUDE. Further, we design a numerical algorithm to obtain inverse uncertainty distribution of the solution. Finally, as an application, we analyse the inverse uncertainty distributions of time integral of CUDEs and design numerical …algorithms to obtain inverse uncertainty distributions of time integral. Show more

Keywords: Complex uncertain differential equations, existence and uniqueness theorem, time integral

DOI: 10.3233/JIFS-211030

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2275-2289, 2021